Monte Carlo Simulations Research Articles

Avoiding pitfalls in molecular simulation of vapor sorption: Example of propane and isobutane in metal-organic frameworks for adsorption cooling applications.

This study introduces recommendations for conducting molecular simulations of vapor adsorption, with an emphasis on enhancing the accuracy, reproducibility, and comparability of results. The first aspect we address is consistency in the implementation of some details of typical molecular models, including tail corrections and cutoff distances, due to their significant influence on generated data. We highlight the importance of explicitly calculating the saturation pressures at relevant temperatures using methods such as Gibbs ensemble Monte Carlo simulations and illustrate some pitfalls in extrapolating saturation pressures using this method. For grand canonical Monte Carlo (GCMC) simulations, the input fugacity is usually calculated using an equation of state, which often requires the critical parameters of the fluid. We show the importance of using critical parameters derived from the simulation with the same model to ensure internal consistency between the simulated explicit adsorbate phase and the implicit bulk phase in GCMC. We show the advantages of presenting isotherms on a relative pressure scale to facilitate easier comparison among models and with experiment. Extending these guidelines to a practical case study, we evaluate the performance of various isoreticular metal-organic frameworks (MOFs) in adsorption cooling applications. This includes examining the advantages of using propane and isobutane as working fluids and identifying MOFs with a superior performance.

SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories.

Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.

Tomographic cross correlations between galaxy surveys and the CMB gravitational lensing potential. Effect of the redshift bin mismatch

Upcoming surveys of the large-scale structure of our Universe will employ a large coverage area of about half of the sky and will significantly increase the observational depth. With these surveys, we will be able to cross-correlate cosmic microwave background (CMB) gravitational lensing and galaxy surveys divided into narrow redshift bins to map the evolution of the cosmological parameters with redshift. We study the effect of the redshift bin mismatch of objects that is due to photometric redshift errors in tomographic cross-correlation measurements. We used the code FLASK to create Monte Carlo simulations of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) and Planck CMB lensing convergence. We simulated log-normal fields and divided galaxies into nine redshift bins with the Gaussian and modified Lorentzian photometric redshift errors. To estimate the parameters, we used angular power spectra of CMB lensing and galaxy density contrast fields and the maximum likelihood estimation method. We show that even with simple Gaussian errors with a standard deviation of $ the galaxy auto-power spectra in tomographic bins are offset by $2-15<!PCT!>$. The estimated cross-power spectra between galaxy clustering and CMB lensing are also biased, with smaller deviations $<5<!PCT!>$. As a result, the $ $ parameter deviates between $0.2-1.2\ due to the redshift bin mismatch of the objects. We propose a computationally fast and robust method based on the scattering matrix approach to correct for the redshift bin mismatch of the objects. The estimates of the parameters in tomographic studies such as the linear galaxy bias, the cross-correlation amplitude, and $ $ are biased due to the redshift bin mismatch of the objects. The biases in these parameters are alleviated with our scattering matrix approach.

Alternative statistical interpretation for the apparent plateaus in the duration distributions of gamma-ray bursts

The existence of a plateau in the short-duration tail of the observed distribution of cosmological long-soft gamma ray bursts (LGRBs) is posited to be the first direct evidence of collapsars. A similar plateau in the short-duration tail of the observed duration distribution of short-hard gamma ray bursts (SGRBs) has been suggested as evidence of compact binary mergers. We present an equally plausible alternative interpretation for this evidence, based on a purely statistical approach. Specifically, we show that the observed plateau in the short-duration tail of the duration distribution of LGRBs can naturally occur in the statistical distributions of strictly positive physical quantities, exacerbated by the effects of mixing with the duration distribution of SGRBs, observational selection effects, and data aggregation (e.g., binning) methodologies. The observed plateau in the short-duration tail of the observed distributions of SGRBs may likewise result from a combination of sample incompleteness and inhomogeneous binning of data. We further confirm the impact of these factors on the observation of a plateau in the duration distributions of GRBs through extensive numerical Monte Carlo simulations. This analysis corroborates and strengthens a purely statistical and sample-incompleteness interpretation of the observed plateau in the duration distribution of LGRBs and SGRBs, without invoking the physics of collapsars or jet-propagation through the stellar envelope.

Efficient optimization of an accelerator neutron source for neutron capture therapy using genetic algorithms.

In recent years, genetic algorithms have been applied in the field of nuclear technology design, producing superior optimization results compared to traditional methods. They can be employed in the design and optimization of beam shaping assemblies (BSA) BSA to obtain the desired neutron beams. But it should be noted that the direct combination of Monte Carlo methods with genetic algorithms requires a significant amount of computational resources and time. Design and optimize BSA more efficiently to achieve neutron beams that meet specified recommendations. We propose an approach of NSGA II with crucial variables which are identified by multivariate statistical techniques. This approach significantly reduces the problem sizes, thus reducing the time required for optimization. We illustrate this methodology using the example of BSA design for AB-BNCT. The computational efficiency has tripled with crucial variables. By using NSGA II, we obtained optimized models conforming to both the new and old version IAEA BNCT guidelines through a single optimization process and subjected them to phantom analysis. The results demonstrate that models obtained through this method can meet the IAEA recommendations with deep advantage depth (AD) and high absorbed ratio (AR). The genetic algorithm with crucial variables displays tremendous potential in addressing BSA optimization challenges.

Reliability assessment of dynamic line rating methods based on conductor temperature estimation

Dynamic line rating techniques are increasingly prevalent in the computation of ampacity for overhead transmission lines. Some of these methods, commonly employed by system operators, rely on real-time measurements to calculate conductor temperature, either directly or indirectly. Despite their widespread usage, the susceptibility of these techniques to measurement errors has been largely disregarded in prior research. In this regard, this paper includes a groundbreaking reliability analysis addressing the impact of measurement errors on various dynamic line rating techniques in overhead lines, based on conductor temperature estimation. Two case studies are presented, incorporating typical errors from simulations. The first case study adopts a standard 24-hour profile for the variables involved in the conductor ampacity calculation. In the second study, Monte Carlo simulations are conducted, in which meteorological variables are randomly assigned with five load levels to evaluate the current’s influence on measurement noise sensitivity. The resulting ampacity errors across all methods considered are remarkably high, with mean values exceeding 150% in specific scenarios. This unequivocally demonstrates that the commonly accepted levels of measurement accuracy in these widely used rating techniques yield estimations falling far below acceptable reliability standards.

A stochastic modelling framework for predicting flexural properties of ultra-thin randomly oriented strands

A stochastic modelling framework is developed to predict the flexural properties of high-strength sheet moulding compounds made of randomly oriented ultra-thin carbon fibre-reinforced thermoplastic prepreg tapes. The model enables reliable designs using ultra-thin randomly oriented strands with less testing, leading to potential applications in automotive primary structures. The stochastic model is based on a Monte Carlo simulation. The flexural modulus is predicted using classical laminate theory, while the flexural strength is predicted by following a Weibull distribution. Fibre discontinuities are considered through stress concentrations introduced by tape overlaps. The results are validated against new 3-point bending and previously reported 4-point bending experimental results. A scaling effect on strength and scatter is predicted, and its implications for structural applications are also discussed.

The research on the failure mechanism and dynamic reliability of lubricated clearance

In the operation of mechanisms with lubricated clearances, uncertain parameters such as the temperature of lubricant, driving speed, and clearance value will lead to dynamic variations for oil film thickness of lubricated clearance. The lubrication may fail when the oil film thickness of bearing surfaces is insufficient, thereby significantly affecting the performance of the mechanism. This paper presents a novel method for the dynamic reliability of lubricated clearances by improved response surface method (IRSM). Firstly, the mathematical model of lubricated clearance is established, and the oil film load of lubricated clearance is defined, then the dynamic model of mechanism considering lubricated clearances is built through Lagrange method. Secondly, considering the failure mechanism of the lubricated clearance, the surrogate model is established using the IRSM with minimum oil film thickness as the failure criterion. Finally, taking the 9-link mechanism with multiple lubricated clearances as a numerical example, the dynamic minimum oil film thickness of the lubricated clearance considering uncertain parameters is computed. The reliability of the lubricated clearance is investigated by the surrogate model, and the applicability of the model is verified by the Monte Carlo method (MCM). The results demonstrate that the IRSM has higher accuracy compared to the traditional response surface method (TRSM). This paper provides a theoretical basis for the study of failure mechanism and reliability of lubricated clearances, and also has practical significance for engineering applications.

Understanding Substrate Effects on 2D MoS2 Growth: A Kinetic Monte Carlo Approach

AbstractControlling the morphology of 2D transition metal dichalcogenides (TMDs) plays a key role in their applications. Although chemical vapor deposition can achieve wafer‐scale growth of 2D TMDs, a comprehensive theoretical framework for effective growth optimization is lacking. Atomistic modeling methods offer a promising approach to delve into the intricate dynamics underlying the growth. In this study, kinetic Monte Carlo (kMC) simulations are employed to identify crucial parameters that govern the morphology of MoS2 flakes grown on diverse substrates. The simulations reveal that large adsorption rates significantly enhance growth speed, which however necessitates rapid edge migration to achieve compact triangles. Substrate etching can tune the adsorption–desorption process of adatoms and enable preferential growth within a specific substrate region, controlling the flake morphology. This study unravels the complex dynamics governing 2D TMD morphology, offering a theoretical framework for decision‐making in the design and optimization of TMD synthesis processes.

Quantum states and static field ionization of a cylindrical confined hydrogen atom: A diffusion Monte Carlo study

AbstractIn this article, the diffusion Monte Carlo (DMC) method is applied to the study of the quantum states of a hydrogen atom confined into a cylindrical potential well. We present an independent reproduction of previous studies based on different methods, in particular the energy eigenvalues for ground and selected excited states and the polarizability of the ground state, both for finite and infinite cylinders. The static field ionization of ground and excited states of the confined atom is discussed, including the determination of the potential energy surface and equilibrium position of the proton. This study provides a further demonstration of the versatility of the DMC method for this and analogous problems.

Comparison of some Bayesian estimation methods for type-I generalized extreme value distribution with simulation

The Weibull distribution is considered one of the Type-I Generalized Extreme Value (GEV) distribution, and it plays a crucial role in modeling extreme events in various fields, such as hydrology, finance, and environmental sciences. Bayesian methods play a strong, decisive role in estimating the parameters of the GEV distribution due to their ability to incorporate prior knowledge and handle small sample sizes effectively. In this research, we compare several shrinkage Bayesian estimation methods based on the squared error and the linear exponential loss functions. They were adopted and compared by the Monte Carlo simulation method. The performance of these methods is assessed based on their accuracy and computational efficiency in estimating the scale parameter of the Weibull distribution. To evaluate their performance, we generate simulated datasets with different sample sizes and varying parameter values. A technique for pre-estimation shrinkage is suggested to enhance the precision of estimation. Simulation experiments proved that the Bayesian shrinkage estimator and shrinkage pre-estimation under the squared loss function method are better than the other methods because they give the least mean square error. Overall, our findings highlight the advantages of shrinkage Bayesian estimation methods for the proposed distribution. Researchers and practitioners in fields reliant on extreme value analysis can benefit from these findings when selecting appropriate Bayesian estimation techniques for modeling extreme events accurately and efficiently.

Data-driven optimization for microgrid control under distributed energy resource variability

The integration of renewable energy resources into the smart grids improves the system resilience, provide sustainable demand-generation balance, and produces clean electricity with minimal leakage currents. However, the renewable sources are intermittent in nature. Therefore, it is necessary to develop scheduling strategy to optimise hybrid PV-wind-controllable distributed generator based Microgrids in grid-connected and stand-alone modes of operation. In this manuscript, a priority-based cost optimization function is developed to show the relative significance of one cost component over another for the optimal operation of the Microgrid. The uncertainties associated with various intermittent parameters in Microgrid have also been introduced in the proposed scheduling methodology. The objective function includes the operating cost of CDGs, the emission cost associated with CDGs, the battery cost, the cost of grid energy exchange, and the cost associated with load shedding. A penalty function is also incorporated in the cost function for violations of any constraints. Multiple scenarios are generated using Monte Carlo simulation to model uncertain parameters of Microgrid (MG). These scenarios consist of the worst as well as the best possible cases, reflecting the microgrid’s real-time operation. Furthermore, these scenarios are reduced by using a k-means clustering algorithm. The reduced procedures for uncertain parameters will be used to obtain the minimum cost of MG with the help of an optimisation algorithm. In this work, a meta-heuristic approach, grey wolf optimisation (GWO), is used to minimize the developed cost optimisation function of MG. The standard LV Microgrid CIGRE test network is used to validate the proposed methodology. Results are obtained for different cases by considering different priorities to the sub-objectives using GWO algorithm. The obtained results are compared with the results of Jaya and PSO (particle swarm optimization) algorithms to validate the efficacy of the GWO method for the proposed optimization problem.

Assembly-inspired multiferroicity with nontrivial Chern insulating phase from exohedral metallofullerenes.

Fullerene-assembled low-dimensional materials have been experimentally realized in polymorphic forms and have attracted significant interest very recently. Here, we predict a two-dimensional (2D) honeycomb lattice material TM2(C60)3 (TM = Cr, Mo, and W) assembled from exohedral metallofullerene clusters TM(C60)3 that could exhibit planar triangular geometries. According to first-principles calculations combined with Monte Carlo simulations, we suggest that these 2D assembled materials exhibit various exotic physical properties, including ferromagnetism, ferroelectricity, and quantum anomalous Hall effect. Interestingly, mechanical strains could effectively tune their magnetic moments and switch the conducting spin channel of the Dirac bands at the Fermi level. Our work provides a new cluster-assembly design strategy toward cluster-assembled 2D materials based on fullerene characters.

Prompt-gamma track-length estimator with time tagging from proton tracking.

The design of prompt-gamma detectors necessitates numerous Monte Carlo simulations to precisely develop and optimize the detection stages in proton therapy. Alongside the advancement of MC simulations, various variance reduction methods have been explored to speed-up calculations. Among these techniques, track-length estimators are interesting scoring methods for achieving both speed and accuracy in Monte Carlo simulations of rare events. This paper introduces an extension of the GATE vpgTLE module that incorporates the prompt-gamma emission time, which is tagged from the proton tracking, enhancing its utility for studies focused on detector design and optimization that rely on time measurements. The results obtained from a clinical radiotherapy plan are presented. We demonstrate that the new vpgTLE tally with time tagging is accurate, except for certain prompt-gamma lines corresponding to long mean-life nuclei.

Measurement of perceived pressures in psychiatry: paper-and-pencil and computerized adaptive version of the P-PSY35 scale

PurposeFormal coercion in psychiatry is widely studied yet much less is known about pressures patients may experience, partly because of the very few measures available. The goal of this study was to validate the P-PSY35 (Pressures in Psychiatry Scale) and provide a paper-and-pencil and a computerised adaptive test (CAT) to measure pressures experienced by patients in psychiatry.MethodsThe P-PSY35 items were developed with users. Patients were evaluated during psychiatric hospitalisation or through an online survey. Mokken scale analysis and Item response theory (IRT) were used to select and estimate the items parameters. A Monte-Carlo simulation was performed to evaluate the number of items needed to transform the paper-and-pencil test into a reliable psychometric CAT.ResultsA total of 274 patients were assessed. The P-PSY35 demonstrated good internal validity, internal consistency, convergent and divergent validity. The P-PSY35 could be substantially shortened while maintaining excellent reliability using the CAT procedure.ConclusionThe P-PSY35 was developed in collaboration with users. It is a psychometrically rigorous tool designed to measure experienced pressures in French-language. The development and successful validation of the P-PSY35 represent a welcome step towards implementing and evaluating programs aimed at reducing negative consequences of coercion.

Modeling of N and P-Type of Coaxial Ge Detectors Using MCNPX and the Effect of Dead Layer Variation on Its Response Function

Abstract This study assessed the response function of a p-type and n-type coaxial high-purity germanium (HPGe) detector via Monte Carlo N-Particle eXtended (MCNPX). MCNPX was employed to model the Coaxial Ge detectors, and for a precise simulation, the dimensions of the dead layer of germanium crystals were added. The dead layer was separated into front and lateral surfaces, and the thickness of each dead layer was modeled. In this work, the simulated detectors have been performed at different energy lines using a radioactive source Eu-152 to study the response function of each with dead layer variations for the front dead layer and study the range of relative deviation of the Monte Carlo simulation outputs from the manufactured declared data. The results proved that the n-type coaxial HPGe detector is more sensitive to the dead layer change than the p-type with a thick change of 0.01 mm. This research has significant effects on the efficiencies of the radiation detection systems in the energy range ∼ (120–1410) keV.

Effective theory for graphene nanoribbons with junctions

Graphene nanoribbons are a promising candidate for fault-tolerant quantum electronics. In this scenario, qubits are realized by localized states that can emerge on junctions in hybrid ribbons formed by two armchair nanoribbons of different widths. We derive an effective theory based on a tight-binding ansatz for the description of hybrid nanoribbons and use it to make accurate predictions of the energy gap and nature of the localization in various hybrid nanoribbon geometries. We use quantum Monte Carlo simulations to demonstrate that the effective theory remains applicable in the presence of Hubbard interactions. We discover, in addition to the well-known localizations on junctions, which we call “Fuji”, a new type of “Kilimanjaro” localization smeared out over a segment of the hybrid ribbon. We show that Fuji localizations in hybrids of width N and N+2 armchair nanoribbons occur around symmetric junctions if and only if N(mod3)=1, while edge-aligned junctions never support strong localization. This behavior cannot be explained relying purely on the topological Z2 invariant, which has been believed to be the origin of the localizations to date. Published by the American Physical Society 2024

Outdoor-to-Indoor mmWave Relaying with Massive MIMO: Impact of Imperfect Channel Estimation

Assuming incomplete knowledge of the channel state information (CSI), we investigate two scenarios involving millimeter wave (mmWave) relaying to support outdoor-to-indoor communications. We proceed to derive the average signal-to-noise ratio (SNR) expressions for two relaying scenarios and quantify the asymptotic SNR. The performance of the two relaying scenarios is evaluated using the outage probability—for which we have derived closed-form equations—the end-to-end channel capacity, and the energy efficiency. The obtained results are compared with those derived assuming complete knowledge of the CSI. The effect of the imperfect CSI is therefore assessed in relation to the reference of perfect CSI. In these scenarios, an outside base station (BS) in an urban cellular network serves several indoor users. In the context of a two-hop full-duplex (FD) relaying scheme, we initially suggest a method in which the base station (BS) utilizes zero-forcing (ZF) precoding, and we take into account the overall channel response. Furthermore, we make the assumption that the base station (BS) engages in precoding only depending on the response of the channel in the first hop; in this second design, the relay precodes (using the response of the second-hop channel), amplifies, and sends the signals. Both techniques utilize massive multiple-input–multiple-output (mMIMO) arrays to permit transmission. We also present Monte Carlo simulation results to assess the accuracy of our analytical results. Finally, the two systems are compared in terms of channel estimation and precoding complexity, the number of antennas, as well as the number of users. Practical deployment recommendations are formulated at the end of this work.

Population pharmacokinetics, dosing optimization and clinical outcomes of biapenem in patients with sepsis

Introduction: Biapenem is a carbapenem antibiotic widely used in Asia, can be used for the treatment of adults and children with infections due to susceptible bacteria. Although biapenem is utilized in the treatment of a diverse range of bacterial infections, current pharmacokinetic data in the context of septic populations remain limited. Consequently, our research aims to evaluate the pharmacokinetics and efficacy of biapenem within a septic population to optimize biapenem therapy.Methods: In this study, we characterized the pharmacokinetics of biapenem in septic patients using a population pharmacokinetic (PPK) approach. The clinical PK data to develop the PPK model were obtained from 317 septic patients admitted to Nanjing Drum Tower Hospital between 2018 and 2022. All patients were randomized to the modeling and validation cohorts at a 3:1 ratio, with PPK modeling and validation performed utilizing the NONMEM software.Results: The model found to best describe the available data was a two-compartment PPK model with first-order elimination characterized by the parameters clearance (CL), central volume (V1), peripheral volume (V2), and intercompartmental clearance (Q). A covariate analysis identified that creatinine clearance (CLCR) was a significant covariate influencing biapenem CL, while blood urea nitrogen (BUN) was a significant covariate influencing biapenem Q. Accoding to the clinical outcome analyses, 70% of the time that the free antimicrobial drug concentration exceeds the MIC (fT>MIC) is associated with favourable clinical outcomes. The PPK model was then used to perform Monte Carlo simulations to evaluate the probability of attaining 70% fT>MIC.Conclusions: A final PPK model of biapenem was established for patients with sepsis. The current daily dosage regimen of 1.2 g may insufficient to achieve 70% fT>MIC in septic patients. The dosage regimen of 600 mg every 6 h appears to be the optimal choice.

Simulated impact of medicaid expansion on the economic burden of opioid use disorder in North Carolina

BackgroundOpioid use disorder (OUD) imposes significant costs on state and local governments. Medicaid expansion may lead to a reduction in the cost burden of OUD to the state. MethodsWe estimated the health care, criminal justice and child welfare costs, and tax revenue losses, attributable to OUD and borne by the state of North Carolina in 2022, and then estimated changes in the same domains following Medicaid expansion in North Carolina (adopted in December 2023). Analyses used existing literature on the national and state-level costs attributable to OUD to estimate individual-level health care, criminal justice, and child welfare system costs, and lost tax revenues. We combined Individual-level costs and prevalence estimates to estimate costs borne by the state before Medicaid expansion. Changes in costs after expansion were computed based on a) medication for opioid use disorder (MOUD) access for new enrollees and b) shifting of responsibility for some health care costs from the state to the federal government. Monte Carlo simulation accounted for the impact of parameter uncertainty. Dollar estimates are from the 2022 price year, and costs following the first year were discounted at 3 %. ResultsIn 2022, North Carolina incurred costs of $749 million (95 % credible interval [CI]: $305 M—$1,526 M) associated with OUD (53 % in health care, 36 % in criminal justice, 7 % in lost tax revenue, and 4 % in child welfare costs). Expanding Medicaid lowered the cost burden of OUD incurred by the state. The state was predicted to save an estimated $72 million per year (95 % CI: $6 M—$241 M) for the first two years and $30 million per year (95 % CI: −$28 M—$176 M) in subsequent years. Over five years, savings totaled $224 million (95 % CI: −$47 M—$949 M). ConclusionMedicaid expansion has the potential to decrease the burden of OUD in North Carolina, and policymakers should expedite its implementation.