Monte Carlo Simulations Research Articles

Exploring Multiple Magnetization Plateaus in Graphdiyne-Like Nanolattice Using Monte Carlo Simulations

In this study, we investigated multiple magnetization plateaus in a graphdiyne-type nanolattice using Monte Carlo simulations within the framework of the Blume–Capel model and the Metropolis algorithm. The analysis revealed distinct magnetization plateaus for specific parameter values, indicating a significant increase in the number of stable magnetic states. The total magnetization ([Formula: see text]) exhibits four plateaus corresponding to critical field levels ([Formula: see text], [Formula: see text], [Formula: see text]) and the saturation field ([Formula: see text]). The effects of the reduced exchange coupling parameter ([Formula: see text]), the crystal field parameter (d), and the temperature (t) on the magnetization plateaus were systematically examined. The magnetization plateau behavior observed in the graphdiyne-like nanolattice was comparable to that of other systems studied using Monte Carlo simulations. Future applications may include the development of advanced magnetic storage devices and quantum computing elements, where precise control of magnetic states is essential.

3Dπ: three-dimensional positron imaging, a novel total-body PET scanner using xenon-doped liquid argon scintillator.

Objective.This paper introduces a novel PET imaging methodology called 3-dimensional positron imaging (3Dπ), which integrates total-body coverage, time-of-flight (TOF) technology, ultra-low dose imaging capabilities, and ultra-fast readout electronics inspired by emerging technology from the DarkSide collaboration.Approach.The study evaluates the performance of 3Dπusing Monte Carlo simulations based on NEMA NU 2-2018 protocols. The methodology employs a homogenous, monolithic scintillator composed of liquid argon (LAr) doped with xenon (Xe) with silicon photomultipliers (SiPMs) operating at cryogenic temperatures.Main results.Substantial improvements in system performance are observed, with the 3Dπsystem achieving a noise equivalent count rate of 3.2 Mcps at 17.3 kBq ml-1, continuing to increase up to 4.3 Mcps at 40 kBq ml-1. Spatial resolution measurements show an average FWHM of 2.7 mm across both axial positions. The system exhibits superior sensitivity, with values reaching 373 kcps MBq-1with a line source at the center of the field of view. Additionally, 3Dπachieves a TOF resolution of 151 ps at 5.3 kBq ml-1, highlighting its potential to produce high-quality images with reduced noise levels.Significance.The study underscores the potential of 3Dπin improving PET imaging performance, offering the potential for shorter scan times and reduced radiation exposure for patients. The Xe-doped LAr offers advantages such as fast scintillation, enhanced light yield, and cost-effectiveness. Future research will focus on optimizing system geometry and further refining reconstruction algorithms to exploit the strengths of 3Dπfor clinical applications.

Quantum magic and multipartite entanglement in the structure of nuclei

Motivated by the Gottesman-Knill theorem, we present a detailed study of the quantum complexity of p-shell and sd-shell nuclei. Valence-space nuclear shell-model wave functions generated by the code are mapped to qubit registers using the Jordan-Wigner mapping (12 qubits for the p shell and 24 qubits for the sd shell), from which measures of the many-body entanglement (n-tangles) and magic (nonstabilizerness) are determined. While exact evaluations of these measures are possible for nuclei with a modest number of active nucleons, Monte Carlo simulations are required for the more complex nuclei. The broadly applicable Pauli-string ÎẐ exact (PSIZe) Markov chain Monte Carlo (MCMC) technique is introduced to accelerate the evaluation of measures of magic in deformed nuclei (with hierarchical wave functions), by factors of ≈8 for some nuclei. Significant multinucleon entanglement is found in the sd shell, dominated by proton-neutron configurations, along with significant measures of magic. This is evident not only for the deformed states, but also for nuclei on the path to instability via regions of shape coexistence and level inversion. These results indicate that quantum-computing resources will accelerate precision simulations of such nuclei and beyond. Published by the American Physical Society 2025

Direct and indirect impacts of the COVID-19 pandemic on life expectancy and person-years of life lost with and without disability: A systematic analysis for 18 European countries, 2020-2022.

The direct and indirect impacts of the COVID-19 pandemic on life expectancy (LE) and years of life lost with and without disability remain unclear. Accounting for pre-pandemic trends in morbidity and mortality, we assessed these impacts in 18 European countries, for the years2020-2022. We used multi-state Markov modeling based on several data sources to track transitions of the population aged 35 or older between eight health states from disease-free, combinations of cardiovascular disease, cognitive impairment, dementia, and disability, through to death. We quantified separately numbers and rates of deaths attributable to COVID-19 from those related to mortality from other causes during 2020-2022, and estimated the proportion of loss of life expectancy and years of life with and without disability that could have been avoided if the pandemic had not occurred. Estimates were disaggregated by COVID-19 versus non-COVID causes of deaths, calendar year, age, sex, disability status, and country. We generated the 95% uncertainty intervals (UIs) using Monte Carlo simulations with 500 iterations. Among the 289 million adult population in the 18 countries, person-years of life lost (PYLL) in millions were 4.7 (95% UI 3.4-6.0) in 2020, 7.1 (95% UI 6.6-7.9) in 2021, and 5.0 (95% UI 4.1-6.2) in 2022, totaling 16.8 (95% UI 12.0-21.8) million. PYLL per capita varied considerably between the 18 countries ranging between 20 and 109 per 1,000 population. About 60% of the total PYLL occurred among persons aged over 80, and 30% in those aged 65-80. If the pandemic were avoided, over half (9.8 million (95% UI 4.7-15.1)) of the 16.8 million PYLL were estimated to have been lived without disability. Of the total PYLL, 11.6-13.2 million were due to registered COVID-19 deaths and 3.6-5.3 million due to non-COVID mortality. Despite a decrease in PYLL attributable to COVID-19 after 2021, PYLL associated with other causes of death continued to increase from 2020 to 2022 in most countries. Lower income countries had higher PYLL per capita as well as a greater proportion of disability-free PYLL during 2020-2022. Similar patterns were observed for life expectancy. In 2021, LE at age 35 (LE-35) declined by up to 2.8 (95% UI 2.3-3.3) years, with over two-thirds being disability-free. With the exception of Sweden, LE-35 in the studied countries did not recover to 2019 levels by 2022. The considerable loss of life without disability and the rise in premature mortality not directly linked to COVID-19 deaths during 2020-2022 suggest a potential broader, longer-term and partially indirect impact of the pandemic, possibly resulting from disruptions in healthcare delivery and services for non-COVID conditions and unintended consequences of COVID-19 containment measures. These findings highlight a need for better pandemic preparedness in Europe, ideally, as part of a more comprehensive global public health agenda.

Theoretical limits of passive daytime radiative cooling in extraterrestrial environments: implications for space exploration

Passive daytime radiative cooling (PDRC) enables sub-ambient cooling without external energy input, a feature that has attracted significant research attention. While most studies have focused on PDRC performance in terrestrial environments, extraterrestrial settings offer the potential for superior cooling due to the absence of convective heat transfer and atmospheric thermal radiation. To address this gap, we investigate the extreme optical properties and radiative cooling performance of PDRC materials in extraterrestrial settings, specifically paints and ceramics, known for their strong solar reflectance, high thermal emissivity, and ease of fabrication. This investigation is grounded in a comprehensive theoretical framework that incorporates Mie scattering, Monte Carlo simulations, effective medium theory, and the transfer matrix method for ultra-broadband spectral simulations from vacuum ultraviolet to far-infrared wavelengths, followed by energy balance analysis in extraterrestrial environments. We explore the influence of particle size, volume fraction, and thickness in paints and ceramics on cooling performance to elucidate the theoretical limit of extraterrestrial PDRC. This study broadens the scope of PDRC research and provides valuable insights into the application of PDRC materials for future deep-space exploration.

Propagation of Systematic Sensor Uncertainty Into the Frequency Domain

Abstract When it comes to the identification of dynamic system parameters, like stiffness and damping, the systematic measurement uncertainty is mostly ignored in the frequency domain because of its complicated and elaborate uncertainty propagation. Nevertheless, the uncertainty is far from being negligible. In order to examine the importance of the systematic measurement uncertainty in the frequency domain, the propagation of systematic uncertainty caused by four different sensor error types is investigated by using Monte Carlo simulations. It is shown that an uncertainty in the bias only affects static measurements, i.e., with an excitation frequency of the system being Ω=0. Furthermore, the uncertainty of the linearity and sensitivity only impacts the magnitude of the Fourier coefficient, whereas the uncertainty in the hysteresis results only in an uncertainty in the phase. Subsequently, it is shown that the linearity and hysteresis uncertainty are completely independent in the frequency domain. The results lead to simple propagation relations applicable to various engineering, science, and industry challenges without the need for the conduction of complex Monte Carlo simulations.

Three‐dimensional gravity inversion based on the Hamiltonian Monte Carlo method

AbstractIn geophysics, Bayesian inversion methods are of significant prominence. Here, we present a novel approach utilizing the Hamiltonian Monte Carlo (HMC) method in gravity inversion for elucidating three‐dimensional (3D) density structures. HMC provides a multi‐dimensional sampling method that demonstrates enhanced optimization efficiency, facilitating the attainment of distant proposals with elevated acceptance probabilities. Its applicability also extends to resolving linear inverse problems. Three synthetic models of cubic bodies, dipping dykes and a combined model were designed for tests. The testes underscore the promising potential of HMC in recovering subsurface density source bodies and giving the uncertainty of the inversion model. Furthermore, an inversion test conducted on the Vinton salt dome yields a reasonable 3D distribution of cap rock, consistent with prior studies in this area. The modelling and field experiments showed that the proposed HMC gravity inversion method had higher accuracy and application potential.

Simulation of atom trajectories in the original Stern–Gerlach experiment

Abstract Following a comprehensive analysis of the historical literature, we model the geometry of the Stern–Gerlach experiment to numerically calculate the magnetic field using the finite-element method. Using this calculated field and Monte Carlo methods, the semiclassical atomic translational dynamics are simulated to produce the well-known quantized end-pattern with matching dimensions. The finite-element method used provides the most accurate description of the Stern–Gerlach magnetic field and end-pattern in the literature, matching the historically reported values and figures.

A comparative analysis of GEANT4, MCNP6 and FLUKA on proton-induced gamma-ray simulation.

Precise range verification is essential in proton therapy to minimize treatment margins due to the steep dose fall-off of proton beams. The emission of secondary radiation from nuclear reactions between incident particles and tissues stands out as a promising method for range verification. Two prominent techniques are PET and Prompt Gamma-Ray Spectroscopy (PGS). PGS holds significant promise due to its real-time capability for range monitoring. This method allows for prompt detection and quantification of any disparities between planned and actual dose delivery, facilitating adaptive treatment strategies. Given the key role of Monte Carlo (MC) codes in understanding the PGS mechanisms during proton therapy, it is essential to address the current lack of validated codes covering the full energy spectrum of emittedgamma-rays. Addressing the need for precise range monitoring in proton therapy, our study aims to develop and validate MC codes for PGS. We focus on analyse MCNP6, GEANT4, and FLUKA codes, conducting rigorous validation process by comparing our simulation results with experimental data. Additionally, we propose optimal models and parameters to refine the accuracy of simulations for prompt gamma-ray (PG)spectra. Various proton data libraries, models and cross-sectionsvalues were used in this study to simulate proton-induced gamma-rays in MCNP6, GEANT4 and FLUKA. To validate these simulations, PGS spectra of PMMA block irradiation were obtained with inorganic scintillator detector for different proton energies, raging from approximately to . GEANT4 was the only MC code capable of successfully reproducing PG lines, while the FLUKA aligned better with experimental data for mid-range energies. At higher energies, FLUKA overestimated the PG line ( ) at , whereas GEANT4 underestimated it; MCNP6 provided the closest match. Additionally, GEANT4, FLUKA, and MCNP6 failed to accurately reproduce the PG line ( ) at , consistent with previous findings. To address this limitation, a new model based on experimental and theoretical data from literature wasdeveloped. This study emphasizes the need for updates to the data tables in MC simulations and underscores the importance of further theoretical and experimental research on PG de-excitation lines relevant to proton therapy. The newly developed model, designed to address discrepancies in the simulation of and de-excitation lines across different toolkits, successfully improved the accuracy of the oxygen de-excitation line, which was previously notwell-reproduced.

A method in non-destructive testing for lead shielding exceeding 25 kg/m2 using 18F.

Non-Destructive Testing (NDT) is a commonly used technique for barrier verification within radiation protection, ensuring compliance with national standards and state regulations. There are currently limited published methods in NDT for lead shielding above 25 kg/m2, and thus this research aimed to develop a reproducible method to aid in 'in the field' NDT for lead barriers exceeding 25 kg/m2 using a Fluorine-18 (18F) source. Due to the fast decay of 18F, the data generated within this research was compiled from Monte Carlo (MC) simulations using the PENELOPE engine, and the PENGEOM geometry system to model the proposed empirical setup. The model predicted the Transmission Factor (TF) through area densities (thickness) of lead attenuators up to 302.7 kg/m2, with results validated by empirical measurements using a Source-to-Detector Distance (SDD) of 38.1 ± 0.05 cm and 52.7 ± 0.05 cm. However, the study was limited by the chosen activity of 18F at approximately 180 MBq, where the simulated TF curves demonstrated correspondence of data up to and including area densities of 162.4 kg/m2 using a 38.1 ± 0.05 cm SDD, and 138.0 kg/m2 with a 52.7 ± 0.05 cm SDD. Beyond these thicknesses, the empirical transmission curves deviated from simulated curves due to measurable transmissions becoming significantly reduced. This research demonstrated that using SDDs above 23 cm would provide sufficient near narrow beam conditions with the proposed experimental configuration for in-the-field NDT. The research aimed to develop an equation and method for NDT using a 18F source for lead barriers greater than 25 kg/m2, with transmission data to be made available upon request to the author.

Three-Dimensional Simulation of Bipolar Resistive Switching Memory with Embedded Conductive Nanocrystals in an Oxide Matrix

In this work, the simulation of deoxidation–oxidation of oxygen vacancies (VOs) in an oxide matrix with embedded conductive nanocrystals (c-NCs) is carried out for the development of bipolar resistive switching memories (BRSMs). We have employed the three-dimensional kinetic Monte Carlo (3D-kMC) method to simulate the RS behavior of BRSMs. The c-NC is modeled as fixed oxygen vacancy (f-VO) clusters, defined as sites with zero recombination probability. The three-dimensional oxygen vacancy configuration (3D-VOC) obtained for each voltage step of the simulation is used to calculate the resistive state and the electrical current. It was found that the c-NC reduces the voltage required to switch the memory state from a high to a low resistive state due to the increase in a nonhomogeneous electrical field between electrodes.

The study on the adsorption characteristics of anthracite under different temperature and pressure conditions.

The study of the adsorption characteristics of coal is of great significance to gas prevention and CO2 geological storage. To explore the adsorption mechanism of coal, this study focuses on columnar anthracite. Adsorption tests on coal rock under a range of physical field conditions were conducted using the volumetric method. The adsorption characteristics of anthracite for CO2, CH4, and N2 gases under different conditions were investigated using Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) methods. The results showed that the adsorption capacities of anthracite for these three gases are in the order of CO2 > CH4 > N2, and that the adsorption capacity increases with increasing gas injection pressure. The CO2/CH4/N2 gas molecule adsorption capacity of the anthracite macromolecular structure model decreases with increasing temperature. The increase in temperature has the greatest influence on the CO2 absorption capacity, followed by the CH4 and N2 adsorption capacities. The research offers a theoretical basis for the control of coal mine gas and the geological storage of CO2.

Voltage-Dependent Electrochemical Carbon Dioxide Reduction Mechanism Unveiled by Kinetic Monte Carlo Simulation.

The voltage-dependent dynamic evolution of the electrocatalytic carbon dioxide reduction reaction (CO2RR) on Cu-based catalysts is still unclear. Herein, a Kinetic Monte Carlo (KMC) model that tracks the evolution of the CO2RR on the Cu (111)/(100) surface is developed. Using the Density Functional Theory calculated energetics of 178 elementary reactions in CO2RR toward C1-C2 multispecies production, the KMC model predicted CO2RR linear sweep voltammetry and potential-dependent product distribution that agree well with experimental observations. Degree of rate control analysis reveals that, on Cu (111), the primary hydrocarbon product is C1 species CH4, and as the working potential increases, its rate-determining step (RDS) changes from CO hydrogenation toward the CHO* formation step into the COH* formation step. The Cu (100) surface is more active toward C2H4 and CH3CH2OH production with CO* symmetric coupling step as RDS. This KMC model provides important insights into the CO2RR dynamics on Cu catalysts.

Dynamic panel models with multi-threshold effects and endogeneity

This paper proposes a dynamic multiple threshold regression model with endogeneity to enhance the flexibility in capturing the underlying regime-switching mechanisms for panel data. To handle endogeneity, we apply the first-differenced GMM framework to estimate the unknown parameters for a given number of thresholds. When the number of thresholds is unknown, a modified information criterion is employed to determine the model complexity. We show that, with a probability tending to one, our method can consistently identify the true number of thresholds. Other theoretical properties of the multi-threshold estimators are also well-established. Moreover, we develop a sup-Wald test statistic to facilitate inference on threshold effects. Monte Carlo simulations demonstrate the favourable performance of our proposals. An investment and financial constraints dataset is analysed with the proposed model to illustrate the asymmetric sensitivity of investment to cash flows. As a byproduct, we develop a user-friendly R package panelthreshold to implement the methodologies in this paper.

Addressing Bias and Data Privacy Concerns in AI-Driven Credit Scoring Systems Through Cybersecurity Risk Assessment

The increasing reliance on artificial intelligence (AI) in credit scoring has raised concerns about algorithmic bias and data privacy, necessitating robust cybersecurity risk assessment frameworks. This study investigates the role of cybersecurity risk assessment in mitigating these risks, utilizing multiple datasets, including the Home Mortgage Disclosure Act (HMDA) dataset, the Equifax Data Breach Report, the Financial Cybersecurity Incidents Database, and the MITRE ATT&CK Financial Sector Threat Intelligence Dataset. We employ statistical fairness metrics, Bayesian Probability Modeling, Markov Chain Analysis, and Monte Carlo Simulations to evaluate the extent of bias, privacy risks, and cybersecurity vulnerabilities. Findings reveal significant disparities in loan approvals, with Black applicants receiving approval rates 28% lower than White applicants (χ² = 59.83, p < 0.001), highlighting systemic bias in AI-driven credit scoring. Data privacy remains a pressing issue, as financial sector breaches affect an average of 5,069,760 individuals per incident. Insider threats pose the greatest risk, with a probability of 0.81 of leading to financial fraud. These findings underscore the urgency of integrating fairness-aware machine learning, enhancing regulatory compliance with AI governance policies, and deploying AI-driven cybersecurity tools to fortify financial AI applications against emerging threats. This research contributes to the broader discourse on ethical AI by providing a structured cybersecurity risk assessment approach to mitigate algorithmic bias and strengthen data privacy protections. Implementing these recommendations will enhance fairness, security, and transparency in AI-driven financial decision-making, ensuring compliance with evolving regulatory frameworks and fostering trust in automated credit scoring systems.

Practical issues in conducting distributional weighting in benefit‐cost analysis

AbstractA commonly expressed concern about distributional weighting in benefit‐cost analysis is that the informational burden is too high and the practical challenges insurmountable. In this paper, we address this concern by conducting distributional weighting on a number of real‐world examples, covering a range of different types of policy impacts. We uncover and explore a number of methodological issues that arise in the process of distributional weighting and provide a simplified set of steps that we believe can be implemented by practitioners with a wide range of expertise. We conduct sensitivity analysis and Monte Carlo simulation to test the robustness of our estimates of weighted net benefits to the various assumptions we make, and find that, in general, distributional weighting is no more vulnerable to modeling assumptions and parameter selection than unweighted benefit‐cost analysis itself. We conclude that the concern about the practicability of distributional weighting is, at least in a range of important cases, unfounded.

Statistical framework for nuclear parameter uncertainties in nucleosynthesis modeling of r- and i-process

Propagating nuclear uncertainties to nucleosynthesis simulations is key to understand the impact of theoretical uncertainties on the predictions, especially for processes far from the stability region, where nuclear properties are scarcely known. While systematic (model) uncertainties have been thoroughly studied, the statistical (parameter) ones have been more rarely explored, as constraining them is more challenging. We present here a methodology to determine coherently parameter uncertainties by anchoring the theoretical uncertainties to the experimentally known nuclear properties through the use of the Backward Forward Monte Carlo method. We use this methodology for two nucleosynthesis processes: the intermediate neutron capture process (i-process) and the rapid neutron capture process (r-process). We determine coherently for the i-process the uncertainties from the (n,γ) rates while we explore the impact of nuclear mass uncertainties for the r-process. The effect of parameter uncertainties on the final nucleosynthesis is in the same order as model uncertainties, suggesting the crucial need for more experimental constraints on key nuclei of interest. We show how key nuclear properties, such as relevant (n,γ) rates impacting the i-process tracers, could enhance tremendously the prediction of stellar evolution models by experimentally constraining them.

Theoretical Evaluation of Urea Derivatives on Fe (110) and Sn (111) Surfaces: DFT and Monte Carlo Simulation Approaches

Present-day theoretical computation methods are quite effective at anticipating how corrosion inhibitors would behave on metal surfaces, conserving time and resources on laboratory testing. The present study aims to predict the inhibitory effectiveness of certain furan derivatives on surfaces of Fe (110) and Sn (111) in acidic media. To compare their fundamental attributes against corrosion as well as their behavior on iron (Fe) and tin (Sn) surfaces in an acid medium, six urea derivatives have been chosen for this purpose: Imidazolidin-2-one (Cpd A), 4-methylimidazolidine-2-one (Cpd B), Tetrahyropyrimidin-2(1H)-one (Cpd C), 1,3-dimethylimidazolidin-2-one (Cpd D), 1,1-diethylurea (Cpd E), 1,3-dimethylurea (Cpd F). The anti-corrosive characteristics of Cpds A - F were generally explored using Density Functional Theory and Monte Carlo simulations. The results indicate that the compounds had low energy gaps, the highest occupied molecular orbital energy, global hardness, as well as ionization energy and high Electron affinity, ELUMO, global softness, and number of transferred electrons, which explained their corrosion inhibition potentials. Electrostatic potential (ESP) and Fukui indices identified the reactive sites with the ability to accept and donate electrons via back-donation to the metal's d-orbital. The Monte Carlo simulation demonstrated a favorable contact between the Fe (110) and Sn (111) surfaces and inhibitory molecules in the acidic medium. In the industrial sectors, these compounds may be secured as corrosion protectors.

Characteristics stochastic analysis of long and narrow deep excavations under soil spatial variability

The mechanical properties of soil, resulting from the weathering of rocks through physical and chemical processes, exhibit spatial variability. This variability introduces uncertainties in the design and characteristics of excavation projects. To address these uncertainties caused by soil spatial variability, safety factors are commonly used in excavation design. However, using the same safety factor for different indicators of soil spatial variability is illogical. Therefore, specialized research on the characteristics of deep excavations in the context of soil spatial variability is necessary, as it provides the theoretical basis for rational excavation design. In this study, we assumed that soil parameters follow a lognormal distribution, while spatial correlation adheres to a Gaussian function. We developed a random finite element algorithm for deep excavations, which incorporated Python programming and the ABAQUS computational platform. This algorithm was created within the framework of random field theory and Monte Carlo simulation. The results of our study indicate that, influenced by soil spatial variability, the lateral wall movements and ground surface settlements exhibit discrete distributions near the deterministic results. The maximum deformation of the excavation follows a normal distribution, while the pattern of ground surface settlements demonstrates diversity and chaotic characteristics. The extent to which soil spatial variability affects deep excavations is correlated with indicators of this variability. As the coefficient of soil spatial variability increases, the diversity and chaotic characteristics of ground surface settlements become more prominent. The locations of maximum ground surface settlement and maximum deformation becomes more scattered. Consequently, the probability of excavation failure increases, and the reliability index of the excavation decreases. In summary, soil spatial variability significantly impacts deformation prediction and safety control during the design and construction stages of deep excavations. Therefore, it is crucial to consider the influence of soil spatial variability when designing deep excavations, based on the variability indicators.

Enhancing Ion Emission: Insights from Molecular Dynamics and Monte Carlo Simulations

Enhancing Ion Emission: Insights from Molecular Dynamics and Monte Carlo Simulations