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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine learning models for rat multigeneration reproductive toxicity prediction

Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%–61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals.

consensus model↗
DOE OSTI2022

Sex comparisons in physiological and cognitive performance during hypoxic challenge

Within the tactical aviation community, human performance research lags in considering potential psychophysiological differences between male and female aviators due to little inclusion of females during the design and development of aircraft systems. A poor understanding of how male and female aviators differ with respect to human performance results in unknown potential sex differences on aeromedically relevant environmental stressors, perchance leading to suboptimal performance, safety, and health guidelines. For example, previous hypoxia studies have excluded female participants or lacked a sizeable sample to examine sex comparisons. As such, progress toward sensor development and improving hypoxia familiarization training are stunted due to limited knowledge of how individual differences, including sex, may or may not underlie hypoxia symptoms and performance impairment. Investigating sex differences bridges the gap between aerospace medicine and operational health, and addressing hypoxia is one of many facets yet to be studied. In the current study, we retrospectively examined N = 6 hypoxia studies with male-female participant samples (total, N = 189; male, n = 118; female, n = 71). We explored sex as a predictor of physiological response, sensory deficits, the severity of cognitive performance declines, and symptom manifestation via linear and binary logistic regression models. We found that the female sex predicted lower peripheral oxygen saturation and the likelihood of headache reporting in response to hypoxic challenge, yet explained little variance when combined with age and body mass index. The sensory and cognitive performance models did not converge, suggesting high intra-individual variability. Together, sex, age, and body mass index were not the most robust predictors in responses to hypoxic challenge; we cannot infer this for sensory deficits and cognitive performance within an experimentally induced hypoxic environment. The findings have implications for improving hypoxia familiarization training, monitoring sensor development, and emergency response and recovery protocols in case of a hypoxia occurrence suitable for all aircrew. We recommend continuing to elucidate the impact of sex and intrapersonal differences in hypoxia and other aeromedically relevant stressors in tactical aviation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Construction of Women’s All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms

Introduction Accurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance. Objective The present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies. Methods The data, consisting of 16 seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method. Results The results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals. Conclusion Compared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.

Liu, Meng↗
DOE OSTI2023

COVID-19 vaccination status, side effects, and perceptions among breast cancer survivors: a cross-sectional study in China

Introduction Breast cancer is the most prevalent malignancy in patients with coronavirus disease 2019 (COVID-19). However, vaccination data of this population are limited. Methods A cross-sectional study of COVID-19 vaccination was conducted in China. Multivariate logistic regression models were used to assess factors associated with COVID-19 vaccination status. Results Of 2,904 participants, 50.2% were vaccinated with acceptable side effects. Most of the participants received inactivated virus vaccines. The most common reason for vaccination was “fear of infection” (56.2%) and “workplace/government requirement” (33.1%). While the most common reason for nonvaccination was “worry that vaccines cause breast cancer progression or interfere with treatment” (72.9%) and “have concerns about side effects or safety” (39.6%). Patients who were employed (odds ratio, OR = 1.783, p = 0.015), had stage I disease at diagnosis (OR = 2.008, p = 0.019), thought vaccines could provide protection (OR = 1.774, p = 0.007), thought COVID-19 vaccines were safe, very safe, not safe, and very unsafe (OR = 2.074, p < 0.001; OR = 4.251, p < 0.001; OR = 2.075, p = 0.011; OR = 5.609, p = 0.003, respectively) were more likely to receive vaccination. Patients who were 1–3 years, 3–5 years, and more than 5 years after surgery (OR = 0.277, p < 0.001; OR = 0.277, p < 0.001, OR = 0.282, p < 0.001, respectively), had a history of food or drug allergies (OR = 0.579, p = 0.001), had recently undergone endocrine therapy (OR = 0.531, p < 0.001) were less likely to receive vaccination. Conclusion COVID-19 vaccination gap exists in breast cancer survivors, which could be filled by raising awareness and increasing confidence in vaccine safety during cancer treatment, particularly for the unemployed individuals.

Xu, Yali↗
DOE OSTI2023

The relationship between urinary selenium levels and risk of gestational diabetes mellitus: A nested case–control study

Background Selenium (Se) is an essential trace element for the human body. Serum Se and urinary Se are also biomarkers to assess Se exposure status. However, studies focusing on the association between urinary Se and the risk of gestational diabetes mellitus (GDM) are rare. Objective To investigate the association between urinary Se and the risk of GDM. Methods A nested case–control study based on a prospective birth cohort in Wuhan, China, which focuses on the effects of prenatal environmental factors exposure on pregnant women and children’s health was conducted. Two hundred and twenty-six cases and 452 controls were included. Maternal urine samples were collected before GDM diagnosis, and the urinary Se levels were determined. We assessed the association of urinary Se with GDM by conditional logistic regression with maternal urinary Se level as a categorical variable, and estimated the association between Se and glucose levels by multiple linear regression. The potential modifier roles of maternal age and fetal sex have also been assessed. Results Lower urinary level of Se was significantly associated with a higher risk of GDM (OR = 2.35 for the tertile 1, 95% CI:1.36-4.06; adjusted OR = 1.79 for the tertile 2, 95%CI:1.09-2.95; p for trend = 0.01). Fetal sex had an interaction with Se in the association with GDM. The association was more pronounced among pregnant women with female fetuses than with male fetuses. Discussion Our study suggested a significant negative association between urinary Se and the risk of GDM, and this association may vary depending on the fetal sex.

Liu, Yuanxia↗
DOE OSTI2024

Assessing heterogeneity of patient and health system delay among TB in a population with internal migrants in China

Backgrounds The diagnostic delay of tuberculosis (TB) contributes to further transmission and impedes the implementation of the End TB Strategy. Therefore, we aimed to describe the characteristics of patient delay, health system delay, and total delay among TB patients in Shanghai, identify areas at high risk for delay, and explore the potential factors of long delay at individual and spatial levels. Method The study included TB patients among migrants and residents in Shanghai between January 2010 and December 2018. Patient and health system delays exceeding 14 days and total delays exceeding 28 days were defined as long delays. Time trends of long delays were evaluated by Joinpoint regression. Multivariable logistic regression analysis was employed to analyze influencing factors of long delays. Spatial analysis of delays was conducted using ArcGIS, and the hierarchical Bayesian spatial model was utilized to explore associated spatial factors. Results Overall, 61,050 TB patients were notified during the study period. Median patient, health system, and total delays were 12 days (IQR: 3–26), 9 days (IQR: 4–18), and 27 days (IQR: 15–43), respectively. Migrants, females, older adults, symptomatic visits to TB-designated facilities, and pathogen-positive were associated with longer patient delays, while pathogen-negative, active case findings and symptomatic visits to non-TB-designated facilities were associated with long health system delays (LHD). Spatial analysis revealed Chongming Island was a hotspot for patient delay, while western areas of Shanghai, with a high proportion of internal migrants and industrial parks, were at high risk for LHD. The application of rapid molecular diagnostic methods was associated with reduced health system delays. Conclusion Despite a relatively shorter diagnostic delay of TB than in the other regions in China, there was vital social-demographic and spatial heterogeneity in the occurrence of long delays in Shanghai. While the active case finding and rapid molecular diagnosis reduced the delay, novel targeted interventions are still required to address the challenges of TB diagnosis among both migrants and residents in this urban setting.

Sun, Ruoyao↗
DOE OSTI2025

The relationship between below average cognitive ability at age 5 years and the child’s experience of school at age 9

Background At age 5, while only embarking on their educational journey, substantial differences in children’s cognitive ability will already exist. The aim of this study was to examine the causal association between below average cognitive ability at age 5 years and child-reported experience of school and self-concept, and teacher-reported class engagement and emotional-behavioural function at age 9 years. Methods This longitudinal cohort study used data from 7,392 children in the Growing Up in Ireland Infant Cohort, who had completed the Picture Similarities and Naming Vocabulary subtests of the British Abilities Scales at age 5. Principal components analysis was used to produce a composite general cognitive ability score for each child. Children with a general cognitive ability score more than 1 standard deviation (SD) below the mean at age 5 were categorised as ‘Below Average Cognitive Ability’ (BACA), and those scoring above this as ‘Typical Cognitive Development’ (TCD). The outcomes of interest, measured at age 9, were child-reported experience of school, child’s self-concept, teacher-reported class engagement, and teacher-reported emotional behavioural function. Binary and multinomial logistic regression models were used to examine the association between BACA and these outcomes. Results Compared to those with TCD, those with BACA had significantly higher odds of never liking school [Adjusted odds ratio (AOR) 1.82, 95% CI 1.37–2.43, p < 0.001], of being picked on (AOR 1.27, 95% CI 1.09–1.48) and of picking on others (AOR 1.53, 95% CI 1.27–1.84). They had significantly higher odds of experiencing low self-concept (AOR 1.20, 95% CI 1.02–1.42) and emotional-behavioural difficulties (AOR 1.34, 95% CI 1.10–1.63, p = 0.003). Compared to those with TCD, children with BACA had significantly higher odds of hardly ever or never being interested, motivated and excited to learn (AOR 2.29, 95% CI 1.70–3.10). Conclusion Children with BACA at school-entry had significantly higher odds of reporting a negative school experience and low self-concept at age 9. They had significantly higher odds of having teacher-reported poor class engagement and problematic emotional-behavioural function at age 9. The findings of this study suggest BACA has a causal role in these adverse outcomes. Early childhood policy and intervention design should be cognisant of the important role of cognitive ability in school and childhood outcomes.

Bowe, Andrea K.↗
DOE OSTI2017

Whole-Genome Sequence Analysis of Antimicrobial Resistance Genes in Streptococcus uberis and Streptococcus dysgalactiae Isolates from Canadian Dairy Herds

The objectives of this study are to determine the occurrence of antimicrobial resistance (AMR) genes using whole-genome sequence (WGS) of Streptococcus uberis (S. uberis) and Streptococcus dysgalactiae (S. dysgalactiae) isolates, recovered from dairy cows in the Canadian Maritime Provinces. A secondary objective included the exploration of the association between phenotypic AMR and the genomic characteristics (genome size, guanine–cytosine content, and occurrence of unique gene sequences). Initially, 91 isolates were sequenced, and of these isolates, 89 were assembled. Furthermore, 16 isolates were excluded due to larger than expected genomic sizes (>2.3 bp × 1,000 bp). In the final analysis, 73 were used with complete WGS and minimum inhibitory concentration records, which were part of the previous phenotypic AMR study, representing 18 dairy herds from the Maritime region of Canada (1). A total of 23 unique AMR gene sequences were found in the bacterial genomes, with a mean number of 8.1 (minimum: 5; maximum: 13) per genome. Overall, there were 10 AMR genes [ANT(6), TEM-127, TEM-163, TEM-89, TEM-95, Linb, Lnub, Ermb, Ermc, and TetS] present only in S. uberis genomes and 2 genes unique (EF-TU and TEM-71) to the S. dysgalactiae genomes; 11 AMR genes [APH(3'), TEM-1, TEM-136, TEM-157, TEM-47, TetM, bl2b, gyrA, parE, phoP, and rpoB] were found in both bacterial species. Two-way tabulations showed association between the phenotypic susceptibility to lincosamides and the presence of linB (P = 0.002) and lnuB (P < 0.001) genes and the between the presence of tetM (P = 0.015) and tetS (P = 0.064) genes and phenotypic resistance to tetracyclines only for the S. uberis isolates. The logistic model showed that the odds of resistance (to any of the phenotypically tested antimicrobials) was 4.35 times higher when there were >11 AMR genes present in the genome, compared with <7 AMR genes (P < 0.001). The odds of resistance was lower for S. dysgalactiae than S. uberis (P = 0.031). When the within-herd somatic cell count was >250,000 cells/mL, a trend toward higher odds of resistance compared with the baseline category of <150,000 cells/mL was observed. When the isolate corresponded to a post-mastitis sample, there were lower odds of resistance when compared with non-clinical isolates (P = 0.01). The results of this study showed the strength of associations between phenotypic AMR resistance of both mastitis pathogens and their genotypic resistome and other epidemiological characteristics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Does Age, Residency, or Feeding Guild Coupled with a Drought Index Predict Avian Health during Fall Migration?

Birds are good indicators of environmental change and are often studied for responses to climate. Many studies focus on breeding birds, while fewer look at the migration period, which is a critical time for many birds. Birds are more susceptible to unusual climatic events during their migration due to the metabolic stress of long-distance movements. In the fall of 2020, an unusual cold weather event coupled with drought and wildfire smoke led to a large avian mortality event in New Mexico. Later analysis pointed to the mortality being largely due to starvation. This was the impetus for our research. We used 11 years of fall bird banding data from two locations, along with local drought indices, to determine what predicts avian health during the migration period. We used fat score data from over 15,000 individual birds to assess whether drought indices, age, diet, or residency influenced avian health using multiple logistic regression. We found that the probability of positive fat scores decreased as drought severity increased for younger, insectivorous, migratory birds. Insectivores had a higher probability of receiving a fat score greater than zero relative to local drought conditions, which is important, since many North American insectivores are in steep decline. Migratory birds showed a greater response than year-round residents, and older birds showed a lower but significant response compared to hatch-year birds. Our results suggest that migratory insectivores in the southwestern United States may be less resilient to drought-related climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Simplified Model of Adenine-Induced Chronic Kidney Disease Using SKH1 Mice

Commonly used adenine-induced chronic kidney disease (CKD) murine models often employ C57BL/6 mice; however, this strain has inherent limitations due to its natural resistance to developing key pathological features of CKD, such as tubulointerstitial fibrosis and inflammation. There have been attempts to overcome these barriers by using multiple concentrations of adenine-supplemented diets or by performing prolonged experiments up to 20 weeks. Here, we demonstrate that SKH1 Elite mice develop clinically relevant CKD phenotypes (e.g., polyuria, proteinuria, inflammation, and renal fibrosis) over the course of only 6 weeks of low-dose (0.15%) adenine supplementation. As a docile, immunocompetent, and hairless strain, SKH1 Elite mice offer several logistical advantages over C57BL/6 mice, including ease of handling and the ability to study dermal conditions, which are often secondary to CKD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Estimating Switchgrass Biomass Yield and Lignocellulose Composition from UAV-Based Indices

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha –1 ). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R 2 = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R 2 = 0.88). Cellulose was best predicted using the NDRE (R 2 = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R 2 = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration.

09 BIOMASS FUELS↗
DOE OSTI2018

Longitudinal Socio-Emotional Learning Intervention for Autism via Smartglasses: Qualitative School Teacher Descriptions of Practicality, Usability, and Efficacy in General and Special Education Classroom Settings

There is considerable demand for special education services for the over half a million students with autism in the United States. While assistive technology may augment educational services, its implementation is often prevented by a number of practical and attitudinal barriers. These barriers are especially pertinent for the newest and thus least familiar digital systems, such as computerized smartglasses loaded with specialized software modules. Computerized smartglasses are a technology that has already been shown to have an ability to deliver educational interventions through augmented reality. With this in mind, we sought to understand how school educators received and assessed the practicality of a smartglasses-based educational intervention in a single-subject study. The intervention was designed to aid with attention and social educational learning in autism. The intervention was delivered twice a day during a two-week study on a 13-year-old student with autism who was attending a mainstream middle school in Massachusetts. Three different school educators delivered the intervention: the student’s general education teacher, special education teacher, and paraprofessional. Educators recorded their attitudes, the practicality of the technology, and its impact on the student and their classroom through the use of a digital log and a series of in-person interviews. Overall, the school educators experienced a positive view of the smartglasses. The smartglasses intervention was found to be logistically practical to implement, easily usable by both the educator and student, and not time-consuming to learn or implement. Educators also identified the experience as being fun for the student, and felt that the student demonstrated improvement in his verbal and non-verbal skills. There were no adverse effects on the other students or the classroom, and the technology did not result in a distraction. These findings suggest that social skills interventions delivered by smartglasses may be practical, useful, and may lead to improvements in social communication skills. Further research on smartglasses may help to clarify the future role for augmenting special education in students with autism.

Keshav, Neha↗
DOE OSTI2017

Development of the IBSAL-SimMOpt Method for the Optimization of Quality in a Corn Stover Supply Chain

Variability on the physical characteristics of feedstock has a relevant effect on the reactor’s reliability and operating cost. Most of the models developed to optimize biomass supply chains have failed to quantify the effect of biomass quality and preprocessing operations required to meet biomass specifications on overall cost and performance. The Integrated Biomass Supply Analysis and Logistics (IBSAL) model estimates the harvesting, collection, transportation, and storage cost while considering the stochastic behavior of the field-to-biorefinery supply chain. This paper proposes an IBSAL-SimMOpt (Simulation-based Multi-Objective Optimization) method for optimizing the biomass quality and costs associated with the efforts needed to meet conversion technology specifications. The method is developed in two phases. For the first phase, a SimMOpt tool that interacts with the extended IBSAL is developed. For the second phase, the baseline IBSAL model is extended so that the cost for meeting and/or penalization for failing in meeting specifications are considered. The IBSAL-SimMOpt method is designed to optimize quality characteristics of biomass, cost related to activities intended to improve the quality of feedstock, and the penalization cost. A case study based on 1916 farms in Ontario, Canada is considered for testing the proposed method. Analysis of the results demonstrates that this method is able to find a high-quality set of non-dominated solutions.

09 BIOMASS FUELS↗
DOE OSTI2024

Analyzing the Effects of Atmospheric Turbulent Fluctuations on the Wake Structure of Wind Turbines and Their Blade Vibrational Dynamics

In recent trends, a rising demand for renewable energy has driven wind turbines to larger proportions, where lighter blade designs are often adopted to reduce the costs associated with logistics and production. This causes modern utility-scale wind turbine blades to be inherently more flexible, and their amplified aeroelastic sensitivity results in complex multi-physics reactions to variant atmospheric conditions, including dynamic patterns of aerodynamic loading at the rotor and vortex structure evolutions within the wake. In this paper, we analyze the influence of inflow variance for wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex wake interactions for a range of flexible NRT blade variations, operating in differing conditions of variant atmospheric flow. CODEF solutions of turbine operation in Steady-In-The-Average (SITA) wind conditions are compared to SITA wind conditions featuring a controlled gust-like pulse overimposed, to isolate the effects of typical wind fluctuations. Finally, simulations of realistic time-varying wind conditions from SWiFT meteorological tower measurements are compared to the solutions of SITA wind conditions. These increasingly complex atmospheric inflow variations are tested to show the differing effects evoked by various patterns of spatiotemporal atmospheric flow fluctuations. An analysis is presented for solutions of wind turbine aeroelastic response and vortex wake evolution, to elucidate the consequences of variant inflow, which pertain to wind turbine dynamics at an individual and farm-collective scale. The comparisons of simulated farm flow for SITA and measured fluctuating wind conditions show that certain regions of the wake contain up to a 12% difference in normalized axial velocity, due to the introduction of wind fluctuations. The findings of this study prove valuable for practical applications in wind farm control and optimization strategies, with particular significance for modern utility-scale wind power plants operating in variant atmospheric conditions.

Farrell, Alayna (ORCID:000000023555720X)↗
DOE OSTI2025

Modeling and Analysis of Wind Turbine Wake Vortex Evolution Due to Time-Constant Spatial Variations in Atmospheric Flow

Modern utility-scale wind turbines are evolving toward larger, lighter, and more flexible designs to meet the growing demand for renewable energy while minimizing logistical costs. However, these advancements in lightweight design result in heightened aeroelastic sensitivity, leading to complex interactions which affect the rotor’s capacity to withstand aerodynamic loading and the cascading effects that manifest in the wake’s vortex-structure evolution under variable atmospheric conditions. In this paper, we analyze the influence of stream-wise fluctuating atmospheric flow conditions on wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex–wake interactions for a range of conditions with spatially variant atmospheric flow. CODEF solutions for turbine operation in wind conditions featuring only one parameter fluctuation are compared to wind conditions with several wind parameter variations in combination. By isolating individual inflow variations and comparing them to multi-parameter scenarios, we determine the contributions of each atmospheric factor to rotor dynamics, wake evolution, and downstream wind farm interactions. The purpose of this paper is to analyze the effects of spatial variations in atmospheric flow on the topological evolution of wind turbine vortex wakes, which constitutes a gap in the current understanding of wind turbine wake dynamics. The insights gained from this study are particularly valuable for the development of wind farm control strategies aimed at mitigating the adverse effects of wake interactions, enhancing energy capture, and improving the overall stability of wind farm operations. With these insights, we aim to contribute to the development of modeling and simulation tools to optimize utility-scale wind power plants operating in diverse atmospheric environments.

Farrell, Alayna (ORCID:000000023555720X)↗
DOE OSTI2024

Leveraging Observations of Untrained Panelists to Screen for Quality of Fresh-Cut Romaine Lettuce

Fresh-cut romaine lettuce’s high perishability challenges ready-to-eat (RTE) salad production. Selecting cultivars less prone to browning and decay is crucial for extending shelf life. Traditional quality evaluation methods using instrumentation and trained panelists are time-consuming and logistically complex. This study investigated the effectiveness of untrained volunteers in assessing fresh-cut romaine lettuce quality. Given that the average consumer in the USA is familiar with the flavor characteristics of romaine lettuce, this study proposed to investigate the value of having untrained volunteers discern the quality of fresh-cut romaine lettuce. Therefore, six romaine lettuce accessions (Green Forest, King Henry, Parris Island Cos, PI 491224, SM13-R2, and Sun Valley) were assessed for sensory quality attributes (browning, green color, decay, and overall quality) and compared with instrumentation analyses (gas composition including O2 and CO2, electrolyte leakage, and color). The results showed significant quality differences (p < 0.05) among the accessions, with some seasonal variability. Very importantly, the consumers’ (n = 159) assessments revealed similar results to those produced by either instrumentation or a trained panel. The consumers provided sensory scores that allowed for the grouping of accessions based on their postharvest quality, which efficiently matched their pedigree relationship. In conclusion, ad hoc consumer panels can be an effective way to characterize the quality of romaine lettuce for RTE salads.

Agriculture↗
DOE OSTI2014

Joint Exposure to Chemical and Nonchemical Neurodevelopmental Stressors in U.S. Women of Reproductive Age in NHANES

Lead (Pb) and methyl mercury (MeHg) are well established neurodevelopmental toxicants (NDTs), but joint exposure to chemical and nonchemical (e.g., maternal stress) stressors has rarely been considered. We characterized exposure to Pb, MeHg and a measure of physiological dysregulation associated with chronic stress and examined race/ethnicity as a predictor of joint NDT exposure. Using data from the 2003-2004 NHANES, potential chronic stress exposure was estimated using allostatic load (AL), a quantitative measure of physiological dysregulation. A Hazard Index was calculated for joint exposure to Pb and MeHg (HI NDT ). Logistic regression was used to assess the relationship between an indicator of elevated joint NDT exposures (HI NDT > 1) and race/ethnicity. The multivariate model was stratified by AL groups to examine effect measure modification. African American (adjusted odds ratio [OR] [95% confidence interval] = 2.2 [1.4, 3.3]) and Mexican American (1.4 [0.7, 2.6]) women were more likely to have an HI NDT > 1 compared to Caucasian women. Chronic stress was identified as an effect measure modifier with the largest ORs among women with high AL scores (African Americans = 4.3 [2.0, 9.5]; Mexican Americans = 4.2 [1.3, 14.1]). Chronic stress was found to modify the association between elevated joint NDT exposure and race/ethnicity, highlighting the importance of evaluating chemical and nonchemical stressor exposures leading to a common endpoint.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mining Product Reviews for Important Product Features of Refurbished iPhones

Problem: Remanufacturers want to increase consumer interest in refurbished products, which motivates the need to understand which product features are important to buyers of refurbished products such as mobile phones. Research Questions: This study addresses two questions. First, which product features are most important for buyers of refurbished iPhones? Second, how do those preferences differ from the preferences of buyers of new iPhones? Methods: Online reviews of iPhones are obtained and converted into a document–term matrix. Using this text model, three subsets of features are identified using statistical analysis of frequency of mention: most frequent, average, and least frequent. A logistic regression (LR) model is then used to identify which features are most predictive of whether a review is for a new or refurbished phone. Results: Buyers of refurbished phones mention battery health, screen/display, shell condition, and brand significantly more often than other features. Directly contrasting reviews of refurbished versus new phones shows that shell condition, brand, speaker, and charger are found to be the most predictive product features indicated in reviews for refurbished phones. Of those, the shell condition is significantly more predictive than the others. Implications: The results identify product features that remanufacturers of iPhones can emphasize to increase customer demand.

Anisi, Atefeh↗