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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances

Purpose: Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. Methods: We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. Results: Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. Conclusions: Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization

Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to system- atically collect high-quality treatment information because it is often stored in unstructured text. Manually extracting and standardizing drug and regimen data is time-intensive. Recent advances in large language models (LLMs) offer a potential solution for automated extraction of structured treatment information from clinical text. Objective: This study systematically evaluates the utility of four LLMs from the Llama family for automated extraction of oncology treatment information from clinical text. This information can guide researchers using cancer registry data to provide insights into cancer care and outcomes beyond clinical trials. Methods: Four instruction-tuned Llama models with varying parameter counts (1B, 3B, 8B, and 70B) were evaluated for their ability to extract treatment information from clinical documents. A unified oncology knowledge base integrating seven major public data sources was developed to standardize and normalize extracted entities—a critical step for harmonizing data from diverse sources. Extracted treatment data were compared against expert-annotated ground truth. Model performance was assessed using accuracy metrics (Precision, Recall, F1-Score) and opera- tional feasibility metrics, including processing speed and structural compliance of the output. Results: A strong positive correlation was observed between model size and extraction accuracy. F1-score improved from 0.609 for the 1B model to 0.710 (3B), 0.807 (8B), and 0.828 (70B). While larger models demonstrated superior accuracy and compliance, they incurred higher computational costs. The modest performance difference between 8B and 70B suggests diminishing returns with increasing model size. Conclusions: LLMs represent a viable technology for automating oncology treatment extraction. The 8B-parameter model emerged as a highly effective option, balancing high accuracy and computational efficiency. Selecting an appropriate LLM for deployment in cancer registries involves a trade-off between desired accuracy and available operational resources. Harmonizing extracted entities with the oncology knowledge base facilitates standardized integration into common data models, enhancing data quality for real-world evidence analyses.

artificial intelligence↗
DOE OSTI2024

Developing expert scientific consensus on the environmental and societal effects of marine artificial structures prior to decommissioning

Thousands of artificial (‘human-made’) structures are present in the marine environment, many at or approaching end-of-life and requiring urgent decisions regarding their decommissioning. No consensus has been reached on which decommissioning option(s) result in optimal environmental and societal outcomes, in part, owing to a paucity of evidence from real-world decommissioning case studies. To address this significant challenge, we asked a worldwide panel of scientists to provide their expert opinion. They were asked to identify and characterise the ecosystem effects of artificial structures in the sea, their causes and consequences, and to identify which, if any, should be retained following decommissioning. Experts considered that most of the pressures driving ecological and societal effects from marine artificial structures (MAS) were of medium severity, occur frequently, and are dependent on spatial scale with local-scale effects of greater magnitude than regional effects. The duration of many effects following decommissioning were considered to be relatively short, in the order of days. Overall, environmental effects of structures were considered marginally undesirable, while societal effects marginally desirable. Experts therefore indicated that any decision to leave MAS in place at end-of-life to be more beneficial to society than the natural environment. However, some individual environmental effects were considered desirable and worthy of retention, especially in certain geographic locations, where structures can support improved trophic linkages, increases in tourism, habitat provision, and population size, and provide stability in population dynamics. The expert analysis consensus that the effects of MAS are both negative and positive for the environment and society, gives no strong support for policy change whether removal or retention is favoured until further empirical evidence is available to justify change to the status quo. The combination of desirable and undesirable effects associated with MAS present a significant challenge for policy- and decision-makers in their justification to implement decommissioning options. Decisions may need to be decided on a case-by-case basis accounting for the trade-off in costs and benefits at a local level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Agent-based simulation and child protection systems: Rationale, implementation, and verification

Simulation models are an important tool used in health care and other disciplines to support operational research and decision-making. In the child protection literature, simulation models are an under-utilized source of research evidence. Here, in this paper, we describe the rationale for and the development of an agent-based simulation of a child protection system in the US. Using the investigation, prevention service, and placement histories of 600,000 children served in an urban child welfare system, we walk the reader through the development of a prototype known as OSPEDALE. The governing equations built into OSPEDALE probabilistically simulate the onset of investigations. Then, drawing from empirical survival distributions, the governing equations trace the probability of subsequent interactions with the system (recurrence of maltreatment, service referrals, and placement) conditional on the characteristics of children, their assessed risk level, and prior child protection system involvement. As an initial test of OSPEDALE's utility, we compare empirical admission counts with counts generated from OSPEDALE. Though the verification step is admittedly simple, the comparison shows that OSPEDALE replicates the empirical count of new admissions closely enough to justify further investment in OSPEDALE. Management of public child protection systems is increasingly research evidence-dependent. The emphasis on research evidence as a decision-support tool has elevated evidence acquired through randomized clinical trials. Though important, the evidence from clinical trials represents only one type of research evidence. Properly specified, simulation models are another source of evidence with real-world relevance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events

Uncertainty quantification (UQ) methods play an important role in reducing errors in weather forecasting. Conventional approaches in UQ for weather forecasting rely on generating an ensemble of forecasts from physics-based simulations to estimate the uncertainty. However, it is computationally expensive to generate many forecasts to predict real-time extreme weather events. Evidential Deep Learning (EDL) is an uncertainty-aware deep learning approach designed to provide confidence about its predictions using only one forecast. It treats learning as an evidence acquisition process where more evidence is interpreted as increased predictive confidence. We apply EDL to storm forecasting using real-world weather datasets and compare its performance with traditional methods. Our findings indicate that EDL not only reduces computational overhead but also enhances predictive uncertainty. This method opens up novel opportunities in research areas such as climate risk assessment, where quantifying the uncertainty about future climate is crucial.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Separation of infrared and bulk in thermal QCD

A new thermal regime of QCD, featuring decoupled scale-invariant infrared glue, has been proposed to exist both in pure-glue (N f =0) and “real-world” (N f =2+1 at physical quark masses) QCD. In this IR phase, elementary degrees of freedom flood the infrared, forming a distinct component independent from the bulk. This behavior necessitates non-analyticities in the theory. In pure-glue QCD, such non-analyticities have been shown to arise via Anderson-like mobility edges in Dirac spectra (λ IR = 0, ±λ A ≠ 0), as manifested in the dimension function d IR (λ). Here, we present the first evidence, based on lattice QCD calculation at a=0.105 fm, that this mechanism is also at work in real-world QCD, thus supporting the existence of the proposed IR regime in nature. An important aspect of our results is that, while at T = 234 MeV we find a dimensional jump between zero modes and lowest near-zero modes very close to unity (d IR = 3 to d IR ≃ 2), similar to the IR phase of pure-glue QCD, at T = 187 MeV we observe a continuous λ-dependence. This suggests that thermal states just above the chiral crossover are non-analytically (in T) connected to thermal state at T = 234 MeV, supporting the key original proposition that the transition into the IR regime occurs at a temperature strictly above the chiral crossover.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Enter the AHU (36th Chamber of ASHRAE): A Multi-site Field Study of ASHRAE G36

Despite being recognized as the best practice for advanced building controls, ASHRAE Guideline 36 (G36) has seen slow adoption in retrofit cases. Decisionmakers lack credible field evidence to justify the time and person-power investment. Most prior analyses have relied on software simulations, which overlook implementation challenges and fail to persuade owners to move from models to real-world deployment. This paper presents a multi-site field study of G36 performance, drawing on measured results from 17 projects across diverse building types and climate zones. The analysis disaggregates outcomes by the most widely adopted air handling unit (AHU) based G36 strategies, including trim-and-respond approaches to supply air temperature (SAT) and duct static pressure (DSP) reset, and economizer controls. Results for controls re programming implementations are encouraging, with HVAC savings ranging from 2% - 49%, with a median of 18%. The range aligns with simulation study findings showing 1% - 46% savings through these three strategies. These findings show that even without capital investment in control infrastructure upgrades, existing building owners are reaping significant benefits from updating HVAC sequences of operation to industry best-practice solutions. The results give practitioners and decisionmakers a reference point on what to expect, which strategies deliver, and how field performance may compare to simulation.

Deshpande, Reva↗
DOE OSTI2026

Lanthanide Separations through Helicate Self-Assembly

Skyrocketing demand for rare-earth elements makes their isolation and reuse from electronic waste an increasingly attractive alternative to mining. Typically, the capture of individual lanthanides from mixtures requires the iterative design of complex, macrocyclic ligands, a sometimes successful but often laborious process. Here we show that the self-assembly of rare-earth ions with a ditopic ligand, L d , results in the selective incorporation of smaller lanthanides and gives rise to impressive separation factors. Comparison to a monotopic ligand, L m , shows that the separation performance of ditopic L d critically depends on the presence of two adjacent binding sites. Isothermal titration calorimetry (ITC) experiments show that the selectivity of L d for smaller ions is thermodynamically driven and provide evidence of positive cooperativity among the two binding sites of L d . The simplicity of the separation procedure reported here, requiring only 1 min of sonication in methanol, shows the potential applicability of this approach to real-world separations. Here, by achieving efficient rare-earth separations in small, synthetically facile helicates, this work shows the promise of self-assembly as a mechanism to drive metal separations.

Alcohols↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2025

Rapid Oxidation of Uranium Steel Alloys: Combustion Synthesis

This project explored the use of combustion synthesis as a rapid, high-temperature method for oxidizing uranium-bearing steel alloys. Traditional laboratory-scale synthesis methods often fail to replicate the thermal and kinetic conditions experienced by real-world particulates, particularly those formed under rapid quenching or high-temperature scenarios. Combustion synthesis offers a promising alternative by enabling fast, localized heating and flexible precursor selection. A series of targeted experiments were conducted using a U 2 NiCrFe 4 alloy as the precursor. The alloy was oxidized using combustion synthesis reactions fueled by uranyl nitrate and glycine, achieving peak temperatures exceeding 1,200 °C. Postreaction analysis using scanning electron microscopy (SEM), elemental mapping, and Raman spectroscopy revealed the formation of iron-based oxides, with limited but detectable evidence of uranium oxide phases such as UO 2 . The results indicate that under the rapid reaction and cooling conditions of combustion synthesis, iron oxides form preferentially, but uranium oxide formation is kinetically limited. These findings validate combustion synthesis as a viable method for simulating the oxidation behavior of uranium steels in extreme environments and lay the groundwork for future studies aimed at enhancing uranium oxide formation through higher temperatures or modified precursor compositions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Flexible Ramping Product Procurement in Day-Ahead Markets

Flexible ramping products (FRPs) emerge as a promising instrument for addressing steep and uncertain ramping needs through market mechanisms. Initial implementations of FRPs in North American electricity markets, however, revealed several shortcomings in existing FRP designs. Here, in many instances, FRP prices failed to signal the true value of ramping capacity, most notably evident in zero FRP prices observed in a myriad of periods during which the system was in acute need for rampable capacity. These periods were marked by scheduled but undeliverable FRPs, often calling for operator out-of-market actions. On top of that, the methods used for procuring FRPs have been primarily rule-based, lacking explicit economic underpinnings. In this paper, we put forth an alternative framework for FRP procurement, which seeks to set FRP requirements and schedule FRP awards such that the expected system operation cost is minimized. Using real-world data from U.S. ISOs, we showcase the relative merits of the framework in (i) reducing the total system operation cost, (ii) improving price formation, (iii) enhancing the the deliverability of FRP awards, and (iv) reducing the need for out-of-market actions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Short-term Electricity Price Forecasting with Constrained Regressors

The volatility of electricity price presents a challenge to market participants as their decision-making process are highly depend on the accuracy of price forecasts. However, there is growing empirical evidence of increasing price volatility and price spikes in electricity markets as a result of variable renewable energy generation, extreme weather events, and other factors. The distribution shift caused by spikes in electricity price data differentiates the forecasting tasks from other renewable energy sources. Moreover, the observations may be compromised by cyberattacks and thus not available in the testing phase. To this end, we propose a Similarity-Enhanced Electricity Decomposition Forecasting model (SEED-Forecaster) to address the missing response problem and spikes capturing in short-term electricity price forecasting. The effectiveness of the proposed framework is tested on real-world electricity price data from California Independent System Operator (CAISO). Numerical results of case studies show that the proposed SEED-Forecsater can enhance forecasting performance, particularly in capturing electricity spikes, even under conditions without regressors during testing stage.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Caught in headlights: Captive white-tailed deer responses to variations in vehicle lighting during imminent collision scenarios

Vehicle collisions with deer (Odocoileus spp.) cause billions of dollars in damages and injure thousands of drivers every year in the United States, and few mitigation methods have proven effective. However, recent research suggests that vehicle lighting might influence white-tailed deer (Odocoileus virginianus; hereafter, deer) responses to oncoming vehicles. Most new vehicles are manufactured with light emitting diode (LED) headlights which differ in total radiance and wavelength of light emitted compared to the previous industry standard of tungstenhalogen (halogen) headlights. Also, frontal vehicle illumination through rear-facing lighting has shown promise in enhancing deer responses to vehicles, but its effectiveness has not been tested under various headlight conditions (headlight type or intensity). As such, it remains unclear how these aspects of vehicle lighting affect deer responses to an approaching vehicle. We used 23 captive, wild-type deer to investigate how variations in vehicle lighting affect deer responses to an approaching vehicle at night, when most collisions occur. We released deer into a 95 m long, 3 m wide chute and approached them from the opposite end with an electric golf cart outfitted with two versions of stock 2017–2020 Ford Fusion headlights (LED and halogen) and a 51 cm rear-facing lightbar to test how vehicle lighting affected deer avoidance behaviors in an imminent, head-on collision scenario. Each deer received eight lighting treatments consisting of unique combinations of headlight type (LED vs. halogen), light intensity (low vs. high beam), and rear-facing lighting (lightbar off vs. on). We measured deer alert and flight behavior using infrared videography. We found that the halogen, high beam, lightbar off treatment had the greatest probability of evoking an alert response. Furthermore, when the lightbar was off, high beams appeared to increase alert probability for halogen headlights. Also, we found evidence that high beam, halogen headlights tend to increase alert probability over high beam, LED headlights, when the lighbar was off. We found no effect of our lighting treatments on deer alert distance, flight probability, or flight initiation distance. Across all behavioral responses, the random effect deer ID explained 0.86–9.19 × more variation than our lighting treatments, reflecting large differences in responses among deer. Overall, we found that vehicle lighting can impact deer behavior during an imminent, head-on collision scenario, although lighting was ineffective at increasing favorable flight behaviors. Future research should investigate how vehicle lighting treatments affect free-ranging, wild deer in a variety of real-world scenarios and at longer approach distances.

White-tailed deer (Odocoileus virginianus) Deer be↗
DOE OSTI2024

Rapid biodegradation of microplastics generated from bio-based thermoplastic polyurethane

The accumulation of microplastics in various ecosystems has now been well documented and recent evidence suggests detrimental effects on various biological processes due to this pollution. Accumulation of microplastics in the natural environment is ultimately due to the chemical nature of widely used petroleum-based plastic polymers, which typically are inaccessible to biological processing. One way to mitigate this crisis is adoption of plastics that biodegrade if released into natural environments. In this work, we generated microplastic particles from a bio-based, biodegradable thermoplastic polyurethane (TPU-FC1) and demonstrated their rapid biodegradation via direct visualization and respirometry. Furthermore, we isolated multiple bacterial strains capable of using TPU-FC1 as a sole carbon source and characterized their depolymerization products. Finally, to visualize biodegradation of TPU materials as real-world products, we generated TPU-coated cotton fabric and an injection molded phone case and documented biodegradation by direct visualization and scanning electron microscopy (SEM), both of which indicated clear structural degradation of these materials and significant biofilm formation.

09 BIOMASS FUELS↗
DOE OSTI2024

Enhanced Safety of Heavy-Duty Vehicles on Highways through Automatic Speed Enforcement – A Simulation Study

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. Here, this paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios. To conduct this analysis, we utilize SUMO, an open-source microscopic traffic simulator, enabling realistic simulations of real-world driving behaviors with and without speed cameras. The simulations also consider empirical distributions of desired headway gaps and reaction times exhibited by human drivers. This study offers a fresh perspective on highway safety by examining how the influence of automatic speed enforcement systems on driver behavior enhances the safety of HDV when faced with challenging situations. By shedding light on these dynamics, we contribute valuable insights to the ongoing research efforts on using technology to create safer roadways, ultimately promoting safer coexistence between heavy-duty and passenger vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Electrochemical Characterization of Dissolved Oxides in Molten FLiNaK

Molten fluoride salts have long been the subject of investigation given their application in a variety of industrial processes. Oxides are a common exogeneous impurity within these systems, and can lead to many operational and regulatory concerns. In recent studies, the electro-oxidation of dissolved metallic oxides has been attributed to a single step oxygen evolution reaction despite voltametric evidence suggesting otherwise. Here, we first use square wave voltammetry to confirm that the oxidation of dissolved oxides in fluorides proceeds via a two-electron transfer reaction and then demonstrate that the presence of a peroxide-mediated redox reaction better explains the electrochemical data. Numerical simulations were then used to support the determination of improved diffusion coefficients to enable electroanalytical measurements of the oxide concentration across at a variety of temperatures. A linear Arrhenius relationship was observed when the experimental data was corrected for ohmic resistance effects. Concentration measurements using the corrected square wave voltammetry data demonstrated high accuracy across a large range of concentrations, while the uncorrected data showed plateauing and high errors. In total, this work serves to rectify previously misconstrued electrochemical data of oxides in molten salts and demonstrate how accurate, in situ concentration measurements can be achieved in real-world systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A comprehensive academic and industrial survey of blockchain technology for the energy sector using fuzzy Einstein decision-making

The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization, leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems. This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in energy, (ii) an in-depth evaluation of the evolution and viability of blockchain initiatives in energy with the help of expert surveys, and (iii) a novel decision-making model using a q-rung orthopair fuzzy Multi-Attributive Border Approximation (q-ROF-MABAC) method under the Einstein operator. The results were compared with existing decision models to validate consistency and robustness. Nine key blockchain use case categories were identified and ranked based on technical, economic, and governance dimensions. The results demonstrated that integrating expert insights into a fuzzy logic framework helps filter out overhyped claims in the literature and prioritize realistic and high-impact applications such as green certificates, grid services, and peer-to-peer energy trading. The model’s rankings remained stable across varying weight configurations, confirming the robustness of the methodology. This study provides an evidence-based decision-support tool for researchers, industry stakeholders, and policymakers to better understand, evaluate, and adopt blockchain technologies in the energy sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Global Explainability of A Deep Abstaining Classifier for Cancer Pathology Reports

We present a global explainability method to characterize sources of errors in a real-world multitask deep abstaining classifier (DAC), in the context of cancer histology prediction. Our multitask classifier, currently deployed for automated annotation of cancer pathology reports from NCI-SEER registries, was trained and evaluated on 1.04 million hand-annotated samples and makes simultaneous predictions of cancer site, subsite, histology, laterality, and behavior for each report. The DAC framework enables the model to abstain on ambiguous reports and confusing classes to achieve the target accuracy on the retained (non-abstained) samples, but at the cost of decreased coverage. Requiring 97% accuracy on the histology task caused our model to retain only 22% of all samples, mostly the less ambiguous and common classes. Local explainability with the GradInp technique provided a computationally efficient way of obtaining contextual reasoning for hundreds of thousands of individual predictions. Our method, involving dimensionality reduction of approximately 13000 aggregated local explanations (ALE), offers a tractable path to true global explainability. It enabled identification of sources of errors in histology classification, globally, as hierarchical complexity among classes, label noise, insufficient information, and conflicting evidence. This suggests several strategies for iterative improvement of our DAC, including well-designed exclusion criteria, focused annotation, and reduced penalties for errors involving hierarchically related classes.

59 BASIC BIOLOGICAL SCIENCES↗