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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Western Burrowing Owl Radio-tracking Study

Summary of western burrowing owl satellite-tracking study on the NNSS for the Fall issue of the Nevada Chapter of The Wildlife Society Newsletter.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Oak Ridge Wang-Landau (OWL)

The code is a set of first-principles based thermodynamic properties calculations for materials properties at finite temperature. It provides a collection of commonly used Monte Carlo algorithms, interfaced with a few external codes that carry out density functional theory calculations, such as Quantum Espresso and Local Self-consistent Multiple Scattering. The code provides high accuracy thermodynamic calculations that are comparable to real-world materials properties.

Li, YingWai↗
DOE OSTI2017

Hanford Site Burrowing Owl Monitoring Report for Calendar Year 2015

The U.S. Department of Energy, Richland Operations Office (DOE-RL) conducts ecological monitoring on the Hanford Site to collect and track data needed to ensure compliance with environmental laws, regulations, and policies governing DOE activities. Ecological monitoring data provide baseline information about the plants, animals, and habitat under DOE-RL stewardship at Hanford required for decision-making under the National Environmental Policy Act (NEPA) and Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). The Hanford Site Comprehensive Land Use Plan (CLUP, DOE/EIS-0222-F) which is the Environmental Impact Statement for Hanford Site activities, helps ensure that DOE-RL, its contractors, and other entities conducting activities on the Hanford Site are in compliance with NEPA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Hanford Site Burrowing Owl Monitoring Report for Calendar Year 2013

The U.S. Department of Energy, Richland Operations Office (DOE-RL) conducts ecological monitoring on the Hanford Site to collect and track data needed to ensure compliance with an array of environmental laws, regulations, and policies governing DOE activities. Ecological monitoring data provide baseline information about the plants, animals, and habitat under DOE-RL stewardship at Hanford required for decision-making under the National Environmental Policy Act (NEPA) and Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). The Hanford Site Comprehensive Land Use Plan (CLUP, DOE/EIS-0222-F) which is the Environmental Impact Statement for Hanford Site activities, helps ensure that DOE-RL, its contractors, and other entities conducting activities on the Hanford Site are in compliance with NEPA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

owl

O4921: Open Wave Library for seismic wave modeling and FWI

Gao, Kai [Los Alamos National Laboratory]↗
DOE OSTI2017

Inventory and Waste Characterization Status Report

This report provides an update to Sassani et al. (2016) and includes: (1) an updated set of inputs (Sections 2.3) on various additional waste forms (WF) covering both DOE-managed spent nuclear fuel (SNF) and DOE-managed (as) high-level waste (HLW) for use in the inventory represented in the geologic disposal safety analyses (GDSA); (2) summaries of evaluations initiated to refine specific characteristics of particular WF for future use (Section 2.4); (3) updated development status of the Online Waste Library (OWL) database (Section 3.1.2) and an updated user guide to OWL (Section 3.1.3); and (4) status updates (Section 3.2) for the OWL inventory content, data entry checking process, and external OWL BETA testing initiated in fiscal year 2017.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

SU-E-P-31: Quantifying the Amount of Missing Tissue in a Digital Breast Tomosynthesis

Purpose: To automatically quantify the amount of missing tissue in a digital breast tomosynthesis system using four stair-stepped chest wall missing tissue gauges in the Tomophan™ from the Phantom Laboratory and image processing from Image Owl. Methods: The Tomophan™ phantom incorporates four stair-stepped missing tissue gauges by the chest wall, allowing measurement of missing chest wall in two different locations along the chest wall at two different heights. Each of the four gauges has 12 steps in 0.5 mm increments rising from the chest wall. An image processing algorithm was developed by Image Owl that first finds the two slices containing the steps then finds the signal through the highest step in all four gauges. Using the signal drop at the beginning of each gauge the distance to the end of the image gives the length of the missing tissue gauge in millimeters. Results: The Tomophan™ was imaged in digital breast tomosynthesis (DBT) systems from various vendors resulting in 46 cases used for testing. The results showed that on average 1.9 mm of 6 mm of the gauges are visible. A small focus group was asked to count the number of visible steps for each case which resulted in a good agreement between observer counts and computed data. Conclusion: First, the results indicate that the amount of missing chest wall can differ between vendors. Secondly it was shown that an automated method to estimate the amount of missing chest wall gauges agreed well with observer assessments. This finding indicates that consistency testing may be simplified using the Tomophan™ phantom and analysis by an automated image processing named Tomo QA. In general the reason for missing chest wall may be due to a function of the beam profile at the chest wall as DBT projects through the angular sampling. Research supported by Image Owl, Inc., The Phantom Laboratory, Inc. and Raforninn ehf; Mallozzi and Healy employed by The Phantom Laboratory, Inc.; Goodenough is a consultant to The Phantom Laboratory, Inc.; Fredriksson, Kristbjornsson, Olafsson, Oskarsdottir and Olafsdottir are employed by Raforninn, Ehf.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

ROBOT: A Tool for Automating Ontology Workflows

BACKGROUND:Ontologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and application-specific subsets, generating standard reports, and generating release files in multiple formats. Similar to more general software development, automation is the key to executing and managing these tasks effectively and to releasing more robust products in standard forms. For ontologies using the Web Ontology Language (OWL), the OWL API Java library is the foundation for a range of software tools, including the Protégé ontology editor. In the Open Biological and Biomedical Ontologies (OBO) community, we recognized the need to package a wide range of low-level OWL API functionality into a library of common higher-level operations and to make those operations available as a command-line tool. RESULTS:ROBOT (a recursive acronym for "ROBOT is an OBO Tool") is an open source library and command-line tool for automating ontology development tasks. The library can be called from any programming language that runs on the Java Virtual Machine (JVM). Most usage is through the command-line tool, which runs on macOS, Linux, and Windows. ROBOT provides ontology processing commands for a variety of tasks, including commands for converting formats, running a reasoner, creating import modules, running reports, and various other tasks. These commands can be combined into larger workflows using a separate task execution system such as GNU Make, and workflows can be automatically executed within continuous integration systems. CONCLUSIONS:ROBOT supports automation of a wide range of ontology development tasks, focusing on OBO conventions. It packages common high-level ontology development functionality into a convenient library, and makes it easy to configure, combine, and execute individual tasks in comprehensive, automated workflows. This helps ontology developers to efficiently create, maintain, and release high-quality ontologies, so that they can spend more time focusing on development tasks. It also helps guarantee that released ontologies are free of certain types of logical errors and conform to standard quality control checks, increasing the overall robustness and efficiency of the ontology development lifecycle.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗