Monte Carlo Simulations Research Articles

Curvature correlators in nonperturbative 2D Lorentzian quantum gravity

Correlation functions are ubiquitous tools in quantum field theory from both a fundamental and a practical point of view. However, up to now their use in theories of quantum gravity beyond perturbative and asymptotically flat regimes has been limited, due to difficulties associated with diffeomorphism invariance and the dynamical nature of geometry. We present an analysis of a manifestly diffeomorphism-invariant, nonperturbative two-point curvature correlator in two-dimensional Lorentzian quantum gravity. It is based on the recently introduced quantum Ricci curvature and uses a lattice regularization of the full path integral in terms of causal dynamical triangulations. We discuss some of the subtleties and ambiguities in defining connected correlators in theories of dynamical geometry, and provide strong evidence from Monte Carlo simulations that the connected two-point curvature correlator in 2D Lorentzian quantum gravity vanishes. This work paves the way for an analogous investigation in higher dimensions.

Quantitative evaluation of uncertainty and interpretability in machine learning-based landslide susceptibility mapping through feature selection and explainable AI

Landslide susceptibility mapping (LSM) is essential for determining risk regions and guiding mitigation strategies. Machine learning (ML) techniques have been broadly utilized, but the uncertainty and interpretability of these models have not been well-studied. This study conducted a comparative analysis and uncertainty assessment of five ML algorithms—Random Forest (RF), Light Gradient-Boosting Machine (LGB), Extreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM)—for LSM in Inje area, South Korea. We optimized these models using Bayesian optimization, a method that refines model performance through probabilistic model-based tuning of hyperparameters. The performance of these algorithms was evaluated using accuracy, Kappa score, and F1 score, with accuracy in detecting landslide-prone locations ranging from 0.916 to 0.947. Among them, the tree-based models (RF, LGB, XGB) showed competitive performance and outperformed the other models. Prediction uncertainty was quantified using bootstrapping and Monte Carlo simulation methods, with the latter providing a more consistent estimate across models. Further, the interpretability of ML predictions was analyzed through sensitivity analysis and SHAP values. We also expanded our investigation to include both the inclusion and exclusion of predictors, providing insights into each significant variable through a comprehensive sensitivity analysis. This paper provides insights into the predictive uncertainty and interpretability of ML algorithms for LSM, contributing to future research in South Korea and beyond.

Vector irradiance modelling in a seawater column for assessing the detection capabilities of an oil-in-water emulsion

The possibility of tracking oil pollution in the sea has been an issue that has been analysed for a long time, and the use of light interactions with the sea polluted with various forms of oil has been the subject of numerous studies. This paper presents the results of the Monte Carlo simulations of the fate of a large number of virtual photons to demonstrate changes in the downwelling vector irradiance and upwelling vector irradiance in oil-free seawater and analogously seawater column polluted with an oil-in-water emulsion. The analyses were carried out for eight wavelengths ranging from 412 to 676 nm, upon the assumption of an oil concentration of 10 ppm, taking into account the data of absorption and scattering properties of the southern Baltic Sea. The most favourable combination of wavelengths for the detection of an oil-in-water emulsion was 555/412 for all tested depths.

Exponentially weighted moving average chart using zero-inflated negative binomial distribution

Zero-inflated models are frequently used to deal with data having many zeros. A commonly used model for over-dispersed data containing zeros is known as the zero-inflated Poisson model. However, to account for the heterogeneity of counts that leads to excess variance besides inflation of zeros in the data using a more flexible model than the zero-inflated Poisson model, a zero-inflated negative binomial (ZINB) is suggested. In the present study, Shewhart and exponentially weighted moving average (EWMA) control charts are suggested to monitor the ZINB data. The charts are compared using the average run length and standard deviation of run length by using extensive Monte Carlo simulations. Besides a comprehensive simulation study assuming different settings of parameters of ZINB, a real data set is used to show the practicality of the proposed charts. The results indicate that the EWMA chart is better than the Shewhart chart.

Negative Barnett effect, negative moment of inertia of the gluon plasma, and thermal evaporation of the chromomagnetic condensate

We discuss the negativity of the moment of inertia of (quark-)gluon plasma in a window of “supervortical” range of temperatures above the deconfining phase transition, T≃(1…1.5)Tc, found recently in numerical Monte Carlo simulations by two independent methods. In our work, we confirm numerically that the origin of this effect is rooted in the thermal evaporation of the nonperturbative chromomagnetic condensate. We argue that the negative moment of inertia of gluon plasma indicates the presence of a novel effect, the negative spin-vortical coupling for gluons resulting in a negative gluonic Barnett effect: the spin polarization of gluons exceeds the total angular momentum of rotating plasma, thus forcing the orbital angular momentum to take negative values in the supervortical range of temperatures. Published by the American Physical Society 2024

An Improved Wiener Path Integral Approach for Stochastic Response Estimation of Nonlinear Systems Under Non-White Excitation

Abstract An improved Wiener path integral (WPI) approach is developed for predicting the stochastic response probability density functions (PDFs) of nonlinear systems under non-white excitation. Specifically, the excitation process is modeled as the output of a filter whose input is Gaussian white noise, the joint response PDF of the nonlinear system is expressed as a functional integral of the stochastic action over the space of all possible trajectories between the initial and final states, and the second-order variation of the stochastic action is recast to a quadratic form and taken into account in the estimation of the joint response PDF. Compared to the standard WPI approach where the second-order variation of the stochastic action is regarded as a constant, the improved WPI approach developed herein considers the fluctuations of the second-order variation of the stochastic action in the computational domain, thus improving the accuracy of the stochastic response estimation. Two numerical examples are illustrated, and the nonstationary response PDFs estimated by the improved WPI approach agree well with the Monte Carlo simulation results.

Internal dosimetry assessment of beta, beta/auger, and alpha decaying radionuclides in targeted radionuclide therapy for prostate cancer: a simulation study

The study employs the Monte Carlo method to calculate the internal dosimetry of beta, beta/Auger and alpha-type decaying radionuclides used in the treatment of prostate cancer. The distributions of dose to the prostate and critical organs were simulated on a virtual phantom using GATE MC software. In GATE simulation, geometric parameters and densities were determined for the prostate as the source organ and the kidneys, liver, testicles, and bladder as critical organs. In the prostate geometry, Lu-177, Ac-225, and Tb-161 with an activity of 370 MBq were identified. Using the DoseActors command, the S values, instant absorbed doses (Gy/s), uncertainties, and total absorbed doses (Gy) were computed and saved as an output file in the TXT format. Dosimetric comparison was made between different TRTs according to the absorbed doses in the source and critical organs. The prostate was found to be the tissue that received the highest instant absorbed dose with 8.397E-03, 1.594E+00 and 1.195E-02 Gy/s for Lu-177, Ac-225 and Tb-161, respectively. The kidney, liver, and testicles were taken lowest instant absorbed doses with 3.888E-08 (mean), 5.679E-08, and 4.302E-07 (mean) Gy/s by Tb-161. Lu-177 gave the lowest instant absorbed doses of 5.731E-07 Gy/s for the bladder. It was found that there was no overdose in any critical organ according to the critical threshold values given to protect the organs from radiation-related toxic effects. However, testicles were additionally evaluated in terms of fertility. Ac-225 and Tb-161 were radionuclides that produced optimal doses for TRT.

Stochastic Gauss-Newton method for estimating absorption and scattering in optical tomography with the Monte Carlo method for light transport

Image reconstruction in optical tomography in the so-called transport regime, where the diffusion approximation is not valid, requires modeling of light transport using the radiative transfer equation. In this work, we approach this problem by utilizing the Monte Carlo method for light transport. In this work, we propose a methodology for absolute imaging of absorption and scattering in this regime utilizing a Monte Carlo method for light transport. The image reconstruction problem is formulated as a minimization problem that is solved using a stochastic Gauss-Newton method. In the construction of the Jacobian matrix for scattering, a perturbation approximation for Monte Carlo is utilized. The approach is evaluated with numerical simulations using an adaptive approach where the number of photon packets is adjusted during the iterations, and with different fixed numbers of photon packets. The simulations show that the Monte Carlo method for light transport can be utilized in the absolute imaging problem of optical tomography and that the absorption and scattering parameters can be estimated simultaneously with good accuracy.

Uncertainty Cost Functions for Wave Energy

Wave energy is considered as an important energy source for people living near coastal areas to meet their basic energy needs. Innovative and new technologies can be used for this energy utilization to run the loads from sea waves. Due to its uncertain power availability, we can use uncertainty cost functions based on probability distributions for the availability of the operation of the wave energy microgrid. These probability distributions are based on several mapping models designed for the wave energy, wave height, and wave speed mapping to make the energy more stable and reliable. In contrast to other renewable energy sources, wave energy is inherently unpredictable, making it impossible to model with a single, globally applicable probability distribution function (PDF). The prediction of the behavior of primary wave energy for this purpose is completely based on several probability distribution functions (PDFs) which can be considered as the best for all conditions. In this paper, we have used the Weibull-Rayleigh probability distribution model to develop the uncertainty cost function for wave energy. The Monte-Carlo process is carried out to get the results supported by the Rayleigh probability distribution model consisting of wave height, and uncertain cost histograms. Cost is minimized by using the Weibull Rayleigh model for both overestimation and underestimation costs of wave energy. Monte-Carlo simulation results are further compared with analytical calculation and error between them.

Stochastic eigenvalue computation for cantilever beam dynamics using eigenvector-based polynomial chaos expansion

This paper presents an innovative technique for ascertaining the eigenfrequencies of beams characterized by a random elastic modulus. Our method leverages Polynomial Chaos Expansion (PCE), uniquely focusing on the expansion of eigen vectors rather than the conventional approach of expanding both eigenvalues and eigenvectors. An exceptional aspect of our proposed methodology lies in its seamless integration with the widely-utilized MATLAB ‘eig’ tool, offering a more accessible and efficient solution. To validate the accuracy of our proposed method, we performed comparisons against Monte Carlo simulations, showcasing excellent agreement with both benchmarks. Notably, the mean of our method demonstrates consistent alignment with Monte Carlo simulations, further affirming its precision. We further investigated the computational cost of our method and contrasted it with Monte Carlo simulations, demonstrating its efficiency and computational advantages.

Impact of IL-6 and IL-10 genotypes on tacrolimus dose requirements in kidney transplant recipients: Monte Carlo analysis.

Introduction: IL-6 and IL-10 may affect the activity of cytochrome P450 (CYP) 3A enzymes involved in tacrolimus (Tac) metabolism. Moreover, the effect of IL-6 and IL-10 on Tac pharmacokinetics may differ with respect to the genetic variations in their genes. Aim: To examine the influence of IL-6 and IL-10 gene polymorphisms on Tac dose requirements and exposure over a 5-year period following kidney transplantation. Univariate and standard multivariate linear regression and Monte Carlo analysis were performed to investigate potential covariates influencing Tac dose-adjusted trough concentration (C0/D) in various post-transplantation periods. Materials & methods: IL-6 (-174G>C), IL-10 (-1082G>A, -819C>T and -592C>A) genotype, Tac daily dose, C0, C0/D and intrapatient variability data were collected from 113 patients. Results: Multivariate regression analysis and accompanied Monte Carlo simulation underscore the importance of considering IL-6 -174G>C and IL-10 -1082G>A gene polymorphisms, alongside Tac metabolic phenotype and post-transplantation period, when tailoring Tac dosage regimen. Conclusion: This study provides valuable insights regarding the individualized adjustment of Tac treatment in various post-transplantation periods.

A phase I clinical study: evaluation of safety, tolerability, and population pharmacokinetic-pharmacodynamic target attainment analysis of etimicin sulfate among healthy Chinese participants.

This phase I clinical study aimed to assess the safety, tolerability, and population pharmacokinetic-pharmacodynamics (PK-PD) target attainment analysis of etimicin sulfate in healthy participants and provide scientific reference for further development of clinical breakpoints. Twenty-four healthy Chinese subjects were enrolled and received etimicin sulfate infusion in this study. A population PK model was constructed for the estimation of the PK profiles of etimicin sulfate. The area under the concentration-time curve divided by minimum inhibitory concentration (AUC0-24h/MIC) and the peak concentration divided by MIC (Cmax/MIC) were selected as the PK/PD indices. The probability of target attainment (PTA) was calculated for each designed dosing regimen using Monte Carlo Simulations. The minimum MIC value with a PTA ≥ 90% for each regimen was considered as the PK/PD cutoff values. Etimicin sulfate demonstrated safety, tolerability and predictable PK characteristics. No deaths or serious adverse events appeared, and seven treatment emergent adverse events (TEAEs) were reported by five participants. All TEAEs were minor and relieved rapidly. A two-compartment model was developed and validated for describing the PK features of etimicin sulfate among Chinese healthy participants. The diagnostic goodness-of-fit plots and visual predictive check plots showed that this developed model could describe these data well. The PTA results showed that etimicin sulfate provided clinical improvement against strains with MIC of 0.5-1 mg/l and below, and antibacterial effect against strains with MIC of 0.25 mg/l and below. However, etimicin sulfate had limited clinical efficacy for clinical isolates with MIC values > 1 mg/l.

Impact of COVID-19 on temporal trends and health risks of urinary metal concentrations among residents of Guangzhou, China

Long-term biomonitoring of urinary metal ions is an essential tool for the epidemiological assessment of chronic exposure levels, enabling us to track changes in metal exposure over time and better understand its health implications. In this study, we evaluated the temporal trends of urinary metal ions among 1962 residents of Guangzhou, China, from 2018 to 2022. The total metal ion concentrations in the urine of the population did not change significantly between 2018 and 2019. With the onset of the COVID-19 pandemic in 2020, urinary total metal ion concentrations began to decline dramatically, reaching their lowest level in 2021. A rebound in concentrations was observed in 2022, which returned to the initial levels observed in 2018. Urine chromium and cadmium concentrations peaked in 2020, while urinary lead levels were the highest in 2021, and urinary nickel concentrations were the highest in 2022. Males consistently displayed higher urinary concentrations of lead and arsenic throughout each year of the study. Furthermore, minors consistently had higher urinary nickel levels than adults, whereas adults consistently had higher urinary cadmium concentrations than minors. Cluster analyses were conducted annually on urinary metal ions to examine the differences in their distribution and to evaluate changes in metal exposure patterns over time. The Monte Carlo simulations indicate that the whole population exhibits a high non-carcinogenic risk from arsenic exposure and significant carcinogenic risks associated with exposure to nickel, arsenic, chromium, and cadmium. The next two years were predicted by a gray prediction model, and the results are tested using mean absolute percentage error which demonstrating high accuracy.

Combined DFT and Monte Carlo simulation studies of potential corrosion inhibition properties of coumarin derivatives.

Corrosion, the degradation of materials due to chemical reactions with their environment presents significant challenges both economically and environmentally. It affects various industries, including construction, transportation, and manufacturing, leading to equipment failures, safety hazards, and increased maintenance costs. Coumarin derivatives have shown promise due to their inherent chemical properties and potential for biodegradability. In this study, a series of the coumarin derivatives were examined in silico to reveal their potential corrosion inhibition properties toward the Fe(110) and Cu(111) surfaces. The compounds investigated include coumarin (2H-chromen-2-one, 1), furanocoumarin (7H-furo[3,2-g]chromen-7-one, 2), dihydrofurano coumarin (2,3-dihydro-7H-furo[3,2-g]chromen-7-one, 3), pyrano coumarin-linear type (8,8-dimethyl-2H,8H-pyrano[3,2-g]chromen-2-one, 4), pyrano coumarin-angular type (8,8-dimethyl-2H,8H-pyrano[2,3-f]chromen-2-one, 5), bicoumarin (3,3'-methylenebis(2H-chromen-2-one), 6), and phenyl coumarin (4-phenyl-2H-chromen-2-one, 7). The findings suggest that the bicoumarin derivative 6 exhibits the lowest adsorption energy with the Fe(110) surface, while the same energy absolute value is about two times lower for the Cu(111) surface. This is due to the formation of a planar configuration of a molecule of 6 on the metal surfaces with the participation of both coumarin fragments upon interacting with the Fe(110) surface, while one coumarin fragment interacts with the Cu(111) surface. Density functional theory (DFT) calculations were employed to study the electronic properties of the coumarin derivatives. The specific computational method used was B3LYP, a hybrid functional that combines with the 6-311 + + G(d,p) basis set. Each coumarin derivative was first subjected to a geometry optimization to find the most stable molecular structure. Electronic properties, dipole moments, and molecular electrostatic potential surfaces were calculated. The Monte Carlo simulations were used to model the adsorption behavior of the coumarin derivatives on metal surfaces, namely, Fe(110) and Cu(111). These simulations allowed to visualize interaction of the studied molecules with the metal surfaces, which is crucial for their function as corrosion inhibitors. The present study provides a comprehensive understanding of the corrosion inhibition potential of the applied coumarin derivatives. The insights gained from these methods can inform the development of effective, sustainable corrosion inhibitors that are both environmentally friendly and highly efficient.

Theoretical simulation of time-related electrical performance of 63NiO/ZnO integrated betavoltaic battery

The temporal electrical performance of a 63NiO/ZnO integrated betavoltaic battery is examined. Utilizing first-principles calculations combined with Monte Carlo simulations, we study the energy band structure and density of states of 63NiO, particularly when 63Ni undergoes a 12.5% decay. Our findings reveal that, when the 63NiO layer is 4 μm thick, the decay's impact is akin to substitution doping. Leveraging this insight, we employed Silvaco ATLAS software to simulate the time-dependent short-circuit current, open-circuit voltage, maximum output power, and energy conversion efficiency of the 63NiO/ZnO integrated betavoltaic battery. These results were compared with those of a NiO/ZnO separate betavoltaic battery. At 6.93 years, the maximum output power of the integrated and separate devices was found to be 10.19 and 9.77 nW/cm2, respectively, corresponding to 8.67% and 88.79% of their initial values. Notably, prior to this point, the integrated device exhibited significantly superior performance; at 4.58 years, it demonstrated 2.28 times higher maximum output power compared to the separate device, followed by only a slight difference in performance thereafter.

Residential consumer enrollment in demand response: An agent based approach

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a ±10% variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector.

Population Pharmacokinetics and Loading Dose Optimization of Intravenous Valproic Acid in Hospitalized Thai Patients.

Our goal is to create a population pharmacokinetic (PK) model and identify the best loading dose (LD) of intravenous valproic acid for hospitalized Thai patients. Data from patients who received intravenous valproic acid and underwent measurement of serum valproic acid concentrations during hospitalization were collected retrospectively. A nonlinear mixed-effects modeling approach was conducted to estimate the PK parameters of valproic acid. Covariates affecting the PK parameters of valproic acid were examined and ranked based on their impact on the model's performance. Monte Carlo simulations of 1000 patients were conducted to estimate the optimal LD of valproic acid. A total of 120 hospitalized patients (51.7% male) with 167 valproic acid concentrations were included in the study. A linear one-compartment model with constant residual error was the best base model. An age-covariate model was the best predictor of valproic acid clearance (CL). The typical values of CL and volume of distribution for valproic acid were 0.77 L/h and 14.56 L, respectively. The LD of 1000-1200 mg intravenous was identified as the pragmatic option as an empirical regimen for hospitalized Thai patients. The recommended time to initiate maintenance dose (MD) is 4-8 h following the LD. The population PK model and optimal LD of valproic acid in hospitalized Thai patients has been established, and it may be advisable to initiate the MD at a later time for the elderly.

Pinball boosting of regression quantiles

An algorithm for boosting regression quantiles using asymmetric least absolute deviations, better known as pinball loss, is proposed. Existing approaches for boosting regression quantiles are essentially equal to least squares boosting of regression means with the single difference that their working residuals are based on pinball loss. All steps of our boosting algorithm are embedded in the well-established framework of quantile regression, and its main components – sequential base learning, fitting, and updating – are based on consistent scoring rules for regression quantiles. The Monte Carlo simulations performed indicate that the pinball boosting algorithm is competitive with existing approaches for boosting regression quantiles in terms of estimation accuracy and variable selection, and that its application to the study of regression quantiles of hedonic price functions allows the estimation of previously infeasible high-dimensional specifications.

Option pricing for Barndorff–Nielsen and Shephard model by supervised deep learning

This paper aims to develop a supervised deep learning scheme to compute call option prices for the Barndorff-Nielsen and Shephard model with a non-martingale asset price process having infinite active jumps. In our deep learning scheme, teaching data are generated through the Monte Carlo method developed by Arai and Imai [(2024). Monte Carlo simulation for Barndorff-Nielsen and Shephard model under change of measure, Mathematics and Computers in Simulation, 218, 223–234]. Moreover, the BNS model includes many parameters, which makes the deep learning accuracy worse. Therefore, we will create another input parameter using the Black–Scholes formula. As a result, the accuracy is improved dramatically.

Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples.

Objective
Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton range in-vivo. To fully utilize these methods, an estimate of the elemental composition of the patient's tissue is often needed. 
Approach
A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample's carbon and oxygen contents. The compositions were also assessed with combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues.
Results
The root-mean-square error for the soft tissue carbon and oxygen content was 8.5wt% and 9.5wt% relative to the CCA result and 2.1wt% and 10.3wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4wt% and 1.1wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous. 
Significance 
Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute to in-vivo dose verification measurements.