Monte Carlo Simulations Research Articles

Exhaustive review of acceleration strategies for Monte Carlo simulations in photon transit

Monte Carlo simulation techniques have become the quintessence and a pivotal nexus of inquiry in the realm of simulating photon movement within biological fabrics. Through the stochastic sampling of tissue archetypes delineated by explicit optical characteristics, Monte Carlo simulations possess the theoretical capacity to render unparalleled accuracy in the depiction of exceedingly intricate phenomena. Nonetheless, the quintessential challenge associated with Monte Carlo simulation methodologies resides in their extended computational duration, which significantly impedes the refinement of their precision. Consequently, this discourse is specifically dedicated to exploring innovations in strategies and technologies aimed at expediting Monte Carlo simulations. It delves into the foundational concepts of various acceleration tactics, evaluates these strategies concerning their speed, accuracy, and practicality, and amalgamates a comprehensive overview and critique of acceleration methodologies for Monte Carlo simulations. Ultimately, the discourse envisages prospective trajectories for the employment of Monte Carlo techniques within the domain of tissue optics.

Design and characterisation of a minibeam collimator utilising Monte Carlo simulation and a clinical linear accelerator

Objective. Spatially fractionated radiotherapy is showing promise as a treatment modality. Initial focus was on beams of photons at low energy produced from a synchrotron but more recently research has expanded to include applications in proton therapy. Interest in photon beams remains and this is the focus of this paper Approach. This study presents a 3D printed tungsten minibeam collimator intended to produce peak-to-valley dose ratios (PVDR) of between seven and ten with a 1 MV, bremsstrahlung generated, photon beam. The design of the collimator is motivated by a Monte Carlo study estimating the PVDR for different collimator designs at different energies. This collimator was characterised on a clinical linear accelerator (Elekta VersaHD) as well as an orthovoltage unit. Main results. The performance of the fabricated collimator was measured on Elekta VersaHD running in unflattened mode with a 6 MV beam. On the Elekta VersaHD units the PVDR was measured to be between approximately 1.5 and 2.0 at 3 cm deep. For measurements with the orthovoltage unit PVDRs of greater than 10 were observed at a depth of 4 cm. Significance. The results confirmed that the predictions from simulation could be reproduced on linear accelerators currently in clinical usage, producing PVDRs between 2–2.5. Using the model to predict PVDRs using 1 MV photon beams, the threshold considered to produce enhanced normal tissue dose tolerance ( > 7) was surpassed. This suggests the possibility of using such techniques with versions of existing Linac technology which have been modified to operate at low energy and high beam currents.

Disorder induced band gap lowering in kesterite type Cu2ZnSnSe4 and Ag2ZnSnSe4: a first-principles and special quasirandom structures investigation

Quaternary chalcogenides, i.e. Cu2ZnSnS4, crystallising in the kesterite crystal structure have already been demonstrated as potential building blocks of thin film solar cells, containing only abundant elements and exhibiting power conversion efficiencies of about 14.9% so far. However, due to the potential presence of several structurally similar polymorphs, the unequivocal identification of their ground state crystal structures required the application of more elaborate neutron diffraction experiments. One particular complication arose from the later identified Cu–Zn disorder, present in virtually all thin film samples. Subsequently, it has been shown experimentally that this unavoidable Cu–Zn disorder leads to a band gap lowering in the respective samples. Additional theoretical investigations, mostly based on Monte-Carlo methods, tried to understand the atomistic origin of this disorder induced band gap lowering. Here, we present theoretical results from first-principles calculations based on density functional theory for the disorder induced band gap lowering in kesterite Cu2ZnSnSe4 and Ag2ZnSnSe4, where the Cu–Zn and Ag–Zn disorder is modelled via a supercell approach and special quasirandom structures. Results of subsequent analyses of structural, electronic, and optical properties are discussed with respect to available experimental results, and will provide additional insight and knowledge towards the atomistic origin of the observed disorder induced band gap lowering in kesterite type materials.

Understanding complex effects on clinical outcomes and biomarker activities of food supplementation in heavy smokers: an approach using bayesian networks

BACKGROUND: Antioxidant administration can significantly alleviate smoking-mediated oxidative stress. In our previous research, using a concentrated mixed juice powder led to significant reduction in oxidative status markers and improved lung function among smokers. OBJECTIVE: To compare the effectiveness of different antioxidant formulations of the retail product Juice Plus+® on oxidative status and lung function in healthy smokers via a a Bayesian Network (BN) analysis. METHODS: At baseline and after 3 months’ supplementation, pulmonary function, cardiovascular risk factors, and oxidative status were assessed in 101 smokers, randomized into three groups: placebo (P), fruit/vegetable (FV), and fruit/vegetable/berry (FVB) group. BN analysis was performed via Monte Carlo simulations. Statistical significance was assigned as p < 0.05. RESULTS: No significant interaction was observed in P (network score –101.4, p = ns), whereas overall fit significantly improved in both FV (network score –185.7, p = 0.01) and FVB (–293.7, p = 0.003). Moreover, P group’s respiratory parameters appeared unrelated to the biochemical structure, whereas those of the FV and, to a greater extent, FVB groups were definitely related to it CONCLUSIONS: BN analysis demonstrated that the FV and FVB formulations contain nutraceutical compounds capable of synergistically acting and improving antioxidant modulation and respiratory function in smoking subjects

The potential of mixed carbon-helium beams for online treatment verification: a simulation and treatment planning study.

Objective.Recently, a new and promising approach for range verification was proposed. This method requires the use of two different ion species. Due to their equal magnetic rigidity, fully ionized carbon and helium ions can be simultaneously accelerated in accelerators like synchrotrons. At sufficiently high treatment energies, helium ions can exit the patient distally, reaching approximately three times the range of carbon ions at an equal energy per nucleon. Therefore, the proposal involves adding a small helium fluence to the carbon ion beam and utilizing helium as an online range probe during radiation therapy. This work aims to develop a software framework for treatment planning and motion verification in range-guided radiation therapy using mixed carbon-helium beams.Approach.The developed framework is based on the open-source treatment planning toolkit matRad. Dose distributions and helium radiographs were simulated using the open-source Monte Carlo package TOPAS. Beam delivery system parameters were obtained from the Heidelberg Ion Therapy Center, and imaging detectors along with reconstruction were facilitated by ProtonVDA. Methods for reconstructing the most likely patient positioning error scenarios and the motion phase of 4DCT are presented for prostate and lung cancer sites.Main results.The developed framework provides the capability to calculate and optimize treatment plans for mixed carbon-helium ion therapy. It can simulate the treatment process and generate helium radiographs for simulated patient geometry, including small beam views. Furthermore, motion reconstruction based on these radiographs seems possible with preliminary validation.Significance.The developed framework can be applied for further experimental work with the promising mixed carbon-helium ion implementation of range-guided radiotherapy. It offers opportunities for adaptation in particle therapy, improving dose accumulation, and enabling patient anatomy reconstruction during radiotherapy.

Insights into Error Control Mechanisms in Biological Processes: Copolymerization and Enzyme-Kinetics Revisited.

The high fidelity observed in biological information processing ranging from replication to translation has stimulated significant research efforts to clarify the underlying microscopic picture. Theoretically, several approaches to analyze the error rates have been proposed. The copolymerization theory describes the addition and removal of monomers at the growing tip of a copolymer, leading to a closed set of nonlinear equations. On the other hand, enzyme-kinetics approaches formulate linear equations of biochemical networks, describing transitions between discrete chemical states. However, it is still unclear whether the error values computed by the two approaches agree. Moreover, there are conflicting interpretations on whether the error is under thermodynamic or kinetic discrimination control. In this work, we examine the error rate in persistent copying biochemical processes by specifically analyzing both theoretical approaches. The initial disagreement of the results between the two theories motivated us to rederive the formula for the error rate in the kinetic model. The error computed with the new method resulted in excellent agreement between both theoretical approaches and with Monte Carlo simulations. Furthermore, our theoretical analysis shows that the kinetic discrimination controls the error, even when the energy difference between adding the right and wrong products is very small. Our theoretical investigation gives important insights into the physical-chemical properties of complex biological processes by providing the quantitative framework to evaluate them.

Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: experimental validation in tissue-mimicking finger phantoms

Objective. Effective treatment within 3–5 months of disease onset significantly improves rheumatoid arthritis (RA) prognosis. Nevertheless, 30% of RA patients fail their first treatment, and it takes 3–6 months to identify failure with current monitoring techniques. Time-domain diffuse optical imaging (TD-DOI) may be more sensitive to RA disease activity and could be used to detect treatment failure. In this report, we present the development of a TD-DOI hand imaging system and validate its ability to measure simulated changes in RA disease activity using tissue-mimicking finger phantoms. Approach. A TD-DOI system was built, based on a single-pixel camera architecture, and used to image solid phantoms which mimicked a proximal interphalangeal finger joint. For reference, in silico images of virtual models of the solid phantoms were also generated using Monte Carlo simulations. Spatiotemporal Fourier components were extracted from both simulated and experimental images, and their ability to distinguish between phantoms representing different RA disease activity was quantified. Main results. Many spatiotemporal Fourier components extracted from TD-DOI images could clearly distinguish between phantoms representing different states of RA disease activity. Significance. A TD-DOI system was built and validated using finger-mimicking solid phantoms. The findings suggest that the system could be used to monitor RA disease activity. This single-pixel TD-DOI system could be used to acquire longitudinal measures of RA disease activity to detect early treatment failure.

Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: theoretical development and in silico validation

Objective. Effective early treatment—within 3–5 months of disease onset—significantly improves rheumatoid arthritis (RA) prognosis. Nevertheless, 1 in 3 patients experiences treatment failure which takes 3–6 months to detect with current monitoring techniques. The aim of this work is to develop a method for extracting quantitative features from data obtained with time-domain diffuse optical imaging (TD-DOI), and demonstrate their sensitivity to RA disease activity. Approach. 80 virtual phantoms of the proximal interphalangeal joint—obtained from a realistic tissue distribution derived from magnetic resonance imaging—were created to simulate RA-induced alterations in 5 physiological parameters. TD-DOI images were generated using Monte Carlo simulations, and Poisson noise was added to each image. Subsequently, each image was convolved with an instrument response function (IRF) to mimic experimental measurements. Various spatiotemporal features were extracted from the images (i.e. statistical moments, temporal Fourier components), corrected for IRF effects, and correlated with the disease index (DI) of each phantom. Main results. Spatiotemporal Fourier components of TD-DOI images were highly correlated with DI despite the confounding effects of noise and the IRF. Moreover, lower temporal frequency components ( ⩽ 0.4 GHz) demonstrated greater sensitivity to small changes in disease activity than previously published spatial features extracted from the same images. Significance. Spatiotemporal components of TD-DOI images may be more sensitive to small changes in RA disease activity than previously reported DOI features. TD-DOI may enable earlier detection of RA treatment failure, which would reduce the time needed to identify treatment failure and improve patient prognosis.

Patient-specific radiation dose for Chinese pediatric patients undergoing whole-body PET/CT examinations

Objective. To assess potential variations in the absorbed dose between Chinese and Caucasian children exposed to 18F-FDG PET scan and to investigate the factors contributing to dose differences, this work employed patient-specific phantoms and our compartment model for calculating the patient-specific absorbed dose in Chinese children. Approach. Data of 29 Chinese pediatric patients undergoing whole-body 18F-FDG PET/CT studies were retrospectively collected, including PET images for activity distributions and corresponding CT images for organ segmentation and phantom construction. A biokinetic compartment model was implemented to obtain cumulated activities. Absorbed radiation dose for both CT and PET component were calculated using Monte Carlo simulations. Regression models were fitted to time integrated activity coefficient (TIAC) and organ absorbed dose for each patient. Main results. TIACs of all the organs in our compartment model and the organ dose for 12 organs were correlated with patients’ weight. Young children have significantly large uptake in brain compared to adults. The distinctions of anatomical and biological characteristics between Chinese and Caucasian children contribute to variations in the absorbed dose of 18F-FDG PET scans. PET contributed more in organ dose than CT did in most organs, especially in brain and bladder. The average effective dose (± SD) was 4.5 mSv (± 1.12 mSv), 7.8 mSv (± 3.2 mSv) and 12.3 mSv (± 3.5 mSv) from CT, PET and their sum respectively. PET contributed 1.7 times higher than CT. Significance. To the best of our knowledge, this work represents the first attempt to estimate patient-specific radiation doses from PET/CT for Chinese pediatric patients. TIACs derived from our methodology in both age groups exhibited significant differences from the that reported in ICRP 128. Substantial differences in absorbed and effective doses were observed between Chinese and Caucasian children across all age groups. These disparities are attributed to markedly distinct anatomical and pharmacokinetic characteristics among adults and pediatric patients, and different racial groups. The application of data derived from adults to pediatric patients introduces considerable uncertainty. Our methodology offers a valuable approach not only for estimating pharmacokinetic characteristics and patient-specific radiation doses in pediatric patients undergoing 18F-FDG studies but also for other cohorts with similar characteristics.

A quantitative assessment of Geant4 for predicting the yield and distribution of positron-emitting fragments in ion beam therapy

Objective. To compare the accuracy with which different hadronic inelastic physics models across ten Geant4 Monte Carlo simulation toolkit versions can predict positron-emitting fragments produced along the beam path during carbon and oxygen ion therapy. Approach. Phantoms of polyethylene, gelatin, or poly(methyl methacrylate) were irradiated with monoenergetic carbon and oxygen ion beams. Post-irradiation, 4D PET images were acquired and parent 11C, 10C and 15O radionuclides contributions in each voxel were determined from the extracted time activity curves. Next, the experimental configurations were simulated in Geant4 Monte Carlo versions 10.0 to 11.1, with three different fragmentation models—binary ion cascade (BIC), quantum molecular dynamics (QMD) and the Liege intranuclear cascade (INCL++) - 30 model-version combinations. Total positron annihilation and parent isotope production yields predicted by each simulation were compared between simulations and experiments using normalised mean squared error and Pearson cross-correlation coefficient. Finally, we compared the depth of the maximum positron annihilation yield and the distal point at which the positron yield decreases to 50% of peak between each model and the experimental results. Main results. Performance varied considerably across versions and models, with no one version/model combination providing the best prediction of all positron-emitting fragments in all evaluated target materials and irradiation conditions. BIC in Geant4 10.2 provided the best overall agreement with experimental results in the largest number of test cases. QMD consistently provided the best estimates of both the depth of peak positron yield (10.4 and 10.6) and the distal 50%-of-peak point (10.2), while BIC also performed well and INCL generally performed the worst across most Geant4 versions. Significance. The best predictions of the spatial distribution of positron annihilations and positron-emitting fragment production along the beam path during carbon and oxygen ion therapy was obtained using Geant4 10.2.p03 with BIC or QMD. These version/model combinations are recommended for future heavy ion therapy research.

Understanding shape selectivity effects of hydroisomerization using a reaction equilibrium model.

We study important aspects of shape selectivity effects of zeolites for hydroisomerization of linear alkanes, which produces a myriad of isomers, particularly for long chain hydrocarbons. To investigate the conditions for achieving an optimal yield of branched hydrocarbons, it is important to understand the role of chemical equilibrium in these reversible reactions. We conduct an extensive analysis of shape selectivity effects of different zeolites for the hydroisomerization of C7 and C8 isomers at chemical reaction equilibrium conditions. The reaction ensemble Monte Carlo method, coupled with grand-canonical Monte Carlo simulations, is commonly used for computing reaction equilibrium of heterogeneous reactions. The computational demands become prohibitive for a large number of reactions. We used a faster alternative in which reaction equilibrium is obtained by imposing chemical equilibrium in the gas phase and phase equilibrium between the gas phase components and the adsorbed phase counterparts. This effectively mimics the chemical equilibrium distribution in the adsorbed phase. Using Henry's law at infinite dilution and mixture adsorption isotherm models at elevated pressures, we calculate the adsorbed loadings in the zeolites. This study shows that zeolites with cage or channel-like structures exhibit significant differences in selectivity for alkane isomers. We also observe a minimal impact of pressure on the gas-phase equilibrium of these reactions at typical experimental reaction temperatures 400-700K. This study marks initial strides in understanding the reaction product distribution for long-chain alkanes.

Effect of the Number of Methyl Groups in DMOF on N2O Adsorption and N2O/N2 Separation.

Nitrous oxide (N2O), as the third largest greenhouse gas in the world, also has great applications in industry, so the purification of N2O from N2 in industrial tail gas is a crucial process for achieving environmental protection and giving full play to its economic value. Based on the polarity difference of N2O and N2, N2O adsorption was researched on DMOF series materials with different polarities and methyl numbers of the ligand. N2O adsorption at 0.1 bar is enhanced, attributed to an increase of the methyl group densities at the benzenedicarboxylate linker. Grand canonical Monte Carlo simulations demonstrate the key role of methyl groups within the pore surface in the preferential N2O affinity. Methyl groups preferentially bind to N2O and thus enhanced low (partial) pressure N2O adsorption and N2O/N2 separation. The result shows that DMOF-TM has the highest N2O adsorption capacity (19.6 cm3/g) and N2O/N2 selectivity (23.2) at 0.1 bar. Breakthrough experiments show that, with an increase of the methyl number, the coadsorption time and retention time also increase, and DMOF-TM has the best N2O/N2 separation performance.

High Curie temperature Heusler alloys RhMnCrZ (Z = Si, Ge) investigated by DFT and Monte Carlo methods

We utilize density functional theory (DFT) to conduct a comprehensive theoretical investigation of the quaternary RhMnCrZ (Z = Si, Ge) Heusler alloys. Our study focuse on analyzing the stability, electronic structure, magnetism, exchange interaction, and Curie temperature of these alloys. The stability of the alloys is verified by calculating and analyzing the formation energy, phonon spectrum, elastic constants, and melting temperature. The calculation results show that the alloys are natural half-metallic ferromagnets with lattice constants of 5.868 Å for RhMnCrSi and 5.980 Å for RhMnCrGe. Meanwhile, the effects of compression and expansion stress on the half-metallic property are simulated from a practical perspective. The magnetic calculation of the alloys shows an integer magnetic moment and adheres to the Slater-Pauling rule, which states that Mt=Zt-24. Based on crystal field theory, orbital hybridization theory, exchange interaction, etc., the coupling effect of electrons and the principle of integer magnetic moments are analyzed. The Heisenberg model is used to calculate the exchange interactions between all atoms. Various exchange interactions between atoms are analyzed, and it is concluded that the dominant mechanism is the ferrimagnetic ordered phase. What’s more, we calculate the Curie temperature of the RhMnCrZ (Z = Si, Ge) alloys, taking into account the exchange interactions. The Curie temperatures of both Heusler alloys are above 900 K, which is significantly higher than room temperature. RhMnCrZ (Z = Si, Ge) alloys show promise for spintronics applications, as indicated by theoretical simulation calculations.

Anisotropic remixing of a phase separated binary colloidal system with particles of different sizes in an external modulation.

We explore the phase behavior of a binary colloidal system under external spatially periodic modulation. We perform Monte Carlo simulations on a binary mixture of big and small repulsive Lennard-Jones particles with a diameter ratio of 2:1. We characterize structure by isotropic and anisotropic pair correlation functions, cluster size distribution, bond angle distribution, order parameter, and specific heat. We observe the demixing of the species in the absence of external modulation. However, the mixing of the species gets enhanced with increasing potential strength along with the alignment of the particles transverse to the modulation. The de-mixing order parameter shows discontinuity with increasing modulation strength, characterizing a first order phase transition. The peak in specific heat increases linearly with the size of the system. We also look into the dynamical behavior of the system via computing Mean Square Displacement (MSD) along both parallel and perpendicular directions to the modulation. We observe a decrease in the diffusion coefficient for both types of particles as we increase the strength of the modulation.

Higher-order asymptotic refinements in a multivariate regression model with general parameterization

This paper derives a general Bartlett correction formula to improve the inference based on the likelihood ratio test in a multivariate model under a quite general parameterization, where the mean vector and the variance-covariance matrix can share the same vector of parameters. This approach includes a number of models as special cases such as non-linear regression models, errors-in-variables models, mixed-effects models with non-linear fixed effects, and mixtures of the previous models. We also employ the Skovgaard adjustment to the likelihood ratio statistic in this class of multivariate models and derive a general expression of the correction factor based on Skovgaard approach. Monte Carlo simulation experiments are carried out to verify the performance of the improved tests, and the numerical results confirm that the modified tests are more reliable than the usual likelihood ratio test. Applications to real data are also presented for illustrative purposes.

Economic Energy Scheduling of electrical micro grid considering optimal participation of the electric vehicles

This research presents a strategy for managing energy scheduling within an electrical microgrid, with a specific focus on enhancing the integration of electric vehicles (EVs). By incorporating Monte Carlo simulation to address uncertainties related to EV charging power and demand-side variables, the study aims to ensure precise outcomes. The economic energy scheduling is conducted on a day-ahead basis, taking these uncertainties into consideration to assess the efficiency of the recommended approach. The primary objective is to reduce the overall system costs, encompassing operational expenditures and EV charging power. To tackle the intricacies of the operational framework, the study utilizes the modified sunflower optimization (MSFO) algorithm to resolve the outlined issue. The simulation findings highlight the superior performance of the proposed optimization algorithms compared to others. The proposed approach leads to minimize cost of microgrid by 4.31%, 3.82% and 1.87% than genetic algorithm (GA), Particle swarm optimization (PSO) algorithm and Teaching learning-based optimization (TLBO) algorithm, respectively.

Nonlinear impact of financial inclusion on tax revenue: evidence from the Monte-Carlo simulation algorithm under the Bayesian approach

PurposeThis article aims to study the nonlinear effect of financial inclusion on tax revenue of 21 low financial development countries (LFDCs) and 22 high financial development countries (HFDCs) from 2004 to 2020.Design/methodology/approachThe study calculates the world average financial development index (FD̅) for all countries using data from the IMF. The average FD of HFDCs is higher than (FD̅). On the other hand, the average FD of LFDCs is lower than (FD̅). Data of 21 LFDCs and 22 HFDCs cover the period 2004–2020. With the small sample problem, we applied the Bayesian method to examine the nonlinear effect of financial inclusion on the tax revenue of the two groups of countries.FindingsUsing the Bayesian method, the results show that financial inclusion negatively impacts tax revenue with an absolute probability of 100% in LFDCs and a lower probability of 92.45% in HFDCs. Additionally, the financial inclusion threshold at LFDCs is 18.90. Below this threshold, financial inclusion promotes tax revenue with a 100% probability. On the contrary, when financial inclusion exceeds the threshold, it will have a negative effect on tax revenue. Similarly, the financial inclusion threshold at HFDCs is 20.14, with a probability of 92.45%.Originality/valueTo the best of the authors’ knowledge, this is the first paper to examine the nonlinear impact of financial inclusion on tax revenue in high and low financial development countries.

A SGS efficiency calibration method for measuring the radioactive waste drum based on Monte Carlo simulation and function model

A SGS efficiency calibration method for measuring the radioactive waste drum based on Monte Carlo simulation and function model

Research on the Validity of Bootstrap LM-Error Test in Spatial Random Effect Models

Under the condition of non-classical distributed errors, the test for spatial dependence in spatial panel data models is still a problem waiting to be solved. In this paper, we apply the FDB (Fast Double Bootstrap) method to spatial panel data models to test spatial dependence. In order to research the validity of the Bootstrap LM-Error test in spatial random effect models under the condition that the error term obeys a normal distribution, heteroscedasticity, or time-series correlation, we construct Bootstrap LM-Error statistics and make use of Monte Carlo simulation from size distortion and power aspects to carry out our research. The Monte Carlo simulation results show that the asymptotic LM-Error test in the spatial random effects model has a large size of distortion when the error term disobeys classical distribution. However, the FDB LM-Error test can effectively correct the size distortion of the asymptotic test with the precondition that there is nearly no loss of power in the FDB test. Obviously, compared to the asymptotic LM-Error test, the FDB LM-Error test is a more valid method to test spatial dependence in a spatial random effects model.

An alternative bounded distribution: regression model and applications

AbstractIn this paper, a new bounded distribution is introduced and some distributional properties of the new distribution are discussed. Moreover, the new distribution is implemented in the field of engineering to the Cpc process capability index. Three unknown parameters of the distribution are estimated with several estimators, and the performances of the estimators are evaluated with a Monte Carlo simulation. A new regression model is introduced based on this new distribution as an alternative to beta and Kumaraswamy models. Furthermore, it is considered one of the first studies where regression model parameters are estimated using least squares, weighted least squares, Cramér–von Mises, and maximum product spacing estimators other than the maximum likelihood. The efficiency of the estimators for the parameters of the regression model is further assessed through a simulation. Real datasets are analyzed to demonstrate the applicability of the new distribution and regression model.