Impact of coking plant to heavy metal characteristics in groundwater of surrounding areas: Spatial distribution, source apportionment and risk assessments.

Coking industry is a potential source of heavy metals (HMs) pollution. However, its impacts to the groundwater of surrounding residential areas have not been well understood. This study investigated the pollution characteristics and health risks of HMs in groundwater nearby a typical coking plant. Nine HMs including Fe, Zn, Mo, As, Cu, Ni, Cr, Pb and Cd were analyzed. The average concentration of total HMs was higher in the nearby area (244.27 µg/L) than that of remote area away the coking plant (89.15 µg/L). The spatial distribution of pollution indices including heavy metal pollution index (HPI), Nemerow index (NI) and contamination degree (CD), all demonstrated higher values at the nearby residential areas, suggesting coking activity could significantly impact the HMs distribution characteristics. Four sources of HMs were identified by Positive Matrix Factorization (PMF) model, which indicated coal washing and coking emission were the dominant sources, accounted for 40.4%, and 31.0%, respectively. Oral ingestion was found to be the dominant exposure pathway with higher exposure dose to children than adults. Hazard quotient (HQ) values were below 1.0, suggesting negligible non-carcinogenic health risks, while potential carcinogenic risks were from Pb and Ni with cancer risk (CR) values > 10-6. Monte Carlo simulation matched well with the calculated results with HMs concentrations to be the most sensitive parameters. This study provides insights into understanding how the industrial coking activities can impact the HMs pollution characteristics in groundwater, thus facilitating the implement of HMs regulation in coking industries.

Comparing Accuracy of Parallel Analysis and Fit Statistics for Estimating the Number of Factors With Ordered Categorical Data in Exploratory Factor Analysis.

Determining the number of factors in exploratory factor analysis (EFA) is crucial because it affects the rest of the analysis and the conclusions of the study. Researchers have developed various methods for deciding the number of factors to retain in EFA, but this remains one of the most difficult decisions in the EFA. The purpose of this study is to compare the parallel analysis with the performance of fit indices that researchers have started using as another strategy for determining the optimal number of factors in EFA. The Monte Carlo simulation was conducted with ordered categorical items because there are mixed results in previous simulation studies, and ordered categorical items are common in behavioral science. The results of this study indicate that the parallel analysis and the root mean square error of approximation (RMSEA) performed well in most conditions, followed by the Tucker-Lewis index (TLI) and then by the comparative fit index (CFI). The robust corrections of CFI, TLI, and RMSEA performed better in detecting misfit underfactored models than the original fit indices. However, they did not produce satisfactory results in dichotomous data with a small sample size. Implications, limitations of this study, and future research directions are discussed.

Determine deformation energy in side impact by incorporating contact area in crash algorithm.

This research paper explores potential enhancements to the CRASH algorithm by proposing a hypothesis that relates deformation to applied stress instead of force. By incorporating stress instead of force, the calculation can account for the contact area, leading to a more precise estimation of impact velocity, particularly in side impacts. An initial evaluation of this energy absorption calculation formula is presented, focusing on side impacts in vehicle "2022 Hyundai Ion." Two side impact reports for the vehicle from the National Highway Traffic Safety Administration (NHTSA) database were utilized. One report involved the vehicle tilted at a 45-degree angle against a fixed pole with a 254 mm diameter, while the other examined the vehicle colliding with a moving deformable barrier (MDB) at various speeds. Additionally, a Monte Carlo simulation was conducted to validate the model's applicability. The verification process involved estimating stiffness coefficients from the first report and employing them to calculate energy absorption during the crash against the moving deformable barrier. The analysis demonstrates promising improvements in accurately calculating deformation energy absorbed during impacts.

Pressure-Dependent Shape and Edge Configurations of MoS2 by Kinetic Monte Carlo Simulation.

Understanding the influence of precursor pressures is crucial for optimizing the properties of MoS2 grown through the chemical vapor deposition (CVD) process. In this study, we use kinetic Monte Carlo (KMC) simulations to investigate how varying the pressures of molybdenum (PMo) and sulfur (PS) impacts the structural properties of MoS2, such as grain shape and edge configurations. The simulations differentiate three distinct regimes─growth, steady-state, and etching─each defined by specific PMo, PS, and the most probable atomic sites for filling or etching. We further explore how these regimes influence the atomic configuration of MoS2, particularly the formation of different edge structures like sulfur zigzag (ZZS), molybdenum zigzag (ZZMo), and their respective derivatives. A pressure diagram based on the equations of state and most probable atomic sites was constructed for each regime and validated by comparing predicted ZZ-derived edges to experimental observations. Additionally, the study examines the impact of etching on various line defects, providing insights into the evolution of the MoS2 edges during the CVD process. These findings underscore the importance of controlling both growth and cessation phases in the CVD process to customize edge configurations, with significant implications for chemical functionalization, catalysis, and the electronic properties of transition metal dichalcogenides.

A Monte Carlo Approach for Simulating Electrical Conductivity in Highly Porous Ceramic Composites: Impact of Internal Structure.

Porous ceramic composites play an important role in several applications. This is due to their unique properties resulting from a combination of various materials. Determination of the composite properties and structure is crucial for their further development and optimization. However, composite analysis often requires complex, expensive, and time-demanding experimental work. Mathematical modeling represents an effective tool to substitute experimental approach. The present study employs a Monte Carlo 3D equivalent electronic circuit network model developed to analyze a highly porous composite on the basis of minimum easily obtainable input parameters. Solid oxide cell electrodes were used as a model example, and this study focuses primarily on materials with a porosity of 55% and higher, characterized by deviation of behavior from those of lower void fraction share. This task is approached by adding to the original Monte Carlo model an additional parameter defining the void phase coalescence phenomenon. The enhanced model accurately simulates electrical conductivity for experimental samples of up to 75% porosity. Using sample composition, single-phase properties, and experimentally determined conductivity, this model allows us to estimate data of the internal structure of the material. This approach offers a rapid and cost-effective method to study material microstructure, providing insights into properties, such as electrical conductivity and heat conductivity. The present research thus contributes to advancing predictive capabilities in understanding and optimizing the performance of composite materials with potential in various technological applications.

Pharmacokinetics of Vancomycin in Healthy Korean Volunteers and Monte Carlo Simulations to Explore Optimal Dosage Regimens in Patients with Normal Renal Function.

Background/Objectives: To date, population pharmacokinetic (PK) studies of vancomycin on healthy Korean adults have not been conducted. This study aimed to investigate the PK properties of vancomycin in healthy volunteers and to identify optimal dosing regimens based on the area under the concentration-time curve (AUC) in adult patients with normal renal function. Methods: We conducted a prospective clinical study, analysing PK samples from 12 healthy participants using noncompartmental analysis and non-linear mixed-effects modelling. The population PK parameters derived were employed in Monte Carlo simulations to evaluate the adequacy of the current dosing regimen and to formulate dosing recommendations. Results: The PK profiles were optimally described by a two-compartment model, with body weight and age as significant covariates affecting total clearance. The simulations indicated that to achieve a therapeutic target-defined as an AUC at steady-state over 24 h of 400-600 mg·h/L-daily doses ranging from 60 to 70 mg/kg are necessary in adults with normal renal function. Conclusions: This study underscores the need to actively adjust dosage and administration based on a vancomycin PK model that adequately reflects the demographic characteristics of patients to meet both safety and efficacy standards.

Heavy metal contamination assessment and source attribution in the Vicinity of an iron slag pile in Hechi, China: Integrating multi-medium analysis.

Heavy metals, such as mercury, cadmium, and nickel, may contaminate human inhabited environments, with critical consequences for human health. This study examines the health impacts of heavy metal pollution from an iron slag pile in Hechi, China, by analyzing heavy metal contamination in water, sediment, soil, and crops. Here, the Nemerow pollution index (NI) indicated severe pollution at most sampling sites, the mean NI of groundwater, and surface water had reached 594.13 and 26.79, respectively. Bioaccumulation of mercury (Hg), cadmium (Cd), and nickel (Ni) was noted in crops, cucumbers showed comparatively lower risk levels. Logarithmic surface water-sediment partition coefficient calculations indicated that heavy metals such as chromium (Cr), ferrum (Fe), zinc (Zn), copper (Cu), Ni, arsenic (As), and lead (Pb) tend to accumulate in sediments. There was a high risk in groundwater (67.48-6590.54) and surface water (13.73-2500.85). Variably influenced by rainfall, these metals can be diluted and mobilized from surface water and sediments, thereby changing the contamination levels and ecological risks. Probabilistic health risk assessments indicated that health risks were higher in children than in adults, the mean total carcinogenic risk values of soil, groundwater, and surface water, were 6.79E-04, 4.20E-06, and 1.15E-6 for children, respectively. Moderate soil pollution is the main health hazard. A Positive Matrix Factorization model attributed over 60% of the pollution to slag stacking. Biotechnologies, solidification/stabilization techniques, field management, and institutional controls, driven by principles of green, low-carbon, and economic efficiency may mitigate. These findings contribute to the management of heavy metal pollution in iron slag pile areas.

Evaluating the Psychometric Properties of the Revised Attitudes Toward Research Scale for Use with MSW Students.

PURPOSE: Evaluating 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. MATERIALS AND METHODS: 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: 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. DISCUSSION: 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. CONCLUSION: 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.

Determination of soil environmental criteria for high-risk trace metals in urban park soils using improved CLEA model.

The trace metals (TMs) accumulated in urban park soils can pose potential threats to human health, making the management of soil quality based on health risks critically important. Based on the human health risk assessment (HHRA) model coupled with Monte Carlo Simulation, this study improved the contaminated land exposure assessment (CLEA) model. Combined with local parameters, the Soil Environmental Criteria (SEC) for high-risk trace metals (TMs) in urban park soils were calculated. Results indicated that all the mean TCR (Total carcinogenic risk) values of seven TMs exceeded the risk threshold of 1E-06, suggesting a higher likelihood of carcinogenic risks for all populations. As and Cr presented the highest potential carcinogenic risks, and were identified as high-risk TMs in the study area. The traditional CLEA model was enhanced by incorporating region-specific data, optimizing exposure parameter calculations, and addressing parameter sensitivity and uncertainty. Using the improved CLEA model, the SEC values for high-risk TMs were calculated, revealing that the SEC values gradually increased from ages 1 to 18, while significantly decreased for individuals over 80 years old. This study effectively addresses issues of parameter uncertainty and sensitivity in the CLEA model, offering new insights for the development of soil environmental quality standards.

A Fast 3D Range-Modulator Delivery Approach: Validation of the FLUKA Model on a Varian ProBeam System Including a Robustness Analysis.

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.

Source apportionment and ecological risk assessment of antibiotics in Dafeng River Basin using PMF and Monte-Carlo simulation.

Antibiotics, prevalent in aquatic ecosystems, pose a grave threat to human health and the ecological well-being. This paper performed a case study on Dafeng River Basin in southern China. Specifically, techniques including positive matrix factorization (PFM) and Monte-Carlo simulation were employed to comprehensively investigate the spatial variations, possible sources, and ecological risks of antibiotics in four groups: sulfonamides (SAs), macrolides (MLs), quinolones (QNs), and tetracyclines (TCs). The major findings were as follows: first, 43 and 39 antibiotics were detected in the surface water and sediments of the basin, respectively, where the respective total content were ND-490.08 ng/L and ND-144.34 µg/kg, and the QNs and TCs were the two dominating groups. Second, the highest antibiotic content in surface water (441.43 ng/L) was observed in the midstream area, whereas the highest concentration in sediments (68.41 µg/kg) was found in the upstream region. Third, the investigation identified five sources of antibiotics discharged to surface water: domestic sewage, agricultural drainage, livestock discharge, sewage treatment plants, and aquaculture; three sources were detected for antibiotics in sediments: aquaculture, sewage treatment plants, and livestock discharge. Fourth, QNs had a significantly higher ecological risk than the other three groups of antibiotics, and livestock discharge (31.4% contribution) and aquaculture (23.4% contribution) were the main sources of risks of antibiotic contamination in Dafeng River Basin. This study is expected to provide some reference for control and risk management of antibiotic pollution in Dafeng River Basin.

Precision comparison of intensity ratios and area ratios in spectral analysis.

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.

Preparation and characterization of a new Gd2O3-epoxy composite for neutron shielding applications.

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.

Development of a novel tool for high-precision focal irradiation using a clinical brachytherapy system.

PURPOSE: This study aims to emphasize the necessity of a focal irradiation tool for small animals and compare the beam characteristics of a tool developed using a brachytherapy system with a linear accelerator (LINAC)-based tool. METHODS: A 1-mm tungsten collimator was designed for a Ir-192 brachytherapy system. The percent depth dose (PDD) and horizontal profile of the collimator were measured and compared with a 4-mm commercial cone in the LINAC. Monte Carlo simulations validated all the measurements. Mouse brains were irradiated using a focal irradiation tool, and immunohistochemistry was performed on the brain samples to assess the dose accuracy. RESULTS: PDD showed that the maximum dose (dmax) for Ir-192 was at the surface in both measurements and simulations. At a depth of 1 mm, the collimator measured doses of 25.6 % and 21.0 %, respectively. At 6 MV in the LINAC, the dmax was observed at depths of 0.7 and 0.8 cm in measurements and simulations, respectively. The full width at half maximum (FWHM) at a depth of 1 mm was 1.0 and 1.1 mm for Ir-192 in the measurements and simulations, respectively. For small cone sizes at dmax, FWHM was 4.0 and 4.1 mm for the measurements and simulations, respectively. Immunohistochemistry results indicated that focal irradiation with Ir-192 affected small superficial brain areas while sparing the contralateral side and subventricular zone. CONCLUSION: The focal irradiation tool accurately delivered doses to small regions and shallow depths in the mouse brain, making it valuable for precise radiotherapy during small animal experiments.

Prediction of refraction error after toric lens implantation with biometric input data uncertainties and power labelling tolerances.

BACKGROUND: The purpose of this study was to simulate the impact of biometric measure uncertainties, lens equivalent and toric power labelling tolerances and axis alignment errors on the refractive outcome after cataract surgery with toric lens implantation. METHODS: In this retrospective non-randomised cross sectional Monte-Carlo simulation study we evaluated a dataset containing 7458 LenStar 900 preoperative biometric measurements. The biometric uncertainties from literature, lens power labelling according to ISO 11979, and axis alignment tolerances of a modern toric lens (Hoya Vivinex) were taken to be normally distributed and used in a Monte-Carlo simulation with 100 000 samples per eye. The target variable was the defocus equivalent (DEQ) derived using the Castrop (DEQC) and the Haigis (DEQH) formulae. RESULTS: Mean/median / 90% quantile DEQC was 0.22/0.21/0.36 D and DEQH was 0.20/0.19/0.32 D. Ignoring the variation in lens power labelling and toric axis alignment the respective DEQC was 0.20/0.19/0.32 D and DEQH was 0.18/0.17/0.29 D. DEQC and DEQH increased with shorter eyes, steeper corneas, equivalent lens power and highly with toric lens power. CONCLUSIONS: According to our simulation results, uncertainties in biometric measures, lens power labelling tolerances, and axis alignment errors are responsible for a significant part of the refraction prediction error after cataract surgery with toric lens implantation. Additional labelling of the exact equivalent and toric power on the lens package could be a step to improve postoperative results.

Assessment of drinking water quality and identifying pollution sources in a chromite mining region.

Water sources near mining regions are often susceptible to contamination from toxic elements. This study employs machine learning (ML) techniques to evaluate drinking water quality and identify pollution sources near a chromite mine in Iran. Human health risks were assessed using both deterministic and probabilistic approaches. Findings revealed that concentrations of calcium (Ca), chromium (Cr), lithium (Li), magnesium (Mg), and sodium (Na) in the water samples exceeded international safety standards. The Unweighted Root Mean Square water quality index (RMS-WQI) and Weighted Quadratic Mean (WQM-WQI) categorized all water samples as 'Fair', with average scores of 67.95 and 67.19, respectively. Of the ML models tested, the Extra Trees (ET) algorithm emerged as the top predictor of WQI, with Mg and strontium (Sr) as key variables influencing the scores. Principal component analysis (PCA) identified three distinct clusters of water quality parameters, highlighting influences from both local geology and anthropogenic activities. The highest average hazard quotient (HQ) for Cr was 1.71 for children, 1.27 for adolescents, and 1.05 for adults. Monte Carlo simulation for health risk assessment indicated median hazard index (HI) of 4.48 for children, 3.58 for teenagers, and 2.98 for adults, all exceeding the acceptable threshold of 1. Total carcinogenic risk (TCR) exceeded the EPA's acceptable level for 99.38 % of children, 98.24 % of teenagers, and 100 % of adults, with arsenic (As) and Cr identified as the main contributors. The study highlights the need for urgent mitigation measures, recommending a 99 % reduction in concentrations of key contaminants to lower both carcinogenic and non-carcinogenic risks to acceptable levels.

Monte Carlo calculations of target fragments from helium and carbon ion interactions with water.

When high energetic heavy ions interact with any target, short range, high linear energy transfer (LET) target fragments are produced. These target fragments (TFs) can give a significant dose to the healthy tissue during heavy ion cancer therapy, and when cosmic radiation interacts with astronauts. This paper presents Monte Carlo simulations, using the Particle and Heavy Ion Transport code System (PHITS), to characterize target fragments from reactions of helium and carbon ions with water. The calculated ranges, LET, doses, and production cross sections are presented. It is shown that protons, deuterons, tritons, alpha particles, 3He, 6He, nitrogen, oxygen, and fluorine ions are the most probable target fragments when carbon and helium ions collide with water. Among the produced target fragments, alpha particles and nitrogen ions give the highest dose to the targets, since the combination of fluence and LETs of these TFs are highest among the produced fragments. The production cross sections of proton and oxygen are the highest among the target fragments cross sections when helium and carbon ions imping on water, because these TFs can be produced through more reaction channels compared to other fragments. These findings are helpful for accurate dose measurement during heavy ion cancer therapy and for shielding of space radiation.

Development of an exposure assessment industrial model for Bacillus cereus in rice matrix containing insect chitosan.

An exposure assessment model for industrial use has been developed by using kinetic data from inactivation and growth of Bacillus cereus spores. It can provide a valuable tool for estimating the concentration of B. cereus after a storage period of 24 h at a specified temperature (20 °C) and for an estimation of the percentage of contaminated portions according to the input data of the model. This model considers a rice-derived product that has undergone a standard cooking process at 95 °C for 20 min. According to the results, the presence of chitosan affects the final microbial load after storage, potentially serving as an additional control measure in the event of cold chain abuse or break. Chitosan's antimicrobial properties likely play a role in reducing microbial growth during storage, thereby contributing to enhanced food safety. In practical terms, this suggests that incorporating chitosan into food products, especially those susceptible to microbial contamination like rice derivatives, could help mitigate risks associated with temperature abuse or cold chain disruptions. By acting as a protective barrier against microbial proliferation, chitosan offers a preventive measure to maintain product quality and safety throughout the supply chain. Considering two scenarios, 104 or 107 as initial contamination the model estimated that the 55 and 100% of portions would be respectively contaminated, according to a Performance Criteria of 4 log reductions.

Geant4 Monte Carlo simulation of human exposure to indoor 222Rn from building materials.

The present study aimed to develop a Monte Carlo model to estimate the annual effective dose due to radon exposure sourced by radon gas in the walls and floor of a standard model room. With the purpose of developing a tool for radon level assessment in dwellings and workplaces, Geant4 toolkit was used to simulate the energy deposited by gamma rays emitted by radioactive radon progeny in a water phantom positioned at three different locations within the model room. The energy deposition was then used to estimate the annual effective dose through a deterministic approach. The simulation outcomes showed good agreement with experimental data, with the ratio between the simulated and the experimental data displaying the overestimation by a factor of approximately 1.09. Both simulation and experimental data fell within the same range, with a relative deviation of 7.7%. Additionally, the influence of various parameters, such as receptor position in the room, wall, and floor thicknesses, wall cover, and building material bulk density, on the annual effective dose due to radon inhalation in the room was evaluated. Geant4 Monte Carlo toolkit proved to be a reliable tool for radon modeling in real exposure situations.

Quantitative estimation of optical properties in bilayer media within the subdiffusive regime using tilted fiber-optic probe diffuse reflectance spectroscopy, part 2: probe design, realization, and experimental validation.

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.