[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.

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.

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.

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.

Variational Bayes inference for hidden Markov diagnostic classification models.

Diagnostic classification models (DCMs) can be used to track the cognitive learning states of students across multiple time points or over repeated measurements. This study developed an effective variational Bayes (VB) inference method for hidden Markov longitudinal general DCMs. The simulations performed in this study verified the validity of the proposed algorithm for satisfactorily recovering true parameters. Simulation and applied data analyses were conducted to compare the proposed VB method to Markov chain Monte Carlo (MCMC) sampling. The results revealed that the parameter estimates provided by the VB method were consistent with the MCMC method with the additional benefit of a faster estimation time. The comparative simulation also indicated differences between the two methods in terms of posterior standard deviation and coverage of 95% credible intervals. Thus, with limited computational resources and time, the proposed VB method can output estimations comparable to that of MCMC.

Important role of turbulence on the distribution of particle radioactivity in the nuclear explosions.

The distribution of particle radioactivity is one of the most important source items of radioactive fallout prediction model for nuclear explosion. For radioactive particles with the diameter larger than 0.5 µm, the influence of turbulent coagulation cannot be ignored. However, few scholars have considered the role of turbulence in the study of the distribution of particle radioactivity. The General Dynamic Equation (GDE), which is solved using the Multi-Monte Carlo method, is used in this study to establish a new model of the distribution of particle radioactivity that takes the impact of turbulent coagulation into account. The results present that the surface and volume distributions of particle radioactivity are closer to those of the Defense Land Fallout Interpretative Code (DELFIC) model, and the relative error of the surface (volume) distribution reduces from 1.243 (0.687) to 0.945 (0.284) when compared to the previous model that simply takes Brownian coagulation into account. The geometric median diameter of total particle radioactivity increases as the solidification temperature increases and as the particle size range increases when considering the influence of various geological conditions. When considering the effects of turbulent coagulation, the differences in the particle radioactivity produced under different geological conditions are smaller than those only considering the Brownian coagulation. This study highlights the importance of turbulent coagulation on the distribution of particle radioactivity in the nuclear explosions.

Monte Carlo simulations of organ and effective doses and dose-length product for dental cone-beam CT.

OBJECTIVES: The use of dental cone-beam CT (CBCT) has increased in recent years. We aimed to calculate the organ and effective doses in dental CBCT using Monte Carlo simulation (MCS) and to correlate the effective dose with the dose-length product (DLP), which is a radiation dose index. METHODS: Organ and effective doses were calculated by MCS using the adult male and female reference phantoms of the International Commission on Radiological Protection publication 110 in a half-rotation scan of the CBCT scanner Veraviewepocs 3Df. The simulations were performed by setting nine protocols in combination with the field-of-view (FOV) and imaging region. In addition, DLPs were calculated by MCS using the virtual CT Dose Index (CTDI) and CBCT phantoms, with the same protocol. RESULTS: The effective doses were 55 and 195 µSv at the minimum FOV of Φ40 × H40 mm and maximum FOV of Φ 80 × H80 mm, respectively. The organs with the major contribution to the effective dose were the red bone marrow (11.0‒12.8%), thyroid gland (4.0‒12.7%), salivary gland (21.8‒33.2%), and remaining tissues (35.1‒45.7%). Positive correlations were obtained between the effective dose and calculated DLP using the CTDI and CBCT phantoms. CONCLUSIONS: Organ and effective doses for each protocol of dental CBCT could be estimated using MCS. There was a positive correlation between the effective dose and DLP, suggesting that DLP can be used to estimate the effective dose of CBCT.

Calibrating GESPECOR model of computing the full-energy peak efficiency of coaxial high-purity germanium detectors by Monte Carlo simulation.

In this work, a classical approach was used for calibrating the GESPECOR detector model for computing the full-energy peak efficiency of p-type coaxial HPGe detectors that is based on the use of linear least squares optimization. The key element of the work is the multiplicative model developed for approximating the values of the full-energy peak efficiency provided by GESPECOR code. It was linearized using the logarithmic transformation to allow an easy use of the linear least squares optimization. A procedure was also developed to estimate the optimal values of the parameters, describing the p-type coaxial HPGe detectors. Its application to a Canberra detector GC3018 showed that it is possible to determine accurate values of the full-energy peak efficiency computed by GESPECOR code using the optimized parameter values.

Enhancing analytical development in the pharmaceutical industry: A DoE-QSRR model for virtual Method Operable Design Region assessment.

Recently, the pharmaceutical industry has increasingly adopted the Analytical Quality by Design (AQbD) approach for analytical development. To facilitate AQbD approach implementation in the development of chromatographic methods for determining cephalosporin antibiotics, an in silico tool capable of performing virtual DoEs was developed enabling to obtain virtual operable regions of method. To this end, the drugs cephalexin, cefazolin, cefotaxime and ceftriaxone were analyzed using four experimental designs, deriving a DoE-QSRR model and employing Monte Carlo method. The DoE-QSRR model and virtual DoEs were validated using data not used in model's construction, obtaining coefficients of determination of 84.72 % for DoE-QSRR model and over 77 % for virtual DoEs. Virtual MODRs were constructed using data from the virtual DoEs. The virtual MODRs were validated by comparing them with experimental MODRs under various scenarios, with overlap areas reaching values exceeding 84 %. Therefore, the in silico tool was considered suitable for indicating analyte trends under different analytical conditions, being capable of performing virtual DoEs for cephalosporin drugs with sufficient assertiveness to guide analytical development and allow obtaining a MODR capable of providing results of adequate quality.

Strategies to convert hospital beds for COVID-19 patients to minimize emergency department overcrowding.

Background: The shortage of hospital beds for COVID-19 patients has been one critical cause of Emergency Department (ED) overcrowding. Purpose: We aimed at elaborating a strategy of conversion of hospital beds, from non-COVID-19 to COVID-19 care, minimizing both ED overcrowding and the number of beds eventually converted. Research Design: Observational retrospective study. Study Sample: We considered the centralized database of all ED admissions in the Lombardy region of Italy during the second "COVID-19 wave" (October to December 2020). Data collection and Analysis: We analyzed all admissions to 82 EDs. We devised a family of Monte Carlo simulations to evaluate the performance of hospital beds' conversion strategies triggered by ED crowding of COVID-19 patients, determining a critical number of beds to be converted when passing an ED-specific crowding threshold. Results: Our results suggest that the maximum number of patients waiting for hospitalization could have been decreased by 70% with the proposed strategy. Such a reduction would have been achieved by converting 30% more hospital beds than the total number converted in the region. Conclusions: The disproportion between reduction in ED crowding and additionally converted beds suggests that a wide margin to improve the efficiency of the conversions exists. The proposed simulation apparatus can be easily generalized to study management policies synchronizing ED output and in-hospital bed availability.

Impact of possible errors in natural language processing-derived data on downstream epidemiologic analysis.

Objective: To assess the impact of potential errors in natural language processing (NLP) on the results of epidemiologic studies. Materials and Methods: We utilized data from three outcomes research studies where the primary predictor variable was generated using NLP. For each of these studies, Monte Carlo simulations were applied to generate datasets simulating potential errors in NLP-derived variables. We subsequently fit the original regression models to these partially simulated datasets and compared the distribution of coefficient estimates to the original study results. Results: Among the four models evaluated, the mean change in the point estimate of the relationship between the predictor variable and the outcome ranged from -21.9% to 4.12%. In three of the four models, significance of this relationship was not eliminated in a single of the 500 simulations, and in one model it was eliminated in 12% of simulations. Mean changes in the estimates for confounder variables ranged from 0.27% to 2.27% and significance of the relationship was eliminated between 0% and 9.25% of the time. No variables underwent a shift in the direction of its interpretation. Discussion: Impact of simulated NLP errors on the results of epidemiologic studies was modest, with only small changes in effect estimates and no changes in the interpretation of the findings (direction and significance of association with the outcome) for either the NLP-generated variables or other variables in the models. Conclusion: NLP errors are unlikely to affect the results of studies that use NLP as the source of data.

Semi-Correlations for Building Up a Simulation of Eye Irritation.

The OECD recognizes that data on a compound's ability to treat eye irritation are essential for the assessment of new compounds on the market. In silico models are frequently used to provide information when experimental data are lacking. Semi-correlations, as they are called, can be useful to build up categorical models for eye irritation. Semi-correlations are latent regressions that can be used when the endpoint is expressed by two values: 1 for an active molecule and 0 for an inactive molecule. The regression line is based on the descriptor values which serve to distribute the data into four classes: true positive, true negative, false positive, and false negative. These values are applied to calculate the corresponding statistical criterion for assessing the predictive potential of the categorical model. In our model, the descriptor is the sum of what are termed correlation weights. These are defined by optimization using the Monte Carlo method. The target function of the optimization is related to the determination coefficient and the mean absolute error for the training set. Our model gives results that are better than those previously reported for the same endpoint.

Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes.

Cluster randomized trials (CRTs) refer to a popular class of experiments in which randomization is carried out at the group level. While methods have been developed for planning CRTs to study the average treatment effect, and more recently, to study the heterogeneous treatment effect, the development for the latter objective has currently been limited to a continuous outcome. Despite the prevalence of binary outcomes in CRTs, determining the necessary sample size and statistical power for detecting differential treatment effects in CRTs with a binary outcome remain unclear. To address this methodological gap, we develop sample size procedures for testing treatment effect heterogeneity in two-level CRTs under a generalized linear mixed model. Closed-form sample size expressions are derived for a binary effect modifier, and in addition, a computationally efficient Monte Carlo approach is developed for a continuous effect modifier. Extensions to multiple effect modifiers are also discussed. We conduct simulations to examine the accuracy of the proposed sample size methods. We present several numerical illustrations to elucidate features of the proposed formulas and to compare our method to the approximate sample size calculation under a linear mixed model. Finally, we use data from the Strategies and Opportunities to Stop Colon Cancer in Priority Populations (STOP CRC) CRT to illustrate the proposed sample size procedure for testing treatment effect heterogeneity.

Evolution Mechanism of Sputtered Film Uniformity with the Erosion Groove Size: Integrated Simulation and Experiment.

In this work, Cu thin films were experimentally fabricated at different target-substrate distances by 2-inch and 4-inch circular planar magnetron targets. Meanwhile, the sputtering deposition of Cu thin films was investigated via an integrated multiscale simulation, where the magnetron sputtering discharge was modeled using the Monte Carlo (MC) method, and the sputtered particle transport was simulated using a coupled Monte Carlo (MC) and molecular dynamics (MD) method. Experimental results indicated that, as the target-substrate distance increased from 30 to 120 mm, the film thickness distribution of the 2-inch target sputtering changed from a bell-shaped curve to a line-shaped curve, while that of the 4-inch target sputtering varied from a saddle-shaped curve to a line-shaped curve. The simulation results were accordant with the experimental results. The simulation results revealed that, at a target-substrate distance of 30 mm, the sputtering particle flow from the 2-inch target overlapped strongly near the substrate center, leading to a bell-shaped film thickness distribution, while the increased diameter of the erosion groove on the 4-inch target reduced the superposition effect of the sputtering particle flow near the substrate center, resulting in a saddle-shaped film thickness distribution. In addition, when the target-substrate distance ranged from 30 to 120 mm, the film thickness uniformity of 4-inch target sputtering was superior to that of 2-inch target sputtering, and the underlying mechanism was discussed in detail.

Metastable atomic-ordered configurations for Al1/2Ga1/2N predicted by Monte-Carlo method based on first-principles calculations.

Metastability of Aln/12Ga1-n/12N (n= 2-10: integer) with the 1-2 monolayer (ML) in-plane configuration towards thec[0001] direction has been demonstrated recently. To theoretically explain the existence of these metastable structures, relatively large calculation cells are needed. However, previous calculations were limited to the use of small calculation cell sizes to estimate the local potential depth (Δσ) of ordered Al1/2Ga1/2N models. In this work, we were able to evaluate large calculation cells based on the interaction energies between proximate Al atoms (δEAl-Al) in AlGaN alloys. To do this,δEAl-Alvalues were estimated by first-principles calculations (FPCs) using a (5a1× 5a2× 5c) cell. Next, a survey of the possible ordered configurations using various large calculation cell models was performed using the estimatedδEAl-Alvalues and the Monte-Carlo method. Then, various Δσvalues were estimated by FPCs and compared with the configurations previously reported by other research groups. We found that the ordered configuration obtained from the (4a1× 2a2× 1c) calculation cell (C42) has the lowest Δσof -9.3 meV/cation and exhibited an in-plane configuration at thec(0001) plane having (-Al-Al-Ga-Ga-) and (-Al-Ga-) sequence arrangements observed along them11-00planes. Hence, we found consistencies between the morphology obtained from experiment and the shape of the primitive cell based on our numerical calculations.

The System of Self-Consistent Models: The Case of Henry's Law Constants.

Data on Henry's law constants make it possible to systematize geochemical conditions affecting atmosphere status and consequently triggering climate changes. The constants of Henry's law are desired for assessing the processes related to atmospheric contaminations caused by pollutants. The most important are those that are capable of long-term movements over long distances. This ability is closely related to the values of Henry's law constants. Chemical changes in gaseous mixtures affect the fate of atmospheric pollutants and ecology, climate, and human health. Since the number of organic compounds present in the atmosphere is extremely large, it is desirable to develop models suitable for predictions for the large pool of organic molecules that may be present in the atmosphere. Here, we report the development of such a model for Henry's law constants predictions of 29,439 compounds using the CORAL software (2023). The statistical quality of the model is characterized by the value of the coefficient of determination for the training and validation sets of about 0.81 (on average).