Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “keywords”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

The spectral world coordinate systems in IRAF/NOAO

The world coordinate system (WCS) for dispersion calibrated spectra used in the IRAF/NOAO spectroscopy packages is described. In particular, the image header keywords which define the coordinates in an 'image' pixel array. These keywords appear both as part of the IRAF image structure and map directly to FITS format. The types of spectra include multidimensional images with spectral dispersion as one axis, one dimensional images, and a special 'multispec' format consisting of multiple, semi-independent, one dimensional spectra in two or three dimensional images. The types of coordinate systems include multidimensional linear coordinates with individual keywords and one dimensional linear and nonlinear coordinates for the 'multispec' images stored in IRAF WCS attributes. The nonlinear world coordinate systems include polynomial, spline, sampled table, and look-up table function representations.

Valdes, F.↗
NASA NTRS2005

EXTENSIBLE DATABASE FRAMEWORK FOR MANAGEMENT OF UNSTRUCTURED AND SEMI-STRUCTURED DOCUMENTS

Method and system for querying a collection of Unstructured or semi-structured documents to identify presence of, and provide context and/or content for, keywords and/or keyphrases. The documents are analyzed and assigned a node structure, including an ordered sequence of mutually exclusive node segments or strings. Each node has an associated set of at least four, five or six attributes with node information and can represent a format marker or text, with the last node in any node segment usually being a text node. A keyword (or keyphrase) is specified. and the last node in each node segment is searched for a match with the keyword. When a match is found at a query node, or at a node determined with reference to a query node, the system displays the context andor the content of the query node.

Gawdiak, Yuri O.↗
NASA NTRS2007

Standardizing Navigation Data: A Status Update

This paper presents the work of the Navigation Working Group of the Consultative Committee for Space Data Systems (CCSDS) on development of standards addressing the transfer of orbit, attitude and tracking data for space objects. Much progress has been made since the initial presentation of the standards in 2004, including the progression of the orbit data standard to an accepted standard, and the near completion of the attitude and tracking data standards. The orbit, attitude and tracking standards attempt to address predominant parameterizations for their respective data, and create a message format that enables communication of the data across space agencies and other entities. The messages detailed in each standard are built upon a keyword = value paradigm, where a fixed list of keywords is provided in the standard where users specify information about their data, and also use keywords to encapsulate their data. The paper presents a primer on the CCSDS standardization process to put in context the state of the message standards, and the parameterizations supported in each standard, then shows examples of these standards for orbit, attitude and tracking data. Finalization of the standards is expected by the end of calendar year 2007.

VanEepoel, John M.↗
NASA NTRS2011

Operational Phase Life Cycle Assessment of Select NASA Ground Test Facilities

NASA's Aeronautics Test Program (ATP) is responsible for many large, high-energy ground test facilities that accomplish the nation s most advanced aerospace research. In order to accomplish these national objectives, significant energy and resources are consumed. A select group of facilities was analyzed using life-cycle assessment (LCA) to determine carbon footprint and environmental impacts. Most of these impacts stem from electricity and natural gas consumption, used directly at the facility and to generate support processes such as compressed air and steam. Other activities were analyzed but determined to be smaller in scale and frequency with relatively negligible environmental impacts. More specialized facilities use R-134a, R-14, jet fuels, or nitrogen gas, and these unique inputs can have a considerable effect on a facility s overall environmental impact. The results of this LCA will be useful to ATP and NASA as the nation looks to identify its top energy consumers and NASA looks to maximize research output and minimize environmental impact. Keywords: NASA, Aeronautics, Wind tunnel, Keyword 4, Keyword 5

Sydnor, George H.↗
NASA NTRS2016

Inflight Radiometric Calibration of New Horizons' Multispectral Visible Imaging Camera (MVIC)

We discuss two semi-independent calibration techniques used to determine the inflight radiometric calibration for the New Horizons Multi-spectral Visible Imaging Camera (MVIC). The first calibration technique compares the measured number of counts (DN) observed from a number of well calibrated stars to those predicted using the component-level calibration. The ratio of these values provides a multiplicative factor that allows a conversation between the preflight calibration to the more accurate inflight one, for each detector. The second calibration technique is a channel-wise relative radiometric calibration for MVIC's blue, near-infrared and methane color channels using Hubble and New Horizons observations of Charon and scaling from the red channel stellar calibration. Both calibration techniques produce very similar results (better than 7% agreement), providing strong validation for the techniques used. Since the stellar calibration described here can be performed without a color target in the field of view and covers all of MVIC's detectors, this calibration was used to provide the radiometric keyword values delivered by the New Horizons project to the Planetary Data System (PDS). These keyword values allow each observation to be converted from counts to physical units; a description of how these keyword values were generated is included. Finally, mitigation techniques adopted for the gain drift observed in the near-infrared detector and one of the panchromatic framing cameras are also discussed.

Howett, C. J. A.↗
NASA NTRS2019

Reusing Data and Metadata to Create New Metadata Through Machine-Learning & Other Programmatic Methods

Recent improvements in natural language processing (NLP) enable metadata to be created programmatically from reused original metadata or even the dataset itself. Transfer-learning applied to NLP has greatly improved performance and reduced training data requirements. In this talk, we’ll compare machine-generated metadata to human-generated metadata and discuss characteristics of metadata and data archives that affect suitability for machine-learning reuse of metadata. Where as human-generated metadata is often populated once, populated from the perspective of data supplier, populated by many individuals with different words for the same thing, and limited in length, machine-generated metadata can be updated any number of times, generated from the perspective of any user, constrained to a standardized set of terms that can be evolved over time, and be any length required. Machine-learning generated metadata offers benefits but also additional needs in terms of version control, process transparency, human-computer interaction, and IT requirements. As a successful example, we’ll discuss how a dataset of abstracts and associated human-tagged keywords from a standardized list of several thousand keywords were used to create a machine-learning model that predicted keyword metadata for open-source code projects on code.nasa.gov. We’ll also discuss a less successful example from data.nasa.gov to show how data archive architecture and characteristics of initial metadata can be strong controls on how easy it is to leverage programmatic methods to reuse metadata to create additional metadata.

Gosses, Justin↗
DOE OSTI2017

Endovascular Therapy Research in Lower Limb Peripheral Arterial Disease Published Over a 5-Year Period: Who is Publishing and Where?

IntroductionPeripheral arterial disease (PAD) is being increasingly managed by endovascular therapies. In this study, we identified the clinical services publishing research as well as the journals of publication over a 5-year period.MethodsTwenty keywords and phrases related to endovascular intervention were identified, and a literature search was performed through the PubMed database from January 2009 to January 2014. Inclusion criteria were English language, study population more than five patients, and matching the keyword search. Eligible studies were collated into a database and classified by journal of publication, PubMed number, article title, publishing clinical service, type of publication, country of origin, and authors.Results825 studies from 114 different journals were identified. 297 papers were excluded. Of the 528 included papers, 204 (39%) were published by Vascular Surgery (VS), 157 (30%) by Interventional Radiology (IR), 101 (19%) by Cardiology, 43 (8%) by Angiology, 6 (1%) by Vascular Medicine, and 17 (3%) from miscellaneous services. 283 (54%) studies originated from Europe, 157 (30%) from North America, 76 (14%) from Asia, 6 from Australia, 3 each from South America and Africa. IR published the most papers on PAD endovascular intervention in Europe with VS second while this trend was reversed in the USA. The 528 papers were published in 98 different journals with retrospective case series (72%), the majority.ConclusionIR continues to play a significant research role in endovascular intervention in PAD, particularly in Europe, and specifically in below the knee intervention, pedal intervention, and drug-eluting technologies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy data set containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Data Mining Scientific Literature Demonstrates Use of Biological and Medical Data Across Scientific Disciplines

The Protein Data Bank (PDB) was established as the 1st open access digital data resource in biology and medicine. Today, the PDB archive contains over 140,000 atomic‐level biomolecular structures determined by crystallography, NMR spectroscopy, and 3D electron microscopy. About 20 new structures are deposited daily. PDB data is used in basic and applied research, patent applications, discovery of lifesaving drugs, product innovation, and education. About 80% of all PDB structures deposited have a “primary citation,” which is the first paper to describe the molecule and its structure and function. In turn, these primary citations have contributed to more than 1 million publications. An initial study by Clarivate Analytics on the Nucleic Acids Research publication demonstrated that PDB data drive high‐impact research in diverse scientific fields. To build on this study, we used Python programming to perform several data mining studies to understand the impact of these structures in fundamental biology, biomedicine, and energy in the scientific literature. The scientific literature database, Web of Science, tracks scientific article metrics such as the number of times cited, subject category, year published, funding source, and keywords. Web of Science data for PDB primary citations was compiled as used as the data set. Two main investigations were performed. In the first, we examined the number of citations per article (data overall and by individual subject categories) to develop “top 10 cited structures” lists and analyzed for trends. In the second investigation, we extended our data mining analysis to consider for each main subject category, the papers with the most citations and the structures they cite, and the most frequently occuring keywords. We have observed different aspects of biological data utility in various subject areas, at times in ways unanticipated from the structures' primary citation. This research demonstrates the value of biological data from the PDB by showing the diverse ways in which the data are being used. Support or Funding Information RCSB PDB is funded by a grant (DBI‐1338415) from the National Science Foundation, the National Institutes of Health, and the US Department of Energy. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Verdiguel, Natalie↗
DOE OSTI2023

Topographic Correction of Optical Remote Sensing Images in Mountainous Areas: A systematic review

Rugged terrain distorts optical remote sensing observations and subsequently impacts land cover classification and biophysical and biochemical parameter retrieval over mountainous areas. Therefore, topographic correction (TC) is a prerequisite for many remote sensing applications. Although various TC methods have been explored over the past four decades to mitigate topographic effects, a systematic and global review of these studies is still lacking. Using a multicomponent bibliometric approach, we extracted bibliometric metadata from 426 publications identified by searching titles, keywords, and abstracts for research on “topographic correction” and “topographic effects” in Scopus and Web of Science (WoS) from 1980 to 2022. Here this systematic review revealed a rapid growth in the number of TC studies since the 1980s, primarily driven by the availability of decametric-resolution remote sensing observations and digital elevation models (DEMs). Most of the research has focused on relatively low-elevation regions, with increasing attention beyond American and European regions, particularly in China. The seasonal distribution of satellite acquisition for TC showed considerable imbalance, mainly concentrated in months with favorable solar illumination conditions (e.g., May to October). Important themes emerged from the keyword analysis, including satellite sensors, DEMs, TC methods, evaluation criteria, and applications.

58 GEOSCIENCES↗
DOE OSTI2024

HAPPA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded

High-performance computing (HPC) systems are increasingly vulnerable to soft errors, which pose significant challenges in maintaining computational accuracy and reliability. Predicting the resilience of HPC applications to these errors is crucial for robust code protection and detailed resilience analysis. In this study, we present HAppA, a modular platform designed for HPC Application Resilience Analysis. Embedding Large Language Models (LLMs), HAppA addresses understanding the context information of long code sequences typical in HPC applications. HAppA implements a novel code representation module that chunks the code into fixed-size segments and aggregates the embeddings of these segments. Three aggregation methods have been explored: MeanPooling, MaxPooling, and LSTM-based techniques. We built a DAtaset for REsilience analysis using Fault Injection (FI), named DARE. Using our DARE dataset, HAppA is trained for regression prediction tasks. Our evaluation results demonstrate the predictive accuracy of HAppA compared to other models, particularly noting that the LSTM-based aggregation method -- HAppA-LSTM -- achieves a mean squared error (MSE) of 0.078 for SDC prediction, surpassing the existing state-of-the-art PARIS model, which recorded an MSE of 0.1172. Additionally, HAppA with the KeyBERT model extracts a list of keywords representing the source code. A comprehensive importance analysis of these keywords further elucidates the code patterns contributing to the error rate. These findings highlight the effectiveness of HAppA in analyzing the resilience of HPC applications and establish a new benchmark for predictive accuracy in resilience.

Jiang, Hailong [Kent State University]↗
DOE OSTI2017

The changing faces of soil organic matter research

For the 70th Anniversary of the establishment of the British Society of Soil Science, this short paper explores the idea that research on soil organic matter has remained a central theme within soil science over the past 70 years, albeit with changing emphasis and application. The number of publications on soil organic matter has increased greatly in recent decades; for example, there were almost 35 000 journal papers with this theme in the decade 2007–2016. Several topics in research on soil organic matter, such as soil fertility, have endured for a number of decades, with publications found on soil organic matter and fertility in the decade 1947–1956. A search with other keywords occurring with soil, such as climate change, biodiversity, fertility, quality, health and security, showed that several topics did not appear before the 1970s and 1980s, but since then the sub-topics and applications have diversified. Carbon is a keyword that has become more associated with publications on soil organic matter; carbon is in over half of soil organic matter publications of the last decade. A closer examination of research on agricultural soil carbon sequestration since 1990 reveals that the focus of papers in the literature has changed over this period. A closer examination of papers on modelling shows that the next generation of soil organic matter models is developing from pseudo first-order decay models using conceptual pools and prescribed controls of turnover time to vertically resolved, microbially explicit models representing mineral surface and plant interactions. Given its higher policy profile during the last 2 years, research on soil organic matter and soil carbon sequestration is predicted to have a bright future.

58 GEOSCIENCES↗
DOE OSTI2018

Percolator: Scalable Pattern Discovery in Dynamic Graphs

We demonstrate Percolator, a distributed system for graph pattern discovery in dynamic graphs. In contrast to conventional mining systems, Percolator advocates efficient pattern mining schemes that (1) support pattern detection with keywords; (2) integrate incremental and parallel pattern mining; and (3) support analytical queries such as trend analysis. The core idea of Percolator is to dynamically decide and verify a small fraction of patterns and their in- stances that must be inspected in response to buffered updates in dynamic graphs, with a total mining cost independent of graph size. We demonstrate a) the feasibility of incremental pattern mining by walking through each component of Percolator, b) the efficiency and scalability of Percolator over the sheer size of real-world dynamic graphs, and c) how the user-friendly GUI of Percolator inter- acts with users to support keyword-based queries that detect, browse and inspect trending patterns. We also demonstrate two user cases of Percolator, in social media trend analysis and academic collaboration analysis, respectively.

Choudhury, Sutanay↗
DOE OSTI2017

Methods for Determining Likelihood of Tweet Deletion

Few works exist that attempt to build predictive models for tweet deletion. Zhou et al. (2015) focus on a subset of deleted tweets – regrettable tweets. These are tweets that the authors believe to contain inappropriate content. Inappropriate can range from vulgar language to sharing private content such as a personal email address. The presence of inappropriate content doesn’t guarantee that a tweet will be deleted, however intuition dictates it can be in an important factor in the tweet being deleted. In their work, the authors create a predictive model for identifying regrettable tweets. It is important to note the authors focus on predicting regrettable tweets that are distinctly not spam and only written in English. Through manual investigation, the authors identify ten major topics including negative sentiment, cursing, and relationships that are prevalent in regrettable tweets. The authors then exploit WordNet and UrbanDictionary to create keyword lists related to the ten topics. Finally, using a combination of existing lexica and the topic keywords as features, the authors build classifiers to test the accuracy of their model. The authors complement 700 manually labeled regrettable tweets with 700 normal tweets to create their evaluation dataset. The authors’ best performance from 10-fold cross-validation was an f1 score of 0.85 using a J48 classifier on a balanced dataset of deleted and non-deleted tweets.

Source record↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
DOE OSTI2022

Nuclear Sciences References (NSR)

Nuclear Science literature database, containing about 250,000 articles covering more than 120 years of nuclear science research. Approximately 80 journals are routinely scanned for relevant articles; it also includes PhD thesis and private communications. Each article in NSR is assigned a distinctive 8-character primary key, known as ‘key number’, and a set of keywords that briefly describe the article's content. These keywords follow strict rules since they are used in web search forms. Typically, an article is incorporated in NSR within a few weeks after its web publication. About 4,000 articles are added per year. All the compilation effort is supported by the US Nuclear Data Program and coordinated by the National Nuclear Data Center at Brookhaven National Laboratory, which is also responsible for its web dissemination.

Basic Nuclear Research, Applied Nuclear Technologi↗
DOE OSTI2022

Global trends in Akkermansia muciniphila research: A bibliometric visualization

Background Akkermansia muciniphila is a member of the gut microbiome, using mucin as sources of carbon, nitrogen, and energy. Since the first discovery of this unique bacterium in 2004, A. muciniphila has been extensively studied. It is considered a promising “next-generation beneficial microbe.” The purpose of this paper is to sort out the research status and summarize the hotspots through bibliometric analysis of the publications of A. muciniphila. Methods The publications about A. muciniphila from January 2004 to February 2022 were obtained from the Web of Science Core Collection. Visualization analyses were performed using three bibliometric tools and GraphPad Prism. Results A total of 1,478 published documents were analyzed. Annual publication number grew from 1 in 2004 to 336 in 2021, with China being the leading producer (33.36%). De Vos, Willem M was the most productive author with the highest H-index (documents = 56, H-index = 37), followed by Cani, Patrice D (documents = 35, H-index = 25). And Scientific Reports published the most papers. PNAS was the keystone taxa in this field, with high betweenness centrality (0.11) and high frequency. The keywords with high frequency in recent years include: oxidative stress, diet, metformin, fecal microbiota transplantation, short-chain fatty acids, polyphenols, microbiota metabolites and so on. The keyword “oxidative stress” was observed to be increasing in frequency recently. Conclusion Over time, the scope of the research on the clinical uses of A. muciniphila has gradually increased, and was gradually deepened and developed toward a more precise level. A. muciniphila is likely to remain a research hotspot in the foreseeable future and may contribute to human health.

Li, Zitong↗
DOE OSTI2022

Design and Implementation of Smart Buildings: A Review of Current Research Trend

The building sector is one of the largest contributors to the world’s total energy use and greenhouse gas emissions. Advancements in building energy technologies have played a critical role in enhancing the energy sustainability of the built environment. Extensive research and new techniques in energy and environmental systems for buildings have recently emerged to address the global challenges. This study reviews existing articles in the literature, mostly since 2000, to explore technological advancement in building energy and environmental systems that can be applied to smart homes and buildings. This review study focuses on an overview of the design and implementation of energy-related smart building technologies, including energy management systems, renewable energy applications, and current advanced smart technologies for optimal function and energy-efficient performance. To review the advancement in building energy-related technologies, a systematic review process is adopted based on available published reviews and research types of articles. Review-type articles are first assessed to explore the current literature on the relevant keywords and to capture major research scopes. Research-type papers are then examined to investigate associated keywords and work scopes, including objectives, focuses, limitations, and future needs. Throughout the comprehensive literature review, this study identifies various techniques of smart home/building applications that have provided detailed solutions or guidelines in different applications to enhance the quality of people’s daily activities and the sustainability of the built environmental system. This paper shows trends in human activities and technology advancements in digital solutions with energy management systems and practical designs. Understanding the overall energy flow between a building and its environmentally connected systems is also important for future buildings and community levels. This paper assists in understanding the pathway toward future smart homes/buildings and their technologies for researchers in related research fields.

renewable system integration↗