Monte Carlo-based Research Articles

The role of ion beam in modification of CuO@NiO@ZnO/GO electrode for supercapacitor application

Recently, mixed transition metals oxides became emerging electrode materials for energy storage devices such as supercapacitors, batteries, etc. due to their high electrochemical performances. In this study, mixed transition metals including Copper oxide (CuO), Nickel oxide (NiO) and Zinc oxide (ZnO) in conjunction with graphene oxide (GO) were used to fabricate electrode materials for the supercapacitor applications adopting hydrothermal process. The features of the as-fabricated electrodes were further modified by irradiating them with 5.0 MeV C++ ions with varying fluence rates. Monte Carlo-based Stopping and Range of Ions in Matter (SRIM) simulation code was utilized to analyze the ion implantation-induced processes inside the target electrode. Electrodes' crystal features, morphologies, elemental compositions, and vibrational and optical characteristics were examined employing X-ray diffraction (XRD), high-resolution transmission electron microscopy (HR-TEM) coupled with energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectroscopy (FTIR) and UV–visible spectroscopy, respectively. Electrochemical impedance spectroscopy (EIS), galvanostatic charge-discharge (GCD), and cyclic voltammetry (CV) were among various techniques engaged to analyze the electrochemical properties of the pristine and post-implanted electrode materials. The electrochemical findings from CV and GCD for the electrode implanted with 5.0 × 1015 ionscm−2, which offered the best result presented enhanced specific capacitances of 1250 and 1350 Fg−1 at a scan rate 1.0 mVs−1 and current density 0.5 Ag−1 respectively. This study revealed that carbon ion implantation at a fluence 5.0 × 1015 ionscm−2 could prompt the enhancement of the electrochemical properties of this fabricated electrode material.

Bayesian inference of initial conditions from non-linear cosmic structures using field-level emulators

ABSTRACT Analysing next-generation cosmological data requires balancing accurate modelling of non-linear gravitational structure formation and computational demands. We propose a solution by introducing a machine learning-based field-level emulator, within the Hamiltonian Monte Carlo-based Bayesian Origin Reconstruction from Galaxies (BORG) inference algorithm. Built on a V-net neural network architecture, the emulator enhances the predictions by first-order Lagrangian perturbation theory to be accurately aligned with full N-body simulations while significantly reducing evaluation time. We test its incorporation in BORG for sampling cosmic initial conditions using mock data based on non-linear large-scale structures from N-body simulations and Gaussian noise. The method efficiently and accurately explores the high-dimensional parameter space of initial conditions, fully extracting the cross-correlation information of the data field binned at a resolution of $1.95\,h^{-1}$ Mpc. Percent-level agreement with the ground truth in the power spectrum and bispectrum is achieved up to the Nyquist frequency $k_\mathrm{N} \approx 2.79h \,\, \mathrm{Mpc}^{-1}$. Posterior resimulations – using the inferred initial conditions for N-body simulations – show that the recovery of information in the initial conditions is sufficient to accurately reproduce halo properties. In particular, we show highly accurate $M_{200\mathrm{c}}$ halo mass function and stacked density profiles of haloes in different mass bins $[0.853,16]\times 10^{14}\,{\rm M}_{\odot }\,h^{-1}$. As all available cross-correlation information is extracted, we acknowledge that limitations in recovering the initial conditions stem from the noise level and data grid resolution. This is promising as it underscores the significance of accurate non-linear modelling, indicating the potential for extracting additional information at smaller scales.

Hosting Capacity in Smart Distribution Systems Using OpenDSS Tool and Monte Carlo-Based Methodology

Hosting Capacity in Smart Distribution Systems Using OpenDSS Tool and Monte Carlo-Based Methodology

Retardation factor scaling for contaminant transport in fractured media

This paper delves into the key factors governing contaminant retardation and the scaling of retardation factor for contaminant transport in naturally fractured media. It employs a geostatistical approach, utilizing hierarchical transition probability and covariance models, to characterize multimodal reactive mineral facies. Subsequently, scale-dependent models are developed to illustrate the transport behaviors of reactive contaminants under equilibrium sorption conditions and quantify the spatial and temporal variations of the effective retardation factor. These models incorporate various factors, including the structural characteristics of reactive minerals (the volume proportions and lengths of different facies types), fracture characteristics (the means and variances of fracture apertures), and the adsorption capacity of contaminants on minerals (sorption coefficients (ln Kd)). The results from these Lagrangian-based models are compared and validated against experimental data of solute transport in a single natural fracture. These findings underscore the remarkable agreement between the derived scale-dependent analytical expressions and the experimental results. Additionally, a global sensitivity analysis was performed using the Monte Carlo-based Sobol’ indices method to understand the influential factors governing contaminant retardation. The study reveals that the primary influential factor is the sorption capacity of contaminants on minerals, while fracture and reactive mineral structural characteristics play a secondary role. These insights provide significant information for understanding the reactive transport processes and the environmental impact of transport parameters in fractured media.

A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets.

In this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity. Due to the nonlinearity and complex observability properties in the BOT problem, a wide area of research has been focused on improving its state estimation accuracy. The objective of this research is to present an accurate approach to estimate the relative position and velocity of the source with respect to the maneuvering observer. This approach is implemented using the iterated extended Kalman filter (IEKF) in an MC-based iterative structure (MC-IEKF). Re-linearizing dynamic and measurement equations using the IEKF along with the MC campaign applied to the initial conditions result in significantly improved accuracy in the estimation process. Furthermore, an observability analysis is conducted to show the effectiveness of the designed maneuver of the observer. A comparison with the widely used UKF algorithm is carried out to demonstrate the performance of the proposed method.

Indirect Force Measurement via Hamiltonian Monte Carlo-Based Probabilistic Model of FSR Sensor

Indirect Force Measurement via Hamiltonian Monte Carlo-Based Probabilistic Model of FSR Sensor

Fetal radiation dose for pregnant patients undergoing CTPA examinations: comparative calculations in a case series with two Monte Carlo-based software products

Fetal radiation dose for pregnant patients undergoing CTPA examinations: comparative calculations in a case series with two Monte Carlo-based software products

Monte Carlo-based Charging Demand Forecasting Model and Market Space Study

With the rapid development of China's new energy vehicle industry, charging infrastructure has gradually become a key factor affecting the further development of the new energy vehicle industry. Scientific and reasonable forecasts of the future development scale of the domestic charging infrastructure market have become an important element supporting the business planning of various related parties such as vehicle enterprises, charging pile enterprises, and the energy industry. Based on the distribution data of electric vehicle traveling and charging characteristics obtained from the industry research, this study proposes a set of charging infrastructure prediction model based on the principle of energy supply-demand balance, predicts the development scale of charging infrastructure in the country and key cities, and further analyzes the market competition and future market margin of the key cities, and evaluates the difficulty of future market entry in the key cities, which provides a basis for further development of the charging infrastructure industry. The difficulty of entering the future market in each key city is assessed, providing business decision support for industry participants. In the future, the charging infrastructure development scale prediction results proposed by this study can be used as the input parameters of the charging pile layout planning model to enhance the scientific nature of the charging infrastructure layout and help the industry to develop in a high-quality manner.

Stochastic simulation of geological cross-sections from boreholes: A random field approach with Markov Chain Monte Carlo method

A reliable geological cross-section is essential to the design and risk assessment of underground structures. Random fields are commonly employed to model geological uncertainty. However, determining the covariance parameters can be a challenging task. In this study, a Bayesian approach is introduced for the simulation of geological formations utilizing available boreholes from a specific site. The cross-section and its associated covariance parameters are learned from geological formations observed in boreholes, using the Markov Chain Monte Carlo-based Bayesian method. A cross-section consisting of seven boreholes in Botanic Gardens, Singapore is used to demonstrate the effectiveness of the proposed method. The results show that the stochastic simulation of geological formations has a sound performance. For the training boreholes, most geological formations have true positive rates and positive predictive values larger than 0.89. For the validation boreholes, almost all geological formations in boreholes are accurately identified. When plenty of boreholes are available, a uniform prior distribution is a better choice to reduce geological uncertainty. Alternatively, when borehole data is limited, a log-normal prior distribution can be used to ensure a more deterministic simulation. For a length of 300-m cross-section of four ordered geological formations, when the number of boreholes is greater than three, the posterior standard deviation of the geological model is less than 10% of the prior, and the effective range of a single borehole is found to be between 21 and 27 m.

THE COMPARISON OF 2D DOSE PATIENT-SPECIFIC QUALITY ASSURANCE BETWEEN MONTE CARLO-CONVOLUTION AND MODIFIED CLARKSON INTEGRATION ALGORITHM

A sophisticated machine of radiotherapy treatment process follows the complexity of the quality assurance (QA) measurement. Non-measurement QA becomes one of the solutions to reduce the medical physicists’ workload. However, this method has not been clinically established. This study compared two non-measurement methods of patient-specific quality assurance (PSQA) to find the feasible algorithm for the adaptive radiotherapy process. Monte Carlo-based (MC) PSQA used a phase space file of the medical linear accelerator (Linac) to obtain the photon energy fluence and forward projected to the isoplane. In contrast, Modified Clarkson Integration-based (MCI) used a non-uniform fluence map in the isoplane. For the modulated intensity, we used a pair of the dynamic log files of the multileaf-collimator (MLC) and then employed them in the algorithms. The dose distributions of MC and MCI methods were compared to the treatment planning system (TPS) using gamma index analysis. We found that the gamma pass rates (GPR) for MC-TPS and MCI-TPS were 99.54% and 99.57%, respectively. Further, the dose distribution in the off-axis region for the MCI method showed lesser accuracy due to the higher secondary dose contribution. The linac log file information can be used and calculated into a 2D dose distribution using both MC and MCI methods, providing high-accuracy results.

A Monte Carlo-based Workflow for Geochemical Anomaly Identification Under Uncertainty and Global Sensitivity Analysis of Model Parameters

A Monte Carlo-based Workflow for Geochemical Anomaly Identification Under Uncertainty and Global Sensitivity Analysis of Model Parameters

A Monte Carlo-Based Approach to Assess the Reinforcement Depassivation Probability of RC Structures: Simulation and Analysis

In this work, an approach is presented to assess the reinforcement depassivation probability of reinforced concrete structures under corrosion induced by carbonation or chloride diffusion. The model consists of coupling mathematical formulations of CO2 and Cl− diffusion with Monte Carlo simulation (MCS). Random events were generated using MCS to create several design life and environmental scenarios. A case study was performed by simulating five Brazilian environmental conditions and distinct mixes of concrete. The effect of input parameters on the reinforcement concrete depassivation probability was evaluated. The results point out that the depassivation probability due to carbonation is more significant in urban centers, and the compressive strength of concrete has the main influence on the depassivation probability. Results also showed that the depassivation probability due to chloride ingress is influenced by, in order of importance, the chloride content on the surface (61.4%), concrete cover (20.3%), compressive strength (7.1%), relative humidity (6.1%), and temperature (5.1%). In addition, an increase in the compressive strength of concrete, from 30 to 50 MPa, can reduce depassivation probability by up to 70%, resulting in a concrete structure that attends the durability limit state. Thus, by incorporating probabilistic approaches, this model can be a valuable tool in the civil construction industry for studying the improvement of durability, reliability, and safety of reinforced concrete structures.

Monte Carlo-Based Microsimulation Approach for Estimating the Collision Probability of Real Traffic Conflicts

During driving in real traffic, often conflict situations arise which do not lead to an accident; however, they offer a good indication of traffic (un)safety. There are many methods and indicators for classifying whether a current driving situation can be considered as a conflict or not (e.g., time to collision, post-encroachment time). However, not many approaches exist to predict how such conflicts will evolve once they have been identified and what the collision probability would be given the conditions of the conflict. Currently available methods make strong assumptions about driver behavior during the conflict, usually do not consider all participants involved in the conflict (only two vehicles), and have limited applicability to various types of real conflicts. In this paper, a Monte Carlo-based microsimulation approach is proposed to estimate the probability of collision for a conflict of any type. Unlike previous approaches, the proposed method can be used to simulate any type of conflict with an arbitrary initial conflict condition and an arbitrary number of vehicles in conflict. The method was developed based on real-world video data of a complex signalized intersection, from which conflicts were identified with the DOCTOR method. As proof of concept, the proposed method was applied to a real conflict involving four vehicles which was extracted from these video data and validated with a real traffic accident. The results show that our approach is capable of simulating real conflicts of various types with multiple participants and predicting their probability of collision.

A New Tree-Level Multi-Objective Forest Harvest Model (MO-PSO): Integrating Neighborhood Indices and PSO Algorithm to Improve the Optimization Effect of Spatial Structure

Accurate, efficient, impersonal harvesting models play a very important role in optimizing stand spatial structural and guiding forest harvest practices. However, existing studies mainly focus on the single-objective optimization and evaluation of forest at the stand- or landscape-level, lacking considerations of tree-level neighborhood interactions. Therefore, the study explored the combination of the PSO algorithm and neighborhood indices to construct a tree-level multi-objective forest harvest model (MO-PSO) covering multi-dimensional spatial characteristics of stands. Taking five natural secondary forest plots and thirty simulated plots as the study area, the MO-PSO was used to simulate and evaluate the process of thinning operations. The results showed that the MO-PSO model was superior to the basic PSO model (PSO) and random thinning model Monte Carlo-based (RD-TH), DBH dominance (DOMI), uniform angle (ANGL), and species mingling (MING) were better than those before thinning. The multi-dimensional stand spatial structure index (L-index) increased by 1.0%~11.3%, indicating that the forest planning model (MO-PSO) could significantly improve the spatial distribution pattern, increase the tree species mixing, and reduce the degree of stand competition. In addition, under the four thinning intensities of 0% (T1), 15% (T2), 30% (T3), and 45% (T4), L-index increased and T2 was the optimal thinning intensity from the perspective of stand spatial structure overall optimization. The study explored the effect of thinning on forest spatial structure by constructing a multi-objective harvesting model, which can help to make reasonable and scientific forest management decisions under the concept of multi-objective forest management.

A Monte Carlo-Based Strategy to Assess Complex Kinetics: Application of the Null-Reaction Method to DAEM.

The distributed activation energy model (DAEM) is widely used in chemical kinetics to statistically describe the occurrence of numerous independent parallel reactions. In this article, we suggest a rethink in the context of a Monte Carlo integral formulation to compute the conversion rate at any time without approximation. After the basics of the DAEM are introduced, the considered equations (under isothermal and dynamic conditions) are respectively expressed into expected values, which in turn are transcribed into Monte Carlo algorithms. To describe the temperature dependence of reactions under dynamic conditions, a new concept of null reaction, inspired from null-event Monte Carlo algorithms, has been introduced. However, only the first-order case is addressed for the dynamic mode due to strong nonlinearities. This strategy is then applied to both analytical and experimental density distribution functions of the activation energy. We show that the Monte Carlo integral formulation is efficient in solving the DAEM without approximation and that it is well-adapted due to the possibility of using any experimental distribution function and any temperature profile. Furthermore, this work is motivated by the need for coupling chemical kinetics and heat transfer in a single Monte Carlo algorithm.

Uncertainty Characterization for Soil Cohesion in a Project Site in Nasiriyah Using Bayesian Methods

High uncertainties arias through the characterization of soil parameters because of the lack of data obtained from geotechnical reports. Reducing these uncertainties may improve the characteristic values of soil parameters. This research aims to probabilistically characterize a soil's cohesion parameter in Nasiriyah. The Bayesian approach has been applied to soil data obtained through a project in Nasiriyah. The soil at the site is classified as lean clay, and the soil cohesion has been evaluated using two Bayesian methods: the ordinary, normal distribution method (OND) and the Marcove Chain Monte Carlo-based Bayesian approach (MCMC) method. The previous knowledge utilized in the Bayesian approach was based on 20 boreholes, and the subjective probability approach has functioned in the prior probability distribution. The OND method deduced a mean value of cohesion of (195.9 kPa) and a standard deviation of (14.68 kPa), (COV) 7.49%. It was noted that the probability distribution has a more significant effect than the previous distribution on the posterior distribution. The MCMC method summarized the probabilistic description of the soil characteristic, through which it reached the mean and the subsequent standard deviation (167.49) kPa (109.8) kPa, respectively, and the coefficient of Variation (COV) was 65.6%. It is considered the most appropriate and common method, especially in high-dimensional data when the results are not well known because it can provide a probabilistic value for the not well-known data.

Monte Carlo-Based Radiobiological Investigation of the Most Optimal Ion Beam Forming SOBP for Particle Therapy.

Proton (p) and carbon (C) ion beams are in clinical use for cancer treatment, although other particles such as He, Be, and B ions have more recently gained attention. Identification of the most optimal ion beam for radiotherapy is a challenging task involving, among others, radiobiological characterization of a beam, which is depth-, energy-, and cell type- dependent. This study uses the FLUKA and MCDS Monte Carlo codes in order to estimate the relative biological effectiveness (RBE) for several ions of potential clinical interest such as p, 4He, 7Li, 10Be, 10B, and 12C forming a spread-out Bragg peak (SOBP). More specifically, an energy spectrum of the projectiles corresponding to a 5-cm SOBP at a depth of 8 cm was used. All secondary particles produced by the projectiles were considered and RBE was determined based on radiation-induced Double Strand Breaks (DSBs), as calculated by MCDS. In an attempt to identify the most optimal ion beam, using the latter data, biological optimization was performed and the obtained depth-dose distributions were inter-compared. The results showed that 12C ions are more effective inside the SOBP region, which comes at the expense of higher dose values at the tail (i.e., after the SOBP). In contrast, p beams exhibit a higher DSOPB/DEntrance ratio, if physical doses are considered. By performing a biological optimization in order to obtain a homogeneous biological dose (i.e., dose × RBE) in the SOBP, the corresponding advantages of p and 12C ions are moderated. 7Li ions conveniently combine a considerably lower dose tail and a DSOPB/DEntrance ratio similar to 12C. This work contributes towards identification of the most optimal ion beam for cancer therapy. The overall results of this work suggest that 7Li ions are of potential interest, although more studies are needed to demonstrate the relevant advantages. Future work will focus on studying more complex beam configurations.

Commissioning of a synchrotron-based proton beam therapy system for use with a Monte Carlo treatment planning system

This work tackles the commissioning and validation of a novel combination of a synchrotron-based proton beam therapy system (Hitachi, Ltd.) for use with a Monte Carlo treatment planning system (TPS). Four crucial aspects in this configuration have been investigated: (1) Monte Carlo-based correction performed by the TPS to the measured integrated depth-dose curves (IDD), (2) circular spot modelling with a single Gaussian function to characterize the synchrotron physical spot, which is elliptical, (3) the modelling of the range shifter that enables using only one set of measurements in open beams, and (4) the Monte Carlo dose calculation model in small fields.Integrated depth-dose curves were measured with a PTW Bragg peak chamber and corrected, with a Monte Carlo model, to account for energy absorbed outside the detector. The elliptical spot was measured by IBA Lynx scintillator, EBT3 films and PTW microDiamond. The accuracy of the TPS (RayStation, RaySearch Laboratories) at spot modelling with a circular Gaussian function was assessed.The beam model was validated using spread-out Bragg peak (SOBP) fields. We took single-point doses at several depths through the central axis using a PTW Farmer chamber, for fields between 2 × 2cm and 30 × 30cm. We checked the range-shifter modelling from open-beam data. We tested clinical cases with film and an ionization chamber array (IBA Matrix).Sigma differences for spots fitted using 2D images and 1D profiles to elliptical and circular Gaussian models were below 0.22 mm. Differences between SOBP measurements at single points and TPS calculations for all fields between 5 × 5 and 30 × 30cm were below 2.3%. Smaller fields had larger differences: up to 3.8% in the 2 × 2cm field. Mean differences at several depths along the central axis were generally below 1%. Differences in range-shifter doses were below 2.4%. Gamma test (3%, 3 mm) results for clinical cases were generally above 95% for Matrix and film.Approaches for modelling synchrotron proton beams have been validated. Dose values for open and range-shifter fields demonstrate accurate Monte Carlo correction for IDDs. Elliptical spots can be successfully modelled using a circular Gaussian, which is accurate for patient calculations and can be used for small fields. A double-Gaussian spot can improve small-field calculations. The range-shifter modelling approach, which reduces clinical commissioning time, is adequate.

Adaptive Bayesian variable clustering via structural learning of breast cancer data.

The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlobased technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.

First-Line Therapy for Type 2 Diabetes With Sodium-Glucose Cotransporter-2 Inhibitors and Glucagon-Like Peptide-1 Receptor Agonists : A Cost-Effectiveness Study.

Guidelines recommend sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP1) receptor agonists as second-line therapy for patients with type 2 diabetes. Expanding their use as first-line therapy has been proposed but the clinical benefits may not outweigh their costs. To evaluate the lifetime cost-effectiveness of a strategy of first-line SGLT2 inhibitors or GLP1 receptor agonists. Individual-level Monte Carlo-based Markov model. Randomized trials, Centers for Disease Control and Prevention databases, RED BOOK, and the National Health and Nutrition Examination Survey. Drug-naive U.S. patients with type 2 diabetes. Lifetime. Health care sector. First-line SGLT2 inhibitors or GLP1 receptor agonists. Life expectancy, lifetime costs, incremental cost-effectiveness ratios (ICERs). First-line SGLT2 inhibitors and GLP1 receptor agonists had lower lifetime rates of congestive heart failure, ischemic heart disease, myocardial infarction, and stroke compared with metformin. First-line SGLT2 inhibitors cost $43000 more and added 1.8 quality-adjusted months versus first-line metformin ($478000 per quality-adjusted life-year [QALY]). First-line injectable GLP1 receptor agonists cost more and reduced QALYs compared with metformin. By removing injection disutility, first-line GLP1 receptor agonists were no longer dominated (ICER, $327000 per QALY). Oral GLP1 receptor agonists were not cost-effective (ICER, $823000 per QALY). To be cost-effective at under $150000 per QALY, costs for SGLT2 inhibitors would need to be under $5 per day and under $6 per day for oral GLP1 receptor agonists. U.S. population and costs not generalizable internationally. As first-line agents, SGLT2 inhibitors and GLP1 receptor agonists would improve type 2 diabetes outcomes, but their costs would need to fall by at least 70% to be cost-effective. American Diabetes Association.