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.

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.

Research and development of the automatic modeling system for Monte Carlo particle transport simulation

在广泛调研和分析现有几何建模方法特点的基础上研发了具有可视化用户界面的自动建模程序系统MCAM。它可以实现多种商用软件CAD模型与McNP模型之间的相互转换，且提供了模型建立、预处理、属性分析等基本功能和计算结果可视化及基于医学映像建模接口等扩展功能。全面系统地介绍了MCAM的设计思想与原理、总体结构、主要功能和国际合作协议框架下的应用测试等情况。实践表明，它是一个实用的MCNP计算辅助工具和核设计与核分析质量保证工具。

Near-surface velocity model construction based on a Monte-Carlo scheme

In tomographic statics seismic data processing, it 1s crucial to cletermme an optimum base for a near-surface model. In this paper, we consider near-surface model base determination as a global optimum problem. Given information from uphole shooting and the first-arrival times from a surface seismic survey, we present a near-surface velocity model construction method based on a Monte-Carlo sampling scheme using a layered equivalent medium assumption. Compared with traditional least-squares first-arrival tomography, this scheme can delineate a clearer, weathering-layer base, resulting in a better implementation of damming correction. Examples using synthetic and field data are used to demonstrate the effectiveness of the proposed scheme.

基于Monte Carlo-Hakanson模型的土壤重金属生态风险评价研究

为了降低土壤重金属生态风险评价中的不确定性,以某实际搬迁后铅蓄电池场地的土壤中重金属Cr、As、Pb、Cu、Zn含量实测值为基础数据,将Monte Carlo模拟引入Hakanson生态危害指数法中,构建Monte Carlo-Hakanson模型,主要通过对重金属含量这一随机变量进行抽样,将得到的大量可靠预测值代入Hakanson指数模型,重复计算模型中的函数值,得到场地生态风险等级及处于各生态风险等级的概率,以此来降低生态风险评价中的不确定性。案例结果如下:研究区生态风险隶属于较高等级,概率为49.44%,Pb的敏感度最高,达到了99.7%,对场地的污染起主导作用。Monte Carlo-Hakanson模型的应用有效降低了类似场地的土壤重金属生态风险评估的不确定性,为获得土壤重金属生态风险评估结果的准确性提供了一个新的思路和方法。

Monte-Carlo模拟在土壤重金属生态风险评价中的应用

离散检测数据的重金属生态风险评估往往受采样数据空间覆盖度有限、检测数据随机波动等问题影响,使评价结果具有较大的不确定性.相对而言,基于地方区域的重金属概率统计以概率的形式解释污染等级的占比,能更有效指导风控方案的制定、污染的修复和场地的管控.本文应用Monte-Carlo模拟结合Hakanson潜在生态风险指数法,对云南省内某小流域土壤中的8种重金属(Cd、Hg、As、Pb、Cr、Cu、Ni和Zn)展开调查,分别从单种金属的污染等级、风险等级,以及整个区域的生态风险三个维度阐述当地的污染状况,系统性评估了当地的生态风险.研究表明Cd是主要污染贡献因子,其生态风险应引起重点关注.本研究证实了Monte-Carlo模拟可有效平衡Hakanson潜在生态风险指数法的随机性,将二者结合可更加全面精确地反映重金属的生态风险.

Reconstructing in vivo spatially offset Raman spectroscopy of human skin tissue using a GPU-accelerated Monte Carlo platform

As one type of spatially offset Raman spectroscopy(SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering during in vivo inverse SORS measurements, the light–tissue interactions when excitation and regenerated Raman photons propagate in skin tissue were studied using Monte Carlo simulation. An eight-layered skin model was first built based on the latest transmission parameters. Then, an open-source platform, Monte Carlo e Xtreme(MCX), was adapted to study the distribution of 785 nm excitation photons inside the model with an inverse spatially shifted annular beam. The excitation photons were converted to emission photons by an inverse distribution method based on excitation flux with spatial offsets Δs of 1 mm, 2 mm, 3 mm and 5 mm. The intrinsic Raman spectra from separated skin layers were measured by continuous linear scanning to improve the simulation accuracy. The obtained results explain why the spectral detection depth gradually increases with increasing spatial offset, and address how the intrinsic Raman spectrum from deep skin layers is distorted by the reabsorption and scattering of the superficial tissue constituents. Meanwhile, it is demonstrated that the spectral contribution from subcutaneous fat will be improved when the offset increases to 5 mm, and the highest detection efficiency for dermal layer spectral detection could be achieved when Δs = 2 mm. Reasonably good matching between the calculated spectrum and the measured in vivo inverse SORS was achieved, thus demonstrating great utility of our modeling method and an approach to help understand the clinical measurements.

基于神经网络-Monte Carlo法的结构系统可靠性分析

针对传统结构可靠性求解方法存在应用局限性和计算误差较大的缺陷,提出了一种基于神经网络-Monte Carlo的结构系统可靠性分析方法,利用神经网络模型预测方法降低Monte Carlo法计算结构可靠性指标的工作量,并与传统可靠性分析方法对比,验证了该方法对结构系统可靠性分析的精度要求及计算效率。结果表明,十五层框架结构系统可靠性分析方法比Monte Carlo法直接计算的失效概率降低了0.03%,且计算工作量减少为1%,该方法的计算结果满足精度要求且计算效率大幅提升。

基于藤Copula-Monte Carlo方法的多事件巨灾债券触发概率模拟

当前全球自然灾害频发,传统的保险业已经无法承担日益严峻的巨灾风险。巨灾债券作为一种新型的金融创新产品,可以为巨灾风险提供更有效的分散方式。文章采用藤Copula模型来刻画多巨灾损失变量间的相关关系,在此基础上,将多巨灾损失变量的条件分位数估计应用到多事件触发巨灾债券的触发值设定。结合全球洪灾数据进行实证分析,通过Monte Carlo方法模拟多事件巨灾债券的触发概率,验证了所构建模型的可行性和优越性。

A Modified Monte Carlo Model of Speckle Tracking of Shear Wave Induced by Acoustic Radiation Force for Acousto-Optic Elasticity Imaging

A modified Monte Carlo model of speckle tracking of shear wave propagation in scattering media is proposed. The established Monte Carlo model mainly concerns the variations of optical electric field and speckle. The two- dimensional intensity distribution and the time evolution of speckles in different probe locations are obtained. The fluctuation of speckle intensity tracks the acoustic-radiation-force shear wave propagation, and especially the reduction of speckle intensity implies attenuation of shear wave. Then, the shear wave velocity is estimated quantitatively on the basis of the time-to-peak algorithm and linear regression processing. The results reveal that a smaller sampling interval yields higher estimation precision and the shear wave velocity is estimated more efficiently by using speckle intensity difference than by using speckle contrast difference according to the estimation error. Hence, the shear wave velocity is estimated to be 2.25 m/s with relatively high accuracy for the estimation error reaches the minimum （0.071）.

A Monte Carlo Model of Light Propagation in Nontransparent Tissue

To sharpen the imaging of structures, it is vital to develop a convenient and efficient quantitative algorithm of the optical coherence tomography (OCT) sampling. In this paper a new Monte Carlo model is set up and how light propagates in bio-tissue is analyzed in virtue of mathematics and physics equations. The relations,in which light intensity of Class 1 and Class 2 light with different wavelengths changes with their permeation depth,and in which Class 1 light intensity (signal light intensity) changes with the probing depth, and in which angularly resolved diffuse reflectance and diffuse transmittance change with the exiting angle, are studied. The results show that Monte Carlo simulation results are consistent with the theory data.

Monte Carlo Simulation-based Decision-making Model for the Water Conservation and Intermediate Water Recycling of Conservationminded Buildings

Through analyzing the life cycle cost of the water conservation and intermediate water application project of conservation-minded buildings,net present value(NPV) was applied as the index to establish the life cycle economic decision-making model.Monte Carlo simulation was used in demonstration cases,modeling analysis of water-saving projects in conservation-minded and traditional buildings verified rationality and practicability of this model and simulation method.It was to provide a referential and operable method for the decision-making of conservation-minded construction projects,as well as a scientific approach and theoretic basis for the precise decision-making of government organs,decision-making departments and developers.

3-D Fracture Network Modelling in Hydropower Engineering Based on Optimal Monte Carlo Simulation

Effectively and accurately modelling the spatial relation of fracture surfaces is crucial in the design and construction of large hydropower dams having a complex underlying geology. However, fracture surfaces are randomly formed and vary greatly with respect to their spatial distribution, which makes the construction of accurate 3-D models challenging. In this study, we use an optimal Monte Carlo simulation and dynamic conditioning to construct a fracture network model. We found the optimal Monte Carlo simulation to effectively reduce the error associated with the Monte Carlo method and use dynamic conditioning to ensure the consistency of the model with the actual distribution of fractures on the excavation faces and outcrops. We applied this novel approach to a hydropower station on the Jinshajiang River, China. The simulation results matched the real sampled values well, confirming that the model is capable of effectively and accurately simulating the spatial relations in a fracture network. © 2017, Tianjin University and Springer-Verlag Berlin Heidelberg.

Monte Carlo study of single-barrier structure based on exclusion model full counting statistics

Different from the usual full counting statistics theoretical work that focuses on the higher order cumulants computation by using cumulant generating function in electrical structures, Monte Carlo simulation of single-barrier structure is performed to obtain time series for two types of widely applicable exclusion models, counter-flows model, and tunnel model. With high-order spectrum analysis of Matlab, the validation of Monte Carlo methods is shown through the extracted first four cumulants from the time series, which are in agreement with those from cumulant generating function. After the comparison between the counter-flows model and the tunnel model in a single barrier structure, it is found that the essential difference between them consists in the strictly holding of Pauli principle in the former and in the statistical consideration of Pauli principle in the latter.

Identification of Water Quality Model Parameter Based on Finite Difference and Monte Carlo

Identification results of water quality model parameter directly affect the accuracy of water quality numerical simulation. To overcome the difficulty of parameter identification caused by the measurement’s uncertainty, a new method which is the coupling of Finite Difference Method and Markov Chain Monte Carlo is developed to identify the parameters of water quality model in this paper. Taking a certain long distance open channel as an example, the effects to the results of parameters identification with different noise are discussed under steady and un-steady non-uniform flow scenarios. And also this proposed method is compared with finite difference method and Nelder Mead Simplex. The results show that it can give better results by the new method. It has good noise resistance and provides a new way to identify water quality model parameters.

Monte Carlo Study of Planar Rotator Model with Weak Dzyaloshinsky-Moriya Interaction

With the help of an improvement Monte Carlo method, the Berezinskii-Kosterlitz-Thouless phase transition arising in two-dimensional planar rotator model with weak Dzyaloshinsky-Moriya （DM） interaction is investigated. The effects of the DM interaction on specific heat, susceptibility, and magnetization are simulated. The critical temperature of transitions is determined by the so-called Binder cumulant and the susceptibility of finite-size scaling. We find that the chiral Z2 symmetry reduced by the DM interactions plays an important role in a two-dimensional XY spin system, typically, the critical temperature is sensitive to weak DM spin couplings.

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.

Microcanonical Monte Carlo Simulation of 2D 4-State Potts Model

Monte Carlo simulation of two dimensional 4 state Potts model has been carried out in microcanonical ensemble. The simulations were done on a 30 × 30 system with periodic boundary conditions. The temperature dependence of energy and order parameter has been calculated. The transition in 4-state Potts model is concluded to be first-order in nature. The transition temperature and latent heat of the first-order transition have been found to be 0.92 and 0.18, respectively.

Using the Optical Glauber Monte Carlo Model to Calculate R_<i>AA</i>Related Results Produced by the ATLAS Collaboration

The energy loss during jet quenching due to the existence of Quark Gluon Plasma (QGP) is calculated by Optical Glaube Monte Carlo model with data collected by ATLAS Collaboration using the LHC detector. An energy loss formula for this situation was modeled and took the form . The nuclear modification factor, RAA, for jets in a 208Pb + 208Pb nucleus collision with rapidity interval of ∣у∣=2.8 and the initial transverse momentum of 50 GeV ≤ pT ≤ 1000 GeV, are compared with various data plots produced by ATLAS Collaboration. RAA results are plotted in different centrality bins, which are defined by the distribution of number of participating nucleons Npart. The RAA value was found to slowly increase at lower transverse momenta and flatten out at higher transverse momenta. The model’s theoretical calculation results turned out to be similar to the plots produced by the ATLAS Collaboration using data from the LHC with small differences for higher systematic uncertainty events.

Effect of head model on Monte Carlo modeling of spatial sensitivity distribution for functional near-infrared spectroscopy

Modeling Light propagation within human head to deduce spatial sensitivity distribution(SSD)is important for Near-infrared spectroscopy(NIRS)/imaging(NIRI)and diffuse correlation tomography.Lots of head models have been used on this issue,including layered head model,artificial simplified head model,MRI slices described head model,and visible human head model.Hereinto,visible Chinese human(VCH)head model is considered to be a most faithful presentation of anatomical structure,and has been highlighted to be employed in modeling light propagation.However,it is not practical for all researchers to use VCH head models and actually increasing number of people are using magnet resonance imaging(MRI)head models.Here,all the above head models were simulated and compared,and we focused on the effect of using di®erent head models on predictions of SSD.Our results were in line with the previous reports on the effect of cerebral cortex folding geometry.Moreover,the in fluence on SSD increases with thefidelity of head models.And surprisingly,the SSD percentages in scalp and gray matter(region of interest)in MRI head model were found to be 80%and 125%higher than in VCH head model.MRI head models induced nonignorable discrepancy in SSD estimation when compared with VCH head model.This study,as we believe,is the first to focus on comparison among full serials of head model on estimating SSD,and provided quantitative evidence for MRI head model users to calibrate their SSD estimation.