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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

60 APPLIED LIFE 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 OSTI2023

Mining the Penicillium expansum Genome for Virulence Genes: A Functional-Based Approach to Discover Novel Loci Mediating Blue Mold Decay of Apple Fruit

Blue mold, a postharvest disease of pome fruits, is caused by the filamentous fungus Penicillium expansum. In addition to the economic losses caused by P. expansum, food safety can be compromised, as this pathogen is mycotoxigenic. In this study, forward and reverse genetic approaches were used to identify genes involved in blue mold infection in apple fruits. For this, we generated a random T-DNA insertional mutant library. A total of 448 transformants were generated and screened for the reduced decay phenotype on apples. Of these mutants, six (T-193, T-275, T-434, T-588, T-625, and T-711) were selected for continued studies and five unique genes were identified of interest. In addition, two deletion mutants (Δt-625 and Δt-588) and a knockdown strain (t-434 KD ) were generated for three loci. Data show that the Δt-588 mutant phenocopied the T-DNA insertion mutant and had virulence penalties during apple fruit decay. We hypothesize that this locus encodes a glyoxalase due to bioinformatic predictions, thus contributing to reduced colony diameter when grown in methylglyoxal (MG). This work presents novel members of signaling networks and additional genetic factors that regulate fungal virulence in the blue mold fungus during apple fruit decay.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Mining for Gluon Saturation at Colliders

Quantum chromodynamics (QCD) is the theory of strong interactions of quarks and gluons collectively called partons, the basic constituents of all nuclear matter. Its non-abelian character manifests in nature in the form of two remarkable properties: color confinement and asymptotic freedom. At high energies, perturbation theory can result in the growth and dominance of very gluon densities at small-x. If left uncontrolled, this growth can result in gluons eternally growing violating a number of mathematical bounds. The resolution to this problem lies by balancing gluon emissions by recombinating gluons at high energies: phenomena of gluon saturation. High energy nuclear and particle physics experiments have spent the past decades quantifying the structure of protons and nuclei in terms of their fundamental constituents confirming predicted extraordinary behavior of matter at extreme density and pressure conditions. In the process they have also measured seemingly unexpected phenomena. We will give a state of the art review of the underlying theoretical and experimental tools and measurements pertinent to gluon saturation physics. We will argue for the need of high energy electron-proton/ion colliders such as the proposed EIC (USA) and LHeC (Europe) to consolidate our knowledge of QCD knowledge in the small x kinematic domains.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Mining for Strong Gravitational Lenses with Self-supervised Learning

We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys’ Data Release 9. Targeting the identification of new strong gravitational lens candidates, we first create a rapid similarity search tool to discover new strong lenses given only a single labeled example. We then show how training a simple linear classifier on the self-supervised representations, requiring only a few minutes on a CPU, can automatically classify strong lenses with great efficiency. We present 1192 new strong lens candidates that we identified through a brief visual identification campaign and release an interactive web-based similarity search tool and the top network predictions to facilitate crowd-sourcing rapid discovery of additional strong gravitational lenses and other rare objects: github.com/georgestein/ssl-legacysurvey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Data Driven Approach to Public Opinion Mining on Autonomous Vehicles: Sentiment Analysis of Social Media Comments Using Large Language Models

In the realm of online identity, social media has emerged as a rich and dynamic source of user-generated content, making it an invaluable resource for understanding public sentiment on a wide range of topics. Individuals often share their raw emotions and candid opinions on these platforms without fear of judgment or backlash. In this study, we conduct a sentiment analysis on user comments collected from various online platforms, with a specific focus on discussions surrounding autonomous vehicles. Leveraging the capabilities of large language models (LLMs), we classify each comment into one of five sentiment categories: Very Negative, Negative, Neutral, Positive, and Very Positive. Our approach demonstrates the effectiveness of LLMs in capturing nuanced contextual sentiment, offering a scalable and state-of-the-art alternative to traditional manual annotation methods. The results reveal key trends and insights into public perception, enabling a deeper understanding of how autonomous vehicle technologies are received by the online community. Our findings underscore the dynamic nature of public sentiment, which is shaped not only by advances in autonomous vehicle technology but also by contextual events such as regulatory developments, political adjustment and safety incidents.

24 POWER TRANSMISSION AND DISTRIBUTION↗