Analysis Code Research Articles

The 2023 US National Seismic Hazard Model: Ground-motion characterization for the conterminous United States

We update the ground-motion characterization for the 2023 National Seismic Hazard Model (NSHM) for the conterminous United States. The update includes the use of new ground-motion models (GMMs) in the Cascadia subduction zone; an adjustment to the central and eastern United States (CEUS) GMMs to reduce misfits with observed data; an updated boundary for the application of GMMs for shallow, crustal earthquakes in active tectonic regions (i.e. western United States (WUS)) and stable continental regions (i.e. CEUS); and the use of improved models for the site response of deep sedimentary basins in the WUS and CEUS. Site response updates include basin models for the California Great Valley and for the Portland and Tualatin basins, Oregon, as well as long-period basin effects from three-dimensional simulations in the Greater Los Angeles region and in the Seattle basin; in the CEUS, we introduce a broadband (0.01- to 10-s period) amplification model for the effects of the passive-margin basins of the Atlantic and Gulf Coastal Plains. In addition, we summarize progress on implementing rupture directivity models into seismic hazard models, although they are not incorporated in the 2023 NSHM. We implement the ground-motion characterization for the 2023 NSHM in the US Geological Survey’s code for probabilistic seismic hazard analysis, nshmp-haz-v2, and present the sensitivity of hazard to these changes. Hazard calculations indicate widespread effects from adjustments to the CEUS GMMs, from the incorporation of Coastal Plain amplification effects, and from the treatment of shallow-basin and out-of-basin sites in the San Francisco Bay Area and Los Angeles region, as well as locally important changes from subduction-zone GMMs, and from updated and new WUS basins.

PSSFSS—An Open-source Code for Analysis of Polarization and Frequency Selective Surfaces

The open-source code PSSFSS for analysis and design of polarization selective surfaces (PSSs), and frequency selective surfaces (FSSs) is presented, beginning with an introduction to the Julia programming language in which the code is written. Analysis methods and algorithms used in PSSFSS are described, highlighting features of Julia that make it attractive for developing this type of application. Usage examples illustrate the code’s ease of use, speed, and accuracy.

Neutronic design of small modular long­life pressurized water reactor using thorium carbide fuel at a power level of 300–500 MWth

This study presents the neutronic design of a small modular long­life Pressurized Water Reactor (PWR) using thorium carbide fuel with 233U fissile material. The target optimization for this study is a reactor designed to operate for 20 years, with excess reacti­vity throughout the reactor operational cycle consistently below 1.00 % dk/k. The analysis involves dividing the reactor core into three fuel regions with 233U enrichment levels ranging from 3 % to 8 %, with a 1 % difference for each fuel region. To achieve optimum conditions, 231Pa was randomly added to the fuel. The fuel volume fraction in this design varied from 30 % to 65 %, with a 5 % incremental variation. Power level variations are also studied within the 300–500 MWth with increments of 50 MWth. Calculations were performed using the Standard Reactor Analysis Code (SRAC) program with the PIJ and CITATION modules for cell and core calculations utilizing JENDL­4.0 nuclide data. Neutronic calculations indicate that the fuel with a 60 % volume fraction achieves optimum conditions at a power level of 300 MWth. The best performance was observed with a fuel volume fraction of 65 %, reaching optimum conditions across power levels ranging from 300 to 500 MWth. For the fuel with the best performance, the power density distributions for low and high power levels follow the same pattern radially and axially. The power peaking factor (PPF) for all fuel configurations approaching the optimum conditions remains below two, a safe limit for the PWR. Other neutronic safety parameters, such as the Doppler coefficient and void fraction coefficient, also stay within the safe limits for the PWR, with both values remaining negative throughout the reactor operational cycle

Unveiling ideological shifts in news trans‑editing

Abstract This article expands on previous research on news trans-editing by examining the relationship between ideology and language. Using a conceptual framework that combines critical discourse analysis with narrative analysis, the study analyses a corpus of trans-edited news articles on the 2014 Hong Kong protests. These articles were collected from Reference News, along with their source texts from various English-language international media. Narrative analysis was conducted using NVivo computer-assisted tools, including cluster analysis, word frequency and matrix coding. The findings reveal significant shifts in the narratives between the original and trans-edited versions, indicating that the trans-editors recontextualised the news narratives by determining the narrators, retroversions and frequency of the narrative texts. These results suggest that news trans-editing is not a neutral process, but is rather influenced by the ideological stance of the news outlet.

Comparison of the neutronic properties of the (Th-233U)O2, (Th-233U)C, and (Th-233U)N fuels in small long-life PWR cores with 300, 400, and 500 MWth of power

Abstract The neutronic characteristics of (Th-233U)O2, (Th-233U)C, and (Th-233U)N have been compared in small long-life pressurized water reactors (PWRs). Neutronic calculations were carried out at 300 MWth, 400 MWth, and 500 MWth with two cladding types: zircaloy-4 and ZIRLO (Zr low oxygen). They were performed using the Standard Reactor Analysis Code (SRAC) and JENDL-4.0 nuclide data, dividing the reactor core into three fuel zones with varying 233U enrichment levels, ranging from 3% to 9% and fluctuating by 1%, employing the PIJ module at the fuel cell level and the CITATION module at the reactor core level. In addition, 231Pa was added as burnable poison (BP). The (Th-233U)N fuel demonstrated superior criticality compared to the other fuel types, as it consistently achieves critical conditions throughout the reactor’s operating cycle with excess reactivity <1.00% dk/k for several fuel configurations at the 300 MWth and 400 MWth power levels. Moreover, the (Th-233U)N and (Th-233U)C fuels exhibited similar and flatter power density distribution patterns compared to the (Th-233U)O2 fuel. The power peaking factor (PPF) value was relatively higher for (Th-233U)O2 fuel than the other two fuels. The (Th-233U)N fuel exhibited the most negative Doppler coefficient, followed by (Th-233U)C and (Th-233U)O2 fuels. Analysis of burnup levels revealed that the (Th-233U)O2 fuel achieved significantly higher burnup than the other two fuels.

Low birthweight and overweight during childhood and young adulthood and the risk of type 2 diabetes in men: a population-based cohort study

Aims/hypothesisThis study aimed to determine the relative contributions of low birthweight and overweight during childhood and young adulthood to the risk of type 2 diabetes in men.MethodsWe included 34,231 men born between1945 and 1961 from the population-based BMI Epidemiology Study (BEST) Gothenburg with data on birthweight and overweight status in childhood (8 years, BMI >17.9 kg/m2) and young adulthood (20 years, BMI >25 kg/m2). Participants were followed from age 30 years until 31 December 2019. Information on type 2 diabetes diagnoses was retrieved from Swedish national registers. HRs and 95% CIs for the risk of early (≤59.4 years) and late (>59.4 years) type 2 diabetes were estimated using Cox proportional hazards regression.ResultsDuring follow-up, a total of 2733 cases of type 2 diabetes were diagnosed. Birthweight below the median (<3.6 kg) and overweight at age 20 (BMI >25 kg/m2), but not overweight at age 8 (BMI >17.9 kg/m2), were associated with an increased risk of early and late type 2 diabetes. Of note, a birthweight below the median followed by overweight at age 20 years was associated with a substantially increased risk of early type 2 diabetes (HR 6.07, 95% CI 5.08, 7.27), and a low birthweight (≤2.5 kg) combined with overweight at age 20 years was associated with a massive risk of early type 2 diabetes (HR 9.94, 95% CI 6.57, 15.05).Conclusions/interpretationLow birthweight and overweight in young adulthood are the major developmental determinants of adult type 2 diabetes risk in men. They contribute in an additive manner to the risk of type 2 diabetes. To reduce the risk of type 2 diabetes, young adult overweight should be avoided, especially in boys with a low birthweight.Data availabilityThe SPSS analysis code, the R analysis code and a data dictionary have been made available in an online repository (https://osf.io/bx2as/).Graphical

Cognitive, behavioral, and affective mechanisms underlying the efficacy of Applied Relaxation in reducing psychopathological symptoms: A randomized controlled trial

BackgroundThis study examined the cognitive, behavioral, and affective mechanisms underlying the efficacy of Applied Relaxation (AR) in reducing psychopathological symptoms. AR is a cognitive-behavioral technique that allows for rapid relaxation at the first sign of stress or tension in daily life. MethodsA randomized controlled trial was conducted in 277 adults (18–55 years) with elevated symptoms of depression, anxiety, or stress but without a 12-month DSM-5 mental disorder at study entry. Participants were randomized to an intervention group receiving AR training (10 weeks, N = 139) and an assessment-only control group (N = 138). Mental health outcomes (depressive, anxiety, and stress symptoms) and potential cognitive (self-efficacy, perceived control), behavioral (coping behaviors) and affective (positive and negative affect) mediators of the intervention efficacy were assessed at baseline and post-assessment in both groups. ResultsStructural equation models indicated that baseline to post reductions in psychopathological symptoms due to AR partially passed through less avoidance-oriented and less other dysfunctional coping (proportion of total effect mediated; ratio of indirect to total effect: resignation: 55.0%, rumination: 27.9%, escape: 27.4%, aggression: 21.3%), less negative affect (46.3%), more positive affect (41.8%), lower external control beliefs (14.5%), and higher self-efficacy (13.3%). ConclusionsOur results suggest that improvements in cognitive, behavioral, and affective mediator variables partially explain the intervention efficacy of AR in improving mental health. Trial registration and data statementThe study protocol has been pre-registered on ClinicalTrials.gov (NCT03311529). The study protocol, minimum dataset, and analysis codes are available at OpARA - Open Access Repository and Archive. Supplementary materials (e.g., the course manual and additional training materials) are available on request from the last author (katja.beesdo-baum@tu-dresden.de).

Cumulative fretting fatigue damage model for steel wire ropes

AbstractFretting fatigue contributes significantly to the fatigue failure process in steel wire ropes at the wire‐to‐wire trellis contact region with partial slip conditions. In this respect, this work demonstrates a new damage‐based fretting fatigue model for the prediction of such a failure process. The model is based on Lemaitre's damage equations for quasi‐brittle material with a damageable micro‐inclusion embedded in an elastic meso‐element. It incorporates the cyclic degradation of the elastic modulus of the drawn steel wire material. The fatigue life model acknowledges the mean stress effect. The constitutive and damage equations are formulated into user material (UMAT) subroutine for integration with Abaqus finite element analysis code. The localized fretting fatigue damage mechanism is simulated with an isolated two‐wire model. The effect of the contact condition with the coefficient of friction of 0.2 and 0.8 on the contact mechanics of the drawn wires is considered. The fretting fatigue mechanism map is established for each simulation case. The simulated results of N0 (no of cycles to initiate damage) and damage variable, D, confirm the fretting fatigue condition as the damage occurs in the slip region of the contact area for both the frictional conditions. The results were found in agreement with the previously establish Ruiz fretting parameter. This study will provide a base for the onward reliability assessment of steel wire ropes.

ESPFEM2D: A MATLAB 2D explicit smoothed particle finite element method code for geotechnical large deformation analysis

ESPFEM2D: A MATLAB 2D explicit smoothed particle finite element method code for geotechnical large deformation analysis

Qualitative Insights into the Factors Impacting Information Sharing in People with Chronic Haematological Malignancies

Background. There are many different haematological malignancy subtypes. Most follow chronic pathways that are uncertain and unpredictable, which may lead to feelings of anxiety and distress. The provision of information can ameliorate such difficulties, but patients are known to have unmet needs in this regard. The aim of this study is to explore experiences of information sharing among patients with chronic blood cancers and the factors impacting this process. Methods. The study is set within a UK cohort of blood cancer patients, where care is provided across 14 hospitals according to national clinical guidelines. Purposive sampling was used to identify expert participants (based on experience), and in-depth qualitative interviews were conducted with 35 patients, 10 with a relative present. The study was intended to inform practice and utilised qualitative description, with thematic content analysis and systematic data coding. Results. Experiences of information sharing varied. Most patients described this positively, but not all. Several barriers and facilitators were identified, which are discussed within five themes: (1) shock affects ability to process information, (2) the importance of time to facilitate information exchange, (3) personal relationships have an impact on meeting information needs, (4) HCP interpersonal skills are central to good information sharing, and (5) communication skills and terminology. Conclusions. Patients with chronic blood cancers prefer to engage in information sharing when they are not in a state of shock, and when they have adequate time to process material that is effectively and sensitively delivered, by HCPs they know and trust.

Emotional responses to climate change in Norway and Ireland: a validation of the Inventory of Climate Emotions (ICE) in two European countries and an inspection of its nomological span.

There is an increasing research interest in emotional responses to climate change and their role in climate action and psycho-social impacts of climate change. At the same time, emotional experience of climate change is multidimensional and influenced by a variety of factors, including the local cultural context. Here, we contribute to the scientific debate about this topic with original quality-controlled data from the general populations in Norway (N = 491) and Ireland (N = 485). We investigate the cross-cultural validity and the nomological span of eight distinct emotional responses to climate change - climate anger, climate contempt, climate enthusiasm, climate powerlessness, climate guilt, climate isolation, climate anxiety, and climate sorrow - measured using the recently introduced Inventory of Climate Emotions. We first validate the 8-factor structure of the Norwegian and English language versions of the ICE. Subsequently, we demonstrate a high degree of cross-cultural measurement invariance for these eight climate emotions. Finally, we explore the relationships between these emotional responses and a range of theoretically relevant variables. In this final step, we show that climate emotions are differentially linked to climate change perceptions, support for mitigation policies, socio-demographic factors, feelings of loneliness and alienation, environmental activism, and the willingness to prioritize the natural environment over one's immediate self-interests. Some of these links are also differentiated by the cultural context. This research presents further evidence for the structural, cross-cultural, and concurrent validity of climate emotions as postulated in the ICE framework. Moreover, it provides tools in the form of validated Norwegian and English language versions of the ICE, the complete R code for the validation analysis, as well as an informed basis for cross-cultural research on emotional responses to climate change.

Development and application of a transient analysis code for heat pipe cooled reactor systems

The heat pipe cooled reactor is a type of nuclear reactor system that utilizes heat pipe technology for core cooling. It characterizes in high reliability, compactness and modularity, making it suitable for deep-space exploration, and other applications. To gain a deeper understanding of the system's dynamic behavior and evaluate its performance, a transient analysis code is developed with the reactor point kinetics model, core heat transfer model, improved heat pipe startup and operation models, and Stirling engine model established. The reliability and accuracy of the models and code are validated by comparing the simulation results of the various models with experimental data based on heat pipe startup, operation experiments and KRUSTY test results. The safety limits are developed for the KRUSTY and this paper conducts the transient analysis on KRUSTY and evaluates the safety performance of KRUSTY in accidents including the unexpected shutdown of the Stirling engine, reactivity insertion accident (RIA), and combined partial Stirling engine failure and RIA accidents. KRUSTY shows good thermal stability and controllability under hypothetical accident conditions where no safety limit is exceeded.

A comparative analysis of convolutional coding and block coding techniques over satellite communication

Reliable data transmission is crucial in the field of satellite communication, and many coding schemes are used to improve this reliability. Two popular coding techniques: convolutional codes and block codes were investigated and compared in this paper. Utilizing a simulated environment, the author investigates the bit error rate (BER) performance of both coding strategies along with a baseline case of no coding. The analysis reveals distinct characteristics and performance attributes of the two coding techniques across a range of signal-to-noise rate (SNR) values. Convolutional codes, with their inherent memory, offer error protection across sequences, while block codes, particularly a Hamming-like 7,3 implementation, provide localized protection for individual blocks. The findings of this study contribute to the understanding of error control strategies in satellite communication, guiding the selection and design of coding methods suitable for various operational scenarios and requirements.

Barriers to research in family medicine-interviews with Portuguese family physician researchers.

There is a need for a deeper understanding of the barriers to research in family medicine (FM) and to consider the perceptions and perspectives of professionals. Our study aims to provide a strategic view for research capacity building in FM. We included the perspective of family physician researchers (FPR) on the existing barriers to investigation in this context. To understand and characterize the barriers to research in FM (personal and structural), from the perspective of Portuguese family physicians who are researchers. A qualitative study, of phenomenological nature, was performed, through the conduction of semi-structured interviews with FPR, from 2019 to 2022. Data analysis and thematic coding were done on MAxQDA®, with inductive and deductive approaches, until data saturation was reached. A total of 12 family physicians/researchers were interviewed. Seven main themes were identified as barriers to research: time, professional valorization, funding, ethics committees, infrastructure, management/institutions, and participants. Each theme is divided into subthemes that make it possible to assess how a barrier can affect researchers in performing research activities. Our study highlights the identification of 7 main barriers. Structuring them into sub-themes not only improved the organization of our results but also provided robust support for the next phase, namely the application of a survey with the aim of gaining a deeper insight into the repercussions that these barriers to FPR have at a national level. This research is crucial to laying the foundations for a policy document that offers well-defined and tailored recommendations to address the barriers we have uncovered.

Testing the nature of gravitational wave propagation using dark sirens and galaxy catalogues

The dark sirens method enables us to use gravitational wave events without electromagnetic counterparts as tools for cosmology and tests of gravity. Furthermore, the dark sirens analysis code gwcosmo can now robustly account for information coming from both galaxy catalogues and the compact object mass distribution. We present here an extension of the gwcosmo code and methodology to constrain parameterized deviations from General Relativity that affect the propagation of gravitational waves under the ΛCDM expansion history of the universe. We show results of our analysis using data from the GWTC-3 gravitational wave catalogues, in preparation for application to the O4 observing run. After testing our pipelines using the First Two Years mock data set, we reanalyse 46 events from GWTC-3, and combine the posterior for BBH and NSBH sampling results for the first time. We obtain joint constraints on H 0 and parameterized deviations from General Relativity in the Power Law + Peak BBH population model. With increased galaxy catalogue support in the future, our work sets the stage for dark sirens to become a powerful tool for testing gravity.

Code development and preliminary validation for lead-cooled fast reactor thermal-hydraulic transient behavior

Lead-cooled fast reactors (LFRs) have a wide range of application scenarios, which require the thermal-hydraulic characteristics of LFRs to be reliable. In the present paper, the Lead-cooled fast reactor Thermal-Hydraulic Analysis Code LETHAC was developed, including the models of pipe, heat exchanger, and pool. To verify the correctness of LETHAC, two experimental facilities and three experimental cases were selected, including GFT and PLOFA tests for NACIE-UP and Test-1 for CIRCE. The calculated results show the same and consistent trend with the experimental data, but there are some discrepancies. It can be found that LETHAC is suitable and reliable in predicting the transient behavior of lead-cooled system.

Facilitating Student Learning With a Chatbot in an Online Math Learning Platform

Chatbots represent a promising technology for engaging students in math learning. Guided by Jerome Bruner’s constructivism and Lev Vygotsky’s Zone of Proximal Development, we designed and developed a chatbot that incorporates scaffolding strategies and social-emotional considerations, and we integrated it into ASSISTments, an online math learning platform. We conducted an experimental study to examine the influence of learning math with the chatbot compared to traditional learning with hints. This study involved 85 middle and high school students from three diverse school settings in the United States. The results revealed no significant differences in students' math learning performance and perceived helpfulness and interest between the chatbot and traditional hints conditions. However, students in the chatbot condition displayed significantly lower confidence in solving a similar problem after the intervention, likely due to the removal of the high level of support provided by the chatbot. Despite this, students’ open responses indicated that a significantly higher number of students had positive attitudes towards chatbots. They appreciated the chatting feature, breaking down a problem into steps, and real-time support. The study concludes with a discussion of the findings and implications for chatbot designers and developers and presents avenues for future research and practice in chatbot-assisted learning. In support of Open Science, this study has been preregistered and both the data and the analysis code used in this study are publicly available at https://osf.io/am3p8/ .

Predicting radiation pneumonitis in lung cancer: a EUD-based machine learning approach for volumetric modulated arc therapy patients.

This study aims to develop an optimal machine learning model that uses lung equivalent uniform dose (lung EUD to predict radiation pneumonitis (RP) occurrence in lung cancer patients treated with volumetric modulated arc therapy (VMAT). We analyzed a cohort of 77 patients diagnosed with locally advanced squamous cell lung cancer (LASCLC) receiving concurrent chemoradiotherapy with VMAT. Patients were categorized based on the onset of grade II or higher radiation pneumonitis (RP 2+). Dose volume histogram data, extracted from the treatment planning system, were used to compute the lung EUD values for both groups using a specialized numerical analysis code. We identified the parameter α, representing the most significant relative difference in lung EUD between the two groups. The predictive potential of variables for RP2+, including physical dose metrics, lung EUD, normal tissue complication probability (NTCP) from the Lyman-Kutcher-Burman (LKB) model, and lung EUD-calibrated NTCP for affected and whole lung, underwent both univariate and multivariate analyses. Relevant variables were then employed as inputs for machine learning models: multiple logistic regression (MLR), support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). Each model's performance was gauged using the area under the curve (AUC), determining the best-performing model. The optimal α-value for lung EUD was 0.3, maximizing the relative lung EUD difference between the RP 2+ and non-RP 2+ groups. A strong correlation coefficient of 0.929 (P< 0.01) was observed between lung EUD (α = 0.3) and physical dose metrics. When examining predictive capabilities, lung EUD-based NTCP for the affected lung (AUC: 0.862) and whole lung (AUC: 0.815) surpassed LKB-based NTCP for the respective lungs. The decision tree (DT) model using lung EUD-based predictors emerged as the superior model, achieving an AUC of 0.98 in both training and validation datasets. The likelihood of developing RP 2+ has shown a significant correlation with the advancements in RT technology. From traditional 3-D conformal RT, lung cancer treatment methodologies have transitioned to sophisticated techniques like static IMRT. Accurately deriving such a dose-effect relationship through NTCP modeling of RP incidence is statistically challenging due to the increased number of degrees-of-freedom. To the best of our knowledge, many studies have not clarified the rationale behind setting the α-value to 0.99 or 1, despite the closely aligned calculated lung EUD and lung mean dose MLD. Perfect independence among variables is rarely achievable in real-world scenarios. Four prominent machine learning algorithms were used to devise our prediction models. The inclusion of lung EUD-based factors substantially enhanced their predictive performance for RP 2+. Our results advocate for the decision tree model with lung EUD-based predictors as the optimal prediction tool for VMAT-treated lung cancer patients. Which could replace conventional dosimetric parameters, potentially simplifying complex neural network structures in prediction models.

Development of a Surrogate System of a Plant Dynamics Simulation Model and an Abnormal Situation Identification System for Nuclear Power Plants Using Deep Neural Networks

In the case of a new nuclear reactor, existing evaluation experience is limited; thus, accidents and troubles may occur as a result of such lack of experience. To deal with such situations, it is desirable to use a virtual nuclear plant to reproduce behaviors under various conditions and identify unknown anomalies from the behaviors. Then, when an abnormal situation occurs, one can quickly determine the cause of the abnormality to operate plant equipment and return the plant to a stable condition as quickly as possible. Two types of deep neural network (DNN) systems have been constructed to support the identification of unknown anomalies and the determination of their causes. One is a surrogate system that can estimate physical quantities of a nuclear power plant in a computational time of several orders less than a physical simulation model. The other is an abnormal situation identification system that can estimate the state of the disturbance causing an anomaly from physical quantities of a nuclear power plant. Both systems are trained and tested using data obtained from the analytical code for incore and plant dynamics (ACCORD), which reproduces the steady and dynamic behavior of the actual High Temperature Engineering Test Reactor (HTTR) under various scenarios. The DNN models are built by adjusting the main hyperparameters. Through these procedures, these systems are shown to be able to perform with a high degree of accuracy.

The Exploration of Modelling for The Student Achievement Predictor

The project is rooted in the analysis of historical student data. It encompasses a comprehensive approach involving data observation, meticulous analysis, thorough comparison, systematic processing, and efficient coding techniques. The ultimate goal is to harness the power of machine learning to predict students' academic achievements and discern the key features that exert the most significant influence on their learning outcomes. In terms of the machine learning model, this study explored and assessed several machine learning algorithms, including but not limited to the K-Neighbors Classifier, Logistic Regression, and Decision Tree Classifier. These models are scrutinized and fine-tuned to ensure their suitability for the task at hand. Furthermore, a pivotal aspect of this project is identifying the features that wield the greatest impact on students' learning achievements. By employing feature selection techniques, this study aims to uncover the critical factors that can make a difference in educational outcomes. This information can guide educational institutions in designing targeted interventions to support student success. The experimental results obtained from this study demonstrated the effectiveness of the employed machine learning methods.