Monte Carlo Simulations Research Articles

Determination of mass attenuation coefficient for common prosthetic materials utilizing Monte Carlo simulation, theoretical analysis, and XCOM software

Abstract The accurate calculation of radiation doses in radiotherapy necessitates precise knowledge of the mass attenuation coefficients ( μ m ) of prosthetic materials. Thus, this study aims to determine the μ m of widely used prosthetic materials through theoretical methods, Monte Carlo (MC) simulation, and the XCOM photon cross-section database for the first time. Calculations were performed for the following medical sources: Pd-103, I-125, Cs -131, Tc-99m, Ba-133, Ir-192, Au-198, Cs-137, Ra-226 and Co-60 with a range of energies between 10 keV and 2 MeV. Outcomes reveal that in all three datasets increasing the energy reduces the μ m values for all provided materials. For instance, this value for MMA and PEG and MCNPX simulations, theoretical calculation, XCOM evaluation varies from 0.41911 to 0.09006 cm2.g−1, 0.39194 to 0.08583 cm2.g−1, 0.39790 to 0.08715 cm2.g−1, and 0.45099 to 0.09029 cm2.g−1, 0.42214 to 0.09050 cm2.g−1, 0.40040 to 0.08090 cm2.g-,1 respectively. Furthermore, for high density prosthetic materials like Steel, Titania, and Zinc oxide, μ m is higher specifically at low energy regions compared to low density prosthetic materials, while increasing the energy from 0.021 to 1.25 MeV, a different scenario is recorded. Moreover, MCNPX results exhibit higher deviations specifically in high energy regions compared to other approaches. However, this approach remains as accurate for prosthetic attenuation evaluation. The results of this study present a detailed classification of a wide range of prosthetic materials in terms of their applications and energy levels, serving as a significant reference for future research.

Increased cell killing effect in neutron capture enhanced proton beam therapy

AbstractThermal neutrons generated in the body during proton beam therapy (PBT) can be used to cause boron neutron capture reactions and have recently been proposed as neutron capture enhanced PBT (NCEPBT). However, the cell killing effect of NCEPBT remains underexplored. Here, we show an increase in the cell killing effect of NCEPBT. Using Monte Carlo simulations, we showed that neutrons generated by proton beam irradiation are uniformly spread on tissue culture plates. Human salivary gland tumor cell line (HSG), human osteosarcoma cell line (MG63), human tongue squamous cell carcinoma cell line (SAS), and human malignant melanoma cell line (G-361) were irradiated with X-rays, proton beams, and proton beams with 10B-enriched boronophenylalanine (boron concentration of 20 and 80 ppm). The relative biological effectiveness (RBE) values of proton beams alone, proton beams with 20 ppm boron, and proton beams with 80 ppm boron for HSG, MG63, SAS, and G-361 were 1.02, 1.07, and 1.23; 1.01, 1.08, and 1.44; 1.05, 1.09, and 1.46; and 1.04, 1.13, and 1.63, respectively. NCEPBT with high boron concentration showed high RBE and a high sensitizing effect. Our results confirm an increase in the cell killing effect of NCEPBT, should aid in its clinical use, and warrant its further investigation.

Inference for big data assisted by small area methods: an application on sustainable development goals sensitivity of enterprises in Italy

Abstract In this study, we proposed a new method for estimating the sensitivity of enterprises in Italy to the United Nation’s sustainable development goals at the provincial level using web-scraping data (a nonprobability sample) because this value is not surveyed by the Italian National Institute of Statistics. The proposed method used a probability sample to reduce the selection bias of estimates obtained from the nonprobability sample in the context of small area estimation and integrated nonprobability and probability samples using a double robust estimator that combined (i) propensity weighting to improve the representativeness of the nonprobability sample and (ii) a statistical model to predict the units that were not in the nonprobability sample. A bootstrap procedure for estimating variance was also proposed. To validate the proposed method, a Monte Carlo simulation was performed. Results showed that the proposed method allowed the correction of bias from the nonprobability sample while maintaining a good level of estimate reliability.

Investigating adsorption properties of CO2 and CH4 in subbituminous coals from Mamu and Nsukka formations: a molecular simulation approach

AbstractThe study aimed to investigate the adsorption properties of methane (CH4) and carbon dioxide (CO2) in subbituminous coals from the Mamu and Nsukka formations, focusing on the CO2-Enhanced Coalbed Methane (ECBM) method. Proximate, ultimate, and FT-IR analyses determined the quality, age, and functional categories of these coals, confirming their subbituminous nature. Using molecular dynamics (MD) simulations, a unique amorphous subbituminous coal model was developed to study adsorption phenomena. Isosteric heat and adsorption isotherms for pure CO2 and CH4 were analyzed, alongside Grand Canonical Monte Carlo (GCMC) simulations to assess CO2 adsorption selectivity in a binary CO2 and CH4 mixture. Results showed that CO2 required more isosteric heat than CH4 in single-component scenarios and demonstrated stronger electrostatic interactions with heteroatom groups in the coal model, explaining its higher adsorption preference. In binary adsorption experiments, CO2 exhibited a higher affinity under specific conditions, particularly influenced by pressure variations. At lower pressures, CO2 selectivity decreased rapidly with increasing temperature, while at higher pressures, the influence of temperature diminished. These findings have established a theoretical and practical basis for optimizing CO2-ECBM extraction in Nigeria, highlighting the preferential adsorption of CO2 over CH4 in subbituminous coals from the Mamu and Nsukka formations under varying pressure and temperature conditions. Implementing CO2-ECBM extraction and storage in Nigeria could boost economic viability and help achieve net-zero goals, using insights from this study to guide policy development. Graphical Abstract

An Effective Method for Estimating the Population Mean That Utilizes Dual Auxiliary Information

To improve the effectiveness of population estimators, researchers have recently implemented dual supplementary information. They employed traditional rankings, the empirical cumulative distribution function, and indicator functions as supplementary sources of information in their analysis. An improved family of population mean estimators is introduced in this article, which utilizes the relative ranks of the auxiliary information’s configurations to incorporate the relevant information. A first-order approximation is employed to derive the mathematical expressions for the bias and the mean-squared error (MSE) of the proposed family of estimators. The empirical analysis is investigated to demonstrate the practicality of the proposed estimators in real-world scenarios. Additionally, the theoretical conclusions are effectively validated by the Monte Carlo simulation integration. Our results unequivocally indicate that the proposed family of estimators surpasses their current counterparts.

Accessing excitations of many-body systems via single-mode approximation within quantum Monte Carlo simulations

Accessing excitations of many-body systems via single-mode approximation within quantum Monte Carlo simulations

Embedded multi-boson exchange: A step beyond quantum cluster theories

We introduce a diagrammatic multi-scale approach to the Hubbard model based on the interaction-irreducible (multi-boson) vertex of a small cluster embedded in a self-consistent medium. The vertex captures short-range correlations up to the length scale of the cluster, while long-range correlations are recovered from a set of diagrammatic equations for the Hedin three-leg vertex. By virtue of the crossing symmetry, the Fierz decoupling ambiguity of the Hubbard interaction is resolved exactly. Our benchmarks for the half-filled Hubbard model on the square lattice are in very good agreement with numerically exact diagrammatic Monte Carlo simulations. Published by the American Physical Society 2024

Characterization, source profiles and risk assessment of polycyclic aromatic hydrocarbons (PAHs) in urban dust of 14 Chinese cities

ABSTRACT Data for 16 USEPA priority PAHs were collected in urban dust from 14 cities in China between 2008 and 2022, and concentration, profile composition, and human health risk were investigated. The mean concentration of PAHs was 6.60 mg kg−1, ranging from 2.88 mg kg−1 to 13.88 mg kg−1, and it was higher than other important cities in neighboring countries of China. Among different individual PAHs, mean fluoranthene (Flr) was dominant, accounting for 15.04%, and low molecular weight PAH (LMW) was almost a third of high molecular weight PAH (HMW). The results of PAH composition profiles suggested that the proportion of different rings of PAHs increased in order: 2-ring PAHs (4.40%)< 6-ring PAHs (15.73%)< 3-ring PAHs (17.77%)< 5-ring PAHs (22.54%)< 4-ring PAHs (39.56%). The results of the source appointment suggested that pyrogenic sources and mixed sources, particularly wood, grass, coal, and diesel combustion, were the main sources of PAHs in urban dust. The results of the health risk assessment illustrated that children were more vulnerable to PAHs, and ingestion was the dominant exposure pathway to PAHs (78.152%, 67.519%, 73.206%, and 72.957% of children, adolescents, adults, and the total lifetime cancer risk (TLCR)). PAH pollution in urban dust from different cities in China was a commonly existing phenomenon, but the mean and maximum of TLCR were acceptable according to the standard established by USEPA (10−4). Based on Monte Carlo simulations, body weight (BW) was the most important uncertain input in three age groups (60.75%-91.22% contribution).

Antiproton annihilation at rest in thin solid targets and comparison with Monte Carlo simulations

AbstractThe mechanism of antiproton–nucleus annihilation at rest is not fully understood, despite substantial previous experimental and theoretical work. In this study we used slow extracted antiprotons from the ASACUSA apparatus at CERN to measure the charged particle multiplicities and their energy deposits from antiproton annihilations at rest on three different nuclei: carbon, molybdenum and gold. The results are compared with predictions from different models in the simulation tools Geant4 and FLUKA. A model that accounts for all the observed features is still missing, as well as measurements at low energies, to validate such models.

Pulsed and Polarized X‐Ray Emission From Neutron Star Surfaces

ABSTRACTThe intense magnetic fields of neutron stars naturally lead to strong anisotropy and polarization of radiation emanating from their surfaces, both being sensitive to the hot spot position on the surface. Accordingly, pulse phase‐resolved intensities and polarizations depend on the angle between the magnetic and spin axes and the observer's viewing direction. In this paper, results are presented from a Monte Carlo simulation of neutron star atmospheres that uses a complex electric field vector formalism to treat polarized radiative transfer due to magnetic Thomson scattering. General relativistic influences on the propagation of light from the stellar surface to a distant observer are taken into account. The paper outlines a range of theoretical predictions for pulse profiles at different X‐ray energies, focusing on magnetars and also neutron stars of lower magnetization. By comparing these models with observed intensity and polarization pulse profiles for the magnetar 1RXS J1708‐40, and the light curve for the pulsar PSR J0821‐4300, constraints on the stellar geometry angles and the size of putative polar cap hot spots are obtained.

High‐Entropy Phosphide Catalyst‐Based Hybrid Electrolyzer: A Cost‐Effective and Mild‐Condition Approach for H2 Liberation from Methanol

AbstractMethanol as a hydrogen carrier provides a practical solution for H2 storage and transport, but traditional reforming faces challenges with low efficiency, CO2 emissions, and the need for specialized infrastructure. In this study, a reliable approach for fabricating low‐cost electrodes is presented by in situ growing high‐entropy phosphide nanoparticles on nickel foam (FeCoNiCuMnP/NF). This cost‐effective design is specifically engineered for alkaline methanol oxidation reactions (MOR), achieving a current density of 10 mA cm−2 at an applied voltage of only 1.32 V, while also demonstrating exceptional selectivity for formate products. Advanced Monte Carlo (ML‐MC) simulations identify copper as the predominant surface element and highlight phosphorus coordination as a key factor in enhancing catalytic activity. The field is advanced with a pioneering hybrid acid/alkali flow electrolyzer system, integrating FeCoNiCuMnP/NF anode and commercial RuIr/Ti cathode to enable indirect hydrogen liberation from methanol. This system requires an electrolytic voltage as low as 0.58 V to achieve a current density of 10 mA cm−2 and remains stable for hydrogen liberation over 300 h of operation. This achievement not only offers a highly efficient alternative to indirectly liberate H2 stored in methanol but also establishes a new benchmark for sustainable and economically viable H2 production.

Dose optimization of piperacillin/tazobactam, cefepime, and ceftazidime for carbapenem-resistant Pseudomonas aeruginosa isolates in Türkiye.

Although CRPA may test susceptible to other β-lactams such as ceftazidime (CAZ), cefepime (FEP), and piperacillin/tazobactam (TZP), reduced potency has been observed. We assessed the adequacy of EUCAST Susceptible (S) or Susceptible Increased Exposure (SIE)/(I) doses for CAZ, FEP, and TZP against CRPA clinical isolates. CRPA isolates were collected from patients at three Turkish hospitals. CAZ, FEP, and TZP MICs were determined using broth microdilution. Monte Carlo simulations were performed to determine the probability of target attainment (PTA) for a free time above the MIC (fT > MIC) targets for various doses of each agent against isolates defined as susceptible. fT > MIC targets were 70% for CAZ or FEP and 50% for TZP. Cumulative fraction of response (CFR) was calculated. Optimal PTA and CFR was 90% target achievement. The percentages of isolates SIE/I to CAZ, FEP, and TZP were 49,8%, 47%, and 31,8% respectively. Reduced potency was noted with 54,1% of CAZ-S isolates having MICs of 4 or 8mg/L. Of the FEP and TZP-S isolates, MICs at the breakpoint (8 and 16mg/L, respectively) were the mode with 45,2 and 53,9% of isolates for each, respectively. At an MIC of 8mg/L for CAZ, the EUCAST standard dose was insufficient (CFR of 85%). 3h infusions of EUCAST SIE doses were required for 90% PTA at MIC of 8mg/L and an optimized CFR of 100%. For FEP, the SIE dose of 2g q8h 0.5h infusion of was effective (CFR 96%), utilization of an extended 3h infusion further optimized the PTA at 8mg/L (CFR 99%). For TZP, the standard dose of 4.5 q6h administered as a 0.5h infusion was inadequate (CFR 86%). A standard TZP dose with an extended infusion (4.5g q8h over 4h) and the SIE dose 4.5g q6h 3h infusion resulted in CFRs > 95%. These data support the EUCAST SIE breakpoints for FEP and TZP. To optimize PTA at the SIE breakpoint for CAZ, prolonged infusion is required.

Virtual X-ray critical dimension metrology via Monte Carlo simulation.

X-ray critical dimension (XCD) metrology is a highly promising technique for achieving sub-nanometer precision in critical dimension measurements at advanced nodes of integrated circuit manufacturing. Compared to XCD experiments utilizing synchrotron radiation sources, those employing compact X-ray sources encounter challenges like extended testing time and increased uncertainty. To evaluate the influence of experimental conditions on measurement results, we developed an ab initio virtual X-ray critical dimension metrology via a Monte Carlo simulation (MC-VXCD). Through calibrating the system parameters of the MC-VXCD to a home-built compact XCD instrument, we achieved excellent consistency between virtual and actual measurement results. The virtual instrument effectively estimated measurement errors stemming from the reduced exposure time, which significantly influences the measurement accuracy and throughput. Furthermore, through the MC-VXCD, we establish the connection between the application scenarios of the XCD metrology and the geometry of XCD instruments, offering a versatile platform for the system design, experimental configuration optimization, data analysis, etc., in XCD metrology.

The prediction of RULs for Weibull or gamma k-out-of-n system with nonidentical components based on component data

ABSTRACT The paper investigates the prediction procedures of the remaining useful lifetime (RUL) of k -out-of- n system with nonidentical Weibull or gamma components based on component data. The distributions of the offline and online RULs of system are derived. Using the generalized pivotal quantity (GPQ) method, the prediction intervals (PIs) of the offline and online RULs for the Weibull k -out-of- n system are derived. For the gamma k -out-of- n system, the PIs of the offline and online RULs are also obtained based on the Cornish-Fisher expansion and the GPQ method. In addition, the bootstrap PIs of the offline and online RULs for the Weibull or gamma k -out-of- n system are proposed. The performance of the proposed PIs is assessed in terms of coverage probability by using the Monte Carlo simulation. Finally, two examples are used to illustrate the proposed procedures.

Techno-economic analysis of a green hydrogen-firing gas turbine in Malaysia via Monte Carlo simulations

Techno-economic analysis of a green hydrogen-firing gas turbine in Malaysia via Monte Carlo simulations

Integrating deep learning and discrete cosine transform for surface waves full waveform inversion

Summary Accurate estimations of near-surface S-wave velocity (Vs) models hold particular significance in geological and engineering investigations. On the one hand, the popular Multichannel Analysis of Surface Waves (MASW) is limited to the 1D and the plane wave assumptions. On the other hand, the more advanced and computationally expensive full-waveform inversion (FWI) approach is often solved within a deterministic framework that hampers an accurate uncertainty assessment and makes the final predictions heavily reliant on the starting model. Here we combine deep learning with Discrete Cosine Transforms (DCT) to solve the FWI of surface waves and to efficiently estimate the inversion uncertainties. Our neural network approach effectively learns the inverse non-linear mapping between DCT-compressed seismograms and DCT-compressed S-velocity models. The incorporation of DCT into the deep learning framework provides several advantages: it notably reduces parameter space dimensionality and alleviates the ill-conditioning of the problem. Additionally, it decreases the complexity of the network architecture and the computational cost for the training phase compared to training in the full domain. A Monte Carlo simulation is also used to propagate the uncertainties from the data to the model space. We first test the implemented inversion method on synthetic data to showcase the generalization capabilities of the trained network and to explore the implications of incorrect noise assumptions in the recorded seismograms and inaccurate wavelet estimations. Further, we demonstrate the applicability of the implemented method to field data. In this case, available borehole information is used to validate our predictions. In both the synthetic and field applications, the predictions provided by the proposed method are compared with those of a deterministic FWI and the outcomes of a network trained in the full data and model spaces. Our experiments confirm that the implemented deep-learning inversion efficiently and successfully solves the FWI problem and yields more accurate and stable results than a network trained without the DCT compression. This opens the possibility to efficiently train a neural network that provides accurate instantaneous predictions of Vs near-surface models and related uncertainties.

LASSO-type instrumental variable selection methods with an application to Mendelian randomization.

Valid instrumental variables (IVs) must not directly impact the outcome variable and must also be uncorrelated with nonmeasured variables. However, in practice, IVs are likely to be invalid. The existing methods can lead to large bias relative to standard errors in situations with many weak and invalid instruments. In this paper, we derive a LASSO procedure for the k-class IV estimation methods in the linear IV model. In addition, we propose the jackknife IV method by using LASSO to address the problem of many weak invalid instruments in the case of heteroscedastic data. The proposed methods are robust for estimating causal effects in the presence of many invalid and valid instruments, with theoretical assurances of their execution. In addition, two-step numerical algorithms are developed for the estimation of causal effects. The performance of the proposed estimators is demonstrated via Monte Carlo simulations as well as an empirical application. We use Mendelian randomization as an application, wherein we estimate the causal effect of body mass index on the health-related quality of life index using single nucleotide polymorphisms as instruments for body mass index.

Bayesian Inference for Estimating Heat Sources Through Temperature Assimilation

Abstract This paper utilizes a Bayesian inference framework to address the two-dimensional (2D) steady-state heat conduction problem, focusing on the estimation of unknown distributed heat sources in a thermally conducting medium with uniform conductivity. The goal is to infer the locations, strength, and shape of heaters by assimilating temperature data in Euclidean space, employing a Fourier series to represent each heater's shape. The Markov Chain Monte Carlo (MCMC) method, incorporating the random-walk Metropolis–Hasting (MH) algorithm and parallel tempering, is utilized for posterior distribution exploration in both unbounded and wall-bounded domains. It is found that multiple solutions arise in cases where the number of temperature sensors is less than the number of unknown states. Moreover, smaller heaters introduce greater uncertainty in estimated strength. To address the challenge of estimating the heater's strength and shape simultaneously due to their strong correlation, our method incorporates sharp priors on one to ensure accurate and feasible solutions of the other. The diffusive nature of heat conduction smooths out any deformations in the temperature contours, especially in the presence of multiple heaters positioned near each other, impacting convergence. In wall-bounded domains with Neumann boundary conditions, the inference of heater parameters tends to be more accurate than in unbounded domains.

External evaluation of neonatal vancomycin population pharmacokinetic models: Moving from first-order equations to Bayesian-guided therapeutic monitoring.

Guidelines for vancomycin therapeutic monitoring recommend using a Bayesian approach with a population pharmacokinetic model to estimate the 24 h area under the concentration-time curve over first-order equations. Thus, we performed an external evaluation of population pharmacokinetic models of vancomycin in neonates and compared Bayesian results with those observed in clinical practice via pharmacokinetic equations to improve therapeutic monitoring by proposing optimized initial dosing nomograms and assessing the feasibility of reduced blood sampling strategies using the most predictive models. Models were identified from the literature and evaluated via an external neonatal population. A priori predictive performance was first assessed by prediction-based diagnostics, then by simulation-based diagnostics and a posteriori analyses only if deemed satisfactory; model-informed vancomycin exposure was also compared with reference first-order pharmacokinetic equations. The best-performing models were ultimately subjected to Monte Carlo simulations to develop new initial dosing nomograms offering the highest probability of achieving therapeutic target. A total of 28 population pharmacokinetic models were evaluated in the external dataset, which includes 72 neonates and 380 vancomycin concentrations. Eleven models had an adequate predictive performance with bias ≤ ± 15% and imprecision 30%, while the Bayesian approach yielded over 75% agreement with reference exposure values in most cases. Nonetheless, Capparelli etal. and Mehrotra etal. models performed the best overall, showing the lowest imprecisions of 16.8% and 16.9%, respectively; both models recommended higher dosage regimens than the theoretical nomogram currently applied to favor therapeutic target attainment. We externally evaluated numerous neonatal population pharmacokinetic models of vancomycin and used the most predictive ones to advocate new initial dosing nomograms. Clinical implementation of the Bayesian approach could reduce the time needed to reach therapeutic target and limit the number of blood samples in newborns compared with traditional pharmacokinetic equations.

Monte Carlo Simulation of Organ Absorbed Dose of Worker's Radiation Exposure in Bone Scintigraphy

Objectives: This study examines individual organ doses and the impact of ionizing radiation sources on effective radiation doses. Methods: In the research, the ICRP-defined adult standing phantom was used as the phantom material in the Visual Monte Carlo Dose Calculation Program (VMC). Subsequently, the incurred doses were calculated by defining different doses, distances, and durations for the 99mTc radioactive source. Results: Exposure times were set at 5 minutes and 20 minutes in comparison. The results indicated that for 5 minutes and 20 minutes at 360 cm, doses remained below the ICRP recommended annual dose limit of 5.7 µSv/h for occupational exposure. Conclusion: Organ absorbed and effective doses varied with exposure time and source-phantom distance. To optimize radiation exposure, people working in radiation fields must make an increased effort to reduce radiation doses following the ALARA principles. Keywords: Effective dose, absorbed dose, VMC software, Monte Carlo