Monte Carlo Simulations Research Articles

Characterization of selected additive manufacturing materials for synchrotron monochromatic imaging and broad-beam radiotherapy at the Australian synchrotron- imaging and medical beamline.

This study aims to characterize radiological properties of selected additive manufacturing (AM) materials utilizing both material extrusion and vat photopolymerization technologies. Monochromatic synchrotron X-ray images and synchrotron treatment beam dosimetry were acquired at the hutch 3B of the Australian synchrotron - imaging and medical beamline (AS-IMBL).
Approach: Eight energies from 30 keV up to 65 keV were used to acquire the attenuation coefficients of the AM materials. Comparison of theoretical, and experimental attenuation data of AM materials and standard solid water for MV linac was performed. Broad-beam dosimetry experiment through attenuated dose measurement and a Geant4 Monte Carlo simulation were done for the studied materials to investigate its attenuation properties specific for a 4 tesla wiggler field with varying synchrotron radiation beam qualities.
Main results: PLA plus matches attenuation coefficients of both soft tissue and brain tissue, while ABS, ASA, and Draft resin have close equivalence to adipose tissue. Lastly, PLA, CPE plus, TPU, and White resins are promising substitute materials for breast tissue. For broad-beam experiment and simulation, many of the studied materials were able to simulate RMI457 SolidWater and bolus within +/-10% for the three synchrotron beam qualities. These results are useful in fabricating phantoms for synchrotron and other related medical radiation applications such as orthovoltage treatments. 
Significance and conclusion: These 3D printing materials were studied as potential substitutes for selected tissues such as breast tissue, adipose tissue, soft-tissue, and brain tissue useful in fabricating 3D printed phantoms for synchrotron imaging, therapy, and orthovoltage applications. Fabricating customizable heterogeneous anthropomorphic phantoms (e.g. breast, head, thorax) and pre-clinical animal phantoms (e.g. rodents, canine) for synchrotron imaging and radiotherapy using AM can be done based on the results of this study.

Escape from textured adsorbing surfaces.

The escape dynamics of sticky particles from textured surfaces is poorly understood despite importance to various scientific and technological domains. In this work, we address this challenge by investigating the escape time of adsorbates from prevalent surface topographies, including holes/pits, pillars, and grooves. Analytical expressions for the probability density function and the mean of the escape time are derived. A particularly interesting scenario is that of very deep and narrow confining spaces within the surface. In this case, the joint effect of the entrapment and stickiness prolongs the escape time, resulting in an effective desorption rate that is dramatically lower than that of the untextured surface. This rate is shown to abide a universal scaling law, which couples the equilibrium constants of adsorption with the relevant confining length scales. While our results are analytical and exact, we also present an approximation for deep and narrow cavities based on an effective description of one-dimensional diffusion that is punctuated by motionless adsorption events. This simple and physically motivated approximation provides high-accuracy predictions within its range of validity and works relatively well even for cavities of intermediate depth. All theoretical results are corroborated with extensive Monte Carlo simulations.

Reliability evaluation in the zero‐failure Weibull case based on double‐modified hierarchical Bayes

AbstractHierarchical Bayes, or E‐Bayes, is frequently used to estimate the failure probability when solving a zero‐failure reliability evaluation model; however, the accuracy of the reliability estimation using these methods is not very good in practice. Due to this, a novel double‐modified hierarchical Bayes (DMH‐Bayes) is proposed for Weibull characteristic data in this study to enhance failure probability estimation and improve reliability point estimation accuracy. Meanwhile, in order to guarantee the preservation of the assessment findings' consistency and confidence level, the parametric Bootstrap method (P‐Bootstrap) and the L‐moment estimation method based on point estimation are introduced to obtain reliability confidence interval estimates. Based on Monte–Carlo simulation testing and analysis of a gyroscope bearing, the new model is confirmed to have better applicability and robustness while improving the accuracy of reliability assessment.

Machine learning insights into predicting biogas separation in metal-organic frameworks

Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation properties of metal-organic frameworks (MOFs). Training data are obtained using grand canonical Monte Carlo simulations of experimental MOFs which have been carefully curated to ensure data quality and structural viability. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R2 values consistently above 0.9 for the validation set. We make prospective predictions on an independent external set of hypothetical MOFs, and examine these predictions in comparison to the results of grand canonical Monte Carlo calculations. The best-performing trained models correctly filter out over 90% of low-performing unseen MOFs, illustrating their applicability to other MOF datasets.

Monte Carlo Simulation of the CO2 Flooding Efficiency at a Core Scale for Different Oil Compositions

The evaluation of water-alternating-gas (WAG) efficiency and profitability is complicated by a large number of reservoir, operating, and economic parameters and constraints. This study aims at understanding the influence of the oil composition on different WAG injections. By employing compositional reservoir modeling and the Monte Carlo method to characterize the diversity of oils occurring in nature, we simulate the microscopic displacement efficiency of CO2 flooding when it is applied to both light- and heavy-oil reservoirs. We find that the economic performance of WAG in both miscible and immiscible scenarios is mainly characterized by the dimensionless injection rate and the oil density at surface conditions. Neither the bubble point pressure nor the minimum miscibility pressure can be used for the quantification of the optimal WAG parameters. We present our estimates of the best strategies for the miscible and immiscible injections and verify some of our previous results for randomly sampled oils. In particular, we demonstrate that CO2 flooding is better to apply at higher-dimensionless injection rates. We show that the injection of CO2 organized at a light-oil reservoir results in a higher profitability of WAG, although this comes at the cost of lower carbon storage efficiency.

Multiscale SUR Estimation of Systematic Risk

Abstract We propose a multiscale version of the seemingly unrelated regressions model, based on wavelet transform-based time series observations. Each regression equation refers to a different time scale, which enables the use of across-scale error covariances in the feasible GLS estimation procedure for efficiency gains. We demonstrate the advantages of the proposed method over OLS with two studies: an empirical study using stock market returns for the main US industrial sectors and a detailed Monte Carlo simulation study with alternative wavelet filters. We also provide explanations for the suitability of the proposed method for estimating long-term systematic risk.

Quantum Monte Carlo study of thin parahydrogen films on graphite

The low-temperature properties of one and two layers of parahydrogen adsorbed on graphite are investigated theoretically through Quantum Monte Carlo simulations. We adopt a microscopic model that explicitly includes the corrugation of the substrate. We study the phase diagram of a monolayer up to second layer promotion, and the possible occurrence of superfluidity in the second layer. We obtain results down to a temperature as low as 8 mK. We find second-layer promotion to occur at a considerably greater coverage than obtained in previous calculations and estimated experimentally; moreover, we find no evidence of a possible finite superfluid response in the second layer, disproving recent theoretical predictions.

Holocene relative sea-level variation and coastal changes in the Bay of Cádiz: New insights on the influence of local subsidence and glacial hydro-isostatic adjustments

The Bay of Cádiz is located in South-western Spain (Andalusia region) and constitutes an example of a typical estuarine salt marsh environment. In this study we reconstruct its Holocene morpho-evolution and relative sea-level change history by assembling a geodatabase of geological sea-level markers derived from boreholes and bibliographic data, standardized to the most recent international guidelines for RSL studies. The identified high-precision sea-level index points were compared to a number of new site-specific glacio-hydro-isostatic adjustment (GIA) models in order to disentangle potential components which influenced the sea-level evolution and finally obtain the vertical displacement (VD) trends that affect this coastal area by using a Bayesian statistical approach including Monte Carlo simulations. In general, the whole area was affected by overall subsidence related to the local sediment compaction, which had an impact on the morpho-evolution of the different zones with a variable entity and completely outclassed the GIA-driven component. Between 6.7 and 3.0 ka BP, the northern sector of the bay was characterized by subsidence rates of about - 0.65 mm/yr while, during the last 3.0 ka, the general trend appears to be homogeneous for both the main sectors of the bay with an average subsidence rate of - 1.6 mm/yr. The Holocene RSL curve from the Gulf of Cádiz aligns with past reconstructions, revealing subtle differential trends in subsidence rates in the northern and southern sectors of the Bay of Cádiz due to varying substratum.

Neural-network quantum states for ultra-cold Fermi gases

Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these properties can be precisely probed experimentally, accurately describing them poses significant theoretical challenges due to strong pairing correlations and the non-perturbative nature of particle interactions. In this work, we introduce a Pfaffian-Jastrow neural-network quantum state featuring a message-passing architecture to efficiently capture pairing and backflow correlations. We benchmark our approach on existing Slater-Jastrow frameworks and state-of-the-art diffusion Monte Carlo methods, demonstrating a performance advantage and the scalability of our scheme. We show that transfer learning stabilizes the training process in the presence of strong, short-ranged interactions, and allows for an effective exploration of the BCS-BEC crossover region. Our findings highlight the potential of neural-network quantum states as a promising strategy for investigating ultra-cold Fermi gases.

A Novel Approach to Integrate Human Biomonitoring Data with Model Predicted Dietary Exposures: A Crop Protection Chemical Case Study Using Lambda-Cyhalothrin.

The appropriate use of human biomonitoring data to model population chemical exposures is challenging, especially for rapidly metabolized chemicals, such as agricultural chemicals. The objective of this study is to demonstrate a novel approach integrating model predicted dietary exposures and biomonitoring data to potentially inform regulatory risk assessments. We use lambda-cyhalothrin as a case study, and for the same representative U.S. population in the National Health and Nutrition Examination Survey (NHANES), an integrated exposure and pharmacokinetic model predicted exposures are calibrated to measurements of the urinary metabolite 3-phenoxybenzoic acid (3PBA), using an approximate Bayesian computing (ABC) methodology. We demonstrate that the correlation between modeled urinary 3PBA and the NHANES 3PBA measurements more than doubled as ABC thresholding narrowed the acceptable tolerance range for predicted versus observed urinary measurements. The median predicted urinary concentrations were closer to the median measured value using ABC than using current regulatory Monte Carlo methods.

Modelling Inflation Dynamics and Global Oil Price Shocks in OAPEC Countries: TVP-VAR

This paper aims to examine the dynamic pass-through effect of oil price shocks on inflation in OAPEC countries using quarterly data ranging from 1990:Q1 to 2022:Q4. The incorporation of the TVP-VAR model along with the Markov Chain Monte Carlo method with stochastic volatility estimation offers a robust framework for capturing the time-varying nature of the relationship. Additionally, the analysis of the Impulse Response Function provides valuable insights into the short-term and long-term dynamics following oil price shocks. The results demonstrate a significant positive pass-through effect of oil prices on inflation in the sample member states, with heterogeneous magnitude and persistence. These variations are attributed to diversity in oil dependency, exchange rate regimes, monetary policy frameworks and the degree of openness to global markets. The study suggests the need for effective inflation management strategies and appropriate policy responses to oil price fluctuations. Furthermore, policymakers may consider adjusting exchange rates, interest rates, or trade policies to manage inflationary pressures effectively.

Thermoplastic polymers as water substitutes

Background. New applications of 3D printing have recently appeared in the fields of radiotherapy and radiology, but the knowledge of many radiological characteristics of the compounds involved is still limited. Therefore, studies are needed to improve our understanding about the transport and interaction of ionizing radiation in these materials. Purpose. The purpose of this study is to perform an analysis of the most important radiation interaction parameters in thermoplastic materials used in Fused Deposition Modeling 3D printing. Additionally, we propose improvements to bring their characteristics closer to those of water and use them as water substitutes in applications such as radiodiagnosis, external radiotherapy, and brachytherapy. Methods. We have calculated different magnitudes as mass linear attenuation, mass energy absorption coefficients, as well as stopping power and electronic density of several thermoplastic materials along with various compounds that have been used as water substitutes and in a new proposed blend. To perform these computations, we have used the XCOM and ESTAR databases from NIST and the EGSnrc code for Montecarlo simulations. Results. From the representation of the calculated interaction parameters, we have been able to establish relationships between their properties and the proportion of certain chemical elements. In addition, studying these same characteristics in different commercial solutions used as substitutes for water phantoms allows us to extrapolate improvements for these polymers. Conclusion. The radiological characteristics of the analyzed thermoplastic materials can be improved by adding some chemical elements with atomic numbers higher than oxygen and by using polyethylene in new blends.

Cooperative states and shift in resonant scattering of an atomic ensemble

Abstract Abstract We investigate the spectral shift known as the collective Lamb shift in forward scattering for a cold dense atomic cloud. The shift results from resonant dipole-dipole interaction mediated by real and virtual photon exchange, forming many-body states displaying various super- and subradiant behaviour. However, the scattering spectrum reflects the overall contributions from these states but also averages out the radiative details associated with the underlying spin orders, causing ambiguity in determination and raising controversy on the scaling property of this shift. We employ a Monte-Carlo simulation to study how the collective states contribute to emission. We thus distinguish two kinds of collective shift that follow different scaling laws. One results from dominant occupation of the near-resonant collective states. This shift is usually small and insensitive to the density or the number of participating atoms. The other comes from large spatial correlation of dipoles, associated with the states of higher degree of emission. This corresponds to larger collective shift that is approximately linearly dependent on the optical depth. We further demonstrate that the spatial spin order plays an essential role in superradiant emission. Our analysis provides a novel perspective for understanding collective scattering and cooperative effects.

Radiation Safety Assessment in Prostate Cancer Treatment: A Predictive Approach for I-125 Brachytherapy

This study uses Monte Carlo simulation and experimental measurements to develop a predictive model for estimating the external dose rate associated with permanent radioactive source implantation in prostate cancer patients. The objective is to estimate the accuracy of the patient’s external dose rate measurement. First, I-125 radioactive sources were implanted into Mylar window water phantoms to simulate the permanent implantation of these sources in patients. Water phantom experimental measurement was combined with Monte Carlo simulation to develop predictive equations, whose performance was verified against external clinical data. The model’s accuracy in predicting the external dose rate in patients with permanently implanted I-125 radioactive sources was high (R2 = 0.999). A comparative analysis of the experimental measurements and the Monte Carlo simulations revealed that the maximum discrepancy between the measured and calculated values for the water phantom was less than 5.00%. The model is practical for radiation safety assessments, enabling the evaluation of radiation exposure risks to individuals around patients with permanently implanted I-125 radioactive sources.

The effect of electron backscatter and charge build up in media on beam current transformer signal for ultra-high dose rate (FLASH) electron beam monitoring

Objective. Beam current transformers (BCT) are promising detectors for real-time beam monitoring in ultra-high dose rate (UHDR) electron radiotherapy. However, previous studies have reported a significant sensitivity of the BCT signal to changes in source-to-surface distance (SSD), field size, and phantom material which have until now been attributed to the fluctuating levels of electrons backscattered within the BCT. The purpose of this study is to evaluate this hypothesis, with the goal of understanding and mitigating the variations in BCT signal due to changes in irradiation conditions. Approach. Monte Carlo simulations and experimental measurements were conducted with a UHDR-capable intra-operative electron linear accelerator to analyze the impact of backscattered electrons on BCT signal. The potential influence of charge accumulation in media as a mechanism affecting BCT signal perturbation was further investigated by examining the effects of phantom conductivity and electrical grounding. Finally, the effectiveness of Faraday shielding to mitigate BCT signal variations is evaluated. Main Results. Monte Carlo simulations indicated that the fraction of electrons backscattered in water and on the collimator plastic at 6 and 9 MeV is lower than 1%, suggesting that backscattered electrons alone cannot account for the observed BCT signal variations. However, our experimental measurements confirmed previous findings of BCT response variation up to 15% for different field diameters. A significant impact of phantom type on BCT response was also observed, with variations in BCT signal as high as 14.1% when comparing measurements in water and solid water. The introduction of a Faraday shield to our applicators effectively mitigated the dependencies of BCT signal on SSD, field size, and phantom material. Significance. Our results indicate that variations in BCT signal as a function of SSD, field size, and phantom material are likely driven by an electric field originating in dielectric materials exposed to the UHDR electron beam. Strategies such as Faraday shielding were shown to effectively prevent these electric fields from affecting BCT signal, enabling reliable BCT-based electron UHDR beam monitoring.

A general ray tracing approach for solving thermal radiation in regular one-dimensional variable index media via the Monte Carlo method

In the heat transfer community, one-dimensional media refers to some media where the temperature and other thermophysical properties vary along one direction. Plane-parallel, concentric cylindrical, and concentric spherical geometries are among of regular one-dimensional media. In this paper, a general ray tracing approach is presented to track the curve paths of energy particles in regular one-dimensional variable index media. The domain of interest is limited by specular sidewalls and the medium is divided into slices with constant temperature and radiative properties. Then, the path of random energy particles emitted from boundary surfaces and volume elements are traced, element by element, until they absorb by diffuse-gray surfaces or the gray medium. After counting the number of absorbed particles by each boundary surface and volume element, the Monte Carlo method is performed to simulate thermal radiation heat transfer. The results are presented for three regular geometries, including plane-parallel, concentric cylindrical, and concentric spherical media, and the effects of radiative properties are investigated by some numerical experiments.

Cost-effectiveness of restorative treatments for permanent molars with severe molar incisor hypomineralization: perspectives for the Brazilian public health system.

To undertake a cost-effectiveness analysis of restorative treatments for a first permanent molar with severe molar incisor hypomineralization from the perspective of the Brazilian public system. Two models were constructed: a one-year decision tree and a ten-year Markov model, each based on a hypothetical cohort of one thousand individuals through Monte Carlo simulation. Eight restorative strategies were evaluated: high viscosity glass ionomer cement (HVGIC);encapsulated GIC; etch and rinse adhesive + composite; self-etch adhesive + composite;preformed stainless steel crown; HVGIC + etch and rinse adhesive + composite; HVGIC + self-etch adhesive + composite, and encapsulated GIC + etch and rinse adhesive + composite. Effectiveness data were sourced from the literature. Micro-costing was applied using 2022 USD market averages with a 5% variation. Incremental cost-effectiveness ratio (ICER), net monetary benefit (%NMB), and the budgetary impact were obtained. Cost-effective treatments included HVGIC (%NMB = 0%/0%), encapsulated GIC (%NMB = 19.4%/ 19.7%), and encapsulated GIC + etch and rinse adhesive + composite (%NMB = 23.4%/ 24.5%) at 1year and 10years, respectively. The benefit gain of encapsulated GIC + etch and rinse adhesive + composite in relation to encapsulated GIC was small when compared to the cost increase at 1year (gain of 3.28% and increase of USD 24.26) and 10years (gain of 4% and increase of USD 15.54). Within the horizon and perspective analyzed, the most cost-effective treatment was encapsulated GIC restoration. This study can provide information for decision-making.

Improved estimators in bell regression model with application

In this paper, we propose the application of shrinkage strategies to estimate coefficients in the Bell regression models when prior information about the coefficients is available. The Bell regression models are well-suited for modelling count data with multiple covariates. Furthermore, we provide a detailed explanation of the asymptotic properties of the proposed estimators, including asymptotic biases and mean squared errors. To assess the performance of the estimators, we conduct numerical studies using Monte Carlo simulations and evaluate their simulated relative efficiency. The results demonstrate that the suggested estimators outperform the unrestricted estimator when prior information is taken into account. Additionally, we present an empirical application to demonstrate the practical utility of the suggested estimators.

Parameter free AEWMA control chart for dispersion in semiconductor manufacturing

The study presents a new parameter free adaptive exponentially weighted moving average (AEWMA) control chart tailored for monitoring process dispersion, utilizing an adaptive approach for determining the smoothing constant. This chart is crafted to adeptly detect shifts within anticipated ranges in process dispersion by dynamically computing the smoothing constant. To assess its effectiveness, the chart’s performance is measured through concise run-length profiles generated from Monte Carlo simulations. A notable aspect is the incorporation of an unbiased estimator in computing the smoothing constant through the suggested function, thereby improving the chart’s capability to identify different levels of increasing and decreasing shifts in process dispersion. The comparison with an established adaptive EWMA-S2 dispersion chart highlights the considerable efficiency of the proposed chart in addressing diverse magnitudes of process dispersion shifts. Additionally, the study includes an application to a real-life dataset, showcasing the practicality and user-friendly nature of the proposed chart in real-world situations.

Latent structure and reliability

There are numerous reliability coefficients and α is the most popular. It is known that different coefficients can be appropriate in specific conditions and that α should not be used indiscriminately. However, some coefficients and conditions, particularly regarding the latent structure, lacked attention in previous research. In four Monte Carlo simulations, this study compared α, λ2, maximized λ4, λ4 based on locally optimal splits, μ2, Gilmer-Feldt, Kaiser-Caffrey α, Heise-Bohrnstedt Ω, Joreskog's ρ, ωtotal, algebraic greatest lower bound, greatest lower bound based on minimum rank factor analysis in every condition and also hierarchical ω and asymptotic ω hierarchical in multidimensional conditions. Findings suggest each of these coefficients can be useful at least in some conditions. Most differences in performance were observed in congeneric conditions and conditions with up to moderate loadings. Some coefficients were found to be more useful than previously considered. Results are discussed in the context of existing theory and previous Monte Carlo studies.