Emergent topological ordered phase for the Ising-XY model revealed by cluster-updating Monte Carlo method

The two-component cold atom systems with anisotropic hopping amplitudes can be phenomenologically described by a two-dimensional Ising-XY coupled model with spatial anisotropy.At low temperatures,theoretical predictions[Phys.Rev.A 72053604(2005)]and[arXiv:0706.1609]indicate the existence of a topological ordered phase characterized by Ising and XY disorder but with 2XY ordering.However,due to ergodic difficulties faced by Monte Carlo methods at low temperatures,this topological phase has not been numerically explored.We propose a linear cluster updating Monte Carlo method,which flips spins without rejection in the anisotropy limit but does not change the energy.Using this scheme and conventional Monte Carlo methods,we succeed in revealing the nature of topological phases with half-vortices and domain walls.In the constructed global phase diagram,Ising and XY-type transitions are very close to each other and differ significantly from the schematic phase diagram reported earlier.We also propose and explore a wide range of quantities,including magnetism,superfluidity,specific heat,susceptibility,and even percolation susceptibility,and obtain consistent and reliable results.Furthermore,we observed first-order transitions characterized by common intersection points in magnetizations for different system sizes,as opposed to the conventional phase transition where Binder cumulants of various sizes share common intersections.The critical exponents of different types of phase transitions are reasonably fitted.The results are useful to help cold atom experiments explore the half-vortex topological phase.

Monte Carlo method for evaluation of surface emission rate measurement uncertainty

The aim of this study is to evaluate the uncertainty of 2πα and 2πβ surface emission rates using the windowless multiwire proportional counter method.This study used the Monte Carlo method (MCM) to validate the conventional Guide to the Expression of Uncertainty in Measurement (GUM) method.A dead time measurement model for the two-source method was established based on the characteristics of a single-channel measurement system,and the voltage threshold correction factor measurement function was indirectly obtained by fitting the threshold correction curve.The uncertainty in the surface emission rate was calculated using the GUM method and the law of propagation of uncertainty.The MCM provided clear definitions for each input quantity and its uncertainty distribution,and the simulation training was realized with a complete and complex mathematical model.The results of the surface emission rate uncertainty evaluation for four radioactive plane sources using both methods showed the uncertainty’s consistency E_(n)<0.070 for the comparison of each source,and the uncertainty results of the GUM were all lower than those of the MCM.However,the MCM has a more objective evaluation process and can serve as a validation tool for GUM results.

Method“Monte Carlo”in healthcare

In public health,simulation modeling stands as an invaluable asset,enabling the evaluation of new systems without their physical implementation,experimentation with existing systems without operational adjustments,and testing system limits without real-world repercussions.In simulation modeling,the Monte Carlo method emerges as a powerful yet underutilized tool.Although the Monte Carlo method has not yet gained widespread prominence in healthcare,its technological capabilities hold promise for substantial cost reduction and risk mitigation.In this review article,we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts.We underscore the significance of experiential insights derived from simulated experimentation,especially in resource-constrained scenarios where time,financial constraints,and limited resources necessitate innovative and efficient approaches.As public health faces increasing challenges,incorporating the Monte Carlo method presents an opportunity for enhanced system construction,analysis,and evaluation.

Monte Carlo方法在移动通信可靠性分析中的应用

文章主要探讨Monte Carlo方法在移动通信可靠性分析中的应用,并引入重要抽样技术对Monte Carlo方法进行优化。首先,文章介绍Monte Carlo方法的基本原理及其在移动通信可靠性分析中的应用。其次,引入重要抽样技术对该方法进行优化。最后,利用优化网络工程工具(Optimized Network Engineering Tools,OPNET)模拟平台构建相应的移动通信网络模型,并对比Monte Carlo方法和本文研究的优化方法在不同样本数量下的性能差异。结果表明,重要抽样技术的引入显著提高了Monte Carlo方法在移动通信可靠性分析中的准确性和效率,减少了方差,验证了该方法在移动通信可靠性分析中的有效性和实用性。

Application of the Monte Carlo Method in Simulative Generation Test Problems Simulative Generation Test Problems

An approach for generating test problems by a computer using the Monte Carlo method based upon user-given characterizations is described.A single point X~* is prespocified by the user to be a solution of the test problems.The approach is flex- ible enough to specify function values,gradients,Hesse,degeneracy degree and ill- conditioned degree at the point X~*.Other numerical features such as indefiniteness, convexity are also under user's control.

Study of the Conductance Characteristic of Doped Polymer by Monte Carlo Method

The conduct mechanism of the doped polymer is considered. In an asymmetrysystem composed of high polymer and doping conductive matte, chain or congeries framework will beformed between the conductive particles to improve the conductance characteristic. In thisprocession, the conductive particles interact to each other. In this paper, we describe theconductance of the doped polymer by Monte Carlo method. The results accord with the experimentsquite well. It can be concluded that there is an evident change of doped polymer from nonconductorto metal.

Monte Carlo Method for the Uncertainty Evaluation of Spatial Straightness Error Based on New Generation Geometrical Product Specification

Straightness error is an important parameter in measuring high-precision shafts. New generation geometrical product speeifieation（GPS） requires the measurement uncertainty characterizing the reliability of the results should be given together when the measurement result is given. Nowadays most researches on straightness focus on error calculation and only several research projects evaluate the measurement uncertainty based on ＂The Guide to the Expression of Uncertainty in Measurement（GUM）＂. In order to compute spatial straightness error（SSE） accurately and rapidly and overcome the limitations of GUM, a quasi particle swarm optimization（QPSO） is proposed to solve the minimum zone SSE and Monte Carlo Method（MCM） is developed to estimate the measurement uncertainty. The mathematical model of minimum zone SSE is formulated. In QPSO quasi-random sequences are applied to the generation of the initial position and velocity of particles and their velocities are modified by the constriction factor approach. The flow of measurement uncertainty evaluation based on MCM is proposed, where the heart is repeatedly sampling from the probability density function（PDF） for every input quantity and evaluating the model in each case. The minimum zone SSE of a shaft measured on a Coordinate Measuring Machine（CMM） is calculated by QPSO and the measurement uncertainty is evaluated by MCM on the basis of analyzing the uncertainty contributors. The results show that the uncertainty directly influences the product judgment result. Therefore it is scientific and reasonable to consider the influence of the uncertainty in judging whether the parts are accepted or rejected, especially for those located in the uncertainty zone. The proposed method is especially suitable when the PDF of the measurand cannot adequately be approximated by a Gaussian distribution or a scaled and shifted t-distribution and the measurement model is non-linear.

Forced propagation method for Monte Carlo fission source convergence acceleration in the RMC

In loosely coupled or large-scale problems with high dominance ratios,slow fission source convergence can take extremely long time,reducing Monte Carlo(MC)criticality calculation efficiency.Although various acceleration methods have been developed,some methods cannot reduce convergence times,whereas others have been limited to specific problem geometries.In this study,a new fission source convergence acceleration(FSCA)method,the forced propagation(FP)method,has been proposed,which forces the fission source to propagate and accelerate fission source convergence.Additionally,some stabilization techniques have been designed to render the method more practical.The resulting stabilized method was then successfully implemented in the MC transport code,and its feasibility and effectiveness were tested using the modified OECD/NEA,one-dimensional slab benchmark,and the Hoogenboom full-core problem.The comparison results showed that the FP method was able to achieve efficient FSCA.

An analysis of the radar backscatter from oil-covered sea surfaces using moment method and Monte-Carlo simulation: preliminary results

An analysis of the radar backscattering from the ocean surface covered by oil spill is presented using a mi- crowave scattering model and Monte-Carlo simulation. In the analysis, a one-dimensional rough sea sur- face is numerically generated with an ocean waveheight spectrum for a given wind velocity. A two-layered medium is then generated by adding a thin oil layer on the simulated rough sea surface. The electric fields backscattered from the sea surface with two-layered medium are computed with the method of moments （MoM）, and the backscattering coefficients are statistically obtained with N independent samples for each oil-spilled surface using the Monte-Carlo technique for various conditions of surface roughness, oil-layer thickness, frequency, polarization and incidence angle. The numerical simulation results are compared with theoretical models for clean sea surfaces and SAR images of an off-spilled sea surface caused by the Hebei （Hebei province, China） Spirit oil tanker in 2007. Further, conditions for better oil spill extraction are sought by the numerical simulation on the effects of wind speed and oil-layer thickness at different inci- dence angles on the backscattering coefficients.

A new hybrid method—combined heat flux method with Monte-Carlo method to analyze thermal radiation

A new hybrid method, Monte-Carlo-Heat-Flux （MCHF） method, was presented to analyze the radiative heat transfer of participating medium in a three-dimensional rectangular enclosure using combined the Monte-Carlo method with the heat flux method. Its accuracy and reliability was proved by comparing the computational results with exact results from classical ＂Zone Method＂.

COMPUTERIZED SIMULATION OF THERMODYNAMICS FOR MOLTEN LiF-KF SOLUTION BY MONTE CARLO METHOD

The structure and behaviour of LiF-KF solution,as a typical common-anion system,has been simulated by Monte Carlo method.The calculation of partial radial distribution function of ions,heat of mixing and potential energy distribution shows that the average distance be- tween Li^+ and F^- ions will significantly narrow after mixing of molten LiF and KF.This is very similar to the lean-on-one-side effect in molten LiF-KCl solution.The calculated heat of mixing is in fair agreement with the measured one.The dominant source of the energy of mixing may be that the decrease of the repulsion energy between cations,the decrease of the attraction energy between cations and anions,and the decrease of the repulsion energy be- tween anions.

Direct Monte Carlo Method Simulation of the Synthesis of Carbon Particle Through Coagulation in the Detonation of Explosives

A model is constructed and used in computing the coagulation probability of free carbon during the detonation of explosives. A direct simulation Monte Carlo (DSMC) program is constructed to simulate the coagulation of free carbon particles. The evaluation of the distribution spectrum of particles in the system is obtained. The simulation result is consistent with the experimental curve.

DETERMINATION OF THE FAVORED PATTERN IN NEURAL SPIKE TRAIN WITH MONTE CARLO METHOD AND TEMPLATE METHOD

The modern neuroelectrical physiology indicates that there are certain patterns inneural spike train which appear more frequently than others.These favored patterns(FP)maybe related to the mechanism of neural information processing in central nervous system.Inthis paper,the quantized Monte Carlo method and template method used in favored patternrecognition are described.The Monte Carlo method determines favored patterns or the possiblefavored patterns,while the template method is used for choosing the FP from the candidates andcounting the number of the FP in the spike train.This work will be pursued for extracting theFP in some complicated spike train.

Seismic Inventory Simulation and Conformance Test Based on the Monte Carlo Method——A Case Study of the Fenhe-Weihe Belt

Artificial earthquake catalogue simulation is one of the ways to effectively improve the incompleteness of the existing earthquake catalogue,the scarcity of large earthquake records and the improvement of seismological research. Based on the Poisson distribution model of seismic activity and the Gutenberg-Richter magnitude-frequency relationship,the Monte Carlo method which can describe the characteristics of the stochastic nature and the physical experiment process is used. This paper simulates the future seismic catalogues of the Fenhe-Weihe seismic belt of different durations and conducts statistical tests on them.The analysis shows that the simulation catalogue meets the set seismic activity parameters and meets the Poisson distribution hypothesis,which can obtain a better simulated earthquake catalogues that meets the seismic activity characteristics. According to the simulated earthquake catalogues,future earthquake trends in this region are analyzed to provide reference for seismic hazard analysis.

Specimen Variation Effects on XRF Analysis by the Monte Carlo Method: Thicknesses, Densities and Particle Sizes

This theoretical study conducted an X-ray fluorescence (XRF) analysis on specimen variation, with emphasis on variations on the thicknesses, density and particle sizes of specimens. The theoretical formula for X-ray fluorescence intensity was derived. These specimen variations were simulated using Monte Carlo Neutron-Particle Transport Code MCNP5. The Cu element X-ray characteristic peak counts were calculated. These variations made a conspicuous impact on the fluorescence intensity X-ray characteristic, in terms of theoretical formulas and calculations. There was a nonlinear relationship between thicknesses and count, except for thin specimens. As the density increased, the count increased in an exponential form for the saturated thick specimens. When the density reached 1 g.cm-3 , the count remained constant. The matrix materials (moisture) could increase the matrix effects. The higher the moisture was, the greater the matrix effect was. Specimen particle size also affects these measurement results. Hence, these specimens must be prepared before measurement. The calculations were consistent with the theoretical formulas.

Calculation of Neutron-Proton Mass Difference by the Monte Carlo Method

Calculation results of the Monte Carlo method of the average energy of the electrostatic interaction between the quarks are presented to the neutron and proton. The proposed model of the distribution of quarks in protons and neutrons is possible to assess the area which included a strong (gluon) interaction. Given the fact that the probability of finding a quark in the field with strong interaction is less than one, there is a good agreement between the experimental and calculated values of the mass difference between the neutron and the proton.

Complex derivatives valuation: applying the Least-Squares Monte Carlo Simulation Method with several polynomial basis

Background:This article investigates the Least-Squares Monte Carlo Method by using different polynomial basis in American Asian Options pricing.The standard approach in the option pricing literature is to choose the basis arbitrarily.By comparing four different polynomial basis we show that the choice of basis interferes in the option's price.Methods:We assess Least-Squares Method performance in pricing four different American Asian Options by using four polynomial basis:Power,Laguerre,Legendre and Hermite A.To every American Asian Option priced,three sets of parameters are used in order to evaluate it properly.Results:We show that the choice of the basis interferes in the option's price by showing that one of them converges to the option's value faster than any other by using fewer simulated paths.In the case of an Amerasian call option,for example,we find that the preferable polynomial basis is Hermite A.For an Amerasian put option,the Power polynomial basis is recommended.Such empirical outcome is theoretically unpredictable,since in principle all basis can be indistinctly used when pricing the derivative.Conclusion:In this article The Least-Squares Monte Carlo Method performance is assessed in pricing four different types of American Asian Options by using four different polynomial basis through three different sets of parameters.Our results suggest that one polynomial basis is best suited to perform the method when pricing an American Asian option.Theoretically all basis can be indistinctly used when pricing the derivative.However,our results does not confirm these.We find that when pricing an American Asian put option,Power A is better than the other basis we have studied here whereas when pricing an American Asian call,Hermite A is better.

Calculation of Neutral Particle Energy Spectra in Tokamak by Using the Monte Carlo Method

Neutral particle energy spectra in the HT-7 tokamak are calculated by using the Monte Carlo method. It can reproduce the spectra measured in experiment. Differences of neutral particle energy spectra in higher and lower electron density plasma are discussed. Results show that the ion temperature given by neutral particle energy spectra is lower than the real ion temperature, but the deviation is within 10% if the ion temperature is less than 800 eV and thecentral chord-averaged electron density does not exceed 3 ×1013 cm-3. But for ion temperature higher than 1000 eV at the central chord-averaged density limit up to 5 ×1013 cm-3, the neutral particle energy spectra can still give the ion temperature within 10% deviation.

Evaluation the Price of Multi-Asset Rainbow Options Using Monte Carlo Method

Solution of the system stochastic differential equations in multi dimensional case using Monte Carlo method had many useful features in compare with the other computational methods. One of them is the solution of boundary value problems to be found at just one point, if required (with associated saving in computation), whereas deterministic methods necessarily find the solution at large number of points simultaneously. This property can be particularly useful in problems such option pricing, where the value of an option is required only at the time of striking, and for the state of the market at that time. In this work we consider a European multi-asset options which mathematically described by the system of stochastic differential equations. We will apply Monte Carlo method for the solution of that system which is the price of Multi-asset rainbow options.

4-10 Classical-trajectory Monte Carlo method for the Investigation of the Dynamics of Ion-atom Collisions

The reaction microscope (COLTRIMS as well) is a novel technique for the investigation of the dynamics ofion-atoms collisions. Exploiting this technique, a large variety of kinematically complete experiments on electrontransfer and ionization have been performed. However, the understanding of these experimental results is farfrom satisfactory, especially for collision energy in the intermediate-energy range. The classical-trajectory MonteCarlo method (CTMC) proposed by Abrines and Percival[1] shed some light on the problem. This method has ademonstrated region of applicability in the intermediate-energy range.