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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Weakly Supervised Machine Learning Procedure for Magnet Quench Diagnostics

Voltage taps remain the standard and reliable diagnostic tool for detecting quenches in superconducting magnets. However, they identify a quench only at the time of voltage rise and do not provide information on earlier physical precursors. In this work, we investigate whether acoustic emission data can reveal precursor activity that occurs before conventional voltage detection using machine learning techniques. We introduce an event selection method and a weakly supervised machine learning procedure to learn data-driven criteria for identifying potential acoustic precursors to quenches. Two Convolutional Neural Network (CNN) architectures are trained: one on acoustic sensor events from our selection procedure and one on the Fast Fourier Transforms (FFTs) of these events. Both networks are trained iteratively using confidence-weighted loss functions to associate certain subsets of training data with a precursor label. We evaluate the performance of these models by examining the time distribution of events classified as potential precursors relative to the quench onset. Results indicate that the proposed approach can possibly distinguish acoustic emission events occurring closer to the quench from earlier acoustic activity during ramping, suggesting the potential for flagging quench precursors in acoustic data.

Khan, Maira [Fermilab] (ORCID:0009000891602387)↗
DOE OSTI2019

Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators

The use of machine learning (ML) models toward development of validated structure–property relationships for two fundamental properties of activated inorganic scintillators for high energy radiation detection, namely the light yield (LY) and the decay time constant, is explored. The ML models are built on easily accessible proxies of materials—interchangeably referred to as features, descriptors or fingerprints—that are carefully selected on the basis of a physical understanding of the scintillation mechanism. Our study indicates that the developed physics-based ML models employing kernel ridge regression (KRR) and AdaBoost algorithm applied on top of a decision tree-based regression are able to “learn” the underlying design rules in a multi-dimensional feature space and thereby enable reasonably accurate predictions of the two target properties on unseen compounds (i.e., on a held-out test set). For instance, within a set of twenty-five cerium- or europium-doped scintillator materials, our analysis reveals a strong correlation between the average ionic part of the dielectric constant and the LY, irrespective of the specific chemistry of the compounds, indicating that the average ionic part of the dielectric constant is a particularly relevant descriptor toward prediction of the LY. Our results also demonstrate that, despite the use of small training datasets, the developed models are able to quickly distinguish high performing chemistries from those with relatively poor performance and therefore can play a crucial role in screening of new compounds with an attractive combination of targeted properties. Finally, the present study provides necessary motivation for future efforts involving ML models with relatively large training datasets, vast feature space explorations, and experimental design in search of promising novel scintillator chemistries.

36 MATERIALS SCIENCE↗
DOE OSTI2020

IRDFF-II: A New Neutron Metrology Library

High quality nuclear data is the most fundamental underpinning for all neutron metrology applications. This paper describes the release of version II of the International Reactor Dosimetry and Fusion File (IRDFF-II) that contains a consistent set of nuclear data for fission and fusion neutron metrology applications up to 60 MeV neutron energy. The library is intended to support: a) applications in research reactors; b) safety and regulatory applications in the nuclear power generation in commercial fission reactors; and c) material damage studies in support of the research and development of advanced fusion concepts. The paper describes the contents of the library, documents the thorough verification process used in its preparation, and provides an extensive set of validation data gathered from a wide range of neutron benchmark fields. The new IRDFF-II library includes 119 metrology reactions, four cover material reactions to support self-shielding corrections, five metrology metrics used by the dosimetry community, and cumulative fission products yields for seven fission products in three different neutron energy regions. In support of characterizing the measurement of the residual nuclei from the dosimetry reactions and the fission product decay modes, the present document lists the recommended decay data, particle emission energies and probabilities for 68 activation products. It also includes neutron spectral characterization data for 29 neutron benchmark fields for the validation of the library contents. Additional six reference fields were assessed (four from plutonium critical assemblies, two measured fields for thermal-neutron induced fission on 233U and 239Pu targets) but not used for validation due to systematic discrepancies in C/E reaction rate values or lack of reaction-rate experimental data. Another ten analytical functions are included that can be useful for calculating average cross sections, average energy, thermal spectrum average cross sections and resonance integrals. The IRDFF-II library and comprehensive documentation is available online at www-nds.iaea.org/IRDFF/. Evaluated cross sections can be compared with experimental data and other evaluations at www-nds.iaea.org/exfor/endf.htm. The new library is expected to become the international reference in neutron metrology for multiple applications.

IAEA IRDFF↗
DOE OSTI2017

Nanoquinacrine caused apoptosis in oral cancer stem cells by disrupting the interaction between GLI1 and β catenin through activation of GSK3β

Presences of cancer stem cells (CSCs) in a bulk of cancer cells are responsible for tumor relapse, metastasis and drug resistance in oral cancer. Due to high drug efflux, DNA repair and self-renewable capacity of CSCs, the conventional chemotherapeutic agents are unable to kill the CSCs. CSCs utilizes Hedgehog (HH-GLI), WNT-β catenin signalling for its growth and development. GSK3β negatively regulates both the pathways in CSCs. Here, we have shown that a nano-formulated bioactive small molecule inhibitor Quinacrine (NQC) caused apoptosis in oral cancer stem cells (OCSCs; isolated from different oral cancer cells and oral cancer patient derived primary cells) by down regulating WNT-β catenin and HH-GLI components through activation of GSK3β. NQC activates GSK3β in transcriptional and translational level and reduces β catenin and GLI1 as well as downstream target gene of both the pathways Cyclin D1, C-Myc. The transcription factor activity of both the pathways was also reduced by NQC treatment. GSK3β, β catenin and GLI1 interacts with each other and NQC disrupts the co-localization and interaction between β catenin and GLI1 in OCSCs in a dose dependent manner through activation of GSK3β. Thus, data suggest NQC caused OCSCs death by disrupting the crosstalk between β catenin and GLI1 by activation of GSK3β. - Graphical abstract: Schematic representation of inhibition of HH-GLI and WNT-β catenin cross talk by NQC through GSK3β. Diagram showing HH-GLI and WNT-β catenin pathway, where GLI1 and β catenin and other components were co-localized in a complex and co-ordinately regulate the downstream target genes. NQC activates GSK3β, which further phosphorylates and activates the β catenin and GLI1. Activated β catenin and GLI1 were degraded by proteosomal degradation pathway and unable to translocate into nucleus as a result growth of cells inhibited. - Highlights: • Nano-formulated quinacrine (NQC) activates GSK3β in oral cancer stem cells (OCSCs) in vitro and ex vivo. • NQC induces apoptosis in OCSCs by inhibiting WNT-β catenin and HH-GLI cascade in vitro and ex vivo. • NQC disrupts the interaction and co-localization between GLI1 and β catenin. • NQC-mediated disruption of the crosstalk between GLI1 and β catenin is GSK3β dependent.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

CHROMOSPHERIC MASS MOTIONS AND INTRINSIC SUNSPOT ROTATIONS FOR NOAA ACTIVE REGIONS 10484, 10486, AND 10488 USING ISOON DATA

This work utilizes Improved Solar Observing Optical Network continuum (630.2 nm) and H{alpha} (656.2 nm) data to: (1) detect and measure intrinsic sunspot rotations occurring in the photosphere and chromosphere, (2) identify and measure chromospheric filament mass motions, and (3) assess any large-scale photospheric and chromospheric mass couplings. Significant results from 2003 October 27-29, using the techniques of Brown et al., indicate significant counter-rotation between the two large sunspots in NOAA AR 10486 on October 29, as well as discrete filament mass motions in NOAA AR 10484 on October 27 that appear to be associated with at least one C-class solar flare.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Comparison of two different methods of image analysis for the assessment of microglial activation in patients with multiple sclerosis using (R)-[N-methyl-carbon-11]PK11195

Chronic active multiple sclerosis (MS) lesions have a rim of activated microglia/macrophages (m/M) leading to ongoing tissue damage, and thus represent a potential treatment target. Activation of this innate immune response in MS has been visualized and quantified using PET imaging with [ 11C]-(R)-PK11195 (PK). Accurate identification of m/M activation in chronic MS lesions requires the sensitivity to detect lower levels of activity within a small tissue volume. We assessed the ability of kinetic modeling of PK PET data to detect m/M activity in different central nervous system (CNS) tissue regions of varying sizes and in chronic MS lesions. Ten patients with MS underwent a single brain MRI and two PK PET scans 2 hours apart. Volume of interest (VOI) masks were generated for the white matter (WM), cortical gray matter (CGM), and thalamus (TH). The distribution volume (VT) was calculated with the Logan graphical method (LGM-VT) utilizing an image-derived input function (IDIF). The binding potential (BPND) was calculated with the reference Logan graphical method (RLGM) utilizing a supervised clustering algorithm (SuperPK) to determine the non-specific binding region. Masks of varying volume were created in the CNS to assess the impact of region size on the various metrics among high and low uptake regions. Chronic MS lesions were also evaluated and individual lesion masks were generated. The highest PK uptake occurred the TH and lowest within the WM, as demonstrated by the mean time activity curves. In the TH, both reference and IDIF based methods resulted in estimates that did not significantly depend on VOI size. However, in the WM, the test-retest reliability of BPND was significantly lower in the smallest VOI, compared to the estimates of LGM-VT. These observations were consistent for all chronic MS lesions examined. In this study, we demonstrate that BPND and LGM-VT are both reliable for quantifying m/M activation in regions of high uptake, however with blood input function LGM-VT is preferred to assess longitudinal m/M activation in regions of relatively low uptake, such as chronic MS lesions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A Perspective on Data and Privacy for AI in Healthcare [Industrial and Governmental Activities]

As large language models continue to push the bounds of AI model size, they are also being trained on unprecedented volumes of data. While individual hospitals are estimated to produce petabytes of data per year, only a small fraction is currently being used for developing AI models. Additionally, with such data resources available, healthcare is well-positioned to benefit from the current trends in AI. Moreover, the inherently multi-modal and longitudinal nature of clinical data – from omics to imaging to unstructured notes – provides a fertile ground for the development and application of cutting-edge architectures like foundation models.

Gounley, John [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2019

Analysis of Multimodal Wearable Sensor Data to Characterize Social Groups and Influence in Organizations

This document reports the results of a 4-month study analyzing data provided to Los Alamos National Laboratory (LANL) by the performer teams for the Multimodal Objective Sensing to Assess Individuals with Context (MOSAIC) project. The MOSAIC performer teams monitored hundreds of volunteers from several organizations in their daily activities, using wearable sensors to capture data about those activities. The project also collected “ground truth” data on individual behavior and psychological state using survey instruments throughout the study. The LANL MOSAIC analysis team approached this data with the goal of capturing and analyzing social dynamics within organizations. We focused on two key analysis objectives: identifying and characterizing groups as the evolve over time, and identifying and modeling patterns of social influence.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Cryogenic thermal modeling of microwave high density signaling

Superconducting quantum computers require microwave control lines running from room temperature to the mixing chamber of a dilution refrigerator. Adding more lines without preliminary thermal modeling to make predictions risks overwhelming the cooling power at each thermal stage. In this paper, we investigate the thermal load of SC-086/50-SCN-CN semi-rigid coaxial cable, which is commonly used for the control and readout lines of a superconducting quantum computer, as we increase the number of lines to a quantum processor. We investigate the makeup of the coaxial cables, verify the materials and dimensions, and experimentally measure the total thermal conductivity of a single cable as a function of the temperature from cryogenic to room temperature values. We also measure the cryogenic DC electrical resistance of the inner conductor as a function of temperature, allowing for the calculation of active thermal loads due to Ohmic heating. Fitting this data produces a numerical thermal conductivity function used to calculate the static heat loads due to thermal transfer within the wires resulting from a temperature gradient. The resistivity data is used to calculate active heat loads, and we use these fits in a cryogenic model of a superconducting quantum processor in a typical Bluefors XLD1000-SL dilution refrigerator, investigating how the thermal load increases with processor sizes ranging from 100 to 225 qubits. We conclude that the theoretical upper limit of the described architecture is approximately 200 qubits. However, including an engineering margin in the cooling power and the available space for microwave readout circuitry at the mixing chamber, the practical limit is approximately 140 qubits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2010

Green Power Marketing in the United States: A Status Report (2009 Data)

This report documents green power marketing activities and trends in the United States. First, aggregate green power sales data for all voluntary purchase markets across the United States are presented. Next, we summarize data on utility green pricing programs offered in regulated electricity markets; green power marketing activity in competitive electricity markets, as well as green power soldto voluntary purchasers in the form of RECs; and renewable energy sold as greenhouse gas offsets in the United States. Finally, this is followed by a discussion of key market trends and issues. The data presented in this report are based primarily on figures provided to NREL by utilities and independent renewable energy marketers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2010

Green Power Marketing in the United States: A Status Report (2009 Data)

This report documents green power marketing activities and trends in the United States. First, aggregate green power sales data for all voluntary purchase markets across the United States are presented. Next, we summarize data on utility green pricing programs offered in regulated electricity markets; green power marketing activity in competitive electricity markets, as well as green power sold to voluntary purchasers in the form of RECs; and renewable energy sold as greenhouse gas offsets in the United States. Finally, this is followed by a discussion of key market trends and issues. The data presented in this report are based primarily on figures provided to NREL by utilities and independent renewable energy marketers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Clean Cities Coalitions: 2016 Activity Report

The U.S. Department of Energy's (DOE's) national network of Clean Cities Coalitions advance the nation's economic, environmental, and energy security by supporting local actions to promote the use of domestic fuels within transportation. The nearly 100 Clean Cities coalitions, whose territory covers 80% of the U.S. population, bring together stakeholders in the public and private sectors to use alternative and renewable fuels, idle-reduction (IR) measures, fuel economy improvements, and new transportation technologies as they emerge. To ensure success, coalitions leverage a robust set of expert resources and tools provided by national laboratories and DOE. Each year, Clean Cities coordinators submit annual reports of their activities and accomplishments for the previous calendar year. Data and information are submitted via an online tool that is maintained as part of the Alternative Fuels Data Center (AFDC) at the National Renewable Energy Laboratory (NREL). Coordinators submit a range of data that characterize the membership, funding, projects, and activities of their coalitions. They also submit data about sales of alternative fuels; use of alternative fuel vehicles (AFVs), plug-in electric vehicles (PEVs), and hybrid electric vehicles (HEVs); IR initiatives; fuel economy improvement activities; and programs to reduce vehicle miles traveled (VMT). NREL analyzes the submitted data to determine how broadly energy use in the U.S. has shifted due to coalition activities, which are summarized in this report.

2016 metrics↗
DOE OSTI2021

Simulation Dataset - Input files

The input data that are required to run the model are divided into fundamental data and advanced data. The fundamental data are essential to run the model, while the advanced data allow optional model features to be activated. The names of all input data files are to be entered as ASCII characters in a tabular format. Spaces, commas, or tabs can separate numeric fields, the use of any other special visible or hidden characters must be avoided. It may be useful to note that all files are generally read in using standard free-format style FORTRAN READ statements. The input data files can be generated/modified using any standard editor, spreadsheet or word processor (in non-document mode), given that the above guidelines are complied with. However, the inclusion of special formatting characters and the insertion of blank lines must be avoided. The input data files may also be generated through the use of special purpose translation programs that convert the input data files that were initially generated for another traffic simulation or transportation planning model into an INTEGRATION format.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Independent Verification Survey of the SPRU Lower Level Hillside Area at the Knolls Atomic Power Laboratory, Niskayuna, New York (Final Report)

During August 10, 2011 through August 19, 2011, and October 23, 2011 through November 4, 2011, ORAU/ORISE conducted verification survey activities at the Separations Process Research Unit (SPRU) site that included in-process inspections, surface scans, and soil sampling of the Lower Level Hillside Area. According to the Type-B Investigation Report, Sr-90 was the primary contributor to the majority of the activity (60 times greater than the Cs-137 activity). The evaluation of the scan data and sample results obtained during verification activities determined that the primary radionuclide of concern, Sr-90, was well below the agreed upon soil cleanup objective (SCO) of 30 pCi/g for the site. However, the concentration of Cs-137 in the four judgmental samples collected in final status survey (FSS) Units A and B was greater than the SCO. Both ORAU and aRc surveys identified higher Cs-137 concentrations in FSS Units A and B; the greatest concentrations were identified in FSS Unit A.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗