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 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 Simulation of Spin Glasses Using Vector Spin Model in 3D

In the present work, the possibility of existence of spin glass phase using classical Heisenberg model with Edwards-Anderson type of interactions has been explored employing Monte Carlo simulation of Binder parameter （g （L, T））. Previous experimental studies show that there is finite temperature phase transition but this study indicates that there is no finite temperature phase transition in 3D Heisenberg vector spin glass model.

Safety analysis of wheel brake system based on STAMP/STPA and Monte Carlo simulation

The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus,we consider system safety as a control problem based on the system-theoretic accident model, the processes(STAMP) model and the system theoretic process analysis(STPA) technique to compensate the deficiency of traditional techniques. Meanwhile,system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly,we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.

Nanocrystallization behaviour of a ternary amorphous alloy during isothermal annealing： a Monte Carlo simulation

The nanocrystallization behaviour of Zr70Cu20Ni10 metallic glass during isothermal annealing is studied by employing a Monte Carlo simulation incorporating with a modified Ising model and a Q-state Potts model. Based on the simulated microstructure and differential scanning calorimetry curves, we find that the low crystal-amorphous interface energy of Ni plays an important role in the nanocrystallization of primary Zr2Ni. It is found that when T〈T1max （where T1max is the temperature with maximum nucleation rate）, the increase of temperature results in a larger growth rate and a much finer mierostrueture for the primary Zr2Ni, which accords with the microstructure evolution in ＂flash annealing＂. Finally, the Zr2Ni/Zr2Cu interface energy σG contributes to the pinning effect of the primary nano-sized Zr2Ni grains in the later formed normal Zr2Cu grains.

Monte Carlo Simulation for the Adsorption of Symmetric Triblock Copolymers

The adsorption behavior of symmetric triblock copolymers, Am/2BnAm/2, from a nonselective solvent at solid-liquid interface has been studied by Monte Carlo simulations on a simple lattice model. Either segment A or segment B is attractive, while the other is non-attractive to the surface. Influences of the adsorption energy, bulk concentration, chain composition and chain length on the microstructure of adsorbed layers are presented. The results show that the total surface coverage and the adsorption amount increases monotonically as the bulk concentration increases. The larger the adsorption energy and the higher the fraction of adsorbing segments, the higher the total surface coverage is exhibited. The product of surface coverage and the proportion of non-attractive segments are nearly independent of the chain length, and the logarithm of the adsorption amount is a linear function of the reciprocal of the reduced temperature. When the adsorption energy is larger, the adsorption amount exhibits a maximum as the fraction of adsorbing segment increases. The adsorption isotherms of copolymers with different length of non-attractive segments can be mapped onto a single curve under given adsorption energy. The adsorption layer thickness decreases as the adsorption energy and the fraction of adsorbing segments increases, but it increases as the length of non-attractive segments increases. The tails mainly govern the adsorption layer thickness.

Magnetic properties of La2CuMnO6 double perovskite ceramic investigated by Monte Carlo simulations

We present a theoretical study of the magnetic properties of the lanthanum copper manganate double perovskite La2CuMnO6 ceramic,using Monte Carlo simulations.We analyze and discuss the ground state phase diagrams in different planes to show the effect of every physical parameter.Based on the Monte Carlo simulations,which combine Metropolis algorithm and Ising model,we explore the thermal behavior of the total magnetization and susceptibility.We also present and discuss the influence of physical parameters such as the external magnetic field,the exchange coupling interactions between magnetic atoms,and the exchange magnetic field on the magnetization of the system.Moreover,the critical temperature of the system is about Tc=70 K,in agreement with the experimental value.Finally,the hysteresis loops of La2CuMnO6 are discussed.

Analysis of typhoon wind hazard in Shenzhen City by Monte-Carlo Simulation

As one of the most serious natural disasters,many typhoons affect southeastern China every year.Taking Shenzhen,a coastal city in southeast China as an example,we employed a Monte-Carlo simulation to generate a large number of virtual typhoons for wind hazard analysis.By analyzing 67-year historical typhoons data from 1949 to 2015 using the Best Track Dataset for Tropical Cyclones over the Western North Pacific recorded by the Shanghai Typhoon Institute,China Meteorological Administration(CMASTI),typhoon characteristic parameters were extracted and optimal statistical distributions established for the parameters in relation to Shenzhen.We employed the Monte-Carlo method to sample each distribution to generate the characteristic parameters of virtual typhoons.In addition,the Yah Meng(YM)wind field model was introduced,and the sensitivity of the YM model to several parameters discussed.Using the YM wind field model,extreme wind speeds were extracted from the virtual typhoons.The extreme wind speeds for different return periods were predicted and compared with the current structural code to provide improved wind load information for wind-resistant structural design.

Variational and diffusion Monte Carlo simulations of a hydrogen molecular ion in a spherical box

The variational and diffusion Monte Carlo approaches are used to study the ground-state properties of a hydrogen molecular ion in a spheroidal box. In this work, we successfully treat the zero-point motion of protons in the same formalism with as of electrons and avoid the Born–Oppenheimer approximation in density function theory. The study shows that the total energy increases with the decrease in volume, and that the distance between protons decreases as the pressure increases.Considering the motion of protons, the kinetic energy of the electron is higher than that of the fixed model under the same conditions and increases by 5%. The kinetic energy of the proton is found to be small under high pressure, which is only a fraction of the kinetic energy of the electron.

Monte Carlo Simulation of Propylene Selective Oxidation and Ammoxidation overβ-Bi2Mo2O9 Catalyst under Anaerobic Condition

A random walk Monte Carlo （RWMC） simulation model of catalytic particle was established on the basis of the structures of bismuth molybdate catalysts and mechanisms of catalytic reactions with propylene selective oxidation and ammoxidation. The simulation results show that rationality of the RWMC model is proved on the basis of pulse experimental data. One of the most remarkable factors affecting catalytic behavior is the transfer of bulk lattice oxygen, which decides the rate of ammonia-consuming and propylene-consuming. The selectivity of main products reaches the maximum after the reduction of catalysts to a certain degree. It is inferred that catalytic performance improves greatly if the ratio of capacity for dehydrogenation from adsorbed propylene molecule on catalytically active site of molybdenum metal-imido group （Mo=NH） to that on catalytically active site of molybdenum metal-oxo group （Mo=O） becomes much higher.

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.

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.

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.

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.

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.

一种Monte Carlo仿真新算法及其对三维个体晶粒长大速率拓扑依赖性理论方程的验证

通过建立一种新的三维晶粒组织及其演变的 Ｍｏｎｔｅ Ｃａｒｌｏ可视化图像仿真算法，验证了作者近年来理论导出的三维个体晶粒长大速率拓扑依赖性方程的正确性．

基于砂控地质建模和Monte Carlo模拟的储量评价方法

提出了基于砂控地质建模和Monte Carlo模拟相结合的地质储量评价方法。通过分析影响储层体积、孔隙度、流体界面、含烃饱和度以及体积系数等储量计算参数不确定性的地质成因,对各参数的不确定性进行评价并给出可能存在的变化范围;在地质建模中体现储量计算参数的不确定性变化;基于地质模型并结合Monte Carlo模拟方法进行地质储量评价。利用本文方法进行储量评价,其结果更客观。

力传感器惯性质量的改进Monte Carlo校准方法

惯性质量是力传感器模型的重要校准参数,也是影响动态力测量精度的关键因素之一。为了消除参数误差对惯性质量校准模型引起的病态,提出一种改进Monte Carlo校准(MMCC)方法。首先,建立力传感器惯性质量、配重质量与测量响应之间的模型;其次,利用伪随机数生成技术,分别对该模型中的配重质量、加速度和电压进行样本空间的全域模拟;然后,根据区间判断准则筛选出满足预设精度的有效样本;最后,结合有效样本的概率,估计出力传感器的惯性质量,并实现动态校准。为了验证本文方法的准确性,利用正弦激振台对Kistler 9331B型力传感器进行动态校准。实验结果表明,惯性质量的估计值为83.91 g,估计误差为0.67%,标准差为0.74 g;动态力的校准误差范围为[-7.88%,11.46%]。校准误差明显低于传统的二次及多次配重法。

三维晶粒长大速率方程的大尺度Potts模型Monte carlo仿真验证

采用Potts模型Monte Carlo方法对3种现存的三维个体晶粒长大速率拓扑依赖性方程进行了仿真验证.结果表明,Rivier速率方程认为晶粒体积变化率dV_f/dt与晶粒面数f成线性关系,与仿真结果明显不符,不适用于描述三维晶粒长大过程的动力学.当晶粒面数f(?)8时,Yu-Liu速率方程和MacPherson-Srolovitz速率方程均与仿真结果很好吻合,表明这两者均可以用来定量描述三维晶粒长大过程的动力学;当f<8时,这两个方程均与仿真结果有显著差异.