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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Investigating Coastal Vegetation Dynamics and Ecosystem Impacts Under Elevated CO 2 and Temperature: A Process‐Based Approach

Coastal forests are increasingly vulnerable to climate change and sea-level rise, with flooding and salinity driving transitions to marsh-dominated ecosystems. Using the coastal version of FATES-Hydro, we conducted 30-year simulations at two coastal forest sites—a broadleaf swamp white oak stand at Lake Erie and a conifer loblolly pine stand at Chesapeake Bay—under historical climate and elevated CO 2 (+100 ppm) and temperature (+1.5°C) scenarios. Elevated CO 2 increased net primary productivity at both sites, while warming alone intensified hydraulic stress and accelerated mortality, particularly in the conifer stand. Simulations show that elevated temperatures intensify vapor pressure deficit and hydraulic stress on trees already experiencing salinity- and submersion-driven water stress, increasing tree mortality beyond what would be expected in a non-water-limited environment. Marsh expansion partially compensated for tree loss at the Lake Erie site but reduced ecosystem productivity in the conifer forest at Chesapeake Bay. In conclusion, our results highlight how differences in stand structure, phenology, and local hydrology modulate ecosystem trajectories under climate change, emphasizing the importance of demographic and community-level processes for predicting the fate of coastal forests.

Ding, Junyan [Barcelona Supercomputing Center (BSC↗
DOE OSTI2023

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

An investigation of transitional management problems for the NSTS at NASA, executive summary

A summary is given of the work of the University of Houston research team for the third quarter of effort in a yearly grant for the National Space Transportation System (NSTS). As such it serves as a resting place for the ideas and concepts developed this quarter with the collaboration of the Management Integration Offices of NASA. Another objective is the hope that the report will help to stimulate the healthy problem solving process already present at NASA. The main goal of the contractual work is to help NASA to find ways and means of moving into a truly operational era with the shuttle program. This work is a continuation of early work and the reader is encouraged to read the final reports of earlier years. Chapter One of the report is an introduction. Chapter Two deals with industrial adaptation and is in two parts: theory and application. In the theory section, impressions of the management system immediately after reflight are discussed. A key issue, in the author's opinion is the seeming lack of purpose of the program. The application section has six appendices: 1988 Demographic Survey, Field Notes of Interview with HL/P South Texas Nuclear Project, a comparison of the Agendas of the current manager with that of a previous manager, a note on compartmentalization to assist in manifesting, a study on launch prediction for STS-26, and a discussion of a statistical decision-making course for upper level managers. Chapter Three deals with theoretical results returned on flow shop scheduling which will be of use downstream. Chapter Four deals with a statistical model developed to predict the flight rate in future years and indicates the program will have trouble making its schedule. Chapter Five covers the constructural effort and shows the work to be on schedule.

Hunsucker, John↗
DOE OSTI2025

An On-Demand Electric Transit Case Study of New Rochelle, New York

This work explores the extent to which an on-demand mobility service utilizing lightweight electric vehicles (EVs) provides community and sustainability benefits in New Rochelle, New York. Travel and survey data from September 2019 through 2023 are used to describe the system and estimate impacts on travelers. The system was found to be used more by women (nearly 60%) and younger demographics (>65% under the age of 42), with peak use in the middle of the day and a grocery store as a top origin and destination. The service is utilized primarily for short trips (86% under 2 miles), and the small, right-sized EVs have carbon dioxide emissions associated with charging the fleet that are roughly one-quarter of the fleet emissions of conventional hybrid vans and nearly 50 times less than a fleet of diesel buses. Mapping current socio-spatial dynamics of travel demand can inform equity performance, as well as assist future planning and service area development and possible extensions to similar smaller, lower-density environments that are nearby and connected to major metropolitan areas. The findings in this case study suggest on-demand electric transit may be a significant and growing space for advancing clean and highly valued public mobility services. Sustainable public transport interventions that consider right-sized, electric, on-demand vehicles can help achieve improved accessibility and reduce energy use and greenhouse gas emissions. This work was presented at the Transportation Research Board (TRB) 2025 Annual Meeting on January 7, 2025.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into microaggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of microbehaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney R. Begerowski↗
NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into micro-aggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of micro-behaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney Begerowski↗
NASA NTRS2010

Filovirus Research: The Need for an Integrated Approach in Time and Space

The episodic appearance of Ebola virus (EBOV) and Marburg virus (MARV) across central Africa over the last 15 years not only underscores the importance of filoviruses as uniquely virulent agents to both human and wildlife communities but also implies a very complex transmission scenario that must be understood if we are to prevent or mitigate filovirus outbreaks in the future. Efforts of a global network of scientists and healthcare workers have expanded our knowledge of filoviruses to meet the growing threat of Ebola and Marburg hemorrhagic fevers in Africa. In recent decades, several newly emerging diseases have resulted in major threats to both affected communities and global public health. Viruses from wildlife hosts in particular, have exhibited a capability for cross-species transmission (CST), and have caused high-impact diseases in humans Such as Ebola and Marburg hemorrhagic fevers, Nipah and severe acute respiratory syndrome (SAILS). It has been estimated that about 60.3% (Jones et al. 2008) of human infectious diseases are of animal origin (zoonoses) and even some important viral diseases that are traditionally considered of human origin, for example measles and smallpox, may very well have their prehistoric origins in wildlife (Wolfe et al 2007). It maybe logical and prudent therefore, to anticipate that there are other, new filoviruses out there that will cross into humans at some point in time. If we anticipate that these will happen and wish to be prepared for and mitigate this potential, then an understanding of filoviruses as a biologic system in the environment will be essential to that process. We will need to know how the ecological dynamic of CST interacts with a 'new' viruse's evolutionary factors to overcome environmental, demographic and host-specific barriers to transmission and infectivity to humans.

Pinzon, Jorge E.↗
DOE OSTI2021

Coupled social and infrastructure approaches for enhancing solar energy adoption. Final Report

The goal of this project was to work with rural electric cooperatives to facilitate the diffusion of solar energy adoption in households located in the rural and semi-urban areas of Virginia by identifying social and behavioral factors that might be unique to rural regions; and develop a model to calculate the solar adoption propensity score for household based on their demographics, social and behavioral characteristics which would provide an objective metric to cooperatives that can be further used to do targeted marketing of rooftop solar panels. This was achieved through the following tasks: (1) Conducted a survey of the members of Virginia electric cooperatives to identify demographic, social, financial and behavioral attributes of individuals who are likely to adopt rooftop solar panels. (2) Developed a highly detailed, data-driven, agent-based model of the population of Virginia, focusing on the rural regions. (3) Developed diffusion models that use social, behavioral, and demographic factors, and peer effects to study their impact on solar adoption in rural areas. (4) Built a prototype tool based on the diffusion model to help study market segmentation in rural areas and made it available to National Rural Electric Cooperative Association (NRECA). (5) Results and recommendations derived from the model were provided to NRECA to be shared with participating cooperatives. (6) Results were published in peer reviewed journals, conference proceedings and book chapters, and ideas disseminated through presentations and newsletters. There were several important methodological contributions made under this project which are detailed in the published papers, including: (1) Built a decision-adjusted model for predicting adoptors with imbalanced training data; (2) designed seeding strategies to maximize adoption given a fixed budget; (3) built a methodology to compare different agent based models; (4) created models to identify important factors that influence decision to adopt solar panels; and (5) built a methodology for building household profiles of solar generation to study the duck curve phenomenon. The team included members from the University of Virginia (lead), National Rural Electric Cooperative Association (NRECA), Arizona State University, Virginia Tech and Sandia National Laboratory. Note that no individual entity or stakeholder has incentive to promote solar in rural regions. Most of the research and work focuses around urban regions where the potential for growth in solar adoption is higher due to higher population density. This puts rural areas at a disadvantage. By improving the diffusion of solar adoption in rural parts of the country, we can not only provide clean energy to rural areas but also promote job growth and improves energy independence.

14 SOLAR ENERGY↗
DOE OSTI2026

New Norms or Old Habits: Evaluating Interlinked Trajectories of Online Shopping and Work Commute Post-Pandemic

The COVID-19 pandemic has significantly shifted travel behaviors, with major changes observed in online shopping and travel to work. Despite considerable research into pandemic-induced changes in travel behavior, it remains uncertain whether these new patterns have persisted or reverted to pre-pandemic norms. This study addresses this uncertainty by evaluating whether shifts in online shopping and work travel during the pandemic have become permanently ingrained in individuals' daily routine. Leveraging data from the 2022 National Household Travel Survey, a bivariate ordered probit model is employed to analyze changes in online shopping and work travel - whether they have increased, decreased, or remained stable compared to pre-pandemic levels across different population segments. The analysis finds that the pandemic did not significantly alter online shopping for home delivery and travel to work for the majority of society. However, a substantial portion of respondents reported increased online shopping for home delivery and reduced travel to work compared to pre-pandemic levels, with online shopping trends appearing more permanent. Segment-wise analysis and model results indicate heterogeneity in behavioral shifts with females engaging more in online shopping, while zero-vehicle households are traveling less to work, compared to pre-pandemic levels. Additionally, increase in online shopping frequency is significantly and negatively correlated with decrease in traveling to work. These findings highlight the need for improved digital infrastructure, flexible work policies, and integrated transportation solutions tailored to evolving demographic and socioeconomic needs in the post-pandemic era. Additionally, the study calls for integrating passenger and freight movement in a single framework rather than treating them in silos.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2002

Effects of Interannual Climate Variability on Water Availability and Productivity in Capoeira and Crops Under Traditional and Alternative Shifting Cultivation

Regenerating forests play an important role in long-term carbon sequestration and sustainable landuse as they act as potentially important carbon and nutrient sinks during the shifting agriculture fallow period. The long-term functioning of capoeira. is increasingly threatened by a shortening fallow period during shifting cultivation due to demographic pressures and associated increased vulnerability to severe climatic events. Declining productivity and functioning of fallow forests of shifting cultivation combined with progressive loss of nutrients by successive burning and cropping activities has resulted in declining agricultural productivity. In addition to the effects of intense land use practices, droughts associated with El Nino events are becoming more frequent and severe in moist tropical forests and negative effects on capoeira productivity could be considerable. In Igarape-Acu (near Belem, Para), we hypothesize that experimental alternative landuse/clearing practices (mulching and fallow vegetation improvement by planting with fast-growing leguminous tree species) may make capoeira and agriculture more resilient to the effects of agricultural pressures and drought through (1) increased biomass, soil organic matter and associated increase in soil water storage, and nutrient retention and (2) greater rooting depth of trees planted for fallow improvement. This experimental practice (moto mechanized chop-and-mulch with fallow improvement) has resulted increased soil moisture during the cropping phase, reduced loss of nutrients and organic matter, and higher rates of secondary-forest biomass accumulation. We present preliminary data on water relations during the dry season of 2001 in capoeira and crops for both traditional slash-and-burn and alternative chop-and-mulch practices. These data will be used to test IKONOS data for the detection of moisture status differences. The principal goal of the research is to determine the extent to which capoeira and agricultural fields are susceptible to extreme climate events (drought) under contrasting landuse/clearing practices.

Guild, Liane S.↗
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 OSTI2024

On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results

Accurate representation of the turbulent exchange of carbon, water, and heat between the land surface and the atmosphere is critical for modelling global energy, water, and carbon cycles in both future climate projections and weather forecasts. Evaluation of models' ability to do this is performed in a wide range of simulation environments, often without explicit consideration of the degree of observational constraint or uncertainty and typically without quantification of benchmark performance expectations. We describe a Model Intercomparison Project (MIP) that attempts to resolve these shortcomings, comparing the surface turbulent heat flux predictions of around 20 different land models provided with in situ meteorological forcing evaluated with measured surface fluxes using quality-controlled data from 170 eddy-covariance-based flux tower sites. Predictions from seven out-of-sample empirical models are used to quantify the information available to land models in their forcing data and so the potential for land model performance improvement. Sites with unusual behaviour, complicated processes, poor data quality, or uncommon flux magnitude are more difficult to predict for both mechanistic and empirical models, providing a means of fairer assessment of land model performance. When examining observational uncertainty, model performance does not appear to improve in low-turbulence periods or with energy-balance-corrected flux tower data, and indeed some results raise questions about whether the energy balance correction process itself is appropriate. In all cases the results are broadly consistent, with simple out-of-sample empirical models, including linear regression, comfortably outperforming mechanistic land models. In all but two cases, latent heat flux and net ecosystem exchange of CO 2 are better predicted by land models than sensible heat flux, despite it seeming to have fewer physical controlling processes. Land models that are implemented in Earth system models also appear to perform notably better than stand-alone ecosystem (including demographic) models, at least in terms of the fluxes examined here. The approach we outline enables isolation of the locations and conditions under which model developers can know that a land model can improve, allowing information pathways and discrete parameterisations in models to be identified and targeted for future model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Assessing Simulations of Forest Hurricane Disturbance and Recovery in Puerto Rico by ELM-FATES Using Field Measurements

In the past three decades, Puerto Rico (PR) experienced five hurricanes that met or exceeded category three, and they caused severe forest structural damage and elevated tree mortality. To improve our mechanistic understanding of hurricane impacts on tropical forests and assess hurricane-affected forest dynamics in Earth system models, we use in situ forest measurements at the Bisley Experimental Watersheds in Northeast PR to evaluate the Functionally Assembled Terrestrial Ecosystem Simulator coupled with the Energy Exascale Earth System Model Land Model (ELM-FATES). The observations show that before Hurricane Hugo, 77.3% of the aboveground biomass (AGB) is from the shade-tolerant plant function type (PFT). The Hugo-induced mortality rates are over ~50%, and they induce a ~39% AGB reduction, which recovers to a level like the pre-Hugo condition in 2014, following a second, lower intensity hurricane, Georges. We perform numerical experiments that simulate damage from Hugo and Georges on the forests, including defoliation, sapwood and structural biomass damage, and hurricane-induced mortality. ELM-FATES can reasonably represent coexistence between the two PFTs–light-demanding and shade-tolerant–for both the pre-Hugo and post-Hugo conditions. The model represents a reasonable size distribution of mid-and large-sized trees although it underestimates AGB, likely due to the overestimated nonhurricane mortality. ELM-FATES temporarily stimulated leaf biomass and diameter increment after Georges, an effect that should be tested with observations of future hurricane defoliation events. This research indicates that addressing model-data mismatches in tree mortality and understory dynamics are essential to simulation of more extreme hurricane effects under climate change.

58 GEOSCIENCES↗
NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗
DOE OSTI2025

Tree architectural characteristics and stem and leaf functional traits for 17 individuals in the Central Amazon

Given recent increases in tree mortality rates in the Amazon forest following extreme drought and wind events, we tested if lower wood density and acquisitive plant functional traits were associated with increased growth and mortality for common co-occurring trees in the Central Amazon. Research was conducted at the ZF2 Research Station located north or Manaus, Brazil, managed by the Instituto Nacional de Pesquisas da Amazônia (INPA). Seventeen trees of different species with similar sizes but a range in wood density (WD) and wood traits were felled, then assessed for 27 different individual functional parameters, including whole tree architecture, stem xylem anatomical and hydraulic traits and leaf traits. Wood logs were collected at DBH, 50% stem length and at 100% stem length (at the base of the canopy). For wood anatomy samples, n=3-6 subsamples from each height. For leaf samples, 30 leaves were collected from the upper sunlit canopy. The methodology is detailed in the accompanying manuscript. The trait data are summarized in this file: "Trait_Summary.CSV". Summary Trait code abbreviations and units are described in this file: "Sample_Info_Traits_Summary.CSV". Stem traits measured along the bole from the base of the tree (DBH, diameter breast height), mid-stem, and base of the canopy are described in these files: "Sapwood_Area_height.CSV"; "Species_Info_height.CSV"; "Sample_Info_height.CSV"

54 ENVIRONMENTAL SCIENCES↗