The safety verification of nuclear systems can be done by analyzing the outputs of Best-Estimate Thermal-Hydraulic (BE-TH)
The safety analysis of nuclear power plant is moving towards a realistic approach in which the simulations performed using best estimate computer codes must be accompanied by an uncertainty analysis, known as the Best Estimate Plus Uncertainties approach. The most popular statistical method used in these analyses is the Wilks' method, which is based on the principle of order statistics for determining a certain coverage of the Figures-of-Merit with an appropriate degree of confidence. However, there exist other statistical techniques that could provide similar or even better results. This paper explores the performance of alternative nonparametric methods as compared to the Wilks' method of obtaining such Figure-of-Merits tolerance intervals. Three methods are investigated, i.e. Hutson and Beran-Hall methods and a bootstrap method. All the techniques have been used to perform the uncertainty analysis of a Large-Break Loss of Coolant Accident. The Figure-of-Merit of interest in this application is the maximum value reached by the Peaking Clad Temperature. In order to analyze the results obtained by the different methods, four performance metrics are proposed to measure the coverage, dispersion, conservativeness, and robustness of the tolerance intervals.
Nuclear policies have experienced an important change since Fukushima Daiichi nuclear plant accident and the safety of spent fuels has been in the spot issue among all the safety concerns. At many countries, the spent fuel of nuclear power plants is not reprocessed so it has to be stored inside the facilities, normally in pools. As nuclear power plants use best estimate codes to perform safety analysis, it is interesting to assess the capacity of such codes to simulate spent fuel pools behavior. This paper uses TRACE thermal-hydraulic code to simulate steady state and transient conditions of spent fuel pools. The steady state results are compared with plant measurements of Maine Yankee with a good agreement between the calculations and the measurements. The transient simulated is a loss of cooling together with a loss of coolant through the transfer channel. TRACE heat transfer radiation model has been activated using the parameters obtained from COBRA thermal-hydraulic code.
A substantial minority of women who experience interpersonal violence will develop posttraumatic stress disorder (PTSD). One critical challenge for preventing PTSD is predicting whose acute posttraumatic stress symptoms will worsen to a clinically significant degree. This 6-month longitudinal study adopted multilevel modeling and exploratory machine learning (ML) methods to predict PTSD onset in 58 young women, ages 18 to 30, who experienced an incident of physical and/or sexual assault in the three months prior to baseline assessment. Women completed baseline assessments of theory-driven cognitive and neurobiological predictors and interview-based measures of PTSD diagnostic status and symptom severity at 1-, 3-, and 6-month follow-ups. Higher levels of self-blame, generalized anxiety disorder severity, childhood trauma exposure, and impairment across multiple domains were associated with a pattern of high and stable posttraumatic stress symptom severity over time. Predictive performance for PTSD onset was similarly strong for a gradient boosting machine learning model including all predictors and a logistic regression model including only baseline posttraumatic stress symptom severity. The present findings provide directions for future work on PTSD prediction among interpersonal violence survivors that could enhance early risk detection and potentially inform targeted prevention programs.
Introduction: Europe has seen a steady increase in the use of prescription opioids, especially in non-cancer indications. Epidemiological data on the patterns of use of opioids is required to optimize prescription. We aim to describe the patterns of opioid therapy initiation for non-cancer pain and characteristics of patients treated in a region with five million inhabitants in the period 2012 to 2018.Methods: Population-based retrospective cohort study of all adult patients initiating opioid therapy for non-cancer pain in the region of Valencia. We described patient characteristics at baseline and the characteristics of baseline and subsequent treatment initiation. We used multinominal regression models to identify individual factors associated with initiation.Results: A total of 957,080 patients initiated 1,509,488 opioid treatments (957,080 baseline initiations, 552,408 subsequent initiations). For baseline initiations, 738,749 were with tramadol (77.19%), 157,098 with codeine (16.41%) 58,436 (6.11%) with long-acting opioids, 1,518 (0.16%) with short-acting opioids and 1,279 (0.13%) with ultrafast drugs. When compared to tramadol, patients initiating with short-acting, long-acting and ultrafast opioids were more likely to be older and had more comorbidities, whereas initiators with codeine were more prone to be healthier and younger. Treatments lasting less than 7 days accounted for 41.82% of initiations, and 11.89% lasted more than 30 days. 19.55% of initiators with ultrafast fentanyl received more than 120 daily Morphine Milligram Equivalents (MME), and 16.12% of patients initiating with long-acting opioids were prescribed more than 90 daily MME (p < 0.001). Musculoskeletal indications accounted for 65.05% of opioid use. Overlap with benzodiazepines was observed in 24.73% of initiations, overlap with gabapentinoids was present in 11.04% of initiations with long-acting opioids and 28.39% of initiators with short-acting opioids used antipsychotics concomitantly. In subsequent initiations, 55.48% of treatments included three or more prescriptions (vs. 17.60% in baseline initiations) and risk of overlap was also increased.Conclusion: Opioids are initiated for a vast array of non-oncological indications, and, despite clinical guidelines, short-acting opioids are used marginally, and a significant number of patients is exposed to potentially high-risk patterns of initiation, such as treatments lasting more than 14 days, treatments surpassing 50 daily MMEs, initiating with long-acting opioids, or hazardous overlapping with other therapies.
h i g h l i g h t s• TRACE modelization for PKL and ROSA/LSTF installations.• Secondary-side depressurization as accident management action.• CET vs PCT relation.• Analysis of differences in the vessel models. a b s t r a c t Experimental facilities are scaled models of commercial nuclear power plants, and are of great importance to improve nuclear power plants safety. Thus, the results obtained in the experiments undertaken in such facilities are essential to develop and improve the models implemented in the thermal-hydraulic codes, which are used in safety analysis. The experiments and inter-comparisons of the simulated results are usually performed in the frame of international programmes in which different groups of several countries simulate the behaviour of the plant under the accidental conditions established, using different codes and models. The results obtained are compared and studied to improve the knowledge on codes performance and nuclear safety.Thus, the Nuclear Energy Agency (NEA), in the nuclear safety work area, auspices several programmes which involve experiments in different experimental facilities. Among the experiments proposed in NEA programmes, one on them consisted of performing a counterpart test between ROSA/LSTF and PKL facilities, with the main objective of determining the effectiveness of late accident management actions in a small break loss of coolant accident (SBLOCA). This study was proposed as a result of the conclusion obtained by the NEA Working Group on the Analysis and Management of Accidents, which analyzed different installations and observed differences in the measurements of core exit temperature (CET) and maximum peak cladding temperature (PCT). In particular, the transient consists of a small break loss of coolant accident (SBLOCA) in a hot leg with additional failure of safety systems but with accident management measures (AM), consisting of a fast secondary-side depressurization, activated by the CET. The paper presents the results obtained in the simulations for both installations using TRACE, observing, in general, a good agreement with the experiments. However, ROSA/LSTF calculations underestimated the maximum PCT value, what might be explained by the higher core level predicted in the simulation compared with the experiment. In PKL calculations, PCT maximum value is slightly higher than in the experiment, and the core level predicted is lower. In the comparison of the evolution of both installations a different timing in the transient events is observed, due to the difference in the pressure vessel design. Thus, when PKL vessel is modified with some of the ROSA/LSTF features, the evolution of the new PKL model behaviour is closer the one observed in ROSA/LSTF calculations.
Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.