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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

CMed: Crowd Analytics for Medical Imaging Data

In this work, we present a visual analytics framework, CMed, for exploring medical image data annotations acquired from crowdsourcing. CMed can be used to visualize, classify, and filter crowdsourced clinical data based on a number of different metrics such as detection rate, logged events, and clustering of the annotations. CMed provides several interactive linked visualization components to analyze the crowd annotation results for a particular video and the associated workers. Additionally, all results of an individual worker can be inspected using multiple linked views in our CMed framework. We allow a crowdsourcing application analyst to observe patterns and gather insights into the crowdsourced medical data, helping him/her design future crowdsourcing applications for optimal output from the workers. We demonstrate the efficacy of our framework with two medical crowdsourcing studies: polyp detection in virtual colonoscopy videos and lung nodule detection in CT thin-slab maximum intensity projection videos. We also provide experts' feedback to show the effectiveness of our framework. Lastly, we share the lessons we learned from our framework with suggestions for integrating our framework into a clinical workflow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Uncovering I/O demands on HPC platforms: Peeking under the hood of Santos Dumont

High-Performance Computing (HPC) platforms are required to solve the most diverse large-scale scientific problems in various research areas, such as biology, chemistry, physics, and health sciences. Researchers use a multitude of scientific softwares, which have different requirements. These include input and output operations, which directly impact performance due to the existing difference in processing and data access speeds. Thus, supercomputers must efficiently handle mixed workload when storing data from the applications. Understanding the set of applications and their performance running in a supercomputer is paramount to understanding the storage system's usage, pinpointing possible bottlenecks, and guiding optimization techniques. This research proposes a methodology and visualization tool to evaluate a supercomputer's data storage infrastructure's performance, taking into account the diverse workload and demands of the system over a long period of operation. As a study case, we focus on the Santos Dumont supercomputer, identifying inefficient usage, problematic performance factors, and providing guidelines on how to tackle those issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Dual particle imaging: Applications in security and environmental imaging

Radiation imaging detectors have been applied in a variety of fields for detection, localization and characterization of radioactive sources. In this work, we describe a handheld detection system (H2DPI) that can simultaneously image neutrons and gamma rays, and reconstruct the energy spectra of the incident particles. This system was applied to the detection and characterization of a plutonium metal source in experiments performed at the National Criticality Experiments Research Center, where the 4.5 kg plutonium source was placed approximately 60 cm from the H2DPI. Neutron and gamma-ray images were measured that show good agreement with the known location of the source. Furthermore, the H2DPI was also used to measure the neutron spectrum, which shows the expected shape of a Watt fission spectrum, and the gamma-ray energy spectrum, which shows the energies common to plutonium metal. Finally, we describe the application of the H2DPI in augmented reality, whereby real-time neutron and gamma-ray imaging data can be streamed to the Microsoft HoloLens to allow the user to visualize the source locations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Overview of SMART Initiative

The objective of the SMART Initiative, i.e., Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to show how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon storage operations in three main areas: real-time visualization, virtual learning, and real-time forecasting. This presentation reports the status of SMART initiative for demonstrating: (a) virtual learning during the pre-injection permitting phase, and (b) ML-assisted operational decision making and visualization.

Siriwardane, Hema↗
DOE OSTI2021

Measurement and analysis of GPU-accelerated applications with HPCToolkit

To address the challenge of performance analysis on the US DOE’s forthcoming exascale supercomputers, Rice University has been extending its HPCToolkit performance tools to support measurement and analysis of GPU-accelerated applications. To help developers understand the performance of accelerated applications as a whole, HPCToolkit’s measurement and analysis tools attribute metrics to calling contexts that span both CPUs and GPUs. To measure GPU-accelerated applications efficiently, HPCToolkit employs a novel wait-free data structure to coordinate monitoring and attribution of GPU performance. To help developers understand the performance of complex GPU code generated from high-level programming models, HPCToolkit constructs sophisticated approximations of call path profiles for GPU computations. To support fine-grained analysis and tuning, HPCToolkit uses PC sampling and instrumentation to measure and attribute GPU performance metrics to source lines, loops, and inlined code. To supplement fine-grained measurements, HPCToolkit can measure GPU kernel executions using hardware performance counters. To provide a view of how an execution evolves over time, HPCToolkit can collect, analyze, and visualize call path traces within and across nodes. Finally, on NVIDIA GPUs, HPCToolkit can derive and attribute a collection of useful performance metrics based on measurements using GPU PC samples. Here, we illustrate HPCToolkit’s new capabilities for analyzing GPU-accelerated applications with several codes developed as part of the Exascale Computing Project.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Enhancing the applicability of multicomponent time-dependent density functional theory

The multicomponent extension of time-dependent density functional theory (TDDFT) within the nuclear-electronic orbital (NEO) framework enables the calculation of both electronic and vibrational excitations simultaneously. In this NEO-TDDFT approach, all electrons and select nuclei, typically protons, are treated quantum mechanically on the same level. Herein, the dependence of the proton vibrational excitation energies on the nuclear and electronic basis sets is examined. Protonic basis sets that include f basis functions in conjunction with substantial electronic basis sets for the quantum hydrogen are found to produce accurate proton vibrational excitation energies that are mostly within ~30 cm -1 of reference values for the molecules studied. The NEO-TDDFT approach is shown to be effective for open-shell as well as closed-shell systems. Additionally, an approach for computing and visualizing the nuclear transition densities associated with the proton vibrational excitations is implemented. These nuclear transition densities are important for characterizing the proton vibrational excitations and determining the spatial orientations of the corresponding vibrational modes. These capabilities are essential for a variety of applications, including the incorporation of anharmonic effects into molecular vibrational frequency calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Advanced Performance Metrics and their Application to the Sensitivity Analysis for Model Validation and Calibration

High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. In this work, results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Visualizing subcellular rearrangements in intact β cells using soft x-ray tomography

Characterizing relationships between cell structures and functions requires mesoscale mapping of intact cells showing subcellular rearrangements following stimulation; however, current approaches are limited in this regard. Here, we report a unique application of soft x-ray tomography to generate three-dimensional reconstructions of whole pancreatic β cells at different time points following glucose-stimulated insulin secretion. Reconstructions following stimulation showed distinct insulin vesicle distribution patterns reflective of altered vesicle pool sizes as they travel through the secretory pathway. Our results show that glucose stimulation caused rapid changes in biochemical composition and/or density of insulin packing, increased mitochondrial volume, and closer proximity of insulin vesicles to mitochondria. Costimulation with exendin-4 (a glucagon-like peptide-1 receptor agonist) prolonged these effects and increased insulin packaging efficiency and vesicle maturation. This study provides unique perspectives on the coordinated structural reorganization and interactions of organelles that dictate cell responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Light-Dependent Changes in the Spatial Localization of Metabolites in Solenostemon scutellarioides (Coleus Henna) Visualized by Matrix-Free Atmospheric Pressure Electrospray Laser Desorption Ionization Mass Spectrometry Imaging

The visualization of foliage color in plants provides immediate insight into some of the compounds that exist in the leaf. However, many non-colored compounds are also present; their cellular distributions are not readily identifiable optically. In this study we evaluate the applicability of mass spectrometry imaging (MSI) via electrospray laser desorption ionization (ELDI) to reveal the spatial distribution of metabolites. ELDI-MSI is a matrix free, atmospheric pressure ionization method that utilizes a UV laser coupled with supplemental ionization by electrospray. We specifically applied ELDI-MSI to determine the spatial distribution of metabolites in Coleus Henna half leaves that were grown with half-sections either fully illuminated or shaded. We monitored dynamic changes in the spatial distribution of metabolites in response to the change of illumination every 7 days for a 28 day period. A novel source-sink relationship was observed between the 2 halves of the experimental leaf. Furthermore, Coleus Henna leaves present visually recognizable sectors associated with the differential accumulation of flavonoids. Thus, we correlated the effect of differential illumination and presence or absence of flavonoids with metabolic changes revealed by the accumulation of carbohydrates, amino acids, and organic acids. Here, the results show the potential of ELDI-MSI to provide spatial information for a variety of plant metabolites with little sample preparation.

Coleus (Solenostemon scuttellarioides)↗
DOE OSTI2020

Direct visualization of electromagnetic wave dynamics by laser-free ultrafast electron microscopy

Integrating femtosecond lasers with electron microscopies has enabled direct imaging of transient structures and morphologies of materials in real time and space. Here, we report the development of a laser-free ultrafast electron microscopy (UEM) offering the same capability but without requiring femtosecond lasers and intricate instrumental modifications. We create picosecond electron pulses for probing dynamic events by chopping a continuous beam with a radio frequency (RF)–driven pulser with the pulse repetition rate tunable from 100 MHz to 12 GHz. As a first application, we studied gigahertz electromagnetic wave propagation dynamics in an interdigitated comb structure. We reveal, on nanometer space and picosecond time scales, the transient oscillating electromagnetic field around the tines of the combs with time-resolved polarization, amplitude, and local field enhancement. This study demonstrates the feasibility of laser-free UEM in real-space visualization of dynamics for many research fields, especially the electrodynamics in devices associated with information processing technology.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Developing titanium micro/nano porous layers on planar thin/tunable LGDLs for high-efficiency hydrogen production

Proton exchange membrane electrolyzer cells (PEMECs) have been considered one of the most promising devices for hydrogen generation and energy storage from water splitting, especially when coupled with sustainable energy resources. Microporous layers (MPLs), which have been widely used in fuel cells for better catalyst access and product/reactant removal, have limited investigations in PEMECs due to harsh environments and carbon corrosion. In this study, the MPLs with both irregular micro (~5 um) and spherical nano (30-50 nm) titanium particles are developed on novel thin/tunable liquid/gas diffusion layers (TT-LGDLs) and are investigated comprehensively both in-situ and ex-situ for the first time. The MPLs change the wettability of the TT-LGDLs and show super hydrophobic property. The results reveal that micro particle MPLs exhibit improved catalytic activity but increased ohmic resistances, and that nano particle MPLs do not impact catalytic activity meaningfully but exhibit even greater increases in ohmic resistance. The effects of the thickness of the MPLs are also investigated and the typical MPL is also studied by in-situ visualization in a transparent PEMEC with a high-speed and micro-scale visualization system (HMVS). The results indicate the strong feasibility of the TT-LGDLs with small pore size and large porosity for high-efficiency and low-cost PEMEC practical applications.

08 HYDROGEN↗
DOE OSTI2025

PySIDT: Subgraph Isomorphic Decision Trees for Molecular Property Prediction

Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constructing and extending traditional property estimation schemes. However, DNNs require large amounts of training data, are challenging to interpret, require large amounts of memory to load even during inference, and have severe difficulties incorporating qualitative chemical knowledge, which are often desired for molecular property prediction tasks. Here, in this study, we present PySIDT (https://github.com/zadorlab/PySIDT), a software for training and running inference on Subgraph Isomorphic Decision Trees (SIDTs). SIDTs are graph-based decision trees made of nodes associated with molecular substructures. Inference is done by descending target molecular structures down the decision tree to nodes with matching subgraph isomorphic substructures and making predictions based on the final (most specific) nodes matched. SIDTs scale down well to dataset sizes much smaller than is feasible for DNNs. As trees of molecular substructures, SIDTs are inherently readable and easy to visualize, making them easy to analyze. They are also straightforward to extend and retrain, facilitate uncertainty estimation, and enable easy integration of expert knowledge. We demonstrate the SIDT approach discussing its application to a diverse range of molecular prediction tasks: rate coefficient estimation, diffusion coefficient estimation, thermochemistry estimation, transition state bond stretch prediction, p K a prediction, stability of molecular structures, stability of surface structures, and prediction of surface lateral interaction energetics. Additionally, we demonstrate the power of the SIDT algorithms in two direct learning curve vanilla comparisons with the popular DNN-based software Chemprop and the popular gradient boosted trees-based software XGBoost on enthalpy of formation and rate coefficient prediction tasks. In particular, in the enthalpy of formation case, vanilla PySIDT is able to outperform vanilla Chemprop and XGBoost across the full range of training/validation set sizes out to 11,560 data points.

Johnson, Matthew Sean [Sandia National Laboratorie↗
DOE OSTI2024

Updates to the Alliance of Genome Resources central infrastructure

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific “landing pages” and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Improving the accuracy and resolution of neutron crystallographic data by three-dimensional profile fitting of Bragg peaks in reciprocal space

Neutron crystallography is a powerful technique for directly visualizing the locations of H atoms in biological macromolecules. This information has provided key new insights into enzyme mechanisms, ligand binding and hydration. However, despite the importance of this information, the application of neutron crystallography in biology has been limited by the relatively low flux of available neutron beams and the large incoherent neutron scattering from hydrogen, both of which contribute to weak diffraction data with relatively low signal-to-background ratios. A method has been developed to fit weak data based on three-dimensional profile fitting of Bragg peaks in reciprocal space by an Ikeda–Carpenter function with a bivariate Gaussian. When applied to data collected from three different proteins, three-dimensional profile fitting yields intensities with higher correlation coefficients (CC 1/2 ) at high resolutions, decreased R free factors, extended resolutions and improved nuclear density maps. Importantly, additional features are revealed in nuclear density maps that may provide additional scientific information. These results suggest that three-dimensional profile fitting will help to extend the capabilities of neutron macromolecular crystallography.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

gcamdata: An R Package for Preparation, Synthesis, and Tracking of Input Data for the GCAM Integrated Human-Earth Systems Model

The increasing data requirements of complex models demand robust, reproducible, and transparent systems to track and prepare models’ inputs. Here we describe version 1.0 of the gcamdata R package that processes raw inputs to produce the hundreds of XML files needed by the GCAM integrated human-earth systems model. It features extensive functional and unit testing, data tracing and visualization, and enforces metadata, documentation, and flexibility in its component data-processing subunits. Although this package is specific to GCAM, many of its structural pieces and approaches should be broadly applicable to, and reusable by, other complex model/data systems aiming to improve transparency, reproducibility, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

LiDAR-Based Point Cloud Data Processing

The presentation includes the procedures of utilizing LiDAR-based point cloud data in WebGL applications, such as capturing data, processing data, importing point cloud into Unity software, and improving application performance. It uses IRC BLD 603 Lab B-12 as an example. It'll be presented at Viz n Learn workshop which is a collaboration series between INL's Applied Visualization Laboratory (AVL) and University of Wyoming (UW)'s Shell 3D Visualization center. Each session will highlight 2 projects from each institution monthly, followed by discussion on areas of collaborative interest and action.

3D↗
DOE OSTI2019

Inspection Technologies for Waste Containers at Canadian Nuclear Laboratories - 19429

To provide data for aging management programs, containers for different forms of nuclear waste, ranging from low-level waste to irradiated nuclear fuel, should be inspected. At Canadian Nuclear Laboratories, visual, ultrasonic, electromagnetic, and hybrid forms of non-destructive evaluation (NDE) are used for in situ inspection of carbon steel containers having various configurations, access issues, and material thickness. NDE applications tailored to the requirements of the task provide data for condition monitoring and operational assessments. The examples of fuel baskets stacked in concrete canisters and sheet metal containers arrayed in warehouses are illustrated here. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

3D MRI in Musculoskeletal Imaging: Current and Future Applications

Purpose of Review: This article addresses current clinical applications, recent literature, and potential future applications of 3-dimensional magnetic resonance imaging (3D MRI) for musculoskeletal (MSK) applications. Recent Findings: The main advantage of 3D MRI over standard 2-dimensional MRI is its ability to reduce partial volume averaging artifacts and create multiplanar reconstruction (MPRs) in any plane with any slice thickness from a single high-resolution isotropic acquisition. 3D MRI acquisitions are particularly useful for the evaluation of articular cartilage, which is prone to volume averaging artifacts, and for the assessment of longitudinally coursing structures such as peripheral nerves and tendons, which are better visualized with non-orthogonal MPRs. 3D MRI is also useful for surface and volumetric analysis of bone and cartilage for preoperative and longitudinal assessments. Current research is focused on decreasing acquisition times and automating segmentation through machine learning, thus overcoming some of the current limitations of 3D MRI and providing new applications for this technique. Summary: 3D MRI is widely used in MSK imaging today, and its use is likely to continue to increase in the future, with recent advancements focused on accelerated acquisition techniques and quantitative imaging.

62 RADIOLOGY AND NUCLEAR MEDICINE↗