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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients’ recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

DeepAndes: A Self-Supervised Vision Foundation Model for Multispectral Remote Sensing Imagery of the Andes

By mapping sites at large scales usingremotely sensed data, archaeologists can generate unique insights into long-term demographic trends, interregional social networks, and human adaptations in the past. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. In addition, while recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multispectral satellite imagery, such as the eight-band data used in our study. In this article, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multispectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for eight-band multispectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pretrained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pretraining in archaeological remote sensing.

Guo, Junlin [Vanderbilt Univ., Nashville, TN (Unit↗
DOE OSTI2024

Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data

This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional MLP, were developed to forecast power outage probabilities, leveraging a rich array of input features gathered from publicly available sources including weather data, weather station locations, power infrastructure maps, socio-economic and demographic statistics, and power outage records. Given a one-hour-ahead weather forecast, the models predict the power outage probability for each census tract, taking into account both the weather prediction and the location's characteristics. The deep learning models employed different loss functions to optimize prediction performance. Our experimental results underscore the significance of socio-economic factors in enhancing the accuracy of power outage predictions at the census tract level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

A Stochastic Framework for Estimating Load Profiles at EV Fast Charging Stations

This paper formulates a methodology for estimating the average daily load profiles of EV fast charging stations over a planning horizon of five to ten years. The developed methodology uses historic vehicle registration data, state-level EV adoption targets, seasonal driving patterns, local demographics, competition, and traffic volume information to predict average station usage. Through Monte Carlo simulations, an average daily load profile is obtained for each month in the planning horizon, and prediction uncertainty is quantified. The proposed framework will facilitate the accurate estimation of energy and demand costs incurred by the charging station over the planning period, thereby informing return-on-investment calculations.

Biswas, Shuchismita↗
DOE OSTI2021

Towards Decarbonization: Establishing a Sustainable, Equitable, Diverse Workforce in the U.S. Photovoltaic Industry

Decarbonizing the U.S. electric grid by 2035 and the broader U.S. economy by 2050 have been identified as targets to avoid more severe climate change effects. Photovoltaics (PV) is expected to play a major role in any decarbonization efforts, and this would involve significant expansion and development of the PV workforce. This work quantifies the size of the workforce required for high levels of PV deployment that could achieve decarbonization. The demographics of the current workforce are reviewed, and existing workforce development initiatives are reviewed with respect to the magnitude needed for decarbonization, with a focus on equity and diversity.

decarbonization↗
DOE OSTI2021

The Value of Diversity in the Renewable Energy Industry and Research Community

Part of creating a successful solar industry is developing a capable workforce. It has been repeatedly shown that organizations and companies are more productive when they have diverse and inclusive cultures. The solar industry in the U.S. performs well in some aspects (non-white workforce, positive work environment) but drastically under-performs in many others. We discuss these metrics and how this impacts the renewable energy industry and the solar research community. We highlight some initial actions which are necessary to begin to remediate these deficiencies, such as demographic tracking, recruiting strategies, and mentoring. We also present the historical and ongoing efforts to improve the diversity and inclusion of the Photovoltaic Specialists Conference, particularly through the work of this year’s Diversity and Inclusion (D&I) oversight committee. This year’s D&I committee is implementing diversity tracking, a women’s lunch, a diversity lunch, childcare resources, pronouns on badges, gender-neutral restrooms, addressing accessibility issues, and publishing a diversity statement and code of conduct to the PVSC website.

diversity↗
DOE OSTI2025

MIC-DP: A Scalable Correlation-Aware Differential Privacy Framework for High-Dimensional Data

Conventional differential privacy (DP) assumes record independence, limiting effectiveness on real-world datasets with temporal, spatial, or structural correlations. These dependencies undermine privacy guarantees and degrade utility in domains like healthcare, IoT, and smart city analytics. We propose Maximum Information Correlated Differential Privacy (MIC-DP), a novel framework that dynamically calibrates noise based on statistical dependencies. MIC-DP uses the Maximum Information Coefficient (MIC) to capture both linear and nonlinear correlations without explicit modeling, enabling adaptive sensitivity adjustment and improved privacy–utility trade-offs. Evaluations on healthcare (MIMIC), demographic (ACI), and synthetic datasets show that MIC-DP reduces mean absolute error (MAE) by up to 5.2% under strict privacy budgets (ϵ≤1), with aggregate utility improvements reaching 18% across datasets and evaluation metrics. MIC-DP provides formal (ϵ,δ)-privacy guarantees, scales efficiently with feature count, and supports deployment in moderate-scale, privacy-sensitive applications. Its tunable performance and runtime efficiency make MIC-DP suitable for privacy-sensitive applications where low-latency analytics and strong privacy guarantees must coexist. These results demonstrate MIC-DP’s effectiveness as a correlation-aware solution for practical DP.

Yang, Wenjun [Univ. of Washington, Tacoma, WA (Uni↗
DOE OSTI2021

Legacy effects of drought on tree growth responses to hurricanes

Natural perturbations, including hurricanes and severe droughts, are becoming more frequent and intense in the tropics, yet our understanding of forest responses to these events is still limited. While the relationships between extreme climatic events and tree mortality are frequently studied, responses of trees that survived such perturbations have been rarely reported. Perturbations and their effects can be quite variable, and trees can respond both positively and negatively to them, depending on their sensitivity and opportunistic behavior. Understanding the potential variation in responses both within and among species is important for building predictions of forest responses. We studied tree growth responses (i.e., changes relative to standard conditions) to hurricanes and drought events in Puerto Rico across six common and broadly distributed species. We examined whether there is a congruence in responses to these contrasting extreme climatic events. We show that trees are particularly susceptible to droughts but display a wide range of responses to hurricanes. Despite the high stochasticity in the impacts of hurricanes, trees that showed reduced growth during previous droughts tend to show reduced growth during the hurricanes as well. Our results demonstrate that trees subjected to different and successive major climatic stresses have consistent declines in their performance. Projected increases in the severity of droughts and tropical storms could have an accumulated, and possibly compound, impact on tree growth. Long-term demographic studies are necessary to understand legacy effects of species responses to extreme climatic perturbations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Otolith 87 Sr / 86 Sr identifies natal origin, movement and life history of Burbot Lota lota in the Kootenai River following 45 years of impoundment

Abstract Hydropower has long been an effective form of renewable energy, but the development of the resource has negatively impacted fishes throughout the world. Formulating recovery plans to mitigate these effects requires a firm understanding of species‐specific provenance, life history and life stage habitat requirements. However, the importance of this information is often only recognised after a substantial population decline, when obtaining it becomes demographically challenging. We provide a case study for this scenario and present a post hoc approach to gain insight into these critical data gaps for Burbot Lota lota in the upper Kootenai River in Montana and British Columbia. The population once supported a robust tribal and recreational harvest but declined following impoundment and these fisheries have remained closed or curtailed for nearly 20 years. The reasons for the decline are unknown and actions to assist recovery have been constrained by limited information regarding natal origin, life history and habitat use. We addressed this issue using strontium isotope ratios ( 87 Sr/ 86 Sr) in otoliths collected over four decades in Lake Koocanusa, the reservoir on the Kootenai River above Libby Dam. Due to the geologic heterogeneity of the basin and resulting variation in water 87 Sr/ 86 Sr, the assignment probability for Burbot to their region of natal origin approached unity. Otolith 87 Sr/ 86 Sr also indicated the population was comprised of mainstem‐ and tributary‐origin forms and their relative abundance changed over time. Mainstem Burbot was most abundant during the 1970s and 2000s, whereas tributary forms dominated between these periods. We suggest ecosystem changes following impoundment contributed to this shift in life history forms and the overall decline of the population. Our findings underscore the need for additional investigations of these impacts to guide restoration efforts.

Dunnigan, James L.↗
DOE OSTI2021

Invasion and rapid adaptation of guppies ( Poecilia reticulata ) across the Hawaiian Archipelago

How much does natural selection, as opposed to genetic drift, admixture, and gene flow, contribute to the evolution of invasive species following introduction to a new environment? Here we assess how evolution can shape biological invasions by examining population genomic variation in non-native guppies (Poecilia reticulata) introduced to the Hawaiian Islands approximately a century ago. By examining 18 invasive populations from four Hawaiian islands and four populations from the native range in northern South America, we reconstructed the history of introductions and evaluated population structure as well as the extent of ongoing gene flow across watersheds and among islands. Patterns of differentiation indicate that guppies have developed significant population structure, with little natural or human-mediated gene flow having occurred among populations following introduction. Demographic modeling and admixture graph analyses together suggest that guppies were initially introduced to O‘ahu and Maui and then translocated to Hawai‘i and Kaua‘i. We detected evidence for only one introduction event from the native range, implying that any adaptive evolution in introduced populations likely utilized the genetic variation present in the founding population. Environmental association tests accounting for population structure identified loci exhibiting signatures of adaptive variation related to predators and landscape characteristics but not nutrient regimes. When paired with high estimates of effective population sizes and detectable population structure, the presence of environment-associated loci supports the role of natural selection in shaping contemporary evolution of Hawaiian guppy populations. Our findings indicate that local adaptation may engender invasion success, particularly in species with life histories that facilitate rapid evolution. Finally, evidence of low gene flow between populations suggests that removal could be an effective approach to control invasive guppies across the Hawaiian archipelago.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A catastrophic tropical drought kills hydraulically vulnerable tree species

Drought-related tree mortality is now a widespread phenomenon predicted to increase in magnitude with climate change. However, the patterns of which species and trees are most vulnerable to drought, and the underlying mechanisms have remained elusive, in part due to the lack of relevant data and difficulty of predicting the location of catastrophic drought years in advance. In this work, we used long-term demographic records and extensive databases of functional traits and distribution patterns to understand the responses of 20–53 species to an extreme drought in a seasonally dry tropical forest in Costa Rica, which occurred during the 2015 El Niño Southern Oscillation event. Overall, species-specific mortality rates during the drought ranged from 0% to 34%, and varied little as a function of tree size. By contrast, hydraulic safety margins correlated well with probability of mortality among species, while morphological or leaf economics spectrum traits did not. This firmly suggests hydraulic traits as targets for future research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Forest responses to simulated elevated CO 2 under alternate hypotheses of size- and age-dependent mortality

Elevated atmospheric carbon dioxide (eCO 2 ) is predicted to increase growth rates of forest trees. The extent to which increased growth translates to changes in biomass is dependent on the turnover time of the carbon, and thus tree mortality rates. Size- or age-dependent mortality combined with increased growth rates could result in either decreased carbon turnover from a speeding up of tree life cycles, or increased biomass from trees reaching larger sizes, respectively. However, most vegetation models currently lack any representation of size- or age-dependent mortality and the effect of eCO 2 on changes in biomass and carbon turnover times is thus a major source of uncertainty in predictions of future vegetation dynamics. Using a reduced-complexity form of the vegetation demographic model the Functionally Assembled Terrestrial Ecosystem Simulator to simulate an idealised tropical forest, we find increases in biomass despite reductions in carbon turnover time in both size- and age-dependent mortality scenarios in response to a hypothetical eCO 2 -driven 25% increase in woody net primary productivity (wNPP). Carbon turnover times decreased by 9.6% in size-dependent mortality scenarios due to a speeding up of tree life cycles, but also by 2.0% when mortality was age-dependent, as larger crowns led to increased light competition. Increases in aboveground biomass (AGB) were much larger when mortality was age-dependent (24.3%) compared with size-dependent (13.4%) as trees reached larger sizes before death. In simulations with a constant background mortality rate, carbon turnover time decreased by 2.1% and AGB increased by 24.0%, however, absolute values of AGB and carbon turnover were higher than in either size- or age-dependent mortality scenario. The extent to which AGB increases and carbon turnover decreases will thus depend on the mechanisms of large tree mortality: if increased size itself results in elevated mortality rates, then this could reduce by about half the increase in AGB relative to the increase in wNPP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Tree crown damage and its effects on forest carbon cycling in a tropical forest

Abstract Crown damage can account for over 23% of canopy biomass turnover in tropical forests and is a strong predictor of tree mortality; yet, it is not typically represented in vegetation models. We incorporate crown damage into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to evaluate how lags between damage and tree recovery or death alter demographic rates and patterns of carbon turnover. We represent crown damage as a reduction in a tree's crown area and leaf and branch biomass, and allow associated variation in the ratio of aboveground to belowground plant tissue. We compare simulations with crown damage to simulations with equivalent instant increases in mortality and benchmark results against data from Barro Colorado Island (BCI), Panama. In FATES, crown damage causes decreases in growth rates that match observations from BCI. Crown damage leads to increases in carbon starvation mortality in FATES, but only in configurations with high root respiration and decreases in carbon storage following damage. Crown damage also alters competitive dynamics, as plant functional types that can recover from crown damage outcompete those that cannot. This is a first exploration of the trade‐off between the additional complexity of the novel crown damage module and improved predictive capabilities. At BCI, a tropical forest that does not experience high levels of disturbance, both the crown damage simulations and simulations with equivalent increases in mortality does a reasonable job of capturing observations. The crown damage module provides functionality for exploring dynamics in forests with more extreme disturbances such as cyclones and for capturing the synergistic effects of disturbances that overlap in space and time.

Needham, Jessica F.↗
DOE OSTI2020

Spatial and temporal patterns in age structure of Golden Eagles wintering in eastern North America

The behavior of wildlife varies seasonally, and that variation can have substantial demographic consequences. This is especially true for long-distance migrants where the use of landscapes varies by season and, sometimes, age cohort. In this study, we tested the hypothesis that distributional patterns of Golden Eagles (Aquila chrysaetos) wintering in eastern North America are age-structured (i.e., birds of similar ages winter together) through the analysis of 370,307 images collected by motion-sensitive trail cameras set over bait during the winters of 2012–2013 and 2013–2014. At nine sites with sufficient data for analysis, we documented 145 eagle visits in 2012–2013 and 146 in 2013–2014. We found significant between-year variation in age structure of wintering eastern Golden Eagles, driven largely by annual differences in the proportion of first-winter birds. However, although many other species show spatial structure in wintering behavior, our analysis revealed no latitudinal organization among age cohorts of wintering eastern Golden Eagles. The lack of age-related latitudinal segregation in wintering behavior does not exclude the possibility that these eagles have sex-based or other types of dominance hierarchies that could result in spatial or temporal segregation. Alternatively, other mechanisms such as food availability or habitat structure may determine the distribution and abundance of Golden Eagles in winter.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Demand Response Analysis for Different Residential Personas in a Comfort-Driven Behavioral Context

Low demand response (DR) participation and high program drop-out rates continue to impede DR goals that could save up to $13 billion in annual grid expansion and electricity demand costs. Yet, the literature lacks a thorough understanding of how different residential customer segments enrolled in DR programs respond to utility signals in view of occupant comfort considerations. The objective of this study is to gain a clear understanding of the effects of four different customer personas on residential DR. Given current data limitations, this work developed an array of hypothetical personas with varied priorities, activity levels, and comfort thresholds based on demographic variables that have been found in previous studies to influence energy consumption. A BEopt DR model for a reference residential single-family building located in Colorado was built to isolate the effect of differences in buildings or climate. The results provide useful evidence on how persona-comfort differences lead to significant deviations in DR goals (especially peak demand reduction), ranging from 0.1% to 20%. This work presents a novel framework representing comfort preferences in DR models. The data generated, albeit synthetic, and the results could inform DR program design considerations of how different people respond to different comfort priorities.

BEopt↗
DOE OSTI2020

The relatively young and rural population may limit the spread and severity of COVID-19 in Africa: a modelling study

A novel coronavirus disease 2019 (COVID-19) has spread to all regions of the world. There is great uncertainty regarding how countries’ characteristics will affect the spread of the epidemic; to date, there are few studies that attempt to predict the spread of the epidemic in African countries. In this paper, we investigate the role of demographic patterns, urbanisation and comorbidities on the possible trajectories of COVID-19 in Ghana, Kenya and Senegal. We use an augmented deterministic Susceptible-Infected-Recovered model to predict the true spread of the disease, under the containment measures taken so far. We disaggregate the infected compartment into asymptomatic, mildly symptomatic and severely symptomatic to match observed clinical development of COVID-19. We also account for age structures, urbanisation and comorbidities (HIV, tuberculosis, anaemia). In our baseline model, we project that the peak of active cases will occur in July, subject to the effectiveness of policy measures. When accounting for the urbanisation, and factoring in comorbidities, the peak may occur between 2 June and 17 June (Ghana), 22 July and 29 August (Kenya) and, finally, 28 May and 15 June (Senegal). Successful containment policies could lead to lower rates of severe infections. While most cases will be mild, we project in the absence of policies further containing the spread, that between 0.78% and 1.03%, 0.61% and 1.22%, and 0.60% and 0.84% of individuals in Ghana, Kenya and Senegal, respectively, may develop severe symptoms at the time of the peak of the epidemic. Compared with Europe, Africa’s younger and rural population may modify the severity of the epidemic. The large youth population may lead to more infections but most of these infections will be asymptomatic or mild, and will probably go undetected. The higher prevalence of underlying conditions must be considered.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Activities and risk factors associated with fall-related injuries among US Army soldiers

Introduction Falls/near falls are the second leading cause of hospitalisation and outpatient visits among US Army soldiers. While numerous studies have evaluated fall-related or near fall-related injuries among elderly adults, few have evaluated this association among young adults. The objective of this study is to describe the characteristics and risk factors associated with fall-related or near fall-related injuries among male US Army soldiers. Methods This is a cross-sectional study of male US Army Airborne Division soldiers (n=5187). Electronic surveys captured demographic, lifestyle, physical training (PT), fitness and injury data during spring/summer of 2016. Multiple logistic regression was used to identify independent risk factors of fall-related or near fall-related injuries, adjusting for potential confounders. Results Primary findings indicated that activities and risk factors associated with fall-related or near fall-related injuries among soldiers included younger age (≤35 years), holding a job that required minimal lifting activities, slower 2-mile run times and not running during personal PT. Conclusions The findings from this study suggest that male US Army soldiers and other physically active men may benefit from (1) obtaining and/or maintaining higher aerobic endurance and muscular strength, and (2) training focused on preventing fall-related injuries during PT, road marching and sports/recreational activities. Moreover, prevention strategies and education should further target younger soldiers (≤35 years old), as younger age is not modifiable.

General & Internal Medicine↗
DOE OSTI2022

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗