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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Helios: Understanding Solar Evolution Through Text Analytics

This proof-of-concept project focused on developing, testing, and validating a range of bibliometric, text analytic, and machine-learning based methods to explore the evolution of three photovoltaic (PV) technologies: Cadmium Telluride (CdTe), Dye-Sensitized solar cells (DSSC), and Multi-junction solar cells. The analytical approach to the work was inspired by previous work by the same team to measure and predict the scientific prominence of terms and entities within specific research domains. The goal was to create tools that could assist domain-knowledgeable analysts in investigating the history and path of technological developments in general, with a focus on analyzing step-function changes in performance, or “breakthroughs,” in particular. The text-analytics platform developed during this project was dubbed Helios. The project relied on computational methods for analyzing large corpora of technical documents. For this project we ingested technical documents from the following sources into Helios: Thomson Scientific Web of Science (papers), the U.S. Patent & Trademark Office (patents), the U.S. Department of Energy (technical documents), the U.S. National Science Foundation (project funding summaries), and a hand curated set of full-text documents from Thomson Scientific and other sources.

14 SOLAR ENERGY↗
DOE OSTI2022

Pollution in the Press: Employing Text Analytics to Understand Regional Water Quality Narratives

Drinking water has and will continue to be at the foundation of our nation’s well-being and there is a growing interest in United States (US) drinking water quality. Nearly 30% of the United States population obtained their water from community water systems that did not meet federal regulations in 2019. Given the heavy interactions between society and drinking water quality, this study integrates social constructionism, environmental injustice, and sociohydrological systems to evaluate local awareness of drinking water quality issues. By employing text analytics, we explore potential drivers of regional water quality narratives within 25 local news sources across the United States. Specifically, we assess the relationship between printed local newspapers and water quality violations in communities as well as the influence of social, political, and economic factors on the coverage of drinking water quality issues. Results suggest that the volume and/or frequency of local drinking water violations is not directly reflected in local news coverage. Additionally, news coverage varied across sociodemographic features, with a negative relationship between Hispanic populations and news coverage of Lead and Copper Rule, and a positive relationship among non-Hispanic white populations. These findings extend current understanding of variations in local narratives to consider nuances of water quality issues and indicate opportunities for increasing equity in environmental risk communication.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Evaluating Text Analytic Frameworks for Mental Health Surveillance

Reducing suicide incidence among US veterans is one of the highest priorities for the US Department of Veterans Affairs (VA). We are implementing a suicide risk detection system, in collaboration with the VA, that would serve as a surveillance system for risk factors appearing in clinical text data. Primary requirements for this system are fast search capability, feature and information extraction, and delivery of data to up-stream natural language processing models. As such, we are evaluating scalable storage solutions on the basis of performance, fault tolerance, and scalability. In this paper we present our current approach to evaluation, preliminary findings, and the work in progress towards a more robust text analysis pipeline.

Mayer, Benjamin W.↗
DOE OSTI2017

ChemAnalyzer: A Text Analytics Platform for Chemical Surveillance

ChemAnalyzer is designed to highlight and summarize important information in chemical incident reportings and other textual sources to reduce the burden on a chemical analyst. By reducing time spent identifying relevant information and summarizing important characteristics, the application (app) enables expedient and thorough surveillance of multiple sources to identify a potential chemical incident.

Gaurav, Sanket↗
DOE OSTI2018

Text-based Analytics for Biosurveillance

The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when). The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when).

Biomedical NLP↗
DOE OSTI2017

Novel Analysis and Visualization of Chemical Events for Public Health Surveillance

Pacific Northwest National Laboratory hosted an intern-based web application development contest in the summer of 2016 centered around developing novel chemical surveillance applications to aid in health situational awareness. Making up the three teams were three graduate students (n=9) from various US schools majoring in non-public health domains, such as computer science and user design. The interns successfully developed three applications that demonstrated a value-add to chemical surveillance—ChemAnalyzer (text analytics), RetroSpect (retrospective analysis of chemical events),and ToxicBusters (geo-based trend analytics). These applications will be the basis for the first chemical surveillance application to be incorporated into the DTRA Biosurveillance Ecosystem (BSVE).Pacific Northwest National Laboratory (PNNL), on behalf the Defense Threat Reduction Agency (DTRA; project number CB10190), hosts an annual intern- based web app development contest. Previous competitions have focused on mobile biosurveillance. The 2016 competition pivoted away from biosurveillance to focus on addressing challenges within the field of chemical surveillance and increasing public health chemical situational awareness. The result of the app will be integrated within the DTRA BSVE. PNNL hosted nine graduate interns for a 10-week period in the summer of 2016 as participants in a summer web application development contest. Students were drawn from such fields as software engineering and user experience and design and placed into three teams of three students. The challenge presented to the interns was to design and develop a fully-functional web application that would address a critical need within the chemical surveillance community. The interns developed their own ideas (vetted by PNNL and DTRA), discovered and integrated their own data sources, and produced their own visualizations and analytics, independent of any assistance outside of that provided in an advisory capacity. The competition end with a judging event with a panel of subject matter experts and cash awards were distributed to the teams. Each team produced a unique application. Although there was mild overlap between some of the ideas, the applications were developed independently and each reflected the unique contributions of the teams. ChemAnalyzer is a text-analytics platform designed to facilitate more data-driven decision, given a corpus of text data about a chemical event. Their platform provided the ability to automatically identify and highlight key words in documents related to chemical events. The keywords are drawn from an ontology installed with the system, as well as any user-identified keywords. The ChemAnalyzer team finished in third place. The RetroSpect team developed a visual analytic tool for performing retrospective analysis and monitoring of chemical events. Their app provided the ability to search and analyze past events, as well as visualization of state and county information for the recorded chemical events. The RetroSpect team finished in second place. The Toxic busters team - the winners of the competition - created a geo-based situational awareness tool for tracking chemical events. Their app featured an updateable map overlay, search functionality for finding specific or related events, incident and city/state/national-level statistics and trends, as well as news and social media integration based on keywords related to chemical surveillance. Each of the apps developed by the teams provides value to an analyst tasked with monitoring chemical events. The apps integrated unique data sources to provides a full picture of a chemical event, and its effects upon the surrounding population. This integrated analytics provides a valuable benefit over existing workflows, where analysts must monitor news, social, and other information sources manually for real-time information. The apps developed by these interns are designed to enable identification and analysis of the incident as quickly as possible, allowing for more timely assessments of the incident and its impacts. The web app development contest provided a unique opportunity for students to learn about the emerging needs in chemical surveillance as it relates to health situational awareness. Students were drawn from a variety of fields and were tasked with developing novel web apps addressing some of the most pressing challenges in the field of chemical surveillance. The ideas generated by the students will help form the basis for future chemical surveillance application development to be integrated with the DTRABSVE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Sandia's Research in Support of COVID-19 Pandemic Response: Computing and Information Sciences

This report summarizes the goals and findings of eight research projects conducted under the Computing and Information Sciences (CIS) Research Foundation and related to the COVID- 19 pandemic. The projects were all formulated in response to Sandia's call for proposals for rapid-response research with the potential to have a positive impact on the global health emergency. Six of the projects in the CIS portfolio focused on modeling various facets of disease spread, resource requirements, testing programs, and economic impact. The two remaining projects examined the use of web-crawlers and text analytics to allow rapid identification of articles relevant to specific technical questions, and categorization of the reliability of content. The portfolio has collectively produced methods and findings that are being applied by a range of state, regional, and national entities to support enhanced understanding and prediction of the pandemic's spread and its impacts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

String correlators on $\text{AdS}_3$: Analytic structure and dual CFT

We continue our study of string correlators on Euclidean \text{AdS}_3 AdS 3 with pure NS-NS flux. The worldsheet and spacetime correlators have a rich analytic structure, which we analyse completely for genus 0 four-point functions. We show that correlators exhibit a simple behaviour near their singularities. The spacetime correlators are meromorphic functions in the \mathrm{SL}(2,\mathbb{R}) S L ( 2 , ℝ ) -spins, whose pole structure is shown to agree with the prediction of a recent proposal for the dual \text{CFT}_2 CFT 2 . Moreover, we also compute the residues of the spacetime correlators for some of the poles exactly and find again a perfect match with the proposal for the dual \text{CFT}_2 CFT 2 , thereby checking the duality for some non-trivial four-point functions exactly. Our computations simplify drastically in the tensionless limit of \mathrm{AdS}_3 \times \mathrm{S}^3 \times \mathbb{T}^4 A d S 3 × S 3 × 𝕋 4 where the behaviour near the poles gives in fact the exact answer. This paper is the third in a series with several installments.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Nuclear cusps and singularities in the nonadditive kinetic potential bifunctional from analytical inversion

The non-additive kinetic potential $v^{\text{NAD}}$ is a key quantity in density-functional theory (DFT) embedding methods, such as frozen density embedding theory and partition DFT. $v^{\text{NAD}}$ is a bi-functional of electron densities $\rho_{\rm B}$ and $\rho_{\rm tot} = \rho_{\rm A} + \rho_{\rm B}$. It can be evaluated using approximate kinetic-energy functionals, but accurate approximations are challenging. The behavior of $v^{\text{NAD}}$ in the vicinity of the nuclei has long been questioned, and singularities were seen in some approximate calculations. In this article, the existence of singularities in $v^{\text{NAD}}$ is analyzed analytically for various choices of $\rho_{\rm B}$ and $\rho_{\rm tot}$, using the nuclear cusp conditions for the density and Kohn-Sham potential. It is shown that no singularities arise from smoothly partitioned ground-state Kohn-Sham densities. We confirm this result by numerical calculations on diatomic test systems HeHe, HeLi$^+$, and H$_2$, using analytical inversion to obtain a numerically exact $v^{\rm NAD}$ for the local density approximation. We examine features of $v^{\rm NAD}$ which can be used for development and testing of approximations to $v^{\rm NAD}[\rho_{\rm B},\rho_{\rm tot}]$ and kinetic-energy functionals.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

TexTonic: Interactive Visualization for Exploration and discovery of Very Large Text Collections

TexTonic is a visual analytic system for interactive exploration of very large unstructured text collections. TexTonic visualizes hierarchical clusters of representative terms, snippets, and documents in a single, multi-scale spatial layout. Exploration is supported by interacting with the visualization and directly manipulating the terms in the visualization using semantic interactions. These semantic interactions steer the underlying analytic model by translating user interactions within the visualization to contextual updates to the supporting data model. The combination of semantic interactions and information visualization at multiple levels of the data hierarchy helps users manage information overload so that they can more effectively explore very large text collections. In this paper we describe TexTonic’s data processing and analytic pipeline, user interface and interaction design principles, and the results of a user study conducted mid-development with experienced data analysts. We also discuss the implications TexTonic could have on visual exploration and discovery tasks

human centered computing, Visual analytics, Inform↗
DOE OSTI2020

Tearing Instability in Alfvén and Kinetic-Alfvén Turbulence

Recently, it has been realized that magnetic plasma turbulence and magnetic field reconnection are inherently related phenomena. Turbulent fluctuations generate regions of sheared magnetic field that become unstable to the tearing instability and reconnection, thus modifying turbulence at the corresponding scales. In this contribution, we give a brief review of some recent results on tearing-mediated magnetic turbulence. We illustrate the main ideas of this rapidly developing field of study by concentrating on two important examples—magnetohydrodynamic Alfvén turbulence and small-scale kinetic-Alfvén turbulence. Due to various potential applications of these phenomena in space physics and astrophysics, we specifically try not to overload the text by heavy analytical derivations but rather present a qualitative discussion accessible to a non-expert in the theories of turbulence and reconnection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗