Monte Carlo-based Method Research Articles

Monte Carlo Simulation and Experimental High-Angle Annular Dark Field Tomography

In the last 10 years, the transmission electron tomography technique was extensively used in biology applications to obtain 3D configurations of small organic structures [1]. Such bright field electron tomography is not generally used in material science applications as the diffraction effects result in strong artefacts during the reconstruction. Bragg’s diffractions are indeed present at certain angles during the acquisition of a bright field tilt series. High angle annular dark field (HAADF) imaging with a scanning transmission electron microscope (STEM) is not significantly affected by dynamic effects. Since the contrast of HAADF depends on material thickness and composition [2], this technique is a good candidate for tomographic acquisitions of materials science samples. However, the reconstruction algorithms have to be adapted and optimum experimental parameters are needed for high resolution three-dimensional (3D) dataset at the nanoscale. The CASINO Monte Carlo software [3] was developed to simulate the effect of parameters such as angular tilt range, angular coverage, beam energy and beam dosage on HAADF-STEM micrograph [4]. In this work, we use a Monte Carlo-based simulation method to accurately model the whole tomographic data acquisition process and test the reconstruction algorithm with these different parameters.

Stochastic Load Modeling for Electric Vehicle Charging Load Case Study: Pattaya City Thailand

This paper presents a Monte Carlo-based method used in modeling electrical load occurring from electric vehicle battery charging and assessing its impact on the distribution system in case of an extensive residential use of electric vehicles. The state of charge and starting time are parameters considered stochastic in nature. As such they can be appropriately represented by a standard distribution function. In this paper a case study is demonstrated using a 22-kV distribution network located in an urban service area of the Provincial Electricity Authority (PEA). Based on the PEA’s typical daily load profile, a new load profile which includes the effect of electric vehicle charging is developed. The expected impact on the line overload and quality standard of voltage caused by electric vehicle charging can be forecasted. Results of the study are applicable for system planning to accommodate an expansion in the network capacity due to the growth in electric vehicle number.

Rainfall Uncertainty in Flood Forecasting: Belgian Case Study of Rivierbeek

AbstractRainfall forecast errors are the key sources of uncertainty in flood forecasting. To quantify this uncertainty operational flood forecasting centers make use of rainfall forecasts obtained by ensemble predicting systems (EPS). The EPS forecasts are generated by perturbing the initial conditions of numerical weather prediction models. Question, however, remains whether these EPS cover the real forecast uncertainty range and whether the EPS-based uncertainty estimates are similar to the ones obtained by statistical methods. Both questions are addressed in this research based on data of a flood forecasting system in Belgium. Comparison is made between the uncertainty bounds generated by EPS and by a Monte Carlo-based statistical method after historical forecasted rainfall uncertainty analysis. The latter analysis calculates the error between forecasted and observed catchment rainfall, taking into account the dependency on lead time and rainfall depth. The forecasted rainfall errors are described by t...

A Bayesian Method for Analyzing Lateral Gene Transfer

Lateral gene transfer (LGT)--which transfers DNA between two non-vertically related individuals belonging to the same or different species--is recognized as a major force in prokaryotic evolution, and evidence of its impact on eukaryotic evolution is ever increasing. LGT has attracted much public attention for its potential to transfer pathogenic elements and antibiotic resistance in bacteria, and to transfer pesticide resistance from genetically modified crops to other plants. In a wider perspective, there is a growing body of studies highlighting the role of LGT in enabling organisms to occupy new niches or adapt to environmental changes. The challenge LGT poses to the standard tree-based conception of evolution is also being debated. Studies of LGT have, however, been severely limited by a lack of computational tools. The best currently available LGT algorithms are parsimony-based phylogenetic methods, which require a pre-computed gene tree and cannot choose between sometimes wildly differing most parsimonious solutions. Moreover, in many studies, simple heuristics are applied that can only handle putative orthologs and completely disregard gene duplications (GDs). Consequently, proposed LGT among specific gene families, and the rate of LGT in general, remain debated. We present a Bayesian Markov-chain Monte Carlo-based method that integrates GD, gene loss, LGT, and sequence evolution, and apply the method in a genome-wide analysis of two groups of bacteria: Mollicutes and Cyanobacteria. Our analyses show that although the LGT rate between distant species is high, the net combined rate of duplication and close-species LGT is on average higher. We also show that the common practice of disregarding reconcilability in gene tree inference overestimates the number of LGT and duplication events.

Heterogeneity effect on neutron shielding in borated concrete and Monte Carlo-based cross section homogenization method for particle dispersed media

A Monte Carlo-based radiation transport calculation method to treat media containing randomly and uniformly dispersed spherical particles was developed by Shmakov et al. The method (hereinafter referred to as “SLD method”) formulates effective homogenized cross sections for particle dispersed media. This paper discusses the applicability of the SLD method to neutron shielding in an ordinary concrete and a low-activation concrete containing small B4C particles. If the absorber particles are not small enough, the heterogeneity effect needs to be treated for accurate dose or activation estimations. The direct heterogeneous representation of the small particles requires long computing time. The use of the SLD method could considerably reduce the computational burdens without reducing its accuracy. The heterogeneity effect on neutron shielding is significant below 10 eV. The homogeneous calculation underestimates the neutron absorption rate of Na by 11 % in the ordinary concrete and of Eu by 14% in the low-activation concrete containing 14-μm-diameter B4C particles. MCNP 4C calculations with the SLD method agree very well with the direct heterogeneous calculations. The SLD method improves the figure of merit by a factor of 100 with respect to the direct heterogeneous calculation.

A Monte Carlo-Based Approach for Groundwater Chemistry Inverse Modeling

Inverse geochemical modeling of groundwater entails identifying a set of geochemical reactions which can explain observed changes in water chemistry between two samples that are spatially related in some sense, such as two points along a flow pathway. A common inversion approach is to solve a set of simultaneous mass and electron balance equations involving water-rock and oxidation-reduction reactions that are consistent with the changes in concentrations of various aqueous components. However, this mass-balance approach does not test the thermodynamic favorability of the resulting model and provides limited insight into the model uncertainties. In this context, a Monte Carlo-based forward-inverse modeling method is proposed that generates probability distributions for model parameters which best match the observed data using the Metro-polis-Hastings search strategy. The forward model is based on the well-vetted PHREEQC geochemical model. The proposed modeling approach is applied to two test applications, one involving an inverse modeling example supplied with the PHREEQC code that entails groundwater interactions with a granitic rock mineral assemblage, and the other concerning the impact of fuel hydrocarbon bioattenuation on groundwater chemistry. In both examples, the forward-inverse approach is able to approximately reproduce observed water quality changes invoking mass transfer reactions that are all thermodynamically favorable.

A New Risk Assessment Method of Escape Route Based on Monte Carlo Method and Event Tree to Emergency Evacuation

Emergency evacuation in public places becomes a research focus in recent decades. One major objective of evacuation is to maximize the safety of the whole evacuation. Because of the high-dense population and complex structure, the large public building faces a unique challenge in developing assessment of risk rating each escape route. The evacuation route evaluation problem can be treated as a combinatorial predicted problem. In this paper, we propose a novel Monte Carlo based evolutionary algorithm which evaluation combining Event Tree Analysis (FTA). A Monte Carlo-based estimation method is developed to propagate parameter uncertainty in QRA. The experiment shows that some recommendations on environmental risk management were proposed for risk mitigation. It is concluded that the integration of Monte Carlo analysis with QRA is a more complete, scientific and reliable measure of risk assessment.

Monte Carlo-based uncertainties of surface UV estimates from models and from spectroradiometers

Although some of the adverse effects of the ultraviolet (UV) radiation may be strictly proportional to cumulative UV doses, others may relate to the frequency of extreme UV events. Therefore, an improved understanding of the UV global climate, including variability and trends, has become of great interest. Variability and trend analyses require quality-ensured surface UV series. The quality of surface UV data depends on their uncertainty. Building upon our prior efforts, we have used a Monte Carlo-based method to compute, under different conditions, the uncertainties affecting UV data rendered by models (1D radiative transfer models) and by spectroradiometers (double monochromator-based and CCD array-based). We found that the uncertainty of spectral UV measurements is driven by the signal-to-noise ratio of the detector, while the uncertainty of spectral UV calculations strongly depends on the uncertainty of the ozone input. The presented uncertainty figures allow comparison of the performance of modern UV gathering techniques (models and instruments), and provide a frame to assess the significance of differences when intercomparing.

Characterisation factors for life cycle impact assessment of sound emissions

Noise is a serious stressor affecting the health of millions of citizens. It has been suggested that disturbance by noise is responsible for a substantial part of the damage to human health. However, no recommended approach to address noise impacts was proposed by the handbook for life cycle assessment (LCA) of the European Commission, nor are characterisation factors (CFs) and appropriate inventory data available in commonly used databases. This contribution provides CFs to allow for the quantification of noise impacts on human health in the LCA framework. Noise propagation standards and international reports on acoustics and noise impacts were used to define the model parameters. Spatial data was used to calculate spatially-defined CFs in the form of 10-by-10-km maps. The results of this analysis were combined with data from the literature to select input data for representative archetypal situations of emission (e.g. urban day with a frequency of 63Hz, rural night at 8000Hz, etc.). A total of 32 spatial and 216 archetypal CFs were produced to evaluate noise impacts at a European level (i.e. EU27). The possibility of a user-defined characterisation factor was added to support the possibility of portraying the situation of full availability of information, as well as a highly-localised impact analysis. A Monte Carlo-based quantitative global sensitivity analysis method was applied to evaluate the importance of the input factors in determining the variance of the output. The factors produced are ready to be implemented in the available LCA databases and software. The spatial approach and archetypal approach may be combined and selected according to the amount of information available and the life cycle under study. The framework proposed and used for calculations is flexible enough to be expanded to account for impacts on target subjects other than humans and to continents other than Europe.

FINDING OPTIMAL STRATEGIES IN TENNIS FROM VIDEO SEQUENCES

The analysis of tennis data from broadcast video in order to identify tactical information is broadly an unexplored field, and the existing analytical approaches usually consist of simply providing general statistical information. In this paper, we present a tennis model based on Markov decision processes (MDPs), which describes the dynamic interaction between the players and we introduce a novel Monte Carlo-based method with an aim to extract optimal strategic information. In order to test the approach with real tennis data, we also present a system that transforms broadcast video tennis sequences into discrete temporal data that is fed into the model. We show that this framework, based on states, actions and rewards, allows for the identification of optimal strategies.

Monte Carlo Based Personalized PageRank on Dynamic Networks

In large-scale networks, the structure of the underlying network changes frequently, and thus the power iteration method for Personalized PageRank computation cannot deal with this kind of dynamic network efficiently. In this paper, we design a Monte Carlo-based incremental method for Personalized PageRank computation. In a dynamic network, first, we do a random walk starting from each node and save the performed walks into a fingerprint database; second, we update the fingerprint database in a fixed time interval with our proposed update algorithm; finally, when a query is issued by a user, we estimate the Personalized PageRank vector by our proposed approximation algorithm. Experiments on real-world networks show that our method can handle multichanges of the underlying network at a time and is more efficient than related work, so it can be used in real incremental Personalized PageRank-based applications.

複素ウェイトを用いたモンテカルロ法によるB1計算の開発と群定数、拡散係数の生成 -集合体計算への適用

複素ウェイトを用いたモンテカルロ法によるB1計算の開発と群定数、拡散係数の生成 -集合体計算への適用

Modelling soil temperature and moisture and corresponding seasonality of photosynthesis and transpiration in a boreal spruce ecosystem

Abstract. Recovery of photosynthesis and transpiration is strongly restricted by low temperatures in air and/or soil during the transition period from winter to spring in boreal zones. The extent to which air temperature (Ta) and soil temperature (Ts) influence the seasonality of photosynthesis and transpiration of a boreal spruce ecosystem was investigated using a process-based ecosystem model (CoupModel) together with eddy covariance (EC) data from one eddy flux tower and nearby soil measurements at Knottåsen, Sweden. A Monte Carlo-based uncertainty method (GLUE) provided prior and posterior distributions of simulations representing a wide range of soil conditions and performance indicators. The simulated results showed sufficient flexibility to predict the measured cold and warm Ts in the moist and dry plots around the eddy flux tower. Moreover, the model presented a general ability to describe both biotic and abiotic processes for the Norway spruce stand. The dynamics of sensible heat fluxes were well described by the corresponding latent heat fluxes and net ecosystem exchange of CO2. The parameter ranges obtained are probably valid to represent regional characteristics of boreal conifer forests, but were not easy to constrain to a smaller range than that produced by the assumed prior distributions. Finally, neglecting the soil temperature response function resulted in fewer behavioural models and probably more compensatory errors in other response functions for regulating the seasonality of ecosystem fluxes.

FAILURE PROGNOSTICS BY A DATA-DRIVEN SIMILARITY-BASED APPROACH

This paper presents a data-driven, similarity-based approach for prognostics of industrial and structural components. The potentiality of the approach is demonstrated on a problem of crack propagation, taken from literature. The crack growth process is described by a nonlinear model affected by nonadditive noises. A comparison is provided with an existing Monte Carlo-based estimation method, known as particle filtering.

An efficient statistically equivalent reduced method on stochastic model updating

The demand for computational efficiency and reduced cost presents a big challenge for the development of more applicable and practical approaches in the field of uncertainty model updating. In this article, a computationally efficient approach, which is a combination of Stochastic Response Surface Method (SRSM) and Monte Carlo inverse error propagation, for stochastic model updating is developed based on a surrogate model. This stochastic surrogate model is determined using the Hermite polynomial chaos expansion and regression-based efficient collocation method. This paper addresses the critical issue of effectiveness and efficiency of the presented method. The efficiency of this method is demonstrated as a large number of computationally demanding full model simulations are no longer essential, and instead, the updating of parameter mean values and variances is implemented on the stochastic surrogate model expressed as an explicit mathematical expression. A three degree-of-freedom numerical model and a double-hat structure formed by a number of bolted joints are employed to illustrate the implementation of the method. Using the Monte Carlo-based method as the benchmark, the effectiveness and efficiency of the proposed method is verified.

基于蒙特卡罗的HPGe伽马谱仪无源效率刻度方法

基于蒙特卡罗的HPGe伽马谱仪无源效率刻度方法

Uncertainty Propagation in Quantitative Risk Assessment Modeling for Fire in Road Tunnels

Road tunnels are critical transportation infrastructures that provide underground passageways for motorists and commuters. Fire in road tunnels in combination with tunnel safety provisions failure may lead to catastrophic consequences, and thus, necessitates a robust and reliable approach to assess tunnel risks. This article proposes a quantitative risk assessment model for fire in road tunnel by taking into consideration two types of uncertainties. A Monte Carlo-based estimation method is developed to propagate parameter uncertainty in quantitative risk assessment model consisting of event tree analysis as well as consequence estimation models. The percentile-based individual risks and α-cut-based societal risks are put up and the risk indices are proven to be very useful for tunnel operators with distinct risk attitudes to assess the safety level of a road tunnel. Finally, the proposed research methodology is applied to Singapore KPE road tunnels.

Computational identification of self‐inhibitory peptides from envelope proteins

Fusion process is known to be the initial step of viral infection and hence targeting the entry process is a promising strategy to design antiviral therapy. The self-inhibitory peptides derived from the enveloped (E) proteins function to inhibit the protein-protein interactions in the membrane fusion step mediated by the viral E protein. Thus, they have the potential to be developed into effective antiviral therapy. Herein, we have developed a Monte Carlo-based computational method with the aim to identify and optimize potential peptide hits from the E proteins. The stability of the peptides, which indicates their potential to bind in situ to the E proteins, was evaluated by two different scoring functions, dipolar distance-scaled, finite, ideal-gas reference state and residue-specific all-atom probability discriminatory function. The method was applied to α-helical Class I HIV-1 gp41, β-sheet Class II Dengue virus (DENV) type 2 E proteins, as well as Class III Herpes Simplex virus-1 (HSV-1) glycoprotein, a E protein with a mixture of α-helix and β-sheet structural fold. The peptide hits identified are in line with the druggable regions where the self-inhibitory peptide inhibitors for the three classes of viral fusion proteins were derived. Several novel peptides were identified from either the hydrophobic regions or the functionally important regions on Class II DENV-2 E protein and Class III HSV-1 gB. They have potential to disrupt the protein-protein interaction in the fusion process and may serve as starting points for the development of novel inhibitors for viral E proteins.

Theoretical Prediction of Metastable Intermediates in the Decomposition of Mg(BH4)2

We have studied the decomposition pathway and products of Mg-borohydride using density functional theory (DFT) calculations of free energy (including vibrational contributions) in conjunction with a Monte Carlo-based crystal structure prediction method, the prototype electrostatic ground state (PEGS) search method. We find that a recently proposed Mg(B3H8)2 intermediate (Chong et al. Chem. Commun.2011, 47, 1330) is energetically highly unfavorable with respect to decomposition into MgB12H12 and hence is not a thermodynamic reaction product. We systematically search for low-energy structures of Mg-triboranes [Mg(B3H8)2, MgB3H7, and Mg3(B3H6)2], closo-borane MgBnHn (n = 6, 7, 8, 9, 10, 11), and Mg(B11H14)2 compounds using PEGS simulations, refining the resulting structures with accurate DFT calculations. We find that none of these compounds break the previously determined thermodynamically stable decomposition path: Mg(BH4)2 → 1/6MgB12H12 + 5/6MgH2 + 13/6H2 → MgB2 + 4H2. However, the reaction [Mg(BH4)2 → 1/3Mg3(B3H6)2 + 2H2] involving a Mg3(B3H6)2 product has an enthalpy close to that of the MgB12H12 pathway and falls within the desired enthalpy window for near-ambient reversibility [20–50 kJ/(mol H2)]. This indicates that (1) if MgB12H12 is kinetically hindered in the decomposition of Mg(BH4)2 such as in the aforementioned reference (Chong et al. Chem. Commun.2011, 47, 1330), Mg3(B3H6)2 might be yielded as a metastable intermediate, and (2) Mg3(B3H6)2 could possibly be rehydrided back to Mg(BH4)2 under modest H2(T,p) conditions. We suggest that the observed intermediate is not [B3H8] but could be another triborane such as [B3H6].

IS YOUR PHYLOGENY INFORMATIVE? MEASURING THE POWER OF COMPARATIVE METHODS

Phylogenetic comparative methods may fail to produce meaningful results when either the underlying model is inappropriate or the data contain insufficient information to inform the inference. The ability to measure the statistical power of these methods has become crucial to ensure that data quantity keeps pace with growing model complexity. Through simulations, we show that commonly applied model choice methods based on information criteria can have remarkably high error rates; this can be a problem because methods to estimate the uncertainty or power are not widely known or applied. Furthermore, the power of comparative methods can depend significantly on the structure of the data. We describe a Monte Carlo-based method which addresses both of these challenges, and show how this approach both quantifies and substantially reduces errors relative to information criteria. The method also produces meaningful confidence intervals for model parameters. We illustrate how the power to distinguish different models, such as varying levels of selection, varies both with number of taxa and structure of the phylogeny. We provide an open-source implementation in the pmc ("Phylogenetic Monte Carlo") package for the R programming language. We hope such power analysis becomes a routine part of model comparison in comparative methods.