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DOE OSTI2025

Cross‐Cultural Validation of the Binge Eating Disorder Screener‐7 ( BEDS ‐7) Across 42 Countries

ABSTRACT Objective This study aimed to evaluate the reliability and validity of the Binge Eating Disorder Screener‐7 (BEDS‐7) across 42 countries and 26 languages, assessing its reliability and validity as a screening tool for binge‐eating disorder (BED) in diverse cultural contexts. Specifically, it sought to enhance early recognition of BED symptoms in primary care settings globally, contributing to a standardized framework for assessing BED. Method The International Sex Survey, a cross‐sectional online study, was conducted in 42 countries and 26 languages. A diverse community sample of 82,243 participants, aged 18 years or older, completed the BEDS‐7 and measures of sexuality, mental health, substance use, and sociodemographic characteristics. Confirmatory factor analyses and tests of measurement invariance were employed to evaluate the reliability and validity of the BEDS‐7 across languages, countries, genders, and sexual orientations. Results The BEDS‐7 demonstrated scalar factorial invariance across languages and countries, indicating consistent factor loadings and item intercepts. In contrast, the screener showed residual invariance across gender and sexual orientation groups, supporting its robustness across these demographics. Kruskal–Wallis tests revealed significant differences in BED symptoms across languages, countries, genders, and sexual orientations, with the highest BED scores observed among queer, pansexual, and gender‐diverse individuals. The BEDS‐7 also demonstrated adequate reliability (Cronbach's alpha > 0.80) and moderate criterion validity. Discussion The findings provide further evidence of the reliability and validity of the BEDS‐7 as a potential screening tool for identifying probable cases of BED globally, facilitating early intervention in primary care settings.

Gewirtz‐Meydan, Ateret [School of Social Work, Fac↗

DOE OSTI2025

Correlation effects in magic-angle twisted bilayer graphene: An auxiliary-field quantum Monte Carlo study

Magic-angle twisted bilayer graphene (MATBG) presents a fascinating platform for investigating the effects of electron interactions in topological flat bands. The Bistritzer-MacDonald (BM) model provides a simplified quantitative description of the flat bands. Introducing long-range Coulomb interactions leads to an interacting BM (IBM) Hamiltonian, a momentum-space continuum description which offers a very natural starting point for many-body studies of MATBG. Accurate and reliable many-body computations in the IBM model are challenging, however, and have been limited mostly to special fillings or smaller lattice sizes. We employ a state-of-the-art auxiliary-field quantum Monte Carlo (AFQMC) method to study the IBM model, which constrains the sign problem to enable accurate treatment of large system sizes. We determine ground-state properties and quantify errors compared to mean-field theory calculations. Our calculations identify correlated metal states and their competition with the insulating Kramers intervalley-coherent state at both half-filling and charge neutrality. Additionally, we investigate one- and three-quarter fillings, and examine the effect of many-body corrections beyond single Slater determinant solutions. We discuss the effect that details of the IBM Hamiltonian have on the results, including different forms of double-counting corrections, and the need to establish and precisely specify many-body Hamiltonians to allow more direct and quantitative comparisons with experiments in MATBG. Published by the American Physical Society 2025

Xiao, Zhi-Yu (ORCID:0000000219531579)↗

DOE OSTI2022

The Promises, Challenges, and Opportunities of Omics for Studying the Plant Holobiont

Microorganisms are critical drivers of biological processes that contribute significantly to plant sustainability and productivity. In recent years, emerging research on plant holobiont theory and microbial invasion ecology has radically transformed how we study plant–microbe interactions. Over the last few years, we have witnessed an accelerating pace of advancements and breadth of questions answered using omic technologies. Herein, we discuss how current state-of-the-art genomics, transcriptomics, proteomics, and metabolomics techniques reliably transcend the task of studying plant–microbe interactions while acknowledging existing limitations impeding our understanding of plant holobionts.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

2020 Summer Resource Adequacy in the ERCOT Region

The study looks at the evolution of the Electric Reliability Council of Texas (ERCOT) market across the last decade to provide an understanding of how the market arrived at its current condition. In addition, the study examines four scenarios and utilizes probabilistic dispatch analysis to assess the potential for resource adequacy and reliability concerns under those scenarios. The study finds that under three of the four scenarios, the region will see an increased risk for Energy Emergency Alerts and need for emergency operator actions to ensure the reliable delivery of electricity to the more than 25 million people who ERCOT serves.

2020↗

DOE OSTI2022

Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Abstract. Runoff is a critical component of the terrestrial water cycle, and Earth system models (ESMs) are essential tools to study its spatiotemporal variability. Runoff schemes in ESMs typically include many parameters so that model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at a global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using a surrogate-assisted Bayesian framework. First, the polynomial chaos expansion machinery with Bayesian compressed sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5∘ × 0.5∘ for 1991–2010. The error metric between the ELM simulations and the benchmark data is selected to construct the surrogates, which facilitates efficient calibration and avoids the more conventional, but challenging, construction of high-dimensional surrogates for the ELM simulated runoff. Second, the Sobol' index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that, in most regions, ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multimodel ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

58 GEOSCIENCES↗

DOE OSTI2024

Simulator Data Analysis to Inform Digitalized Environment Impacts on Human Reliability

The U.S. Nuclear Regulatory Commission (NRC) has developed a human reliability analysis (HRA) method, termed the Integrated Human Event Analysis System for Event and Condition Assessment (IDHEAS-ECA), in order to estimate human error probabilities (HEPs) in risk-informed regulatory applications. To update the quantification part of IDHEAS-ECA, the NRC required human performance and error data from fully digitalized main control rooms (MCRs); therefore, it requested that Idaho National Laboratory (INL) revisit previous data collection studies and investigate how the following three factors impact human reliability: self-checking, peer-checking, and automation. The HRA data collection studies revisited were the Human Reliability Data Extraction (HuREX) project, developed by the Korea Atomic Energy Research Institute (KAERI), and the Simplified Human Error Experimental Program (SHEEP), developed by INL. HuREX is a representative HRA data collection study that collects human reliability data from full-scope simulators staffed by licensed operators. SHEEP, on the other hand, has been proposed to complement such full-scope studies by collecting data via simplified simulators staffed by non-licensed student operators. In the HuREX study, KAERI collected HRA data from fully digitalized MCRs for the Advanced Power Reactor (APR)–1400. The SHEEP data were obtained from simplified simulators that partially mimicked the features of digitalized MCRs. The present report mainly discusses how the impacts of the aforementioned three factors on human errors were derived from these two data collection studies.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Toward the first gluon parton distribution from the LaMET

Abstract We present progress towards the first unpolarized gluon quasi-parton distribution function (PDF) from lattice quantum chromodynamics using high-statistics measurements for hadrons at two valence pion masses M π ≈ 310 and 690 MeV computed on an a ≈ 0.12 fm ensemble with 2 + 1 + 1-flavors of highly improved staggered quark generated by the MILC collaboration. In this study, we consider two gluon operators for which the hybrid-ratio renormalization matching kernels have been recently derived and a third operator that has been used in prior pseudo-PDF studies of the gluon PDFs. We compare the matrix elements for each operator for both the nucleon and pion, at both pion masses, and using two gauge-smearing techniques. Focusing on the more phenomenologically studied nucleon gluon PDF, we compare the ratio and hybrid-ratio renormalized matrix elements at both pion masses and both smearings to those reconstructed from the nucleon gluon PDF from the CT18 global analysis. We identify the best choice of operator to study the gluon PDF and present the first gluon quasi-PDF under some caveats. Additionally, we explore the recent idea of Coulomb gauge fixing to improve signal at large Wilson-line displacement and find it could be a major help in improving the signal in the gluon matrix elements. This work helps identify the best operator for studying the gluon quasi-PDF, shows higher hadron boost momentum is needed to implement hybrid-ratio renormalization reliably, and suggests the need to study more diverse set of operators with their corresponding perturbative calculations for hybrid-ratio renormalization to further gluon quasi-PDF study.

Good, William (ORCID:0000000184281003)↗

DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗

DOE OSTI2022

A reliable workflow for improving nanoscale X-ray fluorescence tomographic analysis on nanoparticle-treated HeLa cells

Abstract Scanning X-ray fluorescence (XRF) tomography provides powerful characterization capabilities in evaluating elemental distribution and differentiating their inter- and intra-cellular interactions in a three-dimensional (3D) space. Scanning XRF tomography encounters practical challenges from the sample itself, where the range of rotation angles is limited by geometric constraints, involving sample substrates or nearby features either blocking or converging into the field of view. This study aims to develop a reliable and efficient workflow that can (1) expand the experimental window for nanoscale tomographic analysis of local areas of interest within a laterally extended specimen, and (2) bridge 3D analysis at micrometer and nanoscales on the same specimen. We demonstrate the workflow using a specimen of HeLa cells exposed to iron oxide core and titanium dioxide shell (Fe3O4/TiO2) nanocomposites. The workflow utilizes iterative and multiscale XRF data collection with intermediate sample processing by focused ion beam (FIB) sample preparation between measurements at different length scales. Initial assessment combined with precise sample manipulation via FIB allows direct removal of sample regions that are obstacles to both incident X-ray beam and outgoing XRF signals, which considerably improves the subsequent nanoscale tomography analysis. This multiscale analysis workflow has advanced bio-nanotechnology studies by providing deep insights into the interaction between nanocomposites and single cells at a subcellular level as well as statistical assessments from measuring a population of cells.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2022

Separating Sensor Anomalies From Process Anomalies in Data-Driven Anomaly Detection

Data-driven anomaly detection over time series data is studied from the perspective of separating data anomalies—corresponding to sensor failures—from process anomalies—that arise from equipment or operational failures. Herein, a semi-supervised approach is proposed that utilizes two predictive models trained on non-anomalous data using two different sensor groups as inputs, and a nested hypothesis test to reliably classify data or process anomalies. Conditions are derived on choice of sensor groups to guarantee reliable detection, and a case study is presented to demonstrate the proposed classification approach.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Sensitivity of fatigue reliability in wind turbines: effects of design turbulence and the Wöhler exponent

Fatigue assessment of wind turbines involves three main sources of uncertainty: material resistance, load, and the damage accumulation model. Many studies focus on increasing the accuracy of fatigue load assessment to improve the fatigue reliability. Probabilistic modeling of the wind's turbulence standard deviation is an example of an approach used for this purpose. Editions 3 and 4 of the IEC standard for the design of wind energy generation systems (IEC 61400-1) suggest different probability distributions as alternatives for the representative turbulence in the normal turbulence model (NTM) of edition 1. There are debates on whether the suggested distributions provide conservative reliability levels, as the established design safety factors are calibrated based on the representative turbulence approach. The current study addresses the debate by comparing annual reliability based on different scenarios of NTM using a probabilistic approach. More importantly, it elaborates on the relative importance of load assessment accuracy in defining the fatigue reliability. Using the DTU 10 MW reference wind turbine and the first-order reliability method (FORM), we study the changes in the annual reliability level and its sensitivity to the three main random inputs. We perform the study considering the blade root flapwise and the tower base fore–aft moments, assuming different fatigue exponents in each load channel. The results show that integration over distributions of turbulence in each mean wind speed results in less conservative annual reliability levels than representative turbulence. The difference in the reliability levels varies according to turbulence distribution and the fatigue exponent. In the case of the tower base, the difference in the annual reliability index after 20 years can be up to 50 %. However, the model and material uncertainty have much higher effects on the reliability levels compared to load uncertainty. Knowledge about such differences in the reliability levels due to the choice of turbulence distribution is especially important, as it impacts the extent of lifetime extension through reliability reassessments.

17 WIND ENERGY↗

DOE OSTI2024

Recent Advances of PyROS: A Pyomo Solver for Nonconvex Two-Stage Robust Optimization in Process Systems Engineering

This poster highlights uncertainty and technical risk reduction capabilities in CCSI2, with a focus on robust optimization. It presents recent advances of the two-stage robust optimization (RO) solver PyROS and applications to advanced energy systems optimization. To demonstrate the computational performance and reliability of PyROS, a benchmarking study on a library of over 8,500 small-scale RO problems is presented. Further, PyROS is used to obtain robust system designs of a MEA-based CO2 absorber under uncertainty in the thermodynamic property models for a variety of CO2 capture rate threshold requirements. Overall, the results demonstrate that the PyROS solver, including recent extensions to multi-stage RO settings, provides a reliable avenue to optimize the design and operation of advanced energy systems subject to various sources of parametric uncertainty.

Sherman, Jason↗

DOE OSTI2024

Recent Advances of PyROS: A Pyomo Solver for Nonconvex Two-Stage Robust Optimization in Process Systems Engineering

Sherman, Jason↗

DOE OSTI2021

Clean Grid Vision: A U.S. Perspective - Chapter 3. Transmission Grid-Supporting Technologies

High renewable penetration will bring different challenges to power system reliability and stability. Reduction of inertia has been of concern for more wide-spread instable events; however, with the advanced control functionalities provided by power electronics interface and proper storage compensation, renewable generators are capable of providing wide-range of grid services that can maintain and improve grid reliability and stability matrices. Many studies and testing has demonstrated those functions and many pilot projects throughout the world had proved the concepts are working.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Evaluating probabilistic deep learning methods for uncertainty quantification of temperature downscaling

Deep learning (DL) has emerged as a promising tool for downscaling coarse-resolution climate data to high-resolution outputs, enabling improved regional climate predictions. A critical aspect of DL-based downscaling is the incorporation of uncertainty quantification (UQ), which enhances the interpretability and reliability of predictions—key factors for climate risk assessment and decision-making. This study develops a DL model to downscale 2 m temperature across the contiguous United States using reanalysis datasets. We systematically evaluate three epistemic UQ methods—deep ensembles (DEns), Monte Carlo dropout (MCD), and Flipout—based on their probabilistic accuracy, downscaling performance, sensitivity to geographical features, and computational efficiency. Results indicate that MCD generally outperforms Flipout and DEns in terms of calibration and downscaling accuracy. However, DEns demonstrate lower calibration errors in coastal regions, indicating its higher confidence within these areas. Flipout, in contrast, is more sensitive to elevation gradients and exhibits higher calibration errors in mountainous regions. Hence, the choice of UQ method for this task depends on the specific requirements of the application. For applications that prioritize overall calibration, downscaling accuracy, and computational efficiency, MCD is a strong candidate. These findings highlight the importance of selecting UQ methods based on application-specific requirements, such as geographical context and computational constraints. By addressing the trade-offs between UQ methods, this study provides actionable insights for improving the reliability, scalability, and utility of DL-based downscaling in climate science.

Environmental sciences↗

DOE OSTI2024

Impacts of Spatial Resolution in a High-Fidelity Capacity Expansion Model: An ERCOT Case Study

Capacity expansion models are important tools in examining the evolution of the electric power sector. Embedded in these tools are many modeling choices with consequential impacts on computational burden and associated analysis. In this study, we adjust the spatial resolution of the Regional Energy Deployment System (ReEDS) to understand the implications of higher-fidelity modeling on energy system projections and model solve times. The native ReEDS regions capture the contiguous United States in 134 balancing areas whereas the regions in the higher-resolution version are defined by over 3,000 U.S. counties. Using both resolutions, we conduct a case study of the Texas Interconnection (The Electric Reliability Council of Texas [ERCOT]) to explore differences in model projections and to inform appropriate applications of high spatial resolution in a large-scale, applied capacity expansion model.

county↗

DOE OSTI2026

Reliability of Digital Communications in Nuclear Facilities and Operations

A comprehensive evaluation of methods to compare the reliability of wired and wireless digital communication networks for use in nuclear power plants. The study underscores the critical role of communication reliability in ensuring operational safety in nuclear power plants. Key objectives include developing a methodology to assess communication technologies’ reliability, particularly for comparing wireless and wired networks, for nuclear facility applications. The framework focuses on technology-agnostic evaluations and emphasizes the importance of reliability metrics spanning safety, security, and monitoring functions. The framework emphasizes that the key performance indicators are application dependent and provides a hierarchy of different network types that will have different reliability requirements. The report considers both existing plants and advanced reactors, including small modular reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Near-zero-field magnetoresistance measurements: A simple method to track atomic-scale defects involved in metal-oxide-semiconductor device reliability

We demonstrate the ability of a relatively new analytical technique, near-zero-field magnetoresistance (NZFMR), to track atomic-scale phenomena involved in the high-field stressing damage of fully processed Si metal-oxide-semiconductor field-effect transistors. We show that the technique is sensitive to both the Pb0 and Pb1 dangling bond centers and that the presence of both centers can be inferred through NZFMR via hyperfine interactions with the central 29Si atoms of the dangling bonds. The NZFMR results also provide evidence for the redistribution of mobile hydrogen atoms at the Si/SiO2 interface and also a potential change in the average dipolar coupling constant between electrons in neighboring defects. This work shows that NZFMR offers significant analytical power for studying technologically relevant semiconductor device reliability problems and has advantages in experimental simplicity over comparable techniques.

Moxim, Stephen J. (ORCID:0000000309659904)↗