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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Association of optical, ultraviolet, and soft X-ray excess emissions in AGNs

Soft X-ray emission (0.5–2.0 keV) plays a pivotal role in regulating the optical and ultraviolet (UV) emission in the active galactic nuclei (AGNs). We collected a sample of 1811 AGNs from the SDSS database and obtained various parameters of Balmer lines, optical continuum, Mg ii line & UV continuum and studied their dependencies on soft X-ray luminosity. Based on the linear regression analysis, we found that FWHM$_{\rm {Mg\,\,\small {II}}}$ ∝ FWHM$_{\text{H}\beta }^{0.554}$ suggesting that UV emission is arising from a region relatively outside the broad-line region (BLR) associated to the Hβ emission and found a strong correlation between optical and UV luminosities (L$_{\rm {Mg\,\,\small {II}}}$ ∝ $L_{\rm {H}\beta }^{0.822}$). It was noticed that the dependency of optical continuum luminosities on soft excess changes with the redshift (L X ∝ L$^{0.596}_{5100\, \mathring{\rm A}}$ for z < 0.5 and L X ∝ L$^{0.429}_{5100\, \mathring{\rm A}}$ for z > 0.5). The full width at half-maximum components of Hβ and Mg II core components were found to be virialized and is not affected by the soft excess emission whereas the wings of Mg II display a dependency. We estimated a relation viz. L X ∝L$^{0.520}_{3000\, \mathring{\rm A}}$ FWHM$^{0.525}_{\rm {Mg\,\,\small {II}}}$ and found to be well in agreement with a proposed physical scenario. All the derived relations were used to understand the intermodulating association of the BLR and disc in the AGNs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Libra

Libra is a Python package that extends support of its parent package, SQLAlchemy. Libra’s primary functionality supports the dynamic creation of object-oriented analogs of SQL tables from a variety of user-defined, text-based schema definition formats and provides quality control and analysis tools and methods

Spears, Brady↗
DOE OSTI2012

NDMAS System and Process Description

Experimental data generated by the Very High Temperature Reactor Program need to be more available to users in the form of data tables on Web pages that can be downloaded to Excel or in delimited text formats that can be used directly for input to analysis and simulation codes, statistical packages, and graphics software. One solution that can provide current and future researchers with direct access to the data they need, while complying with records management requirements, is the Nuclear Data Management and Analysis System (NDMAS). This report describes the NDMAS system and its components, defines roles and responsibilities, describes the functions the system performs, describes the internal processes the NDMAS team uses to carry out the mission, and describes the hardware and software used to meet Very High Temperature Reactor Program needs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Full Integration of Lipidomics Data into Multi-OMIC Functional Enrichment

Lipids have numerous roles critical to living systems. Many of these vital functions are tightly linked or regulated by proteins. Even though the function of many lipid-related enzymes is known, direct links between most individual lipid species and the enzymes that metabolize them are severely lacking. This missing connection impedes biological progress. A resource that allows users to quickly identify protein-lipid relationships will advance our understanding of lipid homeostasis and metabolic perturbed systems. Databases are readily available for mapping small molecule metabolites (i.e., polar metabolites) and specific oxidized fatty acid lipids (e.g., arachidonic acid derivatives) to proteins/enzymes known to be directly involved in metabolism of these molecules (e.g. SMPDB, Reactome, HMDB, BioCyc). This enables the integration of metabolite and protein (as well as transcript) data when performing functional enrichment and metabolic modeling allowing researchers to rapidly examine metabolic pathways from multiple omics sources. Currently, linking lipidomics data with proteomics data is conducted using a time-intensive, user defined, manual process. Here we propose creating a tool that will link lipidomics and proteomics data and also conduct functional enrichment analysis and visualization allowing for full statistical integration. Using text-mining approaches, we will extract lipid references from uniprot and create a database linking lipid species to enzymes directly related to their metabolism. We will also extract directional annotation, which will allow even greater specificity when linking data types and will help eliminate false positives from functional associations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Mining Product Reviews for Important Product Features of Refurbished iPhones

Problem: Remanufacturers want to increase consumer interest in refurbished products, which motivates the need to understand which product features are important to buyers of refurbished products such as mobile phones. Research Questions: This study addresses two questions. First, which product features are most important for buyers of refurbished iPhones? Second, how do those preferences differ from the preferences of buyers of new iPhones? Methods: Online reviews of iPhones are obtained and converted into a document–term matrix. Using this text model, three subsets of features are identified using statistical analysis of frequency of mention: most frequent, average, and least frequent. A logistic regression (LR) model is then used to identify which features are most predictive of whether a review is for a new or refurbished phone. Results: Buyers of refurbished phones mention battery health, screen/display, shell condition, and brand significantly more often than other features. Directly contrasting reviews of refurbished versus new phones shows that shell condition, brand, speaker, and charger are found to be the most predictive product features indicated in reviews for refurbished phones. Of those, the shell condition is significantly more predictive than the others. Implications: The results identify product features that remanufacturers of iPhones can emphasize to increase customer demand.

Anisi, Atefeh↗
DOE OSTI2019

Toward a Deep Learning System for Making Sense of Unlabeled Multimodal Data

Analysts and decision makers are constantly charged with making sense of large amounts of disparate data. With the proliferation of smartphones and the ever-growing array of new sensor technologies, the old adage of swimming in sensors but drowning in data remains especially relevant. As collections of images, video, audio, and text continue to grow, the need for computer-assisted analysis likewise increases. The “data deluge” presents both daunting challenges as well as remarkable opportunities. In this article, we describe Deep Learning approaches for learning representations of data from vast amounts of unlabeled or weakly labeled training sets. These feature representations enable the computer-assisted retrieval and tagging that can help our analysts find patterns in the vast ocean of collected data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Low-latency NuMI Trigger for the CHIPS-5 Neutrino Detector

The CHIPS R&D project aims to develop affordable water Cherenkov detectors for large-scale underwater installations. In 2019, a 5kt prototype detector CHIPS-5 was deployed in northern Minnesota to study neutrinos generated by the nearby NuMI beam. This contribution presents a dedicated low-latency time distribution system for CHIPS-5 that delivers timing signals from the Fermilab accelerator to the detector with sub-nanosecond precision. Exploiting existing NOvA infrastructure, the time distribution system achieves this only with open-source software and conventional network elements. In a time-of-flight study, the presented system has reliably offered a time budget of $610 \pm 330\text{ ms}$ for on-site triggering. This permits advanced analysis in real-time as well as a novel hardware-assisted active triggering mode, which reduces DAQ computing load and network bandwidth outside triggered time windows.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

W2VPCA: A Machine Learning Method for Measuring Attitudes With Natural Language

Company strategy influences many decisions in freight transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables. However, strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent strategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which can be problematic. This study introduces a new machine learning method to generate strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses information on word context. We apply W2VPCA to generate measurements of latent strategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strategies, one focusing on distribution and the other on products, with truck fleet and distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies. While this study focuses on freight behavioral models, W2VPCA may also have applications in behavioral modeling in other domains.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Software for Analyzing Sequences of Flow-Related Images

Spotlight is a computer program for analysis of sequences of images generated in combustion and fluid physics experiments. Spotlight can perform analysis of a single image in an interactive mode or a sequence of images in an automated fashion. The primary type of analysis is tracking of positions of objects over sequences of frames. Features and objects that are typically tracked include flame fronts, particles, droplets, and fluid interfaces. Spotlight automates the analysis of object parameters, such as centroid position, velocity, acceleration, size, shape, intensity, and color. Images can be processed to enhance them before statistical and measurement operations are performed. An unlimited number of objects can be analyzed simultaneously. Spotlight saves results of analyses in a text file that can be exported to other programs for graphing or further analysis. Spotlight is a graphical-user-interface-based program that at present can be executed on Microsoft Windows and Linux operating systems. A version that runs on Macintosh computers is being considered.

Klimek, Robert↗
DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗
NASA NTRS2006

Spotlight-8 Image Analysis Software

Spotlight is a cross-platform GUI-based software package designed to perform image analysis on sequences of images generated by combustion and fluid physics experiments run in a microgravity environment. Spotlight can perform analysis on a single image in an interactive mode or perform analysis on a sequence of images in an automated fashion. Image processing operations can be employed to enhance the image before various statistics and measurement operations are performed. An arbitrarily large number of objects can be analyzed simultaneously with independent areas of interest. Spotlight saves results in a text file that can be imported into other programs for graphing or further analysis. Spotlight can be run on Microsoft Windows, Linux, and Apple OS X platforms.

Klimek, Robert↗
DOE OSTI2016

Hyperthermia and Radiation Therapy in Locoregional Recurrent Breast Cancers: A Systematic Review and Meta-analysis

Purpose: To conduct a systematic review and meta-analysis to evaluate the outcome of hyperthermia (HT) and radiation therapy (RT) in locally recurrent breast cancers (LRBCs). Methods and Materials: A total of 708 abstracts were screened from 8 databases according to the PRISMA guidelines. Single-arm and 2-arm studies, treating LRBCs with HT and RT but without surgery (for local recurrence) or concurrent chemotherapy were considered. The evaluated endpoint was complete response (CR). Results: Thirty-one full text articles, pertaining to 34 studies, were shortlisted for the meta-analysis. Eight were 2-arm (randomized, n=5; nonrandomized, n=3), whereas 26 were single-arm studies. In all, 627 patients were enrolled in 2-arm and 1483 in single-arm studies. Patients were treated with a median of 7 HT sessions, and an average temperature of 42.5°C was attained. Mean RT dose was 38.2 Gy (range, 26-60 Gy). Hyperthermia was most frequently applied after RT. In the 2-arm studies, a CR of 60.2% was achieved with RT + HT versus 38.1% with RT alone (odds ratio 2.64, 95% confidence interval [CI] 1.66-4.18, P<.0001). Risk ratio and risk difference were 1.57 (95% CI 1.25-1.96, P<.0001) and 0.22 (95% CI 0.11-0.33, P<.0001), respectively. In 26 single-arm studies, RT + HT attained a CR of 63.4% (event rate 0.62, 95% CI 0.57-0.66). Moreover, 779 patients had been previously irradiated (696 from single-arm and 83 from 2-arm studies). A CR of 66.6% (event rate 0.64, 95% CI 0.58-0.70) was achieved with HT and reirradiation (mean ± SD dose: 36.7 ± 7.7 Gy). Mean acute and late grade 3/4 toxicities with RT + HT were 14.4% and 5.2%, respectively. Conclusions: Thermoradiation therapy enhances the likelihood of CR rates in LRBCs over RT alone by 22% with minimal acute and late morbidities. For even those previously irradiated, reirradiation with HT provides locoregional control in two-thirds of the patients. Thermoradiation therapy could therefore be considered as an effective and safe palliative treatment option for LRBCs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Data from: "Warming and the dependence of limber pine (Pinus flexilis) establishment on summer soil moisture within and above its current elevation range"

This data package contains data that were used for analysis in “Warming and the dependence of limber pine (Pinus flexilis) establishment on summer soil moisture within and above its current elevation range”, by Moyes et al. 2013. All data collection and field research were completed on Niwot Ridge, Colorado, USA.This data package contains nine comma-separated-values (.csv) files, one text (.txt) file, and two zipped seedling folders that were used for leaf area analysis. One zipped folder contains 468 .jpg photographs of seedlings, and the second contains 468 corresponding Image J-processed .jpg images that include silhouette leaf area values. .csv and .txt files can be opened using any compatible simple text-editor software such as TextEdit (Mac) and Notepad (Windows); .csv’s can also be opened using R and Microsoft Excel. Image files can be opened using Preview (Mac) and Photos (Windows). In addition, there are a total of 31 Microsoft Excel files: three .xlsx files, and 28 raw Li-Cor output .xls files. This data user’s guide is available in .pdf format, and can be opened using Adobe Acrobat Reader, or any other compatible file viewing software. Geospatial data showing field site locations are also included in the archive for use and reference. There are two geospatial formats in this archive: ESRI shapefiles (.shp) and keyhole markup-language (.kml) files. Both file types contain bounding box information, with the former being polygons, and the latter containing corner coordinates for each site. ESRI shapefiles can be opened using any geospatial software compatible with the file type (such as ESRI’s ArcGIS suite and QGIS), and .kml files are compatible with Google Earth and Google Maps.--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Continued changes in climate are projected to alter the geographic distributions of plant species, in part by affecting where individuals can establish from seed. We tested the hypothesis that warming promotes uphill redistribution of subalpine tree populations by reducing cold limitation at high elevation and enhancing drought stress at low elevation. We seeded limber pine (Pinus flexilis) into plots with combinations of infrared heating and water addition treatments, at sites positioned in lower subalpine forest, the treeline ecotone, and alpine tundra. In 2010, first-year seedlings were assessed for physiological performance and survival over the snow-free growing season. Seedlings emerged in midsummer, about 5–8 weeks after snowmelt. Low temperature was not observed to limit seedling photosynthesis or respiration between emergence and October, and thus experimental warming did not appear to reduce cold limitation at high elevation. Instead, gas exchange and water potential from all sites indicated a prevailing effect of summer moisture stress on photosynthesis and carbon balance. Infrared heaters raised soil growing degree days (base 5 °C, p < 0.001) and August–September mean soil temperature (p < 0.001). Despite marked differences in vegetation cover and meteorological conditions across sites, volumetric soil moisture content (θ) at 5–10 cm below 0.16 and 0.08 m^3 m^(−3) consistently corresponded with moderate and severe indications of drought stress in midday stem water potential, stomatal conductance, photosynthesis, and respiration. Seedling survival was greater in watered plots than in heated plots (p = 0.01), and negatively related to soil growing degree days and duration of exposure to θ < 0.08 m^3 m^(−3) in a stepwise linear regression model (p < 0.0001). We concluded that seasonal moisture stress and high soil surface temperature imposed a strong limitation to limber pine seedling establishment across a broad elevation gradient, including at treeline, and that these limitations are likely to be enhanced by further climate warming.--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Maintenance log:Amended file name error in Data User's Guide May 19 2022

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Lower bound for the neutrino magnetic moment from kick velocities induced at the birth of neutron stars

We show that the neutrino chirality flip, which can take place in the core of a neutron star at birth, is an efficient process to allow neutrinos to anisotropically escape, thus providing a to induce the neutron star kick velocities. The process is not subject to the no-go theorem since although the flip from left- to right-handed neutrinos happens at equilibrium, the reverse process does not take place given that right-handed neutrinos do not interact with matter and therefore detailed balance is lost. For simplicity, we model the neutron star core as being made of strange quark matter. We find that the process is efficient when the neutrino magnetic moment is not smaller than [Formula: see text], where [Formula: see text] is the Bohr magneton. When this lower bound is combined with the most stringent upper bound, which uses the luminosity data obtained from the analysis of SN 1987A, our results set a range for the neutrino magnetic moment given by [Formula: see text]. The obtained kick velocities for natal conditions are consistent with the observed ones and span the correct range of radii for typical magnetic field intensities.

Physics↗
DOE OSTI2026

Search for heavy resonances decaying into two Higgs bosons in the $\text {b}{\bar{\text {b}}} \tau ^{+} \tau ^{-}$ final state in proton–proton collisions at $\sqrt{s} = 13\,\text {Te}\hspace{-.08em}\text {V}$

A search is presented for massive narrow-width resonances in the mass range of 1–4.5 TeV , decaying into pairs of Higgs bosons (HH). The search uses proton–proton collision data at a center-of-mass energy of 13 TeV collected with the CMS detector at the CERN LHC during 2016–2018, corresponding to an integrated luminosity of 138 fb -1 . The analysis targets final states where one Higgs boson decays into a pair of bottom quarks and the other into a pair of tau leptons, ${\text {X}} \rightarrow {\text {HH}} \rightarrow \text {b}{\bar{\text {b}}}\,\tau ^{+}\tau ^{-}$. It uses a single large radius jet to reconstruct the ${\text {H}} \rightarrow \text {b}{\bar{\text {b}}}$ decay, while the ${\text {H}} \rightarrow \tau ^{+}\tau ^{-}$ decay products can either be contained within a single large radius jet or appear as two isolated tau leptons. The observed data are consistent with standard model background expectations. Upper limits at 95% confidence level are set on the production cross section for resonant HH production for masses between 1 and 4.5 TeV. This analysis sets the most sensitive limits to date on ${\text {X}} \rightarrow {\text {HH}} \rightarrow \text {b}\bar{\hbox {b}}\, \tau ^{+}\tau ^{-}$ decays in the mass range of 1.4–4.5 TeV.

Hayrapetyan, A. [Yerevan Physics Institute]↗
DOE OSTI2018

Low-mass dark matter search with CDMSlite

The SuperCDMS experiment is designed to directly detect WIMPs (Weakly Interacting Massive Particles) that may constitute the dark matter in our galaxy. During its operation at the Soudan Underground Laboratory, germanium detectors were run in the CDMSlite (Cryogenic Dark Matter Search low ionization threshold experiment) mode to gather data sets with sensitivity specifically for WIMPs with masses ${<}10$ GeV/$c^2$. In this mode, a large detector-bias voltage is applied to amplify the phonon signals produced by drifting charges. This paper presents studies of the experimental noise and its effect on the achievable energy threshold, which is demonstrated to be as low as 56 eV$_{\text{ee}}$ (electron equivalent energy). The detector biasing configuration is described in detail, with analysis corrections for voltage variations to the level of a few percent. Detailed studies of the electric-field geometry, and the resulting successful development of a fiducial parameter, eliminate poorly measured events, yielding an energy resolution ranging from ${\sim}$9 eV$_{\text{ee}}$ at 0 keV to 101 eV$_{\text{ee}}$ at ${\sim}$10 keV$_{\text{ee}}$. New results are derived for astrophysical uncertainties relevant to the WIMP-search limits, specifically examining how they are affected by variations in the most probable WIMP velocity and the galactic escape velocity. These variations become more important for WIMP masses below 10 GeV/$c^2$. Finally, new limits on spin-dependent low-mass WIMP-nucleon interactions are derived, with new parameter space excluded for WIMP masses ${\lesssim}$3 GeV/$c^2$.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Extreme Scale Visual Analytics

Given the scale and complexity of today s data, visual analytics is rapidly becoming a necessity rather than an option for comprehensive exploratory analysis. In this paper, we provide an overview of three applications of visual analytics for addressing the challenges of analyzing climate, text streams, and biosurveilance data. These systems feature varying levels of interaction and high performance computing technology integration to permit exploratory analysis of large and complex data of global significance.

Steed, Chad A↗