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Manufacturing companies strive to identify and manage the effects of unexpected disruptions (risks) on their production processes, which affect their performance and resilience. In this study, we propose a decision framework to capture the impact of interconnected risk sources, on the efficiency of manufacturing companies. The proposed framework utilises a novel mixed-integer linear programming (MILP) model to minimize the time of satisfying the orders while it considers the risk associated with suppliers and manufacturers. The MILP model considers the relationships among (i) material and suppliers and (ii) work centers to measure the propagation of risks throughout the production system. The proposed framework also utilises the Monte Carlo simulation to calculate the associated likelihood of delay and the distribution of the delivery time of orders. To show the complication of the propagation of risk, two distinct scenarios are compared. The first scenario considers zero risks, while the second one assigns probabilistic risk to the suppliers and work centers. The results highlight the magnitude and the complexity of the risk propagation from various interconnected sources through the production system. It also identifies the most vulnerable components of the production system affected more by various types of risk. [ABSTRACT FROM AUTHOR]

In this manuscript, the magnetic and magnetocaloric properties of the Ga2$_{2}$MnNi compound are investigated using Monte Carlo simulations and first‐principles calculations. Initially, the electronic, structural, and magnetic characteristics of the alloy are explored. The findings indicate that the ferromagnetic state in the F4¯3$\bar{4} 3$m (No. 216) structure, with an optimal lattice parameter of 5.88 Å, was the most stable state compared to nonmagnetic (NM) and antiferromagnetic (AFM) states. Phonon dispersion studies confirm the alloy's dynamic stability, while the density of states reveal metallic behavior at the Fermi level. The total magnetic moment is calculated to be 3.48  μB$\left(\mu\right)_{\text{B}}$. Additionally, exchange interactions are computed for Monte Carlo simulations, predicting a Curie temperature (Tc$_{\text{c}}$) of 331 K, consistent with experimental measurements (Tc$_{\text{c}}$ = 330 K). Furthermore, the alloy exhibited a relative cooling power (RCP) of 1133.16 J kg−1 and a magnetocaloric effect of 19.65 J kg−1 K−1 at an applied magnetic field of 5 T. These results indicate that the Ga2$_{2}$MnNi full‐Heusler alloy is a potential option for use in magnetic refrigeration applications. [ABSTRACT FROM AUTHOR]

AbstractThis study was conducted to monitor and evaluate the health risks of multiple organophosphorus pesticide residues in three characteristic fruits of Maragheh district, East Azerbaijan province. The Monte Carlo method was used to quantify the possible non-carcinogenic health risks associated with pesticide residues in adults and children. The residues of 17 pesticides in 36 samples were analysed using the QuEChERS extraction method coupled with gas chromatography-mass spectrometry (GC/MS). Residues of 13 pesticides (76.47%) were detected in grape samples and 15 pesticides (88.23%) in apricot and plum samples. The maximum level found in grape samples was due to chlorpyrifos with 1.2 mg/kg, and in apricot and plum samples to diazinon with 1.6 and 1.3 mg/kg, respectively. All grape, apricot and plum samples contained at least ten pesticides with levels exceeding the Iranian and EU Maximum Residue Limits (MRLs). The processing factor for washing treatment in grape, apricot and plum samples was less than 1, ranging from 0.53 to 0.69. Chlorpyrifos with values of 0.064 and 0.256 in the grape samples and diazinon with values of 0.0443 and 0.177 in the apricot samples and values of 0.0263 and 0.105 in the plum samples contain the highest hazard quotients (HQ) for adults and children, respectively. The cumulative risk assessment due to exposure to several pesticide residues suggests that there is no significant health risk for adults (HI < 1). However, consumption of unwashed grapes may cause adverse effects in children (HI = 1.48). However, for further research, a comprehensive longitudinal study is suggested to assess the long-term effects of exposure to pesticides especially for children. [ABSTRACT FROM AUTHOR]

The Lomax distribution is widely recognized in distributional theory and thoroughly applied in many study fields, particularly in engineering and lifetime studies. Considering the importance of fitting the lifetime datasets, this article deals a weighted exponentiated Lomax model using a novel modification on Lomax distribution. The proposed modification is powerful tool in modeling numerous types of data sets notably skewed, complex, asymmetric and symmetric. Various distributional characterizations are provided. Additionally, the estimation of the model parameter is obtained via two numerous estimation techniques. Monte Carlo simulation based research is conducted to demonstrate the potential of proposed estimator's using some measures, including the average estimate, average bias, and associated mean square error. Two real datasets drawn from lifetime area are illustrated to show the applicability of the suggested distribution. The results underscore the superior performance of the proposed model compared to its competitors, showcasing its effectiveness in accurately representing the analyzed datasets. [ABSTRACT FROM AUTHOR]

A five pressured diatomite clay materials samples were produced as a mixture of diatomaceous earth mineral and sodium silicate for the purpose of this investigation. The forming pressures ranged from 22.84 MPa to 114.24 MPa, and the samples were formed under a variety of conditions. The ratio of Na2SiO3 to the total mass of the produced pressed samples was maintained at precisely 15 wt.% throughout the process. Through the process of increasing the forming pressure, the density of the pressed samples was increased by ~ 11%. For the purpose of determining how the forming pressure will affect the shielding capacity, the Monte Carlo simulation was applied. The linear attenuation of the fabricated pressed diatomite samples improves from 0.123 to 0.136 cm−1 when the forming pressure is increased from 22.84 MPa to 114.24 MPa. By increasing the pressure from 22.84 MPa to 114.24 MPa, the half-value thickness reduced from 5.63 cm to 5.09 cm, and the thickness equivalent = 1 cm of pure Pb metal decreased from 10.09 cm to 9.12 cm. It was found that elevating the forming pressure resulted in an augmentation of the fast neutron removal for the artificially manufactured pressed diatomite samples. [ABSTRACT FROM AUTHOR]

Patient-reported outcomes (PROs) that aim to measure patients' subjective attitudes towards their health or health-related conditions in various fields have been increasingly used in randomised controlled trials (RCTs). PRO data is likely to be bounded, discrete, and skewed. Although various statistical methods are available for the analysis of PROs in RCT settings, there is no consensus on what statistical methods are the most appropriate for use. This study aims to use simulation methods to compare the performance (in terms of bias, empirical standard error, coverage of the confidence interval, Type I error, and power) of three different statistical methods, multiple linear regression (MLR), Tobit regression (Tobit), and median regression (Median), to estimate a range of predefined treatment effects for a PRO in a two-arm balanced RCT. We assumed there was an underlying latent continuous outcome that the PRO was measuring, but the actual scores observed were equally spaced and discrete. This study found that MLR was associated with little bias of the estimated treatment effect, small standard errors, and appropriate coverage of the confidence interval under most scenarios. Tobit performed worse than MLR for analysing PROs with a small number of levels, but it had better performance when analysing PROs with more discrete values. Median showed extremely large bias and errors, associated with low power and coverage for most scenarios especially when the number of possible discrete values was small. We recommend MLR as a simple and universal statistical method for the analysis of PROs in RCT settings. [ABSTRACT FROM AUTHOR]

The formation of ordered structures by Janus-like particles, composed of two parts (A and B), with orientation-dependent interactions on a triangular lattice was studied using Monte Carlo methods. The assumed lattice model allows each particle to take on one of the six orientations. The interaction between the A parts of neighboring particles was assumed to be attractive, while the AB and BB interactions were assumed to be repulsive. Moreover, it was assumed that the interaction between a pair of neighboring particles depended on the degrees to which their AA, AB, and BB parts face each other. It was shown that several ordered phases of different densities and structures may appear, depending on the magnitudes of AB and BB interactions. In particular, we found several structures composed of small clusters consisting of three ( O T ), four ( O R ), and seven (S) particles, surrounded by empty sites, the lamellar phases ( O L , O L 1 , and O L 3 ), the structures with hexagonal symmetry ( R 3 × 3 and K), as well as the structures with more complex symmetry ( R 5 × 5 and L A D) . Several phase diagrams were evaluated, which demonstrated that the stability regions of different ordered phases are primarily determined by the strengths of repulsive AB and BB interactions. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the optimal experimental planning for multi-level stress testing with Type-II censoring when the test units can be put into coherent systems for the experiment. Based on the notion of system signatures of coherent systems and assuming the lifetimes of the test units follow a distribution in a general log-location-scale family of distributions, the maximum likelihood estimators of the model parameters and the Fisher information matrix are derived. For optimal experimental planning, in addition to some commonly used optimality criteria, such as D-optimality, A-optimality, and V-optimality, we also consider the total time of the experiment and the total time on test. Then, motivated by a real-life application in the reliability study of furniture joints, we focus on using series systems in multi-level stress experiments. The methodology is illustrated by considering lognormal and Weibull distributed test units. Numerical and Monte Carlo simulation studies are used to demonstrate the advantages and disadvantages of using series systems in life-testing experiments. A numerical example based on furniture joints with sensitivity analysis is used to elucidate how the proposed methods can be used in planning a life-testing experiment. Finally, some concluding remarks with some future research directions are provided. [ABSTRACT FROM AUTHOR]

Broadband spectral measurements of the ballistic transmission of scattering samples are challenging. The presented work shows an approach that includes a broadband system and an automated adjustment unit for compensation of angular distortions caused by non-plane-parallel samples. The limits of the system in terms of optimal transmission and detected forward scattering influenced by the scattering phase function are investigated. We built and validated a setup that measures the collimated transmission signal in a spectral range from 300 nm to 2150 nm. The system was validated using polystyrene spheres and Mie calculations. The limits of the system in terms of optimal transmission and detected forward scattering were researched. The optimal working parameters of the system, analyzed by simulations using the Monte Carlo method, show that the transmission should be larger than 10% and less than 90% to allow for a reliable measurement with acceptable errors caused by noise and systematic errors of the system. The optimal transmission range is between 25% and 50%. We show that the phase function is important when considering the accuracy of the measurement. For strongly forward-scattering samples, errors of up to 80% can be observed, even for a very small numerical aperture of 6.6 · 10 − 4 , as used in our experimental system. We also show that errors increase with optical thickness as the ballistic transmission decreases and the multiscattered fraction increases. In addition, errors caused by multiple reflections in the sample layer were analyzed and also classified as relevant for classical absorption spectroscopy. [ABSTRACT FROM AUTHOR]

This study aimed to assess and compare the shielding performance of boron-containing materials for neutrons generated in proton therapy and used in boron neutron capture therapy (BNCT). Five composites, including AlB2, Al-B4C, Al-TiB2, Al-BN, and Al-TiB2-BN, were selected as shielding materials, with concrete used as a benchmark. The mass fraction of boron compounds in these materials ranged from 10% to 50%. The Monte Carlo toolkit Geant4 was employed to calculate shielding parameters, including neutron ambient dose equivalent, dose values, and macroscopic cross-section. Results indicated that, compared to concrete, these boron-containing materials more effectively absorb thermal neutrons. When the boron compound exceeds 30 wt.%, these materials exhibit better shielding performance than concrete of the same thickness for neutrons generated by protons. For a given material, its shielding capability increases with boron content. Among the five materials when the material thickness and boron compound content are the same, the shielding performance for neutrons generated by protons, from best to worst, is as follows: Al-TiB2, Al-B4C, AlB2, Al-TiB2-BN, and Al-BN. For BNCT, the shielding performance from best to worst is in the following order: Al-B4C, AlB2, Al-TiB2, Al-TiB2-BN, and Al-BN. The results of this study provide references and guidelines for the selection and optimization of neutron shielding materials in proton therapy and BNCT facilities. [ABSTRACT FROM AUTHOR]

Revisions were recently proposed to the way in which concrete-filled hollow structural section members are handled in CSA S16. These revisions were based on previous research, comparisons to experiments, and a first-order reliability method analysis of existing provisions for compression and flexural members. In this paper, this topic is further expanded by using Monte Carlo Simulations (MCS) to determine the inherent reliability of the previous and new design rules for concrete-filled rectangular hollow section (RHS) and circular hollow section (CHS) beam-columns. A representative set of concrete-filled RHS and CHS members with variations in concrete strength, wall slenderness, effective length, and loading eccentricity are analyzed. Using MCS, the reliability index (β+) of each is determined over a range of live-to-dead load ratios. Inherent β+ values are compared to the code-specified target (i.e., β+ = 3.0 per Annex B of CSA S16) and the CSA S16:19 and AISC 360-16 provisions. [ABSTRACT FROM AUTHOR]

The proportion of explained variance is an important statistic in multiple regression for determining how well the outcome variable is predicted by the predictors. Earlier research on 20 different estimators for the proportion of explained variance, including the exact Olkin–Pratt estimator and the Ezekiel estimator, showed that the exact Olkin–Pratt estimator produced unbiased estimates, and was recommended as a default estimator. In the current study, the same 20 estimators were studied in incomplete data, with missing data being treated using multiple imputation. In earlier research on the proportion of explained variance in multiply imputed data sets, an estimator called R̂SP2 was shown to be the preferred pooled estimator for regular R2. For each of the 20 estimators in the current study, two pooled estimators were proposed: one where the estimator was the average across imputed data sets, and one where R̂SP2 was used as input for the calculation of the specific estimator. Simulations showed that estimates based on R̂SP2 performed best regarding bias and accuracy, and that the Ezekiel estimator was generally the least biased. However, none of the estimators were unbiased at all times, including the exact Olkin–Pratt estimator based on R̂SP2. [ABSTRACT FROM AUTHOR]

We demonstrate that the recently introduced triple generally weighted moving average (GWMA) chart and its counterpart, the double GWMA chart, incorporate sub‐optimal weighting patterns that may assign more weight to certain historical data points at the expense of more recent ones. Moreover, these control charts, when compared to the exponentially weighted moving average (EWMA) chart, exhibit a substantial computational burden. Our findings underscore that a well‐designed EWMA chart offers superior overall performance in comparison to these control charts. [ABSTRACT FROM AUTHOR]

This paper introduces the Monte Carlo simulation (MCS) procedure in combination with an effective finite element method (FEM) based on the refined first-order shear deformation theory (r-FSDT) to examine the random vibration of functionally graded sandwich (FGS) plates with different skin layers subjected to double explosive load (DEL). In the stochastic design methodology, the formulation of the state function for design conditions commonly involves integrating random input parameters, assumed distribution functions, and the stochastic responses derived from problem models. The motion equation of the FGS plate is derived by using Hamilton’s principle. Then, the Newmark-beta method is applied to solve linear second-order ordinary differential equations. Finally, the random vibration of the FGS plate is studied by considering the normal distribution parameters. In general, this research tries to shed light on the effects of geometric parameters and material properties and guide the design of FGS plates subjected to double explosive load with uncertain input parameters. [ABSTRACT FROM AUTHOR]

Asymmetrical probability models are helpful for analyzing skewed data sets since they allow you to describe the form of the distribution and anticipate the chance of extreme events. This article defines a novel approach to continuous moment exponential distribution called the exponentiated generalized moment exponential model. The extension has two additional parameters accounting for the distribution's shape. We extend this distribution probability density, cumulative distribution, hazard rate, and survival functions and establish different key statistical properties. Parameter estimation is obtained using different procedures, notably maximum likelihood estimation, least square, and Bayesian methods. A Monte Carlo simulation experiment is conducted to assess parameter performance and indicator risk measures. This article examines two distinct actual data sets in order to highlight the significance of the proposed model as well as its application in a variety of settings. The new model is compared to a large number of well-known extensions that were developed by other businesses. [ABSTRACT FROM AUTHOR]

The Korean Peninsula, characterized by low-to-moderate seismicity, faces a shortage of strong ground motion records, posing challenges for the seismic safety assessment of critical infrastructures. Given the rarity of large-magnitude earthquakes, generating a variety of earthquakes with rational values of Peak Ground Acceleration (PGA) is essential for robust seismic fragility and risk analysis. To address this, a new stochastic approach is proposed to simulate artificial earthquakes at multiple source-to-site distances and derive the probability distribution of PGA based on recorded data from a single seismic event. Two key source parameters, seismic moment and corner frequency, are treated as random variables with a negative correlation, reflecting their uncertainties and dependence on source-to-site distance. The Monte Carlo simulation with copula sampling of the key source parameters generates Fourier spectra for artificial earthquakes, which are transformed into the time domain to yield PGA distributions at various distances. A comparison with recorded data shows that the proposed method effectively simulates ground motion intensities, with no statistically significant differences between the simulated results and recorded data ( p > 0.05 ). The present method of determining PGA distributions provides a robust framework to enhance seismic risk analysis for the safety assessment of structures. [ABSTRACT FROM AUTHOR]

This study presents a model-based parameter estimation method for integrating and validating uncertainty in expert knowledge and simulation models. The parameters of the models of complex systems are often unknown due to a lack of measurement data. The experience-based knowledge of experts can substitute missing information, which is usually imprecise. The novelty of the present paper is a method based on Monte Carlo (MC) simulation and importance sampling (IS) techniques for integrating uncertain expert knowledge into the system model. Uncertain knowledge about the model parameters is propagated through the system model by MC simulation in the form of a discrete sample, while IS helps to weight the sample elements regarding imprecise knowledge about the outputs in an iterative circle. Thereby, the consistency of expert judgments can be investigated as well. The contributions of this paper include an expert knowledge-based parameter estimation technique and a method for the evaluation of expert judgments according to the estimation results to eliminate incorrect ones. The applicability of the proposed method is introduced through a case study of a Hungarian operating waste separation system. The results verify that the assessments of experts can be efficiently integrated into system models, and their consistency can be evaluated. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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This paper introduces the Particle Swarm Optimization (PSO) algorithm to enhance the Latin Hypercube Sampling (LHS) process. The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation (MCS) to LHS for probabilistic trend calculations. The PSO method optimizes sample distribution, enhances global search capabilities, and significantly boosts computational efficiency. To validate its effectiveness, the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power. The performance was then compared with Latin Hypercubic Important Sampling (LHIS), which integrates significant sampling with the Monte Carlo method. The comparison results indicate that the PSO-enhanced method significantly improves the uniformity and representativeness of the sampling. This enhancement leads to a reduction in data errors and an improvement in both computational accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Copyright of Methods in Ecology & Evolution is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Occasional outliers that might be a natural part of a process may distort the properties of a control chart. In this paper, we show that a recently proposed adaptive EWMA (AE) mean chart is highly sensitive to the outliers. The false alarm rate of the AE chart increases when the proportion and/or magnitude of the outliers increase and vice versa. In order to circumvent this demerit of the AE chart, we propose a truncated normal distribution-based AE (TAE) chart for monitoring the mean of a normal process in the presence of outliers. The zero-state and steady-state average run-length profiles of the proposed chart are estimated using Monte Carlo simulations. Based on detailed run-length comparisons, it is found that the TAE chart may outperform the existing EWMA chart (based on a truncated normal distribution) when detecting various mean shift sizes of an outlier-prone normal process. Illustrative examples are also included in this study to demonstrate the implementation of the existing and proposed charts. [ABSTRACT FROM AUTHOR]

The dosing of fluconazole for young infants remains empirical because of the limited pharmacokinetic (PK) data. We aimed to establish a population PK model and assess the systematic exposure-response of commonly used regimens of fluconazole in Chinese infants. We included infants with a postnatal age of less than 120 days and received intravenous fluconazole. Both scheduled and scavenged plasma samples were collected, and fluconzaole concentration was determined by a validated ultra-performance liquid chromatography-tandem mass spectrometry assay. Population PK analysis was conducted using Phoenix NLME, and then Monte Carlo simulation was conducted to predict the probability of target attainment (PTA) of empirically used regimens of both prophylactic and therapeutic purposes. Based on 304 plasma samples from 183 young infants, fluconazole concentration data was best described by a one-compartment model with first-order elimination. Gestational Age (GA), postnatal age (PNA), and body weight (BW) were included in the final model as CL = 0.02*(GA/214)2.77*(PNA/13)0.24*exp(nCL); V = 1.56*(BW/1435)0.90*exp(nV). Model validation revealed the final model had qualified stability and acceptable predictive properties. Monte Carlo simulation indicated that under the same minimum inhibitory concentration (MIC) value and administration regimen, PTA decreased with GA and PNA. The commonly used prophylactic regimens can meet the clinical need, while higher doses might be needed for treatment of invasive candidiasis. This population PK model of fluconazole discriminated the impact of GA and PNA on CL and BW on V. Dosing adjustment was needed according to the GA and PNA of infants to achieve targeted exposures. [ABSTRACT FROM AUTHOR]

Water contamination is a major environmental issue, especially in rapidly growing industrialized areas like Singrauli. This study addresses research gaps regarding the hydrochemical characterization, health risk assessment, and source identification of contaminants. Hydrochemistry shows the concentrations of Na+, Ca2+, F−, Mn, As, Mo, Sr, and Ni were above the permissible limit for drinking usage. Water quality index (WQI), heavy metal pollution (HMPI), and evaluation indices (HMEI) revealed As, Mn, Cd, Mo, Co, and Ni were the key heavy elements contributing towards aqueous media pollution in the Singrauli area. Additionally, F was also considered one of the major contaminants. In health risk assessment, the higher values of hazard quotient (HQ) for non-carcinogens were associated with Mn, As, Mo, and F; and hazard index (HI) values > 1 were found in 70% and 55% of samples for children and adults, respectively. Carcinogenic risk (CR) for human health was associated with As. CR values in 56.7% (for adults) and 61.7% (for children) of the total samples exceeded 1 × 10–4. Monte Carlo simulation was applied and highlighted the significant risk factors responsible for both carcinogenic and non-carcinogenic health impacts. 19.2%, 7.3%, and 6.9% of the simulated HQ values for adults and 30.1%, 16.9%, and 10.6% for children were above the safe limit for F, As, and Mn, respectively. Additionally, only 43.8% and 24.8% of the simulated HI for adults and children were within the safe limit. Irrespective of age groups, all the simulated values of As in CR were above 1 × 10–6; and 60% (for adults) and 77.1% (for children) of the values were above 1 × 10–4. This outcome emphasizes the urgency of pollution control measures, especially for As, F, and Mn, to safeguard public health. Moreover, a multivariate statistical approach revealed that both geogenic and anthropogenic sources were responsible for contamination. Therefore, regular monitoring, filtration, and purification are mandatory to ensure safe drinking water for human consumption. [ABSTRACT FROM AUTHOR]

Background: Neutron beams utilized for performing BNCT are composed of a mixture of neutrons and gamma rays. Although much of the dose delivered to the cancer cells comes from the high LET particles produced by the boron neutron capture reaction, the dose delivered to the healthy tissues from unwanted gamma rays cannot be ignored. With the increase in the number of accelerators for BNCT, a detector system that is capable of measuring gamma ray dose in a mixed neutron/gamma irradiation field is crucial. Currently, BeO TLDs encased in quartz glass are used to measure gamma ray dose in a BNCT irradiation field. However, this type of TLD is no longer commercially available. A replacement dosimetry system is required to perform the recommended ongoing quality assurance of gamma ray measurement for a clinical BNCT system. Purpose: The purpose of this study is to evaluate the characteristics of a BeO OSLD detector system under a mixed neutron and gamma ray irradiation field and to assess the suitability of the system for routine quality assurance measurements of an accelerator‐based BNCT facility. Methods: The myOSLD system by RadPro International GmbH was evaluated using the accelerator‐based neutron source designed for clinical BNCT (NeuCure BNCT system). The readout constancy, linearity, dose rate effect, and fading effect of the OSLD were evaluated. Free‐in‐air and water phantom measurements were performed and compared with the TLD results and Monte Carlo simulation results. The PHITS Monte Carlo code was used for this study. Results: The readout constancy was found to be stable over a month‐long period and similar to the TLD results. The OSLD readout signal was found to be linear, with a high coefficient of determination (R2 ≥ 0.999) up to a proton charge of 3.6 C. There was no significant signal fading or dose rate dependency. The central axis depth dose and off‐axis dose profile measurements agreed with both the TLD and Monte Carlo simulation results, within one standard deviation. Conclusion: The myOSLD system was characterized using an accelerator system designed for clinical BNCT. The experimental measurements confirmed the OSLD achieved similar, if not superior to, the currently utilized dosimetry system for routine QA of an accelerator‐based BNCT system. The OSLD system would be a suitable replacement for the current TLD system for performing routine QA of gamma ray dose measurement in a BNCT irradiation field. [ABSTRACT FROM AUTHOR]

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan's Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters' robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study's quantile estimates can be employed in codified structural designs with policy consequences. [ABSTRACT FROM AUTHOR]

The current study aims to introduce a new polymeric composite consisting of epoxy resin as the matrix and gadolinium oxide (Gd2O3) as the neutron adsorption ingredient. The shielding performance of the composite was assessed by neutron attenuation experiments with an Am-Be source and polyethylene moderator. The results of these experiments showed an appreciable agreement with the Monte Carlo simulations. Other characteristics of the composite, including mechanical strength, thermal stability, microtexture, and its chemical compositions, were examined using standard tensile test, thermogravimetric analysis, X-ray diffraction, scanning electron microscopy, static light scattering analyses, and Fourier-transform infrared spectroscopy (FTIR). The results indicated that the new composites offer appreciable neutron absorption properties so that samples with 0.5%, 2%, 5%, and 10% Gd2O3 content could reduce the neutron beam intensity by 54%, 63%, 66%, and 70% at a thickness of 4 cm. [ABSTRACT FROM AUTHOR]

Diseased conditions of articular cartilage such as osteoarthritis (OA) are accompanied by a change in the water concentration, together with a decrease in cartilage thickness. These parameters are important indicators of the normal joint functioning. Light scattering in a heterogeneous joint tissue is a complex process and it is important to supplement its study with understanding gained from modelling. In this work we employed Monte Carlo for analysis of the relationship between changes in cartilage parameters and calculated diffuse reflectance spectra. It has been shown that water absorption peak at 980 nm is determined by both water content and cartilage thickness, while changes in diffuse reflectance spectra at 1450 nm are mostly driven only by water content. Since independent assessment of these parameters is clinically relevant, several approaches for their determination were developed. A heuristic algorithm for cartilage thickness determination and its quality metrics (R 2 = 0.96) were obtained. Several regression models were developed using the amplitudes of diffuse reflectance spectra at different wavelengths and it was shown that the use of a wide range of wavelengths (850–1700 nm) allows to determine the cartilage water concentration and its thickness with the accuracy R 2 0.96 and 0.99 while measuring at different probe-detector distances. [ABSTRACT FROM AUTHOR]

AbstractThe utilization of dynamic probability control limits (DPCLs) can prove advantageous in the development of control charts, particularly in scenarios where sample sizes, areas of opportunity, or any other covariate values exhibit temporal fluctuations. In this study, we design the DPCLs-based two adaptive EWMA (AE) and adaptive CUSUM (AC) charts for monitoring the mean of a normally distributed process. We use Monte Carlo simulations to estimate the conditional false alarm rates, zero-state and steady-state (SS) average run-length profiles of the adaptive charts. It is found that the proposed AE and AC charts outperform the existing AE chart in terms of relative mean index. An example is given to demonstrate the implementation of the proposed charts. [ABSTRACT FROM AUTHOR]

This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research. [ABSTRACT FROM AUTHOR]

PurposeMaterials and methodsResultsDiscussionConclusionEvaluating students’ attitudes toward research is essential for instructors of any research methods class, as students’ general attitudes toward research may impact if and how they integrate research into practice decision making. However, few psychometrically sound, multidimensional instruments that can be used with Master of Social Work (MSW) students exist.This work used confirmatory factor analysis to evaluate the psychometric properties of the Revised Attitudes Toward Research Scale with a diverse sample of 396 master’s level social work students. Multiple indicators, multiple causes models and a series of Monte Carlo simulations were then used to assess the relationship between various dimensions of students’ attitudes toward research, their prior research exposure/training, and their sociodemographic characteristics.Results indicate that the overall performance of the measure with this sample of MSW students was strong, and the factor structure was consistent with that found when evaluated with different samples in previous research. Moreover, various sociodemographic characteristics predicted scores on the research usefulness, research anxiety, and positive research predisposition subscales.The use of this tool allows instructors to identify students with high levels of research-related anxiety and those who may not intuitively comprehend the need to understand empirical research findings and integrate them into practice decision making.The authors offer suggestions for integrating this validated tool into social work research methods classes to assist in developing students’ ability to engage in all steps of the evidence-based practice process once in the field. [ABSTRACT FROM AUTHOR]

AbstractStratified sampling is one of the powerful variance reduction methods for analyzing system performance, such as reliability, with stochastic simulation. It divides the input space into disjoint subsets, called strata, to draw samples from each stratum. Partitioning the input space properly and allocating greater computational effort to crucial strata can help accurately estimate system performance with a limited computational budget. How to create strata, however, has yet to be thoroughly examined. Strata design faces the curse of dimensionality and data scarcity as the input dimension increases. We analytically derive the optimal stratification structure that minimizes the estimation variance for univariate problems. Further, reconciling the optimal stratification into decision trees, we devise a robust algorithm for multi-dimensional problems. Numerical experiments and a wind turbine case study demonstrate the superiority of the proposed method in terms of variance reduction, leading to computational efficiency and scalability. [ABSTRACT FROM AUTHOR]

Simple Summary: Ultra-high dose rate radiotherapy with particle beams such as proton and carbon is a major technical challenge. Fast beam delivery and the concentration of the Bragg peak to the tumor must be achieved in the order of milliseconds and can therefore only be realized with 3D range-modulators (3D RM). This work analyzed the robustness of the dose distribution, resulting from a 3D RM for a cube target volume. Firstly, a high-precision dose measurement was performed, and a very good agreement was found between the measured and Monte Carlo simulated dose distribution. Further simulations were conducted to determine how sensitively the resulting dose distribution responds to misalignments of the 3D RM. The results show that the 3D RM must be aligned with accuracy less than 1° to preserve the planned desired dose distribution. As the research and pre-clinical adoption of 3D RMs in particle therapy has become more widespread recently, these results are of high general utility. A 3D range-modulator (RM), optimized for a single energy and a specific target shape, is a promising and viable solution for the ultra-fast dose delivery in particle therapy. The aim of this work was to investigate the impact of potential beam and modulator misalignments on the dose distribution. Moreover, the FLUKA Monte Carlo model, capable of simulating 3D RMs, was adjusted and validated for the 250 MeV single-energy proton irradiation from a Varian ProBeam system. A 3D RM was designed for a cube target shape rotated 45° around two axes using a Varian-internal research version of the Eclipse treatment planning software, and the resulting dose distribution was simulated in a water phantom. Deviations from the ideal alignment were introduced, and the dose distributions from the modified simulations were compared to the original unmodified one. Finally, the FLUKA model and the workflow were validated with base-line data measurements and dose measurements of the manufactured modulator prototype at the HollandPTC facility in Delft. The adjusted FLUKA model, optimized particularly in the scope of a single-energy FLASH irradiation with a PMMA pre-absorber, demonstrated very good agreement with the measured dose distribution resulting from the 3D RM. Dose deviations resulting from modulator-beam axis misalignments depend on the specific 3D RM and its shape, pin aspect ratio, rotation angle, rotation point, etc. A minor modulator shift was found to be more relevant for the distal dose distribution than for the spread-out Bragg Peak (SOBP) homogeneity. On the other hand, a modulator tilt (rotation away from the beam axis) substantially affected not only the depth dose profile, transforming a flat SOBP into a broad, Gaussian-like distribution with increasing rotation angle, but also shifted the lateral dose distribution considerably. This work strives to increase awareness and highlight potential pitfalls as the 3D RM method progresses from a purely research concept to pre-clinical studies and human trials. Ensuring that gantry rotation and the combined weight of RM, PMMA, and aperture do not introduce alignment issues is critical. Given all the other range and positioning uncertainties, etc., not related to the modulator, the RM must be aligned with an accuracy below 1° in order to preserve a clinically acceptable total uncertainty budget. Careful consideration of critical parameters like the pin aspect ratio and possibly a novel robust modulator geometry optimization are potential additional strategies to mitigate the impact of positioning on the resulting dose. Finally, even the rotated cube 3D modulator with high aspect ratio pin structures (~80 mm height to 3 mm pin base width) was found to be relatively robust against a slight misalignment of 0.5° rotation or a 1.5 mm shift in one dimension perpendicular to the beam axis. Given a reliable positioning and QA concept, the additional uncertainties introduced by the 3D RM can be successfully managed adopting the concept into the clinical routine. [ABSTRACT FROM AUTHOR]

The long-debated question in analytical chemistry of which of the area ratio or the intensity ratio is the more precise has yielded no definitive analytical conclusion. To address this issue theoretically, we derived analytical solutions for the lower limits of estimation precision for spectral parameters, including the intensity ratio and area ratio, based on the Cramér–Rao lower bound (CRLB) framework for a Gaussian spectrum. The precisions of spectral parameter estimations from the analytical solutions were consistent with results obtained from Monte Carlo simulations. Our theoretical and simulation results revealed that the precision of estimating the area ratio surpassed that of the intensity ratio by a factor of 2 . Additionally, our experimental results aligned well with both theoretical predictions and simulation outcomes, further validating our approach. This increased precision of the area ratio is due to negative covariance between intensity and bandwidth, rather than the area containing more intensity information, as often misinterpreted. Consequently, and quite counter intuitively, prior bandwidth and intensity related information does not improve the area ratio precision: it worsens it. The analytical solution we derived represents the fundamental limits of spectral parameter measurement precision. Thus, it can be used as an alternative method for estimating the minimum error when experimental measurement uncertainty cannot be determined. [ABSTRACT FROM AUTHOR]

It is an established and already accomplished fact that probability-based methodologies (or probability/statistical distributions) play the roles of imperative tools in illustrating and describing applied phenomena. Paying attention to these previous facts, this paper considers the evolution of a new probability distribution. Thus, the main achievement of this paper leads to a new probability distribution as a result of combining the exponentiated exponential model with a tangent-based stratagem. The new model is called the tangent exponentiated exponential (TEE) distribution. For the TEE distribution, certain distributional properties and estimators of the model parameters are derived mathematically. The appraisal of the estimators of the TEE distribution is accomplished through a simulation study. Moreover, the applicability and virtuoso of the TEE distribution are exemplified via two data sets, which are taken from the music engineering (named Data 1) and reliability sector (named Data 2). By considering the p -value and three other important statistical tests/methods as diagnostic tools, we find that the TEE distribution is the most useful and optimal model for Data 1 and Data 2 compared to certain candidate probability distributions. [ABSTRACT FROM AUTHOR]

The objective of this study is to present a method for developing fragility curves for soil liquefaction that align with seismic hazards using Monte Carlo simulation. This approach can incorporate all uncertainties and variabilities in the input parameters. The seismic parameters, including earthquake magnitude (M) and associated peak ground acceleration (PGA), are jointly considered for the liquefaction assessment. The liquefaction potential and the resulting damages obtained by this method are more realistic. A case study is conducted using data from a sand-boil site in Yuanlin, Changhua County, where liquefaction occurred during the 1999 Chi-Chi earthquake in Taiwan. The findings indicate that the liquefaction potential index, IL, the post-liquefaction settlement, St, and the liquefaction probability index, PW, are all appropriate parameters for assessing liquefaction damages. The fragility curves for soil liquefaction developed through this method can support the performance-based earthquake engineering (PBEE) approach, provide guidance for liquefaction evaluation to the Taiwan Earthquake Loss Estimation System (TELES), and serve as a foundation for scenario simulation and an earthquake early warning system for liquefaction damages. [ABSTRACT FROM AUTHOR]

AbstractPerovskite scintillators have garnered significant interest in the realm of gamma-ray imaging in the past few years. Here, a comprehensive investigation into the gamma-ray imaging properties of CsPbBr3-PP composite scintillators is presented, wherein the Monte Carlo simulation approach offered by Geant4 is utilized. The primary focus is on the point spread function and modulation transfer function of the material, elucidating the nuanced interactions between gamma-ray energy, scintillator thickness, and their resultant imaging capabilities. A key aspect of this study is the exploration of the nonlinear and inverse effect of scintillator thickness and the energy of the photon beam on the imaging quality, highlighting the trade-offs between energy deposition and image resolution.This research underscores the importance of optimizing the scintillator design to balance these factors, catering to specific applications in high-energy detection and imaging. This work not only contributes significantly to the field of material sciences and radiographic imaging, but also provides practical insights for the development of more effective scintillator-based detectors. The findings of this study have broad implications for the design and application of perovskite scintillators in various high-tech industries and scientific research. [ABSTRACT FROM AUTHOR]

Process‐induced deformation (PID) arises in thermoset parts due to internal residual stress developed from their anisotropic properties, resulting in distortions. While passive numerical manufacturing control exists, active manufacturing control is crucial for enhancing the manufacturing process. The work focuses on diagnosing the polymerization reaction, known as the curing process, to consider the influence of uncertainties in thermal loading conditions on the behavior of cure kinetics. This is achieved using a Particle Filter approach, wherein a posterior distribution of cure evolution is recursively approximated based on observed measurements from characterization tests. The algorithm is designed to simultaneously perform the diagnosis and prognosis of the Degree of Cure and PID. This approach adopts the augmented cure formulation to address various scenarios with uncertainties in thermal loading conditions. It offers the advantage of providing comparable PID predictions with minimal computational costs. C‐shaped thermoset parts made of epoxy/carbon fibers with varying thicknesses are cured using the Manufacturing Recommended Curing Cycle, and the predictions with the developed algorithm are validated against experimental measures. Upon validation, the converged prognosis capability of the Particle Filter model is employed to assess the impact of thermal loading uncertainty on cure profiles, which, in turn, affects the final PIDs outcome. Highlights: A Bayesian sampling approach enables the estimation of cure kinetics parameters.The estimated stochastic parameters forecast the process‐induced deformations.The augmented Degree of Cure accounts for uncertainties linked to thermal loadings.Analysis on AS4/8552 C‐shaped parts shows the cure kinetics impact.The framework reduces the computational costs required for active control. [ABSTRACT FROM AUTHOR]

This study advances the application of the first‐to‐signal (FTS) concept to evaluate the effectiveness of multivariate charts when detecting shifts in the mean vector of a multivariate normally distributed process. Using Monte Carlo simulations, we estimate steady‐state average run‐length profiles and the probabilities of multivariate charts being the FTS an out‐of‐control state. Notably, our findings indicate that while the multivariate cumulative sum (MCUSUM) chart serves as a viable alternative to the multivariate exponentially weighted moving average (MEWMA) chart when detecting small shifts in the process mean vector, the latter surpasses the former in detecting moderate‐to‐large shifts. In addition, the Hotelling's T2$T^2$ chart outperforms the MEWMA and MCUSUM charts when detecting large shifts. [ABSTRACT FROM AUTHOR]

This paper investigates the stochastic nonlinear dynamic response of a single-degree-of-freedom dielectric elastomer generator under the combined action of harmonic excitation and stochastic disturbance. First, the nonlinear vibration model of a single-degree-of-freedom dielectric elastomer generator is established. The average method and fourth-order Runge–Kutta method are used to analyze the influence of pre-stretching, the initial membrane length, the charge amount, and the external excitation amplitude on the system response. Considering stochastic disturbance, the stochastic average method is applied to determine the probability density function (PDF) of the system’s steady-state response, and Monte Carlo numerical simulations are compared with the theoretical calculations for verification. The oscillation amplitude of the steady-state response can be increased by increasing the pre-stretching, harmonic excitation amplitude, and stochastic disturbance intensity. In the case of stochastic disturbance, as the intensity of the disturbance increases, the system becomes more likely to exhibit large amplitude responses for longer durations. [ABSTRACT FROM AUTHOR]

AbstractA high-altitude nuclear detonation releases a significant portion of energy as X-rays with a blackbody spectrum. Satellites are particularly vulnerable to prompt soft X-rays (~1 keV) absorbed within a few microns of the surface of the solar array, causing melting and evaporation of its materials. The absorption of soft X-rays in solar cell materials is studied using GEANT4 computer software. Energy deposition as a function of depth (depth-dose profile) is calculated for slab geometries of dielectric and metallic materials. The photo-absorption and Compton scattering of X-rays and the contribution of secondary radiation, such as photo-electrons, Auger-electrons, and fluorescence photons are taken into account. The effect of the production of secondary radiation on the distribution of deposited dose in the near-surface region of materials is investigated. The results presented in this work are validated against published data and provide valuable insights into X-ray absorption by solar cell materials, the redistribution of energy by secondary radiation, and the spatial scale of power density deposition that can be used as a source term for the further thermomechanical analysis of a material’s phase transformations and melting. [ABSTRACT FROM AUTHOR]

Significance: Tissues like skin have a layered structure where each layer's optical properties vary significantly. However, traditional diffuse reflectance spectroscopy assumes a homogeneous medium, often leading to estimations that reflects the properties of neither layer. There's a clear need for probes that can precisely measure the optical properties of layered tissues. Aim: This paper aims to design a diffuse reflectance probe capable of accurately estimating the optical properties of bilayer tissues in the subdiffusive regime. Approach: Using Monte Carlo simulations, we evaluated key geometric factors--fiber placement, tilt angle, diameter, and numerical aperture--on optical property estimation, following the methodology in Part I. A robust design is proposed that balances accurate intrinsic optical property (IOP) calculations with practical experimental constraints. Results: The designed probe, featuring eight illumination and eight detection fibers with varying spacings and tilt angles. The estimation error of the IOP calculation for bilayer phantoms is less than 20% for top layers with thicknesses between 0.2 and 1.0 mm. Conclusion: Building on the approach from Part I and using a precise calibration, the probe effectively quantified and distinguished the IOPs of bilayer samples, particularly those relevant to early skin pathology detection and characterization. [ABSTRACT FROM AUTHOR]

Ship hull vibration is a significant contributor to fatigue crack growth and the major sources of vibrations are found to be the main engine vibration excitation, the wave-induced springing and whipping loads, and the actions of the propeller. In the midship region, wave-induced loads and the main engine are the major contributors, whereas propeller excitation dominates in the aft region of the ship hull. No general method exists to solve all kinds of vibration problems and they need to be evaluated through a case-by-case approach. The complex and uncertain aspects of hull vibration and fatigue crack growth motivate the need for a reliability-based scheme for assessing the resulting fatigue crack propagation. In the present paper, a probabilistic formulation for the failure probability of the occurrence of crack propagation of a secondary hull component is outlined. A generic cargo hold model is analyzed with engine excitation and wave-induced loading as vibration sources, and a stochastic model for vibration response is outlined. The limit state is formulated as the possible occurrence of fatigue crack growth. The secondary hull component considered is a pipe stack support, which is a supporting component that attaches the cargo pipes to the wall inside a cargo tank. Different initial crack sizes are implemented to evaluate the adequacy of the applied stochastic model for vibration response and the accuracy of the estimated failure probability is assessed. [ABSTRACT FROM AUTHOR]

This work aimed to determine the linear and mass attenuation coefficients of soy-lignin bonded Rhizophora spp. particleboard intended for use as a phantom material using Monte Carlo GATE simulation. At a desired density of 1.0 g · cm-3, particleboard constructed of Rhizophora spp. wood trunk bonded with soy flour and lignin was created. The sample's elemental composition was identified using energy dispersive X-ray spectroscopy. The GATE software was used to simulate the setup with the histories of 1 x 107, and comparison was made between the experimental and simulation data. The disparities between the linear and mass attenuation coefficients of the samples experimentally measured and calculated using GATE at low energy photons were quite small. The result revealed a good agreement between the experimental and simulation data, and the attenuation coefficients were in close proximity with XCOM of water. The outcome revealed GATE adequacy for validation of attenuation coefficient measurement in bioresources phantom material for medical physics application. [ABSTRACT FROM AUTHOR]

In this paper, a generalized autoregressive conditional heteroskedasticity model under skew-normal distributions is studied. A maximum likelihood approach is taken and the parameters in the model are estimated based on the expectation-maximization algorithm. The statistical diagnostics is made through the local influence technique, with the normal curvature and diagnostics results established for the model under four perturbation schemes in identifying possible influential observations. A simulation study is conducted to evaluate the performance of our proposed method and a real-world application is presented as an illustrative example. [ABSTRACT FROM AUTHOR]

This study examines population and small sample properties of the standardized fifth and sixth moments – the "higher moments" – for assessing univariate normality against bimodal and selected unimodal alternatives. Population parameters and distributions for selected bimodal mixtures are calculated and contrasted with those for the normal distribution. Using Gram-Charlier series expansion methods, an omnibus goodness of fit test incorporating the higher moments is specified and Monte Carlo simulation used to compare test power with parametric tests based on the standardized third and fourth sample moments: the asymptotic and size corrected versions of the Jarque-Bera score test and the omnibus D'Agostino K2 test. The studentized range and directional tests using the third through sixth moments are also considered. The results demonstrate that incorporating the fifth and sixth moments can provide enhanced parametric normality test power for bimodal normal mixture alternatives but not for various unimodal alternatives. [ABSTRACT FROM AUTHOR]

Recently, several control charts have been proposed in the statistical process monitoring literature based on a belief statistic. In this article we compare the zero‐state average run‐lengths and conditional expected delay profiles of the exponentially weighted moving average (EWMA) and belief statistic‐based (BSB) charts. We show that an ordinary EWMA chart is far better than the BSB chart when detecting delayed shifts in the mean of a normally distributed process. [ABSTRACT FROM AUTHOR]

Optical fiber X-ray sensors have the potential to realize real-time dose monitoring of precision radiotherapy. However, an over-response phenomenon can occur when using an optical fiber sensor (OFXS) filled with inorganic scintillator (Gd2O2S:Tb) to measure the off-axis ratio (OAR) curve. The aim of this paper is to study the mechanism responsible for the over-response. Due to the complex particle distribution present in water phantoms, the Monte Carlo-based code GEANT4 was used to model the response of the scintillator. The energy response of the scintillator to photons and electrons was initially simulated, which subsequently allowed the OAR curve to be simulated and the results were compared with experiment. To analyze the energy distribution of particles in different positions, electron spectroscopy was simulated together with the photon spectrum at the position from the central axis to a distance of 14.5 cm away from the center. Finally, three metal (Al, Cu, Sn) caps were made for the OFXS to prevent the low-energy photons penetrating the OFXS, and the OARs measurements were repeated. The results show that the scintillator exhibits higher sensitivity to photons with energy below 0.5 MeV, while for electrons, the scintillator has a higher sensitivity to high-energy electrons. Simulations for electron spectroscopy and the photon spectrum show that there are many low-energy photons with relatively few low-energy electrons. The OARs measured using the OFXS with metal caps show that the over-response can be mitigated using a high-Z metal cap. The measurements demonstrate that the OAR cure measured using an OFXS fitted with a Sn cap exhibits the closest response to that measured using an IC. [ABSTRACT FROM AUTHOR]

In this paper, a new regression estimator is proposed as an alternative to the ridge estimator in the case of multicollinearity in Bell regression model, called an almost unbiased ridge estimator. Also, we provide the theoretical properties of the new almost unbiased ridge estimator, and some theorems showing under which conditions that the almost unbiased ridge estimator is superior to its competitors. We consider a comprehensive simulation study to demonstrate the superiority of the almost unbiased ridge estimator compared to the usual Bell ridge estimator and the maximum likelihood estimator. The usefulness and superiority of the introduced regression estimator is shown via a real-world data example. According to the results of the simulation study and real-world data example, we conclude that the new almost unbiased ridge regression estimator is superior to its competitors in terms of the mean square error criterion. [ABSTRACT FROM AUTHOR]