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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders

This paper presents a data-driven load modeling methodology for distribution system quasi-static time-series (QSTS) simulation considering both diversity and variability characteristics of distribution loads. Based on our previous work in [1]-[2], a variability library and diversity library have been established based on the realistic high-resolution data collected from actual utility feeders. Given the load profile for the start-of-circuit load of a feeder, the loads on the feeder nodes can be modeled with both diversity and variability instead of being directly scaled from the substation load profile according to the distribution allocation factors. With diversified load models, the load-induced impact on the feeder operation characteristics, such as voltage ramp and regulator operations, can be better considered in QSTS simulation. The proposed modeling methodology has been tested on both the IEEE 123-bus feeder and an actual utility feeder model, and the simulation results have demonstrated the merits of deploying the proposed load modeling methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Data-Driven Load Diversity and Variability Modeling for Quasi-Static Time-Series Simulation on Distribution Feeders: Preprint

This paper presents a data-driven load modeling methodology for distribution system quasi-static time-series (QSTS) simulation considering both diversity and variability characteristics of distribution loads. Based on our previous work in [1]-[2], a variability library and diversity library have been established based on the realistic high-resolution data collected from actual utility feeders. Given the load profile for the start-of-circuit load of a feeder, the loads on the feeder nodes can be modeled with both diversity and variability instead of being directly scaled from the substation load profile according to the distribution allocation factors. With diversified load models, the load-induced impact on the feeder operation characteristics, such as voltage ramp and regulator operations, can be better considered in QSTS simulation. The proposed modeling methodology has been tested on both the IEEE 123-bus feeder and an actual utility feeder model, and the simulation results have demonstrated the merits of deploying the proposed load modeling methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Data for Phylogenetic diversity of light-dependent phosphorylation of Thr78 in Rubisco activase

Rubisco activase is an ATP-dependent chaperone that facilitates dissociation of inhibitory sugar phosphates from the catalytic sites of Rubisco during photosynthesis. In Arabidopsis, Rubisco activase is negatively regulated by dark-dependent phosphorylation of Thr78. The prevalence of Thr78 in Rubisco activase was investigated across sequences from 91 plant species, finding that 29 (∼32%) species shared a threonine in the same position. Analysis of seven C3 species with an antibody raised against a Thr78 phospho-peptide demonstrated that this position is phosphorylated in multiple genera. However, light-dependent dephosphorylation of Thr78 was observed only in Arabidopsis. Further, phosphorylation of Thr78 could not be detected in any of the four C4 grass species examined. The results suggest that despite conservation of Thr78 in Rubisco activase from a wide range of species, a regulatory role for phosphorylation at this site is more limited. This provides a case study for how variation in post-translational regulation can amplify functional divergence across the phylogeny of plants beyond what is explained by sequence variation in a metabolically important protein.

photosynthesis↗
NASA NTRS1991

Integration of diverse remote sensing data sets for geologic mapping and resource exploration

The use of high-quality multispectral images in the visible, near-infrared, shortwave infrared, thermal infrared, and microwave regions of the spectrum for producing thematic maps showing details of the surface geology is reported. The airborne data sets used in the study include the Airborne Visible/Infrared Imaging Spectrometer, the Thermal Infrared Multispectral Scanner, and the airborne SAR. Ancillary data include a digital elevation model, National High Altitude Photography, Landsat Multispectral Scanner data, Landsat Thematic Mapper data, laboratory and field spectral measurements, and traditional geologic mapping. The integrated, multispectral images are shown to provide new geologic information that can be used in mineral deposit models to provide exploration targets.

Kruse, Fred A.↗
DOE OSTI2016

Computer systems for annotation of single molecule fragments

There are provided computer systems for visualizing and annotating single molecule images. Annotation systems in accordance with this disclosure allow a user to mark and annotate single molecules of interest and their restriction enzyme cut sites thereby determining the restriction fragments of single nucleic acid molecules. The markings and annotations may be automatically generated by the system in certain embodiments and they may be overlaid translucently onto the single molecule images. An image caching system may be implemented in the computer annotation systems to reduce image processing time. The annotation systems include one or more connectors connecting to one or more databases capable of storing single molecule data as well as other biomedical data. Such diverse array of data can be retrieved and used to validate the markings and annotations. The annotation systems may be implemented and deployed over a computer network. They may be ergonomically optimized to facilitate user interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Further Development of an ISOCS - based Advanced In Situ Gamma Spectrometry Services Tool for Waste Measurements - 17452

Canberra's In Situ Object Counting System is an established tool used for gamma spectrometry allowing a physical representation of complex geometries and mathematical calculation of the calibration function while avoiding the need for radioisotope standards. The ISOCS Uncertainty Estimator tool can be used to calculate defensible systematic calibration uncertainties. However the uncertainties depend on knowledge of the properties of the item being measured and for poorly characterized materials these can be large, often leading to large costs for waste sentencing. An ISOCS-based advanced in-situ gamma spectrometry services tool has been developed to reduce those uncertainties. This system is based on generating and comparing a range of candidate geometry models that yield figures of merit indicative of improved consistency between modelled data and available diverse measurement data. The approach and results of initial performance testing have previously been published, demonstrating an analysis procedure and enhanced accuracy for measurement of a low density 200 litre waste drum, based on measurements with known sources placed at different positions. It was shown how the accuracy of assay of Am-241 in a light matrix can be significantly improved. Further analysis has now been performed, with reference to measurements of a second drum, containing a standard matrix of higher density, and with a multiple radionuclide line source (Ba-133, Cs-137, Eu-152 and Am-241). These measurements represent a more challenging waste assay scenario, corresponding to potentially much larger assay uncertainties. Our results show how the assay uncertainty can be reduced, by careful application of AIGS, from relative values of the order of 100% to a few 10's of %. Furthermore, we demonstrate, through extended analysis of the test drum data, how an AIGS measurement program can be optimized to reduce the number of required measurement positions substantially. This paper describes these new results with comment on their importance and potential application for real waste assay projects. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

An Advanced ISOCS In Situ Gamma Spectrometry Services Tool to Reduce Uncertainties in Waste Characterization Projects - 16311

Canberra's In Situ Object Counting System (ISOCS) is an established tool used for gamma spectrometry allowing a physical representation of complex geometries and mathematical calculation of the calibration function. It avoids the need for radioisotope standards which can be costly and prohibitively difficult to produce. Uncertainties in the sample - detector geometry lead to uncertainties in the calibrations. Canberra's ISOCS Uncertainty Estimator (IUE) tool offers an automated means of accommodating known model geometry uncertainties, with calculation of robust defensible calibration uncertainties. However these uncertainties may be large leading to potential over-estimation of under-estimation of activity. This can be equated directly to waste disposal costs if one is using ISOCS for waste sentencing to classify wastes as (for example) Transuranic Waste (TRU) or Low Level Waste (LLW). An Advanced ISOCS in-situ Gamma Spectrometry (AIGS) services tool has been developed to reduce the uncertainties. This system is based on generating and comparing geometry models that yield Figures of Merit (FoM) indicative of improved consistency between modelled data and available diverse measurement data. This paper describes the development and features of AIGS, focusing on recent testing with radioisotope sources in a 200 litre drum filled with a simulated waste matrix. The results demonstrate a significant advantage for Am-241 where uncertainties can otherwise amount to a factor of 2 or more. We show how our specialist teams can develop procedures for deployment of standard ISOCS hardware, adapting measurement geometries and procedures through careful planning, to provide data that can be used to greatly reduce the uncertainties in the measured activities. Lastly, we comment on the range of potential applications for this new technique and the wider benefits for the decommissioning industry. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗
DOE OSTI2025

When more data hurts: Optimizing data coverage while mitigating diversity-induced underfitting in an ultrafast machine-learned potential

Machine-learned interatomic potentials (MLIPs) are becoming an essential tool in materials modeling. However, optimizing the generation of training data used to parametrize the MLIPs remains a significant challenge. This is because MLIPs can fail when encountering local environments too different from those present in the training data. The difficulty of determining a priori the environments that will be encountered during molecular dynamics simulation necessitates diverse, high-quality training data. Here, this study investigates how training data diversity affects the performance of MLIPs using the Ultra-Fast force field (UF 3 ) to model amorphous silicon nitride. We employ expert and autonomously generated data to create the training data and fit four force field variants to subsets of the data. Our findings reveal a critical balance in training data diversity: insufficient diversity hinders generalization, while excessive diversity can exceed the MLIP's learning capacity, reducing simulation accuracy. Specifically, we found that the UF 3 variant trained on a subset of the training data, in which nitrogen-rich structures were removed, offered vastly better prediction and simulation accuracy than any other variant. By comparing these UF 3 variants, we highlight the nuanced requirements for creating accurate MLIPs, emphasizing the importance of application-specific training data to achieve optimal performance in modeling complex material behaviors.

ab initio molecular dynamics↗
NASA NTRS2015

Data Recipes: Toward Creating How-To Knowledge Base for Earth Science Data

Both the diversity and volume of Earth science data from satellites and numerical models are growing dramatically, due to an increasing population of measured physical parameters, and also an increasing variety of spatial and temporal resolutions for many data products. To further complicate matters, Earth science data delivered to data archive centers are commonly found in different formats and structures. NASA data centers, managed by the Earth Observing System Data and Information System (EOSDIS), have developed a rich and diverse set of data services and tools with features intended to simplify finding, downloading, and working with these data. Although most data services and tools have user guides, many users still experience difficulties with accessing or reading data due to varying levels of familiarity with data services, tools, and or formats. The data recipe project at Goddard Earth Science Data and Information Services Center (GES DISC) was initiated in late 2012 for enhancing user support. A data recipe is a How-To online explanatory document, with step-by-step instructions and examples of accessing and working with real data (http:disc.sci.gsfc.nasa.govrecipes). The current suite of recipes has been found to be very helpful, especially to first-time-users of particular data services, tools, or data products. Online traffic to the data recipe pages is significant, even though the data recipe topics are still limited. An Earth Science Data System Working Group (ESDSWG) for data recipes was established in the spring of 2014, aimed to initiate an EOSDIS-wide campaign for leveraging the distributed knowledge within EOSDIS and its user communities regarding their respective services and tools. The ESDSWG data recipe group is working on an inventory and analysis of existing data recipes and tutorials, and will provide guidelines and recommendation for writing and grouping data recipes, and for cross linking recipes to data products. This presentation gives an overview of the data recipe activites at GES DISC and ESDSWG. We are seeking requirements and input from a broader data user community to establish a strong knowledge base for Earth science data research and application implementations.

data recipe↗
NASA NTRS1990

Tropical Ocean Global Atmosphere (TOGA) Meteorological and Oceanographic Data Sets for 1985 and 1986

The Tropical Ocean Global Atmosphere (TOGA) Program is a component of the World Meteorological Organization (WMO)/International Council of Scientific Unions (ICSU) World Climate Research Program (WCRP). One of the objectives of TOGA, which began in 1985, is to determine the limits of predictability of monthly mean sea surface temperature variations in tropical regions. The TOGA program created a raison d'etre for an explosive growth of the tropical ocean observing system and a substantial improvement in numerical simulations from atmospheric and oceanic general circulation models. Institutions located throughout the world are involved in the TOGA-distributed active data archive system. The diverse TOGA data sets for 1985 and 1986, including results from general circulation models, are included on a CD-ROM. Variables on the CD-ROM are barometric pressure, surface air temperature, dewpoint temperature Cartesian components of surface wind, surface sensible and latent heat fluxes,Cartesian components of surface wind stress and of an index of surface wind stress, sea level, sea surface temperature, and depth profiles of temperature and current in the upper ocean. Some data sets are global in extent, some are regional and cover portions of an ocean basin. Data on the CD-ROM can be extracted with an Apple Macintosh or an IBM PC.

Halpern, D.↗
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↗
NASA NTRS2005

Diverse Applications of Occultation Data in Ozone Assimilation

Ozone profiles from solar occultation instruments provide invaluable information that can be used to evaluate the quality of assimilated ozone fields, from case studies to long-term phenomena. Occultation data can also be applied to include physical constraints while developing components of an assimilation system. Conversely, assimilation of occultation data can help in their validation, and it provides a framework for evaluation of the impact of occultation data on constraining global ozone fields within models. We illustrate these diverse applications by a series of examples using the ozone assimilation system at NASA/Goddard. In a case study, low ozone in the lower stratosphere due to transport of air from the Tropics to northern high latitudes that was captured by assimilation of Aura Microwave Limb Sounder (MLS) and Solar Backscatter Ultraviolet (SBUV) data, was found to agree with SAGE III data. For long-term monitoring, the quality of a multi-year SBUV-only assimilation was evaluated using monthly-mean time series of POAM, HALOE, and SAGE I1 data. We found realism in the representation of the annual cycle in ozone and in some aspects of interannual variability. Assimilation of POAM data was shown to improve the representation of lower stratospheric ozone, especially over Antarctica. More recently, we assimilated ILAS II ozone data in order to help in their validation. Solar occultation data are used to estimate parameters in a new model for forecast error variances that is being developed. These examples demonstrate the importance of occultation data for ozone assimilation, and potential of assimilation to increase the impact and the value of occultation data.

Markowitz, A.↗
DOE OSTI2017

Data from: Extensive genetic diversity is present within North American switchgrass germplasm

Switchgrass (Panicum virgatum L.) is a perennial native North American grass present in two ecotypes: upland, found primarily in the northern range of switchgrass habitats, and lowland, found largely in the southern reaches of switchgrass habitats. Previous studies focused on a diversity panel of primarily northern switchgrass, so to expand our knowledge of genetic diversity in a broader set of North American switchgrass, exome capture sequence data were generated for 632 additional, primarily lowland individuals. In total, over 37 million single nucleotide polymorphisms (SNPs) were identified and a set of 1.9 million high-confidence SNPs were obtained from 1169 individuals from 140 populations (67 upland, 65 lowland, 8 admixed) were used in downstream analyses of genetic diversity and population structure. Seven separate population groups were identified with moderate genetic differentiation [mean fixation index (Fst) estimate of 0.06] between the lowland and the upland populations. Ecotype-specific and population-specific SNPs were identified for use in germplasm evaluations. Relative to rice (Oryza sativa L.), maize (Zea mays L.), soybean [Glycine max (L.) Merr.], and Medicago truncatula Gaertn., analyses of nucleotide diversity revealed a high degree of genetic diversity (0.0135) across all individuals, consistent with the outcrossing mode of reproduction and the polyploidy of switchgrass. This study supports the hypothesis that repeated glaciation events, ploidy barriers, and restricted gene flow caused by flowering time differences have resulted in distinct gene pools across ecotypes and geographic regions. These data provide a resource to associate alleles with traits of interest for forage, restoration, and biofuel feedstock efforts in switchgrass.

09 BIOMASS FUELS↗