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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

ResOpsUS, a dataset of historical reservoir operations in the contiguous United States

Abstract There are over 52,000 dams in the contiguous US ranging from 0.5 to 243 meters high that collectively hold 600,000 million cubic meters of water. These structures have dramatically affected the river dynamics of every major watershed in the country. While there are national datasets that document dam attributes, there is no national dataset of reservoir operations. Here we present a dataset of historical reservoir inflows, outflows and changes in storage for 679 major reservoirs across the US, called ResOpsUS. All of the data are provided at a daily temporal resolution. Temporal coverage varies by reservoir depending on construction date and digital data availability. Overall, the data spans from 1930 to 2020, although the best coverage is for the most recent years, particularly 1980 to 2020. The reservoirs included in our dataset cover more than half of the total storage of large reservoirs in the US (defined as reservoirs with storage greater 0.1 km 3 ). We document the assembly process of this dataset as well as its contents. Historical operations are also compared to static reservoir attribute datasets for validation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Continuous observations of the surface energy budget and meteorology over the Arctic sea ice during MOSAiC

Abstract The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was a yearlong expedition supported by the icebreaker R/V Polarstern , following the Transpolar Drift from October 2019 to October 2020. The campaign documented an annual cycle of physical, biological, and chemical processes impacting the atmosphere-ice-ocean system. Of central importance were measurements of the thermodynamic and dynamic evolution of the sea ice. A multi-agency international team led by the University of Colorado/CIRES and NOAA-PSL observed meteorology and surface-atmosphere energy exchanges, including radiation; turbulent momentum flux; turbulent latent and sensible heat flux; and snow conductive flux. There were four stations on the ice, a 10 m micrometeorological tower paired with a 23/30 m mast and radiation station and three autonomous Atmospheric Surface Flux Stations. Collectively, the four stations acquired ~928 days of data. This manuscript documents the acquisition and post-processing of those measurements and provides a guide for researchers to access and use the data products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding

Abstract Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million technical drawings with 132,890 object names and 22,394 viewpoints extracted from 14 years of US design patent documents. We demonstrate the usefulness of DeepPatent2 with conceptual captioning. We further provide the potential usefulness of our dataset to facilitate other research areas such as 3D image reconstruction and image retrieval.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Aerial imagery dataset of lost oil wells

Orphaned wells are wells for which the operator is unknown or insolvent. The location of hundreds of thousands of these wells remain unknown in the United States alone. Cost-effective techniques are essential to locate orphaned wells to address environmental problems. In this paper, we present a dataset consisting of 120,948 aerial images of recently documented orphan wells. Each of these 512 × 512 images is paired with segmentation masks that indicate the presence or absence of such well. These images, sourced from the National Agriculture Imagery Program, cover the continental United States with spatial resolutions ranging from 30 centimeters to 1 meter. Additionally, we included negative examples by selecting locations uniformly across the United States. Accompanying metadata includes the IDs and spatial resolution of the original images, which are available for free through the United States Geological Survey, and the pixel coordinates of documented orphaned wells identified in these images. This dataset is intended to support the development of deep-learning models that can help locating undocumented orphan wells from such imagery, thereby blunting the environmental damage they do.

Climate-change mitigation↗
DOE OSTI2023

Knowledge of lactation amenorrhea method among postpartum women in Ethiopia: a facility-based cross-sectional study

While the importance of knowledge about contraceptives in improving their utilization and thereby reducing the risk of unintended pregnancies is well documented, there are limited studies documented about the Lactational Amenorrhea Method (LAM). Thus, understanding the knowledge of postpartum mothers about LAM is essential for designing tailored interventions. This study assessed the level of knowledge about LAM and its associated factors among postpartum mothers in Ethiopia. A facility-based cross-sectional study was conducted among 3148 randomly selected postpartum participants. The study utilized multistage sampling approach in hospitals located across five regions and one city administration in Ethiopia. Data were collected using face-to-face interviews at discharge. A participant was categorized as having knowledge of LAM if she correctly answered the three LAM criteria: amenorrhea, the first 6 months, and exclusive breast feeding. A binary logistic regression model was used to identify factors associated with knowledge of LAM. Variables with p < 0.25 in the binary logistic regression were included in the multiple logistic regression. Then, associations were described using the adjusted odds ratio (AOR) along with the 95% confidence interval (CI), and statistical significance was declared at p < 0.05. Only four in 10 participants (40.6%; 95% CI 38.9–42.3) had knowledge of LAM. Participants who attended college or above educational level (AOR = 2.1, 95% CI 1.5–2.8), those with parity of two (AOR = 2.3; 95% CI 1.6–3.6) or more than two (AOR = 2.4; 95% CI 1.5–4.0), those who expressed a desire for further fertility (AOR = 1.3; 95% CI 1.1–1.5), individuals who received counselling on LAM (AOR = 3.0; 95% CI 2.6–3.7), and those who gave birth in hospital (AOR = 2.6; 95% CI 1.4–2.6) had higher odds of knowledge about LAM, compared to their counter parts. In contrary, participants resided far away from health facilities had 30% lower odd of knowledge about LAM compared to those resided near the health facilities (AOR = 0.70; 95% CI 0.6–0.8). The proportion of participants who had knowledge of LAM was low. Strengthening counseling about LAM during antenatal care and delivery with due attention to women with limited access to health facilities should be considered for increasing their level of knowledge on LAM.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Rapid biodegradation of microplastics generated from bio-based thermoplastic polyurethane

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

09 BIOMASS FUELS↗
DOE OSTI2020

SFCOMPO Database of Spent Nuclear Fuel Assay Data – the Next Frontier

SFCOMPO is the world’s largest database for measured spent nuclear fuel assay data. An international effort coordinated by the Nuclear Energy Agency (NEA) resulted in a significant expansion of the database and its release online in 2017 as a downloadable application. The SFCOMPO Technical Review Group (TRG) was recently formed under the direction of NEA’s Nuclear Science Committee/Working Party on Nuclear Criticality Safety and was mandated to maintain and further coordinate the development of SFCOMPO. This TRG is currently focused on (1) critical evaluation of the experimental assay data by independent experts and (2) development of benchmarks and benchmark models that can be applied to validate burnup codes. This will improve the quality and documentation of the experimental datasets and enable their use by the international community to support code validation for design and safety analysis of spent nuclear fuel transportation, storage, and repository applications. It follows the precedent and draws on the experience gained from similar NEA efforts in the International Reactor Physics Experiment Evaluation Project and the International Criticality Safety Benchmark Experiment Project. Ongoing SFCOMPO evaluations have served as a test bed to develop templates for documenting evaluations, develop review guidance, improve approaches for a global uncertainty analysis, and devise a strategy focused on providing practical information of highest value to the user community. The current effort, status, and associated challenges are discussed.

Ilas, Germina↗
DOE OSTI2023

The Covariance of PFNS Results from the Chi-Nu Experiment

The prompt fission neutron spectrum (PFNS) from neutron-induced fission is a fundamental quantity for the behavior of nuclear reactors, and has been measured many times on a wide variety of nuclei and covering different ranges of incident and emitted neutron energies. However, results from past measurements are frequently called into question in modern nuclear data evaluations because of a lack of thorough experimental documentation and incomplete uncertainty analyses. The Chi-Nu experiment at Los Alamos National Laboratory was designed to produce high-precision measurements of the PFNS of major actinides over a wide range of incident and emitted neutron energies, and with the documentation and covariance analysis required to ensure that the results of this experiment maintain their impact long into the future, thereby avoiding this pitfall of past measurements. In this work we describe the Chi-Nu experiment along with summaries of the treatment of and methods developed to address two important components of the analysis of Chi-Nu data: random-coincidence backgrounds and MCNP simulations. Furthermore, we describe the first results for correlations not just between all data points collected on a single target nucleus, but also between all data points from separate Chi-Nu measurements on 235 U and 239 Pu. These correlations are important for accurately calculating ratios of the PFNS from one actinide to another, which are rare and can be informative for nuclear data evaluation efforts.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Provider Perspectives: Identification and Follow-up of Infants who Are Deaf or Hard of Hearing

Objective Without timely screening, diagnosis, and intervention, hearing loss can cause significant delays in a child's speech, language, social, and emotional development. In 2019, Texas had nearly twice the average rate of loss to follow-up (LFU) or loss to documentation (LTD; i.e., missing documentation of services received) among infants who did not pass their newborn hearing screening compared to the United States overall (51.1 vs. 27.5%). We aimed to identify factors contributing to LFU/LTD among infants who do not pass their newborn hearing screening in Texas. Study Design Data were collected through semistructured qualitative interviews with 56 providers along the hearing care continuum, including hospital newborn hearing screening program staff, audiologists, primary care physicians, and early intervention (EI) program staff located in three rural and urban public health regions in Texas. Following recording and transcription of the interviews, we used qualitative data analysis software to analyze themes using a conventional content analysis approach. Results Frequently cited barriers included problems with family access to care, difficulty contacting patients, problems with communication between providers and referrals, lack of knowledge among providers and parents, and problems using the online reporting system. Providers in rural areas more often mentioned problems with family access to care and contacting families compared to providers in urban areas. Conclusion These findings provide insight into strategies that public health professionals and health care providers can use to work together to help further increase the number of children identified early who may benefit from EI services. Key Points

Obstetrics & Gynecology↗
DOE OSTI2024

A Survey of Multimaterial Treatments for Thermal Radiative Transfer

Arbitrary Lagrangian-Eulerian methods are a popular choice for hydrodynamic modeling in radiation (rad-hydro) simulations. Because these methods involve a relaxation step that moves the mesh relative to material boundaries, multimaterial spatial zones are generally present. Accurate treatments of these zones are needed to resolve various physical phenomena of interest for inertial confinement fusion applications. However, these codes are often paired with single-material, deterministic thermal radiative transfer (TRT) codes that are oblivious to the material compositions of each zone. These single-material TRT codes can only accept homogenized material properties (opacities, specific heats, etc.) from the hydrodynamic code and output homogenized solutions. After each TRT time step, the multimaterial hydrodynamic code must dehomogenize the quantities computed by the TRT package in order to update subzonal material temperatures. The process by which hydrodynamic codes perform this dehomogenization has not been well documented in previous literature, and the methods can vary significantly from code to code. The purpose of this paper is to document, study, and compare existing techniques used for rad-hydro simulations as well as present a new method with potentially promising results. We summarize several methods and give comparisons on infinite-medium problems as well a finite-medium problem for two of the methods.

42 ENGINEERING↗
DOE OSTI2025

Automated Resonance Fitting for Nuclear Data Evaluation

Global and national efforts to deliver high-quality nuclear data to users have a wide-ranging impact, affecting applications in national security, reactor operations, basic science, medicine, and more. Cross section evaluation is a major part of this effort, combining theory and experimentation to produce recommended values and uncertainties for reaction probabilities. Resonance region evaluation is a specialized type of nuclear data evaluation that can require significant manual effort and months of time from expert scientists. In this article, non-convex non-linear optimization methods are combined with concepts of inferential statistics to infer a resonance model from experimental data in an automated manner that is not dependent on prior evaluation(s). This methodology aims to enhance the workflow of a resonance evaluator by minimizing time, effort, and the potential for bias from prior assumptions, while enhancing reproducibility and documentation, thereby addressing well-known challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

First access to ELM-free negative triangularity at low aspect ratio

A plasma scenario with negative triangularity (NT) shaping is achieved on MAST-U for the first time. While edge localized modes (ELMs) are eventually suppressed as the triangularity is decreased below δ ≲ –0.06, an extended period of H-mode operation with Type-III ELMs is sustained at less negative δ even through access to the second stability region for ideal ballooning modes is closed. This documents a qualitative difference from the ELM-free access conditions documented in NT scenarios on conventional aspect ratio machines. The electron temperature at the pedestal top drops across the transition to ELM-free operation, but a steady rise in core temperature as δ is decreased allows for similar normalized β in the ELM-free NT and H-mode positive triangularity shapes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A new Software Training Model at Belle II

The physics output of modern experimental HEP collaborations hinges not only on the quality of its software but also on the ability of the collaborators to make the best possible use of it. With the COVID-19 pandemic making in-person training impossible, the training paradigm at Belle II was shifted from periodic workshops towards guided self-study. To that end, the study material was rebuilt from scratch as a series of modular and hands-on lessons tightly integrated with the software documentation using Sphinx. Each lesson contains multiple exercises that are supplemented with hints and complete solutions. Rather than duplicating information, students are systematically taught to work with the technical reference documentation to find the important sections for themselves. Unit tests ensure that all examples work with different software versions, and feedback buttons make it easy to submit comments for improvements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

EXFOR-NSR PDF database: a system for nuclear knowledge preservation and data curation

Current needs of nuclear science and technology include complete, well-documented, and easily verifiable nuclear data. The complete data records require supporting nuclear bibliography, presently stored in dedicated libraries, in addition, to actual data. Additionally, experimental nuclear reaction data (EXFOR) and Nuclear Science References (NSR) databases contain compilations based on primary (journals) and secondary (conference proceedings, theses, preprints, etc.) publications, and data received from authors via private communications. The secondary library materials and private communications often represent a bottleneck for nuclear data verification, compilation, evaluation, and dissemination activities. To address this issue, bibliographic materials were scanned into PDF (Portable Document Format) files and uploaded in a relational database. The traditional scope of nuclear databases that includes meta-data and numbers derived from data in specialized formats was broadened to accommodate the large volumes of original nuclear data publications. The complete PDF publication files were stored in a relational database as Binary Large OBjects (BLOB). This unique collection of nuclear data compilations and supporting publications generate many opportunities for machine learning applications. The Web interfaces for authorized and public access to the EXFOR-NSR nuclear publications database were implemented at the U.S. National Nuclear Data Center, https://www.nndc.bnl.gov/ and IAEA Nuclear Data Section, https://www-nds.iaea.org/ . The current system is complementary to major nuclear libraries and narrowly focused on nuclear data compilation and evaluation procedures. The contents of the PDF database, details of implementation, and Web interface are described. New capabilities for data curation, knowledge preservation, worldwide dissemination, and natural language processing (NLP) applications are given.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

CONSTAX2: improved taxonomic classification of environmental DNA markers

Abstract Summary CONSTAX—the CONSensus TAXonomy classifier—was developed for accurate and reproducible taxonomic annotation of fungal rDNA amplicon sequences and is based upon a consensus approach of RDP, SINTAX and UTAX algorithms. CONSTAX2 extends these features to classify prokaryotes as well as eukaryotes and incorporates BLAST-based classifiers to reduce classification errors. Additionally, CONSTAX2 implements a conda-installable command-line tool with improved classification metrics, faster training, multithreading support, capacity to incorporate external taxonomic databases and new isolate matching and high-level taxonomy tools, replete with documentation and example tutorials. Availability and implementation CONSTAX2 is available at https://github.com/liberjul/CONSTAXv2, and is packaged for Linux and MacOS from Bioconda with use under the MIT License. A tutorial and documentation are available at https://constax.readthedocs.io/en/latest/. Data and scripts associated with the manuscript are available at https://github.com/liberjul/CONSTAXv2_ms_code. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Automated annotation of scientific texts for ML-based keyphrase extraction and validation

Advanced omics technologies and facilities generate a wealth of valuable data daily; however, the data often lack the essential metadata required for researchers to find, curate, and search them effectively. The lack of metadata poses a significant challenge in the utilization of these data sets. Machine learning (ML)–based metadata extraction techniques have emerged as a potentially viable approach to automatically annotating scientific data sets with the metadata necessary for enabling effective search. Text labeling, usually performed manually, plays a crucial role in validating machine-extracted metadata. However, manual labeling is time-consuming and not always feasible; thus, there is a need to develop automated text labeling techniques in order to accelerate the process of scientific innovation. This need is particularly urgent in fields such as environmental genomics and microbiome science, which have historically received less attention in terms of metadata curation and creation of gold-standard text mining data sets. In this paper, we present two novel automated text labeling approaches for the validation of ML-generated metadata for unlabeled texts, with specific applications in environmental genomics. Our techniques show the potential of two new ways to leverage existing information that is only available for select documents within a corpus to validate ML models, which can then be used to describe the remaining documents in the corpus. The first technique exploits relationships between different types of data sources related to the same research study, such as publications and proposals. The second technique takes advantage of domain-specific controlled vocabularies or ontologies. In this paper, we detail applying these approaches in the context of environmental genomics research for ML-generated metadata validation. Our results show that the proposed label assignment approaches can generate both generic and highly specific text labels for the unlabeled texts, with up to 44% of the labels matching with those suggested by a ML keyword extraction algorithm.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Patient-Reported Outcomes in Pediatric Cancer Registration Trials: A US Food and Drug Administration Perspective

Pediatric patient-reported outcome (PRO) data can help inform the US Food and Drug Administration’s (FDA’s) benefit-risk assessment of cancer therapeutics by quantifying symptom and functional outcomes from the patient’s perspective. This study assessed use of PROs in commercial pediatric oncology trials submitted to the FDA for regulatory review. FDA databases were searched to identify pediatric oncology product applications approved between 1997 and 2020. Sponsor-submitted documents were reviewed to determine whether PRO data were collected, which instruments were used, and the quality of collected data (ie, sample size, completion rates, and use of fit-for-purpose instruments). The role of PROs in each trial (endpoint hierarchy) was also recorded in addition to whether any PRO endpoints were included in product labeling. We reviewed 17 pediatric oncology applications, 4 of which included PRO data: denosumab, tisagenlecleucel, larotrectinib, and selumetinib. In these 4 instances, PROs served as exploratory endpoints and were not incorporated in product labeling. Trials that collected PRO data were phase II or phase I/II single-arm studies with sample sizes of 28 to 88 patients. Symptomatic adverse events (AEs) were characterized using clinician-reported Common Terminology Criteria for Adverse Events (CTCAE) without additional patient self-report. PROs were infrequently used in pediatric cancer registration trials. When PROs were used, PRO data were limited by lack of a clear research objective and corresponding prospective statistical analysis plan. Contemporary PRO symptom libraries, such as the National Cancer Institute’s Pediatric PRO-CTCAE, may provide an opportunity to better evaluate the occurrence and impact of symptomatic AEs, from the patient’s perspective, in pediatric oncology trials.

Oncology↗
DOE OSTI2022

Characterization and rank assignment criteria for the anaerobic fungi (Neocallimastigomycota)

Establishing a solid taxonomic framework is crucial for enabling discovery and documentation efforts. This ensures effective communication between scientists as well as reproducibility of results between laboratories, and facilitates the exchange and preservation of biological material. Such framework can only be achieved by establishing clear criteria for taxa characterization and rank assignment. Within the anaerobic fungi (phylum Neocallimastigomycota), the need for such criteria is especially vital. Difficulties associated with their isolation, maintenance and long-term storage often result in limited availability and loss of previously described taxa. To this end, we provide here a list of morphological, microscopic, phylogenetic and phenotypic criteria for assessment and documentation when characterizing newly obtained Neocallimastigomycota isolates. We also recommend a polyphasic rank-assignment scheme for novel genus-, species- and strain-level designations for newly obtained Neocallimastigomycota isolates.

59 BASIC BIOLOGICAL SCIENCES↗