Shim, Sangwoo; Aspuru-Guzik, Alán
2012-12-14
The reduced density matrix of excitons coupled to a phonon bath at a finite temperature is studied using the path integral Monte Carlo method. Appropriate choices of estimators and importance sampling schemes are crucial to the performance of the Monte Carlo simulation. We show that by choosing the population-normalized estimator for the reduced density matrix, an efficient and physically-meaningful sampling function can be obtained. In addition, the nonadiabatic phonon probability density is obtained as a byproduct during the sampling procedure. For importance sampling, we adopted the Metropolis-adjusted Langevin algorithm. The analytic expression for the gradient of the target probability density function associated with the population-normalized estimator cannot be obtained in closed form without a matrix power series. An approximated gradient that can be efficiently calculated is explored to achieve better computational scaling and efficiency. Application to a simple one-dimensional model system from the previous literature confirms the correctness of the method developed in this manuscript. The displaced harmonic model system within the single exciton manifold shows the numerically exact temperature dependence of the coherence and population of the excitonic system. The sampling scheme can be applied to an arbitrary anharmonic environment, such as multichromophoric systems embedded in the protein complex. The result of this study is expected to stimulate further development of real time propagation methods that satisfy the detailed balance condition for exciton populations. PMID:23249075
Improved importance sampling for Monte Carlo simulation of time-domain optical coherence tomography.
Lima, Ivan T; Kalra, Anshul; Sherif, Sherif S
2011-01-01
We developed an importance sampling based method that significantly speeds up the calculation of the diffusive reflectance due to ballistic and to quasi-ballistic components of photons scattered in turbid media: Class I diffusive reflectance. These components of scattered photons make up the signal in optical coherence tomography (OCT) imaging. We show that the use of this method reduces the computation time of this diffusive reflectance in time-domain OCT by up to three orders of magnitude when compared with standard Monte Carlo simulation. Our method does not produce a systematic bias in the statistical result that is typically observed in existing methods to speed up Monte Carlo simulations of light transport in tissue. This fast Monte Carlo calculation of the Class I diffusive reflectance can be used as a tool to further study the physical process governing OCT signals, e.g., obtain the statistics of the depth-scan, including the effects of multiple scattering of light, in OCT. This is an important prerequisite to future research to increase penetration depth and to improve image extraction in OCT. PMID:21559120
NASA Astrophysics Data System (ADS)
Anagnostopoulos, K.; Azuma, T.; Nishimura, J.
The sign problem is a notorious problem, which occurs in Monte Carlo simulations of a system with a partition function whose integrand is not positive. One way to simulate such a system is to use the factorization method where one enforces sampling in the part of the configuration space which gives important contribution to the partition function. This is accomplished by using constraints on some observables chosen appropriately and minimizing the free energy associated with their joint distribution functions. These observables are maximally correlated with the complex phase. Observables not in this set essentially decouple from the phase and can be calculated without the sign problem in the corresponding "microcanonical" ensemble. These ideas are applied on a simple matrix model with very strong sign problem and the results are found to be consistent with analytic calculations using the Gaussian Expansion Method.
Monte Carlo importance sampling for the MCNP{trademark} general source
Lichtenstein, H.
1996-01-09
Research was performed to develop an importance sampling procedure for a radiation source. The procedure was developed for the MCNP radiation transport code, but the approach itself is general and can be adapted to other Monte Carlo codes. The procedure, as adapted to MCNP, relies entirely on existing MCNP capabilities. It has been tested for very complex descriptions of a general source, in the context of the design of spent-reactor-fuel storage casks. Dramatic improvements in calculation efficiency have been observed in some test cases. In addition, the procedure has been found to provide an acceleration to acceptable convergence, as well as the benefit of quickly identifying user specified variance-reduction in the transport that effects unstable convergence.
Importance sampling : promises and limitations.
West, Nicholas J.; Swiler, Laura Painton
2010-04-01
Importance sampling is an unbiased sampling method used to sample random variables from different densities than originally defined. These importance sampling densities are constructed to pick 'important' values of input random variables to improve the estimation of a statistical response of interest, such as a mean or probability of failure. Conceptually, importance sampling is very attractive: for example one wants to generate more samples in a failure region when estimating failure probabilities. In practice, however, importance sampling can be challenging to implement efficiently, especially in a general framework that will allow solutions for many classes of problems. We are interested in the promises and limitations of importance sampling as applied to computationally expensive finite element simulations which are treated as 'black-box' codes. In this paper, we present a customized importance sampler that is meant to be used after an initial set of Latin Hypercube samples has been taken, to help refine a failure probability estimate. The importance sampling densities are constructed based on kernel density estimators. We examine importance sampling with respect to two main questions: is importance sampling efficient and accurate for situations where we can only afford small numbers of samples? And does importance sampling require the use of surrogate methods to generate a sufficient number of samples so that the importance sampling process does increase the accuracy of the failure probability estimate? We present various case studies to address these questions.
Monte carlo sampling of fission multiplicity.
Hendricks, J. S.
2004-01-01
Two new methods have been developed for fission multiplicity modeling in Monte Carlo calculations. The traditional method of sampling neutron multiplicity from fission is to sample the number of neutrons above or below the average. For example, if there are 2.7 neutrons per fission, three would be chosen 70% of the time and two would be chosen 30% of the time. For many applications, particularly {sup 3}He coincidence counting, a better estimate of the true number of neutrons per fission is required. Generally, this number is estimated by sampling a Gaussian distribution about the average. However, because the tail of the Gaussian distribution is negative and negative neutrons cannot be produced, a slight positive bias can be found in the average value. For criticality calculations, the result of rejecting the negative neutrons is an increase in k{sub eff} of 0.1% in some cases. For spontaneous fission, where the average number of neutrons emitted from fission is low, the error also can be unacceptably large. If the Gaussian width approaches the average number of fissions, 10% too many fission neutrons are produced by not treating the negative Gaussian tail adequately. The first method to treat the Gaussian tail is to determine a correction offset, which then is subtracted from all sampled values of the number of neutrons produced. This offset depends on the average value for any given fission at any energy and must be computed efficiently at each fission from the non-integrable error function. The second method is to determine a corrected zero point so that all neutrons sampled between zero and the corrected zero point are killed to compensate for the negative Gaussian tail bias. Again, the zero point must be computed efficiently at each fission. Both methods give excellent results with a negligible computing time penalty. It is now possible to include the full effects of fission multiplicity without the negative Gaussian tail bias.
Multiple importance sampling for PET.
Szirmay-Kalos, Laszló; Magdics, Milán; Tóth, Balázs
2014-04-01
This paper proposes the application of multiple importance sampling in fully 3-D positron emission tomography to speed up the iterative reconstruction process. The proposed method combines the results of lines of responses (LOR) driven and voxel driven projections keeping their advantages, like importance sampling, performance and parallel execution on graphics processing units. Voxel driven methods can focus on point like features while LOR driven approaches are efficient in reconstructing homogeneous regions. The theoretical basis of the combination is the application of the mixture of the samples generated by the individual importance sampling methods, emphasizing a particular method where it is better than others. The proposed algorithms are built into the Tera-tomo system. PMID:24710165
Annealed Importance Sampling Reversible Jump MCMC algorithms
Karagiannis, Georgios; Andrieu, Christophe
2013-03-20
It will soon be 20 years since reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithms have been proposed. They have significantly extended the scope of Markov chain Monte Carlo simulation methods, offering the promise to be able to routinely tackle transdimensional sampling problems, as encountered in Bayesian model selection problems for example, in a principled and flexible fashion. Their practical efficient implementation, however, still remains a challenge. A particular difficulty encountered in practice is in the choice of the dimension matching variables (both their nature and their distribution) and the reversible transformations which allow one to define the one-to-one mappings underpinning the design of these algorithms. Indeed, even seemingly sensible choices can lead to algorithms with very poor performance. The focus of this paper is the development and performance evaluation of a method, annealed importance sampling RJ-MCMC (aisRJ), which addresses this problem by mitigating the sensitivity of RJ-MCMC algorithms to the aforementioned poor design. As we shall see the algorithm can be understood as being an “exact approximation” of an idealized MCMC algorithm that would sample from the model probabilities directly in a model selection set-up. Such an idealized algorithm may have good theoretical convergence properties, but typically cannot be implemented, and our algorithms can approximate the performance of such idealized algorithms to an arbitrary degree while not introducing any bias for any degree of approximation. Our approach combines the dimension matching ideas of RJ-MCMC with annealed importance sampling and its Markov chain Monte Carlo implementation. We illustrate the performance of the algorithm with numerical simulations which indicate that, although the approach may at first appear computationally involved, it is in fact competitive.
A pure-sampling quantum Monte Carlo algorithm
Ospadov, Egor; Rothstein, Stuart M.
2015-01-14
The objective of pure-sampling quantum Monte Carlo is to calculate physical properties that are independent of the importance sampling function being employed in the calculation, save for the mismatch of its nodal hypersurface with that of the exact wave function. To achieve this objective, we report a pure-sampling algorithm that combines features of forward walking methods of pure-sampling and reptation quantum Monte Carlo (RQMC). The new algorithm accurately samples properties from the mixed and pure distributions simultaneously in runs performed at a single set of time-steps, over which extrapolation to zero time-step is performed. In a detailed comparison, we found RQMC to be less efficient. It requires different sets of time-steps to accurately determine the energy and other properties, such as the dipole moment. We implement our algorithm by systematically increasing an algorithmic parameter until the properties converge to statistically equivalent values. As a proof in principle, we calculated the fixed-node energy, static α polarizability, and other one-electron expectation values for the ground-states of LiH and water molecules. These quantities are free from importance sampling bias, population control bias, time-step bias, extrapolation-model bias, and the finite-field approximation. We found excellent agreement with the accepted values for the energy and a variety of other properties for those systems.
Annealed Importance Sampling for Neural Mass Models
Penny, Will; Sengupta, Biswa
2016-01-01
Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks, and inferences made about the strength of connections, using M/EEG data and Bayesian inference. To date, however, Bayesian methods have been largely restricted to the Variational Laplace (VL) algorithm which assumes that the posterior distribution is Gaussian and finds model parameters that are only locally optimal. This paper explores the use of Annealed Importance Sampling (AIS) to address these restrictions. We implement AIS using proposals derived from Langevin Monte Carlo (LMC) which uses local gradient and curvature information for efficient exploration of parameter space. In terms of the estimation of Bayes factors, VL and AIS agree about which model is best but report different degrees of belief. Additionally, AIS finds better model parameters and we find evidence of non-Gaussianity in their posterior distribution. PMID:26942606
Annealed Importance Sampling for Neural Mass Models.
Penny, Will; Sengupta, Biswa
2016-03-01
Neural Mass Models provide a compact description of the dynamical activity of cell populations in neocortical regions. Moreover, models of regional activity can be connected together into networks, and inferences made about the strength of connections, using M/EEG data and Bayesian inference. To date, however, Bayesian methods have been largely restricted to the Variational Laplace (VL) algorithm which assumes that the posterior distribution is Gaussian and finds model parameters that are only locally optimal. This paper explores the use of Annealed Importance Sampling (AIS) to address these restrictions. We implement AIS using proposals derived from Langevin Monte Carlo (LMC) which uses local gradient and curvature information for efficient exploration of parameter space. In terms of the estimation of Bayes factors, VL and AIS agree about which model is best but report different degrees of belief. Additionally, AIS finds better model parameters and we find evidence of non-Gaussianity in their posterior distribution. PMID:26942606
Cool walking: a new Markov chain Monte Carlo sampling method.
Brown, Scott; Head-Gordon, Teresa
2003-01-15
Effective relaxation processes for difficult systems like proteins or spin glasses require special simulation techniques that permit barrier crossing to ensure ergodic sampling. Numerous adaptations of the venerable Metropolis Monte Carlo (MMC) algorithm have been proposed to improve its sampling efficiency, including various hybrid Monte Carlo (HMC) schemes, and methods designed specifically for overcoming quasi-ergodicity problems such as Jump Walking (J-Walking), Smart Walking (S-Walking), Smart Darting, and Parallel Tempering. We present an alternative to these approaches that we call Cool Walking, or C-Walking. In C-Walking two Markov chains are propagated in tandem, one at a high (ergodic) temperature and the other at a low temperature. Nonlocal trial moves for the low temperature walker are generated by first sampling from the high-temperature distribution, then performing a statistical quenching process on the sampled configuration to generate a C-Walking jump move. C-Walking needs only one high-temperature walker, satisfies detailed balance, and offers the important practical advantage that the high and low-temperature walkers can be run in tandem with minimal degradation of sampling due to the presence of correlations. To make the C-Walking approach more suitable to real problems we decrease the required number of cooling steps by attempting to jump at intermediate temperatures during cooling. We further reduce the number of cooling steps by utilizing "windows" of states when jumping, which improves acceptance ratios and lowers the average number of cooling steps. We present C-Walking results with comparisons to J-Walking, S-Walking, Smart Darting, and Parallel Tempering on a one-dimensional rugged potential energy surface in which the exact normalized probability distribution is known. C-Walking shows superior sampling as judged by two ergodic measures. PMID:12483676
Adaptive Importance Sampling for Control and Inference
NASA Astrophysics Data System (ADS)
Kappen, H. J.; Ruiz, H. C.
2016-03-01
Path integral (PI) control problems are a restricted class of non-linear control problems that can be solved formally as a Feynman-Kac PI and can be estimated using Monte Carlo sampling. In this contribution we review PI control theory in the finite horizon case. We subsequently focus on the problem how to compute and represent control solutions. We review the most commonly used methods in robotics and control. Within the PI theory, the question of how to compute becomes the question of importance sampling. Efficient importance samplers are state feedback controllers and the use of these requires an efficient representation. Learning and representing effective state-feedback controllers for non-linear stochastic control problems is a very challenging, and largely unsolved, problem. We show how to learn and represent such controllers using ideas from the cross entropy method. We derive a gradient descent method that allows to learn feed-back controllers using an arbitrary parametrisation. We refer to this method as the path integral cross entropy method or PICE. We illustrate this method for some simple examples. The PI control methods can be used to estimate the posterior distribution in latent state models. In neuroscience these problems arise when estimating connectivity from neural recording data using EM. We demonstrate the PI control method as an accurate alternative to particle filtering.
Neutrino oscillation parameter sampling with MonteCUBES
NASA Astrophysics Data System (ADS)
Blennow, Mattias; Fernandez-Martinez, Enrique
2010-01-01
We present MonteCUBES ("Monte Carlo Utility Based Experiment Simulator"), a software package designed to sample the neutrino oscillation parameter space through Markov Chain Monte Carlo algorithms. MonteCUBES makes use of the GLoBES software so that the existing experiment definitions for GLoBES, describing long baseline and reactor experiments, can be used with MonteCUBES. MonteCUBES consists of two main parts: The first is a C library, written as a plug-in for GLoBES, implementing the Markov Chain Monte Carlo algorithm to sample the parameter space. The second part is a user-friendly graphical Matlab interface to easily read, analyze, plot and export the results of the parameter space sampling. Program summaryProgram title: MonteCUBES (Monte Carlo Utility Based Experiment Simulator) Catalogue identifier: AEFJ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEFJ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public Licence No. of lines in distributed program, including test data, etc.: 69 634 No. of bytes in distributed program, including test data, etc.: 3 980 776 Distribution format: tar.gz Programming language: C Computer: MonteCUBES builds and installs on 32 bit and 64 bit Linux systems where GLoBES is installed Operating system: 32 bit and 64 bit Linux RAM: Typically a few MBs Classification: 11.1 External routines: GLoBES [1,2] and routines/libraries used by GLoBES Subprograms used:Cat Id ADZI_v1_0, Title GLoBES, Reference CPC 177 (2007) 439 Nature of problem: Since neutrino masses do not appear in the standard model of particle physics, many models of neutrino masses also induce other types of new physics, which could affect the outcome of neutrino oscillation experiments. In general, these new physics imply high-dimensional parameter spaces that are difficult to explore using classical methods such as multi-dimensional projections and minimizations, such as those
Adaptive sample map for Monte Carlo ray tracing
NASA Astrophysics Data System (ADS)
Teng, Jun; Luo, Lixin; Chen, Zhibo
2010-07-01
Monte Carlo ray tracing algorithm is widely used by production quality renderers to generate synthesized images in films and TV programs. Noise artifact exists in synthetic images generated by Monte Carlo ray tracing methods. In this paper, a novel noise artifact detection and noise level representation method is proposed. We first apply discrete wavelet transform (DWT) on a synthetic image; the high frequency sub-bands of the DWT result encode the noise information. The sub-bands coefficients are then combined to generate a noise level description of the synthetic image, which is called noise map in the paper. This noise map is then subdivided into blocks for robust noise level metric calculation. Increasing the samples per pixel in Monte Carlo ray tracer can reduce the noise of a synthetic image to visually unnoticeable level. A noise-to-sample number mapping algorithm is thus performed on each block of the noise map, higher noise value is mapped to larger sample number, and lower noise value is mapped to smaller sample number, the result of mapping is called sample map. Each pixel in a sample map can be used by Monte Carlo ray tracer to reduce the noise level in the corresponding block of pixels in a synthetic image. However, this block based scheme produces blocky artifact as appeared in video and image compression algorithms. We use Gaussian filter to smooth the sample map, the result is adaptive sample map (ASP). ASP serves two purposes in rendering process; its statistics information can be used as noise level metric in synthetic image, and it can also be used by a Monte Carlo ray tracer to refine the synthetic image adaptively in order to reduce the noise to unnoticeable level but with less rendering time than the brute force method.
Monte Carlo Sampling of Negative-temperature Plasma States
John A. Krommes; Sharadini Rath
2002-07-19
A Monte Carlo procedure is used to generate N-particle configurations compatible with two-temperature canonical equilibria in two dimensions, with particular attention to nonlinear plasma gyrokinetics. An unusual feature of the problem is the importance of a nontrivial probability density function R0(PHI), the probability of realizing a set {Phi} of Fourier amplitudes associated with an ensemble of uniformly distributed, independent particles. This quantity arises because the equilibrium distribution is specified in terms of {Phi}, whereas the sampling procedure naturally produces particles states gamma; {Phi} and gamma are related via a gyrokinetic Poisson equation, highly nonlinear in its dependence on gamma. Expansion and asymptotic methods are used to calculate R0(PHI) analytically; excellent agreement is found between the large-N asymptotic result and a direct numerical calculation. The algorithm is tested by successfully generating a variety of states of both positive and negative temperature, including ones in which either the longest- or shortest-wavelength modes are excited to relatively very large amplitudes.
The alias method: A fast, efficient Monte Carlo sampling technique
Rathkopf, J.A.; Edwards, A.L. ); Smidt, R.K. )
1990-11-16
The alias method is a Monte Carlo sampling technique that offers significant advantages over more traditional methods. It equals the accuracy of table lookup and the speed of equal probable bins. The original formulation of this method sampled from discrete distributions and was easily extended to histogram distributions. We have extended the method further to applications more germane to Monte Carlo particle transport codes: continuous distributions. This paper presents the alias method as originally derived and our extensions to simple continuous distributions represented by piecewise linear functions. We also present a method to interpolate accurately between distributions tabulated at points other than the point of interest. We present timing studies that demonstrate the method's increased efficiency over table lookup and show further speedup achieved through vectorization. 6 refs., 2 figs., 1 tab.
Extending the alias Monte Carlo sampling method to general distributions
Edwards, A.L.; Rathkopf, J.A. ); Smidt, R.K. )
1991-01-07
The alias method is a Monte Carlo sampling technique that offers significant advantages over more traditional methods. It equals the accuracy of table lookup and the speed of equal probable bins. The original formulation of this method sampled from discrete distributions and was easily extended to histogram distributions. We have extended the method further to applications more germane to Monte Carlo particle transport codes: continuous distributions. This paper presents the alias method as originally derived and our extensions to simple continuous distributions represented by piecewise linear functions. We also present a method to interpolate accurately between distributions tabulated at points other than the point of interest. We present timing studies that demonstrate the method's increased efficiency over table lookup and show further speedup achieved through vectorization. 6 refs., 12 figs., 2 tabs.
ERIC Educational Resources Information Center
Kim, Su-Young
2012-01-01
Just as growth mixture models are useful with single-phase longitudinal data, multiphase growth mixture models can be used with multiple-phase longitudinal data. One of the practically important issues in single- and multiphase growth mixture models is the sample size requirements for accurate estimation. In a Monte Carlo simulation study, the…
A modified Monte Carlo 'local importance function transform' method
Keady, K. P.; Larsen, E. W.
2013-07-01
The Local Importance Function Transform (LIFT) method uses an approximation of the contribution transport problem to bias a forward Monte-Carlo (MC) source-detector simulation [1-3]. Local (cell-based) biasing parameters are calculated from an inexpensive deterministic adjoint solution and used to modify the physics of the forward transport simulation. In this research, we have developed a new expression for the LIFT biasing parameter, which depends on a cell-average adjoint current to scalar flux (J{sup *}/{phi}{sup *}) ratio. This biasing parameter differs significantly from the original expression, which uses adjoint cell-edge scalar fluxes to construct a finite difference estimate of the flux derivative; the resulting biasing parameters exhibit spikes in magnitude at material discontinuities, causing the original LIFT method to lose efficiency in problems with high spatial heterogeneity. The new J{sup *}/{phi}{sup *} expression, while more expensive to obtain, generates biasing parameters that vary smoothly across the spatial domain. The result is an improvement in simulation efficiency. A representative test problem has been developed and analyzed to demonstrate the advantage of the updated biasing parameter expression with regards to solution figure of merit (FOM). For reference, the two variants of the LIFT method are compared to a similar variance reduction method developed by Depinay [4, 5], as well as MC with deterministic adjoint weight windows (WW). (authors)
Continuous-time quantum Monte Carlo using worm sampling
NASA Astrophysics Data System (ADS)
Gunacker, P.; Wallerberger, M.; Gull, E.; Hausoel, A.; Sangiovanni, G.; Held, K.
2015-10-01
We present a worm sampling method for calculating one- and two-particle Green's functions using continuous-time quantum Monte Carlo simulations in the hybridization expansion (CT-HYB). Instead of measuring Green's functions by removing hybridization lines from partition function configurations, as in conventional CT-HYB, the worm algorithm directly samples the Green's function. We show that worm sampling is necessary to obtain general two-particle Green's functions which are not of density-density type and that it improves the sampling efficiency when approaching the atomic limit. Such two-particle Green's functions are needed to compute off-diagonal elements of susceptibilities and occur in diagrammatic extensions of the dynamical mean-field theory and in efficient estimators for the single-particle self-energy.
A flexible importance sampling method for integrating subgrid processes
NASA Astrophysics Data System (ADS)
Raut, E. K.; Larson, V. E.
2016-01-01
Numerical models of weather and climate need to compute grid-box-averaged rates of physical processes such as microphysics. These averages are computed by integrating subgrid variability over a grid box. For this reason, an important aspect of atmospheric modeling is spatial integration over subgrid scales. The needed integrals can be estimated by Monte Carlo integration. Monte Carlo integration is simple and general but requires many evaluations of the physical process rate. To reduce the number of function evaluations, this paper describes a new, flexible method of importance sampling. It divides the domain of integration into eight categories, such as the portion that contains both precipitation and cloud, or the portion that contains precipitation but no cloud. It then allows the modeler to prescribe the density of sample points within each of the eight categories. The new method is incorporated into the Subgrid Importance Latin Hypercube Sampler (SILHS). The resulting method is tested on drizzling cumulus and stratocumulus cases. In the cumulus case, the sampling error can be considerably reduced by drawing more sample points from the region of rain evaporation.
A flexible importance sampling method for integrating subgrid processes
Raut, E. K.; Larson, V. E.
2016-01-29
Numerical models of weather and climate need to compute grid-box-averaged rates of physical processes such as microphysics. These averages are computed by integrating subgrid variability over a grid box. For this reason, an important aspect of atmospheric modeling is spatial integration over subgrid scales. The needed integrals can be estimated by Monte Carlo integration. Monte Carlo integration is simple and general but requires many evaluations of the physical process rate. To reduce the number of function evaluations, this paper describes a new, flexible method of importance sampling. It divides the domain of integration into eight categories, such as the portion that containsmore » both precipitation and cloud, or the portion that contains precipitation but no cloud. It then allows the modeler to prescribe the density of sample points within each of the eight categories. The new method is incorporated into the Subgrid Importance Latin Hypercube Sampler (SILHS). The resulting method is tested on drizzling cumulus and stratocumulus cases. In the cumulus case, the sampling error can be considerably reduced by drawing more sample points from the region of rain evaporation.« less
A flexible importance sampling method for integrating subgrid processes
Raut, E. K.; Larson, V. E.
2016-01-01
Numerical models of weather and climate need to compute grid-box-averaged rates of physical processes such as microphysics. These averages are computed by integrating subgrid variability over a grid box. For this reason, an important aspect of atmospheric modeling is spatial integration over subgrid scales.
The needed integrals can be estimated by Monte Carlo integration. Monte Carlo integration is simple and general but requires many evaluations of the physical process rate. To reduce the number of function evaluations, this paper describes a new, flexible method of importance sampling. It divides the domain of integration into eight categories, such as the portion that contains both precipitation and cloud, or the portion that contains precipitation but no cloud. It then allows the modeler to prescribe the density of sample points within each of the eight categories.
The new method is incorporated into the Subgrid Importance Latin Hypercube Sampler (SILHS). The resulting method is tested on drizzling cumulus and stratocumulus cases. In the cumulus case, the sampling error can be considerably reduced by drawing more sample points from the region of rain evaporation.
A flexible importance sampling method for integrating subgrid processes
Raut, E. K.; Larson, V. E.
2016-01-29
Numerical models of weather and climate need to compute grid-box-averaged rates of physical processes such as microphysics. These averages are computed by integrating subgrid variability over a grid box. For this reason, an important aspect of atmospheric modeling is spatial integration over subgrid scales. The needed integrals can be estimated by Monte Carlo integration. Monte Carlo integration is simple and general but requires many evaluations of the physical process rate. To reduce the number of function evaluations, this paper describes a new, flexible method of importance sampling. It divides the domain of integration into eight categories, such as the portion that containsmore » both precipitation and cloud, or the portion that contains precipitation but no cloud. It then allows the modeler to prescribe the density of sample points within each of the eight categories. The new method is incorporated into the Subgrid Importance Latin Hypercube Sampler (SILHS). Here, the resulting method is tested on drizzling cumulus and stratocumulus cases. In the cumulus case, the sampling error can be considerably reduced by drawing more sample points from the region of rain evaporation.« less
Experimental validation of plutonium ageing by Monte Carlo correlated sampling
Litaize, O.; Bernard, D.; Santamarina, A.
2006-07-01
Integral measurements of Plutonium Ageing were performed in two homogeneous MOX cores (MISTRAL2 and MISTRALS) of the French MISTRAL Programme between 1996 and year 2000. The analysis of the MISTRAL2 experiment with JEF-2.2 nuclear data library high-lightened an underestimation of {sup 241}Am capture cross section. The next experiment (MISTRALS) did not conclude in the same way. This paper present a new analysis performed with the recent JEFF-3.1 library and a Monte Carlo perturbation method (correlated sampling) available in the French TRIPOLI4 code. (authors)
Reactive Monte Carlo sampling with an ab initio potential
NASA Astrophysics Data System (ADS)
Leiding, Jeff; Coe, Joshua D.
2016-05-01
We present the first application of reactive Monte Carlo in a first-principles context. The algorithm samples in a modified NVT ensemble in which the volume, temperature, and total number of atoms of a given type are held fixed, but molecular composition is allowed to evolve through stochastic variation of chemical connectivity. We discuss general features of the method, as well as techniques needed to enhance the efficiency of Boltzmann sampling. Finally, we compare the results of simulation of NH3 to those of ab initio molecular dynamics (AIMD). We find that there are regions of state space for which RxMC sampling is much more efficient than AIMD due to the "rare-event" character of chemical reactions.
Importance sampling. I. Computing multimodel p values in linkage analysis
Kong, A.; Frigge, M.; Irwin, M.; Cox, N. )
1992-12-01
In linkage analysis, when the lod score is maximized over multiple genetic models, standard asymptotic approximation of the significance level does not apply. Monte Carlo methods can be used to estimate the p value, but procedures currently used are extremely inefficient. The authors propose a Monte Carlo procedure based on the concept of importance sampling, which can be thousands of times more efficient than current procedures. With a reasonable amount of computing time, extremely accurate estimates of the p values can be obtained. Both theoretical results and an example of maturity-onset diabetes of the young (MODY) are presented to illustrate the efficiency performance of their method. Relations between single-model and multimodel p values are explored. The new procedure is also used to investigate the performance of asymptotic approximations in a single model situation. 22 refs., 6 figs., 1 tab.
Importance Sampling of Word Patterns in DNA and Protein Sequences
Chan, Hock Peng; Chen, Louis H.Y.
2010-01-01
Abstract Monte Carlo methods can provide accurate p-value estimates of word counting test statistics and are easy to implement. They are especially attractive when an asymptotic theory is absent or when either the search sequence or the word pattern is too short for the application of asymptotic formulae. Naive direct Monte Carlo is undesirable for the estimation of small probabilities because the associated rare events of interest are seldom generated. We propose instead efficient importance sampling algorithms that use controlled insertion of the desired word patterns on randomly generated sequences. The implementation is illustrated on word patterns of biological interest: palindromes and inverted repeats, patterns arising from position-specific weight matrices (PSWMs), and co-occurrences of pairs of motifs. PMID:21128856
CSnrc: Correlated sampling Monte Carlo calculations using EGSnrc
Buckley, Lesley A.; Kawrakow, I.; Rogers, D.W.O.
2004-12-01
CSnrc, a new user-code for the EGSnrc Monte Carlo system is described. This user-code improves the efficiency when calculating ratios of doses from similar geometries. It uses a correlated sampling variance reduction technique. CSnrc is developed from an existing EGSnrc user-code CAVRZnrc and improves upon the correlated sampling algorithm used in an earlier version of the code written for the EGS4 Monte Carlo system. Improvements over the EGS4 version of the algorithm avoid repetition of sections of particle tracks. The new code includes a rectangular phantom geometry not available in other EGSnrc cylindrical codes. Comparison to CAVRZnrc shows gains in efficiency of up to a factor of 64 for a variety of test geometries when computing the ratio of doses to the cavity for two geometries. CSnrc is well suited to in-phantom calculations and is used to calculate the central electrode correction factor P{sub cel} in high-energy photon and electron beams. Current dosimetry protocols base the value of P{sub cel} on earlier Monte Carlo calculations. The current CSnrc calculations achieve 0.02% statistical uncertainties on P{sub cel}, much lower than those previously published. The current values of P{sub cel} compare well with the values used in dosimetry protocols for photon beams. For electrons beams, CSnrc calculations are reported at the reference depth used in recent protocols and show up to a 0.2% correction for a graphite electrode, a correction currently ignored by dosimetry protocols. The calculations show that for a 1 mm diameter aluminum central electrode, the correction factor differs somewhat from the values used in both the IAEA TRS-398 code of practice and the AAPM's TG-51 protocol.
Consistent Adjoint Driven Importance Sampling using Space, Energy and Angle
Peplow, Douglas E.; Mosher, Scott W; Evans, Thomas M
2012-08-01
For challenging radiation transport problems, hybrid methods combine the accuracy of Monte Carlo methods with the global information present in deterministic methods. One of the most successful hybrid methods is CADIS Consistent Adjoint Driven Importance Sampling. This method uses a deterministic adjoint solution to construct a biased source distribution and consistent weight windows to optimize a specific tally in a Monte Carlo calculation. The method has been implemented into transport codes using just the spatial and energy information from the deterministic adjoint and has been used in many applications to compute tallies with much higher figures-of-merit than analog calculations. CADIS also outperforms user-supplied importance values, which usually take long periods of user time to develop. This work extends CADIS to develop weight windows that are a function of the position, energy, and direction of the Monte Carlo particle. Two types of consistent source biasing are presented: one method that biases the source in space and energy while preserving the original directional distribution and one method that biases the source in space, energy, and direction. Seven simple example problems are presented which compare the use of the standard space/energy CADIS with the new space/energy/angle treatments.
Estimation of cosmological parameters using adaptive importance sampling
Wraith, Darren; Kilbinger, Martin; Benabed, Karim; Prunet, Simon; Cappe, Olivier; Fort, Gersende; Cardoso, Jean-Francois; Robert, Christian P.
2009-07-15
We present a Bayesian sampling algorithm called adaptive importance sampling or population Monte Carlo (PMC), whose computational workload is easily parallelizable and thus has the potential to considerably reduce the wall-clock time required for sampling, along with providing other benefits. To assess the performance of the approach for cosmological problems, we use simulated and actual data consisting of CMB anisotropies, supernovae of type Ia, and weak cosmological lensing, and provide a comparison of results to those obtained using state-of-the-art Markov chain Monte Carlo (MCMC). For both types of data sets, we find comparable parameter estimates for PMC and MCMC, with the advantage of a significantly lower wall-clock time for PMC. In the case of WMAP5 data, for example, the wall-clock time scale reduces from days for MCMC to hours using PMC on a cluster of processors. Other benefits of the PMC approach, along with potential difficulties in using the approach, are analyzed and discussed.
Receiver function inversion by trans-dimensional Monte Carlo sampling
NASA Astrophysics Data System (ADS)
Agostinetti, N. Piana; Malinverno, A.
2010-05-01
A key question in the analysis of an inverse problem is the quantification of the non-uniqueness of the solution. Non-uniqueness arises when properties of an earth model can be varied without significantly worsening the fit to observed data. In most geophysical inverse problems, subsurface properties are parameterized using a fixed number of unknowns, and non-uniqueness has been tackled with a Bayesian approach by determining a posterior probability distribution in the parameter space that combines `a priori' information with information contained in the observed data. However, less consideration has been given to the question whether the data themselves can constrain the model complexity, that is the number of unknowns needed to fit the observations. Answering this question requires solving a trans-dimensional inverse problem, where the number of unknowns is an unknown itself. Recently, the Bayesian approach to parameter estimation has been extended to quantify the posterior probability of the model complexity (the number of model parameters) with a quantity called `evidence'. The evidence can be hard to estimate in a non-linear problem; a practical solution is to use a Monte Carlo sampling algorithm that samples models with different number of unknowns in proportion to their posterior probability. This study presents a method to solve in trans-dimensional fashion the non-linear inverse problem of inferring 1-D subsurface elastic properties from teleseismic receiver function data. The Earth parameterization consists of a variable number of horizontal layers, where little is assumed a priori about the elastic properties, the number of layers, and and their thicknesses. We developed a reversible jump Markov Chain Monte Carlo algorithm that draws samples from the posterior distribution of Earth models. The solution of the inverse problem is a posterior probability distribution of the number of layers, their thicknesses and the elastic properties as a function of
The Importance of Microhabitat for Biodiversity Sampling
Mehrabi, Zia; Slade, Eleanor M.; Solis, Angel; Mann, Darren J.
2014-01-01
Responses to microhabitat are often neglected when ecologists sample animal indicator groups. Microhabitats may be particularly influential in non-passive biodiversity sampling methods, such as baited traps or light traps, and for certain taxonomic groups which respond to fine scale environmental variation, such as insects. Here we test the effects of microhabitat on measures of species diversity, guild structure and biomass of dung beetles, a widely used ecological indicator taxon. We demonstrate that choice of trap placement influences dung beetle functional guild structure and species diversity. We found that locally measured environmental variables were unable to fully explain trap-based differences in species diversity metrics or microhabitat specialism of functional guilds. To compare the effects of habitat degradation on biodiversity across multiple sites, sampling protocols must be standardized and scale-relevant. Our work highlights the importance of considering microhabitat scale responses of indicator taxa and designing robust sampling protocols which account for variation in microhabitats during trap placement. We suggest that this can be achieved either through standardization of microhabitat or through better efforts to record relevant environmental variables that can be incorporated into analyses to account for microhabitat effects. This is especially important when rapidly assessing the consequences of human activity on biodiversity loss and associated ecosystem function and services. PMID:25469770
MARKOV CHAIN MONTE CARLO POSTERIOR SAMPLING WITH THE HAMILTONIAN METHOD
K. HANSON
2001-02-01
The Markov Chain Monte Carlo technique provides a means for drawing random samples from a target probability density function (pdf). MCMC allows one to assess the uncertainties in a Bayesian analysis described by a numerically calculated posterior distribution. This paper describes the Hamiltonian MCMC technique in which a momentum variable is introduced for each parameter of the target pdf. In analogy to a physical system, a Hamiltonian H is defined as a kinetic energy involving the momenta plus a potential energy {var_phi}, where {var_phi} is minus the logarithm of the target pdf. Hamiltonian dynamics allows one to move along trajectories of constant H, taking large jumps in the parameter space with relatively few evaluations of {var_phi} and its gradient. The Hamiltonian algorithm alternates between picking a new momentum vector and following such trajectories. The efficiency of the Hamiltonian method for multidimensional isotropic Gaussian pdfs is shown to remain constant at around 7% for up to several hundred dimensions. The Hamiltonian method handles correlations among the variables much better than the standard Metropolis algorithm. A new test, based on the gradient of {var_phi}, is proposed to measure the convergence of the MCMC sequence.
Armas-Pérez, Julio C.; Londono-Hurtado, Alejandro; Guzmán, Orlando; Hernández-Ortiz, Juan P.; Pablo, Juan J. de
2015-07-28
A theoretically informed coarse-grained Monte Carlo method is proposed for studying liquid crystals. The free energy functional of the system is described in the framework of the Landau-de Gennes formalism. The alignment field and its gradients are approximated by finite differences, and the free energy is minimized through a stochastic sampling technique. The validity of the proposed method is established by comparing the results of the proposed approach to those of traditional free energy minimization techniques. Its usefulness is illustrated in the context of three systems, namely, a nematic liquid crystal confined in a slit channel, a nematic liquid crystal droplet, and a chiral liquid crystal in the bulk. It is found that for systems that exhibit multiple metastable morphologies, the proposed Monte Carlo method is generally able to identify lower free energy states that are often missed by traditional approaches. Importantly, the Monte Carlo method identifies such states from random initial configurations, thereby obviating the need for educated initial guesses that can be difficult to formulate.
Armas-Perez, Julio C.; Londono-Hurtado, Alejandro; Guzman, Orlando; Hernandez-Ortiz, Juan P.; de Pablo, Juan J.
2015-07-27
A theoretically informed coarse-grained Monte Carlo method is proposed for studying liquid crystals. The free energy functional of the system is described in the framework of the Landau-de Gennes formalism. The alignment field and its gradients are approximated by finite differences, and the free energy is minimized through a stochastic sampling technique. The validity of the proposed method is established by comparing the results of the proposed approach to those of traditional free energy minimization techniques. Its usefulness is illustrated in the context of three systems, namely, a nematic liquid crystal confined in a slit channel, a nematic liquid crystal droplet, and a chiral liquid crystal in the bulk. It is found that for systems that exhibit multiple metastable morphologies, the proposed Monte Carlo method is generally able to identify lower free energy states that are often missed by traditional approaches. Importantly, the Monte Carlo method identifies such states from random initial configurations, thereby obviating the need for educated initial guesses that can be difficult to formulate.
Armas-Pérez, Julio C; Londono-Hurtado, Alejandro; Guzmán, Orlando; Hernández-Ortiz, Juan P; de Pablo, Juan J
2015-07-28
A theoretically informed coarse-grained Monte Carlo method is proposed for studying liquid crystals. The free energy functional of the system is described in the framework of the Landau-de Gennes formalism. The alignment field and its gradients are approximated by finite differences, and the free energy is minimized through a stochastic sampling technique. The validity of the proposed method is established by comparing the results of the proposed approach to those of traditional free energy minimization techniques. Its usefulness is illustrated in the context of three systems, namely, a nematic liquid crystal confined in a slit channel, a nematic liquid crystal droplet, and a chiral liquid crystal in the bulk. It is found that for systems that exhibit multiple metastable morphologies, the proposed Monte Carlo method is generally able to identify lower free energy states that are often missed by traditional approaches. Importantly, the Monte Carlo method identifies such states from random initial configurations, thereby obviating the need for educated initial guesses that can be difficult to formulate. PMID:26233107
Advanced interacting sequential Monte Carlo sampling for inverse scattering
NASA Astrophysics Data System (ADS)
Giraud, F.; Minvielle, P.; Del Moral, P.
2013-09-01
The following electromagnetism (EM) inverse problem is addressed. It consists in estimating the local radioelectric properties of materials recovering an object from global EM scattering measurements, at various incidences and wave frequencies. This large scale ill-posed inverse problem is explored by an intensive exploitation of an efficient 2D Maxwell solver, distributed on high performance computing machines. Applied to a large training data set, a statistical analysis reduces the problem to a simpler probabilistic metamodel, from which Bayesian inference can be performed. Considering the radioelectric properties as a hidden dynamic stochastic process that evolves according to the frequency, it is shown how advanced Markov chain Monte Carlo methods—called sequential Monte Carlo or interacting particles—can take benefit of the structure and provide local EM property estimates.
Ensemble bayesian model averaging using markov chain Monte Carlo sampling
Vrugt, Jasper A; Diks, Cees G H; Clark, Martyn P
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.
Predicting outcomes of steady-state 13C isotope tracing experiments using Monte Carlo sampling
2012-01-01
Background Carbon-13 (13C) analysis is a commonly used method for estimating reaction rates in biochemical networks. The choice of carbon labeling pattern is an important consideration when designing these experiments. We present a novel Monte Carlo algorithm for finding the optimal substrate input label for a particular experimental objective (flux or flux ratio). Unlike previous work, this method does not require assumption of the flux distribution beforehand. Results Using a large E. coli isotopomer model, different commercially available substrate labeling patterns were tested computationally for their ability to determine reaction fluxes. The choice of optimal labeled substrate was found to be dependent upon the desired experimental objective. Many commercially available labels are predicted to be outperformed by complex labeling patterns. Based on Monte Carlo Sampling, the dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of 13C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed. Conclusions While 13C analysis is a useful tool in systems biology, high redundancy in measurements limits the information that can be obtained from each experiment. It is however possible to compute potential limitations before an experiment is run and predict whether, and to what degree, the rate of each reaction can be resolved. PMID:22289253
Automatic variance reduction for Monte Carlo simulations via the local importance function transform
Turner, S.A.
1996-02-01
The author derives a transformed transport problem that can be solved theoretically by analog Monte Carlo with zero variance. However, the Monte Carlo simulation of this transformed problem cannot be implemented in practice, so he develops a method for approximating it. The approximation to the zero variance method consists of replacing the continuous adjoint transport solution in the transformed transport problem by a piecewise continuous approximation containing local biasing parameters obtained from a deterministic calculation. He uses the transport and collision processes of the transformed problem to bias distance-to-collision and selection of post-collision energy groups and trajectories in a traditional Monte Carlo simulation of ``real`` particles. He refers to the resulting variance reduction method as the Local Importance Function Transform (LIFI) method. He demonstrates the efficiency of the LIFT method for several 3-D, linearly anisotropic scattering, one-group, and multigroup problems. In these problems the LIFT method is shown to be more efficient than the AVATAR scheme, which is one of the best variance reduction techniques currently available in a state-of-the-art Monte Carlo code. For most of the problems considered, the LIFT method produces higher figures of merit than AVATAR, even when the LIFT method is used as a ``black box``. There are some problems that cause trouble for most variance reduction techniques, and the LIFT method is no exception. For example, the author demonstrates that problems with voids, or low density regions, can cause a reduction in the efficiency of the LIFT method. However, the LIFT method still performs better than survival biasing and AVATAR in these difficult cases.
Schach von Wittenau, A.E.; Cox, L.J.; Bergstrom, P.H., Jr.; Chandler, W.P.; Hartmann-Siantar, C.L.; Hornstein, S.M.
1997-10-31
We outline the techniques used within PEREGRINE, a 3D Monte Carlo code calculation system, to model the photon output from medical accelerators. We discuss the methods used to reduce the phase-space data to a form that is accurately and efficiently sampled.
Azbouche, Ahmed; Belgaid, Mohamed; Mazrou, Hakim
2015-08-01
A fully detailed Monte Carlo geometrical model of a High Purity Germanium detector with a (152)Eu source, packed in Marinelli beaker, was developed for routine analysis of large volume environmental samples. Then, the model parameters, in particular, the dead layer thickness were adjusted thanks to a specific irradiation configuration together with a fine-tuning procedure. Thereafter, the calculated efficiencies were compared to the measured ones for standard samples containing (152)Eu source filled in both grass and resin matrices packed in Marinelli beaker. From this comparison, a good agreement between experiment and Monte Carlo calculation results was obtained highlighting thereby the consistency of the geometrical computational model proposed in this work. Finally, the computational model was applied successfully to determine the (137)Cs distribution in soil matrix. From this application, instructive results were achieved highlighting, in particular, the erosion and accumulation zone of the studied site. PMID:25982445
Sequential Importance Sampling for Rare Event Estimation with Computer Experiments
Williams, Brian J.; Picard, Richard R.
2012-06-25
Importance sampling often drastically improves the variance of percentile and quantile estimators of rare events. We propose a sequential strategy for iterative refinement of importance distributions for sampling uncertain inputs to a computer model to estimate quantiles of model output or the probability that the model output exceeds a fixed or random threshold. A framework is introduced for updating a model surrogate to maximize its predictive capability for rare event estimation with sequential importance sampling. Examples of the proposed methodology involving materials strength and nuclear reactor applications will be presented. The conclusions are: (1) Importance sampling improves UQ of percentile and quantile estimates relative to brute force approach; (2) Benefits of importance sampling increase as percentiles become more extreme; (3) Iterative refinement improves importance distributions in relatively few iterations; (4) Surrogates are necessary for slow running codes; (5) Sequential design improves surrogate quality in region of parameter space indicated by importance distributions; and (6) Importance distributions and VRFs stabilize quickly, while quantile estimates may converge slowly.
On scale invariant features and sequential Monte Carlo sampling for bronchoscope tracking
NASA Astrophysics Data System (ADS)
Luó, Xióngbiao; Feuerstein, Marco; Kitasaka, Takayuki; Natori, Hiroshi; Takabatake, Hirotsugu; Hasegawa, Yoshinori; Mori, Kensaku
2011-03-01
This paper presents an improved bronchoscope tracking method for bronchoscopic navigation using scale invariant features and sequential Monte Carlo sampling. Although image-based methods are widely discussed in the community of bronchoscope tracking, they are still limited to characteristic information such as bronchial bifurcations or folds and cannot automatically resume the tracking procedure after failures, which result usually from problematic bronchoscopic video frames or airway deformation. To overcome these problems, we propose a new approach that integrates scale invariant feature-based camera motion estimation into sequential Monte Carlo sampling to achieve an accurate and robust tracking. In our approach, sequential Monte Carlo sampling is employed to recursively estimate the posterior probability densities of the bronchoscope camera motion parameters according to the observation model based on scale invariant feature-based camera motion recovery. We evaluate our proposed method on patient datasets. Experimental results illustrate that our proposed method can track a bronchoscope more accurate and robust than current state-of-the-art method, particularly increasing the tracking performance by 38.7% without using an additional position sensor.
Lévy-Ciesielski random series as a useful platform for Monte Carlo path integral sampling.
Predescu, Cristian
2005-04-01
We demonstrate that the Lévy-Ciesielski implementation of Lie-Trotter products enjoys several properties that make it extremely suitable for path-integral Monte Carlo simulations: fast computation of paths, fast Monte Carlo sampling, and the ability to use different numbers of time slices for the different degrees of freedom, commensurate with the quantum effects. It is demonstrated that a Monte Carlo simulation for which particles or small groups of variables are updated in a sequential fashion has a statistical efficiency that is always comparable to or better than that of an all-particle or all-variable update sampler. The sequential sampler results in significant computational savings if updating a variable costs only a fraction of the cost for updating all variables simultaneously or if the variables are independent. In the Lévy-Ciesielski representation, the path variables are grouped in a small number of layers, with the variables from the same layer being statistically independent. The superior performance of the fast sampling algorithm is shown to be a consequence of these observations. Both mathematical arguments and numerical simulations are employed in order to quantify the computational advantages of the sequential sampler, the Lévy-Ciesielski implementation of path integrals, and the fast sampling algorithm. PMID:15903818
Decomposition and (importance) sampling techniques for multi-stage stochastic linear programs
Infanger, G.
1993-11-01
The difficulty of solving large-scale multi-stage stochastic linear programs arises from the sheer number of scenarios associated with numerous stochastic parameters. The number of scenarios grows exponentially with the number of stages and problems get easily out of hand even for very moderate numbers of stochastic parameters per stage. Our method combines dual (Benders) decomposition with Monte Carlo sampling techniques. We employ importance sampling to efficiently obtain accurate estimates of both expected future costs and gradients and right-hand sides of cuts. The method enables us to solve practical large-scale problems with many stages and numerous stochastic parameters per stage. We discuss the theory of sharing and adjusting cuts between different scenarios in a stage. We derive probabilistic lower and upper bounds, where we use importance path sampling for the upper bound estimation. Initial numerical results turned out to be promising.
ERIC Educational Resources Information Center
Myers, Nicholas D.; Ahn, Soyeon; Jin, Ying
2011-01-01
Monte Carlo methods can be used in data analytic situations (e.g., validity studies) to make decisions about sample size and to estimate power. The purpose of using Monte Carlo methods in a validity study is to improve the methodological approach within a study where the primary focus is on construct validity issues and not on advancing…
Genheden, Samuel; Cabedo Martinez, Ana I; Criddle, Michael P; Essex, Jonathan W
2014-03-01
We present our predictions for the SAMPL4 hydration free energy challenge. Extensive all-atom Monte Carlo simulations were employed to sample the compounds in explicit solvent. While the focus of our study was to demonstrate well-converged and reproducible free energies, we attempted to address the deficiencies in the general Amber force field force field with a simple QM/MM correction. We show that by using multiple independent simulations, including different starting configurations, and enhanced sampling with parallel tempering, we can obtain well converged hydration free energies. Additional analysis using dihedral angle distributions, torsion-root mean square deviation plots and thermodynamic cycles support this assertion. We obtain a mean absolute deviation of 1.7 kcal mol(-1) and a Kendall's τ of 0.65 compared with experiment. PMID:24488307
Improved importance sampling technique for efficient simulation of digital communication systems
NASA Technical Reports Server (NTRS)
Lu, Dingqing; Yao, Kung
1988-01-01
A new, improved importance sampling (IIS) approach to simulation is considered. Some basic concepts of IS are introduced, and detailed evolutions of simulation estimation variances for Monte Carlo (MC) and IS simulations are given. The general results obtained from these evolutions are applied to the specific previously known conventional importance sampling (CIS) technique and the new IIS technique. The derivation for a linear system with no signal random memory is considered in some detail. For the CIS technique, the optimum input scaling parameter is found, while for the IIS technique, the optimum translation parameter is found. The results are generalized to a linear system with memory and signals. Specific numerical and simulation results are given which show the advantages of CIS over MC and IIS over CIS for simulations of digital communications systems.
NASA Astrophysics Data System (ADS)
Holmes, Jesse Curtis
established that depends on uncertainties in the physics models and methodology employed to produce the DOS. Through Monte Carlo sampling of perturbations from the reference phonon spectrum, an S(alpha, beta) covariance matrix may be generated. In this work, density functional theory and lattice dynamics in the harmonic approximation are used to calculate the phonon DOS for hexagonal crystalline graphite. This form of graphite is used as an example material for the purpose of demonstrating procedures for analyzing, calculating and processing thermal neutron inelastic scattering uncertainty information. Several sources of uncertainty in thermal neutron inelastic scattering calculations are examined, including sources which cannot be directly characterized through a description of the phonon DOS uncertainty, and their impacts are evaluated. Covariances for hexagonal crystalline graphite S(alpha, beta) data are quantified by coupling the standard methodology of LEAPR with a Monte Carlo sampling process. The mechanics of efficiently representing and processing this covariance information is also examined. Finally, with appropriate sensitivity information, it is shown that an S(alpha, beta) covariance matrix can be propagated to generate covariance data for integrated cross sections, secondary energy distributions, and coupled energy-angle distributions. This approach enables a complete description of thermal neutron inelastic scattering cross section uncertainties which may be employed to improve the simulation of nuclear systems.
Adaptive importance sampling of random walks on continuous state spaces
Baggerly, K.; Cox, D.; Picard, R.
1998-11-01
The authors consider adaptive importance sampling for a random walk with scoring in a general state space. Conditions under which exponential convergence occurs to the zero-variance solution are reviewed. These results generalize previous work for finite, discrete state spaces in Kollman (1993) and in Kollman, Baggerly, Cox, and Picard (1996). This paper is intended for nonstatisticians and includes considerable explanatory material.
Optimal sampling efficiency in Monte Carlo sampling with an approximate potential
Coe, Joshua D; Shaw, M Sam; Sewell, Thomas D
2009-01-01
Building on the work of Iftimie et al., Boltzmann sampling of an approximate potential (the 'reference' system) is used to build a Markov chain in the isothermal-isobaric ensemble. At the endpoints of the chain, the energy is evaluated at a higher level of approximation (the 'full' system) and a composite move encompassing all of the intervening steps is accepted on the basis of a modified Metropolis criterion. For reference system chains of sufficient length, consecutive full energies are statistically decorrelated and thus far fewer are required to build ensemble averages with a given variance. Without modifying the original algorithm, however, the maximum reference chain length is too short to decorrelate full configurations without dramatically lowering the acceptance probability of the composite move. This difficulty stems from the fact that the reference and full potentials sample different statistical distributions. By manipulating the thermodynamic variables characterizing the reference system (pressure and temperature, in this case), we maximize the average acceptance probability of composite moves, lengthening significantly the random walk between consecutive full energy evaluations. In this manner, the number of full energy evaluations needed to precisely characterize equilibrium properties is dramatically reduced. The method is applied to a model fluid, but implications for sampling high-dimensional systems with ab initio or density functional theory (DFT) potentials are discussed.
Perera, Meewanage Dilina N; Li, Ying Wai; Eisenbach, Markus; Vogel, Thomas; Landau, David P
2015-01-01
We describe the study of thermodynamics of materials using replica-exchange Wang Landau (REWL) sampling, a generic framework for massively parallel implementations of the Wang Landau Monte Carlo method. To evaluate the performance and scalability of the method, we investigate the magnetic phase transition in body-centered cubic (bcc) iron using the classical Heisenberg model parameterized with first principles calculations. We demonstrate that our framework leads to a significant speedup without compromising the accuracy and precision and facilitates the study of much larger systems than is possible with its serial counterpart.
Improving the sampling efficiency of Monte Carlo molecular simulations: an evolutionary approach
NASA Astrophysics Data System (ADS)
Leblanc, Benoit; Braunschweig, Bertrand; Toulhoat, Hervé; Lutton, Evelyne
We present a new approach in order to improve the convergence of Monte Carlo (MC) simulations of molecular systems belonging to complex energetic landscapes: the problem is redefined in terms of the dynamic allocation of MC move frequencies depending on their past efficiency, measured with respect to a relevant sampling criterion. We introduce various empirical criteria with the aim of accounting for the proper convergence in phase space sampling. The dynamic allocation is performed over parallel simulations by means of a new evolutionary algorithm involving 'immortal' individuals. The method is bench marked with respect to conventional procedures on a model for melt linear polyethylene. We record significant improvement in sampling efficiencies, thus in computational load, while the optimal sets of move frequencies are liable to allow interesting physical insights into the particular systems simulated. This last aspect should provide a new tool for designing more efficient new MC moves.
``Binless Wang-Landau sampling'' - a multicanonical Monte Carlo algorithm without histograms
NASA Astrophysics Data System (ADS)
Li, Ying Wai; Eisenbach, Markus
Inspired by the very successful Wang-Landau (WL) sampling, we innovated a multicanonical Monte Carlo algorithm to obtain the density of states (DOS) for physical systems with continuous state variables. Unlike the original WL scheme where the DOS is obtained as a numerical array of finite resolution, our algorithm assumes an analytical form for the DOS using a well chosen basis set, with coefficients determined iteratively similar to the WL approach. To avoid undesirable artificial errors caused by the discretization of state variables, we get rid of the use of a histogram for keeping track of the number of visits to energy levels, but store the visited states directly for the fitting of coefficients. This new algorithm has the advantage of producing an analytical expression for the DOS, while the original WL sampling can be readily recovered. This research was supported by the Office of Science of the Department of Energy under Contract DE-AC05-00OR22725.
Note: A pure-sampling quantum Monte Carlo algorithm with independent Metropolis
NASA Astrophysics Data System (ADS)
Vrbik, Jan; Ospadov, Egor; Rothstein, Stuart M.
2016-07-01
Recently, Ospadov and Rothstein published a pure-sampling quantum Monte Carlo algorithm (PSQMC) that features an auxiliary Path Z that connects the midpoints of the current and proposed Paths X and Y, respectively. When sufficiently long, Path Z provides statistical independence of Paths X and Y. Under those conditions, the Metropolis decision used in PSQMC is done without any approximation, i.e., not requiring microscopic reversibility and without having to introduce any G(x → x'; τ) factors into its decision function. This is a unique feature that contrasts with all competing reptation algorithms in the literature. An example illustrates that dependence of Paths X and Y has adverse consequences for pure sampling.
Schumaker, Mark F; Kramer, David M
2011-09-01
We have programmed a Monte Carlo simulation of the Q-cycle model of electron transport in cytochrome b(6)f complex, an enzyme in the photosynthetic pathway that converts sunlight into biologically useful forms of chemical energy. Results were compared with published experiments of Kramer and Crofts (Biochim. Biophys. Acta 1183:72-84, 1993). Rates for the simulation were optimized by constructing large numbers of parameter sets using Latin hypercube sampling and selecting those that gave the minimum mean square deviation from experiment. Multiple copies of the simulation program were run in parallel on a Beowulf cluster. We found that Latin hypercube sampling works well as a method for approximately optimizing very noisy objective functions of 15 or 22 variables. Further, the simplified Q-cycle model can reproduce experimental results in the presence or absence of a quinone reductase (Q(i)) site inhibitor without invoking ad hoc side-reactions. PMID:21221830
Fast Monte Carlo simulation of a dispersive sample on the SEQUOIA spectrometer at the SNS
Granroth, Garrett E; Chen, Meili; Kohl, James Arthur; Hagen, Mark E; Cobb, John W
2007-01-01
Simulation of an inelastic scattering experiment, with a sample and a large pixilated detector, usually requires days of time because of finite processor speeds. We report simulations on an SNS (Spallation Neutron Source) instrument, SEQUOIA, that reduce the time to less than 2 hours by using parallelization and the resources of the TeraGrid. SEQUOIA is a fine resolution (∆E/Ei ~ 1%) chopper spectrometer under construction at the SNS. It utilizes incident energies from Ei = 20 meV to 2 eV and will have ~ 144,000 detector pixels covering 1.6 Sr of solid angle. The full spectrometer, including a 1-D dispersive sample, has been simulated using the Monte Carlo package McStas. This paper summarizes the method of parallelization for and results from these simulations. In addition, limitations of and proposed improvements to current analysis software will be discussed.
Performance evaluation of an importance sampling technique in a Jackson network
NASA Astrophysics Data System (ADS)
brahim Mahdipour, E.; Masoud Rahmani, Amir; Setayeshi, Saeed
2014-03-01
Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.
Source of statistical noises in the Monte Carlo sampling techniques for coherently scattered photons
Muhammad, Wazir; Lee, Sang Hoon
2013-01-01
Detailed comparisons of the predictions of the Relativistic Form Factors (RFFs) and Modified Form Factors (MFFs) and their advantages and shortcomings in calculating elastic scattering cross sections can be found in the literature. However, the issues related to their implementation in the Monte Carlo (MC) sampling for coherently scattered photons is still under discussion. Secondly, the linear interpolation technique (LIT) is a popular method to draw the integrated values of squared RFFs/MFFs (i.e. ) over squared momentum transfer (). In the current study, the role/issues of RFFs/MFFs and LIT in the MC sampling for the coherent scattering were analyzed. The results showed that the relative probability density curves sampled on the basis of MFFs are unable to reveal any extra scientific information as both the RFFs and MFFs produced the same MC sampled curves. Furthermore, no relationship was established between the multiple small peaks and irregular step shapes (i.e. statistical noise) in the PDFs and either RFFs or MFFs. In fact, the noise in the PDFs appeared due to the use of LIT. The density of the noise depends upon the interval length between two consecutive points in the input data table of and has no scientific background. The probability density function curves became smoother as the interval lengths were decreased. In conclusion, these statistical noises can be efficiently removed by introducing more data points in the data tables. PMID:22984278
Implementation of unsteady sampling procedures for the parallel direct simulation Monte Carlo method
NASA Astrophysics Data System (ADS)
Cave, H. M.; Tseng, K.-C.; Wu, J.-S.; Jermy, M. C.; Huang, J.-C.; Krumdieck, S. P.
2008-06-01
An unsteady sampling routine for a general parallel direct simulation Monte Carlo method called PDSC is introduced, allowing the simulation of time-dependent flow problems in the near continuum range. A post-processing procedure called DSMC rapid ensemble averaging method (DREAM) is developed to improve the statistical scatter in the results while minimising both memory and simulation time. This method builds an ensemble average of repeated runs over small number of sampling intervals prior to the sampling point of interest by restarting the flow using either a Maxwellian distribution based on macroscopic properties for near equilibrium flows (DREAM-I) or output instantaneous particle data obtained by the original unsteady sampling of PDSC for strongly non-equilibrium flows (DREAM-II). The method is validated by simulating shock tube flow and the development of simple Couette flow. Unsteady PDSC is found to accurately predict the flow field in both cases with significantly reduced run-times over single processor code and DREAM greatly reduces the statistical scatter in the results while maintaining accurate particle velocity distributions. Simulations are then conducted of two applications involving the interaction of shocks over wedges. The results of these simulations are compared to experimental data and simulations from the literature where there these are available. In general, it was found that 10 ensembled runs of DREAM processing could reduce the statistical uncertainty in the raw PDSC data by 2.5-3.3 times, based on the limited number of cases in the present study.
Markov Chain Monte Carlo Sampling Methods for 1D Seismic and EM Data Inversion
Energy Science and Technology Software Center (ESTSC)
2008-09-22
This software provides several Markov chain Monte Carlo sampling methods for the Bayesian model developed for inverting 1D marine seismic and controlled source electromagnetic (CSEM) data. The current software can be used for individual inversion of seismic AVO and CSEM data and for joint inversion of both seismic and EM data sets. The structure of the software is very general and flexible, and it allows users to incorporate their own forward simulation codes and rockmore » physics model codes easily into this software. Although the softwae was developed using C and C++ computer languages, the user-supplied codes can be written in C, C++, or various versions of Fortran languages. The software provides clear interfaces for users to plug in their own codes. The output of this software is in the format that the R free software CODA can directly read to build MCMC objects.« less
Two-phase importance sampling for inference about transmission trees.
Numminen, Elina; Chewapreecha, Claire; Sirén, Jukka; Turner, Claudia; Turner, Paul; Bentley, Stephen D; Corander, Jukka
2014-11-01
There has been growing interest in the statistics community to develop methods for inferring transmission pathways of infectious pathogens from molecular sequence data. For many datasets, the computational challenge lies in the huge dimension of the missing data. Here, we introduce an importance sampling scheme in which the transmission trees and phylogenies of pathogens are both sampled from reasonable importance distributions, alleviating the inference. Using this approach, arbitrary models of transmission could be considered, contrary to many earlier proposed methods. We illustrate the scheme by analysing transmissions of Streptococcus pneumoniae from household to household within a refugee camp, using data in which only a fraction of hosts is observed, but which is still rich enough to unravel the within-household transmission dynamics and pairs of households between whom transmission is plausible. We observe that while probability of direct transmission is low even for the most prominent cases of transmission, still those pairs of households are geographically much closer to each other than expected under random proximity. PMID:25253455
Two-phase importance sampling for inference about transmission trees
Numminen, Elina; Chewapreecha, Claire; Sirén, Jukka; Turner, Claudia; Turner, Paul; Bentley, Stephen D.; Corander, Jukka
2014-01-01
There has been growing interest in the statistics community to develop methods for inferring transmission pathways of infectious pathogens from molecular sequence data. For many datasets, the computational challenge lies in the huge dimension of the missing data. Here, we introduce an importance sampling scheme in which the transmission trees and phylogenies of pathogens are both sampled from reasonable importance distributions, alleviating the inference. Using this approach, arbitrary models of transmission could be considered, contrary to many earlier proposed methods. We illustrate the scheme by analysing transmissions of Streptococcus pneumoniae from household to household within a refugee camp, using data in which only a fraction of hosts is observed, but which is still rich enough to unravel the within-household transmission dynamics and pairs of households between whom transmission is plausible. We observe that while probability of direct transmission is low even for the most prominent cases of transmission, still those pairs of households are geographically much closer to each other than expected under random proximity. PMID:25253455
Baba, Justin S; John, Dwayne O; Koju, Vijay
2015-01-01
The propagation of light in turbid media is an active area of research with relevance to numerous investigational fields, e.g., biomedical diagnostics and therapeutics. The statistical random-walk nature of photon propagation through turbid media is ideal for computational based modeling and simulation. Ready access to super computing resources provide a means for attaining brute force solutions to stochastic light-matter interactions entailing scattering by facilitating timely propagation of sufficient (>10million) photons while tracking characteristic parameters based on the incorporated physics of the problem. One such model that works well for isotropic but fails for anisotropic scatter, which is the case for many biomedical sample scattering problems, is the diffusion approximation. In this report, we address this by utilizing Berry phase (BP) evolution as a means for capturing anisotropic scattering characteristics of samples in the preceding depth where the diffusion approximation fails. We extend the polarization sensitive Monte Carlo method of Ramella-Roman, et al.,1 to include the computationally intensive tracking of photon trajectory in addition to polarization state at every scattering event. To speed-up the computations, which entail the appropriate rotations of reference frames, the code was parallelized using OpenMP. The results presented reveal that BP is strongly correlated to the photon penetration depth, thus potentiating the possibility of polarimetric depth resolved characterization of highly scattering samples, e.g., biological tissues.
Refined elasticity sampling for Monte Carlo-based identification of stabilizing network patterns
Childs, Dorothee; Grimbs, Sergio; Selbig, Joachim
2015-01-01
Motivation: Structural kinetic modelling (SKM) is a framework to analyse whether a metabolic steady state remains stable under perturbation, without requiring detailed knowledge about individual rate equations. It provides a representation of the system’s Jacobian matrix that depends solely on the network structure, steady state measurements, and the elasticities at the steady state. For a measured steady state, stability criteria can be derived by generating a large number of SKMs with randomly sampled elasticities and evaluating the resulting Jacobian matrices. The elasticity space can be analysed statistically in order to detect network positions that contribute significantly to the perturbation response. Here, we extend this approach by examining the kinetic feasibility of the elasticity combinations created during Monte Carlo sampling. Results: Using a set of small example systems, we show that the majority of sampled SKMs would yield negative kinetic parameters if they were translated back into kinetic models. To overcome this problem, a simple criterion is formulated that mitigates such infeasible models. After evaluating the small example pathways, the methodology was used to study two steady states of the neuronal TCA cycle and the intrinsic mechanisms responsible for their stability or instability. The findings of the statistical elasticity analysis confirm that several elasticities are jointly coordinated to control stability and that the main source for potential instabilities are mutations in the enzyme alpha-ketoglutarate dehydrogenase. Contact: dorothee.childs@embl.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26072485
Estimation variance bounds of importance sampling simulations in digital communication systems
NASA Technical Reports Server (NTRS)
Lu, D.; Yao, K.
1991-01-01
In practical applications of importance sampling (IS) simulation, two basic problems are encountered, that of determining the estimation variance and that of evaluating the proper IS parameters needed in the simulations. The authors derive new upper and lower bounds on the estimation variance which are applicable to IS techniques. The upper bound is simple to evaluate and may be minimized by the proper selection of the IS parameter. Thus, lower and upper bounds on the improvement ratio of various IS techniques relative to the direct Monte Carlo simulation are also available. These bounds are shown to be useful and computationally simple to obtain. Based on the proposed technique, one can readily find practical suboptimum IS parameters. Numerical results indicate that these bounding techniques are useful for IS simulations of linear and nonlinear communication systems with intersymbol interference in which bit error rate and IS estimation variances cannot be obtained readily using prior techniques.
NASA Astrophysics Data System (ADS)
Zou, Jinming; Saven, Jeffery G.
2003-02-01
For complex multidimensional systems, Monte Carlo methods are useful for sampling probable regions of a configuration space and, in the context of annealing, for determining "low energy" or "high scoring" configurations. Such methods have been used in protein design as means to identify amino acid sequences that are energetically compatible with a particular backbone structure. As with many other applications of Monte Carlo methods, such searches can be inefficient if trial configurations (protein sequences) in the Markov chain are chosen randomly. Here a mean-field biased Monte Carlo method (MFBMC) is presented and applied to designing and sampling protein sequences. The MFBMC method uses predetermined sequence identity probabilities wi(α) to bias the sequence selection. The wi(α) are calculated using a self-consistent, mean-field theory that can estimate the number and composition of sequences having predetermined values of energetically related foldability criteria. The MFBMC method is applied to both a simple protein model, the 27-mer lattice model, and an all-atom protein model. Compared to conventional Monte Carlo (MC) and configurational bias Monte Carlo (BMC), the MFBMC method converges faster to low energy sequences and samples such sequences more efficiently. The MFBMC method also tolerates faster cooling rates than the MC and BMC methods. The MFBMC method can be applied not only to protein sequence search, but also to a wide variety of polymeric and condensed phase systems.
Clever particle filters, sequential importance sampling and the optimal proposal
NASA Astrophysics Data System (ADS)
Snyder, Chris
2014-05-01
Particle filters rely on sequential importance sampling and it is well known that their performance can depend strongly on the choice of proposal distribution from which new ensemble members (particles) are drawn. The use of clever proposals has seen substantial recent interest in the geophysical literature, with schemes such as the implicit particle filter and the equivalent-weights particle filter. Both these schemes employ proposal distributions at time tk+1 that depend on the state at tk and the observations at time tk+1. I show that, beginning with particles drawn randomly from the conditional distribution of the state at tk given observations through tk, the optimal proposal (the distribution of the state at tk+1 given the state at tk and the observations at tk+1) minimizes the variance of the importance weights for particles at tk overall all possible proposal distributions. This means that bounds on the performance of the optimal proposal, such as those given by Snyder (2011), also bound the performance of the implicit and equivalent-weights particle filters. In particular, in spite of the fact that they may be dramatically more effective than other particle filters in specific instances, those schemes will suffer degeneracy (maximum importance weight approaching unity) unless the ensemble size is exponentially large in a quantity that, in the simplest case that all degrees of freedom in the system are i.i.d., is proportional to the system dimension. I will also discuss the behavior to be expected in more general cases, such as global numerical weather prediction, and how that behavior depends qualitatively on the observing network. Snyder, C., 2012: Particle filters, the "optimal" proposal and high-dimensional systems. Proceedings, ECMWF Seminar on Data Assimilation for Atmosphere and Ocean., 6-9 September 2011.
Sampling Enrichment toward Target Structures Using Hybrid Molecular Dynamics-Monte Carlo Simulations
Yang, Kecheng; Różycki, Bartosz; Cui, Fengchao; Shi, Ce; Chen, Wenduo; Li, Yunqi
2016-01-01
Sampling enrichment toward a target state, an analogue of the improvement of sampling efficiency (SE), is critical in both the refinement of protein structures and the generation of near-native structure ensembles for the exploration of structure-function relationships. We developed a hybrid molecular dynamics (MD)-Monte Carlo (MC) approach to enrich the sampling toward the target structures. In this approach, the higher SE is achieved by perturbing the conventional MD simulations with a MC structure-acceptance judgment, which is based on the coincidence degree of small angle x-ray scattering (SAXS) intensity profiles between the simulation structures and the target structure. We found that the hybrid simulations could significantly improve SE by making the top-ranked models much closer to the target structures both in the secondary and tertiary structures. Specifically, for the 20 mono-residue peptides, when the initial structures had the root-mean-squared deviation (RMSD) from the target structure smaller than 7 Å, the hybrid MD-MC simulations afforded, on average, 0.83 Å and 1.73 Å in RMSD closer to the target than the parallel MD simulations at 310K and 370K, respectively. Meanwhile, the average SE values are also increased by 13.2% and 15.7%. The enrichment of sampling becomes more significant when the target states are gradually detectable in the MD-MC simulations in comparison with the parallel MD simulations, and provide >200% improvement in SE. We also performed a test of the hybrid MD-MC approach in the real protein system, the results showed that the SE for 3 out of 5 real proteins are improved. Overall, this work presents an efficient way of utilizing solution SAXS to improve protein structure prediction and refinement, as well as the generation of near native structures for function annotation. PMID:27227775
Understanding Mars: The Geologic Importance of Returned Samples
NASA Astrophysics Data System (ADS)
Christensen, P. R.
2011-12-01
what are the nature, ages, and origin of the diverse suite of aqueous environments, were any of them habitable, how, when, and why did environments vary through time, and finally, did any of them host life or its precursors? A critical next step toward answering these questions would be provided through the analysis of carefully selected samples from geologically diverse and well-characterized sites that are returned to Earth for detailed study. This sample return campaign is envisioned as a sequence of three missions that collect the samples, place them into Mars orbit, and return them to Earth. Our existing scientific knowledge of Mars makes it possible to select a site at which specific, detailed hypotheses can be tested, and from which the orbital mapping can be validated and extended globally. Existing and future analysis techniques developed in laboratories around the world will provide the means to perform a wide array of tests on these samples, develop hypotheses for the origin of their chemical, isotopic, and morphologic signatures, and, most importantly, perform follow-up measurements to test and validate the findings. These analyses will dramatically improve our understanding of the geologic processes and history of Mars, and through their ties to the global geologic context, will once again revolutionize our understanding of this complex planet.
19 CFR 151.67 - Sampling by importer.
Code of Federal Regulations, 2014 CFR
2014-04-01
... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Wool and Hair § 151.67 Sampling by... quantities from the packages of wool or hair designated for examination, provided the bales or bags...
19 CFR 151.67 - Sampling by importer.
Code of Federal Regulations, 2013 CFR
2013-04-01
... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Wool and Hair § 151.67 Sampling by... quantities from the packages of wool or hair designated for examination, provided the bales or bags...
19 CFR 151.67 - Sampling by importer.
Code of Federal Regulations, 2012 CFR
2012-04-01
... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Wool and Hair § 151.67 Sampling by... quantities from the packages of wool or hair designated for examination, provided the bales or bags...
19 CFR 151.67 - Sampling by importer.
Code of Federal Regulations, 2010 CFR
2010-04-01
... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Wool and Hair § 151.67 Sampling by... quantities from the packages of wool or hair designated for examination, provided the bales or bags...
19 CFR 151.67 - Sampling by importer.
Code of Federal Regulations, 2011 CFR
2011-04-01
... TREASURY (CONTINUED) EXAMINATION, SAMPLING, AND TESTING OF MERCHANDISE Wool and Hair § 151.67 Sampling by... quantities from the packages of wool or hair designated for examination, provided the bales or bags...
Vrugt, Jasper A; Hyman, James M; Robinson, Bruce A; Higdon, Dave; Ter Braak, Cajo J F; Diks, Cees G H
2008-01-01
Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled DiffeRential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, high-dimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems.
Code of Federal Regulations, 2014 CFR
2014-07-01
... requirements for importers who import gasoline into the United States by truck. 80.1349 Section 80.1349... FUELS AND FUEL ADDITIVES Gasoline Benzene Sampling, Testing and Retention Requirements § 80.1349 Alternative sampling and testing requirements for importers who import gasoline into the United States...
NASA Astrophysics Data System (ADS)
Medhat, M. E.; Demir, Nilgun; Akar Tarim, Urkiye; Gurler, Orhan
2014-08-01
Monte Carlo simulations, FLUKA and Geant4, were performed to study mass attenuation for various types of soil at 59.5, 356.5, 661.6, 1173.2 and 1332.5 keV photon energies. Appreciable variations are noted for all parameters by changing the photon energy and the chemical composition of the sample. The simulations parameters were compared with experimental data and the XCOM program. The simulations show that the calculated mass attenuation coefficient values were closer to experimental values better than those obtained theoretically using the XCOM database for the same soil samples. The results indicate that Geant4 and FLUKA can be applied to estimate mass attenuation for various biological materials at different energies. The Monte Carlo method may be employed to make additional calculations on the photon attenuation characteristics of different soil samples collected from other places.
Local three-dimensional earthquake tomography by trans-dimensional Monte Carlo sampling
NASA Astrophysics Data System (ADS)
Piana Agostinetti, Nicola; Giacomuzzi, Genny; Malinverno, Alberto
2015-06-01
Local earthquake tomography is a non-linear and non-unique inverse problem that uses event arrival times to solve for the spatial distribution of elastic properties. The typical approach is to apply iterative linearization and derive a preferred solution, but such solutions are biased by a number of subjective choices: the starting model that is iteratively adjusted, the degree of regularization used to obtain a smooth solution, and the assumed noise level in the arrival time data. These subjective choices also affect the estimation of the uncertainties in the inverted parameters. The method presented here is developed in a Bayesian framework where a priori information and measurements are combined to define a posterior probability density of the parameters of interest: elastic properties in a subsurface 3-D model, hypocentre coordinates and noise level in the data. We apply a trans-dimensional Markov chain Monte Carlo algorithm that asymptotically samples the posterior distribution of the investigated parameters. This approach allows us to overcome the issues raised above. First, starting a number of sampling chains from random samples of the prior probability distribution lessens the dependence of the solution from the starting point. Secondly, the number of elastic parameters in the 3-D subsurface model is one of the unknowns in the inversion, and the parsimony of Bayesian inference ensures that the degree of detail in the solution is controlled by the information in the data, given realistic assumptions for the error statistics. Finally, the noise level in the data, which controls the uncertainties of the solution, is also one of the inverted parameters, providing a first-order estimate of the data errors. We apply our method to both synthetic and field arrival time data. The synthetic data inversion successfully recovers velocity anomalies, hypocentre coordinates and the level of noise in the data. The Bayesian inversion of field measurements gives results
Mielke, Steven L; Truhlar, Donald G
2016-01-21
Using Feynman path integrals, a molecular partition function can be written as a double integral with the inner integral involving all closed paths centered at a given molecular configuration, and the outer integral involving all possible molecular configurations. In previous work employing Monte Carlo methods to evaluate such partition functions, we presented schemes for importance sampling and stratification in the molecular configurations that constitute the path centroids, but we relied on free-particle paths for sampling the path integrals. At low temperatures, the path sampling is expensive because the paths can travel far from the centroid configuration. We now present a scheme for importance sampling of whole Feynman paths based on harmonic information from an instantaneous normal mode calculation at the centroid configuration, which we refer to as harmonically guided whole-path importance sampling (WPIS). We obtain paths conforming to our chosen importance function by rejection sampling from a distribution of free-particle paths. Sample calculations on CH4 demonstrate that at a temperature of 200 K, about 99.9% of the free-particle paths can be rejected without integration, and at 300 K, about 98% can be rejected. We also show that it is typically possible to reduce the overhead associated with the WPIS scheme by sampling the paths using a significantly lower-order path discretization than that which is needed to converge the partition function. PMID:26801023
NASA Astrophysics Data System (ADS)
Mielke, Steven L.; Truhlar, Donald G.
2016-01-01
Using Feynman path integrals, a molecular partition function can be written as a double integral with the inner integral involving all closed paths centered at a given molecular configuration, and the outer integral involving all possible molecular configurations. In previous work employing Monte Carlo methods to evaluate such partition functions, we presented schemes for importance sampling and stratification in the molecular configurations that constitute the path centroids, but we relied on free-particle paths for sampling the path integrals. At low temperatures, the path sampling is expensive because the paths can travel far from the centroid configuration. We now present a scheme for importance sampling of whole Feynman paths based on harmonic information from an instantaneous normal mode calculation at the centroid configuration, which we refer to as harmonically guided whole-path importance sampling (WPIS). We obtain paths conforming to our chosen importance function by rejection sampling from a distribution of free-particle paths. Sample calculations on CH4 demonstrate that at a temperature of 200 K, about 99.9% of the free-particle paths can be rejected without integration, and at 300 K, about 98% can be rejected. We also show that it is typically possible to reduce the overhead associated with the WPIS scheme by sampling the paths using a significantly lower-order path discretization than that which is needed to converge the partition function.
Trans-dimensional Monte Carlo sampling applied to the magnetotelluric inverse problem
NASA Astrophysics Data System (ADS)
Mandolesi, Eric; Piana Agostinetti, Nicola
2015-01-01
The data required to build geological models of the subsurface are often unavailable from direct measurements or well logs. In order to image the subsurface geological structures several geophysical methods have been developed. The magnetotelluric (MT) method uses natural, time-varying electromagnetic (EM) fields as its source to measure the EM impedance of the subsurface. The interpretation of these data is routinely undertaken by solving inverse problems to produce 1D, 2D or 3D electrical conductivity models of the subsurface. In classical MT inverse problems the investigated models are parametrized using a fixed number of unknowns (i.e. fixed number of layers in a 1D model, or a fixed number of cells in a 2D model), and the non-uniqueness of the solution is handled by a regularization term added to the objective function. This study presents a different approach to the 1D MT inverse problem, by using a trans-dimensional Monte Carlo sampling algorithm, where trans-dimensionality implies that the number of unknown parameters is a parameter itself. This construction has been shown to have a built-in Occam razor, so that the regularization term is not required to produce a simple model. The influences of subjective choices in the interpretation process can therefore be sensibly reduced. The inverse problem is solved within a Bayesian framework, where posterior probability distribution of the investigated parameters are sought, rather than a single best-fit model, and uncertainties on the model parameters, and their correlation, can be easily measured.
Hsu, Hsiao-Ping; Binder, Kurt
2012-01-14
Semiflexible macromolecules in dilute solution under very good solvent conditions are modeled by self-avoiding walks on the simple cubic lattice (d = 3 dimensions) and square lattice (d = 2 dimensions), varying chain stiffness by an energy penalty ε(b) for chain bending. In the absence of excluded volume interactions, the persistence length l(p) of the polymers would then simply be l(p) = l(b)(2d - 2)(-1)q(b) (-1) with q(b) = exp(-ε(b)/k(B)T), the bond length l(b) being the lattice spacing, and k(B)T is the thermal energy. Using Monte Carlo simulations applying the pruned-enriched Rosenbluth method (PERM), both q(b) and the chain length N are varied over a wide range (0.005 ≤ q(b) ≤ 1, N ≤ 50,000), and also a stretching force f is applied to one chain end (fixing the other end at the origin). In the absence of this force, in d = 2 a single crossover from rod-like behavior (for contour lengths less than l(p)) to swollen coils occurs, invalidating the Kratky-Porod model, while in d = 3 a double crossover occurs, from rods to Gaussian coils (as implied by the Kratky-Porod model) and then to coils that are swollen due to the excluded volume interaction. If the stretching force is applied, excluded volume interactions matter for the force versus extension relation irrespective of chain stiffness in d = 2, while theories based on the Kratky-Porod model are found to work in d = 3 for stiff chains in an intermediate regime of chain extensions. While for q(b) ≪ 1 in this model a persistence length can be estimated from the initial decay of bond-orientational correlations, it is argued that this is not possible for more complex wormlike chains (e.g., bottle-brush polymers). Consequences for the proper interpretation of experiments are briefly discussed. PMID:22260610
Code of Federal Regulations, 2014 CFR
2014-07-01
... refiners, gasoline importers and producers and importers of certified ethanol denaturant. 80.1630 Section... refiners, gasoline importers and producers and importers of certified ethanol denaturant. (a) Sample and test each batch of gasoline and certified ethanol denaturant. (1) Refiners and importers shall...
NASA Astrophysics Data System (ADS)
Shaw, M. Sam; Coe, Joshua D.; Sewell, Thomas D.
2009-06-01
An optimized version of the Nested Markov Chain Monte Carlo sampling method is applied to the calculation of the Hugoniot for liquid nitrogen. The ``full'' system of interest is calculated using density functional theory (DFT) with a 6-31G* basis set for the configurational energies. The ``reference'' system is given by a model potential fit to the anisotropic pair interaction of two nitrogen molecules from DFT calculations. The EOS is sampled in the isobaric-isothermal (NPT) ensemble with a trial move constructed from many Monte Carlo steps in the reference system. The trial move is then accepted with a probability chosen to give the full system distribution. The P's and T's of the reference and full systems are chosen separately to optimize the computational time required to produce the full system EOS. The method is numerically very efficient and predicts a Hugoniot in excellent agreement with experimental data.
NASA Astrophysics Data System (ADS)
Shaw, M. Sam; Coe, Joshua D.; Sewell, Thomas D.
2009-12-01
An optimized version of the Nested Markov Chain Monte Carlo sampling method is applied to the calculation of the Hugoniot for liquid nitrogen. The "full" system of interest is calculated using density functional theory (DFT) with a 6-31G* basis set for the configurational energies. The "reference" system is given by a model potential fit to the anisotropic pair interaction of two nitrogen molecules from DFT calculations. The EOS is sampled in the isobaric-isothermal (NPT) ensemble with a trial move constructed from many Monte Carlo steps in the reference system. The trial move is then accepted with a probability chosen to give the full system distribution. The P's and T's of the reference and full systems are chosen separately to optimize the computational time required to produce the full system EOS. The method is numerically very efficient and predicts a Hugoniot in excellent agreement with experimental data.
Shaw, Milton Sam; Coe, Joshua D; Sewell, Thomas D
2009-01-01
An optimized version of the Nested Markov Chain Monte Carlo sampling method is applied to the calculation of the Hugoniot for liquid nitrogen. The 'full' system of interest is calculated using density functional theory (DFT) with a 6-31 G* basis set for the configurational energies. The 'reference' system is given by a model potential fit to the anisotropic pair interaction of two nitrogen molecules from DFT calculations. The EOS is sampled in the isobaric-isothermal (NPT) ensemble with a trial move constructed from many Monte Carlo steps in the reference system. The trial move is then accepted with a probability chosen to give the full system distribution. The P's and T's of the reference and full systems are chosen separately to optimize the computational time required to produce the full system EOS. The method is numerically very efficient and predicts a Hugoniot in excellent agreement with experimental data.
NASA Astrophysics Data System (ADS)
Vogel, Thomas; Perez, Danny; Junghans, Christoph
2014-03-01
We show direct formal relationships between the Wang-Landau iteration [PRL 86, 2050 (2001)], metadynamics [PNAS 99, 12562 (2002)] and statistical temperature molecular dynamics [PRL 97, 050601 (2006)], the major Monte Carlo and molecular dynamics work horses for sampling from a generalized, multicanonical ensemble. We aim at helping to consolidate the developments in the different areas by indicating how methodological advancements can be transferred in a straightforward way, avoiding the parallel, largely independent, developments tracks observed in the past.
Aerospace Applications of Weibull and Monte Carlo Simulation with Importance Sampling
NASA Technical Reports Server (NTRS)
Bavuso, Salvatore J.
1998-01-01
Recent developments in reliability modeling and computer technology have made it practical to use the Weibull time to failure distribution to model the system reliability of complex fault-tolerant computer-based systems. These system models are becoming increasingly popular in space systems applications as a result of mounting data that support the decreasing Weibull failure distribution and the expectation of increased system reliability. This presentation introduces the new reliability modeling developments and demonstrates their application to a novel space system application. The application is a proposed guidance, navigation, and control (GN&C) system for use in a long duration manned spacecraft for a possible Mars mission. Comparisons to the constant failure rate model are presented and the ramifications of doing so are discussed.
Williams, Michael S; Ebel, Eric D
2014-11-18
The fitting of statistical distributions to chemical and microbial contamination data is a common application in risk assessment. These distributions are used to make inferences regarding even the most pedestrian of statistics, such as the population mean. The reason for the heavy reliance on a fitted distribution is the presence of left-, right-, and interval-censored observations in the data sets, with censored observations being the result of nondetects in an assay, the use of screening tests, and other practical limitations. Considerable effort has been expended to develop statistical distributions and fitting techniques for a wide variety of applications. Of the various fitting methods, Markov Chain Monte Carlo methods are common. An underlying assumption for many of the proposed Markov Chain Monte Carlo methods is that the data represent independent and identically distributed (iid) observations from an assumed distribution. This condition is satisfied when samples are collected using a simple random sampling design. Unfortunately, samples of food commodities are generally not collected in accordance with a strict probability design. Nevertheless, pseudosystematic sampling efforts (e.g., collection of a sample hourly or weekly) from a single location in the farm-to-table continuum are reasonable approximations of a simple random sample. The assumption that the data represent an iid sample from a single distribution is more difficult to defend if samples are collected at multiple locations in the farm-to-table continuum or risk-based sampling methods are employed to preferentially select samples that are more likely to be contaminated. This paper develops a weighted bootstrap estimation framework that is appropriate for fitting a distribution to microbiological samples that are collected with unequal probabilities of selection. An example based on microbial data, derived by the Most Probable Number technique, demonstrates the method and highlights the
NASA Astrophysics Data System (ADS)
Zhang, G.; Lu, D.; Ye, M.; Gunzburger, M.
2011-12-01
Markov Chain Monte Carlo (MCMC) methods have been widely used in many fields of uncertainty analysis to estimate the posterior distributions of parameters and credible intervals of predictions in the Bayesian framework. However, in practice, MCMC may be computationally unaffordable due to slow convergence and the excessive number of forward model executions required, especially when the forward model is expensive to compute. Both disadvantages arise from the curse of dimensionality, i.e., the posterior distribution is usually a multivariate function of parameters. Recently, sparse grid method has been demonstrated to be an effective technique for coping with high-dimensional interpolation or integration problems. Thus, in order to accelerate the forward model and avoid the slow convergence of MCMC, we propose a new method for uncertainty analysis based on sparse grid interpolation and quasi-Monte Carlo sampling. First, we construct a polynomial approximation of the forward model in the parameter space by using the sparse grid interpolation. This approximation then defines an accurate surrogate posterior distribution that can be evaluated repeatedly at minimal computational cost. Second, instead of using MCMC, a quasi-Monte Carlo method is applied to draw samples in the parameter space. Then, the desired probability density function of each prediction is approximated by accumulating the posterior density values of all the samples according to the prediction values. Our method has the following advantages: (1) the polynomial approximation of the forward model on the sparse grid provides a very efficient evaluation of the surrogate posterior distribution; (2) the quasi-Monte Carlo method retains the same accuracy in approximating the PDF of predictions but avoids all disadvantages of MCMC. The proposed method is applied to a controlled numerical experiment of groundwater flow modeling. The results show that our method attains the same accuracy much more efficiently
Sampson, Andrew; Le Yi; Williamson, Jeffrey F.
2012-02-15
Purpose: To demonstrate potential of correlated sampling Monte Carlo (CMC) simulation to improve the calculation efficiency for permanent seed brachytherapy (PSB) implants without loss of accuracy. Methods: CMC was implemented within an in-house MC code family (PTRAN) and used to compute 3D dose distributions for two patient cases: a clinical PSB postimplant prostate CT imaging study and a simulated post lumpectomy breast PSB implant planned on a screening dedicated breast cone-beam CT patient exam. CMC tallies the dose difference, {Delta}D, between highly correlated histories in homogeneous and heterogeneous geometries. The heterogeneous geometry histories were derived from photon collisions sampled in a geometrically identical but purely homogeneous medium geometry, by altering their particle weights to correct for bias. The prostate case consisted of 78 Model-6711 {sup 125}I seeds. The breast case consisted of 87 Model-200 {sup 103}Pd seeds embedded around a simulated lumpectomy cavity. Systematic and random errors in CMC were unfolded using low-uncertainty uncorrelated MC (UMC) as the benchmark. CMC efficiency gains, relative to UMC, were computed for all voxels, and the mean was classified in regions that received minimum doses greater than 20%, 50%, and 90% of D{sub 90}, as well as for various anatomical regions. Results: Systematic errors in CMC relative to UMC were less than 0.6% for 99% of the voxels and 0.04% for 100% of the voxels for the prostate and breast cases, respectively. For a 1 x 1 x 1 mm{sup 3} dose grid, efficiency gains were realized in all structures with 38.1- and 59.8-fold average gains within the prostate and breast clinical target volumes (CTVs), respectively. Greater than 99% of the voxels within the prostate and breast CTVs experienced an efficiency gain. Additionally, it was shown that efficiency losses were confined to low dose regions while the largest gains were located where little difference exists between the homogeneous and
Kurtz, R.J.; Heasler, P.G.; Baird, D.B.
1994-02-01
This report summarizes the results of three previous studies to evaluate and compare the effectiveness of sampling plans for steam generator tube inspections. An analytical evaluation and Monte Carlo simulation techniques were the methods used to evaluate sampling plan performance. To test the performance of candidate sampling plans under a variety of conditions, ranges of inspection system reliability were considered along with different distributions of tube degradation. Results from the eddy current reliability studies performed with the retired-from-service Surry 2A steam generator were utilized to guide the selection of appropriate probability of detection and flaw sizing models for use in the analysis. Different distributions of tube degradation were selected to span the range of conditions that might exist in operating steam generators. The principal means of evaluating sampling performance was to determine the effectiveness of the sampling plan for detecting and plugging defective tubes. A summary of key results from the eddy current reliability studies is presented. The analytical and Monte Carlo simulation analyses are discussed along with a synopsis of key results and conclusions.
Šoštarić, Marko; Babić, Dinko; Petrinec, Branko; Zgorelec, Željka
2016-07-01
We develop a simple and widely applicable method for determining the self-attenuation correction in gamma-ray spectrometry on environmental samples. The method relies on measurements of the transmission of photons over the matrices of a calibration standard and an analysed sample. Results of this experiment are used in subsequent Monte Carlo simulations in which we first determine the linear attenuation coefficients (μ) of the two matrices and then the self-attenuation correction for the analysed sample. The method is validated by reproducing, over a wide energy range, the literature data for the μ of water. We demonstrate the use of the method on a sample of sand, for which we find that the correction is considerable below ~400keV, where many naturally occurring radionuclides emit gamma rays. At the lowest inspected energy (~60keV), one measures an activity that is by a factor of ~1.8 smaller than its true value. PMID:27157125
Vargas, M Jurado; Timón, A Fernández; Díaz, N Cornejo; Sánchez, D Pérez
2002-12-01
Gamma-ray self-attenuation corrections in the energy range 60-2000 keV were evaluated by means of Monte Carlo calculations for environmental samples in a cylindrical measuring geometry. The dependence of the full-energy peak efficiency on the sample density was obtained for some particular photon energies and, as a result, the corresponding self-attenuation correction factors were obtained. The calculations were performed by assuming that natural materials have mass attenuation coefficients very similar to those of water in the energy range studied. Three different HpGe coaxial detectors were considered: an n-type detector with 44.3% relative efficiency and two p-type detectors of relative efficiencies 20.0% and 30.5%. Our calculations were in very good agreement with the self-attenuation correction factors obtained experimentally by other workers for environmental samples of different densities. This work demonstrates the reliability of Monte Carlo calculations for correcting photon self-attenuation in natural samples. The results also show that the corresponding correction factors are essentially unaffected by the specific coaxial detector used. PMID:12406634
Radiative transfer and spectroscopic databases: A line-sampling Monte Carlo approach
NASA Astrophysics Data System (ADS)
Galtier, Mathieu; Blanco, Stéphane; Dauchet, Jérémi; El Hafi, Mouna; Eymet, Vincent; Fournier, Richard; Roger, Maxime; Spiesser, Christophe; Terrée, Guillaume
2016-03-01
Dealing with molecular-state transitions for radiative transfer purposes involves two successive steps that both reach the complexity level at which physicists start thinking about statistical approaches: (1) constructing line-shaped absorption spectra as the result of very numerous state-transitions, (2) integrating over optical-path domains. For the first time, we show here how these steps can be addressed simultaneously using the null-collision concept. This opens the door to the design of Monte Carlo codes directly estimating radiative transfer observables from spectroscopic databases. The intermediate step of producing accurate high-resolution absorption spectra is no longer required. A Monte Carlo algorithm is proposed and applied to six one-dimensional test cases. It allows the computation of spectrally integrated intensities (over 25 cm-1 bands or the full IR range) in a few seconds, regardless of the retained database and line model. But free parameters need to be selected and they impact the convergence. A first possible selection is provided in full detail. We observe that this selection is highly satisfactory for quite distinct atmospheric and combustion configurations, but a more systematic exploration is still in progress.
Alrefae, T
2014-12-01
A simple method of efficiency calibration for gamma spectrometry was performed. This method, which focused on measuring the radioactivity of (137)Cs in food samples, was based on Monte Carlo simulations available in the free-of-charge toolkit GEANT4. Experimentally, the efficiency values of a high-purity germanium detector were calculated for three reference materials representing three different food items. These efficiency values were compared with their counterparts produced by a computer code that simulated experimental conditions. Interestingly, the output of the simulation code was in acceptable agreement with the experimental findings, thus validating the proposed method. PMID:24214912
Tang, Ke; Zhang, Jinfeng; Liang, Jie
2014-01-01
Loops in proteins are flexible regions connecting regular secondary structures. They are often involved in protein functions through interacting with other molecules. The irregularity and flexibility of loops make their structures difficult to determine experimentally and challenging to model computationally. Conformation sampling and energy evaluation are the two key components in loop modeling. We have developed a new method for loop conformation sampling and prediction based on a chain growth sequential Monte Carlo sampling strategy, called Distance-guided Sequential chain-Growth Monte Carlo (DiSGro). With an energy function designed specifically for loops, our method can efficiently generate high quality loop conformations with low energy that are enriched with near-native loop structures. The average minimum global backbone RMSD for 1,000 conformations of 12-residue loops is Å, with a lowest energy RMSD of Å, and an average ensemble RMSD of Å. A novel geometric criterion is applied to speed up calculations. The computational cost of generating 1,000 conformations for each of the x loops in a benchmark dataset is only about cpu minutes for 12-residue loops, compared to ca cpu minutes using the FALCm method. Test results on benchmark datasets show that DiSGro performs comparably or better than previous successful methods, while requiring far less computing time. DiSGro is especially effective in modeling longer loops (– residues). PMID:24763317
NASA Astrophysics Data System (ADS)
Idiri, Z.; Mazrou, H.; Beddek, S.; Amokrane, A.; Azbouche, A.
2007-07-01
The present paper describes the optimization of sample dimensions of a 241Am-Be neutron source-based Prompt gamma neutron activation analysis (PGNAA) setup devoted for in situ environmental water rejects analysis. The optimal dimensions have been achieved following extensive Monte Carlo neutron flux calculations using MCNP5 computer code. A validation process has been performed for the proposed preliminary setup with measurements of thermal neutron flux by activation technique of indium foils, bare and with cadmium covered sheet. Sensitive calculations were subsequently performed to simulate real conditions of in situ analysis by determining thermal neutron flux perturbations in samples according to chlorine and organic matter concentrations changes. The desired optimal sample dimensions were finally achieved once established constraints regarding neutron damage to semi-conductor gamma detector, pulse pile-up, dead time and radiation hazards were fully met.
Zhang, Zhe; Schindler, Christina E. M.; Lange, Oliver F.; Zacharias, Martin
2015-01-01
The high-resolution refinement of docked protein-protein complexes can provide valuable structural and mechanistic insight into protein complex formation complementing experiment. Monte Carlo (MC) based approaches are frequently applied to sample putative interaction geometries of proteins including also possible conformational changes of the binding partners. In order to explore efficiency improvements of the MC sampling, several enhanced sampling techniques, including temperature or Hamiltonian replica exchange and well-tempered ensemble approaches, have been combined with the MC method and were evaluated on 20 protein complexes using unbound partner structures. The well-tempered ensemble method combined with a 2-dimensional temperature and Hamiltonian replica exchange scheme (WTE-H-REMC) was identified as the most efficient search strategy. Comparison with prolonged MC searches indicates that the WTE-H-REMC approach requires approximately 5 times fewer MC steps to identify near native docking geometries compared to conventional MC searches. PMID:26053419
Zhang, Zhe; Schindler, Christina E M; Lange, Oliver F; Zacharias, Martin
2015-01-01
The high-resolution refinement of docked protein-protein complexes can provide valuable structural and mechanistic insight into protein complex formation complementing experiment. Monte Carlo (MC) based approaches are frequently applied to sample putative interaction geometries of proteins including also possible conformational changes of the binding partners. In order to explore efficiency improvements of the MC sampling, several enhanced sampling techniques, including temperature or Hamiltonian replica exchange and well-tempered ensemble approaches, have been combined with the MC method and were evaluated on 20 protein complexes using unbound partner structures. The well-tempered ensemble method combined with a 2-dimensional temperature and Hamiltonian replica exchange scheme (WTE-H-REMC) was identified as the most efficient search strategy. Comparison with prolonged MC searches indicates that the WTE-H-REMC approach requires approximately 5 times fewer MC steps to identify near native docking geometries compared to conventional MC searches. PMID:26053419
Otsuki, Soichi
2016-02-01
This paper presents a theory describing totally incoherent multiple scattering of turbid spherical samples. It is proved that if reciprocity and mirror symmetry hold for single scattering by a particle, they also hold for multiple scattering in spherical samples. Monte Carlo simulations generate a reduced effective scattering Mueller matrix, which virtually satisfies reciprocity and mirror symmetry. The scattering matrix was factorized by using the symmetric decomposition in a predefined form, as well as the Lu-Chipman polar decomposition, approximately into a product of a pure depolarizer and vertically oriented linear retarding diattenuators. The parameters of these components were calculated as a function of the polar angle. While the turbid spherical sample is a pure depolarizer at low polar angles, it obtains more functions of the retarding diattenuator with increasing polar angle. PMID:26831777
Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Van Leemput, Koen
2013-10-01
Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer's disease classification task. As an additional benefit, the technique also allows one to compute informative "error bars" on the volume estimates of individual structures. PMID:23773521
Iglesias, Juan Eugenio; Sabuncu, Mert Rory; Leemput, Koen Van
2013-01-01
Many segmentation algorithms in medical image analysis use Bayesian modeling to augment local image appearance with prior anatomical knowledge. Such methods often contain a large number of free parameters that are first estimated and then kept fixed during the actual segmentation process. However, a faithful Bayesian analysis would marginalize over such parameters, accounting for their uncertainty by considering all possible values they may take. Here we propose to incorporate this uncertainty into Bayesian segmentation methods in order to improve the inference process. In particular, we approximate the required marginalization over model parameters using computationally efficient Markov chain Monte Carlo techniques. We illustrate the proposed approach using a recently developed Bayesian method for the segmentation of hippocampal subfields in brain MRI scans, showing a significant improvement in an Alzheimer’s disease classification task. As an additional benefit, the technique also allows one to compute informative “error bars” on the volume estimates of individual structures. PMID:23773521
Zhang, Jian; Nielsen, Scott E.; Grainger, Tess N.; Kohler, Monica; Chipchar, Tim; Farr, Daniel R.
2014-01-01
Documenting and estimating species richness at regional or landscape scales has been a major emphasis for conservation efforts, as well as for the development and testing of evolutionary and ecological theory. Rarely, however, are sampling efforts assessed on how they affect detection and estimates of species richness and rarity. In this study, vascular plant richness was sampled in 356 quarter hectare time-unlimited survey plots in the boreal region of northeast Alberta. These surveys consisted of 15,856 observations of 499 vascular plant species (97 considered to be regionally rare) collected by 12 observers over a 2 year period. Average survey time for each quarter-hectare plot was 82 minutes, ranging from 20 to 194 minutes, with a positive relationship between total survey time and total plant richness. When survey time was limited to a 20-minute search, as in other Alberta biodiversity methods, 61 species were missed. Extending the survey time to 60 minutes, reduced the number of missed species to 20, while a 90-minute cut-off time resulted in the loss of 8 species. When surveys were separated by habitat type, 60 minutes of search effort sampled nearly 90% of total observed richness for all habitats. Relative to rare species, time-unlimited surveys had ∼65% higher rare plant detections post-20 minutes than during the first 20 minutes of the survey. Although exhaustive sampling was attempted, observer bias was noted among observers when a subsample of plots was re-surveyed by different observers. Our findings suggest that sampling time, combined with sample size and observer effects, should be considered in landscape-scale plant biodiversity surveys. PMID:24740179
Sample Size Determination for Regression Models Using Monte Carlo Methods in R
ERIC Educational Resources Information Center
Beaujean, A. Alexander
2014-01-01
A common question asked by researchers using regression models is, What sample size is needed for my study? While there are formulae to estimate sample sizes, their assumptions are often not met in the collected data. A more realistic approach to sample size determination requires more information such as the model of interest, strength of the…
Mora, Leonor; Martínez, Indira; Figuera, Lourdes; Segura, Merlyn; Del Valle, Guilarte
2010-12-01
In Sucre state, the Manzanares river is threatened by domestic, agricultural and industrial activities, becoming an environmental risk factor for its inhabitants. In this sense, the presence of protozoans in superficial waters of tributaries of the Manzanares river (Orinoco river, Quebrada Seca, San Juan river), Montes municipality, Sucre state, as well as the analysis of faecal samples from inhabitants of towns bordering these tributaries were evaluated. We collected faecal and water samples from may 2006 through april 2007. The superficial water samples were processed after centrifugation by the direct examination and floculation, using lugol, modified Kinyoun and trichromic colorations. Fecal samples where analyzed by direct examination with physiological saline solution and the modified Ritchie concentration method and using the other colorations techniques above mentioned. The most frequently observed protozoans in superficial waters in the three tributaries were: Amoebas, Blastocystis sp, Endolimax sp., Chilomastix sp. and Giardia sp. Whereas in faecal samples, Blastocystis hominis, Endolimax nana and Entaomeba coli had the greatest frequencies in the three communities. The inhabitants of Orinoco La Peña turned out to be most susceptible to these parasitic infections (77.60%), followed by San Juan River (46.63%) and Quebrada Seca (39.49%). The presence of pathogenic and nonpathogenic protozoans in superficial waters demonstrates the faecal contamination of the tributaries, representing a constant focus of infection for their inhabitants, inferred by the observation of the same species in both types of samples. PMID:21365874
Minimum Sample Size for Cronbach's Coefficient Alpha: A Monte-Carlo Study
ERIC Educational Resources Information Center
Yurdugul, Halil
2008-01-01
The coefficient alpha is the most widely used measure of internal consistency for composite scores in the educational and psychological studies. However, due to the difficulties of data gathering in psychometric studies, the minimum sample size for the sample coefficient alpha has been frequently debated. There are various suggested minimum sample…
Fast and accurate Monte Carlo sampling of first-passage times from Wiener diffusion models
Drugowitsch, Jan
2016-01-01
We present a new, fast approach for drawing boundary crossing samples from Wiener diffusion models. Diffusion models are widely applied to model choices and reaction times in two-choice decisions. Samples from these models can be used to simulate the choices and reaction times they predict. These samples, in turn, can be utilized to adjust the models’ parameters to match observed behavior from humans and other animals. Usually, such samples are drawn by simulating a stochastic differential equation in discrete time steps, which is slow and leads to biases in the reaction time estimates. Our method, instead, facilitates known expressions for first-passage time densities, which results in unbiased, exact samples and a hundred to thousand-fold speed increase in typical situations. In its most basic form it is restricted to diffusion models with symmetric boundaries and non-leaky accumulation, but our approach can be extended to also handle asymmetric boundaries or to approximate leaky accumulation. PMID:26864391
Podtelezhnikov, Alexei A; Wild, David L
2005-10-01
We propose a novel Metropolis Monte Carlo procedure for protein modeling and analyze the influence of hydrogen bonding on the distribution of polyalanine conformations. We use an atomistic model of the polyalanine chain with rigid and planar polypeptide bonds, and elastic alpha carbon valence geometry. We adopt a simplified energy function in which only hard-sphere repulsion and hydrogen bonding interactions between the atoms are considered. Our Metropolis Monte Carlo procedure utilizes local crankshaft moves and is combined with parallel tempering to exhaustively sample the conformations of 16-mer polyalanine. We confirm that Flory's isolated-pair hypothesis (the steric independence between the dihedral angles of individual amino acids) does not hold true in long polypeptide chains. In addition to 3(10)- and alpha-helices, we identify a kink stabilized by 2 hydrogen bonds with a shared acceptor as a common structural motif. Varying the strength of hydrogen bonds, we induce the helix-coil transition in the model polypeptide chain. We compare the propensities for various hydrogen bonding patterns and determine the degree of cooperativity of hydrogen bond formation in terms of the Hill coefficient. The observed helix-coil transition is also quantified according to Zimm-Bragg theory. PMID:16049911
NASA Astrophysics Data System (ADS)
Furuta, T.; Maeyama, T.; Ishikawa, K. L.; Fukunishi, N.; Fukasaku, K.; Takagi, S.; Noda, S.; Himeno, R.; Hayashi, S.
2015-08-01
In this research, we used a 135 MeV/nucleon carbon-ion beam to irradiate a biological sample composed of fresh chicken meat and bones, which was placed in front of a PAGAT gel dosimeter, and compared the measured and simulated transverse-relaxation-rate (R2) distributions in the gel dosimeter. We experimentally measured the three-dimensional R2 distribution, which records the dose induced by particles penetrating the sample, by using magnetic resonance imaging. The obtained R2 distribution reflected the heterogeneity of the biological sample. We also conducted Monte Carlo simulations using the PHITS code by reconstructing the elemental composition of the biological sample from its computed tomography images while taking into account the dependence of the gel response on the linear energy transfer. The simulation reproduced the experimental distal edge structure of the R2 distribution with an accuracy under about 2 mm, which is approximately the same as the voxel size currently used in treatment planning.
Furuta, T; Maeyama, T; Ishikawa, K L; Fukunishi, N; Fukasaku, K; Takagi, S; Noda, S; Himeno, R; Hayashi, S
2015-08-21
In this research, we used a 135 MeV/nucleon carbon-ion beam to irradiate a biological sample composed of fresh chicken meat and bones, which was placed in front of a PAGAT gel dosimeter, and compared the measured and simulated transverse-relaxation-rate (R2) distributions in the gel dosimeter. We experimentally measured the three-dimensional R2 distribution, which records the dose induced by particles penetrating the sample, by using magnetic resonance imaging. The obtained R2 distribution reflected the heterogeneity of the biological sample. We also conducted Monte Carlo simulations using the PHITS code by reconstructing the elemental composition of the biological sample from its computed tomography images while taking into account the dependence of the gel response on the linear energy transfer. The simulation reproduced the experimental distal edge structure of the R2 distribution with an accuracy under about 2 mm, which is approximately the same as the voxel size currently used in treatment planning. PMID:26266894
A new paradigm for petascale Monte Carlo simulation: Replica exchange Wang Landau sampling
Li, Ying Wai; Vogel, Thomas; Wuest, Thomas; Landau, David P
2014-01-01
We introduce a generic, parallel Wang Landau method that is naturally suited to implementation on massively parallel, petaflop supercomputers. The approach introduces a replica-exchange framework in which densities of states for overlapping sub-windows in energy space are determined iteratively by traditional Wang Landau sampling. The advantages and general applicability of the method are demonstrated for several distinct systems that possess discrete or continuous degrees of freedom, including those with complex free energy landscapes and topological constraints.
NASA Astrophysics Data System (ADS)
Feroz, F.; Hobson, M. P.
2008-02-01
In performing a Bayesian analysis of astronomical data, two difficult problems often emerge. First, in estimating the parameters of some model for the data, the resulting posterior distribution may be multimodal or exhibit pronounced (curving) degeneracies, which can cause problems for traditional Markov Chain Monte Carlo (MCMC) sampling methods. Secondly, in selecting between a set of competing models, calculation of the Bayesian evidence for each model is computationally expensive using existing methods such as thermodynamic integration. The nested sampling method introduced by Skilling, has greatly reduced the computational expense of calculating evidence and also produces posterior inferences as a by-product. This method has been applied successfully in cosmological applications by Mukherjee, Parkinson & Liddle, but their implementation was efficient only for unimodal distributions without pronounced degeneracies. Shaw, Bridges & Hobson recently introduced a clustered nested sampling method which is significantly more efficient in sampling from multimodal posteriors and also determines the expectation and variance of the final evidence from a single run of the algorithm, hence providing a further increase in efficiency. In this paper, we build on the work of Shaw et al. and present three new methods for sampling and evidence evaluation from distributions that may contain multiple modes and significant degeneracies in very high dimensions; we also present an even more efficient technique for estimating the uncertainty on the evaluated evidence. These methods lead to a further substantial improvement in sampling efficiency and robustness, and are applied to two toy problems to demonstrate the accuracy and economy of the evidence calculation and parameter estimation. Finally, we discuss the use of these methods in performing Bayesian object detection in astronomical data sets, and show that they significantly outperform existing MCMC techniques. An implementation
Tundisi, J G; Matsumura-Tundisi, T; Tundisi, J E M; Faria, C R L; Abe, D S; Blanco, F; Rodrigues Filho, J; Campanelli, L; Sidagis Galli, C; Teixeira-Silva, V; Degani, R; Soares, F S; Gatti Junior, P
2015-08-01
In this paper the authors describe the limnological approaches, the sampling methodology, and strategy adopted in the study of the Xingu River in the area of influence of future Belo Monte Power Plant. The river ecosystems are characterized by unidirectional current, highly variable in time depending on the climatic situation the drainage pattern an hydrological cycle. Continuous vertical mixing with currents and turbulence, are characteristic of these ecosystems. All these basic mechanisms were taken into consideration in the sampling strategy and field work carried out in the Xingu River Basin, upstream and downstream the future Belo Monte Power Plant Units. PMID:26691072
ERIC Educational Resources Information Center
In'nami, Yo; Koizumi, Rie
2013-01-01
The importance of sample size, although widely discussed in the literature on structural equation modeling (SEM), has not been widely recognized among applied SEM researchers. To narrow this gap, we focus on second language testing and learning studies and examine the following: (a) Is the sample size sufficient in terms of precision and power of…
Kuruvilla Verghese
2002-04-05
This report summarizes the highlights of the research performed under the 1-year NEER grant from the Department of Energy. The primary goal of this study was to investigate the effects of certain design changes in the Fisher Senoscan mammography system and in the degree of breast compression on the discernability of microcalcifications in calcification clusters often observed in mammograms with tumor lesions. The most important design change that one can contemplate in a digital mammography system to improve resolution of calcifications is the reduction of pixel dimensions of the digital detector. Breast compression is painful to the patient and is though to be a deterrent to women to get routine mammographic screening. Calcification clusters often serve as markers (indicators ) of breast cancer.
NASA Astrophysics Data System (ADS)
Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad; Janssen, Hans
2015-02-01
The majority of literature regarding optimized Latin hypercube sampling (OLHS) is devoted to increasing the efficiency of these sampling strategies through the development of new algorithms based on the combination of innovative space-filling criteria and specialized optimization schemes. However, little attention has been given to the impact of the initial design that is fed into the optimization algorithm, on the efficiency of OLHS strategies. Previous studies, as well as codes developed for OLHS, have relied on one of the following two approaches for the selection of the initial design in OLHS: (1) the use of random points in the hypercube intervals (random LHS), and (2) the use of midpoints in the hypercube intervals (midpoint LHS). Both approaches have been extensively used, but no attempt has been previously made to compare the efficiency and robustness of their resulting sample designs. In this study we compare the two approaches and show that the space-filling characteristics of OLHS designs are sensitive to the initial design that is fed into the optimization algorithm. It is also illustrated that the space-filling characteristics of OLHS designs based on midpoint LHS are significantly better those based on random LHS. The two approaches are compared by incorporating their resulting sample designs in Monte Carlo simulation (MCS) for uncertainty propagation analysis, and then, by employing the sample designs in the selection of the training set for constructing non-intrusive polynomial chaos expansion (NIPCE) meta-models which subsequently replace the original full model in MCSs. The analysis is based on two case studies involving numerical simulation of density dependent flow and solute transport in porous media within the context of seawater intrusion in coastal aquifers. We show that the use of midpoint LHS as the initial design increases the efficiency and robustness of the resulting MCSs and NIPCE meta-models. The study also illustrates that this
40 CFR 80.330 - What are the sampling and testing requirements for refiners and importers?
Code of Federal Regulations, 2013 CFR
2013-07-01
... 40 Protection of Environment 17 2013-07-01 2013-07-01 false What are the sampling and testing requirements for refiners and importers? 80.330 Section 80.330 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) REGULATION OF FUELS AND FUEL ADDITIVES Gasoline Sulfur Sampling, Testing and...
40 CFR 80.330 - What are the sampling and testing requirements for refiners and importers?
Code of Federal Regulations, 2010 CFR
2010-07-01
... 40 Protection of Environment 16 2010-07-01 2010-07-01 false What are the sampling and testing requirements for refiners and importers? 80.330 Section 80.330 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) REGULATION OF FUELS AND FUEL ADDITIVES Gasoline Sulfur Sampling, Testing and...
Sampling High-Altitude and Stratified Mating Flights of Red Imported Fire Ant
Technology Transfer Automated Retrieval System (TEKTRAN)
With the exception of an airplane equipped with nets, no method has been developed that successfully samples red imported fire ant, Solenopsis invicta Buren, sexuals in mating/dispersal flights throughout their potential altitudinal trajectories. We developed and tested a method for sampling queens ...
Ledra, Mohammed; El Hdiy, Abdelillah
2015-09-21
A Monte-Carlo simulation algorithm is used to study electron beam induced current in an intrinsic silicon sample, which contains at its surface a linear arrangement of uncapped nanocrystals positioned in the irradiation trajectory around the hemispherical collecting nano-contact. The induced current is generated by the use of electron beam energy of 5 keV in a perpendicular configuration. Each nanocrystal is considered as a recombination center, and the surface recombination velocity at the free surface is taken to be zero. It is shown that the induced current is affected by the distance separating each nanocrystal from the nano-contact. An increase of this separation distance translates to a decrease of the nanocrystals density and an increase of the minority carrier diffusion length. The results reveal a threshold separation distance from which nanocrystals have no more effect on the collection efficiency, and the diffusion length reaches the value obtained in the absence of nanocrystals. A cross-section characterizing the nano-contact ability to trap carriers was determined.
NASA Astrophysics Data System (ADS)
Han, Mancheon; Lee, Choong-Ki; Choi, Hyoung Joon
Hybridization-expansion continuous-time quantum Monte Carlo (CT-HYB) is a popular approach in real material researches because it allows to deal with non-density-density-type interaction. In the conventional CT-HYB, we measure Green's function and find the self energy from the Dyson equation. Because one needs to compute the inverse of the statistical data in this approach, obtained self energy is very sensitive to statistical noise. For that reason, the measurement is not reliable except for low frequencies. Such an error can be suppressed by measuring a special type of higher-order correlation function and is implemented for density-density-type interaction. With the help of the recently reported worm-sampling measurement, we developed an improved self energy measurement scheme which can be applied to any type of interactions. As an illustration, we calculated the self energy for the 3-orbital Hubbard-Kanamori-type Hamiltonian with our newly developed method. This work was supported by NRF of Korea (Grant No. 2011-0018306) and KISTI supercomputing center (Project No. KSC-2015-C3-039)
An Overview of Importance Splitting for Rare Event Simulation
ERIC Educational Resources Information Center
Morio, Jerome; Pastel, Rudy; Le Gland, Francois
2010-01-01
Monte Carlo simulations are a classical tool to analyse physical systems. When unlikely events are to be simulated, the importance sampling technique is often used instead of Monte Carlo. Importance sampling has some drawbacks when the problem dimensionality is high or when the optimal importance sampling density is complex to obtain. In this…
Coalescent: an open-science framework for importance sampling in coalescent theory
Spouge, John L.
2015-01-01
Background. In coalescent theory, computer programs often use importance sampling to calculate likelihoods and other statistical quantities. An importance sampling scheme can exploit human intuition to improve statistical efficiency of computations, but unfortunately, in the absence of general computer frameworks on importance sampling, researchers often struggle to translate new sampling schemes computationally or benchmark against different schemes, in a manner that is reliable and maintainable. Moreover, most studies use computer programs lacking a convenient user interface or the flexibility to meet the current demands of open science. In particular, current computer frameworks can only evaluate the efficiency of a single importance sampling scheme or compare the efficiencies of different schemes in an ad hoc manner. Results. We have designed a general framework (http://coalescent.sourceforge.net; language: Java; License: GPLv3) for importance sampling that computes likelihoods under the standard neutral coalescent model of a single, well-mixed population of constant size over time following infinite sites model of mutation. The framework models the necessary core concepts, comes integrated with several data sets of varying size, implements the standard competing proposals, and integrates tightly with our previous framework for calculating exact probabilities. For a given dataset, it computes the likelihood and provides the maximum likelihood estimate of the mutation parameter. Well-known benchmarks in the coalescent literature validate the accuracy of the framework. The framework provides an intuitive user interface with minimal clutter. For performance, the framework switches automatically to modern multicore hardware, if available. It runs on three major platforms (Windows, Mac and Linux). Extensive tests and coverage make the framework reliable and maintainable. Conclusions. In coalescent theory, many studies of computational efficiency consider only
NASA Astrophysics Data System (ADS)
Mauclaire, L.; McKenzie, J. A.; Schwyn, B.; Bossart, P.
Although microorganisms have been isolated from various deep-subsurface environments, the persistence of microbial activity in claystones buried to great depths and on geological time scales has been poorly studied. The presence of in-situ microbial life in the Opalinus Clay Formation (Mesozoic claystone, 170 million years old) at the Mont Terri Rock Laboratory, Canton Jura, Switzerland was investigated. Opalinus Clay is a host rock candidate for a radioactive waste repository. Particle tracer tests demonstrated the uncontaminated nature of the cored samples, showing their suitability for microbiological investigations. To determine whether microorganisms are a consistent and characteristic component of the Opalinus Clay Formation, two approaches were used: (i) the cultivation of indigenous micoorganisms focusing mainly on the cultivation of sulfate-reducing bacteria, and (ii) the direct detection of molecular biomarkers of bacteria. The goal of the first set of experiments was to assess the presence of cultivable microorganisms within the Opalinus Clay Formation. After few months of incubation, the number of cell ranged from 0.1 to 2 × 10 3 cells ml -1 media. The microorganisms were actively growing as confirmed by the observation of dividing cells, and detection of traces of sulfide. To avoid cultivation bias, quantification of molecular biomarkers (phospholipid fatty acids) was used to assess the presence of autochthonous microorganisms. These molecules are good indicators of the presence of living cells. The Opalinus Clay contained on average 64 ng of PLFA g -1 dry claystone. The detected microbial community comprises mainly Gram-negative anaerobic bacteria as indicated by the ratio of iso/anteiso phospholipids (about 2) and the detection of large amount of β-hydroxy substituted fatty acids. The PLFA composition reveals the presence of specific functional groups of microorganisms in particular sulfate-reducing bacteria ( Desulfovibrio, Desulfobulbus, and
NASA Astrophysics Data System (ADS)
Barclay, Thomas; Quintana, Elisa; Adams, Fred; Ciardi, David; Huber, Daniel; Foreman-Mackey, Daniel; Montet, Benjamin Tyler; Caldwell, Douglas
2015-08-01
Kepler-296 is a binary star system with two M-dwarf components separated by 0.2 arcsec. Five transiting planets have been confirmed to be associated with the Kepler-296 system; given the evidence to date, however, the planets could in principle orbit either star. This ambiguity has made it difficult to constrain both the orbital and physical properties of the planets. Using both statistical and analytical arguments, this paper shows that all five planets are highly likely to orbit the primary star in this system. We performed a Markov-Chain Monte Carlo simulation using a five transiting planet model, leaving the stellar density and dilution with uniform priors. Using importance sampling, we compared the model probabilities under the priors of the planets orbiting either the brighter or the fainter component of the binary. A model where the planets orbit the brighter component, Kepler-296A, is strongly preferred by the data. Combined with our assertion that all five planets orbit the same star, the two outer planets in the system, Kepler-296 Ae and Kepler-296 Af, have radii of 1.53 ± 0.26 and 1.80 ± 0.31 R⊕, respectively, and receive incident stellar fluxes of 1.40 ± 0.23 and 0.62 ± 0.10 times the incident flux the Earth receives from the Sun. This level of irradiation places both planets within or close to the circumstellar habitable zone of their parent star.
Importance sampling for Lambda-coalescents in the infinitely many sites model.
Birkner, Matthias; Blath, Jochen; Steinrücken, Matthias
2011-06-01
We present and discuss new importance sampling schemes for the approximate computation of the sample probability of observed genetic types in the infinitely many sites model from population genetics. More specifically, we extend the 'classical framework', where genealogies are assumed to be governed by Kingman's coalescent, to the more general class of Lambda-coalescents and develop further Hobolth et al.'s (2008) idea of deriving importance sampling schemes based on 'compressed genetrees'. The resulting schemes extend earlier work by Griffiths and Tavaré (1994), Stephens and Donnelly (2000), Birkner and Blath (2008) and Hobolth et al. (2008). We conclude with a performance comparison of classical and new schemes for Beta- and Kingman coalescents. PMID:21296095
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
Li, Weixuan; Lin, Guang
2015-08-01
Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes' rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
Li, Weixuan; Lin, Guang
2015-03-21
Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle these challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.
An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
Li, Weixuan; Lin, Guang
2015-03-21
Parametric uncertainties are encountered in the simulations of many physical systems, and may be reduced by an inverse modeling procedure that calibrates the simulation results to observations on the real system being simulated. Following Bayes’ rule, a general approach for inverse modeling problems is to sample from the posterior distribution of the uncertain model parameters given the observations. However, the large number of repetitive forward simulations required in the sampling process could pose a prohibitive computational burden. This difficulty is particularly challenging when the posterior is multimodal. We present in this paper an adaptive importance sampling algorithm to tackle thesemore » challenges. Two essential ingredients of the algorithm are: 1) a Gaussian mixture (GM) model adaptively constructed as the proposal distribution to approximate the possibly multimodal target posterior, and 2) a mixture of polynomial chaos (PC) expansions, built according to the GM proposal, as a surrogate model to alleviate the computational burden caused by computational-demanding forward model evaluations. In three illustrative examples, the proposed adaptive importance sampling algorithm demonstrates its capabilities of automatically finding a GM proposal with an appropriate number of modes for the specific problem under study, and obtaining a sample accurately and efficiently representing the posterior with limited number of forward simulations.« less
Salter, Tara La Roche; Bunch, Josephine; Gilmore, Ian S
2014-09-16
Many different types of samples have been analyzed in the literature using plasma-based ambient mass spectrometry sources; however, comprehensive studies of the important parameters for analysis are only just beginning. Here, we investigate the effect of the sample form and surface temperature on the signal intensities in plasma-assisted desorption ionization (PADI). The form of the sample is very important, with powders of all volatilities effectively analyzed. However, for the analysis of thin films at room temperature and using a low plasma power, a vapor pressure of greater than 10(-4) Pa is required to achieve a sufficiently good quality spectrum. Using thermal desorption, we are able to increase the signal intensity of less volatile materials with vapor pressures less than 10(-4) Pa, in thin film form, by between 4 and 7 orders of magnitude. This is achieved by increasing the temperature of the sample up to a maximum of 200 °C. Thermal desorption can also increase the signal intensity for the analysis of powders. PMID:25137443
Longuespée, Rémi; Alberts, Deborah; Pottier, Charles; Smargiasso, Nicolas; Mazzucchelli, Gabriel; Baiwir, Dominique; Kriegsmann, Mark; Herfs, Michael; Kriegsmann, Jörg; Delvenne, Philippe; De Pauw, Edwin
2016-07-15
Proteomic methods are today widely applied to formalin-fixed paraffin-embedded (FFPE) tissue samples for several applications in research, especially in molecular pathology. To date, there is an unmet need for the analysis of small tissue samples, such as for early cancerous lesions. Indeed, no method has yet been proposed for the reproducible processing of small FFPE tissue samples to allow biomarker discovery. In this work, we tested several procedures to process laser microdissected tissue pieces bearing less than 3000 cells. Combined with appropriate settings for liquid chromatography mass spectrometry-mass spectrometry (LC-MS/MS) analysis, a citric acid antigen retrieval (CAAR)-based procedure was established, allowing to identify more than 1400 proteins from a single microdissected breast cancer tissue biopsy. This work demonstrates important considerations concerning the handling and processing of laser microdissected tissue samples of extremely limited size, in the process opening new perspectives in molecular pathology. A proof of the proposed method for biomarker discovery, with respect to these specific handling considerations, is illustrated using the differential proteomic analysis of invasive breast carcinoma of no special type and invasive lobular triple-negative breast cancer tissues. This work will be of utmost importance for early biomarker discovery or in support of matrix-assisted laser desorption/ionization (MALDI) imaging for microproteomics from small regions of interest. PMID:26690073
NASA Technical Reports Server (NTRS)
Welzenbach, L. C.; McCoy, T. J.; Glavin, D. P.; Dworkin, J. P.; Abell, P. A.
2012-01-01
turn led to a new wave of Mars exploration that ultimately could lead to sample return focused on evidence for past or present life. This partnership between collections and missions will be increasingly important in the coming decades as we discover new questions to be addressed and identify targets for for both robotic and human exploration . Nowhere is this more true than in the ultimate search for the abiotic and biotic processes that produced life. Existing collections also provide the essential materials for developing and testing new analytical schemes to detect the rare markers of life and distinguish them from abiotic processes. Large collections of meteorites and the new types being identified within these collections, which come to us at a fraction of the cost of a sample return mission, will continue to shape the objectives of future missions and provide new ways of interpreting returned samples.
Devaney, J.J.
1982-04-01
The importance of single, large-angle, nuclear-coulombic, nuclear-hadronic, hadronic-coulombic interference, and multiple nuclear-coulombic scattering is investigated for tritons incident on deuterium, iron, and plutonium for very high temperatures and densities and for ordinary liquid and solid densities at low temperature. Depending on the accuracy desired, we conclude that for 10-keV-temperature DT plasmas it is not necessary to include elastic scattering deflection in reaction-in-flight calculations. For higher temperatures or where angular accuracies greater than 10/sup 0/ are significant or for higher Z targets or for other special circumstances, one must include elastic scattering from coulomb forces.
Ait Kaci Azzou, Sadoune; Larribe, Fabrice; Froda, Sorana
2015-01-01
The effective population size over time (demographic history) can be retraced from a sample of contemporary DNA sequences. In this paper, we propose a novel methodology based on importance sampling (IS) for exploring such demographic histories. Our starting point is the generalized skyline plot with the main difference being that our procedure, skywis plot, uses a large number of genealogies. The information provided by these genealogies is combined according to the IS weights. Thus, we compute a weighted average of the effective population sizes on specific time intervals (epochs), where the genealogies that agree more with the data are given more weight. We illustrate by a simulation study that the skywis plot correctly reconstructs the recent demographic history under the scenarios most commonly considered in the literature. In particular, our method can capture a change point in the effective population size, and its overall performance is comparable with the one of the bayesian skyline plot. We also introduce the case of serially sampled sequences and illustrate that it is possible to improve the performance of the skywis plot in the case of an exponential expansion of the effective population size. PMID:26300910
Egger, C; Maurer, M
2015-04-15
Urban drainage design relying on observed precipitation series neglects the uncertainties associated with current and indeed future climate variability. Urban drainage design is further affected by the large stochastic variability of precipitation extremes and sampling errors arising from the short observation periods of extreme precipitation. Stochastic downscaling addresses anthropogenic climate impact by allowing relevant precipitation characteristics to be derived from local observations and an ensemble of climate models. This multi-climate model approach seeks to reflect the uncertainties in the data due to structural errors of the climate models. An ensemble of outcomes from stochastic downscaling allows for addressing the sampling uncertainty. These uncertainties are clearly reflected in the precipitation-runoff predictions of three urban drainage systems. They were mostly due to the sampling uncertainty. The contribution of climate model uncertainty was found to be of minor importance. Under the applied greenhouse gas emission scenario (A1B) and within the period 2036-2065, the potential for urban flooding in our Swiss case study is slightly reduced on average compared to the reference period 1981-2010. Scenario planning was applied to consider urban development associated with future socio-economic factors affecting urban drainage. The impact of scenario uncertainty was to a large extent found to be case-specific, thus emphasizing the need for scenario planning in every individual case. The results represent a valuable basis for discussions of new drainage design standards aiming specifically to include considerations of uncertainty. PMID:25644630
Akiyama, Tatsuya; Khan, Ashraf A; Cheng, Chorng-Ming; Stefanova, Rossina
2011-09-01
A total of 39 Salmonella enterica serovar Saintpaul strains from imported seafood, pepper and from environmental and clinical samples were analyzed for the presence of virulence genes, antibiotic resistance, plasmid and plasmid replicon types. Pulsed-field gel electrophoresis (PFGE) fingerprinting using the XbaI restriction enzyme and plasmid profiling were performed to assess genetic diversity. None of the isolates showed resistance to ampicillin, chloramphenicol, gentamicin, kanamycin, streptomycin, sulfisoxazole, and tetracycline. Seventeen virulence genes were screened for by PCR. All strains were positive for 14 genes (spiA, sifA, invA, spaN, sopE, sipB, iroN, msgA, pagC, orgA, prgH, lpfC, sitC, and tolC) and negative for three genes (spvB, pefA, and cdtB). Twelve strains, including six from clinical samples and six from seafood, carried one or more plasmids. Large plasmids, sized greater than 50 kb were detected in one clinical and three food isolates. One plasmid was able to be typed as IncI1 by PCR-based replicon typing. There were 25 distinct PFGE-XbaI patterns, clustered to two groups. Cluster A, with 68.5% similarity mainly consists of clinical isolates, while Cluster C, with 67.6% similarity, mainly consisted of shrimp isolates from India. Our findings indicated the genetic diversity of S. Saintpaul in clinical samples, imported seafood, and the environment and that this serotype possesses several virulent genes and plasmids which can cause salmonellosis. PMID:21645810
ROMERO,VICENTE J.
2000-05-04
In order to devise an algorithm for autonomously terminating Monte Carlo sampling when sufficiently small and reliable confidence intervals (CI) are achieved on calculated probabilities, the behavior of CI estimators must be characterized. This knowledge is also required in comparing the accuracy of other probability estimation techniques to Monte Carlo results. Based on 100 trials in a hypothesis test, estimated 95% CI from classical approximate CI theory are empirically examined to determine if they behave as true 95% CI over spectrums of probabilities (population proportions) ranging from 0.001 to 0.99 in a test problem. Tests are conducted for population sizes of 500 and 10,000 samples where applicable. Significant differences between true and estimated 95% CI are found to occur at probabilities between 0.1 and 0.9, such that estimated 95% CI can be rejected as not being true 95% CI at less than a 40% chance of incorrect rejection. With regard to Latin Hypercube sampling (LHS), though no general theory has been verified for accurately estimating LHS CI, recent numerical experiments on the test problem have found LHS to be conservatively over an order of magnitude more efficient than SRS for similar sized CI on probabilities ranging between 0.25 and 0.75. The efficiency advantage of LHS vanishes, however, as the probability extremes of 0 and 1 are approached.
Brown, F.B.; Sutton, T.M.
1996-02-01
This report is composed of the lecture notes from the first half of a 32-hour graduate-level course on Monte Carlo methods offered at KAPL. These notes, prepared by two of the principle developers of KAPL`s RACER Monte Carlo code, cover the fundamental theory, concepts, and practices for Monte Carlo analysis. In particular, a thorough grounding in the basic fundamentals of Monte Carlo methods is presented, including random number generation, random sampling, the Monte Carlo approach to solving transport problems, computational geometry, collision physics, tallies, and eigenvalue calculations. Furthermore, modern computational algorithms for vector and parallel approaches to Monte Carlo calculations are covered in detail, including fundamental parallel and vector concepts, the event-based algorithm, master/slave schemes, parallel scaling laws, and portability issues.
Model reduction algorithms for optimal control and importance sampling of diffusions
NASA Astrophysics Data System (ADS)
Hartmann, Carsten; Schütte, Christof; Zhang, Wei
2016-08-01
We propose numerical algorithms for solving optimal control and importance sampling problems based on simplified models. The algorithms combine model reduction techniques for multiscale diffusions and stochastic optimization tools, with the aim of reducing the original, possibly high-dimensional problem to a lower dimensional representation of the dynamics, in which only a few relevant degrees of freedom are controlled or biased. Specifically, we study situations in which either a reaction coordinate onto which the dynamics can be projected is known, or situations in which the dynamics shows strongly localized behavior in the small noise regime. No explicit assumptions about small parameters or scale separation have to be made. We illustrate the approach with simple, but paradigmatic numerical examples.
NASA Astrophysics Data System (ADS)
Farhat, A.; Menif, M.; Rezig, H.
2013-09-01
This paper analyses the spectral efficiency of Optical Code Division Multiple Access (OCDMA) system using Importance Sampling (IS) technique. We consider three configurations of OCDMA system namely Direct Sequence (DS), Spectral Amplitude Coding (SAC) and Fast Frequency Hopping (FFH) that exploits the Fiber Bragg Gratings (FBG) based encoder/decoder. We evaluate the spectral efficiency of the considered system by taking into consideration the effect of different families of unipolar codes for both coherent and incoherent sources. The results show that the spectral efficiency of OCDMA system with coherent source is higher than the incoherent case. We demonstrate also that DS-OCDMA outperforms both others in terms of spectral efficiency in all conditions.
Shreif, Zeina; Striegel, Deborah A; Periwal, Vipul
2015-09-01
A nucleotide sequence 35 base pairs long can take 1,180,591,620,717,411,303,424 possible values. An example of systems biology datasets, protein binding microarrays, contain activity data from about 40,000 such sequences. The discrepancy between the number of possible configurations and the available activities is enormous. Thus, albeit that systems biology datasets are large in absolute terms, they oftentimes require methods developed for rare events due to the combinatorial increase in the number of possible configurations of biological systems. A plethora of techniques for handling large datasets, such as Empirical Bayes, or rare events, such as importance sampling, have been developed in the literature, but these cannot always be simultaneously utilized. Here we introduce a principled approach to Empirical Bayes based on importance sampling, information theory, and theoretical physics in the general context of sequence phenotype model induction. We present the analytical calculations that underlie our approach. We demonstrate the computational efficiency of the approach on concrete examples, and demonstrate its efficacy by applying the theory to publicly available protein binding microarray transcription factor datasets and to data on synthetic cAMP-regulated enhancer sequences. As further demonstrations, we find transcription factor binding motifs, predict the activity of new sequences and extract the locations of transcription factor binding sites. In summary, we present a novel method that is efficient (requiring minimal computational time and reasonable amounts of memory), has high predictive power that is comparable with that of models with hundreds of parameters, and has a limited number of optimized parameters, proportional to the sequence length. PMID:26092377
Pavlou, Andrew T.; Ji, Wei; Brown, Forrest B.
2016-01-23
Here, a proper treatment of thermal neutron scattering requires accounting for chemical binding through a scattering law S(α,β,T). Monte Carlo codes sample the secondary neutron energy and angle after a thermal scattering event from probability tables generated from S(α,β,T) tables at discrete temperatures, requiring a large amount of data for multiscale and multiphysics problems with detailed temperature gradients. We have previously developed a method to handle this temperature dependence on-the-fly during the Monte Carlo random walk using polynomial expansions in 1/T to directly sample the secondary energy and angle. In this paper, the on-the-fly method is implemented into MCNP6 andmore » tested in both graphite-moderated and light water-moderated systems. The on-the-fly method is compared with the thermal ACE libraries that come standard with MCNP6, yielding good agreement with integral reactor quantities like k-eigenvalue and differential quantities like single-scatter secondary energy and angle distributions. The simulation runtimes are comparable between the two methods (on the order of 5–15% difference for the problems tested) and the on-the-fly fit coefficients only require 5–15 MB of total data storage.« less
40 CFR 80.1348 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2013 CFR
2013-07-01
... Benzene Sampling, Testing and Retention Requirements § 80.1348 What gasoline sample retention requirements... independent laboratory shall also include with the retained sample the test result for benzene as...
40 CFR 80.1348 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2011 CFR
2011-07-01
... Benzene Sampling, Testing and Retention Requirements § 80.1348 What gasoline sample retention requirements... independent laboratory shall also include with the retained sample the test result for benzene as...
Baba, Justin S; Koju, Vijay; John, Dwayne O
2016-01-01
The modulation of the state of polarization of photons due to scatter generates associated geometric phase that is being investigated as a means for decreasing the degree of uncertainty in back-projecting the paths traversed by photons detected in backscattered geometry. In our previous work, we established that polarimetrically detected Berry phase correlates with the mean photon penetration depth of the backscattered photons collected for image formation. In this work, we report on the impact of state-of-linear-polarization (SOLP) filtering on both the magnitude and population distributions of image forming detected photons as a function of the absorption coefficient of the scattering sample. The results, based on Berry phase tracking implemented Polarized Monte Carlo Code, indicate that sample absorption plays a significant role in the mean depth attained by the image forming backscattered detected photons.
40 CFR 80.1348 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2014 CFR
2014-07-01
... Benzene Sampling, Testing and Retention Requirements § 80.1348 What gasoline sample retention requirements... include with the retained sample the test result for benzene as conducted pursuant to § 80.46(e). (b... sample the test result for benzene as conducted pursuant to § 80.47....
DS86 neutron dose: Monte Carlo analysis for depth profile of 152Eu activity in a large stone sample.
Endo, S; Iwatani, K; Oka, T; Hoshi, M; Shizuma, K; Imanaka, T; Takada, J; Fujita, S; Hasai, H
1999-06-01
The depth profile of 152Eu activity induced in a large granite stone pillar by Hiroshima atomic bomb neutrons was calculated by a Monte Carlo N-Particle Transport Code (MCNP). The pillar was on the Motoyasu Bridge, located at a distance of 132 m (WSW) from the hypocenter. It was a square column with a horizontal sectional size of 82.5 cm x 82.5 cm and height of 179 cm. Twenty-one cells from the north to south surface at the central height of the column were specified for the calculation and 152Eu activities for each cell were calculated. The incident neutron spectrum was assumed to be the angular fluence data of the Dosimetry System 1986 (DS86). The angular dependence of the spectrum was taken into account by dividing the whole solid angle into twenty-six directions. The calculated depth profile of specific activity did not agree with the measured profile. A discrepancy was found in the absolute values at each depth with a mean multiplication factor of 0.58 and also in the shape of the relative profile. The results indicated that a reassessment of the neutron energy spectrum in DS86 is required for correct dose estimation. PMID:10494148
2011-01-01
Many European protected areas were legally created to preserve and maintain biological diversity, unique natural features and associated cultural heritage. Built over centuries as a result of geographical and historical factors interacting with human activity, these territories are reservoirs of resources, practices and knowledge that have been the essential basis of their creation. Under social and economical transformations several components of such areas tend to be affected and their protection status endangered. Carrying out ethnobotanical surveys and extensive field work using anthropological methodologies, particularly with key-informants, we report changes observed and perceived in two natural parks in Trás-os-Montes, Portugal, that affect local plant-use systems and consequently local knowledge. By means of informants' testimonies and of our own observation and experience we discuss the importance of local knowledge and of local communities' participation to protected areas design, management and maintenance. We confirm that local knowledge provides new insights and opportunities for sustainable and multipurpose use of resources and offers contemporary strategies for preserving cultural and ecological diversity, which are the main purposes and challenges of protected areas. To be successful it is absolutely necessary to make people active participants, not simply integrate and validate their knowledge and expertise. Local knowledge is also an interesting tool for educational and promotional programs. PMID:22112242
Bojesen, Troels Arnfred
2013-04-01
We present a multihistogram reweighting technique for nonequilibrium Markov chains with discrete energies. The method generalizes the single-histogram method of Yin et al. [Phys. Rev. E 72, 036122 (2005)], making it possible to calculate the time evolution of observables at a posteriori chosen couplings based on a set of simulations performed at other couplings. In the same way as multihistogram reweighting in an equilibrium setting improves the practical reweighting range as well as use of available data compared to single-histogram reweighting, the method generalizes the multihistogram advantages to nonequilibrium simulations. We demonstrate the procedure for the Ising model with Metropolis dynamics, but stress that the method is generally applicable to a range of models and Monte Carlo update schemes. PMID:23679555
Code of Federal Regulations, 2014 CFR
2014-07-01
... producers and importers of certified ethanol denaturant. 80.1644 Section 80.1644 Protection of Environment... ethanol denaturant. (a) Sample and test each batch of certified ethanol denaturant. (1) Producers and importers of certified ethanol denaturant shall collect a representative sample from each batch of...
GeoLab Concept: The Importance of Sample Selection During Long Duration Human Exploration Mission
NASA Technical Reports Server (NTRS)
Calaway, M. J.; Evans, C. A.; Bell, M. S.; Graff, T. G.
2011-01-01
In the future when humans explore planetary surfaces on the Moon, Mars, and asteroids or beyond, the return of geologic samples to Earth will be a high priority for human spaceflight operations. All future sample return missions will have strict down-mass and volume requirements; methods for in-situ sample assessment and prioritization will be critical for selecting the best samples for return-to-Earth.
40 CFR 80.1348 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2012 CFR
2012-07-01
... 40 Protection of Environment 17 2012-07-01 2012-07-01 false What gasoline sample retention... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) REGULATION OF FUELS AND FUEL ADDITIVES Gasoline Benzene Sampling, Testing and Retention Requirements § 80.1348 What gasoline sample retention...
40 CFR 80.1348 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2010 CFR
2010-07-01
... 40 Protection of Environment 16 2010-07-01 2010-07-01 false What gasoline sample retention... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) REGULATION OF FUELS AND FUEL ADDITIVES Gasoline Benzene Sampling, Testing and Retention Requirements § 80.1348 What gasoline sample retention...
Analysis of Host–Parasite Incongruence in Papillomavirus Evolution Using Importance Sampling
Shah, Seena D.; Doorbar, John; Goldstein, Richard A.
2010-01-01
The papillomaviruses (PVs) are a family of viruses infecting several mammalian and nonmammalian species that cause cervical cancer in humans. The evolutionary history of the PVs as it associated with a wide range of host species is not well understood. Incongruities between the phylogenetic trees of various viral genes as well as between these genes and the host phylogenies suggest historical viral recombination as well as violations of strict virus–host cospeciation. The extent of recombination events among PVs is uncertain, however, and there is little evidence to support a theory of PV spread via recent host transfers. We have investigated incongruence between PV genes and hence, the possibility of recombination, using Bayesian phylogenetic methods. We find significant evidence for phylogenetic incongruence among the six PV genes E1, E2, E6, E7, L1, and L2, indicating substantial recombination. Analysis of E1 and L1 phylogenies suggests ancestral recombination events. We also describe a new method for examining alternative host–parasite association mechanisms by applying importance sampling to Bayesian divergence time estimation. This new approach is not restricted by a fixed viral tree topology or knowledge of viral divergence times, multiple parasite taxa per host may be included, and it can distinguish between prior divergence of the virus before host speciation and host transfer of the virus following speciation. Using this method, we find prior divergence of PV lineages associated with the ancestral mammalian host resulting in at least 6 PV lineages prior to speciation of this host. These PV lineages have then followed paths of prior divergence and cospeciation to eventually become associated with the extant host species. Only one significant instance of host transfer is supported, the transfer of the ancestral L1 gene between a Primate and Hystricognathi host based on the divergence times between the υ human type 41 and porcupine PVs. PMID:20093429
Sampling Small Mammals in Southeastern Forests: The Importance of Trapping in Trees
Loeb, S.C.; Chapman, G.L.; Ridley, T.R.
1999-01-01
We investigated the effect of sampling methodology on the richness and abundance of small mammal communities in loblolly pine forests. Trapping in trees using Sherman live traps was included along with routine ground trapping using the same device. Estimates of species richness did not differ among samples in which tree traps were included or excluded. However, diversity indeces (Shannon-Wiener, Simpson, Shannon and Brillouin) were strongly effected. The indeces were significantly greater than if tree samples were included primarily the result of flying squirrel captures. Without tree traps, the results suggested that cotton mince dominated the community. We recommend that tree traps we included in sampling.
Randeniya, S; Mirkovic, D; Titt, U; Guan, F; Mohan, R
2014-06-01
Purpose: In intensity modulated proton therapy (IMPT), energy dependent, protons per monitor unit (MU) calibration factors are important parameters that determine absolute dose values from energy deposition data obtained from Monte Carlo (MC) simulations. Purpose of this study was to assess the sensitivity of MC-computed absolute dose distributions to the protons/MU calibration factors in IMPT. Methods: A “verification plan” (i.e., treatment beams applied individually to water phantom) of a head and neck patient plan was calculated using MC technique. The patient plan had three beams; one posterior-anterior (PA); two anterior oblique. Dose prescription was 66 Gy in 30 fractions. Of the total MUs, 58% was delivered in PA beam, 25% and 17% in other two. Energy deposition data obtained from the MC simulation were converted to Gy using energy dependent protons/MU calibrations factors obtained from two methods. First method is based on experimental measurements and MC simulations. Second is based on hand calculations, based on how many ion pairs were produced per proton in the dose monitor and how many ion pairs is equal to 1 MU (vendor recommended method). Dose distributions obtained from method one was compared with those from method two. Results: Average difference of 8% in protons/MU calibration factors between method one and two converted into 27 % difference in absolute dose values for PA beam; although dose distributions preserved the shape of 3D dose distribution qualitatively, they were different quantitatively. For two oblique beams, significant difference in absolute dose was not observed. Conclusion: Results demonstrate that protons/MU calibration factors can have a significant impact on absolute dose values in IMPT depending on the fraction of MUs delivered. When number of MUs increases the effect due to the calibration factors amplify. In determining protons/MU calibration factors, experimental method should be preferred in MC dose calculations
Bandpass Sampling--An Opportunity to Stress the Importance of In-Depth Understanding
ERIC Educational Resources Information Center
Stern, Harold P. E.
2010-01-01
Many bandpass signals can be sampled at rates lower than the Nyquist rate, allowing significant practical advantages. Illustrating this phenomenon after discussing (and proving) Shannon's sampling theorem provides a valuable opportunity for an instructor to reinforce the principle that innovation is possible when students strive to have a complete…
40 CFR 80.1347 - What are the sampling and testing requirements for refiners and importers?
Code of Federal Regulations, 2011 CFR
2011-07-01
... Benzene Sampling, Testing and Retention Requirements § 80.1347 What are the sampling and testing... benzene requirements of this subpart, except as modified by paragraphs (a)(2), (a)(3) and (a)(4) of this... benzene concentration for compliance with the requirements of this subpart. (ii) Independent...
40 CFR 80.1347 - What are the sampling and testing requirements for refiners and importers?
Code of Federal Regulations, 2014 CFR
2014-07-01
... Benzene Sampling, Testing and Retention Requirements § 80.1347 What are the sampling and testing... benzene requirements of this subpart, except as modified by paragraphs (a)(2), (a)(3) and (a)(4) of this..., 2015, to determine its benzene concentration for compliance with the requirements of this...
40 CFR 80.1347 - What are the sampling and testing requirements for refiners and importers?
Code of Federal Regulations, 2013 CFR
2013-07-01
... Benzene Sampling, Testing and Retention Requirements § 80.1347 What are the sampling and testing... benzene requirements of this subpart, except as modified by paragraphs (a)(2), (a)(3) and (a)(4) of this... benzene concentration for compliance with the requirements of this subpart. (ii) Independent...
The Importance of Sample Processing in Analysis of Asbestos Content in Rocks and Soils
NASA Astrophysics Data System (ADS)
Neumann, R. D.; Wright, J.
2012-12-01
Analysis of asbestos content in rocks and soils using Air Resources Board (ARB) Test Method 435 (M435) involves the processing of samples for subsequent analysis by polarized light microscopy (PLM). The use of different equipment and procedures by commercial laboratories to pulverize rock and soil samples could result in different particle size distributions. It has long been theorized that asbestos-containing samples can be over-pulverized to the point where the particle dimensions of the asbestos no longer meet the required 3:1 length-to-width aspect ratio or the particles become so small that they no longer can be tested for optical characteristics using PLM where maximum PLM magnification is typically 400X. Recent work has shed some light on this issue. ARB staff conducted an interlaboratory study to investigate variability in preparation and analytical procedures used by laboratories performing M435 analysis. With regard to sample processing, ARB staff found that different pulverization equipment and processing procedures produced powders that have varying particle size distributions. PLM analysis of the finest powders produced by one laboratory showed all but one of the 12 samples were non-detect or below the PLM reporting limit; in contrast to the other 36 coarser samples from the same field sample and processed by three other laboratories where 21 samples were above the reporting limit. The set of 12, exceptionally fine powder samples produced by the same laboratory was re-analyzed by transmission electron microscopy (TEM) and results showed that these samples contained asbestos above the TEM reporting limit. However, the use of TEM as a stand-alone analytical procedure, usually performed at magnifications between 3,000 to 20,000X, also has its drawbacks because of the miniscule mass of sample that this method examines. The small amount of powder analyzed by TEM may not be representative of the field sample. The actual mass of the sample powder analyzed by
Aberer, Andre J; Stamatakis, Alexandros; Ronquist, Fredrik
2016-01-01
Sampling tree space is the most challenging aspect of Bayesian phylogenetic inference. The sheer number of alternative topologies is problematic by itself. In addition, the complex dependency between branch lengths and topology increases the difficulty of moving efficiently among topologies. Current tree proposals are fast but sample new trees using primitive transformations or re-mappings of old branch lengths. This reduces acceptance rates and presumably slows down convergence and mixing. Here, we explore branch proposals that do not rely on old branch lengths but instead are based on approximations of the conditional posterior. Using a diverse set of empirical data sets, we show that most conditional branch posteriors can be accurately approximated via a [Formula: see text] distribution. We empirically determine the relationship between the logarithmic conditional posterior density, its derivatives, and the characteristics of the branch posterior. We use these relationships to derive an independence sampler for proposing branches with an acceptance ratio of ~90% on most data sets. This proposal samples branches between 2× and 3× more efficiently than traditional proposals with respect to the effective sample size per unit of runtime. We also compare the performance of standard topology proposals with hybrid proposals that use the new independence sampler to update those branches that are most affected by the topological change. Our results show that hybrid proposals can sometimes noticeably decrease the number of generations necessary for topological convergence. Inconsistent performance gains indicate that branch updates are not the limiting factor in improving topological convergence for the currently employed set of proposals. However, our independence sampler might be essential for the construction of novel tree proposals that apply more radical topology changes. PMID:26231183
Bell, T.L. ); Abdullah, A.; Martin, R.L. ); North, G.R. )
1990-02-28
Estimates of monthly average rainfall based on satellite observations from a low Earth orbit will differ from the true monthly average because the satellite observes a given area only intermittently. This sampling error inherent in satellite monitoring of rainfall would occur even if the satellite instruments could measure rainfall perfectly. The authors estimate the size of this error for a satellite system being studied at NASA, the Tropical Rainfall Measuring Mission (TRMM). They first examine in detail the statistical description of rainfall on scales from 1 to 10{sup 3} km, based on rainfall data from the Global Atmospheric Research Project Atlantic Tropical Experiment (GATE). A TRMM-like satellite is flown over a two-dimensional time-evolving simulation of rainfall using a stochastic model with statistics tuned to agree with GATE statistics. The distribution of sampling errors found from many months of simulated observations is found to be nearly normal, even though the distribution of area-averaged rainfall is far from normal. For a range of orbits likely to be employed in TRMM, sampling error is found to be less than 10% of the mean for rainfall averaged over a 500 {times} 500 km{sup 2} area.
NASA Technical Reports Server (NTRS)
Bell, Thomas L.; Abdullah, A.; Martin, Russell L.; North, Gerald R.
1990-01-01
Estimates of monthly average rainfall based on satellite observations from a low earth orbit will differ from the true monthly average because the satellite observes a given area only intermittently. This sampling error inherent in satellite monitoring of rainfall would occur even if the satellite instruments could measure rainfall perfectly. The size of this error is estimated for a satellite system being studied at NASA, the Tropical Rainfall Measuring Mission (TRMM). First, the statistical description of rainfall on scales from 1 to 1000 km is examined in detail, based on rainfall data from the Global Atmospheric Research Project Atlantic Tropical Experiment (GATE). A TRMM-like satellite is flown over a two-dimensional time-evolving simulation of rainfall using a stochastic model with statistics tuned to agree with GATE statistics. The distribution of sampling errors found from many months of simulated observations is found to be nearly normal, even though the distribution of area-averaged rainfall is far from normal. For a range of orbits likely to be employed in TRMM, sampling error is found to be less than 10 percent of the mean for rainfall averaged over a 500 x 500 sq km area.
40 CFR 80.335 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2011 CFR
2011-07-01
... certify that the procedures meet the requirements of the ASTM procedures required under 40 CFR 80.330. (d... plus a sample of the ethanol used to conduct the handblend testing pursuant to § 80.69 must be retained....
NASA Astrophysics Data System (ADS)
Yagi, Y.; Fukahata, Y.
2007-12-01
As computer technology advanced, it has become possible to observe seismic wave with a higher sampling rate and perform inversion for a larger data set. In general, to obtain a finer image of seismic source processes, waveform data with a higher sampling rate are needed. Then we encounter a problem whether there is no limitation of sampling rate in waveform inversion. In traditional seismic source inversion, covariance components of sampled waveform data have commonly been neglected. In fact, however, observed waveform data are not completely independent of each other at least in time domain, because they are always affected by un-elastic attenuation in the propagation of seismic waves through the Earth. In this study, we have developed a method of seismic source inversion to take the data covariance into account, and applied it to teleseismic P-wave data of the 2003 Boumerdes-Zemmouri, Algeria earthquake. From a comparison of final slip distributions inverted by the new formulation and the traditional formulation, we found that the effect of covariance components is crucial for a data set of higher sampling rates (≥ 5 Hz). For higher sampling rates, the slip distributions by the new formulation look stable, whereas the slip distributions by the traditional formulation tend to concentrate into small patches due to overestimation of the information from observed data. Our result indicates that the un-elastic effect of the Earth gives a limitation to the resolution of inverted seismic source models. It has been pointed out that seismic source models obtained from waveform data analyses are quite different from one another. One possible reason for the discrepancy is the neglect of covariance components. The new formulation must be useful to obtain a standard seismic source model.
Link, W.A.; Nichols, J.D.
1994-01-01
Our purpose here is to emphasize the need to properly deal with sampling variance when studying population variability and to present a means of doing so. We present an estimator for temporal variance of population size for the general case in which there are both sampling variances and covariances associated with estimates of population size. We illustrate the estimation approach with a series of population size estimates for black-capped chickadees (Parus atricapillus) wintering in a Connecticut study area and with a series of population size estimates for breeding populations of ducks in southwestern Manitoba.
IMPORTANCE OF SAMPLE PH ON RECOVERY OF MUTAGENICITY FROM DRINKING WATER BY XAD RESINS
Sample pH and the presence of a chlorine residual were evaluated for their effects on the recovery of mutagenicity in drinking water following concentration by XAD resins. The levels of mutagenicity in the pH 2 concentrates were 7-8 fold higher than those of the pH 8 concentrates...
40 CFR 80.335 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2013 CFR
2013-07-01
... certify that the procedures meet the requirements of the ASTM procedures required under 40 CFR 80.330. (d... 40 Protection of Environment 17 2013-07-01 2013-07-01 false What gasoline sample retention... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) REGULATION OF FUELS AND FUEL ADDITIVES Gasoline...
40 CFR 80.335 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2012 CFR
2012-07-01
... certify that the procedures meet the requirements of the ASTM procedures required under 40 CFR 80.330. (d... 40 Protection of Environment 17 2012-07-01 2012-07-01 false What gasoline sample retention... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) REGULATION OF FUELS AND FUEL ADDITIVES Gasoline...
40 CFR 80.335 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2014 CFR
2014-07-01
... certify that the procedures meet the requirements of the ASTM procedures required under 40 CFR 80.330. (d... 40 Protection of Environment 17 2014-07-01 2014-07-01 false What gasoline sample retention... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) REGULATION OF FUELS AND FUEL ADDITIVES Gasoline...
Sampling efficacy for the imported fire ant Solenopsis invicta (Hymenoptera: Formicidae)
Technology Transfer Automated Retrieval System (TEKTRAN)
The cost-effective detection of incipient invasive ant colonies before their establishment in new ranges is imperative for reducing their global impact and protection of national borders. We examined the sampling efficiency of food-baits, baited and un-baited pitfall traps in detecting isolated red ...
40 CFR 80.335 - What gasoline sample retention requirements apply to refiners and importers?
Code of Federal Regulations, 2010 CFR
2010-07-01
... certify that the procedures meet the requirements of the ASTM procedures required under 40 CFR 80.330. (d... 40 Protection of Environment 16 2010-07-01 2010-07-01 false What gasoline sample retention... PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) REGULATION OF FUELS AND FUEL ADDITIVES Gasoline...
Code of Federal Regulations, 2010 CFR
2010-04-01
...; repacking; examination of merchandise by prospective purchasers. 19.8 Section 19.8 Customs Duties U.S... goods by importer; sampling; repacking; examination of merchandise by prospective purchasers. Importers... conduct of Customs business and no danger to the revenue prospective purchaser may be permitted to...
NASA Astrophysics Data System (ADS)
Baloković, M.; Smolčić, V.; Ivezić, Ž.; Zamorani, G.; Schinnerer, E.; Kelly, B. C.
2012-11-01
We investigate the dichotomy in the radio loudness distribution of quasars by modeling their radio emission and various selection effects using a Monte Carlo approach. The existence of two physically distinct quasar populations, the radio-loud and radio-quiet quasars, is controversial and over the last decade a bimodal distribution of radio loudness of quasars has been both affirmed and disputed. We model the quasar radio luminosity distribution with simple unimodal and bimodal distribution functions. The resulting simulated samples are compared to a fiducial sample of 8300 quasars drawn from the SDSS DR7 Quasar Catalog and combined with radio observations from the FIRST survey. Our results indicate that the SDSS-FIRST sample is best described by a radio loudness distribution which consists of two components, with (12 ± 1)% of sources in the radio-loud component. On the other hand, the evidence for a local minimum in the loudness distribution (bimodality) is not strong and we find that previous claims for its existence were probably affected by the incompleteness of the FIRST survey close to its faint limit. We also investigate the redshift and luminosity dependence of the radio loudness distribution and find tentative evidence that at high redshift radio-loud quasars were rarer, on average louder, and exhibited a smaller range in radio loudness. In agreement with other recent work, we conclude that the SDSS-FIRST sample strongly suggests that the radio loudness distribution of quasars is not a universal function, and that more complex models than presented here are needed to fully explain available observations.
Balokovic, M.; Smolcic, V.; Ivezic, Z.; Zamorani, G.; Schinnerer, E.; Kelly, B. C.
2012-11-01
We investigate the dichotomy in the radio loudness distribution of quasars by modeling their radio emission and various selection effects using a Monte Carlo approach. The existence of two physically distinct quasar populations, the radio-loud and radio-quiet quasars, is controversial and over the last decade a bimodal distribution of radio loudness of quasars has been both affirmed and disputed. We model the quasar radio luminosity distribution with simple unimodal and bimodal distribution functions. The resulting simulated samples are compared to a fiducial sample of 8300 quasars drawn from the SDSS DR7 Quasar Catalog and combined with radio observations from the FIRST survey. Our results indicate that the SDSS-FIRST sample is best described by a radio loudness distribution which consists of two components, with (12 {+-} 1)% of sources in the radio-loud component. On the other hand, the evidence for a local minimum in the loudness distribution (bimodality) is not strong and we find that previous claims for its existence were probably affected by the incompleteness of the FIRST survey close to its faint limit. We also investigate the redshift and luminosity dependence of the radio loudness distribution and find tentative evidence that at high redshift radio-loud quasars were rarer, on average louder, and exhibited a smaller range in radio loudness. In agreement with other recent work, we conclude that the SDSS-FIRST sample strongly suggests that the radio loudness distribution of quasars is not a universal function, and that more complex models than presented here are needed to fully explain available observations.
Importance of sampling design and analysis in animal population studies: a comment on Sergio et al
Kery, M.; Royle, J. Andrew; Schmid, H.
2008-01-01
1. The use of predators as indicators and umbrellas in conservation has been criticized. In the Trentino region, Sergio et al. (2006; hereafter SEA) counted almost twice as many bird species in quadrats located in raptor territories than in controls. However, SEA detected astonishingly few species. We used contemporary Swiss Breeding Bird Survey data from an adjacent region and a novel statistical model that corrects for overlooked species to estimate the expected number of bird species per quadrat in that region. 2. There are two anomalies in SEA which render their results ambiguous. First, SEA detected on average only 6.8 species, whereas a value of 32 might be expected. Hence, they probably overlooked almost 80% of all species. Secondly, the precision of their mean species counts was greater in two-thirds of cases than in the unlikely case that all quadrats harboured exactly the same number of equally detectable species. This suggests that they detected consistently only a biased, unrepresentative subset of species. 3. Conceptually, expected species counts are the product of true species number and species detectability p. Plenty of factors may affect p, including date, hour, observer, previous knowledge of a site and mobbing behaviour of passerines in the presence of predators. Such differences in p between raptor and control quadrats could have easily created the observed effects. Without a method that corrects for such biases, or without quantitative evidence that species detectability was indeed similar between raptor and control quadrats, the meaning of SEA's counts is hard to evaluate. Therefore, the evidence presented by SEA in favour of raptors as indicator species for enhanced levels of biodiversity remains inconclusive. 4. Synthesis and application. Ecologists should pay greater attention to sampling design and analysis in animal population estimation. Species richness estimation means sampling a community. Samples should be representative for the
Smith, R.L.; Harvey, R.W.; LeBlanc, D.R.
1991-01-01
Vertical gradients of selected chemical constituents, bacterial populations, bacterial activity and electron acceptors were investigated for an unconfined aquifer contaminated with nitrate and organic compounds on Cape Cod, Massachusetts, U.S.A. Fifteen-port multilevel sampling devices (MLS's) were installed within the contaminant plume at the source of the contamination, and at 250 and 2100 m downgradient from the source. Depth profiles of specific conductance and dissolved oxygen at the downgradient sites exhibited vertical gradients that were both steep and inversely related. Narrow zones (2-4 m thick) of high N2O and NH4+ concentrations were also detected within the contaminant plume. A 27-fold change in bacterial abundance; a 35-fold change in frequency of dividing cells (FDC), an indicator of bacterial growth; a 23-fold change in 3H-glucose uptake, a measure of heterotrophic activity; and substantial changes in overall cell morphology were evident within a 9-m vertical interval at 250 m downgradient. The existence of these gradients argues for the need for closely spaced vertical sampling in groundwater studies because small differences in the vertical placement of a well screen can lead to incorrect conclusions about the chemical and microbiological processes within an aquifer.Vertical gradients of selected chemical constituents, bacterial populations, bacterial activity and electron acceptors were investigated for an unconfined aquifer contaminated with nitrate and organic compounds on Cape Cod, Massachusetts, USA. Fifteen-port multilevel sampling devices (MLS's) were installed within the contaminant plume at the source of the contamination, and at 250 and 2100 m downgradient from the source. Depth profiles of specific conductance and dissolved oxygen at the downgradient sites exhibited vertical gradients that were both steep and inversely related. Narrow zones (2-4 m thick) of high N2O and NH4+ concentrations were also detected within the contaminant plume
Bassuino, Daniele M; Konradt, Guilherme; Cruz, Raquel A S; Silva, Gustavo S; Gomes, Danilo C; Pavarini, Saulo P; Driemeier, David
2016-07-01
Twenty-six cattle and 7 horses were diagnosed with rabies. Samples of brain and spinal cord were processed for hematoxylin and eosin staining and immunohistochemistry (IHC). In addition, refrigerated fragments of brain and spinal cord were tested by direct fluorescent antibody test and intracerebral inoculation in mice. Statistical analyses and Fisher exact test were performed by commercial software. Histologic lesions were observed in the spinal cord in all of the cattle and horses. Inflammatory lesions in horses were moderate at the thoracic, lumbar, and sacral levels, and marked at the lumbar enlargement level. Gitter cells were present in large numbers in the lumbar enlargement region. IHC staining intensity ranged from moderate to strong. Inflammatory lesions in cattle were moderate in all spinal cord sections, and gitter cells were present in small numbers. IHC staining intensity was strong in all spinal cord sections. Only 2 horses exhibited lesions in the brain, which were located mainly in the obex and cerebellum; different from that observed in cattle, which had lesions in 25 cases. Fisher exact test showed that the odds of detecting lesions caused by rabies in horses are 3.5 times higher when spinal cord sections are analyzed, as compared to analysis of brain samples alone. PMID:27240569
Kranz, Thorsten M; Harroch, Sheila; Manor, Orly; Lichtenberg, Pesach; Friedlander, Yechiel; Seandel, Marco; Harkavy-Friedman, Jill; Walsh-Messinger, Julie; Dolgalev, Igor; Heguy, Adriana; Chao, Moses V; Malaspina, Dolores
2015-08-01
Schizophrenia is a debilitating syndrome with high heritability. Genomic studies reveal more than a hundred genetic variants, largely nonspecific and of small effect size, and not accounting for its high heritability. De novo mutations are one mechanism whereby disease related alleles may be introduced into the population, although these have not been leveraged to explore the disease in general samples. This paper describes a framework to find high impact genes for schizophrenia. This study consists of two different datasets. First, whole exome sequencing was conducted to identify disruptive de novo mutations in 14 complete parent-offspring trios with sporadic schizophrenia from Jerusalem, which identified 5 sporadic cases with de novo gene mutations in 5 different genes (PTPRG, TGM5, SLC39A13, BTK, CDKN3). Next, targeted exome capture of these genes was conducted in 48 well-characterized, unrelated, ethnically diverse schizophrenia cases, recruited and characterized by the same research team in New York (NY sample), which demonstrated extremely rare and potentially damaging variants in three of the five genes (MAF<0.01) in 12/48 cases (25%); including PTPRG (5 cases), SCL39A13 (4 cases) and TGM5 (4 cases), a higher number than usually identified by whole exome sequencing. Cases differed in cognition and illness features based on which mutation-enriched gene they carried. Functional de novo mutations in protein-interaction domains in sporadic schizophrenia can illuminate risk genes that increase the propensity to develop schizophrenia across ethnicities. PMID:26091878
2015-01-01
Solute sampling of explicit bulk-phase aqueous environments in grand canonical (GC) ensemble simulations suffer from poor convergence due to low insertion probabilities of the solutes. To address this, we developed an iterative procedure involving Grand Canonical-like Monte Carlo (GCMC) and molecular dynamics (MD) simulations. Each iteration involves GCMC of both the solutes and water followed by MD, with the excess chemical potential (μex) of both the solute and the water oscillated to attain their target concentrations in the simulation system. By periodically varying the μex of the water and solutes over the GCMC-MD iterations, solute exchange probabilities and the spatial distributions of the solutes improved. The utility of the oscillating-μex GCMC-MD method is indicated by its ability to approximate the hydration free energy (HFE) of the individual solutes in aqueous solution as well as in dilute aqueous mixtures of multiple solutes. For seven organic solutes: benzene, propane, acetaldehyde, methanol, formamide, acetate, and methylammonium, the average μex of the solutes and the water converged close to their respective HFEs in both 1 M standard state and dilute aqueous mixture systems. The oscillating-μex GCMC methodology is also able to drive solute sampling in proteins in aqueous environments as shown using the occluded binding pocket of the T4 lysozyme L99A mutant as a model system. The approach was shown to satisfactorily reproduce the free energy of binding of benzene as well as sample the functional group requirements of the occluded pocket consistent with the crystal structures of known ligands bound to the L99A mutant as well as their relative binding affinities. PMID:24932136
Ali, Nasir; Ahmad, Dawood; Bakht, Jehan
2015-01-01
The present research work was carried out to investigate the antimicrobial (eight bacteria and one fungus) activities of different solvent (ethanol, petroleum ether, chloroform, ethyl acetate and isobutanol) extracted samples from flowers of P. obstusa by disc diffusion method. Analysis of the data revealed that all the five extracts from flowers of P. obstusa showed different ranges of antimicrobial activities. Petroleum ether fractions showed inhibitory activities against all the nine microbial species except Klebsiella pneumonia. Ethyl acetate and isobutanol fractions showed inhibitory effects against all the tested microbial species except Pseudomonas aeruginosa. Chloroform and ethanol extracts had varying levels of inhibitions against all of the tested microorganisms. The most susceptible gram positive bacterium was Bacillus subtilis which was inhibited by all the five extracts while the most resistant gram positive bacterium was Staphylococcus aureus. Erwinia carotovora was the most susceptible gram negative bacterium while Pseudomonas aeruginosa was highly resistant among the gram negative bacteria. PMID:25553696
Krueger-Hadfield, Stacy A; Hoban, Sean M
2016-02-01
The mating system partitions genetic diversity within and among populations and the links between life history traits and mating systems have been extensively studied in diploid organisms. As such most evolutionary theory is focused on species for which sexual reproduction occurs between diploid male and diploid female individuals. However, there are many multicellular organisms with biphasic life cycles in which the haploid stage is prolonged and undergoes substantial somatic development. In particular, biphasic life cycles are found across green, brown and red macroalgae. Yet, few studies have addressed the population structure and genetic diversity in both the haploid and diploid stages in these life cycles. We have developed some broad guidelines with which to develop population genetic studies of haploid-diploid macroalgae and to quantify the relationship between power and sampling strategy. We address three common goals for studying macroalgal population dynamics, including haploid-diploid ratios, genetic structure and paternity analyses. PMID:26987084
NASA Astrophysics Data System (ADS)
Ródenas, J.; Pascual, A.; Zarza, I.; Serradell, V.; Ortiz, J.; Ballesteros, L.
2003-01-01
Germanium crystals have a dead layer that causes a decrease in efficiency, since the layer is not useful for detection, but strongly attenuates photons. The thickness of this inactive layer is not well known due to the existence of a transition zone where photons are increasingly absorbed. Therefore, using data provided by manufacturers in the detector simulation model, some strong discrepancies appear between calculated and measured efficiencies. The Monte Carlo method is applied to simulate the calibration of a HP Ge detector in order to determine the total inactive germanium layer thickness and the active volume that are needed in order to obtain the minimum discrepancy between estimated and experimental efficiency. Calculations and measurements were performed for all of the radionuclides included in a standard calibration gamma cocktail solution. A Marinelli beaker was considered for this analysis, as it is one of the most commonly used sample container for environmental radioactivity measurements. Results indicated that a good agreement between calculated and measured efficiencies is obtained using a value for the inactive germanium layer thickness equal to approximately twice the value provided by the detector manufacturer. For all energy peaks included in the calibration, the best agreement with experimental efficiency was found using a combination of a small thickness of the inactive germanium layer and a small detection active volume.
Tarquini, Gabriele; Nunziante Cesaro, Stella; Campanella, Luigi
2014-01-01
The application of Fourier Transform InfraRed (FTIR) spectroscopy to the analysis of oil residues in fragments of archeological amphorae (3rd century A.D.) from Monte Testaccio (Rome, Italy) is reported. In order to check the possibility to reveal the presence of oil residues in archeological pottery using microinvasive and\\or not invasive techniques, different approaches have been followed: firstly, FTIR spectroscopy was used to study oil residues extracted from roman amphorae. Secondly, the presence of oil residues was ascertained analyzing microamounts of archeological fragments with the Diffuse Reflectance Infrared Spectroscopy (DRIFT). Finally, the external reflection analysis of the ancient shards was performed without preliminary treatments evidencing the possibility to detect oil traces through the observation of the most intense features of its spectrum. Incidentally, the existence of carboxylate salts of fatty acids was also observed in DRIFT and Reflectance spectra of archeological samples supporting the roman habit of spreading lime over the spoil heaps. The data collected in all steps were always compared with results obtained on purposely made replicas. PMID:24274288
NASA Astrophysics Data System (ADS)
García Muñoz, A.; Mills, F. P.
2015-01-01
Context. The interpretation of polarised radiation emerging from a planetary atmosphere must rely on solutions to the vector radiative transport equation (VRTE). Monte Carlo integration of the VRTE is a valuable approach for its flexible treatment of complex viewing and/or illumination geometries, and it can intuitively incorporate elaborate physics. Aims: We present a novel pre-conditioned backward Monte Carlo (PBMC) algorithm for solving the VRTE and apply it to planetary atmospheres irradiated from above. As classical BMC methods, our PBMC algorithm builds the solution by simulating the photon trajectories from the detector towards the radiation source, i.e. in the reverse order of the actual photon displacements. Methods: We show that the neglect of polarisation in the sampling of photon propagation directions in classical BMC algorithms leads to unstable and biased solutions for conservative, optically-thick, strongly polarising media such as Rayleigh atmospheres. The numerical difficulty is avoided by pre-conditioning the scattering matrix with information from the scattering matrices of prior (in the BMC integration order) photon collisions. Pre-conditioning introduces a sense of history in the photon polarisation states through the simulated trajectories. Results: The PBMC algorithm is robust, and its accuracy is extensively demonstrated via comparisons with examples drawn from the literature for scattering in diverse media. Since the convergence rate for MC integration is independent of the integral's dimension, the scheme is a valuable option for estimating the disk-integrated signal of stellar radiation reflected from planets. Such a tool is relevant in the prospective investigation of exoplanetary phase curves. We lay out two frameworks for disk integration and, as an application, explore the impact of atmospheric stratification on planetary phase curves for large star-planet-observer phase angles. By construction, backward integration provides a better
Quantum speedup of Monte Carlo methods
Montanaro, Ashley
2015-01-01
Monte Carlo methods use random sampling to estimate numerical quantities which are hard to compute deterministically. One important example is the use in statistical physics of rapidly mixing Markov chains to approximately compute partition functions. In this work, we describe a quantum algorithm which can accelerate Monte Carlo methods in a very general setting. The algorithm estimates the expected output value of an arbitrary randomized or quantum subroutine with bounded variance, achieving a near-quadratic speedup over the best possible classical algorithm. Combining the algorithm with the use of quantum walks gives a quantum speedup of the fastest known classical algorithms with rigorous performance bounds for computing partition functions, which use multiple-stage Markov chain Monte Carlo techniques. The quantum algorithm can also be used to estimate the total variation distance between probability distributions efficiently. PMID:26528079
Code of Federal Regulations, 2010 CFR
2010-07-01
... 40 Protection of Environment 16 2010-07-01 2010-07-01 false What alternative sampling and testing requirements apply to importers who transport motor vehicle diesel fuel, NRLM diesel fuel, or ECA marine fuel by truck or rail car? 80.583 Section 80.583 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS...
Code of Federal Regulations, 2011 CFR
2011-07-01
... requirements apply to importers who transport motor vehicle diesel fuel, NRLM diesel fuel, or ECA marine fuel... (CONTINUED) AIR PROGRAMS (CONTINUED) REGULATION OF FUELS AND FUEL ADDITIVES Motor Vehicle Diesel Fuel; Nonroad, Locomotive, and Marine Diesel Fuel; and ECA Marine Fuel Sampling and Testing § 80.583...
Dieudonne, C.; Dumonteil, E.; Malvagi, F.; Diop, C. M.
2013-07-01
For several years, Monte Carlo burnup/depletion codes have appeared, which couple a Monte Carlo code to simulate the neutron transport to a deterministic method that computes the medium depletion due to the neutron flux. Solving Boltzmann and Bateman equations in such a way allows to track fine 3 dimensional effects and to get rid of multi-group hypotheses done by deterministic solvers. The counterpart is the prohibitive calculation time due to the time-expensive Monte Carlo solver called at each time step. Therefore, great improvements in term of calculation time could be expected if one could get rid of Monte Carlo transport sequences. For example, it may seem interesting to run an initial Monte Carlo simulation only once, for the first time/burnup step, and then to use the concentration perturbation capability of the Monte Carlo code to replace the other time/burnup steps (the different burnup steps are seen like perturbations of the concentrations of the initial burnup step). This paper presents some advantages and limitations of this technique and preliminary results in terms of speed up and figure of merit. Finally, we will detail different possible calculation scheme based on that method. (authors)
Extending canonical Monte Carlo methods
NASA Astrophysics Data System (ADS)
Velazquez, L.; Curilef, S.
2010-02-01
In this paper, we discuss the implications of a recently obtained equilibrium fluctuation-dissipation relation for the extension of the available Monte Carlo methods on the basis of the consideration of the Gibbs canonical ensemble to account for the existence of an anomalous regime with negative heat capacities C < 0. The resulting framework appears to be a suitable generalization of the methodology associated with the so-called dynamical ensemble, which is applied to the extension of two well-known Monte Carlo methods: the Metropolis importance sampling and the Swendsen-Wang cluster algorithm. These Monte Carlo algorithms are employed to study the anomalous thermodynamic behavior of the Potts models with many spin states q defined on a d-dimensional hypercubic lattice with periodic boundary conditions, which successfully reduce the exponential divergence of the decorrelation time τ with increase of the system size N to a weak power-law divergence \\tau \\propto N^{\\alpha } with α≈0.2 for the particular case of the 2D ten-state Potts model.
Predrag, Stojanovic; Branislava, Kocic; Miodrag, Stojanovic; Biljana, Miljkovic – Selimovic; Suzana, Tasic; Natasa, Miladinovic – Tasic; Tatjana, Babic
2012-01-01
The aim of this study was to fortify the clinical importance and representation of toxigenic and non-toxigenic Clostridium difficile isolated from stool samples of hospitalized patients. This survey included 80 hospitalized patients with diarrhea and positive findings of Clostridium difficile in stool samples, and 100 hospitalized patients with formed stool as a control group. Bacteriological examination of a stool samples was conducted using standard microbiological methods. Stool sample were inoculated directly on nutrient media for bacterial cultivation (blood agar using 5% sheep blood, Endo agar, selective Salmonella Shigella agar, Selenite-F broth, CIN agar and Skirrow’s medium), and to selective cycloserine-cefoxitin-fructose agar (CCFA) (Biomedics, Parg qe tehnicologico, Madrid, Spain) for isolation of Clostridium difficile. Clostridium difficile toxin was detected by ELISA-ridascreen Clostridium difficile Toxin A/B (R-Biopharm AG, Germany) and ColorPAC ToxinA test (Becton Dickinson, USA). Examination of stool specimens for the presence of parasites (causing diarrhea) was done using standard methods (conventional microscopy), commercial concentration test Paraprep S Gold kit (Dia Mondial, France) and RIDA®QUICK Cryptosporidium/Giardia Combi test (R-Biopharm AG, Germany). Examination of stool specimens for the presence of fungi (causing diarrhea) was performed by standard methods. All stool samples positive for Clostridium difficile were tested for Rota, Noro, Astro and Adeno viruses by ELISA – ridascreen (R-Biopharm AG, Germany). In this research we isolated 99 Clostridium difficile strains from 116 stool samples of 80 hospitalized patients with diarrhea. The 53 (66.25%) of patients with diarrhea were positive for toxins A and B, one (1.25%) were positive for only toxin B. Non-toxigenic Clostridium difficile isolated from samples of 26 (32.5%) patients. However, other pathogenic microorganisms of intestinal tract cultivated from samples of 16 patients
Monte Carlo Simulation for Perusal and Practice.
ERIC Educational Resources Information Center
Brooks, Gordon P.; Barcikowski, Robert S.; Robey, Randall R.
The meaningful investigation of many problems in statistics can be solved through Monte Carlo methods. Monte Carlo studies can help solve problems that are mathematically intractable through the analysis of random samples from populations whose characteristics are known to the researcher. Using Monte Carlo simulation, the values of a statistic are…
Monte Carlo techniques for real-time quantum dynamics
Dowling, Mark R. . E-mail: dowling@physics.uq.edu.au; Davis, Matthew J.; Drummond, Peter D.; Corney, Joel F.
2007-01-10
The stochastic-gauge representation is a method of mapping the equation of motion for the quantum mechanical density operator onto a set of equivalent stochastic differential equations. One of the stochastic variables is termed the 'weight', and its magnitude is related to the importance of the stochastic trajectory. We investigate the use of Monte Carlo algorithms to improve the sampling of the weighted trajectories and thus reduce sampling error in a simulation of quantum dynamics. The method can be applied to calculations in real time, as well as imaginary time for which Monte Carlo algorithms are more-commonly used. The Monte-Carlo algorithms are applicable when the weight is guaranteed to be real, and we demonstrate how to ensure this is the case. Examples are given for the anharmonic oscillator, where large improvements over stochastic sampling are observed.
THE IMPORTANCE OF THE MAGNETIC FIELD FROM AN SMA-CSO-COMBINED SAMPLE OF STAR-FORMING REGIONS
Koch, Patrick M.; Tang, Ya-Wen; Ho, Paul T. P.; Chen, Huei-Ru Vivien; Liu, Hau-Yu Baobab; Yen, Hsi-Wei; Lai, Shih-Ping; Zhang, Qizhou; Chen, How-Huan; Ching, Tao-Chung; Girart, Josep M.; Frau, Pau; Li, Hua-Bai; Li, Zhi-Yun; Padovani, Marco; Qiu, Keping; Rao, Ramprasad
2014-12-20
Submillimeter dust polarization measurements of a sample of 50 star-forming regions, observed with the Submillimeter Array (SMA) and the Caltech Submillimeter Observatory (CSO) covering parsec-scale clouds to milliparsec-scale cores, are analyzed in order to quantify the magnetic field importance. The magnetic field misalignment δ—the local angle between magnetic field and dust emission gradient—is found to be a prime observable, revealing distinct distributions for sources where the magnetic field is preferentially aligned with or perpendicular to the source minor axis. Source-averaged misalignment angles (|δ|) fall into systematically different ranges, reflecting the different source-magnetic field configurations. Possible bimodal (|δ|) distributions are found for the separate SMA and CSO samples. Combining both samples broadens the distribution with a wide maximum peak at small (|δ|) values. Assuming the 50 sources to be representative, the prevailing source-magnetic field configuration is one that statistically prefers small magnetic field misalignments |δ|. When interpreting |δ| together with a magnetohydrodynamics force equation, as developed in the framework of the polarization-intensity gradient method, a sample-based log-linear scaling fits the magnetic field tension-to-gravity force ratio (Σ {sub B}) versus (|δ|) with (Σ {sub B}) = 0.116 · exp (0.047 · (|δ|)) ± 0.20 (mean error), providing a way to estimate the relative importance of the magnetic field, only based on measurable field misalignments |δ|. The force ratio Σ {sub B} discriminates systems that are collapsible on average ((Σ {sub B}) < 1) from other molecular clouds where the magnetic field still provides enough resistance against gravitational collapse ((Σ {sub B}) > 1). The sample-wide trend shows a transition around (|δ|) ≈ 45°. Defining an effective gravitational force ∼1 – (Σ {sub B}), the average magnetic-field-reduced star formation efficiency is at least a
Monte Carlo and quasi-Monte Carlo methods
NASA Astrophysics Data System (ADS)
Caflisch, Russel E.
Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N-1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Carlo quadrature is attained using quasi-random (also called low-discrepancy) sequences, which are a deterministic alternative to random or pseudo-random sequences. The points in a quasi-random sequence are correlated to provide greater uniformity. The resulting quadrature method, called quasi-Monte Carlo, has a convergence rate of approximately O((logN)kN-1). For quasi-Monte Carlo, both theoretical error estimates and practical limitations are presented. Although the emphasis in this article is on integration, Monte Carlo simulation of rarefied gas dynamics is also discussed. In the limit of small mean free path (that is, the fluid dynamic limit), Monte Carlo loses its effectiveness because the collisional distance is much less than the fluid dynamic length scale. Computational examples are presented throughout the text to illustrate the theory. A number of open problems are described.
Düppe, H; Gärdsell, P; Hanson, B S; Johnell, O; Nilsson, B E
1996-01-01
OBJECTIVE: To study the effects of participation rate in sampling on "normative" bone mass data. DESIGN: This was a comparison between two randomly selected samples from the same population. The participation rates in the two samples were 61.9% and 83.6%. Measurements were made of bone mass at different skeletal sites and of muscle strength, as well as an assessment of physical activity. SETTING: Malmö, Sweden. SUBJECTS: There were 230 subjects (117 men, 113 women), aged 21 to 42 years. RESULTS: Many subjects participated in both studies (163). Those who took part only in the study with the higher participation rate (67) almost invariably had higher values for bone mass density at the sites measured (up to 7.6% for men) than participants in the study with the lower participation rate. No differences in muscle strength were recorded. CONCLUSION: A high degree of compliance is important to achieve a reliable result in determining normal values in population based studies. PMID:8762383
Tai, Bee-Choo; Grundy, Richard; Machin, David
2011-03-15
Purpose: To accurately model the cumulative need for radiotherapy in trials designed to delay or avoid irradiation among children with malignant brain tumor, it is crucial to account for competing events and evaluate how each contributes to the timing of irradiation. An appropriate choice of statistical model is also important for adequate determination of sample size. Methods and Materials: We describe the statistical modeling of competing events (A, radiotherapy after progression; B, no radiotherapy after progression; and C, elective radiotherapy) using proportional cause-specific and subdistribution hazard functions. The procedures of sample size estimation based on each method are outlined. These are illustrated by use of data comparing children with ependymoma and other malignant brain tumors. The results from these two approaches are compared. Results: The cause-specific hazard analysis showed a reduction in hazards among infants with ependymoma for all event types, including Event A (adjusted cause-specific hazard ratio, 0.76; 95% confidence interval, 0.45-1.28). Conversely, the subdistribution hazard analysis suggested an increase in hazard for Event A (adjusted subdistribution hazard ratio, 1.35; 95% confidence interval, 0.80-2.30), but the reduction in hazards for Events B and C remained. Analysis based on subdistribution hazard requires a larger sample size than the cause-specific hazard approach. Conclusions: Notable differences in effect estimates and anticipated sample size were observed between methods when the main event showed a beneficial effect whereas the competing events showed an adverse effect on the cumulative incidence. The subdistribution hazard is the most appropriate for modeling treatment when its effects on both the main and competing events are of interest.
Jayamani, Indumathy; Cupples, Alison M
2015-07-01
This study investigated the microorganisms involved in hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) degradation from a detonation area at a Navy base. Using Illumina sequencing, microbial communities were compared between the initial sample, samples following RDX degradation, and controls not amended with RDX to determine which phylotypes increased in abundance following RDX degradation. The effect of glucose on these communities was also examined. In addition, stable isotope probing (SIP) using labeled ((13)C3, (15)N3-ring) RDX was performed. Illumina sequencing revealed that several phylotypes were more abundant following RDX degradation compared to the initial soil and the no-RDX controls. For the glucose-amended samples, this trend was strong for an unclassified Pseudomonadaceae phylotype and for Comamonas. Without glucose, Acinetobacter exhibited the greatest increase following RDX degradation compared to the initial soil and no-RDX controls. Rhodococcus, a known RDX degrader, also increased in abundance following RDX degradation. For the SIP study, unclassified Pseudomonadaceae was the most abundant phylotype in the heavy fractions in both the presence and absence of glucose. In the glucose-amended heavy fractions, the 16S ribosomal RNA (rRNA) genes of Comamonas and Anaeromxyobacter were also present. Without glucose, the heavy fractions also contained the 16S rRNA genes of Azohydromonas and Rhodococcus. However, all four phylotypes were present at a much lower level compared to unclassified Pseudomonadaceae. Overall, these data indicate that unclassified Pseudomonadaceae was primarily responsible for label uptake in both treatments. This study indicates, for the first time, the importance of Comamonas for RDX removal. PMID:25721530
NASA Technical Reports Server (NTRS)
Glavin, D. P.; Conrad, P.; Dworkin, J. P.; Eigenbrode, J.; Mahaffy, P. R.
2011-01-01
The search for evidence of life on Mars and elsewhere will continue to be one of the primary goals of NASA s robotic exploration program over the next decade. NASA and ESA are currently planning a series of robotic missions to Mars with the goal of understanding its climate, resources, and potential for harboring past or present life. One key goal will be the search for chemical biomarkers including complex organic compounds important in life on Earth. These include amino acids, the monomer building blocks of proteins and enzymes, nucleobases and sugars which form the backbone of DNA and RNA, and lipids, the structural components of cell membranes. Many of these organic compounds can also be formed abiotically as demonstrated by their prevalence in carbonaceous meteorites [1], though, their molecular characteristics may distinguish a biological source [2]. It is possible that in situ instruments may reveal such characteristics, however, return of the right sample (i.e. one with biosignatures or having a high probability of biosignatures) to Earth would allow for more intensive laboratory studies using a broad array of powerful instrumentation for bulk characterization, molecular detection, isotopic and enantiomeric compositions, and spatially resolved chemistry that may be required for confirmation of extant or extinct Martian life. Here we will discuss the current analytical capabilities and strategies for the detection of organics on the Mars Science Laboratory (MSL) using the Sample Analysis at Mars (SAM) instrument suite and how sample return missions from Mars and other targets of astrobiological interest will help advance our understanding of chemical biosignatures in the solar system.
Liu, Bin
2014-07-01
We describe an algorithm that can adaptively provide mixture summaries of multimodal posterior distributions. The parameter space of the involved posteriors ranges in size from a few dimensions to dozens of dimensions. This work was motivated by an astrophysical problem called extrasolar planet (exoplanet) detection, wherein the computation of stochastic integrals that are required for Bayesian model comparison is challenging. The difficulty comes from the highly nonlinear models that lead to multimodal posterior distributions. We resort to importance sampling (IS) to estimate the integrals, and thus translate the problem to be how to find a parametric approximation of the posterior. To capture the multimodal structure in the posterior, we initialize a mixture proposal distribution and then tailor its parameters elaborately to make it resemble the posterior to the greatest extent possible. We use the effective sample size (ESS) calculated based on the IS draws to measure the degree of approximation. The bigger the ESS is, the better the proposal resembles the posterior. A difficulty within this tailoring operation lies in the adjustment of the number of mixing components in the mixture proposal. Brute force methods just preset it as a large constant, which leads to an increase in the required computational resources. We provide an iterative delete/merge/add process, which works in tandem with an expectation-maximization step to tailor such a number online. The efficiency of our proposed method is tested via both simulation studies and real exoplanet data analysis.
NASA Astrophysics Data System (ADS)
Samei, Ehsan; Kearfott, Kimberlee J.; Gillespie, Timothy J.; Chris Wang, C.-K.
1996-12-01
A method to generate depth-dose distributions due to beta radiation in LiF and soft tissue is proposed. In this method, the EGS4 Monte Carlo radiation transport code is initially used to generate a library of monoenergetic electron depth-dose distributions in the material for electron energies in the range of 10 keV to 5 MeV in 10 keV increments. A polynomial least-squares fit is applied to each distribution. In addition, a theoretical model is developed to generate beta-ray energy spectra of selected radionuclides. A standard Monte Carlo random sampling technique is then employed to sample the spectra and generate the depth-dose distributions in LiF and soft tissue. The proposed method has an advantage over more traditional methods in that the actual radiation transport in the media is performed only once for a set of monoenergetic cases and the beta depth-dose distributions are easily generated by sampling this previously-acquired database in a matter of minutes. This method therefore reduces the demand on computer resources and time. The method can be used to calculate depth-dose distribution due to any beta-emitting nuclide or combination of nuclides with up to ten beta components.
Stockman, Jamila K; Campbell, Jacquelyn C; Celentano, David D
2009-01-01
Objectives Recent evidence suggests that it is important to consider behavioral-specific sexual violence measures in assessing women’s risk behaviors. This study investigated associations of history and types of sexual coercion on HIV risk behaviors in a nationally representative sample of heterosexually active American women. Methods Analyses were based on 5,857 women aged 18–44 participating in the 2002 National Survey of Family Growth. Types of lifetime sexual coercion included: victim given alcohol or drugs, verbally pressured, threatened with physical injury, and physically injured. Associations with HIV risk behaviors were assessed using logistic regression. Results Of 5,857 heterosexually active women, 16.4% reported multiple sex partners and 15.3% reported substance abuse. A coerced first sexual intercourse experience and coerced sex after sexual debut were independently associated with multiple sex partners and substance abuse; the highest risk was observed for women reporting a coerced first sexual intercourse experience. Among types of sexual coercion, alcohol or drug use at coerced sex was independently associated with multiple sex partners and substance abuse. Conclusions Our findings suggest that public health strategies are needed to address the violent components of heterosexual relationships. Future research should utilize longitudinal and qualitative research to characterize the relationship between continuums of sexual coercion and HIV risk. PMID:19734802
2014-01-01
Background Carefully conducted, community-based, longitudinal studies are required to gain further understanding of the nature and timing of respiratory viruses causing infections in the population. However, such studies pose unique challenges for field specimen collection, including as we have observed the appearance of mould in some nasal swab specimens. We therefore investigated the impact of sample collection quality and the presence of visible mould in samples upon respiratory virus detection by real-time polymerase chain reaction (PCR) assays. Methods Anterior nasal swab samples were collected from infants participating in an ongoing community-based, longitudinal, dynamic birth cohort study. The samples were first collected from each infant shortly after birth and weekly thereafter. They were then mailed to the laboratory where they were catalogued, stored at -80°C and later screened by PCR for 17 respiratory viruses. The quality of specimen collection was assessed by screening for human deoxyribonucleic acid (DNA) using endogenous retrovirus 3 (ERV3). The impact of ERV3 load upon respiratory virus detection and the impact of visible mould observed in a subset of swabs reaching the laboratory upon both ERV3 loads and respiratory virus detection was determined. Results In total, 4933 nasal swabs were received in the laboratory. ERV3 load in nasal swabs was associated with respiratory virus detection. Reduced respiratory virus detection (odds ratio 0.35; 95% confidence interval 0.27-0.44) was observed in samples where the ERV3 could not be identified. Mould was associated with increased time of samples reaching the laboratory and reduced ERV3 loads and respiratory virus detection. Conclusion Suboptimal sample collection and high levels of visible mould can impact negatively upon sample quality. Quality control measures, including monitoring human DNA loads using ERV3 as a marker for epithelial cell components in samples should be undertaken to optimize the
NASA Astrophysics Data System (ADS)
Gereben, Orsolya; Petkov, Valeri
2013-11-01
A new method to fit experimental diffraction data with non-periodic structure models for spherical particles was implemented in the reverse Monte Carlo simulation code. The method was tested on x-ray diffraction data for ruthenium (Ru) nanoparticles approximately 5.6 nm in diameter. It was found that the atomic ordering in the ruthenium nanoparticles is quite distorted, barely resembling the hexagonal structure of bulk Ru. The average coordination number for the bulk decreased from 12 to 11.25. A similar lack of structural order has been observed with other nanoparticles (e.g. Petkov et al 2008 J. Phys. Chem. C 112 8907-11) indicating that atomic disorder is a widespread feature of nanoparticles less than 10 nm in diameter.
2014-01-01
Background In biomedical research, response variables are often encountered which have bounded support on the open unit interval - (0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. Methods In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. Results If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar
Monte Carlo Integration Using Spatial Structure of Markov Random Field
NASA Astrophysics Data System (ADS)
Yasuda, Muneki
2015-03-01
Monte Carlo integration (MCI) techniques are important in various fields. In this study, a new MCI technique for Markov random fields (MRFs) is proposed. MCI consists of two successive parts: the first involves sampling using a technique such as the Markov chain Monte Carlo method, and the second involves an averaging operation using the obtained sample points. In the averaging operation, a simple sample averaging technique is often employed. The method proposed in this paper improves the averaging operation by addressing the spatial structure of the MRF and is mathematically guaranteed to statistically outperform standard MCI using the simple sample averaging operation. Moreover, the proposed method can be improved in a systematic manner and is numerically verified by numerical simulations using planar Ising models. In the latter part of this paper, the proposed method is applied to the inverse Ising problem and we observe that it outperforms the maximum pseudo-likelihood estimation.
Two research studies funded and overseen by EPA have been conducted since October 2006 on soil gas sampling methods and variations in shallow soil gas concentrations with the purpose of improving our understanding of soil gas methods and data for vapor intrusion applications. Al...
ERIC Educational Resources Information Center
Osborne, Jason W.
2011-01-01
Large surveys often use probability sampling in order to obtain representative samples, and these data sets are valuable tools for researchers in all areas of science. Yet many researchers are not formally prepared to appropriately utilize these resources. Indeed, users of one popular dataset were generally found "not" to have modeled the analyses…
Code of Federal Regulations, 2014 CFR
2014-07-01
... producers and importers of denatured fuel ethanol and other oxygenates for use by oxygenate blenders. 80... requirements for producers and importers of denatured fuel ethanol and other oxygenates for use by oxygenate blenders. Beginning January 1, 2017, producers and importers of denatured fuel ethanol (DFE) and...
Nansen, Christian; Gumley, Jerome; Groves, Lloyd; Nansen, Maria; Severtson, Dustin; Ridsdill-Smith, Thomas James
2015-07-01
In this study, we analyzed 1145 vacuum samples of redlegged earth mites (RLEM) [Halotydeus destructor (Tucker) (Acari: Penthaleidae)] from 18 sampling events at six locations in pastoral landscapes of Western Australia during three growing seasons (2012-2014) (total of 228,299 RLEM individuals). The specific objectives were to determine: (1) presence/absence effects of a range of vegetation characteristics, (2) possible factors influencing RLEM sampling performance during the course of the season and day, (3) effects of size of area sampled and duration of sampling, (4) the spatial structure of RLEM counts in uniform pastoral vegetation, and (5) develop "best practices" regarding field-based vacuum sampling of surface dwelling soil mites in pastoral landscapes. We found that sampling of completely bare ground will lead to very low RLEM counts but spots with sparse vegetation (presence of bare ground) probably increases the presence of microhabitats for mites to shelter in and therefore lead to higher RLEM counts. RLEM counts were positively associated with the height of vegetation, at least up to about 15 cm in height. In early season (May-August), highest RLEM counts will be obtained in the afternoon hours (2-4 pm), whereas in late season sampling (August-November), highest RLEM counts will be obtained around noon. Higher RLEM counts should be expected from spots with grazed/mowed vegetation including cape weed and without presence of grasses and stubble. Variogram analyses of high-resolution data sets suggested that considerable range of spatial autocorrelation should be expected from fields with fairly uniform vegetation, especially if RLEM population densities are high. We are therefore recommending that samples are collected at least 30 m apart, if the objective is to obtain independent (spatially non-correlated) counts. The results from this study may be used to develop effective sampling protocols deployed in field ecology studies of soil surface dwelling
Energy Science and Technology Software Center (ESTSC)
2010-10-20
The "Monte Carlo Benchmark" (MCB) is intended to model the computatiional performance of Monte Carlo algorithms on parallel architectures. It models the solution of a simple heuristic transport equation using a Monte Carlo technique. The MCB employs typical features of Monte Carlo algorithms such as particle creation, particle tracking, tallying particle information, and particle destruction. Particles are also traded among processors using MPI calls.
Code of Federal Regulations, 2014 CFR
2014-07-01
... producers and importers of denaturant designated as suitable for the manufacture of denatured fuel ethanol... suitable for the manufacture of denatured fuel ethanol meeting federal quality requirements. Beginning January 1, 2017, or on the first day that any producer or importer of ethanol denaturant designates...
Federal Register 2010, 2011, 2012, 2013, 2014
2011-10-20
... plant part) for or capable of propagation, including a tree, a tissue culture, a plantlet culture... data to establish that the plants for planting present a medium or low risk. If a taxon of plants for planting from a certain country is determined to present a medium or low risk, it will be sampled at...
Technology Transfer Automated Retrieval System (TEKTRAN)
Hypoglycin A (HGA) is a toxic amino acid that is naturally produced in unripe ackee fruit. In 1973 the FDA placed a worldwide import alert on ackee fruit, which banned the product from entering the U.S. The FDA has considered establishing a regulatory limit for HGA and lifting the ban, which will re...
Westerbeek, H A; Mooi, W J; Begg, C; Dessing, M; Balm, A J
1992-01-01
Flow cytometric DNA ploidy measurements using deparaffinized tumor specimens were performed on 46 squamous cell carcinomas of the head and neck, including 22 carcinomas of the oropharynx, 18 carcinomas of the larynx and six carcinomas of the oral cavity. Aneuploidy was found in 14 of these tumors with carcinomas of the larynx and oral cavity showing almost equal percentages of DNA aneuploidy (10/18 and 3/6, respectively). In contrast, only 1 of the oropharyngeal carcinomas was aneuploid. Accurate microscopy-controlled sampling of tumor tissue from the histological tissue blocks was found to be mandatory in order to obtain reliable ploidy measurements. PMID:1642865
Abramyan, Tigran M; Snyder, James A; Thyparambil, Aby A; Stuart, Steven J; Latour, Robert A
2016-08-01
Clustering methods have been widely used to group together similar conformational states from molecular simulations of biomolecules in solution. For applications such as the interaction of a protein with a surface, the orientation of the protein relative to the surface is also an important clustering parameter because of its potential effect on adsorbed-state bioactivity. This study presents cluster analysis methods that are specifically designed for systems where both molecular orientation and conformation are important, and the methods are demonstrated using test cases of adsorbed proteins for validation. Additionally, because cluster analysis can be a very subjective process, an objective procedure for identifying both the optimal number of clusters and the best clustering algorithm to be applied to analyze a given dataset is presented. The method is demonstrated for several agglomerative hierarchical clustering algorithms used in conjunction with three cluster validation techniques. © 2016 Wiley Periodicals, Inc. PMID:27292100
Hill, Robert V
2005-08-01
Several mutually exclusive hypotheses have been advanced to explain the phylogenetic position of turtles among amniotes. Traditional morphology-based analyses place turtles among extinct anapsids (reptiles with a solid skull roof), whereas more recent studies of both morphological and molecular data support an origin of turtles from within Diapsida (reptiles with a doubly fenestrated skull roof). Evaluation of these conflicting hypotheses has been hampered by nonoverlapping taxonomic samples and the exclusion of significant taxa from published analyses. Furthermore, although data from soft tissues and anatomical systems such as the integument may be particularly relevant to this problem, they are often excluded from large-scale analyses of morphological systematics. Here, conflicting hypotheses of turtle relationships are tested by (1) combining published data into a supermatrix of morphological characters to address issues of character conflict and missing data; (2) increasing taxonomic sampling by more than doubling the number of operational taxonomic units to test internal relationships within suprageneric ingroup taxa; and (3) increasing character sampling by approximately 25% by adding new data on the osteology and histology of the integument, an anatomical system that has been historically underrepresented in morphological systematics. The morphological data set assembled here represents the largest yet compiled for Amniota. Reevaluation of character data from prior studies of amniote phylogeny favors the hypothesis that turtles indeed have diapsid affinities. Addition of new ingroup taxa alone leads to a decrease in overall phylogenetic resolution, indicating that existing characters used for amniote phylogeny are insufficient to explain the evolution of more highly nested taxa. Incorporation of new data from the soft and osseous components of the integument, however, helps resolve relationships among both basal and highly nested amniote taxa. Analysis of a
Energy Science and Technology Software Center (ESTSC)
2006-05-09
The Monte Carlo example programs VARHATOM and DMCATOM are two small, simple FORTRAN programs that illustrate the use of the Monte Carlo Mathematical technique for calculating the ground state energy of the hydrogen atom.
Ibrahim, Ahmad M; Peplow, Douglas E.; Peterson, Joshua L; Grove, Robert E
2013-01-01
The rigorous 2-step (R2S) method uses three-dimensional Monte Carlo transport simulations to calculate the shutdown dose rate (SDDR) in fusion reactors. Accurate full-scale R2S calculations are impractical in fusion reactors because they require calculating space- and energy-dependent neutron fluxes everywhere inside the reactor. The use of global Monte Carlo variance reduction techniques was suggested for accelerating the neutron transport calculation of the R2S method. The prohibitive computational costs of these approaches, which increase with the problem size and amount of shielding materials, inhibit their use in the accurate full-scale neutronics analyses of fusion reactors. This paper describes a novel hybrid Monte Carlo/deterministic technique that uses the Consistent Adjoint Driven Importance Sampling (CADIS) methodology but focuses on multi-step shielding calculations. The Multi-Step CADIS (MS-CADIS) method speeds up the Monte Carlo neutron calculation of the R2S method using an importance function that represents the importance of the neutrons to the final SDDR. Using a simplified example, preliminarily results showed that the use of MS-CADIS enhanced the efficiency of the neutron Monte Carlo simulation of an SDDR calculation by a factor of 550 compared to standard global variance reduction techniques, and that the increase over analog Monte Carlo is higher than 10,000.
Extra Chance Generalized Hybrid Monte Carlo
NASA Astrophysics Data System (ADS)
Campos, Cédric M.; Sanz-Serna, J. M.
2015-01-01
We study a method, Extra Chance Generalized Hybrid Monte Carlo, to avoid rejections in the Hybrid Monte Carlo method and related algorithms. In the spirit of delayed rejection, whenever a rejection would occur, extra work is done to find a fresh proposal that, hopefully, may be accepted. We present experiments that clearly indicate that the additional work per sample carried out in the extra chance approach clearly pays in terms of the quality of the samples generated.
Chamorro-Premuzic, Tomas; Reimers, Stian; Hsu, Anne; Ahmetoglu, Gorkan
2009-08-01
The present study examined individual differences in artistic preferences in a sample of 91,692 participants (60% women and 40% men), aged 13-90 years. Participants completed a Big Five personality inventory (Goldberg, 1999) and provided preference ratings for 24 different paintings corresponding to cubism, renaissance, impressionism, and Japanese art, which loaded on to a latent factor of overall art preferences. As expected, the personality trait openness to experience was the strongest and only consistent personality correlate of artistic preferences, affecting both overall and specific preferences, as well as visits to galleries, and artistic (rather than scientific) self-perception. Overall preferences were also positively influenced by age and visits to art galleries, and to a lesser degree, by artistic self-perception and conscientiousness (negatively). As for specific styles, after overall preferences were accounted for, more agreeable, more conscientious and less open individuals reported higher preference levels for impressionist, younger and more extraverted participants showed higher levels of preference for cubism (as did males), and younger participants, as well as males, reported higher levels of preferences for renaissance. Limitations and recommendations for future research are discussed. PMID:19026107
NASA Astrophysics Data System (ADS)
Mouslopoulou, Vasiliki; Nicol, Andrew; Walsh, John; Begg, John; Townsend, Dougal; Hristopulos, Dionissios
2013-04-01
The catastrophic earthquakes that recently (September 4th, 2010 and February 22nd, 2011) hit Christchurch, New Zealand, show that active faults, capable of generating large-magnitude earthquakes, can be hidden beneath the Earth's surface. In this study we combine near-surface paleoseismic data with deep (<5 km) onshore seismic-reflection lines to explore the growth of normal faults over short (<27 kyr) and long (>1 Ma) timescales in the Taranaki Rift, New Zealand. Our analysis shows that the integration of different timescale datasets provides a basis for identifying active faults not observed at the ground surface, estimating maximum fault-rupture lengths, inferring maximum short-term displacement rates and improving earthquake hazard assessment. We find that fault displacement rates become increasingly irregular (both faster and slower) on shorter timescales, leading to incomplete sampling of the active-fault population. Surface traces have been recognised for <50% of the active faults and along ∼50% of their lengths. The similarity of along-strike displacement profiles for short and long time intervals suggests that fault lengths and maximum single-event displacements have not changed over the last 3.6 Ma. Therefore, rate changes are likely to reflect temporal adjustments in earthquake recurrence intervals due to fault interactions and associated migration of earthquake activity within the rift.
NASA Astrophysics Data System (ADS)
Mouslopoulou, Vasiliki; Nicol, Andrew; Walsh, John J.; Begg, John G.; Townsend, Dougal B.; Hristopulos, Dionissios T.
2012-03-01
The catastrophic earthquakes that recently (September 4th, 2010 and February 22nd, 2011) hit Christchurch, New Zealand, show that active faults, capable of generating large-magnitude earthquakes, can be hidden beneath the Earth's surface. In this article we combine near-surface paleoseismic data with deep (<5 km) onshore seismic-reflection lines to explore the growth of normal faults over short (<27 kyr) and long (>1 Ma) timescales in the Taranaki Rift, New Zealand. Our analysis shows that the integration of different timescale datasets provides a basis for identifying active faults not observed at the ground surface, estimating maximum fault-rupture lengths, inferring maximum short-term displacement rates and improving earthquake hazard assessment. We find that fault displacement rates become increasingly irregular (both faster and slower) on shorter timescales, leading to incomplete sampling of the active-fault population. Surface traces have been recognised for <50% of the active faults and along ≤50% of their lengths. The similarity of along-strike displacement profiles for short and long time intervals suggests that fault lengths and maximum single-event displacements have not changed over the last 3.6 Ma. Therefore, rate changes are likely to reflect temporal adjustments in earthquake recurrence intervals due to fault interactions and associated migration of earthquake activity within the rift.
Peterson, A. Townsend; Moses, Lina M.; Bausch, Daniel G.
2014-01-01
Lassa fever is a disease that has been reported from sites across West Africa; it is caused by an arenavirus that is hosted by the rodent M. natalensis. Although it is confined to West Africa, and has been documented in detail in some well-studied areas, the details of the distribution of risk of Lassa virus infection remain poorly known at the level of the broader region. In this paper, we explored the effects of certainty of diagnosis, oversampling in well-studied region, and error balance on results of mapping exercises. Each of the three factors assessed in this study had clear and consistent influences on model results, overestimating risk in southern, humid zones in West Africa, and underestimating risk in drier and more northern areas. The final, adjusted risk map indicates broad risk areas across much of West Africa. Although risk maps are increasingly easy to develop from disease occurrence data and raster data sets summarizing aspects of environments and landscapes, this process is highly sensitive to issues of data quality, sampling design, and design of analysis, with macrogeographic implications of each of these issues and the potential for misrepresenting real patterns of risk. PMID:25105746
Peterson, A Townsend; Moses, Lina M; Bausch, Daniel G
2014-01-01
Lassa fever is a disease that has been reported from sites across West Africa; it is caused by an arenavirus that is hosted by the rodent M. natalensis. Although it is confined to West Africa, and has been documented in detail in some well-studied areas, the details of the distribution of risk of Lassa virus infection remain poorly known at the level of the broader region. In this paper, we explored the effects of certainty of diagnosis, oversampling in well-studied region, and error balance on results of mapping exercises. Each of the three factors assessed in this study had clear and consistent influences on model results, overestimating risk in southern, humid zones in West Africa, and underestimating risk in drier and more northern areas. The final, adjusted risk map indicates broad risk areas across much of West Africa. Although risk maps are increasingly easy to develop from disease occurrence data and raster data sets summarizing aspects of environments and landscapes, this process is highly sensitive to issues of data quality, sampling design, and design of analysis, with macrogeographic implications of each of these issues and the potential for misrepresenting real patterns of risk. PMID:25105746
Morokoff, W.J.; Caflisch, R.E.
1995-12-01
The standard Monte Carlo approach to evaluating multidimensional integrals using (pseudo)-random integration nodes is frequently used when quadrature methods are too difficult or expensive to implement. As an alternative to the random methods, it has been suggested that lower error and improved convergence may be obtained by replacing the pseudo-random sequences with more uniformly distributed sequences known as quasi-random. In this paper quasi-random (Halton, Sobol`, and Faure) and pseudo-random sequences are compared in computational experiments designed to determine the effects on convergence of certain properties of the integrand, including variance, variation, smoothness, and dimension. The results show that variation, which plays an important role in the theoretical upper bound given by the Koksma-Hlawka inequality, does not affect convergence, while variance, the determining factor in random Monte Carlo, is shown to provide a rough upper bound, but does not accurately predict performance. In general, quasi-Monte Carlo methods are superior to random Monte Carlo, but the advantage may be slight, particularly in high dimensions or for integrands that are not smooth. For discontinuous integrands, we derive a bound which shows that the exponent for algebraic decay of the integration error from quasi-Monte Carlo is only slightly larger than {1/2} in high dimensions. 21 refs., 6 figs., 5 tabs.
NASA Astrophysics Data System (ADS)
Morokoff, William J.; Caflisch, Russel E.
1995-12-01
The standard Monte Carlo approach to evaluating multidimensional integrals using (pseudo)-random integration nodes is frequently used when quadrature methods are too difficult or expensive to implement. As an alternative to the random methods, it has been suggested that lower error and improved convergence may be obtained by replacing the pseudo-random sequences with more uniformly distributed sequences known as quasi-random. In this paper quasi-random (Halton, Sobol', and Faure) and pseudo-random sequences are compared in computational experiments designed to determine the effects on convergence of certain properties of the integrand, including variance, variation, smoothness, and dimension. The results show that variation, which plays an important role in the theoretical upper bound given by the Koksma-Hlawka inequality, does not affect convergence, while variance, the determining factor in random Monte Carlo, is shown to provide a rough upper bound, but does not accurately predict performance. In general, quasi-Monte Carlo methods are superior to random Monte Carlo, but the advantage may be slight, particularly in high dimensions or for integrands that are not smooth. For discontinuous integrands, we derive a bound which shows that the exponent for algebraic decay of the integration error from quasi-Monte Carlo is only slightly larger than {1}/{2} in high dimensions.
Papadopoulos, Costas; Frontistis, Zacharias; Antonopoulou, Maria; Venieri, Danae; Konstantinou, Ioannis; Mantzavinos, Dionissios
2016-07-01
The sonochemical degradation of ethyl paraben (EP), a representative of the parabens family, was investigated. Experiments were conducted at constant ultrasound frequency of 20kHz and liquid bulk temperature of 30°C in the following range of experimental conditions: EP concentration 250-1250μg/L, ultrasound (US) density 20-60W/L, reaction time up to 120min, initial pH 3-8 and sodium persulfate 0-100mg/L, either in ultrapure water or secondary treated wastewater. A factorial design methodology was adopted to elucidate the statistically important effects and their interactions and a full empirical model comprising seventeen terms was originally developed. Omitting several terms of lower significance, a reduced model that can reliably simulate the process was finally proposed; this includes EP concentration, reaction time, power density and initial pH, as well as the interactions (EP concentration)×(US density), (EP concentration)×(pHo) and (EP concentration)×(time). Experiments at an increased EP concentration of 3.5mg/L were also performed to identify degradation by-products. LC-TOF-MS analysis revealed that EP sonochemical degradation occurs through dealkylation of the ethyl chain to form methyl paraben, while successive hydroxylation of the aromatic ring yields 4-hydroxybenzoic, 2,4-dihydroxybenzoic and 3,4-dihydroxybenzoic acids. By-products are less toxic to bacterium V. fischeri than the parent compound. PMID:26964924
Card, Roderick; Vaughan, Kelly; Bagnall, Mary; Spiropoulos, John; Cooley, William; Strickland, Tony; Davies, Rob; Anjum, Muna F
2016-01-01
Salmonella enterica is a foodborne zoonotic pathogen of significant public health concern. We have characterized the virulence and antimicrobial resistance gene content of 95 Salmonella isolates from 11 serovars by DNA microarray recovered from UK livestock or imported meat. Genes encoding resistance to sulphonamides (sul1, sul2), tetracycline [tet(A), tet(B)], streptomycin (strA, strB), aminoglycoside (aadA1, aadA2), beta-lactam (bla TEM), and trimethoprim (dfrA17) were common. Virulence gene content differed between serovars; S. Typhimurium formed two subclades based on virulence plasmid presence. Thirteen isolates were selected by their virulence profile for pathotyping using the Galleria mellonella pathogenesis model. Infection with a chicken invasive S. Enteritidis or S. Gallinarum isolate, a multidrug resistant S. Kentucky, or a S. Typhimurium DT104 isolate resulted in high mortality of the larvae; notably presence of the virulence plasmid in S. Typhimurium was not associated with increased larvae mortality. Histopathological examination showed that infection caused severe damage to the Galleria gut structure. Enumeration of intracellular bacteria in the larvae 24 h post-infection showed increases of up to 7 log above the initial inoculum and transmission electron microscopy (TEM) showed bacterial replication in the haemolymph. TEM also revealed the presence of vacuoles containing bacteria in the haemocytes, similar to Salmonella containing vacuoles observed in mammalian macrophages; although there was no evidence from our work of bacterial replication within vacuoles. This work shows that microarrays can be used for rapid virulence genotyping of S. enterica and that the Galleria animal model replicates some aspects of Salmonella infection in mammals. These procedures can be used to help inform on the pathogenicity of isolates that may be antibiotic resistant and have scope to aid the assessment of their potential public and animal health risk. PMID:27199965
Card, Roderick; Vaughan, Kelly; Bagnall, Mary; Spiropoulos, John; Cooley, William; Strickland, Tony; Davies, Rob; Anjum, Muna F.
2016-01-01
Salmonella enterica is a foodborne zoonotic pathogen of significant public health concern. We have characterized the virulence and antimicrobial resistance gene content of 95 Salmonella isolates from 11 serovars by DNA microarray recovered from UK livestock or imported meat. Genes encoding resistance to sulphonamides (sul1, sul2), tetracycline [tet(A), tet(B)], streptomycin (strA, strB), aminoglycoside (aadA1, aadA2), beta-lactam (blaTEM), and trimethoprim (dfrA17) were common. Virulence gene content differed between serovars; S. Typhimurium formed two subclades based on virulence plasmid presence. Thirteen isolates were selected by their virulence profile for pathotyping using the Galleria mellonella pathogenesis model. Infection with a chicken invasive S. Enteritidis or S. Gallinarum isolate, a multidrug resistant S. Kentucky, or a S. Typhimurium DT104 isolate resulted in high mortality of the larvae; notably presence of the virulence plasmid in S. Typhimurium was not associated with increased larvae mortality. Histopathological examination showed that infection caused severe damage to the Galleria gut structure. Enumeration of intracellular bacteria in the larvae 24 h post-infection showed increases of up to 7 log above the initial inoculum and transmission electron microscopy (TEM) showed bacterial replication in the haemolymph. TEM also revealed the presence of vacuoles containing bacteria in the haemocytes, similar to Salmonella containing vacuoles observed in mammalian macrophages; although there was no evidence from our work of bacterial replication within vacuoles. This work shows that microarrays can be used for rapid virulence genotyping of S. enterica and that the Galleria animal model replicates some aspects of Salmonella infection in mammals. These procedures can be used to help inform on the pathogenicity of isolates that may be antibiotic resistant and have scope to aid the assessment of their potential public and animal health risk. PMID:27199965
Use of single scatter electron monte carlo transport for medical radiation sciences
Svatos, Michelle M.
2001-01-01
The single scatter Monte Carlo code CREEP models precise microscopic interactions of electrons with matter to enhance physical understanding of radiation sciences. It is designed to simulate electrons in any medium, including materials important for biological studies. It simulates each interaction individually by sampling from a library which contains accurate information over a broad range of energies.
Womersley, J. . Dept. of Physics)
1992-10-01
The D0 detector at the Fermilab Tevatron began its first data taking run in May 1992. For analysis of the expected 25 pb[sup [minus]1] data sample, roughly half a million simulated events will be needed. The GEANT-based Monte Carlo program used to generate these events is described, together with comparisons to test beam data. Some novel techniques used to speed up execution and simplify geometrical input are described.
Observations on variational and projector Monte Carlo methods
Umrigar, C. J.
2015-10-28
Variational Monte Carlo and various projector Monte Carlo (PMC) methods are presented in a unified manner. Similarities and differences between the methods and choices made in designing the methods are discussed. Both methods where the Monte Carlo walk is performed in a discrete space and methods where it is performed in a continuous space are considered. It is pointed out that the usual prescription for importance sampling may not be advantageous depending on the particular quantum Monte Carlo method used and the observables of interest, so alternate prescriptions are presented. The nature of the sign problem is discussed for various versions of PMC methods. A prescription for an exact PMC method in real space, i.e., a method that does not make a fixed-node or similar approximation and does not have a finite basis error, is presented. This method is likely to be practical for systems with a small number of electrons. Approximate PMC methods that are applicable to larger systems and go beyond the fixed-node approximation are also discussed.
Multilevel sequential Monte Carlo samplers
Beskos, Alexandros; Jasra, Ajay; Law, Kody; Tempone, Raul; Zhou, Yan
2016-08-24
Here, we study the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods and leading to a discretisation bias, with the step-size level hL. In addition, the expectation cannot be computed analytically and one often resorts to Monte Carlo methods. In the context of this problem, it is known that the introduction of the multilevel Monte Carlo (MLMC) method can reduce the amount of computational effort to estimate expectations, for a given level of error. This is achieved via a telescoping identity associated to a Monte Carlo approximation of a sequence of probability distributions with discretisation levelsmore » $${\\infty}$$ >h0>h1 ...>hL. In many practical problems of interest, one cannot achieve an i.i.d. sampling of the associated sequence of probability distributions. A sequential Monte Carlo (SMC) version of the MLMC method is introduced to deal with this problem. In conclusion, it is shown that under appropriate assumptions, the attractive property of a reduction of the amount of computational effort to estimate expectations, for a given level of error, can be maintained within the SMC context.« less
Multilevel Monte Carlo simulation of Coulomb collisions
Rosin, M. S.; Ricketson, L. F.; Dimits, A. M.; Caflisch, R. E.; Cohen, B. I.
2014-05-29
We present a new, for plasma physics, highly efficient multilevel Monte Carlo numerical method for simulating Coulomb collisions. The method separates and optimally minimizes the finite-timestep and finite-sampling errors inherent in the Langevin representation of the Landau–Fokker–Planck equation. It does so by combining multiple solutions to the underlying equations with varying numbers of timesteps. For a desired level of accuracy ε , the computational cost of the method is O(ε–2) or (ε–2(lnε)2), depending on the underlying discretization, Milstein or Euler–Maruyama respectively. This is to be contrasted with a cost of O(ε–3) for direct simulation Monte Carlo or binary collision methods.more » We successfully demonstrate the method with a classic beam diffusion test case in 2D, making use of the Lévy area approximation for the correlated Milstein cross terms, and generating a computational saving of a factor of 100 for ε=10–5. Lastly, we discuss the importance of the method for problems in which collisions constitute the computational rate limiting step, and its limitations.« less
Multilevel Monte Carlo simulation of Coulomb collisions
Rosin, M. S.; Ricketson, L. F.; Dimits, A. M.; Caflisch, R. E.; Cohen, B. I.
2014-05-29
We present a new, for plasma physics, highly efficient multilevel Monte Carlo numerical method for simulating Coulomb collisions. The method separates and optimally minimizes the finite-timestep and finite-sampling errors inherent in the Langevin representation of the Landau–Fokker–Planck equation. It does so by combining multiple solutions to the underlying equations with varying numbers of timesteps. For a desired level of accuracy ε , the computational cost of the method is O(ε^{–2}) or (ε^{–2}(lnε)^{2}), depending on the underlying discretization, Milstein or Euler–Maruyama respectively. This is to be contrasted with a cost of O(ε^{–3}) for direct simulation Monte Carlo or binary collision methods. We successfully demonstrate the method with a classic beam diffusion test case in 2D, making use of the Lévy area approximation for the correlated Milstein cross terms, and generating a computational saving of a factor of 100 for ε=10^{–5}. Lastly, we discuss the importance of the method for problems in which collisions constitute the computational rate limiting step, and its limitations.
Composite biasing in Monte Carlo radiative transfer
NASA Astrophysics Data System (ADS)
Baes, Maarten; Gordon, Karl D.; Lunttila, Tuomas; Bianchi, Simone; Camps, Peter; Juvela, Mika; Kuiper, Rolf
2016-05-01
Biasing or importance sampling is a powerful technique in Monte Carlo radiative transfer, and can be applied in different forms to increase the accuracy and efficiency of simulations. One of the drawbacks of the use of biasing is the potential introduction of large weight factors. We discuss a general strategy, composite biasing, to suppress the appearance of large weight factors. We use this composite biasing approach for two different problems faced by current state-of-the-art Monte Carlo radiative transfer codes: the generation of photon packages from multiple components, and the penetration of radiation through high optical depth barriers. In both cases, the implementation of the relevant algorithms is trivial and does not interfere with any other optimisation techniques. Through simple test models, we demonstrate the general applicability, accuracy and efficiency of the composite biasing approach. In particular, for the penetration of high optical depths, the gain in efficiency is spectacular for the specific problems that we consider: in simulations with composite path length stretching, high accuracy results are obtained even for simulations with modest numbers of photon packages, while simulations without biasing cannot reach convergence, even with a huge number of photon packages.
Barfi, Azadeh; Nazem, Habibollah; Saeidi, Iman; Peyrovi, Moazameh; Afsharzadeh, Maryam; Barfi, Behruz; Salavati, Hossein
2016-03-20
In the present study, an efficient and environmental friendly method (called in-syringe reversed dispersive liquid-liquid microextraction (IS-R-DLLME)) was developed to extract three important components (i.e. para-anisaldehyde, trans-anethole and its isomer estragole) simultaneously in different plant extracts (basil, fennel and tarragon), human plasma and urine samples prior their determination using high-performance liquid chromatography. The importance of choosing these plant extracts as samples is emanating from the dual roles of their bioactive compounds (trans-anethole and estragole), which can alter positively or negatively different cellular processes, and necessity to a simple and efficient method for extraction and sensitive determination of these compounds in the mentioned samples. Under the optimum conditions (including extraction solvent: 120 μL of n-octanol; dispersive solvent: 600 μL of acetone; collecting solvent: 1000 μL of acetone, sample pH 3; with no salt), limits of detection (LODs), linear dynamic ranges (LDRs) and recoveries (R) were 79-81 ng mL(-1), 0.26-6.9 μg mL(-1) and 94.1-99.9%, respectively. The obtained results showed that the IS-R-DLLME was a simple, fast and sensitive method with low level consumption of extraction solvent which provides high recovery under the optimum conditions. The present method was applied to investigate the absorption amounts of the mentioned analytes through the determination of the analytes before (in the plant extracts) and after (in the human plasma and urine samples) the consumption which can determine the toxicity levels of the analytes (on the basis of their dosages) in the extracts. PMID:26802527
A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification
NASA Astrophysics Data System (ADS)
Wu, Keyi; Li, Jinglai
2016-09-01
In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parameter y. The performance parameter y is random due to the presence of various sources of uncertainty in the system, and our goal is to estimate the probability density function (PDF) of y. We propose to use the multicanonical Monte Carlo (MMC) method, a special type of adaptive importance sampling algorithms, to compute the PDF of interest. Moreover, we develop an adaptive algorithm to construct local Gaussian process surrogates to further accelerate the MMC iterations. With numerical examples we demonstrate that the proposed method can achieve several orders of magnitudes of speedup over the standard Monte Carlo methods.
Blennow, Mattias
2010-03-30
We introduce the software package MonteCUBES, which is designed to easily and effectively perform Markov Chain Monte Carlo simulations for analyzing neutrino oscillation experiments. We discuss the methods used in the software as well as why we believe that it is particularly useful for simulating new physics effects.
Fast Monte Carlo for radiation therapy: the PEREGRINE Project
Hartmann Siantar, C.L.; Bergstrom, P.M.; Chandler, W.P.; Cox, L.J.; Daly, T.P.; Garrett, D.; House, R.K.; Moses, E.I.; Powell, C.L.; Patterson, R.W.; Schach von Wittenau, A.E.
1997-11-11
The purpose of the PEREGRINE program is to bring high-speed, high- accuracy, high-resolution Monte Carlo dose calculations to the desktop in the radiation therapy clinic. PEREGRINE is a three- dimensional Monte Carlo dose calculation system designed specifically for radiation therapy planning. It provides dose distributions from external beams of photons, electrons, neutrons, and protons as well as from brachytherapy sources. Each external radiation source particle passes through collimator jaws and beam modifiers such as blocks, compensators, and wedges that are used to customize the treatment to maximize the dose to the tumor. Absorbed dose is tallied in the patient or phantom as Monte Carlo simulation particles are followed through a Cartesian transport mesh that has been manually specified or determined from a CT scan of the patient. This paper describes PEREGRINE capabilities, results of benchmark comparisons, calculation times and performance, and the significance of Monte Carlo calculations for photon teletherapy. PEREGRINE results show excellent agreement with a comprehensive set of measurements for a wide variety of clinical photon beam geometries, on both homogeneous and heterogeneous test samples or phantoms. PEREGRINE is capable of calculating >350 million histories per hour for a standard clinical treatment plan. This results in a dose distribution with voxel standard deviations of <2% of the maximum dose on 4 million voxels with 1 mm resolution in the CT-slice plane in under 20 minutes. Calculation times include tracking particles through all patient specific beam delivery components as well as the patient. Most importantly, comparison of Monte Carlo dose calculations with currently-used algorithms reveal significantly different dose distributions for a wide variety of treatment sites, due to the complex 3-D effects of missing tissue, tissue heterogeneities, and accurate modeling of the radiation source.
Dåderman, Anna Maria; Strindlund, Hans; Wiklund, Nils; Fredriksen, Svend-Otto; Lidberg, Lars
2003-10-14
The sedative-hypnotic benzodiazepine flunitrazepam (FZ) is abused worldwide. The purpose of our study was to investigate violence and anterograde amnesia following intoxication with FZ, and how this was legally evaluated in forensic psychiatric investigations with the objective of drawing some conclusions about the importance of urine sample in a case of a suspected intoxication with FZ. The case was a 23-year-old male university student who, intoxicated with FZ (and possibly with other substances such as diazepam, amphetamines or cannabis), first stabbed an acquaintance and, 2 years later, two friends to death. The police investigation files, including video-typed interviews, the forensic psychiatric files, and also results from the forensic autopsy of the victims, were compared with the information obtained from the case. Only partial recovery from anterograde amnesia was shown during a period of several months. Some important new information is contained in this case report: a forensic analysis of blood sample instead of a urine sample, might lead to confusion during police investigation and forensic psychiatric assessment (FPA) of an FZ abuser, and in consequence wrong legal decisions. FZ, alone or combined with other substances, induces severe violence and is followed by anterograde amnesia. All cases of bizarre, unexpected aggression followed by anterograde amnesia should be assessed for abuse of FZ. A urine sample is needed in case of suspected FZ intoxication. The police need to be more aware of these issues, and they must recognise that they play a crucial role in an assessment procedure. Declaring FZ an illegal drug is strongly recommended. PMID:14550609
An Efficient MCMC Algorithm to Sample Binary Matrices with Fixed Marginals
ERIC Educational Resources Information Center
Verhelst, Norman D.
2008-01-01
Uniform sampling of binary matrices with fixed margins is known as a difficult problem. Two classes of algorithms to sample from a distribution not too different from the uniform are studied in the literature: importance sampling and Markov chain Monte Carlo (MCMC). Existing MCMC algorithms converge slowly, require a long burn-in period and yield…
Wormhole Hamiltonian Monte Carlo
Lan, Shiwei; Streets, Jeffrey; Shahbaba, Babak
2015-01-01
In machine learning and statistics, probabilistic inference involving multimodal distributions is quite difficult. This is especially true in high dimensional problems, where most existing algorithms cannot easily move from one mode to another. To address this issue, we propose a novel Bayesian inference approach based on Markov Chain Monte Carlo. Our method can effectively sample from multimodal distributions, especially when the dimension is high and the modes are isolated. To this end, it exploits and modifies the Riemannian geometric properties of the target distribution to create wormholes connecting modes in order to facilitate moving between them. Further, our proposed method uses the regeneration technique in order to adapt the algorithm by identifying new modes and updating the network of wormholes without affecting the stationary distribution. To find new modes, as opposed to redis-covering those previously identified, we employ a novel mode searching algorithm that explores a residual energy function obtained by subtracting an approximate Gaussian mixture density (based on previously discovered modes) from the target density function. PMID:25861551
Pitchure, D J; Ricker, R E; Williams, M E; Claggett, S A
2010-01-01
Since many household systems are fabricated out of metallic materials, changes to the household environment that accelerate corrosion rates will increase the frequency of failures in these systems. Recently, it has been reported that homes constructed with imported wallboard have increased failure rates in appliances, air conditioner heat exchanger coils, and visible corrosion on electrical wiring and other metal components. At the request of the Consumer Product Safety Commission (CPSC), the National Institute of Standards and Technology (NIST) became involved through the Interagency Agreement CPSC-1-09-0023 to perform metallurgical analyses on samples and corrosion products removed from homes constructed using imported wallboard. This document reports on the analysis of the first group of samples received by NIST from CPSC. The samples received by NIST on September 28, 2009 consisted of copper tubing for supplying natural gas and two air conditioner heat exchanger coils. The examinations performed by NIST consisted of photography, metallurgical cross-sectioning, optical microscopy, scanning electron microscopy (SEM), and x-ray diffraction (XRD). Leak tests were also performed on the air conditioner heat exchanger coils. The objective of these examinations was to determine extent and nature of the corrosive attack, the chemical composition of the corrosion product, and the potential chemical reactions or environmental species responsible for accelerated corrosion. A thin black corrosion product was found on samples of the copper tubing. The XRD analysis of this layer indicated that this corrosion product was a copper sulfide phase and the diffraction peaks corresponded with those for the mineral digenite (Cu9S5). Corrosion products were also observed on other types of metals in the air conditioner coils where condensation would frequently wet the metals. The thickness of the corrosion product layer on a copper natural gas supply pipe with a wall thickness of 1
Pitchure, D. J.; Ricker, R. E.; Williams, M. E.; Claggett, S. A.
2010-01-01
Since many household systems are fabricated out of metallic materials, changes to the household environment that accelerate corrosion rates will increase the frequency of failures in these systems. Recently, it has been reported that homes constructed with imported wallboard have increased failure rates in appliances, air conditioner heat exchanger coils, and visible corrosion on electrical wiring and other metal components. At the request of the Consumer Product Safety Commission (CPSC), the National Institute of Standards and Technology (NIST) became involved through the Interagency Agreement CPSC-1-09-0023 to perform metallurgical analyses on samples and corrosion products removed from homes constructed using imported wallboard. This document reports on the analysis of the first group of samples received by NIST from CPSC. The samples received by NIST on September 28, 2009 consisted of copper tubing for supplying natural gas and two air conditioner heat exchanger coils. The examinations performed by NIST consisted of photography, metallurgical cross-sectioning, optical microscopy, scanning electron microscopy (SEM), and x-ray diffraction (XRD). Leak tests were also performed on the air conditioner heat exchanger coils. The objective of these examinations was to determine extent and nature of the corrosive attack, the chemical composition of the corrosion product, and the potential chemical reactions or environmental species responsible for accelerated corrosion. A thin black corrosion product was found on samples of the copper tubing. The XRD analysis of this layer indicated that this corrosion product was a copper sulfide phase and the diffraction peaks corresponded with those for the mineral digenite (Cu9S5). Corrosion products were also observed on other types of metals in the air conditioner coils where condensation would frequently wet the metals. The thickness of the corrosion product layer on a copper natural gas supply pipe with a wall thickness of 1
Cramer, S.N.
1984-01-01
The MORSE code is a large general-use multigroup Monte Carlo code system. Although no claims can be made regarding its superiority in either theoretical details or Monte Carlo techniques, MORSE has been, since its inception at ORNL in the late 1960s, the most widely used Monte Carlo radiation transport code. The principal reason for this popularity is that MORSE is relatively easy to use, independent of any installation or distribution center, and it can be easily customized to fit almost any specific need. Features of the MORSE code are described.
Compressible generalized hybrid Monte Carlo
NASA Astrophysics Data System (ADS)
Fang, Youhan; Sanz-Serna, J. M.; Skeel, Robert D.
2014-05-01
One of the most demanding calculations is to generate random samples from a specified probability distribution (usually with an unknown normalizing prefactor) in a high-dimensional configuration space. One often has to resort to using a Markov chain Monte Carlo method, which converges only in the limit to the prescribed distribution. Such methods typically inch through configuration space step by step, with acceptance of a step based on a Metropolis(-Hastings) criterion. An acceptance rate of 100% is possible in principle by embedding configuration space in a higher dimensional phase space and using ordinary differential equations. In practice, numerical integrators must be used, lowering the acceptance rate. This is the essence of hybrid Monte Carlo methods. Presented is a general framework for constructing such methods under relaxed conditions: the only geometric property needed is (weakened) reversibility; volume preservation is not needed. The possibilities are illustrated by deriving a couple of explicit hybrid Monte Carlo methods, one based on barrier-lowering variable-metric dynamics and another based on isokinetic dynamics.
Interaction picture density matrix quantum Monte Carlo
Malone, Fionn D. Lee, D. K. K.; Foulkes, W. M. C.; Blunt, N. S.; Shepherd, James J.; Spencer, J. S.
2015-07-28
The recently developed density matrix quantum Monte Carlo (DMQMC) algorithm stochastically samples the N-body thermal density matrix and hence provides access to exact properties of many-particle quantum systems at arbitrary temperatures. We demonstrate that moving to the interaction picture provides substantial benefits when applying DMQMC to interacting fermions. In this first study, we focus on a system of much recent interest: the uniform electron gas in the warm dense regime. The basis set incompleteness error at finite temperature is investigated and extrapolated via a simple Monte Carlo sampling procedure. Finally, we provide benchmark calculations for a four-electron system, comparing our results to previous work where possible.
Monte Carlo variance reduction
NASA Technical Reports Server (NTRS)
Byrn, N. R.
1980-01-01
Computer program incorporates technique that reduces variance of forward Monte Carlo method for given amount of computer time in determining radiation environment in complex organic and inorganic systems exposed to significant amounts of radiation.
TH-E-18A-01: Developments in Monte Carlo Methods for Medical Imaging
Badal, A; Zbijewski, W; Bolch, W; Sechopoulos, I
2014-06-15
Monte Carlo simulation methods are widely used in medical physics research and are starting to be implemented in clinical applications such as radiation therapy planning systems. Monte Carlo simulations offer the capability to accurately estimate quantities of interest that are challenging to measure experimentally while taking into account the realistic anatomy of an individual patient. Traditionally, practical application of Monte Carlo simulation codes in diagnostic imaging was limited by the need for large computational resources or long execution times. However, recent advancements in high-performance computing hardware, combined with a new generation of Monte Carlo simulation algorithms and novel postprocessing methods, are allowing for the computation of relevant imaging parameters of interest such as patient organ doses and scatter-to-primaryratios in radiographic projections in just a few seconds using affordable computational resources. Programmable Graphics Processing Units (GPUs), for example, provide a convenient, affordable platform for parallelized Monte Carlo executions that yield simulation times on the order of 10{sup 7} xray/ s. Even with GPU acceleration, however, Monte Carlo simulation times can be prohibitive for routine clinical practice. To reduce simulation times further, variance reduction techniques can be used to alter the probabilistic models underlying the x-ray tracking process, resulting in lower variance in the results without biasing the estimates. Other complementary strategies for further reductions in computation time are denoising of the Monte Carlo estimates and estimating (scoring) the quantity of interest at a sparse set of sampling locations (e.g. at a small number of detector pixels in a scatter simulation) followed by interpolation. Beyond reduction of the computational resources required for performing Monte Carlo simulations in medical imaging, the use of accurate representations of patient anatomy is crucial to the
NASA Astrophysics Data System (ADS)
Kathilankal, J. C.; Fratini, G.; Burba, G. G.
2014-12-01
High-speed, precise gas analyzers used in eddy covariance flux research measure gas content in a known volume, thus essentially measuring gas density. The classical eddy flux equation, however, is based on the dry mole fraction. The relation between dry mole fraction and density is regulated by the ideal gas law and law of partial pressures, and depends on water vapor content, temperature and pressure of air. If the instrument can output precise fast dry mole fraction, the flux processing is significantly simplified and WPL terms accounting for air density fluctuations are no longer required. This will also lead to the reduction in uncertainties associated with the WPL terms. For instruments adopting an open-path design, this method is difficult to use because of complexities with maintaining reliable fast temperature measurements integrated over the entire measuring path, and also because of extraordinary challenges with accurate measurements of fast pressure in the open air flow. For instruments utilizing a traditional long-tube closed-path design, with tube length 1000 or more times the tube diameter, this method can be used when instantaneous fluctuations in the air temperature of the sampled air are effectively dampened, instantaneous pressure fluctuations are regulated or negligible, and water vapor is measured simultaneously with gas, or the sample is dried. For instruments with a short-tube enclosed design, most - but not all - of the temperature fluctuations are attenuated, so calculating unbiased fluxes using fast dry mole fraction output requires high-speed, precise temperature measurements of the air stream inside the cell. In this presentation, authors look at short-term and long-term data sets to assess the importance of high-speed, precise air temperature measurements in the sampling cell of short-tube enclosed gas analyzers. The CO2 and H2O half hourly flux calculations, as well as long-term carbon and water budgets, are examined.
NASA Astrophysics Data System (ADS)
Glavin, D. P.; Brinckerhoff, W. B.; Conrad, P. G.; Dworkin, J. P.; Eigenbrode, J. L.; Getty, S.; Mahaffy, P. R.
2013-12-01
The search for evidence of life on Mars and elsewhere will continue to be one of the primary goals of NASA's robotic exploration program for decades to come. NASA and ESA are currently planning a series of robotic missions to Mars with the goal of understanding its climate, resources, and potential for harboring past or present life. One key goal will be the search for chemical biomarkers including organic compounds important in life on Earth and their geological forms. These compounds include amino acids, the monomer building blocks of proteins and enzymes, nucleobases and sugars which form the backbone of DNA and RNA, and lipids, the structural components of cell membranes. Many of these organic compounds can also be formed abiotically as demonstrated by their prevalence in carbonaceous meteorites [1], though, their molecular characteristics may distinguish a biological source [2]. It is possible that in situ instruments may reveal such characteristics, however, return of the right samples to Earth (i.e. samples containing chemical biosignatures or having a high probability of biosignature preservation) would enable more intensive laboratory studies using a broad array of powerful instrumentation for bulk characterization, molecular detection, isotopic and enantiomeric compositions, and spatially resolved chemistry that may be required for confirmation of extant or extinct life on Mars or elsewhere. In this presentation we will review the current in situ analytical capabilities and strategies for the detection of organics on the Mars Science Laboratory (MSL) rover using the Sample Analysis at Mars (SAM) instrument suite [3] and discuss how both future advanced in situ instrumentation [4] and laboratory measurements of samples returned from Mars and other targets of astrobiological interest including the icy moons of Jupiter and Saturn will help advance our understanding of chemical biosignatures in the Solar System. References: [1] Cronin, J. R and Chang S. (1993
Effect of paper porosity on OCT images: Monte Carlo study
NASA Astrophysics Data System (ADS)
Kirillin, Mikhail Yu.; Priezzhev, Alexander V.; Myllylä, Risto
2008-06-01
Non-invasive measurement of paper porosity is an important problem for papermaking industry. Presently used techniques are invasive and require long time for processing the sample. In recent years optical coherence tomography (OCT) has been proved to be an effective tool for non-invasive study of optically non-uniform scattering media including paper. The aim of present work is to study the potential ability of OCT for sensing the porosity of a paper sample by means of numerical simulations. The paper sample is characterized by variation of porosity along the sample while numerical simulations allow one to consider the samples with constant porosity which is useful for evaluation of the technique abilities. The calculations were performed implementing Monte Carlo-based technique developed earlier for simulation of OCT signals from multilayer paper models. A 9-layer model of paper consisting of five fiber layers and four air layers with non-planar boundaries was considered. The porosity of the samples was varied from 30 to 80% by varying the thicknesses of the layers. The simulations were performed for model paper samples without and with optical clearing agents (benzyl alcohol, 1-pentanol, isopropanol) applied. It was shown that the simulated OCT images of model paper with various porosities significantly differ revealing the potentiality of the OCT technique for sensing the porosity. When obtaining the images of paper samples with optical clearing agents applied, the inner structure of the samples is also revealed providing additional information about the samples under study.
Monte Carlo Test Assembly for Item Pool Analysis and Extension
ERIC Educational Resources Information Center
Belov, Dmitry I.; Armstrong, Ronald D.
2005-01-01
A new test assembly algorithm based on a Monte Carlo random search is presented in this article. A major advantage of the Monte Carlo test assembly over other approaches (integer programming or enumerative heuristics) is that it performs a uniform sampling from the item pool, which provides every feasible item combination (test) with an equal…
abcpmc: Approximate Bayesian Computation for Population Monte-Carlo code
NASA Astrophysics Data System (ADS)
Akeret, Joel
2015-04-01
abcpmc is a Python Approximate Bayesian Computing (ABC) Population Monte Carlo (PMC) implementation based on Sequential Monte Carlo (SMC) with Particle Filtering techniques. It is extendable with k-nearest neighbour (KNN) or optimal local covariance matrix (OLCM) pertubation kernels and has built-in support for massively parallelized sampling on a cluster using MPI.
A quasi-Monte Carlo Metropolis algorithm
Owen, Art B.; Tribble, Seth D.
2005-01-01
This work presents a version of the Metropolis–Hastings algorithm using quasi-Monte Carlo inputs. We prove that the method yields consistent estimates in some problems with finite state spaces and completely uniformly distributed inputs. In some numerical examples, the proposed method is much more accurate than ordinary Metropolis–Hastings sampling. PMID:15956207
Quantum Monte Carlo Endstation for Petascale Computing
Lubos Mitas
2011-01-26
published papers, 15 invited talks and lectures nationally and internationally. My former graduate student and postdoc Dr. Michal Bajdich, who was supported byt this grant, is currently a postdoc with ORNL in the group of Dr. F. Reboredo and Dr. P. Kent and is using the developed tools in a number of DOE projects. The QWalk package has become a truly important research tool used by the electronic structure community and has attracted several new developers in other research groups. Our tools use several types of correlated wavefunction approaches, variational, diffusion and reptation methods, large-scale optimization methods for wavefunctions and enables to calculate energy differences such as cohesion, electronic gaps, but also densities and other properties, using multiple runs one can obtain equations of state for given structures and beyond. Our codes use efficient numerical and Monte Carlo strategies (high accuracy numerical orbitals, multi-reference wave functions, highly accurate correlation factors, pairing orbitals, force biased and correlated sampling Monte Carlo), are robustly parallelized and enable to run on tens of thousands cores very efficiently. Our demonstration applications were focused on the challenging research problems in several fields of materials science such as transition metal solids. We note that our study of FeO solid was the first QMC calculation of transition metal oxides at high pressures.
Path integral Monte Carlo and the electron gas
NASA Astrophysics Data System (ADS)
Brown, Ethan W.
Path integral Monte Carlo is a proven method for accurately simulating quantum mechanical systems at finite-temperature. By stochastically sampling Feynman's path integral representation of the quantum many-body density matrix, path integral Monte Carlo includes non-perturbative effects like thermal fluctuations and particle correlations in a natural way. Over the past 30 years, path integral Monte Carlo has been successfully employed to study the low density electron gas, high-pressure hydrogen, and superfluid helium. For systems where the role of Fermi statistics is important, however, traditional path integral Monte Carlo simulations have an exponentially decreasing efficiency with decreased temperature and increased system size. In this thesis, we work towards improving this efficiency, both through approximate and exact methods, as specifically applied to the homogeneous electron gas. We begin with a brief overview of the current state of atomic simulations at finite-temperature before we delve into a pedagogical review of the path integral Monte Carlo method. We then spend some time discussing the one major issue preventing exact simulation of Fermi systems, the sign problem. Afterwards, we introduce a way to circumvent the sign problem in PIMC simulations through a fixed-node constraint. We then apply this method to the homogeneous electron gas at a large swatch of densities and temperatures in order to map out the warm-dense matter regime. The electron gas can be a representative model for a host of real systems, from simple medals to stellar interiors. However, its most common use is as input into density functional theory. To this end, we aim to build an accurate representation of the electron gas from the ground state to the classical limit and examine its use in finite-temperature density functional formulations. The latter half of this thesis focuses on possible routes beyond the fixed-node approximation. As a first step, we utilize the variational
MORSE Monte Carlo shielding calculations for the zirconium hydride reference reactor
NASA Technical Reports Server (NTRS)
Burgart, C. E.
1972-01-01
Verification of DOT-SPACETRAN transport calculations of a lithium hydride and tungsten shield for a SNAP reactor was performed using the MORSE (Monte Carlo) code. Transport of both neutrons and gamma rays was considered. Importance sampling was utilized in the MORSE calculations. Several quantities internal to the shield, as well as dose at several points outside of the configuration, were in satisfactory agreement with the DOT calculations of the same.
Folk, R.L.; Lynch, F.L.
1997-05-01
Bacterial textures are present on clay minerals in Oligocene Frio Formation sandstones from the subsurface of the Corpus Christi area, Texas. In shallower samples, beads 0.05--0.1 {micro}m in diameter rim the clay flakes; at greater depth these beads become more abundant and eventually are perched on the ends of clay filaments of the same diameter. The authors believe that the beads are nannobacteria (dwarf forms) that have precipitated or transformed the clay minerals during burial of the sediments. Rosettes of chlorite also contain, after HCl etching, rows of 0.1 {micro}m bodies. In contrast, kaolinite shows no evidence of bacterial precipitation. The authors review other examples of bacterially precipitated clay minerals. A danger present in interpretation of earlier work (and much work of others) is the development of nannobacteria-looking artifacts caused by gold coating times in excess of one minute; the authors strongly recommend a 30-second coating time. Bacterial growth of clay minerals may be a very important process both in the surface and subsurface.
Monte Carlo techniques for analyzing deep-penetration problems
Cramer, S.N.; Gonnord, J.; Hendricks, J.S.
1986-02-01
Current methods and difficulties in Monte Carlo deep-penetration calculations are reviewed, including statistical uncertainty and recent adjoint optimization of splitting, Russian roulette, and exponential transformation biasing. Other aspects of the random walk and estimation processes are covered, including the relatively new DXANG angular biasing technique. Specific items summarized are albedo scattering, Monte Carlo coupling techniques with discrete ordinates and other methods, adjoint solutions, and multigroup Monte Carlo. The topic of code-generated biasing parameters is presented, including the creation of adjoint importance functions from forward calculations. Finally, current and future work in the area of computer learning and artificial intelligence is discussed in connection with Monte Carlo applications.
2015-01-01
criteria for paraphilia are too inclusive. Suggestions are given to improve the definition of pathological sexual interests, and the crucial difference between SF and sexual interest is underlined. Joyal CC. Defining “normophilic” and “paraphilic” sexual fantasies in a population‐based sample: On the importance of considering subgroups. Sex Med 2015;3:321–330. PMID:26797067
Monte Carlo simulation of an expanding gas
NASA Technical Reports Server (NTRS)
Boyd, Iain D.
1989-01-01
By application of simple computer graphics techniques, the statistical performance of two Monte Carlo methods used in the simulation of rarefied gas flows are assessed. Specifically, two direct simulation Monte Carlo (DSMC) methods developed by Bird and Nanbu are considered. The graphics techniques are found to be of great benefit in the reduction and interpretation of the large volume of data generated, thus enabling important conclusions to be drawn about the simulation results. Hence, it is discovered that the method of Nanbu suffers from increased statistical fluctuations, thereby prohibiting its use in the solution of practical problems.
Applying Monte Carlo Simulation to Launch Vehicle Design and Requirements Analysis
NASA Technical Reports Server (NTRS)
Hanson, J. M.; Beard, B. B.
2010-01-01
This Technical Publication (TP) is meant to address a number of topics related to the application of Monte Carlo simulation to launch vehicle design and requirements analysis. Although the focus is on a launch vehicle application, the methods may be applied to other complex systems as well. The TP is organized so that all the important topics are covered in the main text, and detailed derivations are in the appendices. The TP first introduces Monte Carlo simulation and the major topics to be discussed, including discussion of the input distributions for Monte Carlo runs, testing the simulation, how many runs are necessary for verification of requirements, what to do if results are desired for events that happen only rarely, and postprocessing, including analyzing any failed runs, examples of useful output products, and statistical information for generating desired results from the output data. Topics in the appendices include some tables for requirements verification, derivation of the number of runs required and generation of output probabilistic data with consumer risk included, derivation of launch vehicle models to include possible variations of assembled vehicles, minimization of a consumable to achieve a two-dimensional statistical result, recontact probability during staging, ensuring duplicated Monte Carlo random variations, and importance sampling.
Shield weight optimization using Monte Carlo transport calculations
NASA Technical Reports Server (NTRS)
Jordan, T. M.; Wohl, M. L.
1972-01-01
Outlines are given of the theory used in FASTER-3 Monte Carlo computer program for the transport of neutrons and gamma rays in complex geometries. The code has the additional capability of calculating the minimum weight layered unit shield configuration which will meet a specified dose rate constraint. It includes the treatment of geometric regions bounded by quadratic and quardric surfaces with multiple radiation sources which have a specified space, angle, and energy dependence. The program calculates, using importance sampling, the resulting number and energy fluxes at specified point, surface, and volume detectors. Results are presented for sample problems involving primary neutron and both primary and secondary photon transport in a spherical reactor shield configuration. These results include the optimization of the shield configuration.
Monte Carlo modeling of an integrating sphere reflectometer.
Prokhorov, Alexander V; Mekhontsev, Sergey N; Hanssen, Leonard M
2003-07-01
The Monte Carlo method has been applied to numerical modeling of an integrating sphere designed for hemispherical-directional reflectance factor measurements. It is shown that a conventional algorithm of backward ray tracing used for estimation of characteristics of the radiation field at a given point has slow convergence for small source-to-sphere-diameter ratios. A newly developed algorithm that substantially improves the convergence by calculation of direct source-induced irradiation for every point of diffuse reflection of rays traced is described. The method developed is applied to an integrating sphere reflectometer for the visible and infrared spectral ranges. Parametric studies of hemispherical radiance distributions for radiation incident onto the sample center were performed. The deviations of measured sample reflectance from the actual reflectance as a result of various factors were computed. The accuracy of the results, adequacy of the reflectance model, and other important aspects of the algorithm implementation are discussed. PMID:12868822
Overy, Catherine; Blunt, N. S.; Shepherd, James J.; Booth, George H.; Cleland, Deidre; Alavi, Ali
2014-12-28
Properties that are necessarily formulated within pure (symmetric) expectation values are difficult to calculate for projector quantum Monte Carlo approaches, but are critical in order to compute many of the important observable properties of electronic systems. Here, we investigate an approach for the sampling of unbiased reduced density matrices within the full configuration interaction quantum Monte Carlo dynamic, which requires only small computational overheads. This is achieved via an independent replica population of walkers in the dynamic, sampled alongside the original population. The resulting reduced density matrices are free from systematic error (beyond those present via constraints on the dynamic itself) and can be used to compute a variety of expectation values and properties, with rapid convergence to an exact limit. A quasi-variational energy estimate derived from these density matrices is proposed as an accurate alternative to the projected estimator for multiconfigurational wavefunctions, while its variational property could potentially lend itself to accurate extrapolation approaches in larger systems.
Castro, André L; Dias, Mário; Reis, Flávio; Teixeira, Helena M
2014-10-01
Gamma-Hydroxybutyric Acid (GHB) is an endogenous compound with a story of clinical use, since the 1960's. However, due to its secondary effects, it has become a controlled substance, entering the illicit market for recreational and "dance club scene" use, muscle enhancement purposes and drug-facilitated sexual assaults. Its endogenous context can bring some difficulties when interpreting, in a forensic context, the analytical values achieved in biological samples. This manuscript reviewed several crucial aspects related to GHB forensic toxicology evaluation, such as its post-mortem behaviour in biological samples; endogenous production values, whether in in vivo and in post-mortem samples; sampling and storage conditions (including stability tests); and cut-off reference values evaluation for different biological samples, such as whole blood, plasma, serum, urine, saliva, bile, vitreous humour and hair. This revision highlights the need of specific sampling care, storage conditions, and cut-off reference values interpretation in different biological samples, essential for proper practical application in forensic toxicology. PMID:25287794
NASA Astrophysics Data System (ADS)
Dytman, Steven
2011-10-01
Every neutrino experiment requires a Monte Carlo event generator for various purposes. Historically, each series of experiments developed their own code which tuned to their needs. Modern experiments would benefit from a universal code (e.g. PYTHIA) which would allow more direct comparison between experiments. GENIE attempts to be that code. This paper compares most commonly used codes and provides some details of GENIE.
NASA Astrophysics Data System (ADS)
Mendoza-Borunda, R.; Herrero-Bervera, E.; Canon-Tapia, E.
2012-12-01
Recent work has suggested the convenience of dyke sampling along several profiles parallel and perpendicular to its walls to increase the probability of determining a geologically significant magma flow direction using anisotropy of magnetic susceptibility (AMS) measurements. For this work, we have resampled in great detail some dykes from the Kapaa Quarry, Koolau Volcano in Oahu Hawaii, comparing the results of a more detailed sampling scheme with those obtained previously with a traditional sampling scheme. In addition to the AMS results we will show magnetic properties, including magnetic grain sizes, Curie points and AMS measured at two different frequencies on a new MFK1-FA Spinner Kappabridge. Our results thus far provide further empirical evidence supporting the occurrence of a definite cyclic fabric acquisition during the emplacement of at least some of the dykes. This cyclic behavior can be captured using the new sampling scheme, but might be easily overlooked if the simple, more traditional sampling scheme is used. Consequently, previous claims concerning the advantages of adopting a more complex sampling scheme are justified since this approach can serve to reduce the uncertainty in the interpretation of AMS results.
DETERMINING UNCERTAINTY IN PHYSICAL PARAMETER MEASUREMENTS BY MONTE CARLO SIMULATION
A statistical approach, often called Monte Carlo Simulation, has been used to examine propagation of error with measurement of several parameters important in predicting environmental transport of chemicals. These parameters are vapor pressure, water solubility, octanol-water par...
An enhanced Monte Carlo outlier detection method.
Zhang, Liangxiao; Li, Peiwu; Mao, Jin; Ma, Fei; Ding, Xiaoxia; Zhang, Qi
2015-09-30
Outlier detection is crucial in building a highly predictive model. In this study, we proposed an enhanced Monte Carlo outlier detection method by establishing cross-prediction models based on determinate normal samples and analyzing the distribution of prediction errors individually for dubious samples. One simulated and three real datasets were used to illustrate and validate the performance of our method, and the results indicated that this method outperformed Monte Carlo outlier detection in outlier diagnosis. After these outliers were removed, the value of validation by Kovats retention indices and the root mean square error of prediction decreased from 3.195 to 1.655, and the average cross-validation prediction error decreased from 2.0341 to 1.2780. This method helps establish a good model by eliminating outliers. © 2015 Wiley Periodicals, Inc. PMID:26226927
Interaction picture density matrix quantum Monte Carlo.
Malone, Fionn D; Blunt, N S; Shepherd, James J; Lee, D K K; Spencer, J S; Foulkes, W M C
2015-07-28
The recently developed density matrix quantum Monte Carlo (DMQMC) algorithm stochastically samples the N-body thermal density matrix and hence provides access to exact properties of many-particle quantum systems at arbitrary temperatures. We demonstrate that moving to the interaction picture provides substantial benefits when applying DMQMC to interacting fermions. In this first study, we focus on a system of much recent interest: the uniform electron gas in the warm dense regime. The basis set incompleteness error at finite temperature is investigated and extrapolated via a simple Monte Carlo sampling procedure. Finally, we provide benchmark calculations for a four-electron system, comparing our results to previous work where possible. PMID:26233116
NASA Astrophysics Data System (ADS)
Yagi, Yuji; Fukahata, Yukitoshi
2008-10-01
Nominally continuous data in space and/or time is obtained in various observations in geophysics. Due to an enhanced technology of computers, we can now invert such observed data with a very high sampling rate. Densely sampled observed data are usually not completely independent of each other, and so we must take this effect into account. As for seismic waveform data, they have at least temporal correlation due to the effect of inelastic attenuation of the Earth. Taking the data covariance into account, we have developed a method of seismic source inversion and applied it to teleseismic P-wave data of the 2003 Boumerdes-Zemmouri, Algeria earthquake. From the comparison of the final slip distributions inverted with and without the covariance components, we found that the effect of covariance components is crucial for a data set of higher sampling rates (>=5 Hz). If we neglect the covariance components, the inverted results become unstable due to overestimation of the information from observed data. So far, it has been widely believed that we can obtain a finer image of seismic source processes, by inverting waveform data with a higher sampling rate. However, the covariance components of observed data originated from inelastic effect of the Earth give a limitation on the resolution of inverted seismic source models.
Chin, P.W. . E-mail: mary.chin@physics.org
2005-10-15
This project developed a solution for verifying external photon beam radiotherapy. The solution is based on a calibration chain for deriving portal dose maps from acquired portal images, and a calculation framework for predicting portal dose maps. Quantitative comparison between acquired and predicted portal dose maps accomplishes both geometric (patient positioning with respect to the beam) and dosimetric (two-dimensional fluence distribution of the beam) verifications. A disagreement would indicate that beam delivery had not been according to plan. The solution addresses the clinical need for verifying radiotherapy both pretreatment (without the patient in the beam) and on treatment (with the patient in the beam). Medical linear accelerators mounted with electronic portal imaging devices (EPIDs) were used to acquire portal images. Two types of EPIDs were investigated: the amorphous silicon (a-Si) and the scanning liquid ion chamber (SLIC). The EGSnrc family of Monte Carlo codes were used to predict portal dose maps by computer simulation of radiation transport in the beam-phantom-EPID configuration. Monte Carlo simulations have been implemented on several levels of high throughput computing (HTC), including the grid, to reduce computation time. The solution has been tested across the entire clinical range of gantry angle, beam size (5 cmx5 cm to 20 cmx20 cm), and beam-patient and patient-EPID separations (4 to 38 cm). In these tests of known beam-phantom-EPID configurations, agreement between acquired and predicted portal dose profiles was consistently within 2% of the central axis value. This Monte Carlo portal dosimetry solution therefore achieved combined versatility, accuracy, and speed not readily achievable by other techniques.
Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies
Hampton, Jerrad; Doostan, Alireza
2015-01-01
Sampling orthogonal polynomial bases via Monte Carlo is of interest for uncertainty quantification of models with random inputs, using Polynomial Chaos (PC) expansions. It is known that bounding a probabilistic parameter, referred to as coherence, yields a bound on the number of samples necessary to identify coefficients in a sparse PC expansion via solution to an ℓ{sub 1}-minimization problem. Utilizing results for orthogonal polynomials, we bound the coherence parameter for polynomials of Hermite and Legendre type under their respective natural sampling distribution. In both polynomial bases we identify an importance sampling distribution which yields a bound with weaker dependence on the order of the approximation. For more general orthonormal bases, we propose the coherence-optimal sampling: a Markov Chain Monte Carlo sampling, which directly uses the basis functions under consideration to achieve a statistical optimality among all sampling schemes with identical support. We demonstrate these different sampling strategies numerically in both high-order and high-dimensional, manufactured PC expansions. In addition, the quality of each sampling method is compared in the identification of solutions to two differential equations, one with a high-dimensional random input and the other with a high-order PC expansion. In both cases, the coherence-optimal sampling scheme leads to similar or considerably improved accuracy.
Xu, Fuchao; Giovanoulis, Georgios; van Waes, Sofie; Padilla-Sanchez, Juan Antonio; Papadopoulou, Eleni; Magnér, Jorgen; Haug, Line Småstuen; Neels, Hugo; Covaci, Adrian
2016-07-19
We compared the human exposure to organophosphate flame retardants (PFRs) via inhalation, dust ingestion, and dermal absorption using different sampling and assessment strategies. Air (indoor stationary air and personal ambient air), dust (floor dust and surface dust), and hand wipes were sampled from 61 participants and their houses. We found that stationary air contains higher levels of ΣPFRs (median = 163 ng/m(3), IQR = 161 ng/m(3)) than personal air (median = 44 ng/m(3), IQR = 55 ng/m(3)), suggesting that the stationary air sample could generate a larger bias for inhalation exposure assessment. Tris(chloropropyl) phosphate isomers (ΣTCPP) accounted for over 80% of ΣPFRs in both stationary and personal air. PFRs were frequently detected in both surface dust (ΣPFRs median = 33 100 ng/g, IQR = 62 300 ng/g) and floor dust (ΣPFRs median = 20 500 ng/g, IQR = 30 300 ng/g). Tris(2-butoxylethyl) phosphate (TBOEP) accounted for 40% and 60% of ΣPFRs in surface and floor dust, respectively, followed by ΣTCPP (30% and 20%, respectively). TBOEP (median = 46 ng, IQR = 69 ng) and ΣTCPP (median = 37 ng, IQR = 49 ng) were also frequently detected in hand wipe samples. For the first time, a comprehensive assessment of human exposure to PFRs via inhalation, dust ingestion, and dermal absorption was conducted with individual personal data rather than reference factors of the general population. Inhalation seems to be the major exposure pathway for ΣTCPP and tris(2-chloroethyl) phosphate (TCEP), while participants had higher exposure to TBOEP and triphenyl phosphate (TPHP) via dust ingestion. Estimated exposure to ΣPFRs was the highest with stationary air inhalation (median =34 ng·kg bw(-1)·day(-1), IQR = 38 ng·kg bw(-1)·day(-1)), followed by surface dust ingestion (median = 13 ng·kg bw(-1)·day(-1), IQR = 28 ng·kg bw(-1)·day(-1)), floor dust ingestion and personal air inhalation. The median dermal exposure on hand wipes was 0.32 ng·kg bw(-1)·day(-1) (IQR
Monte Carlo methods in genetic analysis
Lin, Shili
1996-12-31
Many genetic analyses require computation of probabilities and likelihoods of pedigree data. With more and more genetic marker data deriving from new DNA technologies becoming available to researchers, exact computations are often formidable with standard statistical methods and computational algorithms. The desire to utilize as much available data as possible, coupled with complexities of realistic genetic models, push traditional approaches to their limits. These methods encounter severe methodological and computational challenges, even with the aid of advanced computing technology. Monte Carlo methods are therefore increasingly being explored as practical techniques for estimating these probabilities and likelihoods. This paper reviews the basic elements of the Markov chain Monte Carlo method and the method of sequential imputation, with an emphasis upon their applicability to genetic analysis. Three areas of applications are presented to demonstrate the versatility of Markov chain Monte Carlo for different types of genetic problems. A multilocus linkage analysis example is also presented to illustrate the sequential imputation method. Finally, important statistical issues of Markov chain Monte Carlo and sequential imputation, some of which are unique to genetic data, are discussed, and current solutions are outlined. 72 refs.
Barlow, Daniel E; Biffinger, Justin C; Cockrell-Zugell, Allison L; Lo, Michael; Kjoller, Kevin; Cook, Debra; Lee, Woo Kyung; Pehrsson, Pehr E; Crookes-Goodson, Wendy J; Hung, Chia-Suei; Nadeau, Lloyd J; Russell, John N
2016-08-01
AFM-IR is a combined atomic force microscopy-infrared spectroscopy method that shows promise for nanoscale chemical characterization of biological-materials interactions. In an effort to apply this method to quantitatively probe mechanisms of microbiologically induced polyurethane degradation, we have investigated monolayer clusters of ∼200 nm thick Pseudomonas protegens Pf-5 bacteria (Pf) on a 300 nm thick polyether-polyurethane (PU) film. Here, the impact of the different biological and polymer mechanical properties on the thermomechanical AFM-IR detection mechanism was first assessed without the additional complication of polymer degradation. AFM-IR spectra of Pf and PU were compared with FTIR and showed good agreement. Local AFM-IR spectra of Pf on PU (Pf-PU) exhibited bands from both constituents, showing that AFM-IR is sensitive to chemical composition both at and below the surface. One distinct difference in local AFM-IR spectra on Pf-PU was an anomalous ∼4× increase in IR peak intensities for the probe in contact with Pf versus PU. This was attributed to differences in probe-sample interactions. In particular, significantly higher cantilever damping was observed for probe contact with PU, with a ∼10× smaller Q factor. AFM-IR chemical mapping at single wavelengths was also affected. We demonstrate ratioing of mapping data for chemical analysis as a simple method to cancel the extreme effects of the variable probe-sample interactions. PMID:27403761
Russian roulette efficiency in Monte Carlo resonant absorption calculations
Ghassoun; Jehouani
2000-10-01
The resonant absorption calculation in media containing heavy resonant nuclei is one of the most difficult problems treated in reactor physics. Deterministic techniques need many approximations to solve this kind of problem. On the other hand, the Monte Carlo method is a reliable mathematical tool for evaluating the neutron resonance escape probability. But it suffers from large statistical deviations of results and long computation times. In order to overcome this problem, we have used the Splitting and Russian Roulette technique coupled separately to the survival biasing and to the importance sampling for the energy parameter. These techniques have been used to calculate the neutron resonance absorption in infinite homogenous media containing hydrogen and uranium characterized by the dilution (ratio of the concentrations of hydrogen to uranium). The punctual neutron source energy is taken at Es = 2 MeV and Es = 676.45 eV, whereas the energy cut-off is fixed at Ec = 2.768 eV. The results show a large reduction of computation time and statistical deviation, without altering the mean resonance escape probability compared to the usual analog simulation. The Splitting and Russian Roulette coupled to the survival biasing method is found to be the best methods for studying the neutron resonant absorption, particularly for high energies. A comparison is done between the Monte Carlo and deterministic methods based on the numerical solution of the neutron slowing down equations by the iterative method results for several dilutions. PMID:11003535
Monte Carlo algorithm for simulating fermions on Lefschetz thimbles
NASA Astrophysics Data System (ADS)
Alexandru, Andrei; Başar, Gökçe; Bedaque, Paulo
2016-01-01
A possible solution of the notorious sign problem preventing direct Monte Carlo calculations for systems with nonzero chemical potential is to deform the integration region in the complex plane to a Lefschetz thimble. We investigate this approach for a simple fermionic model. We introduce an easy to implement Monte Carlo algorithm to sample the dominant thimble. Our algorithm relies only on the integration of the gradient flow in the numerically stable direction, which gives it a distinct advantage over the other proposed algorithms. We demonstrate the stability and efficiency of the algorithm by applying it to an exactly solvable fermionic model and compare our results with the analytical ones. We report a very good agreement for a certain region in the parameter space where the dominant contribution comes from a single thimble, including a region where standard methods suffer from a severe sign problem. However, we find that there are also regions in the parameter space where the contribution from multiple thimbles is important, even in the continuum limit.
Inhomogeneous Monte Carlo simulations of dermoscopic spectroscopy
NASA Astrophysics Data System (ADS)
Gareau, Daniel S.; Li, Ting; Jacques, Steven; Krueger, James
2012-03-01
Clinical skin-lesion diagnosis uses dermoscopy: 10X epiluminescence microscopy. Skin appearance ranges from black to white with shades of blue, red, gray and orange. Color is an important diagnostic criteria for diseases including melanoma. Melanin and blood content and distribution impact the diffuse spectral remittance (300-1000nm). Skin layers: immersion medium, stratum corneum, spinous epidermis, basal epidermis and dermis as well as laterally asymmetric features (eg. melanocytic invasion) were modeled in an inhomogeneous Monte Carlo model.
NASA Astrophysics Data System (ADS)
Pandya, Tara M.; Johnson, Seth R.; Evans, Thomas M.; Davidson, Gregory G.; Hamilton, Steven P.; Godfrey, Andrew T.
2016-03-01
This work discusses the implementation, capabilities, and validation of Shift, a massively parallel Monte Carlo radiation transport package authored at Oak Ridge National Laboratory. Shift has been developed to scale well from laptops to small computing clusters to advanced supercomputers and includes features such as support for multiple geometry and physics engines, hybrid capabilities for variance reduction methods such as the Consistent Adjoint-Driven Importance Sampling methodology, advanced parallel decompositions, and tally methods optimized for scalability on supercomputing architectures. The scaling studies presented in this paper demonstrate good weak and strong scaling behavior for the implemented algorithms. Shift has also been validated and verified against various reactor physics benchmarks, including the Consortium for Advanced Simulation of Light Water Reactors' Virtual Environment for Reactor Analysis criticality test suite and several Westinghouse AP1000® problems presented in this paper. These benchmark results compare well to those from other contemporary Monte Carlo codes such as MCNP5 and KENO.
Path Integral Monte Carlo Methods for Fermions
NASA Astrophysics Data System (ADS)
Ethan, Ethan; Dubois, Jonathan; Ceperley, David
2014-03-01
In general, Quantum Monte Carlo methods suffer from a sign problem when simulating fermionic systems. This causes the efficiency of a simulation to decrease exponentially with the number of particles and inverse temperature. To circumvent this issue, a nodal constraint is often implemented, restricting the Monte Carlo procedure from sampling paths that cause the many-body density matrix to change sign. Unfortunately, this high-dimensional nodal surface is not a priori known unless the system is exactly solvable, resulting in uncontrolled errors. We will discuss two possible routes to extend the applicability of finite-temperatue path integral Monte Carlo. First we extend the regime where signful simulations are possible through a novel permutation sampling scheme. Afterwards, we discuss a method to variationally improve the nodal surface by minimizing a free energy during simulation. Applications of these methods will include both free and interacting electron gases, concluding with discussion concerning extension to inhomogeneous systems. Support from DOE DE-FG52-09NA29456, DE-AC52-07NA27344, LLNL LDRD 10- ERD-058, and the Lawrence Scholar program.
NASA Technical Reports Server (NTRS)
2003-01-01
MGS MOC Release No. MOC2-387, 10 June 2003
This is a Mars Global Surveyor (MGS) Mars Orbiter Camera (MOC) wide angle view of the Charitum Montes, south of Argyre Planitia, in early June 2003. The seasonal south polar frost cap, composed of carbon dioxide, has been retreating southward through this area since spring began a month ago. The bright features toward the bottom of this picture are surfaces covered by frost. The picture is located near 57oS, 43oW. North is at the top, south is at the bottom. Sunlight illuminates the scene from the upper left. The area shown is about 217 km (135 miles) wide.
Marcus, Ryan C.
2012-07-25
MCMini is a proof of concept that demonstrates the possibility for Monte Carlo neutron transport using OpenCL with a focus on performance. This implementation, written in C, shows that tracing particles and calculating reactions on a 3D mesh can be done in a highly scalable fashion. These results demonstrate a potential path forward for MCNP or other Monte Carlo codes.
Fast Lattice Monte Carlo Simulations of Polymers
NASA Astrophysics Data System (ADS)
Wang, Qiang; Zhang, Pengfei
2014-03-01
The recently proposed fast lattice Monte Carlo (FLMC) simulations (with multiple occupancy of lattice sites (MOLS) and Kronecker δ-function interactions) give much faster/better sampling of configuration space than both off-lattice molecular simulations (with pair-potential calculations) and conventional lattice Monte Carlo simulations (with self- and mutual-avoiding walk and nearest-neighbor interactions) of polymers.[1] Quantitative coarse-graining of polymeric systems can also be performed using lattice models with MOLS.[2] Here we use several model systems, including polymer melts, solutions, blends, as well as confined and/or grafted polymers, to demonstrate the great advantages of FLMC simulations in the study of equilibrium properties of polymers.
Chorin, Alexandre J.
2007-12-12
A sampling method for spin systems is presented. The spin lattice is written as the union of a nested sequence of sublattices, all but the last with conditionally independent spins, which are sampled in succession using their marginals. The marginals are computed concurrently by a fast algorithm; errors in the evaluation of the marginals are offset by weights. There are no Markov chains and each sample is independent of the previous ones; the cost of a sample is proportional to the number of spins (but the number of samples needed for good statistics may grow with array size). The examples include the Edwards-Anderson spin glass in three dimensions.
NASA Astrophysics Data System (ADS)
Tiana, G.; Villa, F.; Zhan, Y.; Capelli, R.; Paissoni, C.; Sormanni, P.; Heard, E.; Giorgetti, L.; Meloni, R.
2015-01-01
Simplified models, including implicit-solvent and coarse-grained models, are useful tools to investigate the physical properties of biological macromolecules of large size, like protein complexes, large DNA/RNA strands and chromatin fibres. While advanced Monte Carlo techniques are quite efficient in sampling the conformational space of such models, the availability of realistic potentials is still a limitation to their general applicability. The recent development of a computational scheme capable of designing potentials to reproduce any kind of experimental data that can be expressed as thermal averages of conformational properties of the system has partially alleviated the problem. Here we present a program that implements the optimization of the potential with respect to the experimental data through an iterative Monte Carlo algorithm and a rescaling of the probability of the sampled conformations. The Monte Carlo sampling includes several types of moves, suitable for different kinds of system, and various sampling schemes, such as fixed-temperature, replica-exchange and adaptive simulated tempering. The conformational properties whose thermal averages are used as inputs currently include contact functions, distances and functions of distances, but can be easily extended to any function of the coordinates of the system.
NASA Astrophysics Data System (ADS)
Devour, Brian M.; Bell, Eric F.
2016-06-01
We study the relative dust attenuation-inclination relation in 78 721 nearby galaxies using the axis ratio dependence of optical-near-IR colour, as measured by the Sloan Digital Sky Survey, the Two Micron All Sky Survey, and the Wide-field Infrared Survey Explorer. In order to avoid to the greatest extent possible attenuation-driven biases, we carefully select galaxies using dust attenuation-independent near- and mid-IR luminosities and colours. Relative u-band attenuation between face-on and edge-on disc galaxies along the star-forming main sequence varies from ˜0.55 mag up to ˜1.55 mag. The strength of the relative attenuation varies strongly with both specific star formation rate and galaxy luminosity (or stellar mass). The dependence of relative attenuation on luminosity is not monotonic, but rather peaks at M3.4 μm ≈ -21.5, corresponding to M* ≈ 3 × 1010 M⊙. This behaviour stands seemingly in contrast to some older studies; we show that older works failed to reliably probe to higher luminosities, and were insensitive to the decrease in attenuation with increasing luminosity for the brightest star-forming discs. Back-of-the-envelope scaling relations predict the strong variation of dust optical depth with specific star formation rate and stellar mass. More in-depth comparisons using the scaling relations to model the relative attenuation require the inclusion of star-dust geometry to reproduce the details of these variations (especially at high luminosities), highlighting the importance of these geometrical effects.
Parallelizing Monte Carlo with PMC
Rathkopf, J.A.; Jones, T.R.; Nessett, D.M.; Stanberry, L.C.
1994-11-01
PMC (Parallel Monte Carlo) is a system of generic interface routines that allows easy porting of Monte Carlo packages of large-scale physics simulation codes to Massively Parallel Processor (MPP) computers. By loading various versions of PMC, simulation code developers can configure their codes to run in several modes: serial, Monte Carlo runs on the same processor as the rest of the code; parallel, Monte Carlo runs in parallel across many processors of the MPP with the rest of the code running on other MPP processor(s); distributed, Monte Carlo runs in parallel across many processors of the MPP with the rest of the code running on a different machine. This multi-mode approach allows maintenance of a single simulation code source regardless of the target machine. PMC handles passing of messages between nodes on the MPP, passing of messages between a different machine and the MPP, distributing work between nodes, and providing independent, reproducible sequences of random numbers. Several production codes have been parallelized under the PMC system. Excellent parallel efficiency in both the distributed and parallel modes results if sufficient workload is available per processor. Experiences with a Monte Carlo photonics demonstration code and a Monte Carlo neutronics package are described.
Monte Carlo Methods in the Physical Sciences
Kalos, M H
2007-06-06
I will review the role that Monte Carlo methods play in the physical sciences. They are very widely used for a number of reasons: they permit the rapid and faithful transformation of a natural or model stochastic process into a computer code. They are powerful numerical methods for treating the many-dimensional problems that derive from important physical systems. Finally, many of the methods naturally permit the use of modern parallel computers in efficient ways. In the presentation, I will emphasize four aspects of the computations: whether or not the computation derives from a natural or model stochastic process; whether the system under study is highly idealized or realistic; whether the Monte Carlo methodology is straightforward or mathematically sophisticated; and finally, the scientific role of the computation.
Monte Carlo variance reduction approaches for non-Boltzmann tallies
Booth, T.E.
1992-12-01
Quantities that depend on the collective effects of groups of particles cannot be obtained from the standard Boltzmann transport equation. Monte Carlo estimates of these quantities are called non-Boltzmann tallies and have become increasingly important recently. Standard Monte Carlo variance reduction techniques were designed for tallies based on individual particles rather than groups of particles. Experience with non-Boltzmann tallies and analog Monte Carlo has demonstrated the severe limitations of analog Monte Carlo for many non-Boltzmann tallies. In fact, many calculations absolutely require variance reduction methods to achieve practical computation times. Three different approaches to variance reduction for non-Boltzmann tallies are described and shown to be unbiased. The advantages and disadvantages of each of the approaches are discussed.
Monte Carlo techniques for analyzing deep penetration problems
Cramer, S.N.; Gonnord, J.; Hendricks, J.S.
1985-01-01
A review of current methods and difficulties in Monte Carlo deep-penetration calculations is presented. Statistical uncertainty is discussed, and recent adjoint optimization of splitting, Russian roulette, and exponential transformation biasing is reviewed. Other aspects of the random walk and estimation processes are covered, including the relatively new DXANG angular biasing technique. Specific items summarized are albedo scattering, Monte Carlo coupling techniques with discrete ordinates and other methods, adjoint solutions, and multi-group Monte Carlo. The topic of code-generated biasing parameters is presented, including the creation of adjoint importance functions from forward calculations. Finally, current and future work in the area of computer learning and artificial intelligence is discussed in connection with Monte Carlo applications. 29 refs.
Monte Carlo study of thermal fluctuations and Fermi-arc formation in d-wave superconductors
NASA Astrophysics Data System (ADS)
Zhong, Yong-Wei; Li, Tao; Han, Qiang
2011-07-01
From the perspective of thermal fluctuations, we investigate the pseudogap phenomena in underdoped high-temperature curpate superconductors. We present a local update Monte Carlo procedure based on the Green’s function method to sample the fluctuating pairing field. The Chebyshev polynomial method is applied to calculate the single-particle spectral function directly and efficiently. The evolution of Fermi arcs as a function of temperature is studied by examining the spectral function at Fermi energy as well as the loss of spectral weight. Our results signify the importance of the vortexlike phase fluctuations on the formation of Fermi arcs.
An alternative Monte Carlo approach to the thermal radiative transfer problem
Booth, Thomas E.
2011-02-20
The usual Monte Carlo approach to the thermal radiative transfer problem is to view Monte Carlo as a solution technique for the nonlinear thermal radiative transfer equations. The equations contain time derivatives which are approximated by introducing small time steps. An alternative approach avoids time steps by using Monte Carlo to directly sample the time at which the next event occurs. That is, the time is advanced on a natural event-by-event basis rather than by introducing an artificial time step.
Isotropic Monte Carlo Grain Growth
Energy Science and Technology Software Center (ESTSC)
2013-04-25
IMCGG performs Monte Carlo simulations of normal grain growth in metals on a hexagonal grid in two dimensions with periodic boundary conditions. This may be performed with either an isotropic or a misorientation - and incliantion-dependent grain boundary energy.
Intergenerational Correlation in Monte Carlo k-Eigenvalue Calculation
Ueki, Taro
2002-06-15
This paper investigates intergenerational correlation in the Monte Carlo k-eigenvalue calculation of a neutron effective multiplicative factor. To this end, the exponential transform for path stretching has been applied to large fissionable media with localized highly multiplying regions because in such media an exponentially decaying shape is a rough representation of the importance of source particles. The numerical results show that the difference between real and apparent variances virtually vanishes for an appropriate value of the exponential transform parameter. This indicates that the intergenerational correlation of k-eigenvalue samples could be eliminated by the adjoint biasing of particle transport. The relation between the biasing of particle transport and the intergenerational correlation is therefore investigated in the framework of collision estimators, and the following conclusion has been obtained: Within the leading order approximation with respect to the number of histories per generation, the intergenerational correlation vanishes when immediate importance is constant, and the immediate importance under simulation can be made constant by the biasing of particle transport with a function adjoint to the source neutron's distribution, i.e., the importance over all future generations.
Wagner, John C; Peplow, Douglas E.; Mosher, Scott W
2014-01-01
This paper presents a new hybrid (Monte Carlo/deterministic) method for increasing the efficiency of Monte Carlo calculations of distributions, such as flux or dose rate distributions (e.g., mesh tallies), as well as responses at multiple localized detectors and spectra. This method, referred to as Forward-Weighted CADIS (FW-CADIS), is an extension of the Consistent Adjoint Driven Importance Sampling (CADIS) method, which has been used for more than a decade to very effectively improve the efficiency of Monte Carlo calculations of localized quantities, e.g., flux, dose, or reaction rate at a specific location. The basis of this method is the development of an importance function that represents the importance of particles to the objective of uniform Monte Carlo particle density in the desired tally regions. Implementation of this method utilizes the results from a forward deterministic calculation to develop a forward-weighted source for a deterministic adjoint calculation. The resulting adjoint function is then used to generate consistent space- and energy-dependent source biasing parameters and weight windows that are used in a forward Monte Carlo calculation to obtain more uniform statistical uncertainties in the desired tally regions. The FW-CADIS method has been implemented and demonstrated within the MAVRIC sequence of SCALE and the ADVANTG/MCNP framework. Application of the method to representative, real-world problems, including calculation of dose rate and energy dependent flux throughout the problem space, dose rates in specific areas, and energy spectra at multiple detectors, is presented and discussed. Results of the FW-CADIS method and other recently developed global variance reduction approaches are also compared, and the FW-CADIS method outperformed the other methods in all cases considered.
Continuous-time quantum Monte Carlo impurity solvers
NASA Astrophysics Data System (ADS)
Gull, Emanuel; Werner, Philipp; Fuchs, Sebastian; Surer, Brigitte; Pruschke, Thomas; Troyer, Matthias
2011-04-01
representations of quantum dots and molecular conductors and play an increasingly important role in the theory of "correlated electron" materials as auxiliary problems whose solution gives the "dynamical mean field" approximation to the self-energy and local correlation functions. Solution method: Quantum impurity models require a method of solution which provides access to both high and low energy scales and is effective for wide classes of physically realistic models. The continuous-time quantum Monte Carlo algorithms for which we present implementations here meet this challenge. Continuous-time quantum impurity methods are based on partition function expansions of quantum impurity models that are stochastically sampled to all orders using diagrammatic quantum Monte Carlo techniques. For a review of quantum impurity models and their applications and of continuous-time quantum Monte Carlo methods for impurity models we refer the reader to [2]. Additional comments: Use of dmft requires citation of this paper. Use of any ALPS program requires citation of the ALPS [1] paper. Running time: 60 s-8 h per iteration.
A Monte Carlo Approach to Biomedical Time Series Search
Woodbridge, Jonathan; Mortazavi, Bobak; Sarrafzadeh, Majid; Bui, Alex A.T.
2016-01-01
Time series subsequence matching (or signal searching) has importance in a variety of areas in health care informatics. These areas include case-based diagnosis and treatment as well as the discovery of trends and correlations between data. Much of the traditional research in signal searching has focused on high dimensional R-NN matching. However, the results of R-NN are often small and yield minimal information gain; especially with higher dimensional data. This paper proposes a randomized Monte Carlo sampling method to broaden search criteria such that the query results are an accurate sampling of the complete result set. The proposed method is shown both theoretically and empirically to improve information gain. The number of query results are increased by several orders of magnitude over approximate exact matching schemes and fall within a Gaussian distribution. The proposed method also shows excellent performance as the majority of overhead added by sampling can be mitigated through parallelization. Experiments are run on both simulated and real-world biomedical datasets.
Implications of Monte Carlo Statistical Errors in Criticality Safety Assessments
Pevey, Ronald E.
2005-09-15
Most criticality safety calculations are performed using Monte Carlo techniques because of Monte Carlo's ability to handle complex three-dimensional geometries. For Monte Carlo calculations, the more histories sampled, the lower the standard deviation of the resulting estimates. The common intuition is, therefore, that the more histories, the better; as a result, analysts tend to run Monte Carlo analyses as long as possible (or at least to a minimum acceptable uncertainty). For Monte Carlo criticality safety analyses, however, the optimization situation is complicated by the fact that procedures usually require that an extra margin of safety be added because of the statistical uncertainty of the Monte Carlo calculations. This additional safety margin affects the impact of the choice of the calculational standard deviation, both on production and on safety. This paper shows that, under the assumptions of normally distributed benchmarking calculational errors and exact compliance with the upper subcritical limit (USL), the standard deviation that optimizes production is zero, but there is a non-zero value of the calculational standard deviation that minimizes the risk of inadvertently labeling a supercritical configuration as subcritical. Furthermore, this value is shown to be a simple function of the typical benchmarking step outcomes--the bias, the standard deviation of the bias, the upper subcritical limit, and the number of standard deviations added to calculated k-effectives before comparison to the USL.
Efficient estimation of decay parameters in acoustically coupled-spaces using slice sampling.
Jasa, Tomislav; Xiang, Ning
2009-09-01
Room-acoustic energy decay analysis of acoustically coupled-spaces within the Bayesian framework has proven valuable for architectural acoustics applications. This paper describes an efficient algorithm termed slice sampling Monte Carlo (SSMC) for room-acoustic decay parameter estimation within the Bayesian framework. This work combines the SSMC algorithm and a fast search algorithm in order to efficiently determine decay parameters, their uncertainties, and inter-relationships with a minimum amount of required user tuning and interaction. The large variations in the posterior probability density functions over multidimensional parameter spaces imply that an adaptive exploration algorithm such as SSMC can have advantages over the exiting importance sampling Monte Carlo and Metropolis-Hastings Markov Chain Monte Carlo algorithms. This paper discusses implementation of the SSMC algorithm, its initialization, and convergence using experimental data measured from acoustically coupled-spaces. PMID:19739741
Monte Carlo simulation of a clearance box monitor used for nuclear power plant decommissioning.
Bochud, François O; Laedermann, Jean-Pascal; Bailat, Claude J; Schuler, Christoph
2009-05-01
When decommissioning a nuclear facility it is important to be able to estimate activity levels of potentially radioactive samples and compare with clearance values defined by regulatory authorities. This paper presents a method of calibrating a clearance box monitor based on practical experimental measurements and Monte Carlo simulations. Adjusting the simulation for experimental data obtained using a simple point source permits the computation of absolute calibration factors for more complex geometries with an accuracy of a bit more than 20%. The uncertainty of the calibration factor can be improved to about 10% when the simulation is used relatively, in direct comparison with a measurement performed in the same geometry but with another nuclide. The simulation can also be used to validate the experimental calibration procedure when the sample is supposed to be homogeneous but the calibration factor is derived from a plate phantom. For more realistic geometries, like a small gravel dumpster, Monte Carlo simulation shows that the calibration factor obtained with a larger homogeneous phantom is correct within about 20%, if sample density is taken as the influencing parameter. Finally, simulation can be used to estimate the effect of a contamination hotspot. The research supporting this paper shows that activity could be largely underestimated in the event of a centrally-located hotspot and overestimated for a peripherally-located hotspot if the sample is assumed to be homogeneously contaminated. This demonstrates the usefulness of being able to complement experimental methods with Monte Carlo simulations in order to estimate calibration factors that cannot be directly measured because of a lack of available material or specific geometries. PMID:19359851
CPMC-Lab: A MATLAB package for Constrained Path Monte Carlo calculations
NASA Astrophysics Data System (ADS)
Nguyen, Huy; Shi, Hao; Xu, Jie; Zhang, Shiwei
2014-12-01
We describe CPMC-Lab, a MATLAB program for the constrained-path and phaseless auxiliary-field Monte Carlo methods. These methods have allowed applications ranging from the study of strongly correlated models, such as the Hubbard model, to ab initio calculations in molecules and solids. The present package implements the full ground-state constrained-path Monte Carlo (CPMC) method in MATLAB with a graphical interface, using the Hubbard model as an example. The package can perform calculations in finite supercells in any dimensions, under periodic or twist boundary conditions. Importance sampling and all other algorithmic details of a total energy calculation are included and illustrated. This open-source tool allows users to experiment with various model and run parameters and visualize the results. It provides a direct and interactive environment to learn the method and study the code with minimal overhead for setup. Furthermore, the package can be easily generalized for auxiliary-field quantum Monte Carlo (AFQMC) calculations in many other models for correlated electron systems, and can serve as a template for developing a production code for AFQMC total energy calculations in real materials. Several illustrative studies are carried out in one- and two-dimensional lattices on total energy, kinetic energy, potential energy, and charge- and spin-gaps.
Molecular simulation of shocked materials using the reactive Monte Carlo method.
Brennan, John K; Rice, Betsy M
2002-08-01
We demonstrate the applicability of the reactive Monte Carlo (RxMC) simulation method [J. K. Johnson, A. Z. Panagiotopoulos, and K. E. Gubbins, Mol. Phys. 81, 717 (1994); W. R. Smith and B. Tríska, J. Chem. Phys. 100, 3019 (1994)] for calculating the shock Hugoniot properties of a material. The method does not require interaction potentials that simulate bond breaking or bond formation; it requires only the intermolecular potentials and the ideal-gas partition functions for the reactive species that are present. By performing Monte Carlo sampling of forward and reverse reaction steps, the RxMC method provides information on the chemical equilibria states of the shocked material, including the density of the reactive mixture and the mole fractions of the reactive species. We illustrate the methodology for two simple systems (shocked liquid NO and shocked liquid N2), where we find excellent agreement with experimental measurements. The results show that the RxMC methodology provides an important simulation tool capable of testing models used in current detonation theory predictions. Further applications and extensions of the reactive Monte Carlo method are discussed. PMID:12241148
MC21 analysis of the nuclear energy agency Monte Carlo performance benchmark problem
Kelly, D. J.; Sutton, T. M.; Wilson, S. C.
2012-07-01
Due to the steadily decreasing cost and wider availability of large scale computing platforms, there is growing interest in the prospects for the use of Monte Carlo for reactor design calculations that are currently performed using few-group diffusion theory or other low-order methods. To facilitate the monitoring of the progress being made toward the goal of practical full-core reactor design calculations using Monte Carlo, a performance benchmark has been developed and made available through the Nuclear Energy Agency. A first analysis of this benchmark using the MC21 Monte Carlo code was reported on in 2010, and several practical difficulties were highlighted. In this paper, a newer version of MC21 that addresses some of these difficulties has been applied to the benchmark. In particular, the confidence-interval-determination method has been improved to eliminate source correlation bias, and a fission-source-weighting method has been implemented to provide a more uniform distribution of statistical uncertainties. In addition, the Forward-Weighted, Consistent-Adjoint-Driven Importance Sampling methodology has been applied to the benchmark problem. Results of several analyses using these methods are presented, as well as results from a very large calculation with statistical uncertainties that approach what is needed for design applications. (authors)
Parallel domain decomposition methods in fluid models with Monte Carlo transport
Alme, H.J.; Rodrigues, G.H.; Zimmerman, G.B.
1996-12-01
To examine the domain decomposition code coupled Monte Carlo-finite element calculation, it is important to use a domain decomposition that is suitable for the individual models. We have developed a code that simulates a Monte Carlo calculation ( ) on a massively parallel processor. This code is used to examine the load balancing behavior of three domain decomposition ( ) for a Monte Carlo calculation. Results are presented.
A Monte Carlo Study of Marginal Maximum Likelihood Parameter Estimates for the Graded Model.
ERIC Educational Resources Information Center
Ankenmann, Robert D.; Stone, Clement A.
Effects of test length, sample size, and assumed ability distribution were investigated in a multiple replication Monte Carlo study under the 1-parameter (1P) and 2-parameter (2P) logistic graded model with five score levels. Accuracy and variability of item parameter and ability estimates were examined. Monte Carlo methods were used to evaluate…
A Monte Carlo Approach to the Design, Assembly, and Evaluation of Multistage Adaptive Tests
ERIC Educational Resources Information Center
Belov, Dmitry I.; Armstrong, Ronald D.
2008-01-01
This article presents an application of Monte Carlo methods for developing and assembling multistage adaptive tests (MSTs). A major advantage of the Monte Carlo assembly over other approaches (e.g., integer programming or enumerative heuristics) is that it provides a uniform sampling from all MSTs (or MST paths) available from a given item pool.…
An Efficient Monte Carlo Method for Modeling Radiative Transfer in Protoplanetary Disks
NASA Technical Reports Server (NTRS)
Kim, Stacy
2011-01-01
Monte Carlo methods have been shown to be effective and versatile in modeling radiative transfer processes to calculate model temperature profiles for protoplanetary disks. Temperatures profiles are important for connecting physical structure to observation and for understanding the conditions for planet formation and migration. However, certain areas of the disk such as the optically thick disk interior are under-sampled, or are of particular interest such as the snow line (where water vapor condenses into ice) and the area surrounding a protoplanet. To improve the sampling, photon packets can be preferentially scattered and reemitted toward the preferred locations at the cost of weighting packet energies to conserve the average energy flux. Here I report on the weighting schemes developed, how they can be applied to various models, and how they affect simulation mechanics and results. We find that improvements in sampling do not always imply similar improvements in temperature accuracies and calculation speeds.
Reverse Monte Carlo ray-tracing for radiative heat transfer in combustion systems
NASA Astrophysics Data System (ADS)
Sun, Xiaojing
Radiative heat transfer is a dominant heat transfer phenomenon in high temperature systems. With the rapid development of massive supercomputers, the Monte-Carlo ray tracing (MCRT) method starts to see its applications in combustion systems. This research is to find out if Monte-Carlo ray tracing can offer more accurate and efficient calculations than the discrete ordinates method (DOM). Monte-Carlo ray tracing method is a statistical method that traces the history of a bundle of rays. It is known as solving radiative heat transfer with almost no approximation. It can handle nonisotropic scattering and nongray gas mixtures with relative ease compared to conventional methods, such as DOM and spherical harmonics method, etc. There are two schemes in Monte-Carlo ray tracing method: forward and backward/reverse. Case studies and the governing equations demonstrate the advantages of reverse Monte-Carlo ray tracing (RMCRT) method. The RMCRT can be easily implemented for domain decomposition parallelism. In this dissertation, different efficiency improvements techniques for RMCRT are introduced and implemented. They are the random number generator, stratified sampling, ray-surface intersection calculation, Russian roulette, and important sampling. There are two major modules in solving the radiative heat transfer problems: the RMCRT RTE solver and the optical property models. RMCRT is first fully verified in gray, scattering, absorbing and emitting media with black/nonblack, diffuse/nondiffuse bounded surface problems. Sensitivity analysis is carried out with regard to the ray numbers, the mesh resolutions of the computational domain, optical thickness of the media and effects of variance reduction techniques (stratified sampling, Russian roulette). Results are compared with either analytical solutions or benchmark results. The efficiency (the product of error and computation time) of RMCRT has been compared to DOM and suggest great potential for RMCRT's application
Exploring Mass Perception with Markov Chain Monte Carlo
ERIC Educational Resources Information Center
Cohen, Andrew L.; Ross, Michael G.
2009-01-01
Several previous studies have examined the ability to judge the relative mass of objects in idealized collisions. With a newly developed technique of psychological Markov chain Monte Carlo sampling (A. N. Sanborn & T. L. Griffiths, 2008), this work explores participants; perceptions of different collision mass ratios. The results reveal…
Ordinal Hypothesis in ANOVA Designs: A Monte Carlo Study.
ERIC Educational Resources Information Center
Braver, Sanford L.; Sheets, Virgil L.
Numerous designs using analysis of variance (ANOVA) to test ordinal hypotheses were assessed using a Monte Carlo simulation. Each statistic was computed on each of over 10,000 random samples drawn from a variety of population conditions. The number of groups, population variance, and patterns of population means were varied. In the non-null…
Monte Carlo autofluorescence modeling of cervical intraepithelial neoplasm progression
NASA Astrophysics Data System (ADS)
Chu, S. C.; Chiang, H. K.; Wu, C. E.; He, S. Y.; Wang, D. Y.
2006-02-01
Monte Carlo fluorescence model has been developed to estimate the autofluorescent spectra associated with the progression of the Exo-Cervical Intraepithelial Neoplasm (CIN). We used double integrating spheres system and a tunable light source system, 380 to 600 nm, to measure the reflection and transmission spectra of a 50 μm thick tissue, and used Inverse Adding-Doubling (IAD) method to estimate the absorption (μa) and scattering (μs) coefficients. Human cervical tissue samples were sliced vertically (longitudinal) by the frozen section method. The results show that the absorption and scattering coefficients of cervical neoplasia are 2~3 times higher than normal tissues. We applied Monte Carlo method to estimate photon distribution and fluorescence emission in the tissue. By combining the intrinsic fluorescence information (collagen, NADH, and FAD), the anatomical information of the epithelium, CIN, stroma layers, and the fluorescence escape function, the autofluorescence spectra of CIN at different development stages were obtained.We have observed that the progression of the CIN results in gradually decreasing of the autofluorescence intensity of collagen peak intensity. In addition, the existence of the CIN layer formeda barrier that blocks the autofluorescence escaping from the stroma layer due to the strong extinction(scattering and absorption) of the CIN layer. To our knowledge, this is the first study measuring the CIN optical properties in the visible range; it also successfully demonstrates the fluorescence model forestimating autofluorescence spectra of cervical tissue associated with the progression of the CIN tissue;this model is very important in assisting the CIN diagnosis and treatment in clinical medicine.
Superposition Enhanced Nested Sampling
NASA Astrophysics Data System (ADS)
Martiniani, Stefano; Stevenson, Jacob D.; Wales, David J.; Frenkel, Daan
2014-07-01
The theoretical analysis of many problems in physics, astronomy, and applied mathematics requires an efficient numerical exploration of multimodal parameter spaces that exhibit broken ergodicity. Monte Carlo methods are widely used to deal with these classes of problems, but such simulations suffer from a ubiquitous sampling problem: The probability of sampling a particular state is proportional to its entropic weight. Devising an algorithm capable of sampling efficiently the full phase space is a long-standing problem. Here, we report a new hybrid method for the exploration of multimodal parameter spaces exhibiting broken ergodicity. Superposition enhanced nested sampling combines the strengths of global optimization with the unbiased or athermal sampling of nested sampling, greatly enhancing its efficiency with no additional parameters. We report extensive tests of this new approach for atomic clusters that are known to have energy landscapes for which conventional sampling schemes suffer from broken ergodicity. We also introduce a novel parallelization algorithm for nested sampling.
Filtering with State-Observation Examples via Kernel Monte Carlo Filter.
Kanagawa, Motonobu; Nishiyama, Yu; Gretton, Arthur; Fukumizu, Kenji
2016-02-01
This letter addresses the problem of filtering with a state-space model. Standard approaches for filtering assume that a probabilistic model for observations (i.e., the observation model) is given explicitly or at least parametrically. We consider a setting where this assumption is not satisfied; we assume that the knowledge of the observation model is provided only by examples of state-observation pairs. This setting is important and appears when state variables are defined as quantities that are very different from the observations. We propose kernel Monte Carlo filter, a novel filtering method that is focused on this setting. Our approach is based on the framework of kernel mean embeddings, which enables nonparametric posterior inference using the state-observation examples. The proposed method represents state distributions as weighted samples, propagates these samples by sampling, estimates the state posteriors by kernel Bayes' rule, and resamples by kernel herding. In particular, the sampling and resampling procedures are novel in being expressed using kernel mean embeddings, so we theoretically analyze their behaviors. We reveal the following properties, which are similar to those of corresponding procedures in particle methods: the performance of sampling can degrade if the effective sample size of a weighted sample is small, and resampling improves the sampling performance by increasing the effective sample size. We first demonstrate these theoretical findings by synthetic experiments. Then we show the effectiveness of the proposed filter by artificial and real data experiments, which include vision-based mobile robot localization. PMID:26654205
Proton Upset Monte Carlo Simulation
NASA Technical Reports Server (NTRS)
O'Neill, Patrick M.; Kouba, Coy K.; Foster, Charles C.
2009-01-01
The Proton Upset Monte Carlo Simulation (PROPSET) program calculates the frequency of on-orbit upsets in computer chips (for given orbits such as Low Earth Orbit, Lunar Orbit, and the like) from proton bombardment based on the results of heavy ion testing alone. The software simulates the bombardment of modern microelectronic components (computer chips) with high-energy (.200 MeV) protons. The nuclear interaction of the proton with the silicon of the chip is modeled and nuclear fragments from this interaction are tracked using Monte Carlo techniques to produce statistically accurate predictions.
Somasundaram, E.; Palmer, T. S.
2013-07-01
In this paper, the work that has been done to implement variance reduction techniques in a three dimensional, multi group Monte Carlo code - Tortilla, that works within the frame work of the commercial deterministic code - Attila, is presented. This project is aimed to develop an integrated Hybrid code that seamlessly takes advantage of the deterministic and Monte Carlo methods for deep shielding radiation detection problems. Tortilla takes advantage of Attila's features for generating the geometric mesh, cross section library and source definitions. Tortilla can also read importance functions (like adjoint scalar flux) generated from deterministic calculations performed in Attila and use them to employ variance reduction schemes in the Monte Carlo simulation. The variance reduction techniques that are implemented in Tortilla are based on the CADIS (Consistent Adjoint Driven Importance Sampling) method and the LIFT (Local Importance Function Transform) method. These methods make use of the results from an adjoint deterministic calculation to bias the particle transport using techniques like source biasing, survival biasing, transport biasing and weight windows. The results obtained so far and the challenges faced in implementing the variance reduction techniques are reported here. (authors)
Bold Diagrammatic Monte Carlo for Fermionic and Fermionized Systems
NASA Astrophysics Data System (ADS)
Svistunov, Boris
2013-03-01
In three different fermionic cases--repulsive Hubbard model, resonant fermions, and fermionized spins-1/2 (on triangular lattice)--we observe the phenomenon of sign blessing: Feynman diagrammatic series features finite convergence radius despite factorial growth of the number of diagrams with diagram order. Bold diagrammatic Monte Carlo technique allows us to sample millions of skeleton Feynman diagrams. With the universal fermionization trick we can fermionize essentially any (bosonic, spin, mixed, etc.) lattice system. The combination of fermionization and Bold diagrammatic Monte Carlo yields a universal first-principle approach to strongly correlated lattice systems, provided the sign blessing is a generic fermionic phenomenon. Supported by NSF and DARPA
Monte Carlo tests of the ELIPGRID-PC algorithm
Davidson, J.R.
1995-04-01
The standard tool for calculating the probability of detecting pockets of contamination called hot spots has been the ELIPGRID computer code of Singer and Wickman. The ELIPGRID-PC program has recently made this algorithm available for an IBM{reg_sign} PC. However, no known independent validation of the ELIPGRID algorithm exists. This document describes a Monte Carlo simulation-based validation of a modified version of the ELIPGRID-PC code. The modified ELIPGRID-PC code is shown to match Monte Carlo-calculated hot-spot detection probabilities to within {plus_minus}0.5% for 319 out of 320 test cases. The one exception, a very thin elliptical hot spot located within a rectangular sampling grid, differed from the Monte Carlo-calculated probability by about 1%. These results provide confidence in the ability of the modified ELIPGRID-PC code to accurately predict hot-spot detection probabilities within an acceptable range of error.
SPQR: a Monte Carlo reactor kinetics code. [LMFBR
Cramer, S.N.; Dodds, H.L.
1980-02-01
The SPQR Monte Carlo code has been developed to analyze fast reactor core accident problems where conventional methods are considered inadequate. The code is based on the adiabatic approximation of the quasi-static method. This initial version contains no automatic material motion or feedback. An existing Monte Carlo code is used to calculate the shape functions and the integral quantities needed in the kinetics module. Several sample problems have been devised and analyzed. Due to the large statistical uncertainty associated with the calculation of reactivity in accident simulations, the results, especially at later times, differ greatly from deterministic methods. It was also found that in large uncoupled systems, the Monte Carlo method has difficulty in handling asymmetric perturbations.
Monte Carlo calculations of nuclei
Pieper, S.C.
1997-10-01
Nuclear many-body calculations have the complication of strong spin- and isospin-dependent potentials. In these lectures the author discusses the variational and Green`s function Monte Carlo techniques that have been developed to address this complication, and presents a few results.
Synchronous Parallel Kinetic Monte Carlo
Mart?nez, E; Marian, J; Kalos, M H
2006-12-14
A novel parallel kinetic Monte Carlo (kMC) algorithm formulated on the basis of perfect time synchronicity is presented. The algorithm provides an exact generalization of any standard serial kMC model and is trivially implemented in parallel architectures. We demonstrate the mathematical validity and parallel performance of the method by solving several well-understood problems in diffusion.
Frequency domain optical tomography using a Monte Carlo perturbation method
NASA Astrophysics Data System (ADS)
Yamamoto, Toshihiro; Sakamoto, Hiroki
2016-04-01
A frequency domain Monte Carlo method is applied to near-infrared optical tomography, where an intensity-modulated light source with a given modulation frequency is used to reconstruct optical properties. The frequency domain reconstruction technique allows for better separation between the scattering and absorption properties of inclusions, even for ill-posed inverse problems, due to cross-talk between the scattering and absorption reconstructions. The frequency domain Monte Carlo calculation for light transport in an absorbing and scattering medium has thus far been analyzed mostly for the reconstruction of optical properties in simple layered tissues. This study applies a Monte Carlo calculation algorithm, which can handle complex-valued particle weights for solving a frequency domain transport equation, to optical tomography in two-dimensional heterogeneous tissues. The Jacobian matrix that is needed to reconstruct the optical properties is obtained by a first-order "differential operator" technique, which involves less variance than the conventional "correlated sampling" technique. The numerical examples in this paper indicate that the newly proposed Monte Carlo method provides reconstructed results for the scattering and absorption coefficients that compare favorably with the results obtained from conventional deterministic or Monte Carlo methods.
Monte Carlo studies of nuclei and quantum liquid drops
Pandharipande, V.R.; Pieper, S.C.
1989-01-01
The progress in application of variational and Green's function Monte Carlo methods to nuclei is reviewed. The nature of single-particle orbitals in correlated quantum liquid drops is discussed, and it is suggested that the difference between quasi-particle and mean-field orbitals may be of importance in nuclear structure physics. 27 refs., 7 figs., 2 tabs.
APS undulator and wiggler sources: Monte-Carlo simulation
Xu, S.L.; Lai, B.; Viccaro, P.J.
1992-02-01
Standard insertion devices will be provided to each sector by the Advanced Photon Source. It is important to define the radiation characteristics of these general purpose devices. In this document,results of Monte-Carlo simulation are presented. These results, based on the SHADOW program, include the APS Undulator A (UA), Wiggler A (WA), and Wiggler B (WB).
Monte Carlo methods to calculate impact probabilities
NASA Astrophysics Data System (ADS)
Rickman, H.; Wiśniowski, T.; Wajer, P.; Gabryszewski, R.; Valsecchi, G. B.
2014-09-01
Context. Unraveling the events that took place in the solar system during the period known as the late heavy bombardment requires the interpretation of the cratered surfaces of the Moon and terrestrial planets. This, in turn, requires good estimates of the statistical impact probabilities for different source populations of projectiles, a subject that has received relatively little attention, since the works of Öpik (1951, Proc. R. Irish Acad. Sect. A, 54, 165) and Wetherill (1967, J. Geophys. Res., 72, 2429). Aims: We aim to work around the limitations of the Öpik and Wetherill formulae, which are caused by singularities due to zero denominators under special circumstances. Using modern computers, it is possible to make good estimates of impact probabilities by means of Monte Carlo simulations, and in this work, we explore the available options. Methods: We describe three basic methods to derive the average impact probability for a projectile with a given semi-major axis, eccentricity, and inclination with respect to a target planet on an elliptic orbit. One is a numerical averaging of the Wetherill formula; the next is a Monte Carlo super-sizing method using the target's Hill sphere. The third uses extensive minimum orbit intersection distance (MOID) calculations for a Monte Carlo sampling of potentially impacting orbits, along with calculations of the relevant interval for the timing of the encounter allowing collision. Numerical experiments are carried out for an intercomparison of the methods and to scrutinize their behavior near the singularities (zero relative inclination and equal perihelion distances). Results: We find an excellent agreement between all methods in the general case, while there appear large differences in the immediate vicinity of the singularities. With respect to the MOID method, which is the only one that does not involve simplifying assumptions and approximations, the Wetherill averaging impact probability departs by diverging toward
Calculating Pi Using the Monte Carlo Method
NASA Astrophysics Data System (ADS)
Williamson, Timothy
2013-11-01
During the summer of 2012, I had the opportunity to participate in a research experience for teachers at the center for sustainable energy at Notre Dame University (RET @ cSEND) working with Professor John LoSecco on the problem of using antineutrino detection to accurately determine the fuel makeup and operating power of nuclear reactors. During full power operation, a reactor may produce 1021 antineutrinos per second with approximately 100 per day being detected. While becoming familiar with the design and operation of the detectors, and how total antineutrino flux could be obtained from such a small sample, I read about a simulation program called Monte Carlo. Further investigation led me to the Monte Carlo method page of Wikipedia2 where I saw an example of approximating pi using this simulation. Other examples where this method was applied were typically done with computer simulations2 or purely mathematical.3 It is my belief that this method may be easily related to the students by performing the simple activity of sprinkling rice on an arc drawn in a square. The activity that follows was inspired by those simulations and was used by my AP Physics class last year with very good results.
Fission Matrix Capability for MCNP Monte Carlo
Carney, Sean E.; Brown, Forrest B.; Kiedrowski, Brian C.; Martin, William R.
2012-09-05
In a Monte Carlo criticality calculation, before the tallying of quantities can begin, a converged fission source (the fundamental eigenvector of the fission kernel) is required. Tallies of interest may include powers, absorption rates, leakage rates, or the multiplication factor (the fundamental eigenvalue of the fission kernel, k{sub eff}). Just as in the power iteration method of linear algebra, if the dominance ratio (the ratio of the first and zeroth eigenvalues) is high, many iterations of neutron history simulations are required to isolate the fundamental mode of the problem. Optically large systems have large dominance ratios, and systems containing poor neutron communication between regions are also slow to converge. The fission matrix method, implemented into MCNP[1], addresses these problems. When Monte Carlo random walk from a source is executed, the fission kernel is stochastically applied to the source. Random numbers are used for: distances to collision, reaction types, scattering physics, fission reactions, etc. This method is used because the fission kernel is a complex, 7-dimensional operator that is not explicitly known. Deterministic methods use approximations/discretization in energy, space, and direction to the kernel. Consequently, they are faster. Monte Carlo directly simulates the physics, which necessitates the use of random sampling. Because of this statistical noise, common convergence acceleration methods used in deterministic methods do not work. In the fission matrix method, we are using the random walk information not only to build the next-iteration fission source, but also a spatially-averaged fission kernel. Just like in deterministic methods, this involves approximation and discretization. The approximation is the tallying of the spatially-discretized fission kernel with an incorrect fission source. We address this by making the spatial mesh fine enough that this error is negligible. As a consequence of discretization we get a
Modelling cerebral blood oxygenation using Monte Carlo XYZ-PA
NASA Astrophysics Data System (ADS)
Zam, Azhar; Jacques, Steven L.; Alexandrov, Sergey; Li, Youzhi; Leahy, Martin J.
2013-02-01
Continuous monitoring of cerebral blood oxygenation is critically important for the management of many lifethreatening conditions. Non-invasive monitoring of cerebral blood oxygenation with a photoacoustic technique offers advantages over current invasive and non-invasive methods. We introduce a Monte Carlo XYZ-PA to model the energy deposition in 3D and the time-resolved pressures and velocity potential based on the energy absorbed by the biological tissue. This paper outlines the benefits of using Monte Carlo XYZ-PA for optimization of photoacoustic measurement and imaging. To the best of our knowledge this is the first fully integrated tool for photoacoustic modelling.
Overview of the MCU Monte Carlo Software Package
NASA Astrophysics Data System (ADS)
Kalugin, M. A.; Oleynik, D. S.; Shkarovsky, D. A.
2014-06-01
MCU (Monte Carlo Universal) is a project on development and practical use of a universal computer code for simulation of particle transport (neutrons, photons, electrons, positrons) in three-dimensional systems by means of the Monte Carlo method. This paper provides the information on the current state of the project. The developed libraries of constants are briefly described, and the potentialities of the MCU-5 package modules and the executable codes compiled from them are characterized. Examples of important problems of reactor physics solved with the code are presented.
Application of the Theory of Functional Monte Carlo Algorithms to Optimization of the DSMC Method
NASA Astrophysics Data System (ADS)
Plotnikov, M. Yu.; Shkarupa, E. V.
2008-12-01
Some approaches to error analysis and optimization of the Direct Simulation Monte Carlo method are presented. The main idea of this work is the construction of the relations between the sample size and the number of cells which guarantee the attainment of the given error level on the base of the theory of functional Monte Carlo algorithms. The optimal (in the sense of the obtained upper error bound) values of the sample size and the number of cells are constructed.
NASA Astrophysics Data System (ADS)
Shein, Norman P.
A nonselective Rayleigh fading channel model using a time-variant complex multiplier z(t) is considered. Performing a Monte Carlo simulation of this channel requires samples of z(t) with appropriate correlation (fading power spectrum). For an important f-4 spectrum, there is a simple digital implementation that generates uniformly spaced samples. However, many communications systems have faded signals which appear only intermittently at the receiver. Nonuniformly spaced samples are better suited to a simulation of this situation. The author presents an algorithm for efficiently generating nonuniformly spaced correlated samples which have a specified f-4 power spectrum.
TOPICAL REVIEW: Monte Carlo modelling of external radiotherapy photon beams
NASA Astrophysics Data System (ADS)
Verhaegen, Frank; Seuntjens, Jan
2003-11-01
An essential requirement for successful radiation therapy is that the discrepancies between dose distributions calculated at the treatment planning stage and those delivered to the patient are minimized. An important component in the treatment planning process is the accurate calculation of dose distributions. The most accurate way to do this is by Monte Carlo calculation of particle transport, first in the geometry of the external or internal source followed by tracking the transport and energy deposition in the tissues of interest. Additionally, Monte Carlo simulations allow one to investigate the influence of source components on beams of a particular type and their contaminant particles. Since the mid 1990s, there has been an enormous increase in Monte Carlo studies dealing specifically with the subject of the present review, i.e., external photon beam Monte Carlo calculations, aided by the advent of new codes and fast computers. The foundations for this work were laid from the late 1970s until the early 1990s. In this paper we will review the progress made in this field over the last 25 years. The review will be focused mainly on Monte Carlo modelling of linear accelerator treatment heads but sections will also be devoted to kilovoltage x-ray units and 60Co teletherapy sources.
Monte Carlo modelling of external radiotherapy photon beams.
Verhaegen, Frank; Seuntjens, Jan
2003-11-01
An essential requirement for successful radiation therapy is that the discrepancies between dose distributions calculated at the treatment planning stage and those delivered to the patient are minimized. An important component in the treatment planning process is the accurate calculation of dose distributions. The most accurate way to do this is by Monte Carlo calculation of particle transport, first in the geometry of the external or internal source followed by tracking the transport and energy deposition in the tissues of interest. Additionally, Monte Carlo simulations allow one to investigate the influence of source components on beams of a particular type and their contaminant particles. Since the mid 1990s, there has been an enormous increase in Monte Carlo studies dealing specifically with the subject of the present review, i.e., external photon beam Monte Carlo calculations, aided by the advent of new codes and fast computers. The foundations for this work were laid from the late 1970s until the early 1990s. In this paper we will review the progress made in this field over the last 25 years. The review will be focused mainly on Monte Carlo modelling of linear accelerator treatment heads but sections will also be devoted to kilovoltage x-ray units and 60Co teletherapy sources. PMID:14653555
Monte Carlo treatment planning for photon and electron beams
NASA Astrophysics Data System (ADS)
Reynaert, N.; van der Marck, S. C.; Schaart, D. R.; Van der Zee, W.; Van Vliet-Vroegindeweij, C.; Tomsej, M.; Jansen, J.; Heijmen, B.; Coghe, M.; De Wagter, C.
2007-04-01
During the last few decades, accuracy in photon and electron radiotherapy has increased substantially. This is partly due to enhanced linear accelerator technology, providing more flexibility in field definition (e.g. the usage of computer-controlled dynamic multileaf collimators), which led to intensity modulated radiotherapy (IMRT). Important improvements have also been made in the treatment planning process, more specifically in the dose calculations. Originally, dose calculations relied heavily on analytic, semi-analytic and empirical algorithms. The more accurate convolution/superposition codes use pre-calculated Monte Carlo dose "kernels" partly accounting for tissue density heterogeneities. It is generally recognized that the Monte Carlo method is able to increase accuracy even further. Since the second half of the 1990s, several Monte Carlo dose engines for radiotherapy treatment planning have been introduced. To enable the use of a Monte Carlo treatment planning (MCTP) dose engine in clinical circumstances, approximations have been introduced to limit the calculation time. In this paper, the literature on MCTP is reviewed, focussing on patient modeling, approximations in linear accelerator modeling and variance reduction techniques. An overview of published comparisons between MC dose engines and conventional dose calculations is provided for phantom studies and clinical examples, evaluating the added value of MCTP in the clinic. An overview of existing Monte Carlo dose engines and commercial MCTP systems is presented and some specific issues concerning the commissioning of a MCTP system are discussed.
A separable shadow Hamiltonian hybrid Monte Carlo method
NASA Astrophysics Data System (ADS)
Sweet, Christopher R.; Hampton, Scott S.; Skeel, Robert D.; Izaguirre, Jesús A.
2009-11-01
Hybrid Monte Carlo (HMC) is a rigorous sampling method that uses molecular dynamics (MD) as a global Monte Carlo move. The acceptance rate of HMC decays exponentially with system size. The shadow hybrid Monte Carlo (SHMC) was previously introduced to reduce this performance degradation by sampling instead from the shadow Hamiltonian defined for MD when using a symplectic integrator. SHMC's performance is limited by the need to generate momenta for the MD step from a nonseparable shadow Hamiltonian. We introduce the separable shadow Hamiltonian hybrid Monte Carlo (S2HMC) method based on a formulation of the leapfrog/Verlet integrator that corresponds to a separable shadow Hamiltonian, which allows efficient generation of momenta. S2HMC gives the acceptance rate of a fourth order integrator at the cost of a second-order integrator. Through numerical experiments we show that S2HMC consistently gives a speedup greater than two over HMC for systems with more than 4000 atoms for the same variance. By comparison, SHMC gave a maximum speedup of only 1.6 over HMC. S2HMC has the additional advantage of not requiring any user parameters beyond those of HMC. S2HMC is available in the program PROTOMOL 2.1. A Python version, adequate for didactic purposes, is also in MDL (http://mdlab.sourceforge.net/s2hmc).
Scalable Domain Decomposed Monte Carlo Particle Transport
NASA Astrophysics Data System (ADS)
O'Brien, Matthew Joseph
In this dissertation, we present the parallel algorithms necessary to run domain decomposed Monte Carlo particle transport on large numbers of processors (millions of processors). Previous algorithms were not scalable, and the parallel overhead became more computationally costly than the numerical simulation. The main algorithms we consider are: • Domain decomposition of constructive solid geometry: enables extremely large calculations in which the background geometry is too large to fit in the memory of a single computational node. • Load Balancing: keeps the workload per processor as even as possible so the calculation runs efficiently. • Global Particle Find: if particles are on the wrong processor, globally resolve their locations to the correct processor based on particle coordinate and background domain. • Visualizing constructive solid geometry, sourcing particles, deciding that particle streaming communication is completed and spatial redecomposition. These algorithms are some of the most important parallel algorithms required for domain decomposed Monte Carlo particle transport. We demonstrate that our previous algorithms were not scalable, prove that our new algorithms are scalable, and run some of the algorithms up to 2 million MPI processes on the Sequoia supercomputer.
Multiple-time-stepping generalized hybrid Monte Carlo methods
NASA Astrophysics Data System (ADS)
Escribano, Bruno; Akhmatskaya, Elena; Reich, Sebastian; Azpiroz, Jon M.
2015-01-01
Performance of the generalized shadow hybrid Monte Carlo (GSHMC) method [1], which proved to be superior in sampling efficiency over its predecessors [2-4], molecular dynamics and hybrid Monte Carlo, can be further improved by combining it with multi-time-stepping (MTS) and mollification of slow forces. We demonstrate that the comparatively simple modifications of the method not only lead to better performance of GSHMC itself but also allow for beating the best performed methods, which use the similar force splitting schemes. In addition we show that the same ideas can be successfully applied to the conventional generalized hybrid Monte Carlo method (GHMC). The resulting methods, MTS-GHMC and MTS-GSHMC, provide accurate reproduction of thermodynamic and dynamical properties, exact temperature control during simulation and computational robustness and efficiency. MTS-GHMC uses a generalized momentum update to achieve weak stochastic stabilization to the molecular dynamics (MD) integrator. MTS-GSHMC adds the use of a shadow (modified) Hamiltonian to filter the MD trajectories in the HMC scheme. We introduce a new shadow Hamiltonian formulation adapted to force-splitting methods. The use of such Hamiltonians improves the acceptance rate of trajectories and has a strong impact on the sampling efficiency of the method. Both methods were implemented in the open-source MD package ProtoMol and were tested on a water and a protein systems. Results were compared to those obtained using a Langevin Molly (LM) method [5] on the same systems. The test results demonstrate the superiority of the new methods over LM in terms of stability, accuracy and sampling efficiency. This suggests that putting the MTS approach in the framework of hybrid Monte Carlo and using the natural stochasticity offered by the generalized hybrid Monte Carlo lead to improving stability of MTS and allow for achieving larger step sizes in the simulation of complex systems.
Multiple-time-stepping generalized hybrid Monte Carlo methods
Escribano, Bruno; Akhmatskaya, Elena; Reich, Sebastian; Azpiroz, Jon M.
2015-01-01
Performance of the generalized shadow hybrid Monte Carlo (GSHMC) method [1], which proved to be superior in sampling efficiency over its predecessors [2–4], molecular dynamics and hybrid Monte Carlo, can be further improved by combining it with multi-time-stepping (MTS) and mollification of slow forces. We demonstrate that the comparatively simple modifications of the method not only lead to better performance of GSHMC itself but also allow for beating the best performed methods, which use the similar force splitting schemes. In addition we show that the same ideas can be successfully applied to the conventional generalized hybrid Monte Carlo method (GHMC). The resulting methods, MTS-GHMC and MTS-GSHMC, provide accurate reproduction of thermodynamic and dynamical properties, exact temperature control during simulation and computational robustness and efficiency. MTS-GHMC uses a generalized momentum update to achieve weak stochastic stabilization to the molecular dynamics (MD) integrator. MTS-GSHMC adds the use of a shadow (modified) Hamiltonian to filter the MD trajectories in the HMC scheme. We introduce a new shadow Hamiltonian formulation adapted to force-splitting methods. The use of such Hamiltonians improves the acceptance rate of trajectories and has a strong impact on the sampling efficiency of the method. Both methods were implemented in the open-source MD package ProtoMol and were tested on a water and a protein systems. Results were compared to those obtained using a Langevin Molly (LM) method [5] on the same systems. The test results demonstrate the superiority of the new methods over LM in terms of stability, accuracy and sampling efficiency. This suggests that putting the MTS approach in the framework of hybrid Monte Carlo and using the natural stochasticity offered by the generalized hybrid Monte Carlo lead to improving stability of MTS and allow for achieving larger step sizes in the simulation of complex systems.
Exploring theory space with Monte Carlo reweighting
Gainer, James S.; Lykken, Joseph; Matchev, Konstantin T.; Mrenna, Stephen; Park, Myeonghun
2014-10-13
Theories of new physics often involve a large number of unknown parameters which need to be scanned. Additionally, a putative signal in a particular channel may be due to a variety of distinct models of new physics. This makes experimental attempts to constrain the parameter space of motivated new physics models with a high degree of generality quite challenging. We describe how the reweighting of events may allow this challenge to be met, as fully simulated Monte Carlo samples generated for arbitrary benchmark models can be effectively re-used. Specifically, we suggest procedures that allow more efficient collaboration between theorists and experimentalists in exploring large theory parameter spaces in a rigorous way at the LHC.
Exploring theory space with Monte Carlo reweighting
Gainer, James S.; Lykken, Joseph; Matchev, Konstantin T.; Mrenna, Stephen; Park, Myeonghun
2014-10-13
Theories of new physics often involve a large number of unknown parameters which need to be scanned. Additionally, a putative signal in a particular channel may be due to a variety of distinct models of new physics. This makes experimental attempts to constrain the parameter space of motivated new physics models with a high degree of generality quite challenging. We describe how the reweighting of events may allow this challenge to be met, as fully simulated Monte Carlo samples generated for arbitrary benchmark models can be effectively re-used. Specifically, we suggest procedures that allow more efficient collaboration between theorists andmore » experimentalists in exploring large theory parameter spaces in a rigorous way at the LHC.« less
Zimmerman, G.B.
1997-06-24
Monte Carlo methods appropriate to simulate the transport of x-rays, neutrons, ion and electrons in Inertial Confinement Fusion targets are described and analyzed. The Implicit Monte Carlo method of x-ray transport handles symmetry within indirect drive ICF hohlraums well, but can be improved 50X in efficiency by angular biasing the x-rays towards the fuel capsule. Accurate simulation of thermonuclear burns nd burn diagnostics involves detailed particle source spectra, charged particle ranges, inflight reaction kinematics, corrections for bulk and thermal Doppler effects and variance reduction to obtain adequate statistics for rare events. It is found that the effects of angular Coulomb scattering must be included in models of charged particle transport through heterogeneous materials.
Shell model Monte Carlo methods
Koonin, S.E.; Dean, D.J.
1996-10-01
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 {gamma}-soft nuclei, and calculation of double beta-decay matrix elements. Finally, prospects for further progress in such calculations are discussed. 87 refs.
Fixed-sample optimization using a probability density function
Barnett, R.N.; Sun, Zhiwei; Lester, W.A. Jr. |
1997-12-31
We consider the problem of optimizing parameters in a trial function that is to be used in fixed-node diffusion Monte Carlo calculations. We employ a trial function with a Boys-Handy correlation function and a one-particle basis set of high quality. By employing sample points picked from a positive definite distribution, parameters that determine the nodes of the trial function can be varied without introducing singularities into the optimization. For CH as a test system, we find that a trial function of high quality is obtained and that this trial function yields an improved fixed-node energy. This result sheds light on the important question of how to improve the nodal structure and, thereby, the accuracy of diffusion Monte Carlo.
Trahan, Travis J.; Gentile, Nicholas A.
2012-09-10
Statistical uncertainty is inherent to any Monte Carlo simulation of radiation transport problems. In space-angle-frequency independent radiative transfer calculations, the uncertainty in the solution is entirely due to random sampling of source photon emission times. We have developed a modification to the Implicit Monte Carlo algorithm that eliminates noise due to sampling of the emission time of source photons. In problems that are independent of space, angle, and energy, the new algorithm generates a smooth solution, while a standard implicit Monte Carlo solution is noisy. For space- and angle-dependent problems, the new algorithm exhibits reduced noise relative to standard implicit Monte Carlo in some cases, and comparable noise in all other cases. In conclusion, the improvements are limited to short time scales; over long time scales, noise due to random sampling of spatial and angular variables tends to dominate the noise reduction from the new algorithm.
Application de la methode des sous-groupes au calcul Monte-Carlo multigroupe
NASA Astrophysics Data System (ADS)
Martin, Nicolas
This thesis is dedicated to the development of a Monte Carlo neutron transport solver based on the subgroup (or multiband) method. In this formalism, cross sections for resonant isotopes are represented in the form of probability tables on the whole energy spectrum. This study is intended in order to test and validate this approach in lattice physics and criticality-safety applications. The probability table method seems promising since it introduces an alternative computational way between the legacy continuous-energy representation and the multigroup method. In the first case, the amount of data invoked in continuous-energy Monte Carlo calculations can be very important and tend to slow down the overall computational time. In addition, this model preserves the quality of the physical laws present in the ENDF format. Due to its cheap computational cost, the multigroup Monte Carlo way is usually at the basis of production codes in criticality-safety studies. However, the use of a multigroup representation of the cross sections implies a preliminary calculation to take into account self-shielding effects for resonant isotopes. This is generally performed by deterministic lattice codes relying on the collision probability method. Using cross-section probability tables on the whole energy range permits to directly take into account self-shielding effects and can be employed in both lattice physics and criticality-safety calculations. Several aspects have been thoroughly studied: (1) The consistent computation of probability tables with a energy grid comprising only 295 or 361 groups. The CALENDF moment approach conducted to probability tables suitable for a Monte Carlo code. (2) The combination of the probability table sampling for the energy variable with the delta-tracking rejection technique for the space variable, and its impact on the overall efficiency of the proposed Monte Carlo algorithm. (3) The derivation of a model for taking into account anisotropic
A User's Manual for MASH V1.5 - A Monte Carlo Adjoint Shielding Code System
C. O. Slater; J. M. Barnes; J. O. Johnson; J.D. Drischler
1998-10-01
The Monte Carlo ~djoint ~ielding Code System, MASH, calculates neutron and gamma- ray environments and radiation protection factors for armored military vehicles, structures, trenches, and other shielding configurations by coupling a forward discrete ordinates air- over-ground transport calculation with an adjoint Monte Carlo treatment of the shielding geometry. Efficiency and optimum use of computer time are emphasized. The code system includes the GRTUNCL and DORT codes for air-over-ground transport calculations, the MORSE code with the GIFT5 combinatorial geometry package for adjoint shielding calculations, and several peripheral codes that perform the required data preparations, transformations, and coupling functions. The current version, MASH v 1.5, is the successor to the original MASH v 1.0 code system initially developed at Oak Ridge National Laboratory (ORNL). The discrete ordinates calculation determines the fluence on a coupling surface surrounding the shielding geometry due to an external neutron/gamma-ray source. The Monte Carlo calculation determines the effectiveness of the fluence at that surface in causing a response in a detector within the shielding geometry, i.e., the "dose importance" of the coupling surface fluence. A coupling code folds the fluence together with the dose importance, giving the desired dose response. The coupling code can determine the dose response as a function of the shielding geometry orientation relative to the source, distance from the source, and energy response of the detector. This user's manual includes a short description of each code, the input required to execute the code along with some helpful input data notes, and a representative sample problem.
A user's manual for MASH 1. 0: A Monte Carlo Adjoint Shielding Code System
Johnson, J.O.
1992-03-01
The Monte Carlo Adjoint Shielding Code System, MASH, calculates neutron and gamma-ray environments and radiation protection factors for armored military vehicles, structures, trenches, and other shielding configurations by coupling a forward discrete ordinates air-over-ground transport calculation with an adjoint Monte Carlo treatment of the shielding geometry. Efficiency and optimum use of computer time are emphasized. The code system include the GRTUNCL and DORT codes for air-over-ground transport calculations, the MORSE code with the GIFT5 combinatorial geometry package for adjoint shielding calculations, and several peripheral codes that perform the required data preparations, transformations, and coupling functions. MASH is the successor to the Vehicle Code System (VCS) initially developed at Oak Ridge National Laboratory (ORNL). The discrete ordinates calculation determines the fluence on a coupling surface surrounding the shielding geometry due to an external neutron/gamma-ray source. The Monte Carlo calculation determines the effectiveness of the fluence at that surface in causing a response in a detector within the shielding geometry, i.e., the dose importance'' of the coupling surface fluence. A coupling code folds the fluence together with the dose importance, giving the desired dose response. The coupling code can determine the dose response a a function of the shielding geometry orientation relative to the source, distance from the source, and energy response of the detector. This user's manual includes a short description of each code, the input required to execute the code along with some helpful input data notes, and a representative sample problem (input data and selected output edits) for each code.
Reduced Variance for Material Sources in Implicit Monte Carlo
Urbatsch, Todd J.
2012-06-25
Implicit Monte Carlo (IMC), a time-implicit method due to Fleck and Cummings, is used for simulating supernovae and inertial confinement fusion (ICF) systems where x-rays tightly and nonlinearly interact with hot material. The IMC algorithm represents absorption and emission within a timestep as an effective scatter. Similarly, the IMC time-implicitness splits off a portion of a material source directly into the radiation field. We have found that some of our variance reduction and particle management schemes will allow large variances in the presence of small, but important, material sources, as in the case of ICF hot electron preheat sources. We propose a modification of our implementation of the IMC method in the Jayenne IMC Project. Instead of battling the sampling issues associated with a small source, we bypass the IMC implicitness altogether and simply deterministically update the material state with the material source if the temperature of the spatial cell is below a user-specified cutoff. We describe the modified method and present results on a test problem that show the elimination of variance for small sources.
Markov chain Monte Carlo methods: an introductory example
NASA Astrophysics Data System (ADS)
Klauenberg, Katy; Elster, Clemens
2016-02-01
When the Guide to the Expression of Uncertainty in Measurement (GUM) and methods from its supplements are not applicable, the Bayesian approach may be a valid and welcome alternative. Evaluating the posterior distribution, estimates or uncertainties involved in Bayesian inferences often requires numerical methods to avoid high-dimensional integrations. Markov chain Monte Carlo (MCMC) sampling is such a method—powerful, flexible and widely applied. Here, a concise introduction is given, illustrated by a simple, typical example from metrology. The Metropolis-Hastings algorithm is the most basic and yet flexible MCMC method. Its underlying concepts are explained and the algorithm is given step by step. The few lines of software code required for its implementation invite interested readers to get started. Diagnostics to evaluate the performance and common algorithmic choices are illustrated to calibrate the Metropolis-Hastings algorithm for efficiency. Routine application of MCMC algorithms may be hindered currently by the difficulty to assess the convergence of MCMC output and thus to assure the validity of results. An example points to the importance of convergence and initiates discussion about advantages as well as areas of research. Available software tools are mentioned throughout.
Polarized light in birefringent samples (Conference Presentation)
NASA Astrophysics Data System (ADS)
Chue-Sang, Joseph; Bai, Yuqiang; Ramella-Roman, Jessica
2016-02-01
Full-field polarized light imaging provides the capability of investigating the alignment and density of birefringent tissue such as collagen abundantly found in scars, the cervix, and other sites of connective tissue. These can be indicators of disease and conditions affecting a patient. Two-dimensional polarized light Monte Carlo simulations which allow the input of an optical axis of a birefringent sample relative to a detector have been created and validated using optically anisotropic samples such as tendon yet, unlike tendon, most collagen-based tissues is significantly less directional and anisotropic. Most important is the incorporation of three-dimensional structures for polarized light to interact with in order to simulate more realistic biological environments. Here we describe the development of a new polarization sensitive Monte Carlo capable to handle birefringent materials with any spatial distribution. The new computational platform is based on tissue digitization and classification including tissue birefringence and principle axis of polarization. Validation of the system was conducted both numerically and experimentally.
Sun, Li-Zhen; Chen, Shi-Jie
2016-07-12
Experiments have suggested that ion correlation and fluctuation effects can be potentially important for multivalent ions in RNA folding. However, most existing computational methods for the ion electrostatics in RNA folding tend to ignore these effects. The previously reported tightly bound ion (TBI) model can treat ion correlation and fluctuation but its applicability to biologically important RNAs is severely limited by the low computational efficiency. Here, on the basis of Monte Carlo sampling for the many-body ion distribution, we develop a new computational model, the Monte Carlo tightly bound ion (MCTBI) model, for ion-binding properties around an RNA. Because of an enhanced sampling algorithm for ion distribution, the model leads to a significant improvement in computational efficiency. For example, for a 160-nt RNA, the model causes a more than 10-fold increase in the computational efficiency, and the improvement in computational efficiency is more pronounced for larger systems. Furthermore, unlike the earlier model that describes ion distribution using the number of bound ions around each nucleotide, the current MCTBI model is based on the three-dimensional coordinates of the ions. The higher efficiency of the model allows us to treat the ion effects for medium to large RNA molecules, RNA-ligand complexes, and RNA-protein complexes. This new model together with proper RNA conformational sampling and the energetics model may serve as a starting point for further development for the ion effects in RNA folding and conformational changes and for large nucleic acid systems. PMID:27311366
NASA Astrophysics Data System (ADS)
Aimi, Takeshi; Imada, Masatoshi
2007-08-01
We examine Gaussian-basis Monte Carlo (GBMC) method introduced by Corney and Drummond. This method is based on an expansion of the density-matrix operator \\hatρ by means of the coherent Gaussian-type operator basis \\hatΛ and does not suffer from the minus sign problem. The original method, however, often fails in reproducing the true ground state and causes systematic errors of calculated physical quantities because the samples are often trapped in some metastable or symmetry broken states. To overcome this difficulty, we combine the quantum-number projection scheme proposed by Assaad, Werner, Corboz, Gull, and Troyer in conjunction with the importance sampling of the original GBMC method. This improvement allows us to carry out the importance sampling in the quantum-number-projected phase-space. Some comparisons with the previous quantum-number projection scheme indicate that, in our method, the convergence with the ground state is accelerated, which makes it possible to extend the applicability and widen the range of tractable parameters in the GBMC method. The present scheme offers an efficient practical way of computation for strongly correlated electron systems beyond the range of system sizes, interaction strengths and lattice structures tractable by other computational methods such as the quantum Monte Carlo method.
Invariance on Multivariate Results: A Monte Carlo Study of Canonical Coefficients.
ERIC Educational Resources Information Center
Thompson, Bruce
In the present study Monte Carlo methods were employed to evaluate the degree to which canonical function and structure coefficients may be differentially sensitive to sampling error. Sampling error influences were investigated across variations in variable and sample (n) sizes, and across variations in average within-set correlation sizes and in…
Monte Carlo simulations in Nuclear Medicine
Loudos, George K.
2007-11-26
Molecular imaging technologies provide unique abilities to localise signs of disease before symptoms appear, assist in drug testing, optimize and personalize therapy, and assess the efficacy of treatment regimes for different types of cancer. Monte Carlo simulation packages are used as an important tool for the optimal design of detector systems. In addition they have demonstrated potential to improve image quality and acquisition protocols. Many general purpose (MCNP, Geant4, etc) or dedicated codes (SimSET etc) have been developed aiming to provide accurate and fast results. Special emphasis will be given to GATE toolkit. The GATE code currently under development by the OpenGATE collaboration is the most accurate and promising code for performing realistic simulations. The purpose of this article is to introduce the non expert reader to the current status of MC simulations in nuclear medicine and briefly provide examples of current simulated systems, and present future challenges that include simulation of clinical studies and dosimetry applications.
Monte Carlo simulations in Nuclear Medicine
NASA Astrophysics Data System (ADS)
Loudos, George K.
2007-11-01
Molecular imaging technologies provide unique abilities to localise signs of disease before symptoms appear, assist in drug testing, optimize and personalize therapy, and assess the efficacy of treatment regimes for different types of cancer. Monte Carlo simulation packages are used as an important tool for the optimal design of detector systems. In addition they have demonstrated potential to improve image quality and acquisition protocols. Many general purpose (MCNP, Geant4, etc) or dedicated codes (SimSET etc) have been developed aiming to provide accurate and fast results. Special emphasis will be given to GATE toolkit. The GATE code currently under development by the OpenGATE collaboration is the most accurate and promising code for performing realistic simulations. The purpose of this article is to introduce the non expert reader to the current status of MC simulations in nuclear medicine and briefly provide examples of current simulated systems, and present future challenges that include simulation of clinical studies and dosimetry applications.
Active neutron multiplicity analysis and Monte Carlo calculations
NASA Astrophysics Data System (ADS)
Krick, M. S.; Ensslin, N.; Langner, D. G.; Miller, M. C.; Siebelist, R.; Stewart, J. E.; Ceo, R. N.; May, P. K.; Collins, L. L., Jr.
Active neutron multiplicity measurements of high-enrichment uranium metal and oxide samples have been made at Los Alamos and Y-12. The data from the measurements of standards at Los Alamos were analyzed to obtain values for neutron multiplication and source-sample coupling. These results are compared to equivalent results obtained from Monte Carlo calculations. An approximate relationship between coupling and multiplication is derived and used to correct doubles rates for multiplication and coupling. The utility of singles counting for uranium samples is also examined.
NASA Astrophysics Data System (ADS)
Boninsegni, M.; Prokof'Ev, N. V.; Svistunov, B. V.
2006-09-01
A detailed description is provided of a new worm algorithm, enabling the accurate computation of thermodynamic properties of quantum many-body systems in continuous space, at finite temperature. The algorithm is formulated within the general path integral Monte Carlo (PIMC) scheme, but also allows one to perform quantum simulations in the grand canonical ensemble, as well as to compute off-diagonal imaginary-time correlation functions, such as the Matsubara Green function, simultaneously with diagonal observables. Another important innovation consists of the expansion of the attractive part of the pairwise potential energy into elementary (diagrammatic) contributions, which are then statistically sampled. This affords a complete microscopic account of the long-range part of the potential energy, while keeping the computational complexity of all updates independent of the size of the simulated system. The computational scheme allows for efficient calculations of the superfluid fraction and off-diagonal correlations in space-time, for system sizes which are orders of magnitude larger than those accessible to conventional PIMC. We present illustrative results for the superfluid transition in bulk liquid He4 in two and three dimensions, as well as the calculation of the chemical potential of hcp He4 .
Monte Carlo Strategies for Selecting Parameter Values in Simulation Experiments.
Leigh, Jessica W; Bryant, David
2015-09-01
Simulation experiments are used widely throughout evolutionary biology and bioinformatics to compare models, promote methods, and test hypotheses. The biggest practical constraint on simulation experiments is the computational demand, particularly as the number of parameters increases. Given the extraordinary success of Monte Carlo methods for conducting inference in phylogenetics, and indeed throughout the sciences, we investigate ways in which Monte Carlo framework can be used to carry out simulation experiments more efficiently. The key idea is to sample parameter values for the experiments, rather than iterate through them exhaustively. Exhaustive analyses become completely infeasible when the number of parameters gets too large, whereas sampled approaches can fare better in higher dimensions. We illustrate the framework with applications to phylogenetics and genetic archaeology. PMID:26012871
Efficient, Automated Monte Carlo Methods for Radiation Transport
Kong, Rong; Ambrose, Martin; Spanier, Jerome
2012-01-01
Monte Carlo simulations provide an indispensible model for solving radiative transport problems, but their slow convergence inhibits their use as an everyday computational tool. In this paper, we present two new ideas for accelerating the convergence of Monte Carlo algorithms based upon an efficient algorithm that couples simulations of forward and adjoint transport equations. Forward random walks are first processed in stages, each using a fixed sample size, and information from stage k is used to alter the sampling and weighting procedure in stage k + 1. This produces rapid geometric convergence and accounts for dramatic gains in the efficiency of the forward computation. In case still greater accuracy is required in the forward solution, information from an adjoint simulation can be added to extend the geometric learning of the forward solution. The resulting new approach should find widespread use when fast, accurate simulations of the transport equation are needed. PMID:23226872
Ab initio Monte Carlo investigation of small lithium clusters.
Srinivas, S.
1999-06-16
Structural and thermal properties of small lithium clusters are studied using ab initio-based Monte Carlo simulations. The ab initio scheme uses a Hartree-Fock/density functional treatment of the electronic structure combined with a jump-walking Monte Carlo sampling of nuclear configurations. Structural forms of Li{sub 8} and Li{sub 9}{sup +} clusters are obtained and their thermal properties analyzed in terms of probability distributions of the cluster potential energy, average potential energy and configurational heat capacity all considered as a function of the cluster temperature. Details of the gradual evolution with temperature of the structural forms sampled are examined. Temperatures characterizing the onset of structural changes and isomer coexistence are identified for both clusters.
NASA Technical Reports Server (NTRS)
Jordan, T. M.
1970-01-01
The theory used in FASTER-III, a Monte Carlo computer program for the transport of neutrons and gamma rays in complex geometries, is outlined. The program includes the treatment of geometric regions bounded by quadratic and quadric surfaces with multiple radiation sources which have specified space, angle, and energy dependence. The program calculates, using importance sampling, the resulting number and energy fluxes at specified point, surface, and volume detectors. It can also calculate minimum weight shield configuration meeting a specified dose rate constraint. Results are presented for sample problems involving primary neutron, and primary and secondary photon, transport in a spherical reactor shield configuration.
NASA Technical Reports Server (NTRS)
Jordan, T. M.
1970-01-01
A description of the FASTER-III program for Monte Carlo Carlo calculation of photon and neutron transport in complex geometries is presented. Major revisions include the capability of calculating minimum weight shield configurations for primary and secondary radiation and optimal importance sampling parameters. The program description includes a users manual describing the preparation of input data cards, the printout from a sample problem including the data card images, definitions of Fortran variables, the program logic, and the control cards required to run on the IBM 7094, IBM 360, UNIVAC 1108 and CDC 6600 computers.
Novel Quantum Monte Carlo Approaches for Quantum Liquids
NASA Astrophysics Data System (ADS)
Rubenstein, Brenda M.
Quantum Monte Carlo methods are a powerful suite of techniques for solving the quantum many-body problem. By using random numbers to stochastically sample quantum properties, QMC methods are capable of studying low-temperature quantum systems well beyond the reach of conventional deterministic techniques. QMC techniques have likewise been indispensible tools for augmenting our current knowledge of superfluidity and superconductivity. In this thesis, I present two new quantum Monte Carlo techniques, the Monte Carlo Power Method and Bose-Fermi Auxiliary-Field Quantum Monte Carlo, and apply previously developed Path Integral Monte Carlo methods to explore two new phases of quantum hard spheres and hydrogen. I lay the foundation for a subsequent description of my research by first reviewing the physics of quantum liquids in Chapter One and the mathematics behind Quantum Monte Carlo algorithms in Chapter Two. I then discuss the Monte Carlo Power Method, a stochastic way of computing the first several extremal eigenvalues of a matrix too memory-intensive to be stored and therefore diagonalized. As an illustration of the technique, I demonstrate how it can be used to determine the second eigenvalues of the transition matrices of several popular Monte Carlo algorithms. This information may be used to quantify how rapidly a Monte Carlo algorithm is converging to the equilibrium probability distribution it is sampling. I next present the Bose-Fermi Auxiliary-Field Quantum Monte Carlo algorithm. This algorithm generalizes the well-known Auxiliary-Field Quantum Monte Carlo algorithm for fermions to bosons and Bose-Fermi mixtures. Despite some shortcomings, the Bose-Fermi Auxiliary-Field Quantum Monte Carlo algorithm represents the first exact technique capable of studying Bose-Fermi mixtures of any size in any dimension. In Chapter Six, I describe a new Constant Stress Path Integral Monte Carlo algorithm for the study of quantum mechanical systems under high pressures. While
Regenerative Markov Chain Monte Carlo for any distribution.
Minh, D.
2012-01-01
While Markov chain Monte Carlo (MCMC) methods are frequently used for difficult calculations in a wide range of scientific disciplines, they suffer from a serious limitation: their samples are not independent and identically distributed. Consequently, estimates of expectations are biased if the initial value of the chain is not drawn from the target distribution. Regenerative simulation provides an elegant solution to this problem. In this article, we propose a simple regenerative MCMC algorithm to generate variates for any distribution
Sampling and Sample Preparation
NASA Astrophysics Data System (ADS)
Morawicki, Rubén O.
Quality attributes in food products, raw materials, or ingredients are measurable characteristics that need monitoring to ensure that specifications are met. Some quality attributes can be measured online by using specially designed sensors and results obtained in real time (e.g., color of vegetable oil in an oil extraction plant). However, in most cases quality attributes are measured on small portions of material that are taken periodically from continuous processes or on a certain number of small portions taken from a lot. The small portions taken for analysis are referred to as samples, and the entire lot or the entire production for a certain period of time, in the case of continuous processes, is called a population. The process of taking samples from a population is called sampling. If the procedure is done correctly, the measurable characteristics obtained for the samples become a very accurate estimation of the population.
Monte Carlo radiation transport: A revolution in science
Hendricks, J.
1993-04-01
When Enrico Fermi, Stan Ulam, Nicholas Metropolis, John von Neuman, and Robert Richtmyer invented the Monte Carlo method fifty years ago, little could they imagine the far-flung consequences, the international applications, and the revolution in science epitomized by their abstract mathematical method. The Monte Carlo method is used in a wide variety of fields to solve exact computational models approximately by statistical sampling. It is an alternative to traditional physics modeling methods which solve approximate computational models exactly by deterministic methods. Modern computers and improved methods, such as variance reduction, have enhanced the method to the point of enabling a true predictive capability in areas such as radiation or particle transport. This predictive capability has contributed to a radical change in the way science is done: design and understanding come from computations built upon experiments rather than being limited to experiments, and the computer codes doing the computations have become the repository for physics knowledge. The MCNP Monte Carlo computer code effort at Los Alamos is an example of this revolution. Physicians unfamiliar with physics details can design cancer treatments using physics buried in the MCNP computer code. Hazardous environments and hypothetical accidents can be explored. Many other fields, from underground oil well exploration to aerospace, from physics research to energy production, from safety to bulk materials processing, benefit from MCNP, the Monte Carlo method, and the revolution in science.
NASA Astrophysics Data System (ADS)
Gingrich, Douglas M.
2010-11-01
We describe the Monte Carlo event generator for black hole production and decay in proton-proton collisions - QBH version 1.02. The generator implements a model for quantum black hole production and decay based on the conservation of local gauge symmetries and democratic decays. The code in written entirely in C++ and interfaces to the PYTHIA 8 Monte Carlo code for fragmentation and decays. Program summaryProgram title: QBH Catalogue identifier: AEGU_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGU_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 10 048 No. of bytes in distributed program, including test data, etc.: 118 420 Distribution format: tar.gz Programming language: C++ Computer: x86 Operating system: Scientific Linux, Mac OS X RAM: 1 GB Classification: 11.6 External routines: PYTHIA 8130 ( http://home.thep.lu.se/~torbjorn/pythiaaux/present.html) and LHAPDF ( http://projects.hepforge.org/lhapdf/) Nature of problem: Simulate black hole production and decay in proton-proton collision. Solution method: Monte Carlo simulation using importance sampling. Running time: Eight events per second.
Tryggestad, E; Armour, M; Iordachita, I; Verhaegen, F; Wong, J W
2011-01-01
Our group has constructed the small animal radiation research platform (SARRP) for delivering focal, kilo-voltage radiation to targets in small animals under robotic control using cone-beam CT guidance. The present work was undertaken to support the SARRP’s treatment planning capabilities. We have devised a comprehensive system for characterizing the radiation dosimetry in water for the SARRP and have developed a Monte Carlo dose engine with the intent of reproducing these measured results. We find that the SARRP provides sufficient therapeutic dose rates ranging from 102 to 228 cGy min−1 at 1 cm depth for the available set of high-precision beams ranging from 0.5 to 5 mm in size. In terms of depth–dose, the mean of the absolute percentage differences between the Monte Carlo calculations and measurement is 3.4% over the full range of sampled depths spanning 0.5–7.2 cm for the 3 and 5 mm beams. The measured and computed profiles for these beams agree well overall; of note, good agreement is observed in the profile tails. Especially for the smallest 0.5 and 1 mm beams, including a more realistic description of the effective x-ray source into the Monte Carlo model may be important. PMID:19687532
Research in the Mont Terri Rock laboratory: Quo vadis?
NASA Astrophysics Data System (ADS)
Bossart, Paul; Thury, Marc
During the past 10 years, the 12 Mont Terri partner organisations ANDRA, BGR, CRIEPI, ENRESA, FOWG (now SWISSTOPO), GRS, HSK, IRSN, JAEA, NAGRA, OBAYASHI and SCK-CEN have jointly carried out and financed a research programme in the Mont Terri Rock Laboratory. An important strategic question for the Mont Terri project is what type of new experiments should be carried out in the future. This question has been discussed among partner delegates, authorities, scientists, principal investigators and experiment delegates. All experiments at Mont Terri - past, ongoing and future - can be assigned to the following three categories: (1) process and mechanism understanding in undisturbed argillaceous formations, (2) experiments related to excavation- and repository-induced perturbations and (3) experiments related to repository performance during the operational and post-closure phases. In each of these three areas, there are still open questions and hence potential experiments to be carried out in the future. A selection of key issues and questions which have not, or have only partly been addressed so far and in which the project partners, but also the safety authorities and other research organisations may be interested, are presented in the following. The Mont Terri Rock Laboratory is positioned as a generic rock laboratory, where research and development is key: mainly developing methods for site characterisation of argillaceous formations, process understanding and demonstration of safety. Due to geological constraints, there will never be a site specific rock laboratory at Mont Terri. The added value for the 12 partners in terms of future experiments is threefold: (1) the Mont Terri project provides an international scientific platform of high reputation for research on radioactive waste disposal (= state-of-the-art research in argillaceous materials); (2) errors are explicitly allowed (= rock laboratory as a “playground” where experience is often gained through
The X-43A Six Degree of Freedom Monte Carlo Analysis
NASA Technical Reports Server (NTRS)
Baumann, Ethan; Bahm, Catherine; Strovers, Brian; Beck, Roger
2008-01-01
This report provides an overview of the Hyper-X research vehicle Monte Carlo analysis conducted with the six-degree-of-freedom simulation. The methodology and model uncertainties used for the Monte Carlo analysis are presented as permitted. In addition, the process used to select hardware validation test cases from the Monte Carlo data is described. The preflight Monte Carlo analysis indicated that the X-43A control system was robust to the preflight uncertainties and provided the Hyper-X project an important indication that the vehicle would likely be successful in accomplishing the mission objectives. The X-43A inflight performance is compared to the preflight Monte Carlo predictions and shown to exceed the Monte Carlo bounds in several instances. Possible modeling shortfalls are presented that may account for these discrepancies. The flight control laws and guidance algorithms were robust enough as a result of the preflight Monte Carlo analysis that the unexpected in-flight performance did not have undue consequences. Modeling and Monte Carlo analysis lessons learned are presented.
The X-43A Six Degree of Freedom Monte Carlo Analysis
NASA Technical Reports Server (NTRS)
Baumann, Ethan; Bahm, Catherine; Strovers, Brian; Beck, Roger; Richard, Michael
2007-01-01
This report provides an overview of the Hyper-X research vehicle Monte Carlo analysis conducted with the six-degree-of-freedom simulation. The methodology and model uncertainties used for the Monte Carlo analysis are presented as permitted. In addition, the process used to select hardware validation test cases from the Monte Carlo data is described. The preflight Monte Carlo analysis indicated that the X-43A control system was robust to the preflight uncertainties and provided the Hyper-X project an important indication that the vehicle would likely be successful in accomplishing the mission objectives. The X-43A in-flight performance is compared to the preflight Monte Carlo predictions and shown to exceed the Monte Carlo bounds in several instances. Possible modeling shortfalls are presented that may account for these discrepancies. The flight control laws and guidance algorithms were robust enough as a result of the preflight Monte Carlo analysis that the unexpected in-flight performance did not have undue consequences. Modeling and Monte Carlo analysis lessons learned are presented.
Heterogeneous Ice Nuclei Measurements in Monte Cimone, Italy
NASA Astrophysics Data System (ADS)
Rudich, Y.; Reicher, N.; Schrod, J.; Bingemer, H. G.
2013-12-01
Supercooled liquid droplets may coexist with ice crystals below the freezing point in mixed phase clouds. Although pure liquid droplets will not freeze spontaneously until the homogeneous freezing temperature -38°C, ice crystals exist at warmer temperatures due to the presence of ice nuclei (IN), that allow heterogeneous freezing on their surface. Only a small portion of the natural and anthropogenic aerosols serve as ice nuclei. Each aerosol type has its own ability to create and grow ice. IN ability varies with chemical and physical properties and with the environmental characteristics, as temperature and humidity. In this study, samples of aerosol particles were collected on a daily basis over a period of two weeks, on top of Monte Cimone in Italy (44.18°N, 10.70°E, 2165m asl), as part of the PEGASOS (Pan-European Gas-AeroSOl-climate interaction Study) project. The aerosols precipitated electrostatically onto a silicon wafer for an offline measurement of the ice nucleation ability, using the FRankfurt Ice Nuclei Deposition FreezinG Experiment (FRIDGE). The FRIDGE is a vacuum diffusion chamber that generates the sub-freezing temperatures and the supersaturations above ice, simulating conditions that exist inside a mixed phase cloud. On top of the chamber, a camera monitors the formation of ice crystals and a new counting algorithm reports the number concentration of ice crystals. During this campaign, a Saharan dust storm reached the sampling area and the ice nuclei concentrations were higher compared to the daily ice nuclei concentrations for the rest of the campaign. This result supports the previous findings that dust particles are among the most effective and important natural sources of ice nuclei.
1-D EQUILIBRIUM DISCRETE DIFFUSION MONTE CARLO
T. EVANS; ET AL
2000-08-01
We present a new hybrid Monte Carlo method for 1-D equilibrium diffusion problems in which the radiation field coexists with matter in local thermodynamic equilibrium. This method, the Equilibrium Discrete Diffusion Monte Carlo (EqDDMC) method, combines Monte Carlo particles with spatially discrete diffusion solutions. We verify the EqDDMC method with computational results from three slab problems. The EqDDMC method represents an incremental step toward applying this hybrid methodology to non-equilibrium diffusion, where it could be simultaneously coupled to Monte Carlo transport.
Multidimensional stochastic approximation Monte Carlo.
Zablotskiy, Sergey V; Ivanov, Victor A; Paul, Wolfgang
2016-06-01
Stochastic Approximation Monte Carlo (SAMC) has been established as a mathematically founded powerful flat-histogram Monte Carlo method, used to determine the density of states, g(E), of a model system. We show here how it can be generalized for the determination of multidimensional probability distributions (or equivalently densities of states) of macroscopic or mesoscopic variables defined on the space of microstates of a statistical mechanical system. This establishes this method as a systematic way for coarse graining a model system, or, in other words, for performing a renormalization group step on a model. We discuss the formulation of the Kadanoff block spin transformation and the coarse-graining procedure for polymer models in this language. We also apply it to a standard case in the literature of two-dimensional densities of states, where two competing energetic effects are present g(E_{1},E_{2}). We show when and why care has to be exercised when obtaining the microcanonical density of states g(E_{1}+E_{2}) from g(E_{1},E_{2}). PMID:27415383
Monte Carlo surface flux tallies
Favorite, Jeffrey A
2010-11-19
Particle fluxes on surfaces are difficult to calculate with Monte Carlo codes because the score requires a division by the surface-crossing angle cosine, and grazing angles lead to inaccuracies. We revisit the standard practice of dividing by half of a cosine 'cutoff' for particles whose surface-crossing cosines are below the cutoff. The theory behind this approximation is sound, but the application of the theory to all possible situations does not account for two implicit assumptions: (1) the grazing band must be symmetric about 0, and (2) a single linear expansion for the angular flux must be applied in the entire grazing band. These assumptions are violated in common circumstances; for example, for separate in-going and out-going flux tallies on internal surfaces, and for out-going flux tallies on external surfaces. In some situations, dividing by two-thirds of the cosine cutoff is more appropriate. If users were able to control both the cosine cutoff and the substitute value, they could use these parameters to make accurate surface flux tallies. The procedure is demonstrated in a test problem in which Monte Carlo surface fluxes in cosine bins are converted to angular fluxes and compared with the results of a discrete ordinates calculation.
Multidimensional stochastic approximation Monte Carlo
NASA Astrophysics Data System (ADS)
Zablotskiy, Sergey V.; Ivanov, Victor A.; Paul, Wolfgang
2016-06-01
Stochastic Approximation Monte Carlo (SAMC) has been established as a mathematically founded powerful flat-histogram Monte Carlo method, used to determine the density of states, g (E ) , of a model system. We show here how it can be generalized for the determination of multidimensional probability distributions (or equivalently densities of states) of macroscopic or mesoscopic variables defined on the space of microstates of a statistical mechanical system. This establishes this method as a systematic way for coarse graining a model system, or, in other words, for performing a renormalization group step on a model. We discuss the formulation of the Kadanoff block spin transformation and the coarse-graining procedure for polymer models in this language. We also apply it to a standard case in the literature of two-dimensional densities of states, where two competing energetic effects are present g (E1,E2) . We show when and why care has to be exercised when obtaining the microcanonical density of states g (E1+E2) from g (E1,E2) .
Quantum Monte Carlo Calculations Applied to Magnetic Molecules
Larry Engelhardt
2006-08-09
We have calculated the equilibrium thermodynamic properties of Heisenberg spin systems using a quantum Monte Carlo (QMC) method. We have used some of these systems as models to describe recently synthesized magnetic molecules, and-upon comparing the results of these calculations with experimental data-have obtained accurate estimates for the basic parameters of these models. We have also performed calculations for other systems that are of more general interest, being relevant both for existing experimental data and for future experiments. Utilizing the concept of importance sampling, these calculations can be carried out in an arbitrarily large quantum Hilbert space, while still avoiding any approximations that would introduce systematic errors. The only errors are statistical in nature, and as such, their magnitudes are accurately estimated during the course of a simulation. Frustrated spin systems present a major challenge to the QMC method, nevertheless, in many instances progress can be made. In this chapter, the field of magnetic molecules is introduced, paying particular attention to the characteristics that distinguish magnetic molecules from other systems that are studied in condensed matter physics. We briefly outline the typical path by which we learn about magnetic molecules, which requires a close relationship between experiments and theoretical calculations. The typical experiments are introduced here, while the theoretical methods are discussed in the next chapter. Each of these theoretical methods has a considerable limitation, also described in Chapter 2, which together serve to motivate the present work. As is shown throughout the later chapters, the present QMC method is often able to provide useful information where other methods fail. In Chapter 3, the use of Monte Carlo methods in statistical physics is reviewed, building up the fundamental ideas that are necessary in order to understand the method that has been used in this work. With these
Markov Chain Monte Carlo and Irreversibility
NASA Astrophysics Data System (ADS)
Ottobre, Michela
2016-06-01
Markov Chain Monte Carlo (MCMC) methods are statistical methods designed to sample from a given measure π by constructing a Markov chain that has π as invariant measure and that converges to π. Most MCMC algorithms make use of chains that satisfy the detailed balance condition with respect to π; such chains are therefore reversible. On the other hand, recent work [18, 21, 28, 29] has stressed several advantages of using irreversible processes for sampling. Roughly speaking, irreversible diffusions converge to equilibrium faster (and lead to smaller asymptotic variance as well). In this paper we discuss some of the recent progress in the study of nonreversible MCMC methods. In particular: i) we explain some of the difficulties that arise in the analysis of nonreversible processes and we discuss some analytical methods to approach the study of continuous-time irreversible diffusions; ii) most of the rigorous results on irreversible diffusions are available for continuous-time processes; however, for computational purposes one needs to discretize such dynamics. It is well known that the resulting discretized chain will not, in general, retain all the good properties of the process that it is obtained from. In particular, if we want to preserve the invariance of the target measure, the chain might no longer be reversible. Therefore iii) we conclude by presenting an MCMC algorithm, the SOL-HMC algorithm [23], which results from a nonreversible discretization of a nonreversible dynamics.
Quantum Monte Carlo studies on small molecules
NASA Astrophysics Data System (ADS)
Galek, Peter T. A.; Handy, Nicholas C.; Lester, William A., Jr.
The Variational Monte Carlo (VMC) and Fixed-Node Diffusion Monte Carlo (FNDMC) methods have been examined, through studies on small molecules. New programs have been written which implement the (by now) standard algorithms for VMC and FNDMC. We have employed and investigated throughout our studies the accuracy of the common Slater-Jastrow trial wave function. Firstly, we have studied a range of sizes of the Jastrow correlation function of the Boys-Handy form, obtained using our optimization program with analytical derivatives of the central moments in the local energy. Secondly, we have studied the effects of Slater-type orbitals (STOs) that display the exact cusp behaviour at nuclei. The orbitals make up the all important trial determinant, which determines the fixed nodal surface. We report all-electron calculations for the ground state energies of Li2, Be2, H2O, NH3, CH4 and H2CO, in all cases but one with accuracy in excess of 95%. Finally, we report an investigation of the ground state energies, dissociation energies and ionization potentials of NH and NH+. Recent focus paid in the literature to these species allow for an extensive comparison with other ab initio methods. We obtain accurate properties for the species and reveal a favourable tendency for fixed-node and other systematic errors to cancel. As a result of our accurate predictions, we are able to obtain a value for the heat of formation of NH, which agrees to within less than 1 kcal mol-1 to other ab initio techniques and 0.2 kcal mol-1 of the experimental value.
Monte Carlo method for the evaluation of symptom association.
Barriga-Rivera, A; Elena, M; Moya, M J; Lopez-Alonso, M
2014-08-01
Gastroesophageal monitoring is limited to 96 hours by the current technology. This work presents a computational model to investigate symptom association in gastroesophageal reflux disease with larger data samples proving important deficiencies of the current methodology that must be taking into account in clinical evaluation. A computational model based on Monte Carlo analysis was implemented to simulate patients with known statistical characteristics Thus, sets of 2000 10-day-long recordings were simulated and analyzed using the symptom index (SI), the symptom sensitivity index (SSI), and the symptom association probability (SAP). Afterwards, linear regression was applied to define the dependency of these indexes with the number of reflux, the number of symptoms, the duration of the monitoring, and the probability of association. All the indexes were biased estimators of symptom association and therefore they do not consider the effect of chance: when symptom and reflux were completely uncorrelated, the values of the indexes under study were greater than zero. On the other hand, longer recording reduced variability in the estimation of the SI and the SSI while increasing the value of the SAP. Furthermore, if the number of symptoms remains below one-tenth of the number of reflux episodes, it is not possible to achieve a positive value of the SSI. A limitation of this computational model is that it does not consider feeding and sleeping periods, differences between reflux episodes or causation. However, the conclusions are not affected by these limitations. These facts represent important limitations in symptom association analysis, and therefore, invasive treatments must not be considered based on the value of these indexes only until a new methodology provides a more reliable assessment. PMID:23082973
Event group importance measures for top event frequency analyses
1995-07-31
Three traditional importance measures, risk reduction, partial derivative, nd variance reduction, have been extended to permit analyses of the relative importance of groups of underlying failure rates to the frequencies of resulting top events. The partial derivative importance measure was extended by assessing the contribution of a group of events to the gradient of the top event frequency. Given the moments of the distributions that characterize the uncertainties in the underlying failure rates, the expectation values of the top event frequency, its variance, and all of the new group importance measures can be quantified exactly for two familiar cases: (1) when all underlying failure rates are presumed independent, and (2) when pairs of failure rates based on common data are treated as being equal (totally correlated). In these cases, the new importance measures, which can also be applied to assess the importance of individual events, obviate the need for Monte Carlo sampling. The event group importance measures are illustrated using a small example problem and demonstrated by applications made as part of a major reactor facility risk assessment. These illustrations and applications indicate both the utility and the versatility of the event group importance measures.
Reconstruction of Human Monte Carlo Geometry from Segmented Images
NASA Astrophysics Data System (ADS)
Zhao, Kai; Cheng, Mengyun; Fan, Yanchang; Wang, Wen; Long, Pengcheng; Wu, Yican
2014-06-01
Human computational phantoms have been used extensively for scientific experimental analysis and experimental simulation. This article presented a method for human geometry reconstruction from a series of segmented images of a Chinese visible human dataset. The phantom geometry could actually describe detailed structure of an organ and could be converted into the input file of the Monte Carlo codes for dose calculation. A whole-body computational phantom of Chinese adult female has been established by FDS Team which is named Rad-HUMAN with about 28.8 billion voxel number. For being processed conveniently, different organs on images were segmented with different RGB colors and the voxels were assigned with positions of the dataset. For refinement, the positions were first sampled. Secondly, the large sums of voxels inside the organ were three-dimensional adjacent, however, there were not thoroughly mergence methods to reduce the cell amounts for the description of the organ. In this study, the voxels on the organ surface were taken into consideration of the mergence which could produce fewer cells for the organs. At the same time, an indexed based sorting algorithm was put forward for enhancing the mergence speed. Finally, the Rad-HUMAN which included a total of 46 organs and tissues was described by the cuboids into the Monte Carlo Monte Carlo Geometry for the simulation. The Monte Carlo geometry was constructed directly from the segmented images and the voxels was merged exhaustively. Each organ geometry model was constructed without ambiguity and self-crossing, its geometry information could represent the accuracy appearance and precise interior structure of the organs. The constructed geometry largely retaining the original shape of organs could easily be described into different Monte Carlo codes input file such as MCNP. Its universal property was testified and high-performance was experimentally verified
Global Monte Carlo Simulation with High Order Polynomial Expansions
William R. Martin; James Paul Holloway; Kaushik Banerjee; Jesse Cheatham; Jeremy Conlin
2007-12-13
The functional expansion technique (FET) was recently developed for Monte Carlo simulation. The basic idea of the FET is to expand a Monte Carlo tally in terms of a high order expansion, the coefficients of which can be estimated via the usual random walk process in a conventional Monte Carlo code. If the expansion basis is chosen carefully, the lowest order coefficient is simply the conventional histogram tally, corresponding to a flat mode. This research project studied the applicability of using the FET to estimate the fission source, from which fission sites can be sampled for the next generation. The idea is that individual fission sites contribute to expansion modes that may span the geometry being considered, possibly increasing the communication across a loosely coupled system and thereby improving convergence over the conventional fission bank approach used in most production Monte Carlo codes. The project examined a number of basis functions, including global Legendre polynomials as well as “local” piecewise polynomials such as finite element hat functions and higher order versions. The global FET showed an improvement in convergence over the conventional fission bank approach. The local FET methods showed some advantages versus global polynomials in handling geometries with discontinuous material properties. The conventional finite element hat functions had the disadvantage that the expansion coefficients could not be estimated directly but had to be obtained by solving a linear system whose matrix elements were estimated. An alternative fission matrix-based response matrix algorithm was formulated. Studies were made of two alternative applications of the FET, one based on the kernel density estimator and one based on Arnoldi’s method of minimized iterations. Preliminary results for both methods indicate improvements in fission source convergence. These developments indicate that the FET has promise for speeding up Monte Carlo fission source
Farr, W M; Mandel, I; Stevens, D
2015-06-01
Selection among alternative theoretical models given an observed dataset is an important challenge in many areas of physics and astronomy. Reversible-jump Markov chain Monte Carlo (RJMCMC) is an extremely powerful technique for performing Bayesian model selection, but it suffers from a fundamental difficulty and it requires jumps between model parameter spaces, but cannot efficiently explore both parameter spaces at once. Thus, a naive jump between parameter spaces is unlikely to be accepted in the Markov chain Monte Carlo (MCMC) algorithm and convergence is correspondingly slow. Here, we demonstrate an interpolation technique that uses samples from single-model MCMCs to propose intermodel jumps from an approximation to the single-model posterior of the target parameter space. The interpolation technique, based on a kD-tree data structure, is adaptive and efficient in modest dimensionality. We show that our technique leads to improved convergence over naive jumps in an RJMCMC, and compare it to other proposals in the literature to improve the convergence of RJMCMCs. We also demonstrate the use of the same interpolation technique as a way to construct efficient 'global' proposal distributions for single-model MCMCs without prior knowledge of the structure of the posterior distribution, and discuss improvements that permit the method to be used in higher dimensional spaces efficiently. PMID:26543580
Farr, W. M.; Mandel, I.; Stevens, D.
2015-01-01
Selection among alternative theoretical models given an observed dataset is an important challenge in many areas of physics and astronomy. Reversible-jump Markov chain Monte Carlo (RJMCMC) is an extremely powerful technique for performing Bayesian model selection, but it suffers from a fundamental difficulty and it requires jumps between model parameter spaces, but cannot efficiently explore both parameter spaces at once. Thus, a naive jump between parameter spaces is unlikely to be accepted in the Markov chain Monte Carlo (MCMC) algorithm and convergence is correspondingly slow. Here, we demonstrate an interpolation technique that uses samples from single-model MCMCs to propose intermodel jumps from an approximation to the single-model posterior of the target parameter space. The interpolation technique, based on a kD-tree data structure, is adaptive and efficient in modest dimensionality. We show that our technique leads to improved convergence over naive jumps in an RJMCMC, and compare it to other proposals in the literature to improve the convergence of RJMCMCs. We also demonstrate the use of the same interpolation technique as a way to construct efficient ‘global’ proposal distributions for single-model MCMCs without prior knowledge of the structure of the posterior distribution, and discuss improvements that permit the method to be used in higher dimensional spaces efficiently. PMID:26543580
NASA Astrophysics Data System (ADS)
Dick, G. J.; Andersson, A.; Banfield, J. F.
2007-12-01
Our understanding of environmental microbiology has been greatly enhanced by community genome sequencing of DNA recovered directly the environment. Community genomics provides insights into the diversity, community structure, metabolic function, and evolution of natural populations of uncultivated microbes, thereby revealing dynamics of how microorganisms interact with each other and their environment. Recent studies have demonstrated the potential for reconstructing near-complete genomes from natural environments while highlighting the challenges of analyzing community genomic sequence, especially from diverse environments. A major challenge of shotgun community genome sequencing is identification of DNA fragments from minor community members for which only low coverage of genomic sequence is present. We analyzed community genome sequence retrieved from biofilms in an acid mine drainage (AMD) system in the Richmond Mine at Iron Mountain, CA, with an emphasis on identification and assembly of DNA fragments from low-abundance community members. The Richmond mine hosts an extensive, relatively low diversity subterranean chemolithoautotrophic community that is sustained entirely by oxidative dissolution of pyrite. The activity of these microorganisms greatly accelerates the generation of AMD. Previous and ongoing work in our laboratory has focused on reconstrucing genomes of dominant community members, including several bacteria and archaea. We binned contigs from several samples (including one new sample and two that had been previously analyzed) by tetranucleotide frequency with clustering by Self-Organizing Maps (SOM). The binning, evaluated by comparison with information from the manually curated assembly of the dominant organisms, was found to be very effective: fragments were correctly assigned with 95% accuracy. Improperly assigned fragments often contained sequences that are either evolutionarily constrained (e.g. 16S rRNA genes) or mobile elements that are
Monte Carlo methods: Application to hydrogen gas and hard spheres
NASA Astrophysics Data System (ADS)
Dewing, Mark Douglas
2001-08-01
Quantum Monte Carlo (QMC) methods are among the most accurate for computing ground state properties of quantum systems. The two major types of QMC we use are Variational Monte Carlo (VMC), which evaluates integrals arising from the variational principle, and Diffusion Monte Carlo (DMC), which stochastically projects to the ground state from a trial wave function. These methods are applied to a system of boson hard spheres to get exact, infinite system size results for the ground state at several densities. The kinds of problems that can be simulated with Monte Carlo methods are expanded through the development of new algorithms for combining a QMC simulation with a classical Monte Carlo simulation, which we call Coupled Electronic-Ionic Monte Carlo (CEIMC). The new CEIMC method is applied to a system of molecular hydrogen at temperatures ranging from 2800K to 4500K and densities from 0.25 to 0.46 g/cm3. VMC requires optimizing a parameterized wave function to find the minimum energy. We examine several techniques for optimizing VMC wave functions, focusing on the ability to optimize parameters appearing in the Slater determinant. Classical Monte Carlo simulations use an empirical interatomic potential to compute equilibrium properties of various states of matter. The CEIMC method replaces the empirical potential with a QMC calculation of the electronic energy. This is similar in spirit to the Car-Parrinello technique, which uses Density Functional Theory for the electrons and molecular dynamics for the nuclei. The challenges in constructing an efficient CEIMC simulation center mostly around the noisy results generated from the QMC computations of the electronic energy. We introduce two complementary techniques, one for tolerating the noise and the other for reducing it. The penalty method modifies the Metropolis acceptance ratio to tolerate noise without introducing a bias in the simulation of the nuclei. For reducing the noise, we introduce the two-sided energy
Of bugs and birds: Markov Chain Monte Carlo for hierarchical modeling in wildlife research
Link, W.A.; Cam, E.; Nichols, J.D.; Cooch, E.G.
2002-01-01
Markov chain Monte Carlo (MCMC) is a statistical innovation that allows researchers to fit far more complex models to data than is feasible using conventional methods. Despite its widespread use in a variety of scientific fields, MCMC appears to be underutilized in wildlife applications. This may be due to a misconception that MCMC requires the adoption of a subjective Bayesian analysis, or perhaps simply to its lack of familiarity among wildlife researchers. We introduce the basic ideas of MCMC and software BUGS (Bayesian inference using Gibbs sampling), stressing that a simple and satisfactory intuition for MCMC does not require extraordinary mathematical sophistication. We illustrate the use of MCMC with an analysis of the association between latent factors governing individual heterogeneity in breeding and survival rates of kittiwakes (Rissa tridactyla). We conclude with a discussion of the importance of individual heterogeneity for understanding population dynamics and designing management plans.
Ground-state properties of LiH by reptation quantum Monte Carlo methods.
Ospadov, Egor; Oblinsky, Daniel G; Rothstein, Stuart M
2011-05-01
We apply reptation quantum Monte Carlo to calculate one- and two-electron properties for ground-state LiH, including all tensor components for static polarizabilities and hyperpolarizabilities to fourth-order in the field. The importance sampling is performed with a large (QZ4P) STO basis set single determinant, directly obtained from commercial software, without incurring the overhead of optimizing many-parameter Jastrow-type functions of the inter-electronic and internuclear distances. We present formulas for the electrical response properties free from the finite-field approximation, which can be problematic for the purposes of stochastic estimation. The α, γ, A and C polarizability values are reasonably consistent with recent determinations reported in the literature, where they exist. A sum rule is obeyed for components of the B tensor, but B(zz,zz) as well as β(zzz) differ from what was reported in the literature. PMID:21445452
COSMOABC: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation
NASA Astrophysics Data System (ADS)
Ishida, E. E. O.; Vitenti, S. D. P.; Penna-Lima, M.; Cisewski, J.; de Souza, R. S.; Trindade, A. M. M.; Cameron, E.; Busti, V. C.
2015-11-01
Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present COSMOABC, a Python ABC sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled COSMOABC with the NUMCOSMO library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. COSMOABC is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://goo.gl/SmB8EX.
Characterization, nanoparticle self-organization, and Monte Carlo simulation of magnetoliposomes
NASA Astrophysics Data System (ADS)
Salvador, Michele Aparecida; Costa, Anderson Silva; Gaeti, Marilisa; Mendes, Livia Palmerston; Lima, Eliana Martins; Bakuzis, Andris Figueiroa; Miotto, Ronei
2016-02-01
In this work we have developed and implement a new approach for the study of magnetoliposomes using Monte Carlo simulations. Our model is based on interaction among nanoparticles considering magnetic dipolar, van der Waals, ionic-steric, and Zeeman interaction potentials. The ionic interaction between nanoparticles and the lipid bilayer is represented by an ionic repulsion electrical surface potential that depends on the nanoparticle-lipid bilayer distance and the concentration of ions in the solution. A direct comparison among transmission electron microscopy, vibrating sample magnetometer, dynamic light scattering, nanoparticle tracking analysis, and experimentally derived static magnetic birefringence and simulation data allow us to validate our implementation. Our simulations suggest that confinement plays an important role in aggregate formation.
Detector-selection technique for Monte Carlo transport in azimuthally symmetric geometries
Hoffman, T.J.; Tang, J.S.; Parks, C.V.
1982-01-01
Many radiation transport problems contain geometric symmetries which are not exploited in obtaining their Monte Carlo solutions. An important class of problems is that in which the geometry is symmetric about an axis. These problems arise in the analyses of a reactor core or shield, spent fuel shipping casks, tanks containing radioactive solutions, radiation transport in the atmosphere (air-over-ground problems), etc. Although amenable to deterministic solution, such problems can often be solved more efficiently and accurately with the Monte Carlo method. For this class of problems, a technique is described in this paper which significantly reduces the variance of the Monte Carlo-calculated effect of interest at point detectors.
SU-E-T-188: Film Dosimetry Verification of Monte Carlo Generated Electron Treatment Plans
Enright, S; Asprinio, A; Lu, L
2014-06-01
Purpose: The purpose of this study was to compare dose distributions from film measurements to Monte Carlo generated electron treatment plans. Irradiation with electrons offers the advantages of dose uniformity in the target volume and of minimizing the dose to deeper healthy tissue. Using the Monte Carlo algorithm will improve dose accuracy in regions with heterogeneities and irregular surfaces. Methods: Dose distributions from GafChromic{sup ™} EBT3 films were compared to dose distributions from the Electron Monte Carlo algorithm in the Eclipse{sup ™} radiotherapy treatment planning system. These measurements were obtained for 6MeV, 9MeV and 12MeV electrons at two depths. All phantoms studied were imported into Eclipse by CT scan. A 1 cm thick solid water template with holes for bonelike and lung-like plugs was used. Different configurations were used with the different plugs inserted into the holes. Configurations with solid-water plugs stacked on top of one another were also used to create an irregular surface. Results: The dose distributions measured from the film agreed with those from the Electron Monte Carlo treatment plan. Accuracy of Electron Monte Carlo algorithm was also compared to that of Pencil Beam. Dose distributions from Monte Carlo had much higher pass rates than distributions from Pencil Beam when compared to the film. The pass rate for Monte Carlo was in the 80%–99% range, where the pass rate for Pencil Beam was as low as 10.76%. Conclusion: The dose distribution from Monte Carlo agreed with the measured dose from the film. When compared to the Pencil Beam algorithm, pass rates for Monte Carlo were much higher. Monte Carlo should be used over Pencil Beam for regions with heterogeneities and irregular surfaces.
Pfeiffer, Christine M; Sternberg, Maya R; Schleicher, Rosemary L; Rybak, Michael E
2013-06-01
Biochemical indicators of water-soluble vitamin (WSV) status were measured in a nationally representative sample of the U.S. population in NHANES 2003-2006. To examine whether demographic differentials in nutritional status were related to and confounded by certain variables, we assessed the association of sociodemographic (age, sex, race-ethnicity, education, income) and lifestyle (dietary supplement use, smoking, alcohol consumption, BMI, physical activity) variables with biomarkers of WSV status in adults (aged ≥ 20 y): serum and RBC folate, serum pyridoxal-5'-phosphate (PLP), serum 4-pyridoxic acid, serum total cobalamin (vitamin B-12), plasma total homocysteine (tHcy), plasma methylmalonic acid (MMA), and serum ascorbic acid. Age (except for PLP) and smoking (except for MMA) were generally the strongest significant correlates of these biomarkers (|r| ≤ 0.43) and together with supplement use explained more of the variability compared with the other covariates in bivariate analysis. In multiple regression models, sociodemographic and lifestyle variables together explained from 7 (vitamin B-12) to 29% (tHcy) of the biomarker variability. We observed significant associations for most biomarkers (≥ 6 of 8) with age, sex, race-ethnicity, supplement use, smoking, and BMI and for some biomarkers with PIR (5 of 8), education (1 of 8), alcohol consumption (4 of 8), and physical activity (5 of 8). We noted large estimated percentage changes in biomarker concentrations between race-ethnic groups (from -24 to 20%), between supplement users and nonusers (from -12 to 104%), and between smokers and nonsmokers (from -28 to 8%). In summary, age, sex, and race-ethnic differentials in biomarker concentrations remained significant after adjusting for sociodemographic and lifestyle variables. Supplement use and smoking were important correlates of biomarkers of WSV status. PMID:23576641
Structural mapping of Maxwell Montes
NASA Technical Reports Server (NTRS)
Keep, Myra; Hansen, Vicki L.
1993-01-01
Four sets of structures were mapped in the western and southern portions of Maxwell Montes. An early north-trending set of penetrative lineaments is cut by dominant, spaced ridges and paired valleys that trend northwest. To the south the ridges and valleys splay and graben form in the valleys. The spaced ridges and graben are cut by northeast-trending graben. The northwest-trending graben formed synchronously with or slightly later than the spaced ridges. Formation of the northeast-trending graben may have overlapped with that of the northwest-trending graben, but occurred in a spatially distinct area (regions of 2 deg slope). Graben formation, with northwest-southeast extension, may be related to gravity-sliding. Individually and collectively these structures are too small to support the immense topography of Maxwell, and are interpreted as parasitic features above a larger mass that supports the mountain belt.
Wet-based glaciation in Phlegra Montes, Mars.
NASA Astrophysics Data System (ADS)
Gallagher, Colman; Balme, Matt
2016-04-01
Eskers are sinuous landforms composed of sediments deposited from meltwaters in ice-contact glacial conduits. This presentation describes the first definitive identification of eskers on Mars still physically linked with their parent system (1), a Late Amazonian-age glacier (~150 Ma) in Phlegra Montes. Previously described Amazonian-age glaciers on Mars are generally considered to have been dry based, having moved by creep in the absence of subglacial water required for sliding, but our observations indicate significant sub-glacial meltwater routing. The confinement of the Phlegra Montes glacial system to a regionally extensive graben is evidence that the esker formed due to sub-glacial melting in response to an elevated, but spatially restricted, geothermal heat flux rather than climate-induced warming. Now, however, new observations reveal the presence of many assemblages of glacial abrasion forms and associated channels that could be evidence of more widespread wet-based glaciation in Phlegra Montes, including the collapse of several distinct ice domes. This landform assemblage has not been described in other glaciated, mid-latitude regions of the martian northern hemisphere. Moreover, Phlegra Montes are flanked by lowlands displaying evidence of extensive volcanism, including contact between plains lava and piedmont glacial ice. These observations provide a rationale for investigating non-climatic forcing of glacial melting and associated landscape development on Mars, and can build on insights from Earth into the importance of geothermally-induced destabilisation of glaciers as a key amplifier of climate change. (1) Gallagher, C. and Balme, M. (2015). Eskers in a complete, wet-based glacial system in the Phlegra Montes region, Mars, Earth and Planetary Science Letters, 431, 96-109.
Large-cell Monte Carlo renormalization of irreversible growth processes
NASA Technical Reports Server (NTRS)
Nakanishi, H.; Family, F.
1985-01-01
Monte Carlo sampling is applied to a recently formulated direct-cell renormalization method for irreversible, disorderly growth processes. Large-cell Monte Carlo renormalization is carried out for various nonequilibrium problems based on the formulation dealing with relative probabilities. Specifically, the method is demonstrated by application to the 'true' self-avoiding walk and the Eden model of growing animals for d = 2, 3, and 4 and to the invasion percolation problem for d = 2 and 3. The results are asymptotically in agreement with expectations; however, unexpected complications arise, suggesting the possibility of crossovers, and in any case, demonstrating the danger of using small cells alone, because of the very slow convergence as the cell size b is extrapolated to infinity. The difficulty of applying the present method to the diffusion-limited-aggregation model, is commented on.
Estimation of beryllium ground state energy by Monte Carlo simulation
NASA Astrophysics Data System (ADS)
Kabir, K. M. Ariful; Halder, Amal
2015-05-01
Quantum Monte Carlo method represent a powerful and broadly applicable computational tool for finding very accurate solution of the stationary Schrödinger equation for atoms, molecules, solids and a variety of model systems. Using variational Monte Carlo method we have calculated the ground state energy of the Beryllium atom. Our calculation are based on using a modified four parameters trial wave function which leads to good result comparing with the few parameters trial wave functions presented before. Based on random Numbers we can generate a large sample of electron locations to estimate the ground state energy of Beryllium. Our calculation gives good estimation for the ground state energy of the Beryllium atom comparing with the corresponding exact data.
Estimation of beryllium ground state energy by Monte Carlo simulation
Kabir, K. M. Ariful; Halder, Amal
2015-05-15
Quantum Monte Carlo method represent a powerful and broadly applicable computational tool for finding very accurate solution of the stationary Schrödinger equation for atoms, molecules, solids and a variety of model systems. Using variational Monte Carlo method we have calculated the ground state energy of the Beryllium atom. Our calculation are based on using a modified four parameters trial wave function which leads to good result comparing with the few parameters trial wave functions presented before. Based on random Numbers we can generate a large sample of electron locations to estimate the ground state energy of Beryllium. Our calculation gives good estimation for the ground state energy of the Beryllium atom comparing with the corresponding exact data.
Bayesian Monte Carlo method for nuclear data evaluation
NASA Astrophysics Data System (ADS)
Koning, A. J.
2015-12-01
A Bayesian Monte Carlo method is outlined which allows a systematic evaluation of nuclear reactions using the nuclear model code TALYS and the experimental nuclear reaction database EXFOR. The method is applied to all nuclides at the same time. First, the global predictive power of TALYS is numerically assessed, which enables to set the prior space of nuclear model solutions. Next, the method gradually zooms in on particular experimental data per nuclide, until for each specific target nuclide its existing experimental data can be used for weighted Monte Carlo sampling. To connect to the various different schools of uncertainty propagation in applied nuclear science, the result will be either an EXFOR-weighted covariance matrix or a collection of random files, each accompanied by the EXFOR-based weight.
Preliminar results of paleontological salvage at Belo Monte Powerplant construction.
Tomassi, H Z; Almeida, C M; Ferreira, B C; Brito, M B; Barberi, M; Rodrigues, G C; Teixeira, S P; Capuzzo, J P; Gama-Júnior, J M; Santos, M G K G
2015-08-01
In this paper some preliminary fossil specimens are presented. They represent a collection sampled by Belo Monte's Programa de Salvamento do Patrimônio Paleontológico (PSPP), which includes unprecedented invertebrate fauna and fossil vertebrates from Pitinga, Jatapu, Manacapuru, Maecuru e Alter do Chão formations from Amazonas basin, Brazil. The Belo Monte paleontological salvage was able to recover 495 microfossil samples and 1744 macrofossil samples on 30 months of sampling activities, and it is still ongoing. The macrofossils identified are possible plant remains, ichnofossils, graptolites, brachiopods, molluscs, athropods, Agnatha, palynomorphs (miosphores, acritarchs, algae cysts, fungi spores and unidentified types) and unidentified fossils. However, deep scientific research is not part of the scope of the program, and this collection must be further studied by researchers who visit Museu Paraense Emilio Goeldi, where the fossils will be housed. More material will be collected until the end of the program. The collection sampled allows a mosaic composition with the necessary elements to assign, in later papers, taxonomic features which may lead to accurate species identification and palaeoenvironmental interpretations. PMID:26691100
An Evaluation of a Markov Chain Monte Carlo Method for the Rasch Model.
ERIC Educational Resources Information Center
Kim, Seock-Ho
2001-01-01
Examined the accuracy of the Gibbs sampling Markov chain Monte Carlo procedure for estimating item and person (theta) parameters in the one-parameter logistic model. Analyzed four empirical datasets using the Gibbs sampling, conditional maximum likelihood, marginal maximum likelihood, and joint maximum likelihood methods. Discusses the conditions…
A Comparison of Item Sampling Plans in the Application of Multiple Matrix Sampling.
ERIC Educational Resources Information Center
Gressard, Risa P.; Loyd, Brenda H.
1991-01-01
A Monte Carlo study, which simulated 10,000 examinees' responses to four tests, investigated the effect of item stratification on parameter estimation in multiple matrix sampling of achievement data. Practical multiple matrix sampling is based on item stratification by item discrimination and a sampling plan with moderate number of subtests. (SLD)
Sample Size Tables, "t" Test, and a Prevalent Psychometric Distribution.
ERIC Educational Resources Information Center
Sawilowsky, Shlomo S.; Hillman, Stephen B.
Psychology studies often have low statistical power. Sample size tables, as given by J. Cohen (1988), may be used to increase power, but they are based on Monte Carlo studies of relatively "tame" mathematical distributions, as compared to psychology data sets. In this study, Monte Carlo methods were used to investigate Type I and Type II error…
Monte Carlo Production Management at CMS
NASA Astrophysics Data System (ADS)
Boudoul, G.; Franzoni, G.; Norkus, A.; Pol, A.; Srimanobhas, P.; Vlimant, J.-R.
2015-12-01
The analysis of the LHC data at the Compact Muon Solenoid (CMS) experiment requires the production of a large number of simulated events. During the RunI of LHC (20102012), CMS has produced over 12 Billion simulated events, organized in approximately sixty different campaigns each emulating specific detector conditions and LHC running conditions (pile up). In order to aggregate the information needed for the configuration and prioritization of the events production, assure the book-keeping of all the processing requests placed by the physics analysis groups, and to interface with the CMS production infrastructure, the web- based service Monte Carlo Management (McM) has been developed and put in production in 2013. McM is based on recent server infrastructure technology (CherryPy + AngularJS) and relies on a CouchDB database back-end. This contribution covers the one and half year of operational experience managing samples of simulated events for CMS, the evolution of its functionalities and the extension of its capability to monitor the status and advancement of the events production.
Atomistic Monte Carlo Simulation of Lipid Membranes
Wüstner, Daniel; Sklenar, Heinz
2014-01-01
Biological membranes are complex assemblies of many different molecules of which analysis demands a variety of experimental and computational approaches. In this article, we explain challenges and advantages of atomistic Monte Carlo (MC) simulation of lipid membranes. We provide an introduction into the various move sets that are implemented in current MC methods for efficient conformational sampling of lipids and other molecules. In the second part, we demonstrate for a concrete example, how an atomistic local-move set can be implemented for MC simulations of phospholipid monomers and bilayer patches. We use our recently devised chain breakage/closure (CBC) local move set in the bond-/torsion angle space with the constant-bond-length approximation (CBLA) for the phospholipid dipalmitoylphosphatidylcholine (DPPC). We demonstrate rapid conformational equilibration for a single DPPC molecule, as assessed by calculation of molecular energies and entropies. We also show transition from a crystalline-like to a fluid DPPC bilayer by the CBC local-move MC method, as indicated by the electron density profile, head group orientation, area per lipid, and whole-lipid displacements. We discuss the potential of local-move MC methods in combination with molecular dynamics simulations, for example, for studying multi-component lipid membranes containing cholesterol. PMID:24469314
Monte Carlo simulation framework for TMT
NASA Astrophysics Data System (ADS)
Vogiatzis, Konstantinos; Angeli, George Z.
2008-07-01
This presentation describes a strategy for assessing the performance of the Thirty Meter Telescope (TMT). A Monte Carlo Simulation Framework has been developed to combine optical modeling with Computational Fluid Dynamics simulations (CFD), Finite Element Analysis (FEA) and controls to model the overall performance of TMT. The framework consists of a two year record of observed environmental parameters such as atmospheric seeing, site wind speed and direction, ambient temperature and local sunset and sunrise times, along with telescope azimuth and elevation with a given sampling rate. The modeled optical, dynamic and thermal seeing aberrations are available in a matrix form for distinct values within the range of influencing parameters. These parameters are either part of the framework parameter set or can be derived from them at each time-step. As time advances, the aberrations are interpolated and combined based on the current value of their parameters. Different scenarios can be generated based on operating parameters such as venting strategy, optical calibration frequency and heat source control. Performance probability distributions are obtained and provide design guidance. The sensitivity of the system to design, operating and environmental parameters can be assessed in order to maximize the % of time the system meets the performance specifications.
Pattern Recognition for a Flight Dynamics Monte Carlo Simulation
NASA Technical Reports Server (NTRS)
Restrepo, Carolina; Hurtado, John E.
2011-01-01
The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.
ALEPH2 - A general purpose Monte Carlo depletion code
Stankovskiy, A.; Van Den Eynde, G.; Baeten, P.; Trakas, C.; Demy, P. M.; Villatte, L.
2012-07-01
The Monte-Carlo burn-up code ALEPH is being developed at SCK-CEN since 2004. A previous version of the code implemented the coupling between the Monte Carlo transport (any version of MCNP or MCNPX) and the ' deterministic' depletion code ORIGEN-2.2 but had important deficiencies in nuclear data treatment and limitations inherent to ORIGEN-2.2. A new version of the code, ALEPH2, has several unique features making it outstanding among other depletion codes. The most important feature is full data consistency between steady-state Monte Carlo and time-dependent depletion calculations. The last generation general-purpose nuclear data libraries (JEFF-3.1.1, ENDF/B-VII and JENDL-4) are fully implemented, including special purpose activation, spontaneous fission, fission product yield and radioactive decay data. The built-in depletion algorithm allows to eliminate the uncertainties associated with obtaining the time-dependent nuclide concentrations. A predictor-corrector mechanism, calculation of nuclear heating, calculation of decay heat, decay neutron sources are available as well. The validation of the code on the results of REBUS experimental program has been performed. The ALEPH2 has shown better agreement with measured data than other depletion codes. (authors)
Quantum Monte Carlo methods and lithium cluster properties
Owen, R.K.
1990-12-01
Properties of small lithium clusters with sizes ranging from n = 1 to 5 atoms were investigated using quantum Monte Carlo (QMC) methods. Cluster geometries were found from complete active space self consistent field (CASSCF) calculations. A detailed development of the QMC method leading to the variational QMC (V-QMC) and diffusion QMC (D-QMC) methods is shown. The many-body aspect of electron correlation is introduced into the QMC importance sampling electron-electron correlation functions by using density dependent parameters, and are shown to increase the amount of correlation energy obtained in V-QMC calculations. A detailed analysis of D-QMC time-step bias is made and is found to be at least linear with respect to the time-step. The D-QMC calculations determined the lithium cluster ionization potentials to be 0.1982(14) [0.1981], 0.1895(9) [0.1874(4)], 0.1530(34) [0.1599(73)], 0.1664(37) [0.1724(110)], 0.1613(43) [0.1675(110)] Hartrees for lithium clusters n = 1 through 5, respectively; in good agreement with experimental results shown in the brackets. Also, the binding energies per atom was computed to be 0.0177(8) [0.0203(12)], 0.0188(10) [0.0220(21)], 0.0247(8) [0.0310(12)], 0.0253(8) [0.0351(8)] Hartrees for lithium clusters n = 2 through 5, respectively. The lithium cluster one-electron density is shown to have charge concentrations corresponding to nonnuclear attractors. The overall shape of the electronic charge density also bears a remarkable similarity with the anisotropic harmonic oscillator model shape for the given number of valence electrons.
Quantum Monte Carlo methods and lithium cluster properties. [Atomic clusters
Owen, R.K.
1990-12-01
Properties of small lithium clusters with sizes ranging from n = 1 to 5 atoms were investigated using quantum Monte Carlo (QMC) methods. Cluster geometries were found from complete active space self consistent field (CASSCF) calculations. A detailed development of the QMC method leading to the variational QMC (V-QMC) and diffusion QMC (D-QMC) methods is shown. The many-body aspect of electron correlation is introduced into the QMC importance sampling electron-electron correlation functions by using density dependent parameters, and are shown to increase the amount of correlation energy obtained in V-QMC calculations. A detailed analysis of D-QMC time-step bias is made and is found to be at least linear with respect to the time-step. The D-QMC calculations determined the lithium cluster ionization potentials to be 0.1982(14) (0.1981), 0.1895(9) (0.1874(4)), 0.1530(34) (0.1599(73)), 0.1664(37) (0.1724(110)), 0.1613(43) (0.1675(110)) Hartrees for lithium clusters n = 1 through 5, respectively; in good agreement with experimental results shown in the brackets. Also, the binding energies per atom was computed to be 0.0177(8) (0.0203(12)), 0.0188(10) (0.0220(21)), 0.0247(8) (0.0310(12)), 0.0253(8) (0.0351(8)) Hartrees for lithium clusters n = 2 through 5, respectively. The lithium cluster one-electron density is shown to have charge concentrations corresponding to nonnuclear attractors. The overall shape of the electronic charge density also bears a remarkable similarity with the anisotropic harmonic oscillator model shape for the given number of valence electrons.
Visibility assessment : Monte Carlo characterization of temporal variability.
Laulainen, N.; Shannon, J.; Trexler, E. C., Jr.
1997-12-12
Current techniques for assessing the benefits of certain anthropogenic emission reductions are largely influenced by limitations in emissions data and atmospheric modeling capability and by the highly variant nature of meteorology. These data and modeling limitations are likely to continue for the foreseeable future, during which time important strategic decisions need to be made. Statistical atmospheric quality data and apportionment techniques are used in Monte-Carlo models to offset serious shortfalls in emissions, entrainment, topography, statistical meteorology data and atmospheric modeling. This paper describes the evolution of Department of Energy (DOE) Monte-Carlo based assessment models and the development of statistical inputs. A companion paper describes techniques which are used to develop the apportionment factors used in the assessment models.
Excited states of methylene from quantum Monte Carlo.
Zimmerman, Paul M; Toulouse, Julien; Zhang, Zhiyong; Musgrave, Charles B; Umrigar, C J
2009-09-28
The ground and lowest three adiabatic excited states of methylene are computed using the variational Monte Carlo and diffusion Monte Carlo (DMC) methods using progressively larger Jastrow-Slater multideterminant complete active space (CAS) wave functions. The highest of these states has the same symmetry, (1)A(1), as the first excited state. The DMC excitation energies obtained using any of the CAS wave functions are in excellent agreement with experiment, but single-determinant wave functions do not yield accurate DMC energies of the states of (1)A(1) symmetry, indicating that it is important to include in the wave function Slater determinants that describe static (strong) correlation. Excitation energies obtained using recently proposed pseudopotentials [Burkatzki et al., J. Chem. Phys. 126, 234105 (2007)] differ from the all-electron excitation energies by at most 0.04 eV. PMID:19791848
Conformational sampling techniques.
Hatfield, Marcus P D; Lovas, Sándor
2014-01-01
The potential energy hyper-surface of a protein relates the potential energy of the protein to its conformational space. This surface is useful in determining the native conformation of a protein or in examining a statistical-mechanical ensemble of structures (canonical ensemble). In determining the potential energy hyper-surface of a protein three aspects must be considered; reducing the degrees of freedom, a method to determine the energy of each conformation and a method to sample the conformational space. For reducing the degrees of freedom the choice of solvent, coarse graining, constraining degrees of freedom and periodic boundary conditions are discussed. The use of quantum mechanics versus molecular mechanics and the choice of force fields are also discussed, as well as the sampling of the conformational space through deterministic and heuristic approaches. Deterministic methods include knowledge-based statistical methods, rotamer libraries, homology modeling, the build-up method, self-consistent electrostatic field, deformation methods, tree-based elimination and eigenvector following routines. The heuristic methods include Monte Carlo chain growing, energy minimizations, metropolis monte carlo and molecular dynamics. In addition, various methods to enhance the conformational search including the deformation or smoothing of the surface, scaling of system parameters, and multi copy searching are also discussed. PMID:23947647
Obtaining representative ground water samples is important for site assessment and
remedial performance monitoring objectives. Issues which must be considered prior to initiating a ground-water monitoring program include defining monitoring goals and objectives, sampling point...
A Post-Monte-Carlo Sensitivity Analysis Code
Energy Science and Technology Software Center (ESTSC)
2000-04-04
SATOOL (Sensitivity Analysis TOOL) is a code for sensitivity analysis, following an uncertainity analysis with Monte Carlo simulations. Sensitivity analysis identifies those input variables, whose variance contributes dominatly to the variance in the output. This analysis can be used to reduce the variance in the output variables by redefining the "sensitive" variables with greater precision, i.e. with lower variance. The code identifies a group of sensitive variables, ranks them in the order of importance andmore » also quantifies the relative importance among the sensitive variables.« less
Monte Carlo Shower Counter Studies
NASA Technical Reports Server (NTRS)
Snyder, H. David
1991-01-01
Activities and accomplishments related to the Monte Carlo shower counter studies are summarized. A tape of the VMS version of the GEANT software was obtained and installed on the central computer at Gallaudet University. Due to difficulties encountered in updating this VMS version, a decision was made to switch to the UNIX version of the package. This version was installed and used to generate the set of data files currently accessed by various analysis programs. The GEANT software was used to write files of data for positron and proton showers. Showers were simulated for a detector consisting of 50 alternating layers of lead and scintillator. Each file consisted of 1000 events at each of the following energies: 0.1, 0.5, 2.0, 10, 44, and 200 GeV. Data analysis activities related to clustering, chi square, and likelihood analyses are summarized. Source code for the GEANT user subprograms and data analysis programs are provided along with example data plots.
Valence-bond quantum Monte Carlo algorithms defined on trees.
Deschner, Andreas; Sørensen, Erik S
2014-09-01
We present a class of algorithms for performing valence-bond quantum Monte Carlo of quantum spin models. Valence-bond quantum Monte Carlo is a projective T=0 Monte Carlo method based on sampling of a set of operator strings that can be viewed as forming a treelike structure. The algorithms presented here utilize the notion of a worm that moves up and down this tree and changes the associated operator string. In quite general terms, we derive a set of equations whose solutions correspond to a whole class of algorithms. As specific examples of this class of algorithms, we focus on two cases. The bouncing worm algorithm, for which updates are always accepted by allowing the worm to bounce up and down the tree, and the driven worm algorithm, where a single parameter controls how far up the tree the worm reaches before turning around. The latter algorithm involves only a single bounce where the worm turns from going up the tree to going down. The presence of the control parameter necessitates the introduction of an acceptance probability for the update. PMID:25314561
Autocorrelation and Dominance Ratio in Monte Carlo Criticality Calculations
Ueki, Taro; Brown, Forrest B.; Parsons, D. Kent; Kornreich, Drew E.
2003-11-15
The cycle-to-cycle correlation (autocorrelation) in Monte Carlo criticality calculations is analyzed concerning the dominance ratio of fission kernels. The mathematical analysis focuses on how the eigenfunctions of a fission kernel decay if operated on by the cycle-to-cycle error propagation operator of the Monte Carlo stationary source distribution. The analytical results obtained can be summarized as follows: When the dominance ratio of a fission kernel is close to unity, autocorrelation of the k-effective tallies is weak and may be negligible, while the autocorrelation of the source distribution is strong and decays slowly. The practical implication is that when one analyzes a critical reactor with a large dominance ratio by Monte Carlo methods, the confidence interval estimation of the fission rate and other quantities at individual locations must account for the strong autocorrelation. Numerical results are presented for sample problems with a dominance ratio of 0.85-0.99, where Shannon and relative entropies are utilized to exclude the influence of initial nonstationarity.
A new lattice Monte Carlo method for simulating dielectric inhomogeneity
NASA Astrophysics Data System (ADS)
Duan, Xiaozheng; Wang, Zhen-Gang; Nakamura, Issei
We present a new lattice Monte Carlo method for simulating systems involving dielectric contrast between different species by modifying an algorithm originally proposed by Maggs et al. The original algorithm is known to generate attractive interactions between particles that have different dielectric constant than the solvent. Here we show that such attractive force is spurious, arising from incorrectly biased statistical weight caused by the particle motion during the Monte Carlo moves. We propose a new, simple algorithm to resolve this erroneous sampling. We demonstrate the application of our algorithm by simulating an uncharged polymer in a solvent with different dielectric constant. Further, we show that the electrostatic fields in ionic crystals obtained from our simulations with a relatively small simulation box correspond well with results from the analytical solution. Thus, our Monte Carlo method avoids the need for the Ewald summation in conventional simulation methods for charged systems. This work was supported by the National Natural Science Foundation of China (21474112 and 21404103). We are grateful to Computing Center of Jilin Province for essential support.
First principles Monte Carlo simulations of aggregation in the vapor phase of hydrogen fluoride
McGrath, Matthew J.; Ghogomu, Julius. N.; Mundy, Christopher J.; Kuo, I-F. Will; Siepmann, J. Ilja
2010-01-01
The aggregation of superheated hydrogen fluoride vapor is explored through the use of Monte Carlo simulations employing Kohn-Sham density functional theory with the exchange/correlation functional of Becke-Lee-Yang-Parr to describe the molecular interactions. Simulations were carried out in the canonical ensemble for a system consisting of ten molecules at constant density (2700 Å^{3}/molecule) and at three different temperatures (T = 310, 350, and 390 K). Aggregation-volume-bias and configurational-bias Monte Carlo approaches (along with pre-sampling with an approximate potential) were employed to increase the sampling efficiency of cluster formation and destruction.
Advantages of Analytical Transformations in Monte Carlo Methods for Radiation Transport
McKinley, M S; Brooks III, E D; Daffin, F
2004-12-13
Monte Carlo methods for radiation transport typically attempt to solve an integral by directly sampling analog or weighted particles, which are treated as physical entities. Improvements to the methods involve better sampling, probability games or physical intuition about the problem. We show that significant improvements can be achieved by recasting the equations with an analytical transform to solve for new, non-physical entities or fields. This paper looks at one such transform, the difference formulation for thermal photon transport, showing a significant advantage for Monte Carlo solution of the equations for time dependent transport. Other related areas are discussed that may also realize significant benefits from similar analytical transformations.
Monte Carlo simulation of particle acceleration at astrophysical shocks
NASA Technical Reports Server (NTRS)
Campbell, Roy K.
1989-01-01
A Monte Carlo code was developed for the simulation of particle acceleration at astrophysical shocks. The code is implemented in Turbo Pascal on a PC. It is modularized and structured in such a way that modification and maintenance are relatively painless. Monte Carlo simulations of particle acceleration at shocks follow the trajectories of individual particles as they scatter repeatedly across the shock front, gaining energy with each crossing. The particles are assumed to scatter from magnetohydrodynamic (MHD) turbulence on both sides of the shock. A scattering law is used which is related to the assumed form of the turbulence, and the particle and shock parameters. High energy cosmic ray spectra derived from Monte Carlo simulations have observed power law behavior just as the spectra derived from analytic calculations based on a diffusion equation. This high energy behavior is not sensitive to the scattering law used. In contrast with Monte Carlo calculations diffusive calculations rely on the initial injection of supra-thermal particles into the shock environment. Monte Carlo simulations are the only known way to describe the extraction of particles directly from the thermal pool. This was the triumph of the Monte Carlo approach. The question of acceleration efficiency is an important one in the shock acceleration game. The efficiency of shock waves efficient to account for the observed flux of high energy galactic cosmic rays was examined. The efficiency of the acceleration process depends on the thermal particle pick-up and hence the low energy scattering in detail. One of the goals is the self-consistent derivation of the accelerated particle spectra and the MHD turbulence spectra. Presumably the upstream turbulence, which scatters the particles so they can be accelerated, is excited by the streaming accelerated particles and the needed downstream turbulence is convected from the upstream region. The present code is to be modified to include a better
Monte Carlo simulation of particle acceleration at astrophysical shocks
NASA Astrophysics Data System (ADS)
Campbell, Roy K.
1989-09-01
A Monte Carlo code was developed for the simulation of particle acceleration at astrophysical shocks. The code is implemented in Turbo Pascal on a PC. It is modularized and structured in such a way that modification and maintenance are relatively painless. Monte Carlo simulations of particle acceleration at shocks follow the trajectories of individual particles as they scatter repeatedly across the shock front, gaining energy with each crossing. The particles are assumed to scatter from magnetohydrodynamic (MHD) turbulence on both sides of the shock. A scattering law is used which is related to the assumed form of the turbulence, and the particle and shock parameters. High energy cosmic ray spectra derived from Monte Carlo simulations have observed power law behavior just as the spectra derived from analytic calculations based on a diffusion equation. This high energy behavior is not sensitive to the scattering law used. In contrast with Monte Carlo calculations diffusive calculations rely on the initial injection of supra-thermal particles into the shock environment. Monte Carlo simulations are the only known way to describe the extraction of particles directly from the thermal pool. This was the triumph of the Monte Carlo approach. The question of acceleration efficiency is an important one in the shock acceleration game. The efficiency of shock waves efficient to account for the observed flux of high energy galactic cosmic rays was examined. The efficiency of the acceleration process depends on the thermal particle pick-up and hence the low energy scattering in detail. One of the goals is the self-consistent derivation of the accelerated particle spectra and the MHD turbulence spectra. Presumably the upstream turbulence, which scatters the particles so they can be accelerated, is excited by the streaming accelerated particles and the needed downstream turbulence is convected from the upstream region. The present code is to be modified to include a better
A user`s manual for MASH 1.0: A Monte Carlo Adjoint Shielding Code System
Johnson, J.O.
1992-03-01
The Monte Carlo Adjoint Shielding Code System, MASH, calculates neutron and gamma-ray environments and radiation protection factors for armored military vehicles, structures, trenches, and other shielding configurations by coupling a forward discrete ordinates air-over-ground transport calculation with an adjoint Monte Carlo treatment of the shielding geometry. Efficiency and optimum use of computer time are emphasized. The code system include the GRTUNCL and DORT codes for air-over-ground transport calculations, the MORSE code with the GIFT5 combinatorial geometry package for adjoint shielding calculations, and several peripheral codes that perform the required data preparations, transformations, and coupling functions. MASH is the successor to the Vehicle Code System (VCS) initially developed at Oak Ridge National Laboratory (ORNL). The discrete ordinates calculation determines the fluence on a coupling surface surrounding the shielding geometry due to an external neutron/gamma-ray source. The Monte Carlo calculation determines the effectiveness of the fluence at that surface in causing a response in a detector within the shielding geometry, i.e., the ``dose importance`` of the coupling surface fluence. A coupling code folds the fluence together with the dose importance, giving the desired dose response. The coupling code can determine the dose response a a function of the shielding geometry orientation relative to the source, distance from the source, and energy response of the detector. This user`s manual includes a short description of each code, the input required to execute the code along with some helpful input data notes, and a representative sample problem (input data and selected output edits) for each code.
Improved Monte Carlo Renormalization Group Method
DOE R&D Accomplishments Database
Gupta, R.; Wilson, K. G.; Umrigar, C.
1985-01-01
An extensive program to analyze critical systems using an Improved Monte Carlo Renormalization Group Method (IMCRG) being undertaken at LANL and Cornell is described. Here we first briefly review the method and then list some of the topics being investigated.
Monte Carlo Ion Transport Analysis Code.
Energy Science and Technology Software Center (ESTSC)
2009-04-15
Version: 00 TRIPOS is a versatile Monte Carlo ion transport analysis code. It has been applied to the treatment of both surface and bulk radiation effects. The media considered is composed of multilayer polyatomic materials.
Monte Carlo Transport for Electron Thermal Transport
NASA Astrophysics Data System (ADS)
Chenhall, Jeffrey; Cao, Duc; Moses, Gregory
2015-11-01
The iSNB (implicit Schurtz Nicolai Busquet multigroup electron thermal transport method of Cao et al. is adapted into a Monte Carlo transport method in order to better model the effects of non-local behavior. The end goal is a hybrid transport-diffusion method that combines Monte Carlo Transport with a discrete diffusion Monte Carlo (DDMC). The hybrid method will combine the efficiency of a diffusion method in short mean free path regions with the accuracy of a transport method in long mean free path regions. The Monte Carlo nature of the approach allows the algorithm to be massively parallelized. Work to date on the method will be presented. This work was supported by Sandia National Laboratory - Albuquerque and the University of Rochester Laboratory for Laser Energetics.
NASA Astrophysics Data System (ADS)
A. O., Q.; Gardner, R. P.
1995-12-01
A new Monte Carlo method for modelling photon transport in the presence of deep-penetration and streaming effects by combining a subspace weight window and biasing schemes has been developed. This method is based on use of an importance map from which an importance subspace is identified for a given particle transport system. Biasing schemes, including direction biasing and the exponential transform, are applied to drive particles into the importance subspace. The subspace weight window approach used consists of splitting and Russian Roulette that acts as a particle weight stabilizer in the subspace to control weight fluctuations caused by the biasing schemes. This approach has been implemented in the optimization of the McLDL code, a specific purpose Monte Carlo code for modelling the spectral response of dual-spaced γ-γ litho-density logging tools. which are highly collimated, deep-penetration, three-dimensional, and low-yield photon transport systems. The McLDL code has been tested on a computational benchmark tool and benchmarked experimentally against laboratory test pit data for a commercial γ-γ litho-density logging tool (the Z-Densilog). The Monte Carlo Multiply Scattered Components (MCMSC) approach has been developed in conjunction with the McLDL code and Library Least-Squares (LLS) analysis. The MCMSC approach consists of constructing component libraries (1 4, 5 8 scatters, etc.) of γ-ray scattered spectra for a reference formation and borehole with the McLDL Monte Carlo code. Then the LLS approach is used with these library spectra to obtain empirical relationships between formation and borehole parameters and the component amounts. These, in turn, can be used to construct the spectra for samples with a range of formation and borehole parameters. This approach should significantly reduce the amount of experimental effort or extent of the Monte Carlo calculations necessary for complete logging tool calibration while maintaining a close physical
Neutron stimulated emission computed tomography: a Monte Carlo simulation approach.
Sharma, A C; Harrawood, B P; Bender, J E; Tourassi, G D; Kapadia, A J
2007-10-21
A Monte Carlo simulation has been developed for neutron stimulated emission computed tomography (NSECT) using the GEANT4 toolkit. NSECT is a new approach to biomedical imaging that allows spectral analysis of the elements present within the sample. In NSECT, a beam of high-energy neutrons interrogates a sample and the nuclei in the sample are stimulated to an excited state by inelastic scattering of the neutrons. The characteristic gammas emitted by the excited nuclei are captured in a spectrometer to form multi-energy spectra. Currently, a tomographic image is formed using a collimated neutron beam to define the line integral paths for the tomographic projections. These projection data are reconstructed to form a representation of the distribution of individual elements in the sample. To facilitate the development of this technique, a Monte Carlo simulation model has been constructed from the GEANT4 toolkit. This simulation includes modeling of the neutron beam source and collimation, the samples, the neutron interactions within the samples, the emission of characteristic gammas, and the detection of these gammas in a Germanium crystal. In addition, the model allows the absorbed radiation dose to be calculated for internal components of the sample. NSECT presents challenges not typically addressed in Monte Carlo modeling of high-energy physics applications. In order to address issues critical to the clinical development of NSECT, this paper will describe the GEANT4 simulation environment and three separate simulations performed to accomplish three specific aims. First, comparison of a simulation to a tomographic experiment will verify the accuracy of both the gamma energy spectra produced and the positioning of the beam relative to the sample. Second, parametric analysis of simulations performed with different user-defined variables will determine the best way to effectively model low energy neutrons in tissue, which is a concern with the high hydrogen content in
NASA Astrophysics Data System (ADS)
Vrugt, Jasper A.; Ter Braak, Cajo J. F.; Clark, Martyn P.; Hyman, James M.; Robinson, Bruce A.
2008-12-01
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 differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a five-parameter rainfall-runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average watershed precipitation, information that is of utmost importance for testing hydrologic theory, diagnosing structural errors in models, and appropriately benchmarking rainfall measurement devices.
Monte Carlo beam capture and charge breeding simulation
Kim, J.S.; Liu, C.; Edgell, D.H.; Pardo, R.
2006-03-15
A full six-dimensional (6D) phase space Monte Carlo beam capture charge-breeding simulation code examines the beam capture processes of singly charged ion beams injected to an electron cyclotron resonance (ECR) charge breeder from entry to exit. The code traces injected beam ions in an ECR ion source (ECRIS) plasma including Coulomb collisions, ionization, and charge exchange. The background ECRIS plasma is modeled within the current frame work of the generalized ECR ion source model. A simple sample case of an oxygen background plasma with an injected Ar +1 ion beam produces lower charge breeding efficiencies than experimentally obtained. Possible reasons for discrepancies are discussed.
A semianalytic Monte Carlo code for modelling LIDAR measurements
NASA Astrophysics Data System (ADS)
Palazzi, Elisa; Kostadinov, Ivan; Petritoli, Andrea; Ravegnani, Fabrizio; Bortoli, Daniele; Masieri, Samuele; Premuda, Margherita; Giovanelli, Giorgio
2007-10-01
LIDAR (LIght Detection and Ranging) is an optical active remote sensing technology with many applications in atmospheric physics. Modelling of LIDAR measurements appears useful approach for evaluating the effects of various environmental variables and scenarios as well as of different measurement geometries and instrumental characteristics. In this regard a Monte Carlo simulation model can provide a reliable answer to these important requirements. A semianalytic Monte Carlo code for modelling LIDAR measurements has been developed at ISAC-CNR. The backscattered laser signal detected by the LIDAR system is calculated in the code taking into account the contributions due to the main atmospheric molecular constituents and aerosol particles through processes of single and multiple scattering. The contributions by molecular absorption, ground and clouds reflection are evaluated too. The code can perform simulations of both monostatic and bistatic LIDAR systems. To enhance the efficiency of the Monte Carlo simulation, analytical estimates and expected value calculations are performed. Artificial devices (such as forced collision, local forced collision, splitting and russian roulette) are moreover foreseen by the code, which can enable the user to drastically reduce the variance of the calculation.
Calculation of the entropy of random coil polymers with the hypothetical scanning Monte Carlo method
NASA Astrophysics Data System (ADS)
White, Ronald P.; Meirovitch, Hagai
2005-12-01
Hypothetical scanning Monte Carlo (HSMC) is a method for calculating the absolute entropy S and free energy F from a given MC trajectory developed recently and applied to liquid argon, TIP3P water, and peptides. In this paper HSMC is extended to random coil polymers by applying it to self-avoiding walks on a square lattice—a simple but difficult model due to strong excluded volume interactions. With HSMC the probability of a given chain is obtained as a product of transition probabilities calculated for each bond by MC simulations and a counting formula. This probability is exact in the sense that it is based on all the interactions of the system and the only approximation is due to finite sampling. The method provides rigorous upper and lower bounds for F, which can be obtained from a very small sample and even from a single chain conformation. HSMC is independent of existing techniques and thus constitutes an independent research tool. The HSMC results are compared to those obtained by other methods, and its application to complex lattice chain models is discussed; we emphasize its ability to treat any type of boundary conditions for which a reference state (with known free energy) might be difficult to define for a thermodynamic integration process. Finally, we stress that the capability of HSMC to extract the absolute entropy from a given sample is important for studying relaxation processes, such as protein folding.
White, Ronald P; Meirovitch, Hagai
2005-12-01
Hypothetical scanning Monte Carlo (HSMC) is a method for calculating the absolute entropy S and free energy F from a given MC trajectory developed recently and applied to liquid argon, TIP3P water, and peptides. In this paper HSMC is extended to random coil polymers by applying it to self-avoiding walks on a square lattice--a simple but difficult model due to strong excluded volume interactions. With HSMC the probability of a given chain is obtained as a product of transition probabilities calculated for each bond by MC simulations and a counting formula. This probability is exact in the sense that it is based on all the interactions of the system and the only approximation is due to finite sampling. The method provides rigorous upper and lower bounds for F, which can be obtained from a very small sample and even from a single chain conformation. HSMC is independent of existing techniques and thus constitutes an independent research tool. The HSMC results are compared to those obtained by other methods, and its application to complex lattice chain models is discussed; we emphasize its ability to treat any type of boundary conditions for which a reference state (with known free energy) might be difficult to define for a thermodynamic integration process. Finally, we stress that the capability of HSMC to extract the absolute entropy from a given sample is important for studying relaxation processes, such as protein folding. PMID:16356071
A novel parallel-rotation algorithm for atomistic Monte Carlo simulation of dense polymer systems
NASA Astrophysics Data System (ADS)
Santos, S.; Suter, U. W.; Müller, M.; Nievergelt, J.
2001-06-01
We develop and test a new elementary Monte Carlo move for use in the off-lattice simulation of polymer systems. This novel Parallel-Rotation algorithm (ParRot) permits moving very efficiently torsion angles that are deeply inside long chains in melts. The parallel-rotation move is extremely simple and is also demonstrated to be computationally efficient and appropriate for Monte Carlo simulation. The ParRot move does not affect the orientation of those parts of the chain outside the moving unit. The move consists of a concerted rotation around four adjacent skeletal bonds. No assumption is made concerning the backbone geometry other than that bond lengths and bond angles are held constant during the elementary move. Properly weighted sampling techniques are needed for ensuring detailed balance because the new move involves a correlated change in four degrees of freedom along the chain backbone. The ParRot move is supplemented with the classical Metropolis Monte Carlo, the Continuum-Configurational-Bias, and Reptation techniques in an isothermal-isobaric Monte Carlo simulation of melts of short and long chains. Comparisons are made with the capabilities of other Monte Carlo techniques to move the torsion angles in the middle of the chains. We demonstrate that ParRot constitutes a highly promising Monte Carlo move for the treatment of long polymer chains in the off-lattice simulation of realistic models of dense polymer systems.
A new method for commissioning Monte Carlo treatment planning systems
NASA Astrophysics Data System (ADS)
Aljarrah, Khaled Mohammed
2005-11-01
The Monte Carlo method is an accurate method for solving numerical problems in different fields. It has been used for accurate radiation dose calculation for radiation treatment of cancer. However, the modeling of an individual radiation beam produced by a medical linear accelerator for Monte Carlo dose calculation, i.e., the commissioning of a Monte Carlo treatment planning system, has been the bottleneck for the clinical implementation of Monte Carlo treatment planning. In this study a new method has been developed to determine the parameters of the initial electron beam incident on the target for a clinical linear accelerator. The interaction of the initial electron beam with the accelerator target produces x-ray and secondary charge particles. After successive interactions in the linac head components, the x-ray photons and the secondary charge particles interact with the patient's anatomy and deliver dose to the region of interest. The determination of the initial electron beam parameters is important for estimating the delivered dose to the patients. These parameters, such as beam energy and radial intensity distribution, are usually estimated through a trial and error process. In this work an easy and efficient method was developed to determine these parameters. This was accomplished by comparing calculated 3D dose distributions for a grid of assumed beam energies and radii in a water phantom with measurements data. Different cost functions were studied to choose the appropriate function for the data comparison. The beam parameters were determined on the light of this method. Due to the assumption that same type of linacs are exactly the same in their geometries and only differ by the initial phase space parameters, the results of this method were considered as a source data to commission other machines of the same type.
Application of MINERVA Monte Carlo simulations to targeted radionuclide therapy.
Descalle, Marie-Anne; Hartmann Siantar, Christine L; Dauffy, Lucile; Nigg, David W; Wemple, Charles A; Yuan, Aina; DeNardo, Gerald L
2003-02-01
Recent clinical results have demonstrated the promise of targeted radionuclide therapy for advanced cancer. As the success of this emerging form of radiation therapy grows, accurate treatment planning and radiation dose simulations are likely to become increasingly important. To address this need, we have initiated the development of a new, Monte Carlo transport-based treatment planning system for molecular targeted radiation therapy as part of the MINERVA system. The goal of the MINERVA dose calculation system is to provide 3-D Monte Carlo simulation-based dosimetry for radiation therapy, focusing on experimental and emerging applications. For molecular targeted radionuclide therapy applications, MINERVA calculates patient-specific radiation dose estimates using computed tomography to describe the patient anatomy, combined with a user-defined 3-D radiation source. This paper describes the validation of the 3-D Monte Carlo transport methods to be used in MINERVA for molecular targeted radionuclide dosimetry. It reports comparisons of MINERVA dose simulations with published absorbed fraction data for distributed, monoenergetic photon and electron sources, and for radioisotope photon emission. MINERVA simulations are generally within 2% of EGS4 results and 10% of MCNP results, but differ by up to 40% from the recommendations given in MIRD Pamphlets 3 and 8 for identical medium composition and density. For several representative source and target organs in the abdomen and thorax, specific absorbed fractions calculated with the MINERVA system are generally within 5% of those published in the revised MIRD Pamphlet 5 for 100 keV photons. However, results differ by up to 23% for the adrenal glands, the smallest of our target organs. Finally, we show examples of Monte Carlo simulations in a patient-like geometry for a source of uniform activity located in the kidney. PMID:12667310
POWER ANALYSIS FOR COMPLEX MEDIATIONAL DESIGNS USING MONTE CARLO METHODS
Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.
2013-01-01
Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, three-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models. PMID:23935262
Full modelling of the MOSAIC animal PET system based on the GATE Monte Carlo simulation code
NASA Astrophysics Data System (ADS)
Merheb, C.; Petegnief, Y.; Talbot, J. N.
2007-02-01
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™ 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™ 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 18F 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
Monte Carlo Volcano Seismic Moment Tensors
NASA Astrophysics Data System (ADS)
Waite, G. P.; Brill, K. A.; Lanza, F.
2015-12-01
Inverse modeling of volcano seismic sources can provide insight into the geometry and dynamics of volcanic conduits. But given the logistical challenges of working on an active volcano, seismic networks are typically deficient in spatial and temporal coverage; this potentially leads to large errors in source models. In addition, uncertainties in the centroid location and moment-tensor components, including volumetric components, are difficult to constrain from the linear inversion results, which leads to a poor understanding of the model space. In this study, we employ a nonlinear inversion using a Monte Carlo scheme with the objective of defining robustly resolved elements of model space. The model space is randomized by centroid location and moment tensor eigenvectors. Point sources densely sample the summit area and moment tensors are constrained to a randomly chosen geometry within the inversion; Green's functions for the random moment tensors are all calculated from modeled single forces, making the nonlinear inversion computationally reasonable. We apply this method to very-long-period (VLP) seismic events that accompany minor eruptions at Fuego volcano, Guatemala. The library of single force Green's functions is computed with a 3D finite-difference modeling algorithm through a homogeneous velocity-density model that includes topography, for a 3D grid of nodes, spaced 40 m apart, within the summit region. The homogenous velocity and density model is justified by long wavelength of VLP data. The nonlinear inversion reveals well resolved model features and informs the interpretation through a better understanding of the possible models. This approach can also be used to evaluate possible station geometries in order to optimize networks prior to deployment.
Improved diffusion Monte Carlo and the Brownian fan
NASA Astrophysics Data System (ADS)
Weare, J.; Hairer, M.
2012-12-01
Diffusion Monte Carlo (DMC) is a workhorse of stochastic computing. It was invented forty years ago as the central component in a Monte Carlo technique for estimating various characteristics of quantum mechanical systems. Since then it has been used in applied in a huge number of fields, often as a central component in sequential Monte Carlo techniques (e.g. the particle filter). DMC computes averages of some underlying stochastic dynamics weighted by a functional of the path of the process. The weight functional could represent the potential term in a Feynman-Kac representation of a partial differential equation (as in quantum Monte Carlo) or it could represent the likelihood of a sequence of noisy observations of the underlying system (as in particle filtering). DMC alternates between an evolution step in which a collection of samples of the underlying system are evolved for some short time interval, and a branching step in which, according to the weight functional, some samples are copied and some samples are eliminated. Unfortunately for certain choices of the weight functional DMC fails to have a meaningful limit as one decreases the evolution time interval between branching steps. We propose a modification of the standard DMC algorithm. The new algorithm has a lower variance per workload, regardless of the regime considered. In particular, it makes it feasible to use DMC in situations where the ``naive'' generalization of the standard algorithm would be impractical, due to an exponential explosion of its variance. We numerically demonstrate the effectiveness of the new algorithm on a standard rare event simulation problem (probability of an unlikely transition in a Lennard-Jones cluster), as well as a high-frequency data assimilation problem. We then provide a detailed heuristic explanation of why, in the case of rare event simulation, the new algorithm is expected to converge to a limiting process as the underlying stepsize goes to 0. This is shown
pyNSMC: A Python Module for Null-Space Monte Carlo Uncertainty Analysis
NASA Astrophysics Data System (ADS)
White, J.; Brakefield, L. K.
2015-12-01
The null-space monte carlo technique is a non-linear uncertainty analyses technique that is well-suited to high-dimensional inverse problems. While the technique is powerful, the existing workflow for completing null-space monte carlo is cumbersome, requiring the use of multiple commandline utilities, several sets of intermediate files and even a text editor. pyNSMC is an open-source python module that automates the workflow of null-space monte carlo uncertainty analyses. The module is fully compatible with the PEST and PEST++ software suites and leverages existing functionality of pyEMU, a python framework for linear-based uncertainty analyses. pyNSMC greatly simplifies the existing workflow for null-space monte carlo by taking advantage of object oriented design facilities in python. The core of pyNSMC is the ensemble class, which draws and stores realized random vectors and also provides functionality for exporting and visualizing results. By relieving users of the tedium associated with file handling and command line utility execution, pyNSMC instead focuses the user on the important steps and assumptions of null-space monte carlo analysis. Furthermore, pyNSMC facilitates learning through flow charts and results visualization, which are available at many points in the algorithm. The ease-of-use of the pyNSMC workflow is compared to the existing workflow for null-space monte carlo for a synthetic groundwater model with hundreds of estimable parameters.
Review of Fast Monte Carlo Codes for Dose Calculation in Radiation Therapy Treatment Planning
Jabbari, Keyvan
2011-01-01
An important requirement in radiation therapy is a fast and accurate treatment planning system. This system, using computed tomography (CT) data, direction, and characteristics of the beam, calculates the dose at all points of the patient's volume. The two main factors in treatment planning system are accuracy and speed. According to these factors, various generations of treatment planning systems are developed. This article is a review of the Fast Monte Carlo treatment planning algorithms, which are accurate and fast at the same time. The Monte Carlo techniques are based on the transport of each individual particle (e.g., photon or electron) in the tissue. The transport of the particle is done using the physics of the interaction of the particles with matter. Other techniques transport the particles as a group. For a typical dose calculation in radiation therapy the code has to transport several millions particles, which take a few hours, therefore, the Monte Carlo techniques are accurate, but slow for clinical use. In recent years, with the development of the ‘fast’ Monte Carlo systems, one is able to perform dose calculation in a reasonable time for clinical use. The acceptable time for dose calculation is in the range of one minute. There is currently a growing interest in the fast Monte Carlo treatment planning systems and there are many commercial treatment planning systems that perform dose calculation in radiation therapy based on the Monte Carlo technique. PMID:22606661
NASA Astrophysics Data System (ADS)
Lin, Lin; Zhang, Mei
2015-02-01
The scaling Monte Carlo method and Gaussian model are applied to simulate the transportation of light beam with arbitrary waist radius. Much of the time, Monte Carlo simulation is performed for pencil or cone beam where the initial status of the photon is identical. In practical application, incident light is always focused on the sample to form approximate Gauss distribution on the surface. With alteration of focus position in the sample, the initial status of the photon will not be identical any more. Using the hyperboloid method, the initial reflect angle and coordinates are generated statistically according to the size of Gaussian waist and focus depth. Scaling calculation is performed with baseline data from standard Monte Carlo simulation. The scaling method incorporated with the Gaussian model was tested, and proved effective over a range of scattering coefficients from 20% to 180% relative to the value used in baseline simulation. In most cases, percentage error was less than 10%. The increasing of focus depth will result in larger error of scaled radial reflectance in the region close to the optical axis. In addition to evaluating accuracy of scaling the Monte Carlo method, this study has given implications for inverse Monte Carlo with arbitrary parameters of optical system.
Monte Carlo simulation with fixed steplength for diffusion processes in nonhomogeneous media
NASA Astrophysics Data System (ADS)
Ruiz Barlett, V.; Hoyuelos, M.; Mártin, H. O.
2013-04-01
Monte Carlo simulation is one of the most important tools in the study of diffusion processes. For constant diffusion coefficients, an appropriate Gaussian distribution of particle's steplengths can generate exact results, when compared with integration of the diffusion equation. It is important to notice that the same method is completely erroneous when applied to non-homogeneous diffusion coefficients. A simple alternative, jumping at fixed steplengths with appropriate transition probabilities, produces correct results. Here, a model for diffusion of calcium ions in the neuromuscular junction of the crayfish is used as a test to compare Monte Carlo simulation with fixed and Gaussian steplength.
Burnup calculation methodology in the serpent 2 Monte Carlo code
Leppaenen, J.; Isotalo, A.
2012-07-01
This paper presents two topics related to the burnup calculation capabilities in the Serpent 2 Monte Carlo code: advanced time-integration methods and improved memory management, accomplished by the use of different optimization modes. The development of the introduced methods is an important part of re-writing the Serpent source code, carried out for the purpose of extending the burnup calculation capabilities from 2D assembly-level calculations to large 3D reactor-scale problems. The progress is demonstrated by repeating a PWR test case, originally carried out in 2009 for the validation of the newly-implemented burnup calculation routines in Serpent 1. (authors)
Direct Simulation Monte Carlo: Recent Advances and Applications
NASA Astrophysics Data System (ADS)
Oran, E. S.; Oh, C. K.; Cybyk, B. Z.
The principles of and procedures for implementing direct simulation Monte Carlo (DSMC) are described. Guidelines to inherent and external errors common in DSMC applications are provided. Three applications of DSMC to transitional and nonequilibrium flows are considered: rarefied atmospheric flows, growth of thin films, and microsystems. Selected new, potentially important advances in DSMC capabilities are described: Lagrangian DSMC, optimization on parallel computers, and hybrid algorithms for computations in mixed flow regimes. Finally, the limitations of current computer technology for using DSMC to compute low-speed, high-Knudsen-number flows are outlined as future challenges.
Approaching chemical accuracy with quantum Monte Carlo.
Petruzielo, F R; Toulouse, Julien; Umrigar, C J
2012-03-28
A quantum Monte Carlo study of the atomization energies for the G2 set of molecules is presented. Basis size dependence of diffusion Monte Carlo atomization energies is studied with a single determinant Slater-Jastrow trial wavefunction formed from Hartree-Fock orbitals. With the largest basis set, the mean absolute deviation from experimental atomization energies for the G2 set is 3.0 kcal/mol. Optimizing the orbitals within variational Monte Carlo improves the agreement between diffusion Monte Carlo and experiment, reducing the mean absolute deviation to 2.1 kcal/mol. Moving beyond a single determinant Slater-Jastrow trial wavefunction, diffusion Monte Carlo with a small complete active space Slater-Jastrow trial wavefunction results in near chemical accuracy. In this case, the mean absolute deviation from experimental atomization energies is 1.2 kcal/mol. It is shown from calculations on systems containing phosphorus that the accuracy can be further improved by employing a larger active space. PMID:22462844
Quantum Monte Carlo calculations on positronium compounds
NASA Astrophysics Data System (ADS)
Jiang, Nan
The stability of compounds containing one or more positrons in addition to electrons and nuclei has been the focus of extensive scientific investigations. Interest in these compounds stems from the important role they play in the process of positron annihilation, which has become a useful technique in material science studies. Knowledge of these compounds comes mostly from calculations which are presently less difficult than laboratory experiments. Owing to the small binding energies of these compounds, quantum chemistry methods beyond the molecular orbital approximation must be used. Among them, the quantum Monte Carlo (QMC) method is most appealing because it is easy to implement, gives exact results within the fixed nodes approximation, and makes good use of existing approximate wavefunctions. Applying QMC to small systems like PsH for binding energy calculation is straightforward. To apply it to systems with heavier atoms, to systems for which the center-of-mass motion needs to be separated, and to calculate annihilation rates, special techniques must be developed. In this project a detailed study and several advancements to the QMC method are carried out. Positronium compounds PsH, Ps2, PsO, and Ps2O are studied with algorithms we developed. Results for PsH and Ps2 agree with the best accepted to date. Results for PsO confirm the stability of this compound, and are in fair agreement with an earlier calculation. Results for Ps2O establish the stability of this compound and give an approximate annihilation rate for the first time. Discussions will include an introduction to QMC methods, an in-depth discussion on the QMC formalism, presentation of new algorithms developed in this study, and procedures and results of QMC calculations on the above mentioned positronium compounds.
NASA Astrophysics Data System (ADS)
Alexander, Andrew William
Within the field of medical physics, Monte Carlo radiation transport simulations are considered to be the most accurate method for the determination of dose distributions in patients. The McGill Monte Carlo treatment planning system (MMCTP), provides a flexible software environment to integrate Monte Carlo simulations with current and new treatment modalities. A developing treatment modality called energy and intensity modulated electron radiotherapy (MERT) is a promising modality, which has the fundamental capabilities to enhance the dosimetry of superficial targets. An objective of this work is to advance the research and development of MERT with the end goal of clinical use. To this end, we present the MMCTP system with an integrated toolkit for MERT planning and delivery of MERT fields. Delivery is achieved using an automated "few leaf electron collimator" (FLEC) and a controller. Aside from the MERT planning toolkit, the MMCTP system required numerous add-ons to perform the complex task of large-scale autonomous Monte Carlo simulations. The first was a DICOM import filter, followed by the implementation of DOSXYZnrc as a dose calculation engine and by logic methods for submitting and updating the status of Monte Carlo simulations. Within this work we validated the MMCTP system with a head and neck Monte Carlo recalculation study performed by a medical dosimetrist. The impact of MMCTP lies in the fact that it allows for systematic and platform independent large-scale Monte Carlo dose calculations for different treatment sites and treatment modalities. In addition to the MERT planning tools, various optimization algorithms were created external to MMCTP. The algorithms produced MERT treatment plans based on dose volume constraints that employ Monte Carlo pre-generated patient-specific kernels. The Monte Carlo kernels are generated from patient-specific Monte Carlo dose distributions within MMCTP. The structure of the MERT planning toolkit software and
DNest3: Diffusive Nested Sampling
NASA Astrophysics Data System (ADS)
Brewer, Brendon
2016-04-01
DNest3 is a C++ implementation of Diffusive Nested Sampling (ascl:1010.029), a Markov Chain Monte Carlo (MCMC) algorithm for Bayesian Inference and Statistical Mechanics. Relative to older DNest versions, DNest3 has improved performance (in terms of the sampling overhead, likelihood evaluations still dominate in general) and is cleaner code: implementing new models should be easier than it was before. In addition, DNest3 is multi-threaded, so one can run multiple MCMC walkers at the same time, and the results will be combined together.
Monte Carlo simulation of two-component aerosol processes
NASA Astrophysics Data System (ADS)
Huertas, Jose Ignacio
Aerosol processes have been extensively used for production of nanophase materials. However when temperatures and number densities are high, particle agglomeration is a serious drawback for these techniques. This problem can be addressed by encapsulating the particles with a second material before they agglomerate. These particles will agglomerate but the primary particles within them will not. When the encapsulation is later removed, the resulting powder will contain only weakly agglomerated particles. To demonstrate the applicability of the particle encapsulation method for the production of high purity unagglomerated nanosize materials, tungsten (W) and tungsten titanium alloy (W-Ti) particles were synthesized in a sodium/halide flame. The particles were characterized by XRD, SEM, TEM and EDAX. The particles appeared unagglomerated, cubic and hexagonal in shape, and had a size of 30-50 nm. No contamination was detected even after extended exposure to atmospheric conditions. The nanosized W and W-Ti particles were consolidated into pellets of 6 mm diameter and 6-8 mm long. Hardness measurements indicate values 4 times that of conventional tungsten. 100% densification was achieved by hipping the samples. To study the particle encapsulation method, a code to simulate particle formation in two component aerosols was developed. The simulation was carried out using a Monte Carlo technique. This approach allowed for the treatment of both probabilistic and deterministic events. Thus, the coagulation term of the general dynamic equation (GDE) was Monte Carlo simulated, and the condensation term was solved analytically and incorporated into the model. The model includes condensation, coagulation, sources, and sinks for two-component aerosol processes. The Kelvin effect has been included in the model as well. The code is general and does not suffer from problems associated with mass conservation, high rates of condensation and approximations on particle composition. It has
NASA Astrophysics Data System (ADS)
Warren, Gary Lee, Jr.
2005-11-01
Quantum Monte Carlo (QMC) methods are a class of powerful computer simulation techniques for solving the many-body Schrodinger equation. These techniques deliver essentially exact results and boast favorable computational scaling with system size. Calculations provide a full quantum mechanical treatment and may be carried to arbitrary precision. These characteristics make QMC a promising choice for the investigation of doped helium clusters, where quantum effects are substantial. Stochastic in nature, QMC methods are susceptible to statistical bias and error, which must be carefully controlled. Moreover, the relationship between the finite sampling error and the statistical uncertainty in observables has never been systematically investigated. Estimates of arbitrary observables are often substandard and can be plagued by statistical uncertainties an order of magnitude or greater than those for corresponding estimates of the energy. In this work, we present an analysis of how finite populations, importance sampling, and dimensionality affect the statistical uncertainties in QMC estimates of arbitrary observables. We find that the uncertainty depends exponentially on the dimensionality of the system, independent of the observable or nature of the system. This provides insight into the minimal population sizes and importance sampling requirements necessary to obtain useful QMC estimates of properties in high-dimensional systems. With this understanding, we develop new, more robust energy optimization procedures for cluster wavefunctions. We also implement a high quality eight parameter ansatz for the investigation of both pure and doped helium cluster systems. Compared to exact DMC results, the optimized wavefunctions recover over 90% of the total energy for clusters of size n ≤ 20. Finally, we apply this knowledge directly to the study of the solvation behavior of neutral calcium and magnesium impurities in helium nanodroplets. Diffusion Monte Carlo calculations
Finding organic vapors - a Monte Carlo approach
NASA Astrophysics Data System (ADS)
Vuollekoski, Henri; Boy, Michael; Kerminen, Veli-Matti; Kulmala, Markku
2010-05-01
drawbacks in accuracy, the inability to find diurnal variation and the lack of size resolution. Here, we aim to shed some light onto the problem by applying an ad hoc Monte Carlo algorithm to a well established aerosol dynamical model, the University of Helsinki Multicomponent Aerosol model (UHMA). By performing a side-by-side comparison with measurement data within the algorithm, this approach has the significant advantage of decreasing the amount of manual labor. But more importantly, by basing the comparison on particle number size distribution data - a quantity that can be quite reliably measured - the accuracy of the results is good.
Monte Carlo simulation of classical spin models with chaotic billiards
NASA Astrophysics Data System (ADS)
Suzuki, Hideyuki
2013-11-01
It has recently been shown that the computing abilities of Boltzmann machines, or Ising spin-glass models, can be implemented by chaotic billiard dynamics without any use of random numbers. In this paper, we further numerically investigate the capabilities of the chaotic billiard dynamics as a deterministic alternative to random Monte Carlo methods by applying it to classical spin models in statistical physics. First, we verify that the billiard dynamics can yield samples that converge to the true distribution of the Ising model on a small lattice, and we show that it appears to have the same convergence rate as random Monte Carlo sampling. Second, we apply the billiard dynamics to finite-size scaling analysis of the critical behavior of the Ising model and show that the phase-transition point and the critical exponents are correctly obtained. Third, we extend the billiard dynamics to spins that take more than two states and show that it can be applied successfully to the Potts model. We also discuss the possibility of extensions to continuous-valued models such as the XY model.
A Monte Carlo Evaluation of Estimated Parameters of Five Shrinkage Estimate Formuli.
ERIC Educational Resources Information Center
Newman, Isadore; And Others
1979-01-01
A Monte Carlo simulation was employed to determine the accuracy with which the shrinkage in R squared can be estimated by five different shrinkage formulas. The study dealt with the use of shrinkage formulas for various sample sizes, different R squared values, and different degrees of multicollinearity. (Author/JKS)
ERIC Educational Resources Information Center
Kim, Jee-Seon; Bolt, Daniel M.
2007-01-01
The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain…
A Monte Carlo Study of Recovery of Weak Factor Loadings in Confirmatory Factor Analysis
ERIC Educational Resources Information Center
Ximenez, Carmen
2006-01-01
The recovery of weak factors has been extensively studied in the context of exploratory factor analysis. This article presents the results of a Monte Carlo simulation study of recovery of weak factor loadings in confirmatory factor analysis under conditions of estimation method (maximum likelihood vs. unweighted least squares), sample size,…
Automated Factor Slice Sampling.
Tibbits, Matthew M; Groendyke, Chris; Haran, Murali; Liechty, John C
2014-01-01
Markov chain Monte Carlo (MCMC) algorithms offer a very general approach for sampling from arbitrary distributions. However, designing and tuning MCMC algorithms for each new distribution, can be challenging and time consuming. It is particularly difficult to create an efficient sampler when there is strong dependence among the variables in a multivariate distribution. We describe a two-pronged approach for constructing efficient, automated MCMC algorithms: (1) we propose the "factor slice sampler", a generalization of the univariate slice sampler where we treat the selection of a coordinate basis (factors) as an additional tuning parameter, and (2) we develop an approach for automatically selecting tuning parameters in order to construct an efficient factor slice sampler. In addition to automating the factor slice sampler, our tuning approach also applies to the standard univariate slice samplers. We demonstrate the efficiency and general applicability of our automated MCMC algorithm with a number of illustrative examples. PMID:24955002
Automated Factor Slice Sampling
Tibbits, Matthew M.; Groendyke, Chris; Haran, Murali; Liechty, John C.
2013-01-01
Markov chain Monte Carlo (MCMC) algorithms offer a very general approach for sampling from arbitrary distributions. However, designing and tuning MCMC algorithms for each new distribution, can be challenging and time consuming. It is particularly difficult to create an efficient sampler when there is strong dependence among the variables in a multivariate distribution. We describe a two-pronged approach for constructing efficient, automated MCMC algorithms: (1) we propose the “factor slice sampler”, a generalization of the univariate slice sampler where we treat the selection of a coordinate basis (factors) as an additional tuning parameter, and (2) we develop an approach for automatically selecting tuning parameters in order to construct an efficient factor slice sampler. In addition to automating the factor slice sampler, our tuning approach also applies to the standard univariate slice samplers. We demonstrate the efficiency and general applicability of our automated MCMC algorithm with a number of illustrative examples. PMID:24955002
Quantum Monte Carlo calculations of light nuclei
Pieper, S.C.
1998-12-01
Quantum Monte Carlo calculations using realistic two- and three-nucleon interactions are presented for nuclei with up to eight nucleons. We have computed the ground and a few excited states of all such nuclei with Greens function Monte Carlo (GFMC) and all of the experimentally known excited states using variational Monte Carlo (VMC). The GFMC calculations show that for a given Hamiltonian, the VMC calculations of excitation spectra are reliable, but the VMC ground-state energies are significantly above the exact values. We find that the Hamiltonian we are using (which was developed based on {sup 3}H,{sup 4}He, and nuclear matter calculations) underpredicts the binding energy of p-shell nuclei. However our results for excitation spectra are very good and one can see both shell-model and collective spectra resulting from fundamental many-nucleon calculations. Possible improvements in the three-nucleon potential are also be discussed. {copyright} {ital 1998 American Institute of Physics.}
Quantum Monte Carlo calculations of light nuclei
Pieper, Steven C.
1998-12-21
Quantum Monte Carlo calculations using realistic two- and three-nucleon interactions are presented for nuclei with up to eight nucleons. We have computed the ground and a few excited states of all such nuclei with Greens function Monte Carlo (GFMC) and all of the experimentally known excited states using variational Monte Carlo (VMC). The GFMC calculations show that for a given Hamiltonian, the VMC calculations of excitation spectra are reliable, but the VMC ground-state energies are significantly above the exact values. We find that the Hamiltonian we are using (which was developed based on {sup 3}H,{sup 4}He, and nuclear matter calculations) underpredicts the binding energy of p-shell nuclei. However our results for excitation spectra are very good and one can see both shell-model and collective spectra resulting from fundamental many-nucleon calculations. Possible improvements in the three-nucleon potential are also be discussed.
Quantum Monte Carlo calculations of light nuclei.
Pieper, S. C.
1998-08-25
Quantum Monte Carlo calculations using realistic two- and three-nucleon interactions are presented for nuclei with up to eight nucleons. We have computed the ground and a few excited states of all such nuclei with Greens function Monte Carlo (GFMC) and all of the experimentally known excited states using variational Monte Carlo (VMC). The GFMC calculations show that for a given Hamiltonian, the VMC calculations of excitation spectra are reliable, but the VMC ground-state energies are significantly above the exact values. We find that the Hamiltonian we are using (which was developed based on {sup 3}H, {sup 4}He, and nuclear matter calculations) underpredicts the binding energy of p-shell nuclei. However our results for excitation spectra are very good and one can see both shell-model and collective spectra resulting from fundamental many-nucleon calculations. Possible improvements in the three-nucleon potential are also be discussed.
Spatial Correlations in Monte Carlo Criticality Simulations
NASA Astrophysics Data System (ADS)
Dumonteil, E.; Malvagi, F.; Zoia, A.; Mazzolo, A.; Artusio, D.; Dieudonné, C.; De Mulatier, C.
2014-06-01
Temporal correlations arising in Monte Carlo criticality codes have focused the attention of both developers and practitioners for a long time. Those correlations affects the evaluation of tallies of loosely coupled systems, where the system's typical size is very large compared to the diffusion/absorption length scale of the neutrons. These time correlations are closely related to spatial correlations, both variables being linked by the transport equation. Therefore this paper addresses the question of diagnosing spatial correlations in Monte Carlo criticality simulations. In that aim, we will propose a spatial correlation function well suited to Monte Carlo simulations, and show its use while simulating a fuel pin-cell. The results will be discussed, modeled and interpreted using the tools of branching processes of statistical mechanics. A mechanism called "neutron clustering", affecting simulations, will be discussed in this frame.
Hunt, R.J.; Steuer, J.J.; Mansor, M.T.C.; Bullen, T.D.
2001-01-01
Recharge areas of spring systems can be hard to identify, but they can be critically important for protection of a spring resource. A recharge area for a spring complex in southern Wisconsin was delineated using a variety of complementary techniques. A telescopic mesh refinement (TMR) model was constructed from an existing regional-scale ground water flow model. This TMR model was formally optimized using parameter estimation techniques; the optimized "best fit" to measured heads and fluxes was obtained by using a horizontal hydraulic conductivity 200% larger than the original regional model for the upper bedrock aquifer and 80% smaller for the lower bedrock aquifer. The uncertainty in hydraulic conductivity was formally considered using a stochastic Monte Carlo approach. Two-hundred model runs used uniformly distributed, randomly sampled, horizontal hydraulic conductivity values within the range given by the TMR optimized values and the previously constructed regional model. A probability distribution of particles captured by the spring, or a "probabilistic capture zone," was calculated from the realistic Monte Carlo results (136 runs of 200). In addition to portions of the local surface watershed, the capture zone encompassed areas outside of the watershed - demonstrating that the ground watershed and surface watershed do not coincide. Analysis of water collected from the site identified relatively large contrasts in chemistry, even for springs within 15 m of one another. The differences showed a distinct gradation from Ordovician-carbonate-dominated water in western spring vents to Cambrian-sandstone-influenced water in eastern spring vents. The difference in chemistry was attributed to distinctive bedrock geology as demonstrated by overlaying the capture zone derived from numerical modeling over a bedrock geology map for the area. This finding gives additional confidence to the capture zone calculated by modeling.
NASA Technical Reports Server (NTRS)
Gayda, J.
1994-01-01
A specialized, microstructural lattice model, termed MCFET for combined Monte Carlo Finite Element Technique, has been developed to simulate microstructural evolution in material systems where modulated phases occur and the directionality of the modulation is influenced by internal and external stresses. Since many of the physical properties of materials are determined by microstructure, it is important to be able to predict and control microstructural development. MCFET uses a microstructural lattice model that can incorporate all relevant driving forces and kinetic considerations. Unlike molecular dynamics, this approach was developed specifically to predict macroscopic behavior, not atomistic behavior. In this approach, the microstructure is discretized into a fine lattice. Each element in the lattice is labeled in accordance with its microstructural identity. Diffusion of material at elevated temperatures is simulated by allowing exchanges of neighboring elements if the exchange lowers the total energy of the system. A Monte Carlo approach is used to select the exchange site while the change in energy associated with stress fields is computed using a finite element technique. The MCFET analysis has been validated by comparing this approach with a closed-form, analytical method for stress-assisted, shape changes of a single particle in an infinite matrix. Sample MCFET analyses for multiparticle problems have also been run and, in general, the resulting microstructural changes associated with the application of an external stress are similar to that observed in Ni-Al-Cr alloys at elevated temperatures. This program is written in FORTRAN for use on a 370 series IBM mainframe. It has been implemented on an IBM 370 running VM/SP and an IBM 3084 running MVS. It requires the IMSL math library and 220K of RAM for execution. The standard distribution medium for this program is a 9-track 1600 BPI magnetic tape in EBCDIC format.
Aneesu-Rahman Prize Lecture: The ``sign problem'' in Quantum Monte Carlo
NASA Astrophysics Data System (ADS)
Ceperley, D. M.
1998-03-01
Quantum simulation methods have been quite successful in giving exact results for certain systems, primarily bosons(Ceperley, D.M. , Rev. Mod. Phys. 67), 279 (1995).. Use of the same techniques in general quantum systems leads to the so-called ``sign problem''; the results are correct but the methods are very inefficient. There are two important questions to ask of a proposed method. Given enough computer time can arbitrarily accurate results be obtained? If so, how long does it take to achieve a given error? There are several methods (released-node or transient estimate) that are exact; the difficulty is in finding a method which also scales well with the number of quantum degrees of freedom. Exact methods, in general, scale exponentially with the number of fermions and in the inverse temperature (or accuracy). At root, the fact that wavefunction is complex or changes sign, gives rise to the poor scaling and the ``sign problem.'' It is not the fermion nature of the system, per se, that causes the difficulty. The desired state is not the absolute ground state. Methods which cancel random walks from positive and negative regions have also been limited to quite small systems because they scale poorly. There are a variety of approximate simulation methods which do scale well, such as variational Monte Carlo, and a variety of fixed-node methods (restricted Path Integral Monte Carlo at non-zero temperature and constrained path methods for lattice models) which fix only boundary conditions not the sampling function. For many systems, the variational and fixed-node methods can be very accurate. The lecture notes and references are on my group's homepage.
Biondo, Elliott D; Ibrahim, Ahmad M; Mosher, Scott W; Grove, Robert E
2015-01-01
Detailed radiation transport calculations are necessary for many aspects of the design of fusion energy systems (FES) such as ensuring occupational safety, assessing the activation of system components for waste disposal, and maintaining cryogenic temperatures within superconducting magnets. Hybrid Monte Carlo (MC)/deterministic techniques are necessary for this analysis because FES are large, heavily shielded, and contain streaming paths that can only be resolved with MC. The tremendous complexity of FES necessitates the use of CAD geometry for design and analysis. Previous ITER analysis has required the translation of CAD geometry to MCNP5 form in order to use the AutomateD VAriaNce reducTion Generator (ADVANTG) for hybrid MC/deterministic transport. In this work, ADVANTG was modified to support CAD geometry, allowing hybrid (MC)/deterministic transport to be done automatically and eliminating the need for this translation step. This was done by adding a new ray tracing routine to ADVANTG for CAD geometries using the Direct Accelerated Geometry Monte Carlo (DAGMC) software library. This new capability is demonstrated with a prompt dose rate calculation for an ITER computational benchmark problem using both the Consistent Adjoint Driven Importance Sampling (CADIS) method an the Forward Weighted (FW)-CADIS method. The variance reduction parameters produced by ADVANTG are shown to be the same using CAD geometry and standard MCNP5 geometry. Significant speedups were observed for both neutrons (as high as a factor of 7.1) and photons (as high as a factor of 59.6).
NASA Astrophysics Data System (ADS)
Suewattana, Malliga; Purwanto, Wirawan; Zhang, Shiwei; Krakauer, Henry; Walter, Eric J.
2007-06-01
The phaseless auxiliary-field quantum Monte Carlo (AF QMC) method [S. Zhang and H. Krakauer, Phys. Rev. Lett. 90, 136401 (2003)] is used to carry out a systematic study of the dissociation and ionization energies of second-row group 3A-7A atoms and dimers: Al, Si, P, S, and Cl. In addition, the P2 dimer is compared to the third-row As2 dimer, which is also triply bonded. This method projects the many-body ground state by means of importance-sampled random walks in the space of Slater determinants. The Monte Carlo phase problem, due to the electron-electron Coulomb interaction, is controlled via the phaseless approximation, with a trial wave function ∣ΨT⟩ . As in previous calculations, a mean-field single Slater determinant is used as ∣ΨT⟩ . The method is formulated in the Hilbert space defined by any chosen one-particle basis. The present calculations use a plane wave basis under periodic boundary conditions with norm-conserving pseudopotentials. Computational details of the plane wave AF QMC method are presented. The isolated systems chosen here allow a systematic study of the various algorithmic issues. We show the accuracy of the plane wave method and discuss its convergence with respect to parameters such as the supercell size and plane wave cutoff. The use of standard norm-conserving pseudopotentials in the many-body AF QMC framework is examined.
Fast quantum Monte Carlo on a GPU
NASA Astrophysics Data System (ADS)
Lutsyshyn, Y.
2015-02-01
We present a scheme for the parallelization of quantum Monte Carlo method on graphical processing units, focusing on variational Monte Carlo simulation of bosonic systems. We use asynchronous execution schemes with shared memory persistence, and obtain an excellent utilization of the accelerator. The CUDA code is provided along with a package that simulates liquid helium-4. The program was benchmarked on several models of Nvidia GPU, including Fermi GTX560 and M2090, and the Kepler architecture K20 GPU. Special optimization was developed for the Kepler cards, including placement of data structures in the register space of the Kepler GPUs. Kepler-specific optimization is discussed.
Geodesic Monte Carlo on Embedded Manifolds.
Byrne, Simon; Girolami, Mark
2013-12-01
Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions across the manifold and the solution of the equations describing geodesic flows in the Hamilton-Jacobi representation. This paper takes the differential geometric basis of Markov chain Monte Carlo further by considering methods to simulate from probability distributions that themselves are defined on a manifold, with common examples being classes of distributions describing directional statistics. Proposal mechanisms are developed based on the geodesic flows over the manifolds of support for the distributions, and illustrative examples are provided for the hypersphere and Stiefel manifold of orthonormal matrices. PMID:25309024
Monte Carlo dose computation for IMRT optimization*
NASA Astrophysics Data System (ADS)
Laub, W.; Alber, M.; Birkner, M.; Nüsslin, F.
2000-07-01
A method which combines the accuracy of Monte Carlo dose calculation with a finite size pencil-beam based intensity modulation optimization is presented. The pencil-beam algorithm is employed to compute the fluence element updates for a converging sequence of Monte Carlo dose distributions. The combination is shown to improve results over the pencil-beam based optimization in a lung tumour case and a head and neck case. Inhomogeneity effects like a broader penumbra and dose build-up regions can be compensated for by intensity modulation.
Monte Carlo simulation of neutron scattering instruments
Seeger, P.A.
1995-12-31
A library of Monte Carlo subroutines has been developed for the purpose of design of neutron scattering instruments. Using small-angle scattering as an example, the philosophy and structure of the library are described and the programs are used to compare instruments at continuous wave (CW) and long-pulse spallation source (LPSS) neutron facilities. The Monte Carlo results give a count-rate gain of a factor between 2 and 4 using time-of-flight analysis. This is comparable to scaling arguments based on the ratio of wavelength bandwidth to resolution width.
Geodesic Monte Carlo on Embedded Manifolds
Byrne, Simon; Girolami, Mark
2013-01-01
Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions across the manifold and the solution of the equations describing geodesic flows in the Hamilton–Jacobi representation. This paper takes the differential geometric basis of Markov chain Monte Carlo further by considering methods to simulate from probability distributions that themselves are defined on a manifold, with common examples being classes of distributions describing directional statistics. Proposal mechanisms are developed based on the geodesic flows over the manifolds of support for the distributions, and illustrative examples are provided for the hypersphere and Stiefel manifold of orthonormal matrices. PMID:25309024
A New Sample Size Formula for Regression.
ERIC Educational Resources Information Center
Brooks, Gordon P.; Barcikowski, Robert S.
The focus of this research was to determine the efficacy of a new method of selecting sample sizes for multiple linear regression. A Monte Carlo simulation was used to study both empirical predictive power rates and empirical statistical power rates of the new method and seven other methods: those of C. N. Park and A. L. Dudycha (1974); J. Cohen…
Antonelli, Maria-Rosaria; Pierangelo, Angelo; Novikova, Tatiana; Validire, Pierre; Benali, Abdelali; Gayet, Brice; De Martino, Antonello
2011-01-01
Polarimetric imaging is emerging as a viable technique for tumor detection and staging. As a preliminary step towards a thorough understanding of the observed contrasts, we present a set of numerical Monte Carlo simulations of the polarimetric response of multilayer structures representing colon samples in the backscattering geometry. In a first instance, a typical colon sample was modeled as one or two scattering “slabs” with monodisperse non absorbing scatterers representing the most superficial tissue layers (the mucosa and submucosa), above a totally depolarizing Lambertian lumping the contributions of the deeper layers (muscularis and pericolic tissue). The model parameters were the number of layers, their thicknesses and morphology, the sizes and concentrations of the scatterers, the optical index contrast between the scatterers and the surrounding medium, and the Lambertian albedo. With quite similar results for single and double layer structures, this model does not reproduce the experimentally observed stability of the relative magnitudes of the depolarizing powers for incident linear and circular polarizations. This issue was solved by considering bimodal populations including large and small scatterers in a single layer above the Lambertian, a result which shows the importance of taking into account the various types of scatterers (nuclei, collagen fibers and organelles) in the same model. PMID:21750762
Begy, Robert-Csaba; Cosma, Constantin; Timar, Alida; Fulea, Dan
2009-05-01
The 1001 keV gamma line of (234m)Pa became important in gamma spectrometric measurements of samples with (238)U content with the advent of development of HpGe detectors of great dimension and high efficiency. In this study the emission probability of the 1001 keV (Y(gamma)) peak of (234m)Pa, was determined by gamma-ray spectrometric measurements performed on glass with Uranium content using Monte Carlo simulation code for efficiency calibration. This method of calculation was not applied for the values quoted in literature so far, at least to our knowledge. The measurements gave an average of 0.836 +/- 0.022%, a value that is in very good agreement to some of the recent results previously presented. PMID:19384056
... using capillary blood sampling. Disadvantages to capillary blood sampling include: Only a limited amount of blood can be drawn using this method. The procedure has some risks (see below). Capillary ...
Miao, Han; Li, Jianfeng; Chen, Daoyong
2016-05-18
Nanofibers are well-known nanomaterials that are promising for many important applications. Since sample preparation for the applications usually starts from a nanofiber solution, characterization of the original conformation of nanofibers in the solution is significant because the conformation affects remarkably the behavior of nanofibers in the samples. However, the characterization is very difficult by existing methods: light scattering can only roughly evaluate the conformation in solution; cryo-TEM is laborious, time-consuming, and challenging technically, and thus difficult to study a system statistically. Herein we report a novel and reliable method to recover the 3D original image of nanofibers in solution through theoretical analysis and Monte-Carlo simulations of TEM images of the nanofibers. Firstly, six kinds of monodisperse nanofibers with the same composition and inner structure but different contour lengths were prepared by the method developed in our laboratory. Then, each kind of nanofiber deposited on the substrate of the TEM sample was measured by TEM and meanwhile simulated by the Monte Carlo method. By matching the simulation results with the TEM results, we determined information about the nanofibers including their rigidity and the interaction between the nanofibers and the substrate. Furthermore, for each kind of nanofiber, based on the information, 3D images of the nanofibers in solution can be re-constructed, and then the average gyration radius and hydrodynamic radius can be calculated, which were compared with the corresponding values measured experimentally to demonstrate the reliability of this method. PMID:27101798
Entropic effects in large-scale Monte Carlo simulations.
Predescu, Cristian
2007-07-01
The efficiency of Monte Carlo samplers is dictated not only by energetic effects, such as large barriers, but also by entropic effects that are due to the sheer volume that is sampled. The latter effects appear in the form of an entropic mismatch or divergence between the direct and reverse trial moves. We provide lower and upper bounds for the average acceptance probability in terms of the Rényi divergence of order 1/2 . We show that the asymptotic finitude of the entropic divergence is the necessary and sufficient condition for nonvanishing acceptance probabilities in the limit of large dimension. Furthermore, we demonstrate that the upper bound is reasonably tight by showing that the exponent is asymptotically exact for systems made up of a large number of independent and identically distributed subsystems. For the last statement, we provide an alternative proof that relies on the reformulation of the acceptance probability as a large deviation problem. The reformulation also leads to a class of low-variance estimators for strongly asymmetric distributions. We show that the entropy divergence causes a decay in the average displacements with the number of dimensions n that are simultaneously updated. For systems that have a well-defined thermodynamic limit, the decay is demonstrated to be n(-1/2) for random-walk Monte Carlo and n(-1/6) for smart Monte Carlo (SMC). Numerical simulations of the Lennard-Jones 38 (LJ(38)) cluster show that SMC is virtually as efficient as the Markov chain implementation of the Gibbs sampler, which is normally utilized for Lennard-Jones clusters. An application of the entropic inequalities to the parallel tempering method demonstrates that the number of replicas increases as the square root of the heat capacity of the system. PMID:17677591
The First 24 Years of Reverse Monte Carlo Modelling, Budapest, Hungary, 20-22 September 2012
NASA Astrophysics Data System (ADS)
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
Infinite variance in fermion quantum Monte Carlo calculations
NASA Astrophysics Data System (ADS)
Shi, Hao; Zhang, Shiwei
2016-03-01
For important classes of many-fermion problems, quantum Monte Carlo (QMC) methods allow exact calculations of ground-state and finite-temperature properties without the sign problem. The list spans condensed matter, nuclear physics, and high-energy physics, including the half-filled repulsive Hubbard model, the spin-balanced atomic Fermi gas, and lattice quantum chromodynamics calculations at zero density with Wilson Fermions, and is growing rapidly as a number of problems have been discovered recently to be free of the sign problem. In these situations, QMC calculations are relied on to provide definitive answers. Their results are instrumental to our ability to understand and compute properties in fundamental models important to multiple subareas in quantum physics. It is shown, however, that the most commonly employed algorithms in such situations have an infinite variance problem. A diverging variance causes the estimated Monte Carlo statistical error bar to be incorrect, which can render the results of the calculation unreliable or meaningless. We discuss how to identify the infinite variance problem. An approach is then proposed to solve the problem. The solution does not require major modifications to standard algorithms, adding a "bridge link" to the imaginary-time path integral. The general idea is applicable to a variety of situations where the infinite variance problem may be present. Illustrative results are presented for the ground state of the Hubbard model at half-filling.
Modeling and Computer Simulation: Molecular Dynamics and Kinetic Monte Carlo
Wirth, B.D.; Caturla, M.J.; Diaz de la Rubia, T.
2000-10-10
Recent years have witnessed tremendous advances in the realistic multiscale simulation of complex physical phenomena, such as irradiation and aging effects of materials, made possible by the enormous progress achieved in computational physics for calculating reliable, yet tractable interatomic potentials and the vast improvements in computational power and parallel computing. As a result, computational materials science is emerging as an important complement to theory and experiment to provide fundamental materials science insight. This article describes the atomistic modeling techniques of molecular dynamics (MD) and kinetic Monte Carlo (KMC), and an example of their application to radiation damage production and accumulation in metals. It is important to note at the outset that the primary objective of atomistic computer simulation should be obtaining physical insight into atomic-level processes. Classical molecular dynamics is a powerful method for obtaining insight about the dynamics of physical processes that occur on relatively short time scales. Current computational capability allows treatment of atomic systems containing as many as 10{sup 9} atoms for times on the order of 100 ns (10{sup -7}s). The main limitation of classical MD simulation is the relatively short times accessible. Kinetic Monte Carlo provides the ability to reach macroscopic times by modeling diffusional processes and time-scales rather than individual atomic vibrations. Coupling MD and KMC has developed into a powerful, multiscale tool for the simulation of radiation damage in metals.
Monte Carlo study of the percolation in two-dimensional polymer systems.
Pawłowska, Monika; Sikorski, Andrzej
2013-10-01
The structure of a two-dimensional film formed by adsorbed polymer chains was studied by means of Monte Carlo simulations. The polymer chains were represented by linear sequences of lattice beads and positions of these beads were restricted to vertices of a two-dimensional square lattice. Two different Monte Carlo methods were employed to determine the properties of the model system. The first was the random sequential adsorption (RSA) and the second one was based on Monte Carlo simulations with a Verdier-Stockmayer sampling algorithm. The methodology concerning the determination of the percolation thresholds for an infinite chain system was discussed. The influence of the chain length on both thresholds was presented and discussed. It was shown that the RSA method gave considerably lower thresholds for longer chains. This behavior can be explained by a different pool of chain conformations used in the calculations in both methods under consideration. PMID:23765040
Drug Use on Mont Blanc: A Study Using Automated Urine Collection
Robach, Paul; Trebes, Gilles; Lasne, Françoise; Buisson, Corinne; Méchin, Nathalie; Mazzarino, Monica; de la Torre, Xavier; Roustit, Matthieu; Kérivel, Patricia; Botré, Francesco; Bouzat, Pierre
2016-01-01
Mont Blanc, the summit of Western Europe, is a popular but demanding high-altitude ascent. Drug use is thought to be widespread among climbers attempting this summit, not only to prevent altitude illnesses, but also to boost physical and/or psychological capacities. This practice may be unsafe in this remote alpine environment. However, robust data on medication during the ascent of Mont Blanc are lacking. Individual urine samples from male climbers using urinals in mountain refuges on access routes to Mont Blanc (Goûter and Cosmiques mountain huts) were blindly and anonymously collected using a hidden automatic sampler. Urine samples were screened for a wide range of drugs, including diuretics, glucocorticoids, stimulants, hypnotics and phosphodiesterase 5 (PDE-5) inhibitors. Out of 430 samples analyzed from both huts, 35.8% contained at least one drug. Diuretics (22.7%) and hypnotics (12.9%) were the most frequently detected drugs, while glucocorticoids (3.5%) and stimulants (3.1%) were less commonly detected. None of the samples contained PDE-5 inhibitors. Two substances were predominant: the diuretic acetazolamide (20.6%) and the hypnotic zolpidem (8.4%). Thirty three samples were found positive for at least two substances, the most frequent combination being acetazolamide and a hypnotic (2.1%). Based on a novel sampling technique, we demonstrate that about one third of the urine samples collected from a random sample of male climbers contained one or several drugs, suggesting frequent drug use amongst climbers ascending Mont Blanc. Our data suggest that medication primarily aims at mitigating the symptoms of altitude illnesses, rather than enhancing performance. In this hazardous environment, the relatively high prevalence of hypnotics must be highlighted, since these molecules may alter vigilance. PMID:27253728
Drug Use on Mont Blanc: A Study Using Automated Urine Collection.
Robach, Paul; Trebes, Gilles; Lasne, Françoise; Buisson, Corinne; Méchin, Nathalie; Mazzarino, Monica; de la Torre, Xavier; Roustit, Matthieu; Kérivel, Patricia; Botré, Francesco; Bouzat, Pierre
2016-01-01
Mont Blanc, the summit of Western Europe, is a popular but demanding high-altitude ascent. Drug use is thought to be widespread among climbers attempting this summit, not only to prevent altitude illnesses, but also to boost physical and/or psychological capacities. This practice may be unsafe in this remote alpine environment. However, robust data on medication during the ascent of Mont Blanc are lacking. Individual urine samples from male climbers using urinals in mountain refuges on access routes to Mont Blanc (Goûter and Cosmiques mountain huts) were blindly and anonymously collected using a hidden automatic sampler. Urine samples were screened for a wide range of drugs, including diuretics, glucocorticoids, stimulants, hypnotics and phosphodiesterase 5 (PDE-5) inhibitors. Out of 430 samples analyzed from both huts, 35.8% contained at least one drug. Diuretics (22.7%) and hypnotics (12.9%) were the most frequently detected drugs, while glucocorticoids (3.5%) and stimulants (3.1%) were less commonly detected. None of the samples contained PDE-5 inhibitors. Two substances were predominant: the diuretic acetazolamide (20.6%) and the hypnotic zolpidem (8.4%). Thirty three samples were found positive for at least two substances, the most frequent combination being acetazolamide and a hypnotic (2.1%). Based on a novel sampling technique, we demonstrate that about one third of the urine samples collected from a random sample of male climbers contained one or several drugs, suggesting frequent drug use amongst climbers ascending Mont Blanc. Our data suggest that medication primarily aims at mitigating the symptoms of altitude illnesses, rather than enhancing performance. In this hazardous environment, the relatively high prevalence of hypnotics must be highlighted, since these molecules may alter vigilance. PMID:27253728
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. PMID:19659557
Coherent Scattering Imaging Monte Carlo Simulation
NASA Astrophysics Data System (ADS)
Hassan, Laila Abdulgalil Rafik
Conventional mammography has poor contrast between healthy and cancerous tissues due to the small difference in attenuation properties. Coherent scatter potentially provides more information because interference of coherently scattered radiation depends on the average intermolecular spacing, and can be used to characterize tissue types. However, typical coherent scatter analysis techniques are not compatible with rapid low dose screening techniques. Coherent scatter slot scan imaging is a novel imaging technique which provides new information with higher contrast. In this work a simulation of coherent scatter was performed for slot scan imaging to assess its performance and provide system optimization. In coherent scatter imaging, the coherent scatter is exploited using a conventional slot scan mammography system with anti-scatter grids tilted at the characteristic angle of cancerous tissues. A Monte Carlo simulation was used to simulate the coherent scatter imaging. System optimization was performed across several parameters, including source voltage, tilt angle, grid distances, grid ratio, and shielding geometry. The contrast increased as the grid tilt angle increased beyond the characteristic angle for the modeled carcinoma. A grid tilt angle of 16 degrees yielded the highest contrast and signal to noise ratio (SNR). Also, contrast increased as the source voltage increased. Increasing grid ratio improved contrast at the expense of decreasing SNR. A grid ratio of 10:1 was sufficient to give a good contrast without reducing the intensity to a noise level. The optimal source to sample distance was determined to be such that the source should be located at the focal distance of the grid. A carcinoma lump of 0.5x0.5x0.5 cm3 in size was detectable which is reasonable considering the high noise due to the usage of relatively small number of incident photons for computational reasons. A further study is needed to study the effect of breast density and breast thickness
Monte Carlo Simulation of Secondary Fluorescence using a New Graphical Interface for PENELOPE
NASA Astrophysics Data System (ADS)
Pinard, P. T.; Demers, H.; Llovet, X.; Gauvin, R.; Salvat, F.
2011-12-01
Secondary fluorescence is not a negligible factor in the chemical concentration measurement of many minerals (quartz, olivine, etc.) using the electron probe microanalysis (EPMA) technique (Llovet and Galán, 2003). The importance of this phenomenon depends on the chemical species present in the mineral but also, in case of heterogeneous samples, on their relative location to the measurement position. Monte Carlo codes are useful tools to select the optimal measurement conditions as well as to correct afterwards the results for phenomenon such as secondary fluorescence. PENELOPE (Salvat et al., 2011) is a Fortran Monte Carlo code for simulation of coupled electron-photon transport in matter that allows a detailed interpretation of experimental results of electron spectroscopy and microscopy. PENEPMA is a dedicated main program of PENELOPE designed to perform simulations with the same parameters as in actual EPMA measurements. Complex geometries can be defined to emulate the internal structure of a sample. Photon interactions are simulated in chronological succession, therefore allowing the calculation of secondary fluorescence. These features combined with the use of the most reliable physical interaction models make PENEPMA a unique Monte Carlo code for EPMA analysis. However, the original version of PENEPMA had a steep learning curve as it required the user to manually create several input files to run a single simulation. To facilitate the use of the code, a graphical interface was recently developed. Written in the cross-platform programming language Python, it simplifies the setup of simulations and the analysis of the results. It also includes optimized simulation parameters which increases the efficiency of the simulations (i.e. reduces the computation time) by a factor of up to 8. In this communication, we describe the structure and capabilities of this graphical interface. It not only eases the definition of the problem, but also provides more extensive