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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]

Uncertainty analysis is essential for estimating variability within specified tolerances, particularly in three-dimensional (3D) assembly tolerance analysis. This study introduces a novel analytical approach for assessing assembly deviations, integrating the Jacobian–Torsor model with the bootstrap technique. The Jacobian–Torsor model combines the efficiency of representing tolerances with the adaptability of the Jacobian matrix for their propagation. This computerized method, based on the unified Jacobian–Torsor approach, focuses on cam-clamping devices, specifically the fastening flange component. The novelty of this study lies in the application of the bootstrap technique, a Monte Carlo Simulation approach, for uncertainty analysis to estimate variability within specified tolerances. A comprehensive comparison of statistical methods—bootstrap, stratified sampling, Bayesian statistics, and analytical methods—demonstrates the advantages of the Bootstrap approach. The results emphasize its user-friendliness and precision, even with complex shapes. The primary aim is to highlight the utility of the unified Jacobian–Torsor method for tolerance analysis. An experiment involving the fastening flange assembly illustrates the practical application of this approach. The findings confirm the effectiveness of the proposed method, demonstrating its accuracy and reliability for cam-clamping devices in real-world assembly scenarios with intricate geometries. [ABSTRACT FROM AUTHOR]

This paper considers optimal pricing in a system with limited substitutable resources, such as certain goods or services. Prices for the different resources have to be set and then customers with heterogeneous preferences show up sequentially. Customers, of n types, select an item from the m available resources, depending on their valuations of the resources and the prices. The goal is to analyze this optimization problem, characterize a set of candidates to optimal solutions and provide methods for solving it. We prove that this problem is NP-hard to approximate within a factor O (n 1 - ε) for any fixed ε > 0 . Another important contribution is to prove that, the space of prices (which in principle is a continuous domain in R m ), can be reduced to a finite set of vectors of cardinality m m - 2 n m 2 m . For a deterministic version of the problem, where the customer types are known to the firm, we provide a mathematical program that chooses the best set of prices. We report extensive computational results showing the usefulness of our exact approach to solve medium size problems with up to 200 customers and different assortments of products and customer types. We then show how to approximate the stochastic model by a small number of solutions of deterministic scenarios solved using a mixed-integer linear program. [ABSTRACT FROM AUTHOR]

Our study assesses the performance of a threshold vector error correction model (TVECM) for the analysis of spatial price transmission. We specifically determine how inference about price regimes and the process of price adjustment to the established long-run equilibrium is affected when the stochastic transaction cost is exogenously or endogenously determined in fixed and flexible threshold models. The study first generates non-stationary but cointegrated price series and transaction costs based on true price transmission parameters: percentage of violations of spatial equilibrium, speed of price adjustment, threshold and inaction band using Monte Carlo simulations. Then, the generated data are used to estimate TVECMs under various assumptions on transaction cost variation and threshold specification. Finally, estimates of price transmission parameters are compared with the true parameters to evaluate the performance of model specifications. Our results indicate that the flexible TVECM accounting for transaction cost variation outperforms the standard fixed TVECM, which is consistent with findings from many previous studies in the literature. Our study also finds that although the flexible TVECM performs better than the fixed TVECM, it may not fully address the limitation of linear models when a real-world data represents a complex nonlinearity in the spatial price transmission. [ABSTRACT FROM AUTHOR]

In this paper, Bayesian estimates are derived for the location and scale parameters of the Laplace distribution based on complete, Type-I, and Type-II censored samples under different prior settings. Subsequently, Bayesian point and interval estimates, as well as the associated statistical inference, are discussed in detail. The developed methods are then applied to two real data sets for illustrative purposes. Moreover, a detailed Monte Carlo simulation study is carried out for evaluating the performance of the inferential methods developed here. Finally, we provide a brief discussion of the established results to demonstrate their practical utility and present some associated problems of further interest. Overall, this study fills an existing gap in the development of Bayesian inferential techniques for the parameters of the two-parameter Laplace distribution, making this research innovative and offering more investigative implications. It showcases the potential for broader methodological applications of Bayesian inference for complex real-world data sets, especially in scenarios involving different forms of censoring. This research provides a critical tool for statistical analysis in different fields such as engineering and finance, where the Laplace distribution is frequently adopted as a fundamental model. [ABSTRACT FROM AUTHOR]

Objective. This work proposes, for the first time, an image-based end-to-end self-normalization framework for positron emission tomography (PET) using conditional generative adversarial networks (cGANs). Approach. We evaluated different approaches by exploring each of the following three methodologies. First, we used images that were either unnormalized or corrected for geometric factors, which encompass all time-invariant factors, as input data types. Second, we set the input tensor shape as either a single axial slice (2D) or three contiguous axial slices (2.5D). Third, we chose either Pix2Pix or polarized self-attention (PSA) Pix2Pix, which we developed for this work, as a deep learning network. The targets for all approaches were the axial slices of images normalized using the direct normalization method. We performed Monte Carlo simulations of ten voxelized phantoms with the SimSET simulation tool and produced 26,000 pairs of axial image slices for training and testing. Main results. The results showed that 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the best performance among all the methods we tested. All approaches improved general image quality figures of merit peak signal to noise ratio (PSNR) and structural similarity index (SSIM) from ∼15 % to ∼55 %, and 2.5D PSA Pix2Pix showed the highest PSNR (28.074) and SSIM (0.921). Lesion detectability, measured with region of interest (ROI) PSNR, SSIM, normalized contrast recovery coefficient, and contrast-to-noise ratio, was generally improved for all approaches, and 2.5D PSA Pix2Pix trained with geometric-factors-corrected input images achieved the highest ROI PSNR (28.920) and SSIM (0.973). Significance. This study demonstrates the potential of an image-based end-to-end self-normalization framework using cGANs for improving PET image quality and lesion detectability without the need for separate normalization scans. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Metal nanoparticles (MNPs) labeled with radioisotopes (RIs) are utilized as radio‐enhancers due to their ability to amplify the radiation dose in their immediate vicinity. A thorough understanding of nanoscale dosimetry around MNPs enables their effective application in radiotherapy. However, nanoscale dosimetry around MNPs still requires further investigation.This study aims to provide insight into the radio‐enhancement effects of MNPs by elucidating nanoscale dosimetry surrounding MNPs labeled with Auger‐emitting RIs. We particularly focus on distinguishing the respective dose contributions of photons and electrons emitted by Auger‐emitting RIs in the context of dose enhancement.A 50 nm diameter NP of silver (Ag) core and gold (Au) shell (Ag@Au NP) was assumed to emit mono‐energetic electrons and photons (3, 5, 10, 20, and 30 keV), or the energy spectrum corresponding to one of three Auger‐emitting RIs (103Pd, 125I, and 131Cs) from the Ag core. Nanoscale radial dose distributions around a single radioactive Ag@Au NP were evaluated in spherical shells of water. Monte Carlo simulations were conducted using single‐event and track structure transport methods implemented in MCNP6.2 and Geant4‐DNA‐Au physics, respectively. To evaluate the extent of radio‐enhancement by the Ag@Au NP, two scenarios were considered: Ag@Au NPs (Au shell included) and Ag@water NPs (Au shell replaced by water).The radial doses of 10, 20, and 30 keV electrons estimated by both codes were comparable. However, the radial doses of 3 and 5 keV electrons by MCNP6.2 were much larger near the NP surface than those by Geant4. There was a dose enhancement of a few % to tens % by the Au shell in the region of the NP surface to 10 µm, depending on the electron energy. The radial doses of photons with the Au shell were higher up to their secondary electron ranges than those without the Au shell. The maximum dose enhancement factor of photons occurred at 20 keV and was 63.4 by MCNP6.2 and 50.5 by Geant4. The overall radial doses of electrons were 1–2 orders of magnitude larger than those of photons. As a result, in cases of RIs emitting both electrons and photons, the radial doses up to electron ranges were dominantly governed by electrons. The dose enhancement estimated by both codes for the RIs ranged from a few % except in the immediate vicinity of the NP surface.Given the dominant contribution of electrons to radial doses of MNP labeled with Auger‐emitting RIs, physical dose enhancement expected by interactions with photons was hindered. Since there are no available RIs emitting exclusively photons, achieving enhanced physical doses within a cell through a combination of MNPs and RIs appears currently unattainable. The radial doses of photons near the NP surface exhibited considerable discrepancies between the codes, primarily attributed to low‐energy electrons. The difference may arise from higher cross‐sections of Au inelastic scattering in Geant4‐DNA‐Au compared to MCNP6.2. [ABSTRACT FROM AUTHOR]

Abstract–The Weibull distribution is the most applied model in reliability field for lifetime analysis. The Weibull location parameter, characterizing the minimum possible life, plays a significant role in engineering applications. In this paper, we consider the interval estimation on the location parameter when the product's lifetime follows the three-parameter Weibull distribution with a known shape parameter. A novel approach based on the relationship between the minimum order statistics, the location parameter, and the sample size is developed to construct confidence intervals for the Weibull location parameter. Thereafter, we compare it with other two interval estimation approaches by the performances of the coverage probability and the average length via simulations and a real application. The results show that the proposed method outperforms the pivot quantity (PQ) method and the bias-corrected and accelerated (Bca) bootstrap method in small and medium samples in terms of coverage probability. [ABSTRACT FROM AUTHOR]

MAPK has been reported as a key oncogenic pathway for canine histiocytic sarcoma, which can be pharmacologically targeted with trametinib, a small inhibitor of MEK1/2. Preliminary data showed promising antitumor activity in in vitro and in vivo models and represented a proof of concept to translate the findings from bench to bedside. In this phase I, dose escalating study using a 3 + 3 cohort design, trametinib was evaluated in 18 dogs with cancer. Adverse events were graded according to VCOG‐CTCAE v2. Blood samples and tumour biopsies were collected for pharmacokinetic and pharmacodynamic assessment. Trametinib was well tolerated with a maximum tolerated dose of 0.5 mg/m2/day, PO. Dose‐limiting toxicities included systemic hypertension, proteinuria, lethargy and elevated ALP, and were all Grade 3. The drug exposures increased more than linearly with dose since the elimination of trametinib was saturable. At a dose of 500 μg Q24h (0.5 mg/m2/day in a 30 kg dog), approximately 70% of dogs had an average steady‐state concentration of 10 ng/mL, achieved after approximately 2 weeks. This threshold was associated with clinical efficacy in humans. Target engagement was not observed in biospecimens collected on Days 0 and 7. In conclusion, trametinib was considered safe in dogs with cancer, and the dose of 0.5 mg/m2/day was the recommended dose for phase II studies. [ABSTRACT FROM AUTHOR]

The intrinsic characteristics of the monolithic crystal detector are spatially inconsistent, which leads to the position dependence of the detector on the energy response of the γ-ray as well as the peak shift of the response spectrum of the detector, that is, the "position-energy" shift. The "position-energy" shift will cause the energy resolution of the detector to deteriorate and affect the energy linearity of the detector. Thus, a crucial challenge in enhancing the position consistency of detector energy response, improving energy resolution, and ensuring accurate isotope identification is the reduction or elimination of this "position-energy" offset. The "position-energy" response correction method is proposed in this paper to improve the position consistency of detector energy response. Firstly, Monte Carlo simulation is used to model monolithic LaBr3(Ce) crystal detectors of different sizes. Secondly, the effective detection region of the detector model is evenly divided into 25 blocks, then the spectral peak position of each incident region is extracted, and the spectral peak correction function matrix of 25 incident regions and the center position is established. Finally, 25 incident regional peaks are modified according to the modified function matrix, so that the spectral peaks in each region are consistent with the peaks in the center, and the modified spectral responses of the detector are obtained. The simulation results show that this method can effectively solve the "position-energy" migration problem of monolithic crystal detectors of different sizes and improve the peak consistency of each detector region. The energy resolution of the 662 keV characteristic peak of the Cs-137 point source can be improved from 4.5% to 3.9%, and the linear deviation of energy can be reduced from 2.1% to 1.2%. [ABSTRACT FROM AUTHOR]

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

This study reviews global levels of ochratoxin A (OTA) in infant formula and cereal-based foods, using Monte Carlo simulation to assess risks. The review found 24 studies on global OTA levels in infant food and cereal-based products, using databases including PubMed, Scopus, Web of Science and Embase until March 2024. We estimated OTA exposure in infant food based on concentration, intake and body weight. The exposure and hazard quotient margin were calculated using BMDL10 and TDI values. Monte Carlo simulation evaluated human health risks from OTA in infant formula and cereal-based foods. A global study from 14 countries shows varying levels, surpassing EU limits in Tunisia, Ecuador, the USA, and generally in Africa, notably in infant cereals, which had higher levels than formula. Globally, OTA was present in 29.3% of the 3348 samples analyzed, with Lebanon at 95.2% and Brazil at 0%. Analysis indicates only non-carcinogenic risk for infants. While health risks for infants are mostly low, ongoing research and monitoring are vital to minimize OTA exposure in infant food. [ABSTRACT FROM AUTHOR]

To assess the dietary exposure risks of pesticide residues in vegetables for the general population, the presence of 39 pesticides was determined in 70 samples, of which 13 were detected. The most frequently detected pesticide was bifenthrin, with a detection rate of 35.7%, mainly found in gingers, followed by isoprocarb and acephate (11.4%) and dimethoate (8.6%). In 17.1% of the samples two or more pesticide residues were found. Acute, chronic and chronic cumulative dietary exposure risk was assessed. Chronic exposure risks were determined by Monte Carlo Simulation (MCS). Estimated chronic exposure to carbofuran, omethoate, disulfoton and dimethoate of approximately 49%, 52%, 40% and 3%, respectively, were at non-carcinogenic human risk. Acute exposure risk to acephate was considered to be of concern due to the high acute hazard quotient (aHQ). [ABSTRACT FROM AUTHOR]

We establish a novel family of Kumaraswamy-X probability distributions in the present investigation. We discussed the KumaraswamyExponential univariate probability distribution. The new distribution with three parameters possesses density function with unimodal and reverse J-shape and hazard rate function of bathtub shaped. We study various statistical properties for it and derive the expressions for its density function, distribution function, survival and hazard rate function, Probability weighted Moments, lth moment, moment generating function, quantile function and Shannon entropy. For the derived distribution order statistics is also discussed. The parameters are estimated using the maximum likelihood estimation approach, and the performance of the estimators was evaluated using a Monte Carlo simulation. Through extensive Monte Carlo simulations and comparative analyses, we assess the performance of the Kumaraswamy-X distribution against other common probability distributions used in engineering contexts. When we apply it to real datasets, it offers a more suitable fit than other existing distributions. We explore the characteristics and potential applications of the Kumaraswamy-X distribution in the context of engineering problems through a comprehensive simulation-based investigation. [ABSTRACT FROM AUTHOR]

This article considers the challenge of designing football group draw mechanisms, which have a uniform distribution over all valid draw assignments, but are also entertaining, practical and transparent. Although this problem is trivial in completely symmetric problems, it becomes challenging when there are draw constraints that are not exchangeable across each of the competing teams, so that symmetry breaks down. We explain how to simulate the FIFA sequential draw method and compute the nonuniformity of its draws by comparison with a uniform rejection sampler. We then propose several practical methods of achieving the uniform distribution while still using balls and bowls in a way which is suitable for a televised draw. The solutions can also be carried out interactively. The general methodology we provide can readily be transported to different competition draws and is not restricted to football events. [ABSTRACT FROM AUTHOR]

This paper presents the details of the analyses which were conducted to study the effects of steel reinforcing bars with unintended high strength on the behaviors of reinforced concrete (RC) columns. The influence of these bars on the column strength and strength-reduction factors were investigated. The former was studied with the help of column axial load-moment interaction diagrams, while a reliability analysis was carried out for the latter. Four different column cross sections reinforced with reinforcement ratios varying from 1 to 4% were included in the analysis. Other variables included concrete compressive and reinforcing bar yield strengths. The effects of the aforementioned variables were also considered on the development length of the reinforcing bars in tension and compression. It was found that the use of reinforcing bars with unintended high strength could change column behavior to compression-controlled at a lesser axial load level, which is accompanied by a reduction in the curvature capacity. Modifications have been suggested to control the negative effects of unintended high strength of bars on the column behavior and bar development length. Strength-reduction factors for RC sections ranging from compression-controlled to tension-controlled regions have also been proposed, which differ from those suggested by the prevalent code of practice. [ABSTRACT FROM AUTHOR]

Characterizing and quantifying livestock carbon emissions helps mitigate temperature rise and potential climate risks. Here, the spatial and temporal characteristics of China's carbon emissions of meat, egg, and milk are analyzed from the perspective of production and consumption. A driving factor decomposition model of carbon emissions of meat, egg, and milk production and consumption is established using the logarithmic mean Divisia index (LMDI). Using a static scenario analysis and dynamic Monte Carlo simulations (MCS), the evolution trajectory of carbon peak in meat, egg, and milk production and consumption in China during 2021–2030 is analyzed. China's carbon emissions from meat, egg, and milk production and consumption have risen in 2020, reaching 794.1 and 465.88 Mt, respectively. Moreover, western China has the highest carbon emissions in production (33.84%), which are concentrated in Sichuan, Inner Mongolia, and Xinjiang. For consumption, the carbon emissions are mainly concentrated in the eastern region (42.96%), and Guangdong is the primary source of carbon emissions in eastern China. The key drivers of carbon emissions for this production and consumption are the farmland output value and residential income effects, respectively. Conversely, reverse urbanization, agricultural industry structure, and food money purchasing power effects play significant suppressive roles. Under the business-as-usual scenario (BAU), China's carbon emissions from meat, egg, and milk production and consumption in 2030 will increase by 40.1% and 4.86% from 2020, respectively. The middle low-carbon scenario (Mid-LC) mitigates carbon emission growth by 24.51% and − 5.7%, respectively, for the production and consumption of meat, egg, and milk in 2030 compared to 2020. In the highly low-carbon scenario (Hig-LC), production and consumption will achieve a carbon peak by 2030. Ultimately, this study highlights the importance of reducing carbon emissions from livestock on production and consumption, and provides help for China's agricultural green development from the geospatial characteristics, drivers, and future trends of carbon emissions at both the production and consumption sides. [ABSTRACT FROM AUTHOR]

In this study, data from 23 domestic cremation facilities measured for 5 years (from 2016 to 2020) of 13 domestic cremation facilities was used to calculate the emission factors of dioxins (PCDD/DFs) of cremation facilities that use Liquified Natural Gas (LNG) as combustion fuel. We performed a statistical analysis for the first time to estimate the emission factor using limited measurement results from cremation facilities that are not easily accessible. We attempted to identify the emission concentration of dioxin as a representative persistent organic pollutants substance and developed a statistically based dioxin emission factor. The concentration of dioxins (PCDD/DFs) in the cremation facility ranged 0.001–4.440 ng I-TEQ/Sm3 with an average concentration of 0.719 ng I-TEQ/Sm3. The emission factor calculated using the 23 measured data showed 0.010–21.485 μg I-TEQ/Cremation. A Monte Carlo Simulation was conducted using probability density distribution and parameter estimation. Consequently, 10,000 emission factors were selected, and the Pareto distribution was predicted to be the most appropriate probability density distribution. The emission factor values through Monte Carlo simulation showed a minimum value of 1.490 × 10–08 μg I-TEQ/Cremation and a maximum value of 7816 μg I-TEQ/Cremation. The average value was calculated as 39.920 μg I-TEQ/Cremation. Each parameter of the Pareto distribution is shape parameter (α) 1.026, location parameter (μ) – 1.021, and scale parameter (λ) 1.021. As a result of this study, the median value of the cumulative density function was selected as a representative value for the dioxin emission factor of cremation facilities and the emission factor was 0.986 μg I-TEQ/ Cremation. The 95% confidence interval in the Pareto distribution was presented as 0.026 μg I-TEQ/Cremation to 36.216 μg I-TEQ/ Cremation. [ABSTRACT FROM AUTHOR]

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Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS‐flex method has been developed as an efficient modeling tool that combines coarse‐grained simulations with all‐atom detail. Available both as a web server and a standalone package, CABS‐flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command‐line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS‐flex across various structure–function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS‐flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa) is one of the essential staple foods highly consumed globally, with nearly 40% to 45% of the global population estimated to consume rice. Therefore, consumable rice should have low levels of harmful elements. This study investigates fluoride (F¯) content in raw rice (uncooked rice) and evaluates Probabilistic Health Risk Assessment (PHRA) through Monte Carlo simulation (MCS) due to the consumption of F¯ enriched rice. The literature review reveals that limited studies have been conducted on the investigation of F¯ in raw rice. The fluoride (F¯) concentration in raw rice varies across the studied countries, indicating the need for additional studies to facilitate a more accurate comparison. The F¯ content in raw rice varied among the studied countries, making it difficult to definitively state that the concentration of F¯ in one country is higher. However, the concentration of F¯ in raw rice in India is notably elevated. This study also highlighted the importance of investigating the F¯ content in raw rice. The study will be highly helpful for policymakers to formulate guidelines for water used for irrigation. [ABSTRACT FROM AUTHOR]

Proton dose calculation in media other than water may be of interest for either research purposes or clinical practice. Current study aims to quantify the required parameters for analytical proton dosimetry in muscle, bone, and PMMA. Required analytical dosimetry parameters were extracted from ICRU-49 report and Janni study. Geant4 Toolkit was also used for Bragg curve simulation inside the investigated media at different proton energies. Calculated and simulated dosimetry data were compared using gamma analysis. Simulated and calculated Bragg curves are consistent, a fact that confirms the validity of reported parameters for analytical proton dosimetry inside considered media. Furthermore, derived analytical parameters for these media are different from those of water. Listed parameters can be reliably utilized for analytical proton dosimetry inside muscle, bone, and PMMA. Furthermore, accurate proton dosimetry inside each medium demands dedicated analytical parameters and one is not allowed to use the water coefficients for non-water media. [ABSTRACT FROM AUTHOR]

Modern seismic risk assessment software includes seismic hazard analysis algorithms or relies on precomputed hazard maps. In both cases, a major step is incorporating the impacts of the local geological and geotechnical conditions. The common practice to address this phenomenon, often referred to as site effect, is to use shear wave velocity (Vs)–depth correlations and a deterministic geological model as a proxy for Vs mapping. The Vs of the top 30 m (Vs30) and often the fundamental site period (T0) are then used as predictors of the potential amplification of seismic shaking. Recognising that uncertainty in local soil properties inevitably affects the seismic site response, a stochastic approach for evaluating Vs30 and T0 is proposed herein, considering a combination of probabilistic Vs–depth correlations and a probabilistic 3D geological model. Monte Carlo simulations are then applied to study the impact of the uncertainties on the seismic site characterisation model. The generated stochastic maps consist of Vs30 and T0 realisations accompanied with spatial distribution of uncertainty as an indication of areas where further fieldwork is needed to improve predictions. The developed methodology is currently being used in a homemade risk assessment software. [ABSTRACT FROM AUTHOR]

This paper proposes a simple reliability-based design approach from a quantile value perspective. The proposed approach allows the design variable to be efficiently determined without implementing repetitive "trial and error" procedures for a given target reliability index. Only a single run of Monte Carlo simulation is needed to calculate the threshold values of the design variable after the design variable function (DVF) is derived from the limit state function. The threshold values are then sorted in ascending order, and a quantile value is chosen as the desired design variable such that MCS samples with threshold values larger, or smaller than this value are undesired samples. Three illustrative examples are presented following the development of the proposed method and its implementation procedure. Results are validated by calculating the failure probabilities of desired designs using direct MCS, and they are also favorably comparable with those reported in literature. This method can play a complementary role to existing RBD methods due to its advantages of simplicity, efficiency, and robustness. [ABSTRACT FROM AUTHOR]

Due to increasing environmental concerns and demand for clean energy resources, photovoltaic (PV) systems are becoming more prevalent. Considering that in several instances, customers pay for both energy and power, PV installations not only must reduce the customers' energy purchases but also lower their peak demand for maximum financial benefits. However, in many cases, the peak demand does not coincide with the peak of photovoltaic generation. To address this issue, excess energy generated during low-demand periods can be stored in a battery, which can then be used to meet peak demand. Determining the optimal size of photovoltaic and battery components while ensuring system performance and financial benefits is significantly challenging. This study proposes a novel statistical methodology for optimizing PV-battery system size. In the proposed method, the PV-battery system must meet peak demand thresholds with a specific probability. Further, cost and benefit functions are used for financial evaluation. Finally, Monte Carlo simulations, developed using time series clustering and a Bayesian model are utilized to assess system performance and financial feasibility. [ABSTRACT FROM AUTHOR]

The structure and phase transition of a two‐dimensional (2D)$$ \left(2\mathrm{D}\right) $$ dusty plasma, confined by an anisotropic power‐law potential and interacting via a screened Coulomb potential, were investigated using Monte Carlo simulations. The study varied the number of particles (N$$ N $$), screening strength (k$$ k $$), eccentricity parameter (α$$ \alpha $$), and confinement power parameter (n$$ n $$) to characterize the system's structural properties. [ABSTRACT FROM AUTHOR]

In the context of autonomous driving, the augmentation of existing data through simulations provides an elegant solution to the challenge of capturing the full range of adverse weather conditions in training datasets. However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment. Consequently, in this work, Monte Carlo simulations are employed to assess the relevance of multiple-scattered light to the detected LiDAR signal in different types of fog, with scattering phase functions calculated from Mie theory considering real particle size distributions. Bidirectional path tracing is used within the self-developed GPU-accelerated Monte Carlo software to compensate for the unfavorable photon statistics associated with the limited detection aperture of the LiDAR geometry. To validate the Monte Carlo software, an analytical solution of the radiative transfer equation for the time-resolved radiance in terms of scattering orders is derived, thereby providing an explicit representation of the double-scattered contributions. The results of the simulations demonstrate that the shape of the detected signal can be significantly impacted by multiple-scattered light, depending on LiDAR geometry and visibility. In particular, double-scattered light can dominate the overall signal at low visibilities. This indicates that considering higher scattering orders is essential for improving AI-based perception models. [ABSTRACT FROM AUTHOR]

The two‐parameter exponential distribution (TPED) is often used to model time‐between‐events data. In this paper, we propose CUmulative SUM and exponentially weighted moving average charts for simultaneously monitoring the parameters (location and scale) of the TPED. A key feature of the proposed charts is their straightforward post‐signal diagnostics. Monte Carlo simulations are used to estimate the zero‐state and steady‐state average run‐length (ARL) profiles of the proposed charts. The ARL performances of existing and proposed charts are assessed in terms of expected weighted run‐length and relative mean index. It is found that the proposed charts outperform the existing charts. A real dataset is used to illustrate the implementation of the proposed charts. [ABSTRACT FROM AUTHOR]

The Jarque–Bera test is commonly used in statistics and econometrics to test the hypothesis that sample elements adhere to a normal distribution with an unknown mean and variance. This paper proposes several modifications to this test, allowing for testing hypotheses that the considered sample comes from: a normal distribution with a known mean (variance unknown); a normal distribution with a known variance (mean unknown); a normal distribution with a known mean and variance. For given significance levels, α = 0.05 and α = 0.01 , we compare the power of our normality test with the most well-known and popular tests using the Monte Carlo method: Kolmogorov–Smirnov (KS), Anderson–Darling (AD), Cramér–von Mises (CVM), Lilliefors (LF), and Shapiro–Wilk (SW) tests. Under the specific distributions, 1000 datasets were generated with the sample sizes n = 25 , 50 , 75 , 100 , 150 , 200 , 250 , 500 , and 1000. The simulation study showed that the suggested tests often have the best power properties. Our study also has a methodological nature, providing detailed proofs accessible to undergraduate students in statistics and probability, unlike the works of Jarque and Bera. [ABSTRACT FROM AUTHOR]

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making. [ABSTRACT FROM AUTHOR]

Exposure to ionizing radiation can induce genetic aberrations via unrepaired DNA strand breaks. To investigate quantitatively the dose–effect relationship at the molecular level, we irradiated dry pBR322 plasmid DNA with 3 MeV protons and assessed fragmentation yields at different radiation doses using long-read sequencing from Oxford Nanopore Technologies. This technology applied to a reference DNA model revealed dose-dependent fragmentation, as evidenced by read length distributions, showing no discernible radiation sensitivity in specific genetic sequences. In addition, we propose a method for directly measuring the single-strand break (SSB) yield. Furthermore, through a comparative study with a collection of previous works on dry DNA irradiation, we show that the irradiation protocol leads to biases in the definition of ionizing sources. We support this scenario by discussing the size distributions of nanopore sequencing reads in the light of Geant4 and Geant4-DNA simulation toolkit predictions. We show that integrating long-read sequencing technologies with advanced Monte Carlo simulations paves a promising path toward advancing our comprehension and prediction of radiation-induced DNA fragmentation. [ABSTRACT FROM AUTHOR]

Statistical modeling of the practical data sets is a useful method that guides researchers to make decisions under uncertainty in applied sectors. To date, a rich number of probability distributions have appeared that have been developed in different ways and implemented in different fields. A vast literature of the available distributions have been constructed by adding new additional parameters, varying from one to eight. This specific piece of the research work also prompts a new probabilistic model, namely, the weighted sine flexible-Weibull distribution. The weighted sine flexible-Weibull distribution consists of the flexible Weibull extension model and a sine-oriented methodology. For the weighted sine flexible-Weibull distribution, the quartile-based properties are derived. Besides these properties, the estimators for the new model are also derived. The estimation part of the weighted sine flexible-Weibull distribution is further extended by conducting a simulation study. Finally, the weighted sine flexible-Weibull distribution is illustrated by considering the injury rate in logistics and public transport systems that are reported after the safety factors are considered. Based on several evaluation criteria, it is established that the weighted sine flexible-Weibull distribution provides the optimal fitting to the injury rate data. [ABSTRACT FROM AUTHOR]

As offshore wind farms move into deeper waters, around 80 m, the high costs necessitate replacing bottom-fixed turbines with floating offshore wind turbines, which require mooring systems to maintain stability within design limits. Data from previous projects in China indicate that mooring systems can constitute about 20% of the total investment. Thus, reducing mooring system costs can significantly benefit the development of next-generation floating wind farms. This paper discusses the reliability analysis of mooring chains for floating wind turbines to optimize inspection plans and strategies, thereby saving on maintenance costs over their design lifetime. A case study on S-N curve based fatigue reliability analysis is conducted using both Monte Carlo Simulation and First Order Reliability Method (FORM), with consistent results from both methods. Additionally, three sensitivity analysis cases identify key parameters for the fatigue reliability analysis. [ABSTRACT FROM AUTHOR]

The degradation of sediments in urban environments worldwide is driven by population growth, urbanization, and industrialization, highlighting the need for thorough quality assessment and management strategies. As a result of these anthropogenic activities, benzene, toluene, ethylbenzene, xylenes, and styrene (BTEXS) are persistently released into the environment, polluting sediment. This study employed self-organizing maps (SOMs), positive matrix factorization (PMF), and Monte Carlo simulation of source-oriented health risks to comprehensively investigate sediment in an urban shallow lake in a mid-sized city in central Serbia. The results indicated a mean ∑BTEXS concentration of 225 µg/kg, with toluene as the dominant congener, followed by m,p-xylene, benzene, ethylbenzene, o-xylene, and styrene. Three contamination sources were identified: waste solvents and plastic waste due to intensive recreational activities, and vehicle exhaust from heavy traffic surrounding the lake. Both non-carcinogenic and carcinogenic health risks were below the permissible limits. However, children were more susceptible to health risks. Benzene from vehicle exhaust is the most responsible for non-carcinogenic and carcinogenic health risks in both population groups. The results of this study can help researchers to find a suitable perspective on the dynamics and impacts of BTEXS in lake sediments. [ABSTRACT FROM AUTHOR]

As a renewable resource, geothermal energy plays an increasingly important role in global and regional energy structures. Influenced by regional tectonic activities, multi-stage thermal evolution, and continuous subsidence, the subsurface temperatures in the Yinggehai Basin has been consistently rising, resulting in the formation of multiple geothermal reservoirs. The Neogene Huangliu Formation, with its high geothermal gradients, suitable burial depths, considerable thickness, and wide distribution, provides excellent geological conditions for substantial geothermal resources. However, the thermal storage characteristics and geothermal resources of this formation have not been fully assessed, limiting their effective development. This study systematically collected and analyzed drilling, geological, and geophysical data to examine these reservoirs' geometric structures, thermal properties, and physical characteristics. Further, we quantitatively evaluated the geothermal resource potential of the Huangliu Formation and its respective reservoirs through volumetric estimation and Monte Carlo simulations, pointing zones with high geothermal prospects and formulating targeted development strategies. The findings indicate: (1) The Yinggehai Basin exhibits an average geothermal gradient of 39.4 ± 4.7 °C/km and an average terrestrial heat flow of 77.4 ± 19.1 mW/m2, demonstrating a favorable geothermal background; (2) The central depression of the Huangliu Formation harbors considerable geothermal resource potential, with an average reservoir temperature of 140.9 °C, and a total geothermal resource quantified at approximately 2.75 × 1020 J, equivalent to 93.95 × 108 tec. Monte Carlo projections estimate the maximum potential resource at about 3.10 × 1020 J, approximately 105.9 ×108 tec. (3) Additionally, the R14 and R23 reservoirs have been identified as possessing the highest potential for geothermal resource development. The study also proposes a comprehensive utilization model that integrates offshore geothermal power generation with multiple applications. These findings provide a method for the evaluation of geothermal resources in the Yinggehai Basin and lay a foundation for the sustainable development of resources. [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]

Correlation coefficients and linear regression values computed from group averages can differ from correlation coefficients and linear regression values computed using individual scores. This observation known as the ecological fallacy often assumes that all the individual scores are available from a population. In many situations, one must use a sample from the larger population. In such cases, the computed correlation coefficient and linear regression values will depend on the sample that is chosen and the underlying sampling distribution. The sampling distribution of correlation coefficients and linear regression values for group averages will be identical to the sampling distribution for individuals for normally distributed variables for random samples drawn from infinitely large continuous distributions. However, data that is acquired in practice is often acquired when sampling without replacement from a finite population. Our objective is to demonstrate through Monte Carlo simulations that the sampling distributions for correlation and linear regression will also be similar for individuals and group averages when sampling without replacement from normally distributed variables. These simulations suggest that when a random sample from a population is selected, the correlation coefficients and linear regression values computed from individual scores will not be more accurate in estimating the entire population values compared to samples when group averages are used as long as the sample size is the same. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusions Omalizumab is an anti‐immunoglobulin E (IgE) monoclonal antibody that was first approved by the United States (US) Food and Drug Administration (FDA) for the treatment of allergic asthma in 2003. The pivotal trials supporting the initial approval of omalizumab used dosing determined by patient's baseline IgE and body weight, with the goal of reducing the mean free IgE level to approximately 25 ng/mL or less. While the underlying parameters supporting the dosing table remained the same, subsequent studies and analyses have resulted in approved alternative versions of the dosing table, including the European Union (EU) asthma dosing table, which differs in weight bands and maximum allowable baseline IgE and omalizumab dose. In this study, we leveraged modelling and simulation approaches to predict and compare the free IgE reduction and forced expiratory volume in 1 second (FEV1) improvement with omalizumab dosing based on the US and EU asthma dosing tables.Previously established population pharmacokinetic‐IgE and IgE‐FEV1 models were used to predict and compare post‐treatment free IgE and FEV1 based on the US and EU dosing tables. Clinical trial simulations (with virtual asthma populations) and Monte Carlo simulations were performed to provide both breadth and depth in the comparisons.The US and EU asthma dosing tables were predicted to result in generally comparable free IgE suppression and FEV1 improvement.Despite the similar free IgE and FEV1 outcomes from simulations, this has not been clinically validated with respect to the registrational endpoint of reduction in annualized asthma exacerbations. [ABSTRACT FROM AUTHOR]

AbstractNumerous biased estimators are known to circumvent the multicollinearity problem in linear measurement error models. This article proposes a general biased estimator with the ridge regression and the Liu estimators as special cases. The efficiency of the suggested estimator is compared with ridge regression and Liu estimators under the mean squared error matrix criterion. In addition, a Monte Carlo simulation study and a numerical evaluation have been conducted to elucidate the superiority of the new general biased estimator over other estimators. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptation of the conventional approximate Bayesian computation sequential Monte Carlo (ABC-SMC) sampling algorithm for parameter estimation in the presence of uncertainties, coined combinatorial ABC-SMC. Inference of this type is used in situations where there does not exist a closed form of the associated likelihood function, which is replaced by a simulating model capable of producing artificial data. In the literature, conventional ABC-SMC is utilised to perform inference on continuous parameters. The novel scheme presented here has been developed to perform inference on parameters that are high-dimensional binary, rather than continuous. By altering the form of the proposal distribution from which to sample candidates in subsequent iterations (referred to as waves), high-dimensional binary variables may be targeted and inferred by the scheme. The efficacy of the proposed scheme is demonstrated through application to vibration data obtained in a structural dynamics experiment on a fibre-optic sensor simulated as a finite plate with uncertain boundary conditions at its edges. Results indicate that the method provides sound inference on the plate boundary conditions, which is validated through subsequent application of the method to multiple vibration datasets. Comparisons between appropriate forms of the metric function used in the scheme are also developed to highlight the effect of this element in the schemes convergence. [ABSTRACT FROM AUTHOR]

Low-altitude airspace is developing rapidly, but the utilization rate of airspace resources is low. Therefore, in order to solve the problem of the safe operation of the fusion of large UAVs and manned aircraft in the same airspace, this paper analyzes the theoretical calculation of the collision risk of the fusion operation of manned aircraft and UAVs at Feng Ming Airport in Zigong, verifying that while assessing the safety spacing of 10 km in the lateral direction, it further simulates the possibility of calculating the theoretical smaller safety spacing. The study will propose a new theory of error spacing safety margin and improve it according to the traditional Event collision risk model, combining the error spacing safety margin to establish an improved collision model more suitable for the fusion operation of manned and unmanned aircraft and reduce the redundancy of calculation. The error factors affecting manned and unmanned aircraft at Zigong Airport are analyzed, and theoretical calculations are analyzed by combining the actual data of Zigong Airport. Finally, the Monte Carlo simulation method is used to solve the error, substitute the calculation results, and simulate a section of the trajectory of the fusion operation for the reverse argument. The theoretical calculation results show that the collision risk from 10 km to 8 km satisfies the lateral target safety level (TSL) specified by ICAO under both traditional and improved models. The collision risk calculated by the improved model incorporating the error spacing safety margin is smaller, which enhances the safety of the model calculations. The results of the study can provide theoretical references for the fusion operation of manned and unmanned aircraft. [ABSTRACT FROM AUTHOR]

Planetary gear transmission mechanisms (PGTMs) are widely used in mechanical transmission systems due to their compact structure and high transmission efficiency. To implement the reliability design and optimization of a PGTM, a novel multi-objective dynamic reliability optimization approach is proposed. First, a multi-objective reliability optimization model is established. Furthermore, considering the strength degradation of gears during service, a dynamic reliability analysis is conducted based on the theory of nonlinear fatigue damage accumulation. In addition, to improve computing efficiency, a random forest surrogate model based on the particle swarm optimization algorithm is proposed. Finally, an adaptive multi-objective evolutionary algorithm based on decomposition (AMOEA/D) is designed to optimize the mechanism, along with an adaptive neighborhood updating strategy and a hybrid crossover operator. The feasibility and superiority of the proposed approach are verified through an NGW planetary gear reducer. The results show that the proposed surrogate model can reduce the calculation cost and has high accuracy. The AMOEA/D algorithm can improve transmission efficiency, reduce gear volume and ensure reliability at the same time. It can provide guidance for actual gear production. [ABSTRACT FROM AUTHOR]

In this paper, we propose the application of shrinkage strategies to estimate coefficients in the Bell regression models when prior information about the coefficients is available. The Bell regression models are well-suited for modelling count data with multiple covariates. Furthermore, we provide a detailed explanation of the asymptotic properties of the proposed estimators, including asymptotic biases and mean squared errors. To assess the performance of the estimators, we conduct numerical studies using Monte Carlo simulations and evaluate their simulated relative efficiency. The results demonstrate that the suggested estimators outperform the unrestricted estimator when prior information is taken into account. Additionally, we present an empirical application to demonstrate the practical utility of the suggested estimators. [ABSTRACT FROM AUTHOR]

Background: Electron beams are used at extended distances ranging between 300 to 700 cm to uniformly cover the entirety of the patient's skin for total skin electron therapy (TSET). Even with electron beams utilizing the high dose rate total skin electron (HDTSe) mode from the Varian 23iX or TrueBeam accelerators, the dose rate is only 2500 cGy/min at source‐to‐surface distance (SSD) = 100 cm. At extended distances, the decrease in dose rate leads to long beam delivery times that can limit or even prevent the use of the treatment for patients who, in their weakened condition, may be unable to stand on their own for extended periods of time. Previously, to increase dose rate, a customized 6 MeV electron beam was created by removing the x‐ray target, flattening filter, beam monitor chamber, and so forth. from the beam path (Chen, et at IJROBP 59, 2004) for TSET. Using this scattering‐foil free (SFF) electron beam requires the treatment distance be extended to 700 cm to achieve dose uniformity from the single beam. This room size requirement has limited the widespread use of the 6 MeV‐SFF beam. Purpose: This study explores an application of a dual‐field technique with a 6 MeV‐SFF beam to provide broad and uniform electron fields to reduce the treatment distances in order to overcome treatment room size limitations. Methods: The EGSnrc system was used to generate incident beams. Gantry angles between 6 MeV‐SFF dual‐fields were optimized to achieve the similar patient skin dose distribution resulting from a standard 6 MeV‐HDTSe dual‐field configuration. The patient skin dose comparisons were performed based on the patient treatment setup geometries using dose‐volume‐histograms. Results: Similar dose coverage can be achieved between 6 MeV‐SFF and 6 MeV‐HDTSe beams by reducing gantry angles between dual‐field geometries by 8° and 7° at treatment distances of 400 and 500 cm, respectively. To achieve 95% mean dose to the first 5 mm of skin depth in the torso area, the mean dose to depths of 5–10 mm and 10–15 mm below the skin surface was 74% (74%) and 49% (50%) of the prescribed dose when using 6 MeV‐SFF (6 MeV‐HDTSe) beam, respectively. Conclusions: The 6 MeV‐SFF electron beam is feasible to provide similar TSET skin dose coverage at SSD ≥ 400 cm using a dual‐field technique. The dose rate of the 6 MeV‐SFF beam is about 4 times that of current available 6 MeV‐HDTSe beams at treatment distances of 400–500 cm, which significantly shortens the treatment beam‐on time and makes TSET available to patients in weakened conditions. [ABSTRACT FROM AUTHOR]

Background: Since 2011, the International Commission on Radiological Protection (ICRP) has recommended an annual eye lens dose limit of 20 mSv for radiation workers, averaged over 5 years, with no year exceeding 50 mSv. However, limited research has been conducted on dose rate conversion coefficients (DCCs) for direct contamination of the eye. Purpose: This study aimed to accurately determine DCCs for the eye lens and cornea for ocular contamination with radionuclides used in nuclear medicine. Methods: DCCs for 37 radionuclides used in nuclear medicine were determined using two different methods. Method 1 involved conducting Monte Carlo (MC) simulations of an ICRU cylinder to determine the absorbed dose at a depth of 3 mm resulting from a point source. The accuracy of this simulation approach was validated by experimental thermoluminescent dosimeter (TLD) measurements for 18F, 68Ga, 99mTc, and 177Lu. In method 2, average DCCs were calculated for the eye lens (complete and radiosensitive parts) and the cornea for both a point source and thin surface contamination centered on the cornea using MC simulations on the adult mesh‐type reference computational phantom of the eye from the ICRP (MRCP). Results: DCCs determined from TLD measurements showed excellent agreement (deviations: +1.4%, +4.7%, −3.1%, and −2.5% for 18F, 68Ga, 99mTc, and 177Lu, respectively) compared to MC simulations of the experimental set‐up. For the 37 radionuclides, DCCs of the complete eye‐lens for a point source ranged from 2.53 × 10−7 to 4.15 × 10−2 mGy MBq−1 s−1 for the adult MRCPs, being substantially smaller compared to DCCs determined via MC simulations of a ICRU cylinder. In general, point source and surface contamination showed comparable DCCs for the eye lens. Radionuclides emitting low‐energy beta radiation or conversion electrons (e.g., 177Lu, 99mTc) showed low DCCs as the radiation does not penetrate to the depth of the eye lens, while radionuclides emitting high‐energy beta radiation (e.g., 90Y) showed high DCCs. Overall, DCCs for the radiosensitive part of the eye lens were larger (up to a factor of 3) compared to the complete eye lens. DCCs for the cornea were larger than for the eye lens with a factor that strongly depended on the emitted radiation type. Especially alpha emitters (e.g., 211At, 223Ra) showed high DCCs for the cornea because of the short range of alpha radiation, leading to local maxima in the cornea and not reaching the eye lens. Conclusion: DCCs at a depth of 3 mm in an ICRU cylinder and adult MRCP DCCs for both the complete and sensitive parts of the eye lens and cornea were determined for 37 radionuclides having applications in nuclear medicine. These DCCs are highly useful in radiation safety assessments and radiation dose calculations in ocular contamination incidents. [ABSTRACT FROM AUTHOR]

Background: Single time point measurement approach and hybrid dosimetry were proposed to simplify the dosimetry process. It is anticipated that utilizing patient‐specific S‐value would enable more accurate dosimetry assessment based on imaging compared to using the conventional MIRD S‐values. Purpose: We performed planar image‐based dosimetry scaled with a single SPECT image for the entire treatment cycle using patient‐specific S‐values (PSS dosimetry) of organs. PSS dosimetry could further simplify the dosimetry procedure compared with a conventional 2D planar/3D SPECT hybrid dosimetry, as PSS dosimetry requires only one SPECT/CT image for the treatment of the entire cycle, whereas the conventional hybrid dosimetry requires a SPECT/CT image for each treatment cycle. Methods: 177Lu‐DOTATATE SPECT/CT and planar image datasets acquired from Seoul National University Hospital (SNUH, Seoul, Republic of Korea) were utilized for the evaluation. Images were acquired 4, 24, 48, and 120 h after patients' intravenous injection of 177Lu‐DOTATATE. Dose estimations based on a Monte Carlo (MC) simulation using the Geant4 Application for Emission Tomography (GATE) (v.8.2) were considered as the reference. Planar image‐based dosimetry scaled with a single SPECT image was performed using the patient‐specific S‐value (PSS). Briefly, the CT image was considered as the patient's anatomical reference and PSSs were quantified using the multiple voxel S‐value (VSV) method. Then, PSS dosimetry was performed by obtaining activity information from sequential planar images and a scaling factor derived from a single SPECT/planar image pair. Hybrid dosimetry using sequential planar images and a single SPECT image was performed for comparison. The absorbed doses of the kidneys, bone marrow (BM) in the lumbar spine, liver, and spleen calculated using the PSS and hybrid dosimetries were compared with the reference MC results. Results: The mean differences (MDs) of the self‐absorption S‐values between S‐value of OLINDA/EXM and PSS for the kidneys, liver, and spleen were −0.04%, −2.39%, and −2.62%, respectively. However, the differences in the self‐absorption S‐values were significantly higher for the BM (84.99%) and the remainder of the body (ROB) (280.84%). The absorbed doses estimated by the PSS and hybrid dosimetries showed relatively high errors compared with MC simulation result, regardless of the organ. In contrast, the PSS and hybrid dosimetries produced similar dose estimates. For the entire cycles of the treatment, the MDs of absorbed doses between PSS and hybrid dosimetries were −3.31%, −6.04%, 3.37%, and −2.17% for the kidneys, BM, liver, and spleen, respectively. Through a correlation analysis and the Wilcoxon signed‐rank test, we concluded that there was no significant difference between the results obtained by the two dosimetry methods. Conclusions: As the PSS was derived using CT images with actual anatomical information and organ‐specific volume of interest (VOI), PSS dosimetry provided reliable results. PSS dosimetry was robust in estimating the absorbed dose for the later treatment cycles. Therefore, PSS dosimetry outperformed hybrid dosimetry in terms of dose estimation for a greater number of treatment cycles. [ABSTRACT FROM AUTHOR]