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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Method, system and computer-readable media for measuring impedance of an energy storage device

Real-time battery impedance spectrum is acquired using a one-time record. Fast Summation Transformation (FST) is a parallel method of acquiring a real-time battery impedance spectrum using a one-time record that enables battery diagnostics. An excitation current to a battery is a sum of equal amplitude sine waves of frequencies that are octave harmonics spread over a range of interest. A sample frequency is also octave and harmonically related to all frequencies in the sum. A time profile of this sampled signal has a duration that is a few periods of the lowest frequency. A voltage response of the battery, average deleted, is an impedance of the battery in a time domain. Since the excitation frequencies are known and octave and harmonically related, a simple algorithm, FST, processes the time profile by rectifying relative to sine and cosine of each frequency. Another algorithm yields real and imaginary components for each frequency.

25 ENERGY STORAGE↗
DOE OSTI2021

Methods, systems, and computer-readable storage media for enhanced phase-contrast x-ray imaging

Systems and methods that directly image attenuation-based object grid, use a source grid to improve imaging of the object grid using a high-energy polychromatic source, and use a detector grid having gratings oriented substantially orthogonally to that of the object grid, can address artifacts and beam hardening effects that limit the quality and discriminatory power of high-energy x-ray imaging that includes phase contrast.

Miller, Erin A.↗
DOE OSTI2023

Methods, systems, and computer-readable storage media for enhanced phase-contrast x-ray imaging

Systems and methods that directly image attenuation-based object grid, use a source grid to improve imaging of the object grid using a high-energy polychromatic source, and use a detector grid having gratings oriented substantially orthogonally to that of the object grid, can address artifacts and beam hardening effects that limit the quality and discriminatory power of high-energy x-ray imaging that includes phase contrast.

Miller, Erin A.↗
DOE OSTI2016

Radiation Oncology and Online Patient Education Materials: Deviating From NIH and AMA Recommendations

Purpose: Physicians encourage patients to be informed about their health care options, but much of the online health care–related resources can be beneficial only if patients are capable of comprehending it. This study's aim was to assess the readability level of online patient education resources for radiation oncology to conclude whether they meet the general public's health literacy needs as determined by the guidelines of the United States National Institutes of Health (NIH) and the American Medical Association (AMA). Methods: Radiation oncology–related internet-based patient education materials were downloaded from 5 major professional websites (American Society for Radiation Oncology, American Association of Physicists in Medicine, American Brachytherapy Society, (RadiologyInfo.org), and Radiation Therapy Oncology Group). Additional patient education documents were downloaded by searching for key radiation oncology phrases using Google. A total of 135 articles were downloaded and assessed for their readability level using 10 quantitative readability scales that are widely accepted in the medical literature. Results: When all 10 assessment tools for readability were taken into account, the 135 online patient education articles were written at an average grade level of 13.7 ± 2.0. One hundred nine of the 135 articles (80.7%) required a high school graduate's comprehension level (12th-grade level or higher). Only 1 of the 135 articles (0.74%) met the AMA and NIH recommendations for patient education resources to be written between the third-grade and seventh-grade levels. Conclusion: Radiation oncology websites have patient education material written at an educational level above the NIH and AMA recommendations; as a result, average American patients may not be able to fully understand them. Rewriting radiation oncology patient education resources would likely contribute to the patients' understanding of their health and treatment options, making each physician-patient interaction more productive and efficient.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

PDF Entity Annotation Tool (PEAT)

While different text mining approaches – including the use of Artificial Intelligence (AI) and other machine based methods - continue to expand at a rapid pace, the tools used by researchers to create the labeled datasets required for training, modeling, and evaluation remain rudimentary. Labeled datasets contain the target attributes the machine is going to learn; for example, training an algorithm to delineate between images of a car or truck would generally require a set of images with a quantitative description of the underlying features of each vehicle type. Development of labeled textual data that can be used to build natural language machine learning models for scientific literature is not currently integrated into existing manual workflows used by domain experts. Published literature is rich with important information, such as different types of embedded text, plots, and tables that can all be used as inputs to train ML/natural language processing (NLP) models, when extracted and prepared in machine readable formats. Currently, both normalized data extraction of use to domain experts and extraction to support development of ML/NLP models are labor intensive and cumbersome manual processes. Automatic extraction of data and information from formats such as PDFs that are optimized for layout and human readability, not machine readability. The PDF (Portable Document Format) Entity Annotation Tool (PEAT) was developed with the goal of allowing users to annotate publications within their current print format, while also allowing those annotations to be captured in a machine-readable format. One of the main issues with traditional annotation tools is that they require transforming the PDF into plain text to facilitate the annotation process. While doing so lessens the technical challenges of annotating data, the user loses all structure and provenance that was inherent in the underlying PDF. Also, textual data extraction from PDFs can be an error prone process. Challenges include identifying sequential blocks of text and a multitude of document formats (multiple columns, font encodings, etc.). As a result of these challenges, using existing tools for development of NLP/ML models directly from PDFs is difficult because the generated outputs are not interoperable. We created a system that allows annotations to be completed on the original PDF document structure, with no plain text extraction. The result is an application that allows for easier and more accurate annotations. In addition, by including a feature that grants the user the ability to easily create a schema, we have developed a system that can be used to annotate text for different domain-centric schemas of relevance to subject matter experts. Different knowledge domains require distinct schemas and annotation tags to support machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Epi Archive: Automated Synthesis of Global Notifiable Disease Data

ObjectiveLANL has built software that automatically collects global notifiable disease data, synthesizes the data, and makes it available to humans and computers within the Biosurveillance Ecosystem (BSVE) as a novel data stream. These data have many applications including improving the prediction and early warning of disease events.IntroductionMost countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health.While most nations likely store incident data in a machine-readable format, governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues1.A survey conducted by LANL of notifiable disease data reporting in over fifty countries identified only a few websites that report data in a machine-readable format. The majority (>70%) produce reports as PDF files on a regular basis. The bulk of the PDF reports present data in a structured tabular format, while some report in natural language.The structure and format of PDF reports change often; this adds to the complexity of identifying and parsing the desired data. Not all websites publish in English, and it is common to find typos and clerical errors.LANL has developed a tool, Epi Archive, to collect global notifiable disease data automatically and continuously and make it uniform and readily accessible.MethodsWe conducted a survey of the national notifiable disease reporting systems notating how the data are reported and in what formats. We determined the minimal metadata that is required to contextualize incident counts properly, as well as optional metadata that is commonly found.The development of software to regularly ingest notifiable disease data and make it available involves three or four main steps: scraping, detecting, parsing and persisting.Scraping: we examine website design and determine reporting mechanisms for each country/website, as well as what varies across the reporting mechanisms. We then designed and wrote code to automate the downloading of the data for each country. We store all artifacts presented as files (PDF, XLSX, etc.) in their original form, along with appropriate metadata for parsing and data provenance.Detecting: This step is required when parsing structured non-machine-readable data such as tabular data in PDF files. We combined the Nurminen methodology of PDF table detection with in-house heuristics to find the desired data within PDF reports2.Parsing: We determined what to extract from each dataset and parsed these data into uniform data structures, correctly accommodating the variations in metadata (e.g., time interval definitions) and the various human languages.Persisting: We store the data in the Epi Archive database and make it available on the internet and through the BSVE. The data is persisted into a structured and normalized SQL database.ResultsThe Epi Archive tool currently contains national and/or subnational notifiable disease data from twenty nations. When a user accesses the Epi Archive site, they are prompted with four fields: country, subregion, disease of interest, and date duration. Upon form submission, a time series is generated from the users’ specifications. The generated graph can then be downloaded into a CSV file if a user is interested in performing personal analysis. Additionally, the data from Epi Archive can be reached through a REST API (Representational State Transfer Application Programming Interface).ConclusionsLANL, as part of a currently funded DTRA effort, is automatically and continually collecting global notifiable disease data. While 20 nations are in production, more are being brought online in the near future. These data are already being utilized and will have many applications including improving the prediction and early warning of disease events.References[1] van Panhuis WG, Paul P, Emerson C, et al. A systematic review of barriers to data sharing in public health. BMC Public Health. 2014. 14:1144. doi:10.1186/1471-2458-14-1144[2] Nurminen, Anssi. "Algorithmic extraction of data in tables in PDF documents." (2013).

Khalsa, Hari S.↗
DOE OSTI2024

Mist

Determining the appropriate material data is often a bottleneck for performing calculations/simulations of industrial/experimental processes and resulting material structures and properties. Beyond the time it takes to find the appropriate values in the literature, many judgement calls are involved in choosing the values. These judgement calls can lead to inconsistencies between steps in research workflow, where different material parameter values are used. Mist solves this problem by providing a mechanism to store, share, and use material information in convenient human-readable and machine-readable formats. Mist has an extensible ontology for defining a wide variety of material information, currently focused on metal alloy applications. Examples include: alloy composition, density, liquidus temperature, and the coefficient of thermal expansion. Mist converts between standardized machine-readable data formats (e.g. JSON), specialized input format for simulation tools, and human-readable documents (e.g. LaTeX, Markdown). For parameters defined by an equation (e.g. a polynomial function) or a list of tabulated values, Mist can evaluate parameter values at requested conditions. Mist also provides an API for direct usage of the Mist data structures in calculations, if supported.

DeWitt, Stephen [Oak Ridge National Laboratory (OR↗
DOE OSTI2017

Epi Archive: automated data collection of notifiable disease data

LANL has built a software program that automatically collects global notifiable disease data—particularly data stored in files—and makes it available and shareable within the Biosurveillance Ecosystem(BSVE) as a new data source. This will improve the prediction and early warning of disease events and other applications. Most countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health as exemplified by the Biosurveillance Ecosystem(BSVE).While most nations do likely store their data in a machine-readable format, the governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues. For example, an attempt by LANL to obtain a weekly version of openly available monthly data, reported by the Australian government, resulted in an onerous bureaucratic reply. The obstacles to obtaining data included: paperwork to request data from each of the Australian states and territories, a long delay to obtain data (up to 3 months) and extensive limitations on the data’s use that prohibit collaboration and sharing. This type of experience when attempting to contact public health departments or ministries of health for data is not uncommon. A survey conducted by LANL of notifiable disease data reporting in 52 countries identified only 10 as being machine-readable and 42 being reported in pdf files on a regular basis. Within the 42 nations that report in pdf files, 32 report in a structured, tabular format and10 in a non-structured way. As a result, LANL has developed a tool-Epi Archive (formerly known as EPIC)-to automatically and continuously collect global notifiable disease data and make it readily accessible. We conducted a survey of the national notifiable disease reporting systems notating how the data is reported in two important dimensions: date standards and case definitions. The development of software to regularly ingests notifiable disease data frand makes this data available involved four main steps scraping, extracting, parsing and persisting. For scraping: we would examine website designs and determine reporting mechanisms for each country/website as well as what varies across the reporting mechanisms. We then designed and wrote code to automate the downloading of report pdf files, for each country. We stored report pdfs along with appropriate metadata for extracting and parsing. For extracting: we developed software that can extract notifiable disease data presented in tabular form from a pdf file. We combined the methodology of figure placement detection with the in-house developed table extraction and annotation heuristics. For parsing: we determined what to extract from each pdf dataset from the survey conducted. We then parsed the extracted data into uniform data structures correctly accommodating the dimensions surveyed and the various human languages. This task involved ingesting notifiable disease data in many disparate formats extracted from pdf files and coalescing the data into a standardized format. For persisting: We then store the data in the Epi Archive PostgreSQL database and make it available through the BSVE. The EpiArchive tool currently contains subnational notifiable disease data from 10 nations. When a user accesses the EpiArchive site, they are prompted with four fields: country, region, disease, and date duration. These fields allow the user to specify the location(down to the state level), the disease of interest, and the duration of interest. Upon form submission, a time series is generated from the users’ specifications. The generated time series can then be downloaded into a csv file if a user is interested in performing personal analysis. Additionally, the data from EpiArchive can be reached through an API. LANL as part of a currently funded DTRA effort so that it will automatically and continuously collect global notifiable disease data—particularly data stored in pdf files—and make it available and shareable within the Biosurveillance Ecosystem (BSVE) as a new data source. This will provide data to analytics and users that will improve the prediction and early warning of disease events and other applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2024

ENDFtk: A robust tool for reading and writing ENDF-formatted nuclear data

ENDFtk is a recently developed C++ and Python interface to interact with ENDF-6 formatted nuclear data files. It provides a robust and complete interface, allowing the reading and writing of all formats currently part of the ENDF-6 formats manual, as well as some non-ENDF formats used by the NJOY processing code. It provides an interface that mimics the names in the ENDF-6 formats manual as well as an equivalent interface using human-readable attribute names. It is robust and powerful enogh for nuclear data experts to develop complex applications, while also simple enough to be used non-experts to retrieve and manipulate evaluated nuclear data. ENDFtk offers the ability to easily interrogate and manipulate data either in large-scale code projects or in simple Python scripts. Here, in this paper, a brief overview of the interface is given, as well as more substantial examples demonstrating plotting simple data, interacting with more complex data, and writing new data to files. ENDFtk is open source and available for download via GitHub (https://github.com/njoy/ENDFtk).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Exploiting Colorimetry for Fidelity in Data Visualization

Advances in multimodal characterization methods fuel the generation of increasing immense hyper-dimensional data sets. Color mapping is employed for conveying higher dimensional data in two-dimensional (2D) representations for human consumption without relying on multiple projections. How one constructs these color maps, however, critically affects how accurately one perceives data. For simple scalar fields, perceptually uniform color maps and color selection have been shown to improve data readability and interpretation across research fields. In this work, we review core concepts underlying the design of perceptually uniform color maps and extend the concepts from scalar fields to two-dimensional vector fields and three-component composition fields frequently found in materials-chemistry research to enable high-fidelity visualization. Additionally, we develop the software tools PAPUC and CMPUC to enable researchers to utilize these colorimetry principles and employ perceptually uniform color spaces for rigorously meaningful color mapping of higher dimensional data representations. Last, we demonstrate how these approaches deliver immediate improvements in data readability and interpretation in microscopies and spectroscopies routinely used in discerning materials structure, chemistry, and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

RASPA3

RASPA3, a molecular simulation code for computing adsorption and diffusion in nanoporous materials and thermodynamic and transport properties of fluids. It implements force field based classical Monte Carlo/molecular dynamics in various ensembles. RASPA3 is rewritten from the ground up in C++23 with speed and code readability in mind. Transition-matrix Monte Carlo is added to compute the density of states and free energies. The Monte Carlo code for rigid molecules is based on quaternions, and the atomic positions needed in the energy evaluation are recreated from the center of mass position and quaternion orientation. The expanded ensemble methodology for fractional molecules, with a scaling parameter λ between 0 and 1, now also keeps track of analytic expressions of dU/dλ, allowing independent verification of the chemical potential using thermodynamic integration. The source code is freely available under the MIT license on GitHub.

Dubbeldam, David↗
DOE OSTI2026

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,200 new measurements from 903 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Most of the 118 reviews are updated, including many that are heavily revised.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗