This original work investigated the optical properties and Monte-Carlo (MC) based simulation of light propagation in the flavedo of Nanfeng tangerine (NF) and Gannan navel orange (GN) infected by Penicillium italicum. The increase of absorption coefficient (μa) at around 482 nm and the decrease at around 675 nm were both observed in infected NF and GN during storage, indicating the accumulation of carotenoids and loss of chlorophyll. Particularly, the μa in NF varied more intensively than GN, but the limited differences of reduced scattering coefficient (μs\') were detected while postharvest infection. Besides, MC simulation of light propagation indicated that the photon packets weight and penetration depth at 482 nm in NF were reduced more than in GN flavedo, while there were almost no changes at the relatively low absorption wavelength of 926 nm. The simulated absorption energy at 482 nm in NF and GN presented more changes than those at 675 nm during infection, thus could provide better detection of citrus diseases. Furthermore, PLS-DA models can discriminate healthy and infected citrus, with the accuracy of 95.24 % for NF and 98.67 % for GN, respectively. Consequently, these results can provide theoretical fundamentals to improve modelling prediction robustness and accuracy.

UNASSIGNED: Task Group 43 (TG-43) formalism does not consider the tissue and applicator heterogeneities. This study is to compare the effect of model-based dose calculation algorithms, like Advanced Collapsed Cone Engine (ACE), on dose calculation with the TG-43 dose calculation formalism in patients with cervical carcinoma.
UNASSIGNED: 20 patients of cervical carcinoma treated with a high dose rate of intracavitary brachytherapy were prospectively studied. The target volume and organs at risk (OARs) were contoured in the Oncentra treatment planning system (Elekta, Veenendaal, The Netherlands). All patients were planned with cobalt-60 (Co-60) and iridium-192 (Ir-192) sources with doses of 21 Gy in 3 fractions. These plans were calculated with TG-43 formalism and a model-based dose calculation algorithm ACE. The dosimetric parameters of TG-43 and ACE-based plans were compared in terms of target coverage and OAR doses.
UNASSIGNED: For Co-60-based plans, the percentage differences in the D90 and V100 values for high-risk clinical target volume (HR-CTV) were 0.36 ± 0.43% and 0.17 ± 0.31%, respectively. For the bladder, rectum and sigmoid, the percentage differences for D2cc volumes were -0.50 ± 0.51%, -0.16 ± 0.53% and -0.37 ± 1.21%, respectively. For Ir-192-based plans, the percentage difference in the D90 for HR-CTV was 0.54 ± 0.79%, while V100 was 0.24 ± 0.29%. For the bladder, rectum and sigmoid, the doses to 2cc volume were 0.35 ± 1.06%, 0.99 ± 0.74% and 0.74 ± 1.92%, respectively. No significant differences were found in the dosimetric parameters calculated with ACE and TG-43.
UNASSIGNED: The ACE algorithm reduced doses to OARs and targets. However, ACE and TG-43 did not show significant differences in the dosimetric parameters of the target and OARs with both sources.

UNASSIGNED: Questions about the accuracy of pulse oximeters in measuring arterial oxygen saturation ( SpO 2 ) in individuals with darker skin pigmentation have resurfaced since the COVID-19 pandemic. This requires investigation to improve patient safety, clinical decision making, and research.
UNASSIGNED: We aim to use computational modeling to identify the potential causes of inaccuracy in SpO 2 measurement in individuals with dark skin and suggest practical solutions to minimize bias.
UNASSIGNED: An in silico model of the human finger was developed to explore how changing melanin concentration and arterial oxygen saturation ( SaO 2 ) affect pulse oximeter calibration algorithms using the Monte Carlo (MC) technique. The model generates calibration curves for Fitzpatrick skin types I, IV, and VI and an SaO 2 range between 70% and 100% in transmittance mode. SpO 2 was derived by inputting the computed ratio of ratios for light and dark skin into a widely used calibration algorithm equation to calculate bias ( SpO 2 - SaO 2 ). These were validated against an experimental study to suggest the validity of the Monte Carlo model. Further work included applying different multiplication factors to adjust the moderate and dark skin calibration curves relative to light skin.
UNASSIGNED: Moderate and dark skin calibration curve equations were different from light skin, suggesting that a single algorithm may not be suitable for all skin types due to the varying behavior of light in different epidermal melanin concentrations, especially at 660 nm. The ratio between the mean bias in White and Black subjects in the cohort study was 6.6 and 5.47 for light and dark skin, respectively, from the Monte Carlo model. A linear multiplication factor of 1.23 and exponential factor of 1.8 were applied to moderate and dark skin calibration curves, resulting in similar alignment.
UNASSIGNED: This study underpins the careful re-assessment of pulse oximeter designs to minimize bias in SpO 2 measurements across diverse populations.

This study aims to develop a nonparametric mixed exponentially weighted moving average-moving average (NPEWMA-MA) sign control chart for monitoring shifts in process location, particularly when the distribution of a critical quality characteristic is either unknown or non-normal. In literature, the variance expression of the mixed exponentially weighted moving average-moving average (EWMA-MA) statistic is calculated by allowing sequential moving averages to be independent, and thus the exclusion of covariance terms results in an inaccurate variance expression. Furthermore, the effectiveness of the EWMA-MA control chart deteriorates when the distribution of a critical quality characteristic deviates from normality. The proposed NPEWMA-MA sign control chart addresses these by utilizing the corrected variance of the EWMA-MA statistic and incorporating the nonparametric sign test into the EWMA-MA charting structure. The chart integrates the moving average (MA) statistic into the exponentially weighted moving average (EWMA) statistic. The EWMA-MA charting statistic assigns more weight to recent w samples, with weights for previous observations decling exponentially. Monte Carlo simulations assess the chart\'s performance using various run length (RL) characteristics such as average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL). Additional measures for overall performance include the average extra quadratic loss (AEQL) and relative mean index (RMI). The proposed NPEWMA-MA sign control chart demonstrates superior performance compared to existing nonparametric control charts across different symmetrical and asymmetric distributions. It efficiently detects process shifts, as validated through both a simulated study and a real-life example from a combined cycle power plant.

Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffers from biased estimation. Therefore, we propose the multivariate Bernoulli detector for competing risks with discrete times involving a multivariate change point model on the cause-specific baseline hazards. Through the prior on the number of change points and their location, we impose dependence between change points across risks, as well as allowing for data-driven learning of their number. Then, conditionally on these change points, a multivariate Bernoulli prior is used to infer which risks are involved. Focus of posterior inference is cause-specific hazard rates and dependence across risks. Such dependence is often present due to subject-specific changes across time that affect all risks. Full posterior inference is performed through a tailored local-global Markov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentation trick and MCMC updates from nonconjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches.

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.

This research examines the seismic hazard impact on railway infrastructure along the U.S. West Coast (Washington, Oregon and California), using machine learning to explore how measures of seismic hazard such as fault density, earthquake frequency, and ground shaking relate to railway infrastructure accidents. By comparing linear and non-linear models, it finds non-linear approaches superior, particularly noting that higher fault densities and stronger peak ground shaking correlate with increased infrastructure accident rates. Shallow earthquakes with magnitudes of 3.5 or greater and hypocentral depths <20 km also exhibit a pronounced correlation with the incidence of railway infrastructure accidents The study extends to financial impact analysis through Net Present Value and Monte Carlo Simulation, and evaluates damage costs from 2000-2023 to guide financial planning and risk management strategies. It highlights the crucial role of advanced financial tools in optimizing maintenance and long-term planning that could result in better preparedness in high seismic hazard regions and emphasizes the need for robust risk management strategies in enhancing railway operational safety that considers the local and regional tectonic and seismic activity and local ground shaking intensity.

Early use of targeted radionuclide therapy (TRT) to eradicate disseminated tumor cells (DTCs) might offer cure. Selection of appropriate radionuclides is required. This work highlights the potential of 103Pd (T1/2 = 16.991 d) which decays to 103mRh (T1/2 = 56.12 min) then to stable 103Rh with emission of Auger and conversion electrons. Methods: The Monte Carlo track structure code CELLDOSE was used to assess absorbed doses in single cells (14-μm diameter; 10-μm nucleus) and clusters of 19 cells. The radionuclide was distributed on the cell surface, within the cytoplasm, or in the nucleus. Absorbed doses from 103Pd, 177Lu and 161Tb were compared after energy normalization. The impact of non-uniform cell targeting, and the potential benefit from dual-targeting was investigated. Additional results related to 103mRh, if used directly, are provided. Results: In the single cell, and depending on radionuclide distribution, 103Pd delivered 7- to 10-fold higher nuclear absorbed dose and 9- to 25-fold higher membrane dose than 177Lu. In the 19-cell clusters, 103Pd absorbed doses also largely exceeded 177Lu. In both situations, 161Tb stood in-between 103Pd and 177Lu. Non-uniform targeting, considering four unlabeled cells within the cluster, resulted in moderate-to-severe dose heterogeneity. For example, with intranuclear 103Pd, unlabeled cells received only 14% of the expected nuclear dose. Targeting with two 103Pd-labeled radiopharmaceuticals minimized dose heterogeneity. Conclusion: 103Pd, a next-generation Auger emitter, can deliver substantially higher absorbed doses than 177Lu to single tumor cells and cell clusters. This may open new horizons for the use of TRT in adjuvant or neoadjuvant settings, or for targeting minimal residual disease.

Interrupted time series (ITS) designs are increasingly used for estimating the effect of shocks in natural experiments. Currently, ITS designs are often used in scenarios with many time points and simple data structures. This research investigates the performance of ITS designs when the number of time points is limited and with complex data structures. Using a Monte Carlo simulation study, we empirically derive the performance-in terms of power, bias and precision- of the ITS design. Scenarios are considered with multiple interventions, a low number of time points and different effect sizes based on a motivating example of the learning loss due to COVID school closures. The results of the simulation study show the power of the step change depends mostly on the sample size, while the power of the slope change depends on the number of time points. In the basic scenario, with both a step and a slope change and an effect size of 30% of the pre-intervention slope, the required sample size for detecting a step change is 1,100 with a minimum of twelve time points. For detecting a slope change the required sample size decreases to 500 with eight time points. To decide if there is enough power researchers should inspect their data, hypothesize about effect sizes and consider an appropriate model before applying an ITS design to their research. This paper contributes to the field of methodology in two ways. Firstly, the motivation example showcases the difficulty of employing ITS designs in cases which do not adhere to a single intervention. Secondly, models are proposed for more difficult ITS designs and their performance is tested.

Traditional method of determining closure and initiation stress of fractured rocks by analyzing the stress-strain curve has problems such as strong subjectivity and large errors. This study utilized the rock closure stress values and onset stress values determined by three traditional methods, namely, axial strain method, fracture volume method and empirical value taking method, as the base database. The Student t distribution theory was used to obtain a confidence interval based on its overall distribution of values and to achieve a combination of the advantages of multiple methods. Within confidence interval, the Monte Carlo stochastic simulation was used to determine the convergence interval of the second stage to further improve the accuracy. Finally, mean value of the randomly sampled values after reaching the convergence stage was taken as the probability value of rock closure and crack initiation stress. The results showed that the 3 traditional methods for calculating rock closure and initiation stresses are significantly different. In contrast, the proposed method biases more towards multi-numerical distribution intervals and also considers the preference effects of different calculation methods. In addition, this method does not show any extreme values that deviate from the confidence intervals, and it has strong accuracy and stability compared to other methods.