Monte Carlo Simulations Research Articles

Population Pharmacokinetic Modeling of Unbound Meropenem in Patients Undergoing Continuous Renal Replacement Therapy: An Observational Cohort Study.

The most effective dosing strategy of meropenem for patients undergoing continuous renal replacement therapy (CRRT) remains uncertain. This study aimed to analyze the population pharmacokinetics (popPKs) of unbound meropenem and establish an appropriate dosing approach. This prospective study involved 19 patients for the development of a popPK model and an additional 10 for its validation. Ethical approval was obtained. The clearance of unbound meropenem was influenced by the sequential organ failure assessment (SOFA) score [=2.22 × (SOFA score/12)^1.88] and the effluent flow rate from the CRRT device, with an interindividual variability of 44.5%. The volume of distribution was affected by the simplified acute physiology score II [=23.1 × (simplified acute physiology score II/52)^1.54]. Monte Carlo simulations suggested meropenem doses ranging from 1.0 to 3.0 g/d using continuous infusion to achieve a target time above the 4 times of minimum inhibitory concentration of the unbound form (% f T >4×MIC ) of 100% for definitive therapy. For empirical therapy, a dose of 1.0 g/d using continuous infusion was recommended to target % f T >MIC of 100%. This study developed a popPK model for unbound meropenem in patients undergoing CRRT and formulated dosing guidelines. UMIN000024321.

Monte Carlo Simulations of Spent Nuclear Fuel Dry Storage Cask Measurements with a New External Remote Monitoring System for Developing Safeguards Approaches

Until a long-term solution for the disposal of spent nuclear fuel (SNF) is available, interim dry casks will be increasingly used for the storage of SNF discharged from civilian nuclear power reactors. Dry casks containing commercial SNF may hold several significant quantities of plutonium, so appropriate nuclear material safeguards monitoring is needed. An external remote monitoring system (RMS) has been developed to advance dry cask safeguards monitoring beyond the current method of containment and surveillance used to maintain continuity of knowledge. In this study, neutron transport simulations of SNF assemblies in a dry cask were performed for several special nuclear material diversion scenarios. The simulations considered various loading patterns and fuel storage durations as long as 100 years. For each fuel loading pattern and storage time investigated, the simulation results were used to calculate the required measurement time to achieve a nondetection probability β ≤ 10% for the diversion of any single fuel assembly in the cask. The calculations were performed for false alarm probabilities α as low as 0.0001% (or 10−6). A Monte Carlo postprocessing approach was developed to consider the impact on the required measurement time of uncertainty in the burnup of fuel assemblies. The study found that the external RMS is well suited for the surveillance of SNF in dry cask storage for nuclear safeguards or other purposes and is able to detect the diversion of a single SNF assembly even after decades of storage and with a very low false alarm probability.

Monte Carlo simulations of glass-forming liquids beyond Metropolis.

Monte Carlo simulations are widely employed to measure the physical properties of glass-forming liquids in thermal equilibrium. Combined with local Monte Carlo moves, the Metropolis algorithm can also be used to simulate the relaxation dynamics, thus offering an efficient alternative to molecular dynamics. Monte Carlo simulations are, however, more versatile because carefully designed Monte Carlo algorithms can more efficiently sample the rugged free energy landscape characteristic of glassy systems. After a brief overview of Monte Carlo studies of glass-formers, we define and implement a series of Monte Carlo algorithms in a three-dimensional model of polydisperse hard spheres. We show that the standard local Metropolis algorithm is the slowest and that implementing collective moves or breaking detailed balance enhances the efficiency of the Monte Carlo sampling. We use time correlation functions to provide a microscopic interpretation of these observations. Seventy years after its invention, the Monte Carlo method remains the most efficient and versatile tool to compute low-temperature properties in supercooled liquids.

Microcanonical Monte Carlo of Lennard-Jones microclusters.

A novel statistical mechanical methodology is applied to clusters of N ≤ 7 atoms. Exact statistical analogs for any energy derivative of entropy ∂mS/∂Em are used in rigorous microcanonical Monte Carlo simulations to vastly enlarge the pool of measurable thermodynamic properties relative to previous work. All analogs are given for two alternative partition functions of the microcanonical ensemble. Coarse grained energy distributions are used to establish the existence of melting transitions. LJ7, LJ5, and LJ4 are found to exhibit trimodal distributions, a feature not being reported before. Varieties of combinations of entropy derivatives are tested for a direct detection of the melting region. It is shown that for such a purpose, derivatives of at least fourth order are necessary.

Creep of Calcium Hydroxide from Stress-induced Dissolution: Kinetic Monte Carlo Simulations

Creep of Calcium Hydroxide from Stress-induced Dissolution: Kinetic Monte Carlo Simulations

MOSP: A user-interface package for simulating metal nanoparticle's structure and reactivity under operando conditions.

The structures of metal nanoparticles (NPs) significantly influence their catalytic reactivities. Recent in situ experimental observations of dramatic structural changes in NPs underscore the need to establish a dynamic structure-property relationship that accounts for the reconstruction of NPs in reactive environments. Here, we present the MOSP, a free and open-source graphical user interface (GUI) package designed to simulate the structure and reactivity of metal NPs under operando conditions. MOSP integrates two models: the multiscale structure reconstruction model predicting equilibrium metal NP structures under specific reaction conditions and the kinetic Monte Carlo model simulating the reaction dynamics. This combination allows for the exploration of the dynamic structure-property relationships of NPs. MOSP enhances user accessibility through its intuitive GUI, facilitating easy input, post-processing, and visualization of simulation data. This article is the release note of MOSP, focusing on its implementation and functionality.

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.

Sustainable Design of Pavements: Predicting Pavement Service Life

Pavement service life is an important factor that affects both the whole life cost and carbon footprint of a pavement. The service life of a pavement is affected by several different parameters which can be broadly classified into climate conditions, binder and mixture properties, pavement design, workmanship, and maintenance strategies. The current practice for determining service life of pavements involves the use of pavement design tools, which are used while constructing a new pavement or performing a reconstruction/resurfacing or pavement maintenance. In addition, field measurements using ground penetration radar, falling weight deflectometer, traffic speed deflectometer, and other techniques are also used to assess the condition of an existing pavement. The information from these measurements is then combined with pavement design software to predict potential pavement service life. The accuracy of the predicted pavement service life is affected by the associated uncertainties in the parameters that affect pavement life. The following paper presents various approaches that could be potentially used to determine the associated uncertainties in the estimation of pavement service life. The various uncertainty quantification techniques have been applied to a specific design, and the outcomes are discussed in this paper. The Monte Carlo simulation method, a system-level uncertainty quantification technique, can estimate a probabilistic pavement service life. The other uncertainty quantification schemes are software specific and provide probabilistic life factors by assumed statistical distributions. Hence, the Monte Carlo simulation technique could be one potential method that can be used for estimating a generalized pavement service utilizing predictions from various design software.

Using Early Rejection Markov Chain Monte Carlo and Gaussian Processes to Accelerate ABC Methods

Approximate Bayesian computation (ABC) is a class of Bayesian inference algorithms that targets problems with intractable or unavailable likelihood functions. It uses synthetic data drawn from the simulation model to approximate the posterior distribution. However, ABC is computationally intensive for complex models in which simulating synthetic data is very expensive. In this article, we propose an early rejection Markov chain Monte Carlo (ejMCMC) sampler based on Gaussian processes to accelerate inference speed. We early reject samples in the first stage of the kernel using a discrepancy model, in which the discrepancy between the simulated and observed data is modeled by Gaussian process (GP). Hence, synthetic data is generated only if the parameter space is worth exploring. We demonstrate through theory, simulation experiments, and real data analysis that the new algorithm significantly improves inference efficiency compared to existing early-rejection MCMC algorithms. In addition, we employ our proposed method within an ABC sequential Monte Carlo (SMC) sampler. In our numerical experiments, we use examples of ordinary differential equations, stochastic differential equations, and delay differential equations to demonstrate the effectiveness of the proposed algorithm. We develop an R package that is available at https://github.com/caofff/ejMCMC.

Spatiotemporal patterns in the active cyclic Potts model

Abstract The nonequilibrium dynamics of a cycling three state Potts model is studied on a square lattice using Monte Carlo simulations and continuum theory. This model is relevant to chemical reactions on a catalytic surface and to molecular transport across a membrane. Several characteristic modes are formed depending on the flipping energies between successive states and the contact energies between neighboring sites. Under cyclic symmetry conditions, cycling homogeneous phases and spiral waves form at low and high flipping energies, respectively. In the intermediate flipping energy regime, these two modes coexist temporally in small systems and/or at low contact energies. Under asymmetric conditions, we observed small biphasic domains exhibiting amoeba-like locomotion and temporal coexistence of spiral waves and a dominant non-cyclic one-state phase. An increase in the flipping energy between two successive states, say state 0 and state 1, while keeping the other flipping energies constant, induces the formation of the third phase (state 2), owing to the suppression of the nucleation of state 0 domains. Under asymmetric conditions regarding the contact energies, two different modes can appear depending on the initial state, due to a hysteresis phenomenon.

Radar shielding jamming method based on random phase modulation of digital coding metasurface

In this paper, a novel radar jamming method based on random phase modulation of digital coding metasurface was proposed. For typical radar system, the modulation model of coding metasurface and the echo model are deduced firstly. Then, the ordered statistics greatest of the constant false alarm rate (OSGO-CFAR) detection model was chosen to analyze the radar detection performance. Through serials of simulation, the influence of modulation modes, modulation switching interval, the number of coding elements and the signal-to-noise ratio (SNR) on radar detection performance is analyzed. Finally, the Monte Carlo simulation is performed. The result shows that as the modulation switching interval of metasurface decreases, the spectrum energy distribution of the target becomes more dispersed and the detection probability of the target decreases obviously. Traditional radar detectors will become ineffective, and the shielding of the target will be achieved. This implies a new radar jamming method which can realize target shielding instead of traditional stealth by reducing the radar cross section.

Optimization based data enrichment using stochastic dynamical system models

We develop a general framework for state estimation in systems modeled with noise-polluted continuous time dynamics and discrete time noisy measurements. Our approach is based on maximum likelihood estimation and employs the calculus of variations to derive optimality conditions for continuous time functions. We make no prior assumptions on the form of the mapping from measurements to state-estimate or on the distributions of the noise terms, making the framework more general than Kalman filtering/smoothing where this mapping is assumed to be linear and the noises Gaussian. The optimal solution that arises is interpreted as a continuous time spline, the structure and temporal dependency of which is determined by the system dynamics and the distributions of the process and measurement noise. Similar to Kalman smoothing, the optimal spline yields increased data accuracy at instants when measurements are taken, in addition to providing continuous time estimates outside the measurement instances. We demonstrate the utility and generality of our approach via illustrative examples that render both linear and nonlinear data filters depending on the particular system. Application of the proposed approach to a Monte Carlo simulation exhibits significant performance improvement in comparison to a common existing method.

Origin and fate of the pseudogap in the doped Hubbard model

The relationship between the pseudogap and underlying ground-state phases has not yet been rigorously established. We investigated the doped two-dimensional Hubbard model at finite temperature using controlled diagrammatic Monte Carlo calculations, allowing for the computation of spectral properties in the infinite-size limit and with arbitrary momentum resolution. We found three distinct regimes as a function of doping and interaction strength: a weakly correlated metal, a correlated metal with strong interaction effects, and a pseudogap regime at low doping. We show that the pseudogap forms both at weak coupling, when the magnetic correlation length is large, and at strong coupling, when it is shorter. As the temperature goes to zero, the pseudogap regime extrapolates precisely to the ordered stripe phase found by ground-state methods.

Stress–strength reliability estimation of s-out-of-k multicomponent systems based on copula function for dependent strength elements under progressively censored sample

The main objective of this research is to determine the reliability of multicomponent stress–strength under progressive Type II censoring. The model comprises k independent strength components, and each component has two elements that are statistically dependent and exposed to the same random stress. To assess the correlation between their elements, we are utilizing the Gumbel copula and assuming that its parameter is unknown. The system is deemed operational only if at least certain number of strength variables s ( 1 ≤ s ≤ k ) surpass the random stress. We are using the Chen distribution to model the stress and strength variables, with varying shape parameters. Different methods are employed to estimate the unknown parameters, including maximum likelihood, maximum product of spacings, Cramér–von-Mises, and bootstrap confidence intervals. The effectiveness of these estimation techniques is evaluated through Monte Carlo simulation, and a real data set is analyzed to illustrate our procedures.

Dynamics of Two Planets near a 2:1 Resonance: Case Studies of Known and Synthetic Exosystems on a Grid of Initial Configurations

The distribution of period ratios for 580 known two-planet systems is apparently nonuniform, with several sharp peaks and troughs. In particular, the vicinity of the 2:1 commensurability seems to have a deficit of systems. Using Monte Carlo simulations and an empirically inferred population distribution of period ratios, we prove that this apparent dearth of near-resonant systems is not statistically significant. The excess of systems with period ratios in the wider vicinity of the 2:1 resonance is significant, however. Long-term WHFast integrations of a synthetic two-planet system on a grid period ratios from 1.87 through 2.12 reveal that the eccentricity and inclination exchange mechanism between non-resonant planets represents the orbital evolution very well in all cases, except at the exact 2:1 mean motion resonance. This resonance destroys the orderly exchange of eccentricity, while the exchange of inclination still takes place. Additional simulations of the Kepler-113 system on a grid of initial inclinations show that the secular periods of eccentricity and inclination variations are well fitted by a simple hyperbolic cosine function of the initial mutual inclination. We further investigate the six known two-planet systems with period ratios within 2% of the exact 2:1 resonance (TOI-216, KIC 5437945, Kepler-384, HD 82943, HD 73526, HD 155358) on a grid of initial inclinations and for two different initial periastron longitudes corresponding to the aligned and anti-aligned states. All these systems are found to be long-term stable except HD 73526, which is likely a false positive. The periodic orbital momentum exchange is still at work in some of these systems, albeit with much shorter cycling periods of a few years.

Optimization of optical properties of nanocomposite films incorporating CWO and ITO nanoparticles for energy-saving window applications

Cesium tungsten oxide (CWO) and indium tin oxide (ITO) nanoparticles are potential candidates for application in energy-saving windows. However, most optical studies on these nanocomposite films lack systematic evaluation and design methods. In this work, the optical properties of spherical and cylindrical CWO and ITO nanoparticles under different geometric parameters based on the Lorenz–Mie and T-matrix theories are investigated, and spectral responses of CWO-PDMS and ITO-PDMS windows are calculated by solving the radiative transfer equation (RTE) using the Monte Carlo method. By evaluating and optimizing the geometric parameters of the nanocomposite films, energy-saving windows exhibit excellent optical performance, with a visible light transmittance that meets the indoor needs of the human eye (Tlum is about 0.6), and can shield most near-infrared light, especially CWO-PDMS windows (TNIR=0.04). Finally, a building energy consumption simulation analysis based on Energy Plus is conducted in three different cities: Jinan, Hong Kong, and Singapore. The results indicate that by adjusting the geometric parameters of nanoparticles, energy-saving windows can effectively reduce energy consumption in tropical and subtropical regions. This work provides guidance for the subsequent commercialization and experimental analysis of spectral selective composite films.

3D printing of realistic body phantoms: Comparison of measured and simulated organ doses on the example of a CT scan on a pregnant woman

AbstractBackgroundMedical examinations or treatment of pregnant women using ionizing radiation are sometimes unavoidable. In such cases, the risk of harm to the embryo and fetus after exposure to ionizing radiation must be carefully estimated. However, no commercially available anthropomorphic body phantoms of pregnant women are available for dose measurements. A promising possibility for the production of body phantoms for patient groups that are not adequately represented by the phantoms of reference persons is 3D printing. However, this approach is still in the evaluation phase.PurposeTo print the abdomen of a woman in the late stage of pregnancy and compare the dose distribution measured using thermoluminescence dosimeters (TLDs) in the printed phantom for two different computed tomography (CT) protocols with the corresponding results of Monte Carlo simulations on voxel models of the pregnant woman.Materials and methodsThe physical phantom was produced through multi‐material extrusion printing using different print materials identified in previous studies to simulate homogeneous soft tissues and the mean compositions of maternal and fetal bones. The 3D printed abdomen was combined with a conventionally produced anthropomorphic female phantom to obtain a whole‐body phantom of a pregnant woman. Dose values resulting from two different CT scans acquired at tube voltages of 80 and 120 kV were measured using TLDs positioned in the physical phantom and cross‐validated with the results of Monte Carlo simulations performed for two different voxel models. The first was a voxelized model of the produced phantom itself and the second a realistic digital model of a pregnant woman. Representative CT values of the materials used in the printed phantom were determined from the acquired CT images.ResultsThe CT values of maternal and fetal tissue structures in the phantom are comparable to CT values of real human tissues. The difference between most organ doses measured in the 3D printed phantom and simulated in the voxel models was below 20% and equivalent within the measurement uncertainties. Only the dose to the fetal head was up to 50% higher and not equivalent for the realistic model and the 80 kV‐protocol. As expected, the agreement was better for the voxelized than for the realistic model. For both models a slight energy dependence was observed, with larger deviations for the 80‐kV protocol especially for organs located in the pelvic region.ConclusionIndividualized physical body phantoms, such as that of a pregnant woman, can be produced using 3D printing. The good agreement between measured and simulated doses to the fetus cross‐validates both dosimetric methods. Therefore, this study demonstrates the suitability of 3D printing phantoms for patients not adequately represented by commercially available body phantoms of reference persons.

RayStation/GATE Monte Carlo simulation framework for verification of proton therapy based on the 12N imaging

Abstract Objective:
12N, having a half-life of 11 milliseconds, is a very promising positron emitter to provide near real-time feedback in proton therapy. There is currently no framework for comparing and validating positron emission imaging of 12N. This work describes the development and validation of a Monte Carlo framework to calculate the images of 12N, as well as long-lived isotopes, originating from activation by protons.
Approach:
The available dual-panel Biograph mCT PET scanner was modeled in GATE and validated by comparing the simulated sensitivity map with the measured one. The distributions of 12N and long-lived isotopes were calculated by RayStation and used as the input of GATE simulations. The RayStation/GATE combination was verified using proton beam irradiations of homogeneous phantoms. A 120 MeV pulsed pencil beam with 108 protons per pulse was used. Two-dimensional images were created from the GATE output and compared with the images based on the measurements and the 1D longitudinal projection of the full 2D image was used to calculate the 12N activity range.
Main results:
The simulated sensitivity in the center of the FoV (5.44%) agrees well with the measured one (5.41%). The simulated and measured 2D sensitivity maps agree in good detail. The relative difference between the measured and simulated positron activity range for both 12N and long-lived isotopes is less than 1%. The broadening of the 12N images relative to those of the longer-lived isotopes can be understood in terms of the large positron range of 12N.
Significance:
We developed and validated a Monte Carlo framework based on RayStation/GATE to support the in-beam PET method for quality assurance of proton therapy. The novel inclusion of the very short-lived isotope 12N makes the framework useful for developing near real-time verification. This represents a significant step towards translating 12N real-time in vivo verification to the clinic.

Minimum profile Hellinger distance estimation for single-index models

Single-index models are the most commonly used covariate models and are widely used in statistical applications. Such models include linear models and generalised linear models as special cases. This article investigates efficient and robust estimates for single-index models. For this purpose, we employ the minimum distance approach which in general is automatically robust with respect to the stability of the quantity being estimated. In particular, the minimum Hellinger distance approach introduced by Beran [(1977), ‘Minimum Hellinger Distance Estimators for Parametric Models’, Annals of Statistics, 5, 445–463] produces estimators that are asymptotically efficient at the model density and simultaneously possess excellent robustness properties. In this paper, we construct a minimum profile Hellinger distance estimator (MPHDE) for single-index models. We prove the consistency of the proposed MPHDE and examine its finite-sample performance and robustness properties via Monte Carlo simulation studies and real data analysis.

Functional Time Series Analysis and Visualization Based on Records

In many phenomena, data are collected on a large scale and at different frequencies. In this context, functional data analysis (FDA) has become an important statistical methodology for analyzing and modeling such data. The approach of FDA is to assume that data are continuous functions and that each continuous function is considered as a single observation. Thus, FDA deals with large-scale and complex data. However, visualization and exploratory data analysis, which are very important in practice, can be challenging due to the complexity of the continuous functions. Here we introduce a type of record concept for functional data, and we propose some nonparametric tools based on the record concept for functional data observed over time (functional time series). We study the properties of the trajectory of the number of record curves under different scenarios. Also, we propose a unit root test based on the number of records. The trajectory of the number of records over time and the unit root test can be used for visualization and exploratory data analysis. We illustrate the advantages of our proposal through a Monte Carlo simulation study. We also illustrate our method on two different datasets: Daily wind speed curves at Yanbu, Saudi Arabia and annual mortality rates in France. Overall, we can identify the type of functional time series being studied based on the number of record curves observed. Supplementary materials for this article are available online.