Technical note: A GPU-based shared Monte Carlo method for fast photon transport in multi-energy x-ray exposures.

BACKGROUND: The Monte Carlo (MC) method is an accurate technique for particle transport calculation due to the precise modeling of physical interactions. Nevertheless, the MC method still suffers from the problem of expensive computational cost, even with graphics processing unit (GPU) acceleration. Our previous works have investigated the acceleration strategies of photon transport simulation for single-energy CT. But for multi-energy CT, conventional individual simulation leads to unnecessary redundant calculation, consuming more time. PURPOSE: This work proposes a novel GPU-based shared MC scheme (gSMC) to reduce unnecessary repeated simulations of similar photons between different spectra, thereby enhancing the efficiency of scatter estimation in multi-energy x-ray exposures. METHODS: The shared MC method selects shared photons between different spectra using two strategies. Specifically, we introduce spectral region classification strategy to select photons with the same initial energy from different spectra, thus generating energy-shared photon groups. Subsequently, the multi-directional sampling strategy is utilized to select energy-and-direction-shared photons, which have the same initial direction, from energy-shared photon groups. Energy-and-direction-shared photons perform shared simulations, while others are simulated individually. Finally, all results are integrated to obtain scatter distribution estimations for different spectral cases. RESULTS: The efficiency and accuracy of the proposed gSMC are evaluated on the digital phantom and clinical case. The experimental results demonstrate that gSMC can speed up the simulation in the digital case by ∼37.8% and the one in the clinical case by ∼20.6%, while keeping the differences in total scatter results within 0.09%, compared to the conventional MC package, which performs an individual simulation. CONCLUSIONS: The proposed GPU-based shared MC simulation method can achieve fast photon transport calculation for multi-energy x-ray exposures.

Modelling Gd-diamond and Gd-SiC neutron detectors.

A custom Monte Carlo (MC) computer model was developed to simulate thermal neutron absorption in, and subsequent photon and electron emission from, natural Gd with a view to using the material as a neutron conversion layer for neutron detectors. The MC code also modelled photon and electron detection with two dissimilar detectors: a thick (500 µm) single crystal diamond detector; and a thin (5.15 µm) commercial off the shelf (COTS) 4H-SiC photodiode detector. The detectors' quantum detection efficiencies (QE) for hard X-rays and γ-rays were relatively low in comparison to their QE for electrons, thus making it possible to collect electron spectra from the Gd layer neutron conversion products which were not overwhelmed by photon emissions from the Gd. The MC code was utilised to determine the optimal thickness of Gd for the efficient detection of a thermal neutron flux. These radiation hard and spectroscopic detectors paired with natural Gd could find utility as robust and compact thermal neutron detectors for nuclear science and engineering, space science, and other applications.

Probabilistic Analysis of Critical Speed Values of a Rotating Machine as a Function of the Change of Dynamic Parameters.

Real-world rotordynamic systems exhibit inherent uncertainties in manufacturing tolerances, material properties, and operating conditions. This study presents a Monte Carlo simulation approach using MSC Adams View and Adams Insight to investigate the impact of these uncertainties on the performance of a Laval/Jeffcott rotor model. Key uncertainties in bearing damping, bearing clearance, and mass imbalance were modeled with probabilistic distributions. The Monte Carlo analysis revealed the probabilistic nature of critical speeds, vibration amplitudes, and overall system stability. The findings highlight the importance of probabilistic methods in robust rotordynamic design and provide insights for establishing manufacturing tolerances and operational limits.

Assessment of the Absorbed Dose Variations in the Thyroid Gland Exposed to Orthopantomography (OPG) while Swallowing: A Novel Approach to Radiation Protection.

Background: The reliance on specialized diagnostic techniques is on the rise across various medical fields, including dentistry. While orthopantomogram (OPG), offers many advantages in terms of dental diagnosis, it also poses potential risks to sensitive organs, notably the thyroid gland. Objective: This study aimed to evaluate the fluctuations in the absorbed dose within the thyroid gland during swallowing while undergoing an OPG procedure. Material and Methods: In this computational simulation study, the BEAMnrc Monte Carlo code was employed to model an OPG machine, using 700 million particles across the energy range of 60-75 keV, which is standard for OPG procedures. The Monte Carlo (MC) model was cross-verified by comparing the derived spectra with those in the IPEM Report 78. A head and neck phantom was constructed using CT scan images with a slice thickness of 5 mm. This phantom underwent simulated beam exposure under two conditions: pre-swallow and post-swallow. Subsequently, the percentage depth dose was measured and contrasted across different depths. Results: After swallowing, there was an increase in the absorbed dose across all three regions of the thyroid (right, left, and center). Notably, regions near the hyoid bone exhibited a particularly significant increase in dose. In certain areas, the absorbed dose even tripled when compared to the pre-swallowing state. Conclusion: The findings indicate that during OPG imaging, swallowing can lead to an increased radiation dose to the thyroid gland. Given the thyroid's heightened sensitivity to radiation, such an increase in dosage is noteworthy.

Models for the No-Observed-Effect Concentration (NOEC) and Maximal Half-Effective Concentration (EC50).

Typical in silico models for ecotoxicology focus on a few endpoints, but there is a need to increase the diversity of these models. This study proposes models using the NOEC for the harlequin fly (Chironomus riparius) and EC50 for swollen duckweed (Lemna gibba) for the first time. The data were derived from the EFSA OpenFoodTox database. The models were based on the correlation weights of molecular features used to calculate the 2D descriptor in CORAL software. The Monte Carlo method was used to calculate the correlation weights of the algorithms. The determination coefficients of the best models for the external validation set were 0.74 (NOAEC) and 0.85 (EC50).

Research on Spatial Magnetic Field Distribution of Magnetic Fluids Based on Microstructure.

This study focuses on the spatial magnetic field distribution of magnetic fluids, an extraordinary class of fluids composed of magnetic nanoparticles (MNPs), employing the Monte Carlo method to simulate the microstructure of magnetic fluids under an external magnetic field. On that basis, a model was established through magnetic dipole theory to delve into the spatial magnetic field distribution of magnetic fluids. The findings reveal that the application of a magnetic field leads to the formation of chain-like structures within the magnetic fluids, resulting in inhomogeneous spatial magnetic field distribution. The size and concentration of MNPs are crucial determinants that significantly affect the microstructure of magnetic fluid and its spatial magnetic field distribution. Furthermore, environmental conditions such as external magnetic field strength or temperature can also regulate the positions of MNPs within magnetic fluids and the spatial magnetic field distribution of the magnetic fluids. These observations enrich the comprehension of the fundamental mechanisms of magnetic fluids and their response to diverse factors, advancing the growing comprehension on the characteristics and applications of these remarkable magnetic fluids.

Statistical Parameters Extracted from Radar Sea Clutter Simulated under Different Operational Conditions.

A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on.

Application of the adaptive Monte Carlo method for uncertainty evaluation in the determination of total testosterone in human serum by triple isotope dilution mass spectrometry.

The measurement uncertainty is a crucial quantitative parameter for assessing the reliability of the result. The study aimed to propose a new budget for uncertainty evaluation of a reference measurement procedure for the determination of total testosterone in human serum. The adaptive Monte Carlo method (aMCM) was used for the propagation of probability distributions assigned to various input quantities to determine the uncertainty of the testosterone concentration. The basic principles of the propagation and the statistical analysis were described based on the experimental results of the quality control serum sample. The analysis of the number of Monte Carlo trials was discussed. The procedure of validation of the GUM uncertainty framework using the aMCM was also provided. The number of Monte Carlo trials was 2.974 × 106 when the results had stabilized. The total testosterone concentration was 16.02 nmol/L, and the standard uncertainty was 0.30 nmol/L. The coverage interval at coverage probability of 95% was 15.45 to 16.62 nmol/L, while the probability distribution for testosterone concentration was approximately described by a Gaussian distribution. The validation of results was not passed as the expanded uncertainty result obtained by the aMCM was slightly lower, about 7%, than that by the GUM uncertainty framework with consistent results of the concentration.

A method to accelerate computational efficiency by more than two orders of magnitude for Monte Carlo simulations of electron-solid interactions.

A method has been developed to increase computational efficiency in Monte Carlo simulations of electron transport and interactions in matter. The method serves as the computational engine for the open-source code AMCSET (Aggie Monte Carlo Simulations of Electron and Ion Transport). The key is to combine n consecutive neighboring free flying distances into groups. Within each group, both flying distance and Mott scattering angles are obtained using Monte Carlo sampling under an equal energy approximation. This reduces the number of integrations of the tabulated differential Mott scattering cross-section in scattering angle selection, i.e., from 1000 to 1 if n = 1000. The method increases efficiency by more than 100 times. At the same time, the calculation still guarantees accuracy in calculating electron trajectory, excitation/ionization energy deposition, elastic scattering energy deposition, and displacement creation. For demonstration, 10 MeV electron bombardments of pure Fe with n up to 1000 are used as examples. The method, due to the availability of tabulated scattering cross-sections, is applicable for targets of the entire elemental table up to Z = 118, and for electron energies up to 900 MeV.

Monte Carlo modelling of cyclotron and radioisotope center (CYRIC) at Tohoku University: a radiation protection study.

Protection against ionizing radiations is important in laboratories with radioactive materials and high energy cyclotron beams. The Cyclotron and Radioisotope Center (CYRIC) located in Tohoku University in Miyagi prefecture, Japan and is a well-known nuclear science laboratory with cyclotron beams and substantial number of high activity radioactive materials. Considering this, it is important to perform complete radiation transport computations to ensure the safety of non-occupational and occupational workers. In the present work, we have developed a complete 3-dimensional model of the main cyclotron building and radiation labs using Monte Carlo method. We have found that the dispersed photons and neutrons inside and in the surrounding of the CYRIC building pose no significant risk to occupational and non-occupational workers. The present work and the developed models would be useful in the field of radiation protection.

Quantitative microbial risk assessment of gastrointestinal illness due to recreational exposure to E. coli and enterococci on the southern coasts of the Caspian Sea.

Background: Gastrointestinal illness refers to a broad range of diseases that affect the digestive system, including infections caused by bacteria, viruses, and parasites. Quantitative Microbial Risk Assessment (QMRA) is a powerful tool used to evaluate the risks associated with microbial pathogens in various environments. The main objective of this study was to conduct a quantitative assessment of gastrointestinal illnesses that occur as a result of exposure to E. coli and enterococci during recreational activities on the southern coasts of the Caspian Sea. Methods: Samples were collected from the recreational beaches along the border line of the Caspian Sea. The samples were analyzed for the presence and enumeration of E. coli and enterococci using the microplate method and membrane filtration techniques. Then, the annual and daily infection risks were computed using the Monte Carlo simulation approach. Results: The results revealed that the risk of daily and annual infections on the coasts of Babolsar was higher than that on the coasts of Sari. Also, in the recreational waters of these beaches, the risk of infection by enterococci was higher than that posed by E. coli. In Babolsar, the average annual infection risk caused by E. coli and enterococci was 0.365 and 1 for children and 0.181 and 0.986 for adults. Also, in Sari, the average annual infection risk caused by E. coli and enterococci was 0.060 and 0.908 for children and 0.027 and 0.815 for adults. In addition, children were more likely than adults to become infected. Conclusion: In light of the study's findings, due to the entry of untreated urban wastewater into the southern part of the Caspian Sea (northern Iran) and the high risk of infectious diseases for children, more control and health measures are necessary for children's swimming.

[Research Photon Energy Spectrum of Medical Linear Accelerator by Monte Carlo Method].

Objective: The distribution of the photon energy spectrum in isocenter plane of the medical linear accelerator and the influence of secondary collimator on the photon energy spectrum are studied. Methods Use the BEAMnrc program to simulate the transmission of the 6 MeV electrons and photons in 5 cm×5 cm,10 cm×10 cm,15 cm×15 cm and 20 cm×20 cm fields in treatment head of the medical linear accelerator, where a phase space file was set up at the isocenter plane to record the particle information passing through this plane. The BEAMdp program is used to analyze the phase space file, in order to obtain the distribution of the photon energy spectrum in isocenter plane and the influence of secondary collimator on the photon energy spectrum. Results: By analyzing the photon energy spectrum of a medical linear accelerator with a nominal energy of 6 MV, it is found that the secondary collimator has little effect on the photon energy spectrum; different fields have different photon energy spectrum distributions; the photon energy spectrum in different central regions of the same field have the same normalized distribution. Conclusion: In the dose calculation of radiation therapy, the influence of photon energy spectrum should be carefully considered.

Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automated technologies face limitations in accurately identifying dicentric chromosomes (DCs), resulting in decreased precision for radiation dose estimation. Furthermore, in the process of identifying DCs through automatic or semi-automatic methods, the resulting distribution could demonstrate under-dispersion or over-dispersion, which results in significant deviations from the Poisson distribution. In response to these issues, we developed an algorithm that employs deep learning to automatically identify chromosomes and perform fully automatic and accurate estimation of diverse radiation doses, adhering to a Poisson distribution. MATERIALS AND METHODS: The dataset utilized for the dose estimation algorithm was generated from 30 healthy donors, with samples created across seven doses, ranging from 0 to 4 Gy. The procedure encompasses several steps: extracting images for dose estimation, counting chromosomes, and detecting DC and fragments. To accomplish these tasks, we utilize a diverse array of artificial neural networks (ANNs). The identification of DCs was accomplished using a detection mechanism that integrates both deep learning-based object detection and classification methods. Based on these detection results, dose-response curves were constructed. A dose estimation was carried out by combining a regression-based ANN with the Monte-Carlo method. RESULTS: In the process of extracting images for dose analysis and identifying DCs, an under-dispersion tendency was observed. To rectify the discrepancy, classification ANN was employed to identify the results of DC detection. This approach led to satisfaction of Poisson distribution criteria by 32 out of the initial pool of 35 data points. In the subsequent stage, dose-response curves were constructed using data from 25 donors. Data provided by the remaining five donors served in performing dose estimations, which were subsequently calibrated by incorporating a regression-based ANN. Of the 23 points, 22 fell within their respective confidence intervals at p < .05 (95%), except for those associated with doses at levels below 0.5 Gy, where accurate calculation was obstructed by numerical issues. The accuracy of dose estimation has been improved for all radiation levels, with the exception of 1 Gy. CONCLUSIONS: This study successfully demonstrates a high-precision dose estimation method across a general range up to 4 Gy through fully automated detection of DCs, adhering strictly to Poisson distribution. Incorporating multiple ANNs confirms the ability to perform fully automated radiation dose estimation. This approach is particularly advantageous in scenarios such as large-scale radiological incidents, improving operational efficiency and speeding up procedures while maintaining consistency in assessments. Moreover, it reduces potential human error and enhances the reliability of results.

A Novel Treatment Planning Design for Intraoperative Electron Radiation Therapy (IOERT) using an Electronic Board.

Background: Intraoperative Irradiation Therapy (IORT) refers to the delivery of radiation during surgery and needs the computed- thickness of the target as one of the most significant factors. Objective: This paper aimed to compute target thickness and design a radiation pattern distributing the irradiation uniformly throughout the target. Material and Methods: The Monte Carlo code was used to simulate the experimental setup in this simulation study. The electron flux variations on an electronic board's metallic layer were studied for different thicknesses of the target tissue and validated with experimental data of the electronic board. Results: Based on the electron number for different Poly Methyl Methacrylate (PMMA) phantom thicknesses at various energies, 6 MeV electrons are suitable to determine the target thickness. Uniformity in radiation and corresponding time for each target were investigated. The iso-dose and percentage depth dose curves show that higher energies are suitable for treatment and distribute uniform radiation throughout the target. Increasing the phantom thickness leads to rising radiation time based on the radiation time corresponding to these energies. The tissue thickness of each section is determined, and the radiation time is managed by scanning the target. Conclusion: Calculation of the thickness of the remaining tissue and irradiation time are needed after incomplete tumor removal in IORT for various remaining tissues. The patients should be protected from overexposure to uniform irradiation of tissues since the radiation dose is prescribed and checked by an oncologist.

The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES.

Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.

Does the accounting of the local symmetry fragments in quasi-SMILES improve the predictive potential of the QSAR models of toxicity toward tadpoles?

Models of toxicity to tadpoles have been developed as single parameters based on special descriptors which are sums of correlation weights, molecular features, and experimental conditions. This information is presented by quasi-SMILES. Fragments of local symmetry (FLS) are involved in the development of the model and the use of FLS correlation weights improves their predictive potential. In addition, the index of ideality correlation (IIC) and correlation intensity index (CII) are compared. These two potential predictive criteria were tested in models built through Monte Carlo optimization. The CII was more effective than IIC for the models considered here.

On the uncertainty quantification of the active uterine contraction during the second stage of labor simulation.

Uterine contractions in the myometrium occur at multiple scales, spanning both organ and cellular levels. This complex biological process plays an essential role in the fetus delivery during the second stage of labor. Several finite element models of active uterine contractions have already been developed to simulate the descent of the fetus through the birth canal. However, the developed models suffer severe reliability issues due to the uncertain parameters. In this context, the present study aimed to perform the uncertainty quantification (UQ) of the active uterine contraction simulation to advance our understanding of pregnancy mechanisms with more reliable indicators. A uterus model with and without fetus was developed integrating a transversely isotropic Mooney-Rivlin material with two distinct fiber orientation architectures. Different contraction patterns with complex boundary conditions were designed and applied. A global sensitivity study was performed to select the most valuable parameters for the uncertainty quantification (UQ) process using a copula-based Monte Carlo method. As results, four critical material parameters ( C 1 , C 2 , K , Ca 0 ) of the active uterine contraction model were identified and used for the UQ process. The stress distribution on the uterus during the fetus descent, considering first and second fiber orientation families, ranged from 0.144 to 1.234 MPa and 0.044 to 1.619 MPa, respectively. The simulation outcomes revealed also the segment-specific contraction pattern of the uterus tissue. The present study quantified, for the first time, the effect of uncertain parameters of the complex constitutive model of the active uterine contraction on the fetus descent process. As perspectives, a full maternal pelvis model will be coupled with reinforcement learning to automatically identify the delivery mechanism behind the cardinal movements of the fetus during the active expulsion process.

Methods for the Design and Analysis of Analytical Ultracentrifugation Experiments.

Analytical ultracentrifugation experiments play an integral role in the solution-phase characterization of biological macromolecules and their interactions. This unit discusses the design of sedimentation velocity and sedimentation equilibrium experiments performed with a Beckman Proteomelab XL-A or XL-I analytical ultracentrifuge and with a Beckman Optima AUC. Instrument settings and experimental design considerations are explained, and strategies for the analysis of experimental data with the UltraScan data analysis software package are presented. Special attention is paid to the strengths and weaknesses of the available detectors, and guidance is provided on how to extract maximum information from analytical ultracentrifugation experiments. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.

Statistical power analysis and sample size planning for moderated mediation models.

Conditional process models, including moderated mediation models and mediated moderation models, are widely used in behavioral science research. However, few studies have examined approaches to conduct statistical power analysis for such models and there is also a lack of software packages that provide such power analysis functionalities. In this paper, we introduce new simulation-based methods for power analysis of conditional process models with a focus on moderated mediation models. These simulation-based methods provide intuitive ways for sample-size planning based on regression coefficients in a moderated mediation model as well as selected variance and covariance components. We demonstrate how the methods can be applied to five commonly used moderated mediation models using a simulation study, and we also assess the performance of the methods through the five models. We implement our approaches in the WebPower R package and also in Web apps to ease their application.

Neutron flux evaluation algorithm with a combination of Monte Carlo and removal-diffusion calculation methods for boron neutron capture therapy.

BACKGROUND: In Japan, the clinical treatment of boron neutron capture therapy (BNCT) has been applied to unresectable, locally advanced, and recurrent head and neck carcinomas using an accelerator-based neutron source since June of 2020. Considering the increase in the number of patients receiving BNCT, efficiency of the treatment planning procedure is becoming increasingly important. Therefore, novel and rapid dose calculation algorithms must be developed. We developed a novel algorithm for calculating neutron flux, which comprises of a combination of a Monte Carlo (MC) method and a method based on the removal-diffusion (RD) theory (RD calculation method) for the purpose of dose calculation of BNCT. PURPOSE: We present the details of our novel algorithm and the verification results of the calculation accuracy based on the MC calculation result. METHODS: In this study, the "MC-RD" calculation method was developed, wherein the RD calculation method was used to calculate the thermalization process of neutrons and the MC method was used to calculate the moderation process. The RD parameters were determined by MC calculations in advance. The MC-RD calculation accuracy was verified by comparing the results of the MC-RD and MC calculations with respect to the neutron flux distributions in each of the cubic and head phantoms filled with water. RESULTS: Comparing the MC-RD calculation results with those of MC calculations, it was found that the MC-RD calculation accurately reproduced the thermal neutron flux distribution inside the phantom, with the exception of the region near the surface of the phantom. CONCLUSIONS: The MC-RD calculation method is useful for the evaluation of the neutron flux distribution for the purpose of BNCT dose calculation, except for the region near the surface.