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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Customizing Countermeasure Prescriptions using Predictive Measures of Sensorimotor Adaptability

Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the readapation phase following a return to a gravitational environment. These alterations may lead to disruption in the ability to perform mission critical functional tasks during and after these gravitational transitions. Astronauts show significant inter-subject variation in adaptive capability following gravitational transitions. The ability to predict the manner and degree to which each individual astronaut will be affected would improve the effectiveness of a countermeasure comprised of a training program designed to enhance sensorimotor adaptability. Due to this inherent individual variability we need to develop predictive measures of sensorimotor adaptability that will allow us to predict, before actual space flight, which crewmember will experience challenges in adaptive capacity. Thus, obtaining this information will allow us to design and implement better sensorimotor adaptability training countermeasures that will be customized for each crewmember's unique adaptive capabilities. Therefore the goals of this project are to: 1) develop a set of predictive measures capable of identifying individual differences in sensorimotor adaptability, and 2) use this information to design sensorimotor adaptability training countermeasures that are customized for each crewmember's individual sensorimotor adaptive characteristics. To achieve these goals we are currently pursuing the following specific aims: Aim 1: Determine whether behavioral metrics of individual sensory bias predict sensorimotor adaptability. For this aim, subjects perform tests that delineate individual sensory biases in tests of visual, vestibular, and proprioceptive function. Aim 2: Determine if individual capability for strategic and plastic-adaptive responses predicts sensorimotor adaptability. For this aim, each subject's strategic and plastic-adaptive motor learning abilities are assessed using a test of locomotor function designed specifically to delineate both mechanisms. Aim 3: Develop predictors of sensorimotor adaptability using brain structural and functional metrics. We will measure individual differences in regional brain volumes (structural MRI), white matter integrity (diffusion tensor imaging, or DTI), functional network integrity (resting state functional connectivity MRI), and sensorimotor adaptation task-related functional brain activation (functional MRI). We decided to complete the data collection for Specific Aims 1, 2 and 3 simultaneously on the same subjects to increase data capture. By having the same subjects perform all three specific aims we can enhance our ability to detect how a wider range of factors can predict adaptability in a specific individual. This provides a much richer database and potentially a better understanding of the predictive power of the selected factors. In this presentation I will discuss preliminary data obtained to date.

Bloomberg, J. J.↗
DOE OSTI2012

Automated Expert Modeling and Student Evaluation

AEMASE searches a database of recorded events for combinations of events that are of interest. It compares matching combinations to a statistical model to determine similarity to previous events of interest and alerts the user as new matching examples are found. AEMASE is currently used by weapons tactics instructors to find situations of interest in recorded tactical training scenarios. AEMASE builds on a sub-component, the Relational Blackboard (RBB), which is being released as open-source software. AEMASE builds on RBB adding interactive expert model construction (automated knowledge capture) and re-evaluation of scenario data.

Source record↗
DOE OSTI2017

Fission Reaction Event Yield Algorithm FREYA 2.0.2

The purpose of this paper is to present the main differences between FREYA versions 1.0 and 2.0.2. FREYA (Fission Reaction Event Yield Algorithm) is a fission event generator which models complete fission events. As such, it automatically includes fluctuations as well as correlations between observables, resulting from conservation of energy and momentum. The main differences between the two versions are: additional fissionable isotopes, angular momentum conservation, Giant Dipole Resonance form factor for the statistical emission of photons, improved treatment of fission photon emission using RIPL database, and dependence on the incident neutron direction. FREYA 2.0.2 has been integrated into the LLNL Fission Library 2.0.2, which has itself been integrated into MCNP6.2, TRIPOLI-4.10, and can be called from Geant4.10.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Final Co-op Presentation Fall 2023

This presentation is an overview of the four projects I worked on during my first co-op term. The first project was a 3D-printed support for a horn that was being X-rayed. My second project was a website that compiled all data available on outgassing and created a comprehensive database. My third project was creating a cart for a PIP-II absorber that needs to be transported down the beamline using NX. My fourth and final project was testing a prototype of a collimator.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Assessing Adverse Events of Postprostatectomy Radiation Therapy for Prostate Cancer: Evaluation of Outcomes in the Regione Emilia-Romagna, Italy

Purpose: Although the likelihood of radiation-related adverse events influences treatment decisions regarding radiation therapy after prostatectomy for eligible patients, the data available to inform decisions are limited. This study was designed to evaluate the genitourinary, gastrointestinal, and sexual adverse events associated with postprostatectomy radiation therapy and to assess the influence of radiation timing on the risk of adverse events. Methods: The Regione Emilia-Romagna Italian Longitudinal Health Care Utilization Database was queried to identify a cohort of men who received radical prostatectomy for prostate cancer during 2003 to 2009, including patients who received postprostatectomy radiation therapy. Patients with prior radiation therapy were excluded. Outcome measures were genitourinary, gastrointestinal, and sexual adverse events after prostatectomy. Rates of adverse events were compared between the cohorts who did and did not receive postoperative radiation therapy. Multivariable Cox proportional hazards models were developed for each class of adverse events, including models with radiation therapy as a time-varying covariate. Results: A total of 9876 men were included in the analyses: 2176 (22%) who received radiation therapy and 7700 (78%) treated with prostatectomy alone. In multivariable Cox proportional hazards models, the additional exposure to radiation therapy after prostatectomy was associated with increased rates of gastrointestinal (rate ratio [RR] 1.81; 95% confidence interval [CI] 1.44-2.27; P<.001) and urinary nonincontinence events (RR 1.83; 95% CI 1.83-2.80; P<.001) but not urinary incontinence events or erectile dysfunction. The addition of the time from prostatectomy to radiation therapy interaction term was not significant for any of the adverse event outcomes (P>.1 for all outcomes). Conclusion: Radiation therapy after prostatectomy is associated with an increase in gastrointestinal and genitourinary adverse events. However, the timing of radiation therapy did not influence the risk of radiation therapy–associated adverse events in this cohort, which contradicts the commonly held clinical tenet that delaying radiation therapy reduces the risk of adverse events.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Mining Materials Design Rules from Data: The Example of Polymer Dielectrics

Mining of currently available and evolving materials databases to discover structure–chemistry–property relationships is critical to developing an accelerated materials design framework. The design of new and advanced polymeric dielectrics for capacitive energy storage has been hampered by the lack of sufficient data encompassing wide enough chemical spaces. Here, data mining and analysis techniques are applied on a recently presented computational data set of around 1100 organic polymers, organometallic polymers, and related molecular crystals, in order to obtain qualitative understanding of the origins of dielectric and electronic properties. By probing the relationships between crucial chemical and structural features of materials and their dielectric constant and band gap, design rules are devised for optimizing either property. Learning from this data set provides guidance to experiments and to future computations, as well as a way of expanding the pool of promising polymer candidates for dielectric applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data

The accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based on the quantum description of the electronic structure, while widely used, are computationally expensive and demand substantial expertise. Emerging universal machine learning interatomic potentials (uMLIPs) offer a transformative alternative by employing pre-trained neural network surrogates to predict interatomic forces directly from atomic coordinates. This approach dramatically reduces computation time and minimizes the need for technical knowledge. In this paper, we produce a phonon database comprising nearly 5000 inorganic crystals to benchmark the performance of several leading uMLIPs. We further assess these models in real-world applications by using them to analyze experimental inelastic neutron scattering data collected on a variety of materials. Through detailed comparisons, we identify the strengths and limitations of these uMLIPs, providing insights into their accuracy and suitability for fast calculations of phonons and related properties, as well as the potential for real-time interpretation of neutron scattering spectra. Our findings highlight how the rapid advancement of AI in science is revolutionizing experimental research and data analysis.

inelastic neutron scattering↗
DOE OSTI2023

Session Introduction: Graph Representations and Algorithms in Biomedicine

Connectivity is a fundamental property of biological systems: on the cellular level, proteins interact with each other to form protein-protein interaction networks (PPIs); on the organism level, neurons are arranged in a network; and on a community-level, species can have complex relationships with one another that drive the development and balance of an ecosystem. Graphs, representations of systems consisting of entities as vertices and their connections as edges, are a useful structure to characterize many such systems. Such models can be used to understand biological systems that naturally have a network structure, including PPIs, biological neurons, and ecosystems. In today’s information age, graph representations and algorithms (often in combination with machine learning techniques) are used to organize massive amounts of related data, much of which may be heterogeneous or unstructured, and identify patterns that represent novel biological insights. PSB’s 2023 session “Graph Representations and algorithms in Biomedicine,” encompasses modern developments in graph theory and its applications to various fields of biomedicine. This session includes a wide range of research - knowledge graphs built from text-mined health data, heterogeneous networks using multi-omic databases, and graphs refined to represent uncertainty or improve memory usage.

Chrisman, Brianna S.↗
DOE OSTI2013

Transcriptome Analysis in Sheepgrass (Leymus chinensis). A Dominant Perennial Grass of the Eurasian Steppe

BACKGROUND: Sheepgrass [Leymus chinensis (Trin.) Tzvel.] is an important perennial forage grass across the Eurasian Steppe and is known for its adaptability to various environmental conditions. However, insufficient data resources in public databases for sheepgrass limited our understanding of the mechanism of environmental adaptations, gene discovery and molecular marker development. RESULTS: The transcriptome of sheepgrass was sequenced using Roche 454 pyrosequencing technology. We assembled 952,328 high-quality reads into 87,214 unigenes, including 32,416 contigs and 54,798 singletons. There were 15,450 contigs over 500 bp in length. BLAST searches of our database against Swiss-Prot and NCBI non-redundant protein sequences (nr) databases resulted in the annotation of 54,584 (62.6%) of the unigenes. Gene Ontology (GO) analysis assigned 89,129 GO term annotations for 17,463 unigenes. We identified 11,675 core Poaceae-specific and 12,811 putative sheepgrass-specific unigenes by BLAST searches against all plant genome and transcriptome databases. A total of 2,979 specific freezing-responsive unigenes were found from this RNAseq dataset. We identified 3,818 EST-SSRs in 3,597 unigenes, and some SSRs contained unigenes that were also candidates for freezing-response genes. Characterizations of nucleotide repeats and dominant motifs of SSRs in sheepgrass were also performed. Similarity and phylogenetic analysis indicated that sheepgrass is closely related to barley and wheat. CONCLUSIONS: This research has greatly enriched sheepgrass transcriptome resources. The identified stress-related genes will help us to decipher the genetic basis of the environmental and ecological adaptations of this species and will be used to improve wheat and barley crops through hybridization or genetic transformation. The EST-SSRs reported here will be a valuable resource for future gene-phenotype studies and for the molecular breeding of sheepgrass and other Poaceae species.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

Combustion of interacting droplet arrays in a microgravity environment

This research program involves the study of one and two dimensional arrays of droplets in a buoyant-free environment. The purpose of the work is to extend the database and theories that exist for single droplets into the regime where droplet interactions are important. The eventual goal being to use the results of this work as inputs to models on spray combustion where droplets seldom burn individually; instead the combustion history of a droplet is strongly influenced by the presence of the neighboring droplets. Throughout the course of the work, a number of related aspects of isolated droplet combustion have also been investigated. This paper will review our progress in microgravity droplet array combustion, advanced diagnostics (specifically L2) applied to isolated droplet combustion, and radiative extinction large droplet flames. A small-scale droplet combustion experiment being developed for the Space Shuttle will also be described.

Dietrich, Daniel L.↗
NASA NTRSDate not supplied

Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network

Transverse cirrus bands (TCBs) are bands of upper-level clouds regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle. However, these hypothesized relationships have not been rigorously tested due to the subjective nature of TCBs in satellite images. A machine learning technique that successfully identifies TCBs objectively in imagery from the GOES-16 Advanced Baseline Imager (ABI) has been developed to solve this problem. The technique uses a U-Net convolutional neural network (CNN) that assigns a probability to each pixel in an image based on the likelihood of the pixel being associated with a TCB. Using the U-Net CNN, a database of TCBs from 2019 to 2022 was developed for the Atlantic tropical cyclone basin by defining an appropriate probability threshold that defines the difference between TCB and non-TCB pixels. This threshold is where the Jaccard score, calculated using manually identified TCBs and model identified TCBs, is maximized. Statistics for TCB occurrence will also be presented, including the relationships between TCBs and storm relative motion, shear relative direction, cardinal direction, tropical cyclone intensity, tropical cyclone intensification rates, and time of day.

John Mark Mayhall↗
DOE OSTI2025

An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks

MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗
DOE OSTI2017

Recent aspects of uranium toxicology in medical geology

Uranium (U) is a chemo-toxic, radiotoxic and even a carcinogenic element. Due to its radioactivity, the effects of U on humans health have been extensively investigated. Prolonged U exposure may cause kidney disease and cancer. The geological distribution of U radionuclides is still a great concern for human health. Uranium in groundwater, frequently used as drinking water, and general environmental pollution with U raise concerns about the potential public health problem in several areas of Asia. The particular paleo-geological hallmark of India and other Southern Asiatic regions enhances the risk of U pollution in rural and urban communities. This paper highlights different health and environmental aspects of U as well as uptake and intake. It discusses levels of U in soil and water and the related health issues. Also described are different issues of U pollution, such as U and fertilizers, occupational exposure in miners, use and hazards of U in weapons (depleted U), U and plutonium as catalysts in the reaction between DNA and H{sub 2}O{sub 2,} and recycling of U from groundwater to surface soils in irrigation. For use in medical geology and U research, large databases and data warehouses are currently available in Europe and the United States. - Highlights: • Uranium (U) is a chemo-toxic, radiotoxic and even a carcinogenic element. • Effects of U on humans health have been extensively studied in recent years. • Prolonged U exposure may cause kidney disease and cancer. • Drinking water is a source of U exposure in some parts of the world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

THE ZURICH ENVIRONMENTAL STUDY (ZENS) OF GALAXIES IN GROUPS ALONG THE COSMIC WEB. V. PROPERTIES AND FREQUENCY OF MERGING SATELLITES AND CENTRALS IN DIFFERENT ENVIRONMENTS

We use the Zurich Environmental Study database to investigate the environmental dependence of the merger fraction Γ and merging galaxy properties in a sample of ∼1300 group galaxies with M > 10{sup 9.2} M {sub ☉} and 0.05 < z < 0.0585. In all galaxy mass bins investigated in our study, we find that Γ decreases by a factor of ∼2-3 in groups with halo masses M {sub HALO} > 10{sup 13.5} M {sub ☉} relative to less massive systems, indicating a suppression of merger activity in large potential wells. In the fiducial case of relaxed groups only, we measure a variation of ΔΓ/Δlog (M {sub HALO}) ∼ –0.07 dex{sup –1}, which is almost independent of galaxy mass and merger stage. At galaxy masses >10{sup 10.2} M {sub ☉}, most mergers are dry accretions of quenched satellites onto quenched centrals, leading to a strong increase of Γ with decreasing group-centric distance at these mass scales. Both satellite and central galaxies in these high-mass mergers do not differ in color and structural properties from a control sample of nonmerging galaxies of equal mass and rank. At galaxy masses of <10{sup 10.2} M {sub ☉} where we mostly probe satellite-satellite pairs and mergers between star-forming systems close pairs (projected distance <10-20 kpc) show instead ∼2 × enhanced (specific) star formation rates and ∼1.5 × larger sizes than similar mass, nonmerging satellites. The increase in both size and star formation rate leads to similar surface star formation densities in the merging and control-sample satellite populations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Perioperative Use of Nonsteroidal Anti-inflammatory Drugs Decreases the Risk of Recurrence of Cancer After Colorectal Resection: A Cohort Study Based on Prospective Data

Background: Perioperative use of nonsteroidal anti-inflammatory drugs (NSAIDs) is known to reduce inflammatory response in relation to surgery. Inflammation may promote recurrence of cancer, thus inhibition by use of NSAIDs could reduce recurrence after surgery. Objective: The aim of this study was to examine the association between perioperative use of NSAIDs and cancer recurrence, as well as disease-free survival (DFS) and mortality after colorectal cancer surgery. Methods: This was a cohort study based on data from a prospective clinical database, electronic medical records, and nationwide registers, and included patients from six major colorectal centers in Denmark. The primary outcome was cancer recurrence, while secondary outcomes included 5-year mortality and DFS. Results: Overall, 2308 patients undergoing colorectal cancer surgery between 1 January 2006 and 31 December 2009 were included. A total of 909 patients received at least 2 days of treatment with NSAIDs, of whom 702 (77.2%) received ibuprofen and 204 (22.4%) received diclofenac. Cox regression analysis adjusting for NSAIDs resulted in decreased recurrence risk (adjusted hazard ratio [HR{sub adjusted}] 0.84, 95% confidence interval [CI] 0.72–0.99; p = 0.042). Competing risk analysis confirmed the finding, with an HR{sub adjusted} of 0.76 (95% CI 0.60–0.97; p = 0.026). There was no significant effect on mortality or DFS. Sensitivity analysis of the effect of ibuprofen reported an HR{sub adjusted} of 0.83 (95% CI 0.70–1.00; p = 0.047). In restricted analyses of localized disease only (Union for International Cancer Control [UICC] I–II) and elective surgery only, no effect was found (localized: HR{sub adjusted} 0.81, 95% CI 0.62–1.06, p = 0.12; elective: HR{sub adjusted} 0.85, 95% CI 0.72–1.01, p = 0.063). Conclusions: Perioperative use of NSAIDs was associated with a reduced risk of cancer recurrence after resection for colorectal cancer. No effect on 5-year mortality or DFS was found.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants

Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1994

Remote sensing/global change. A special bibliography

The first portion of this bibliography contains citations (with abstracts, when available) to unclassified literature contained in the NASA STI Database. These citations also appeared in issues of the abstract journal 'Scientific and Technical Aerospace Reports (STAR)', or in other announcement products offered by the NASA STI Program. The citations appear in ascending accession number order. A second section provides several indexes to the citations. They are subject term, personal author, report number, and accession number. The citations are included for the following disciplines as they relate to remote sensing and global change: astronautics, engineering, geosciences, life sciences, mathematical and computer sciences, social sciences, and space sciences.

Source record↗
NASA NTRS1998

Parameterizations of Pion Energy Spectrum in Nucleon-Nucleon Collisions

The effects of pion (PI) production are expected to play an important role in radiation exposures in the upper atmosphere or on the Martian surface. Nuclear databases for describing pion production are developed for radiation transport codes to support these studies. We analyze the secondary energy spectrum of pions produced in nucleon-nucleon (NN) collisions in the relativistic one-pion exchange model. Parametric formulas of the isospin cross sections for one-pion production channels are discussed and are used to renormalize the model spectrum. Energy spectra for the deuteron related channels (NN yields dPi) are also described.

Cucinotta, Franics A.↗