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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Effect of Transcatheter Arterial Microembolization on Phantom Limb Pain Persisting for 17 Years

Phantom limb pain is a frequent consequence of the amputation; currently available treatments are far from satisfactory. The present report describes a case in which transcatheter arterial microembolization had a remarkable curative effect on phantom limb pain persisting for 17 years. The patient began feeling phantom limb sensations and brief intermittent pain following lower limb amputation above the knee in 2001. The frequency, intensity, and duration of the pain increased over time, and it was resistant to conservative treatments. Following transcatheter arterial microembolization in 2018, patient immediately experienced marked improvement. The pain has been infrequent, much less intense, and did not interfere with daily life 6 months posttreatment. Transcatheter arterial microembolization could be an alternative treatment option for phantom limb pain. Level of Evidence: IV, Case Report.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Fusion Imaging and Virtual Navigation to Guide Percutaneous Thermal Ablation of Hepatocellular Carcinoma: A Review of the Literature

As medical imaging advancements have improved the detectability of hepatocellular carcinoma (HCC) in early stages, the approach to percutaneous thermal ablation for curative treatment has concomitantly advanced. Although many centers are adopting cross-sectional imaging to guide percutaneous ablation, the majority of procedures are still performed under ultrasound (US) guidance worldwide. Challenges to ultrasound guidance may present due to relatively poor resolution particularly with small or isoechoic lesions, or due to intervening structures such as the bowel or diaphragm that obstruct lesional visualization. Fusion imaging (FI) systems have been employed to address these challenges. By merging or synchronizing the real-time images from US with a previously obtained cross-sectional study, FI mitigates the inherent limitations of each individual imaging modality and expands procedural feasibility and technical outcomes. This manuscript reviews the current literature on the use of FI during percutaneous thermal ablation of HCC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Five-Years Outcome Analysis of 142 Consecutive Hepatocellular Carcinoma Patients Treated with Doxorubicin Eluting Microspheres 30–60 μm: Results from a Single-Centre Prospective Phase II Trial

Purpose: To assess prospectively long-term results of doxorubicin-loaded HepaSphere 30–60 μm in consecutive patients with hepatocellular carcinoma (HCC) not amenable to curative treatments. Patients and Methods: Single-center study from June 2011 to December 2015 in 151 patients treated with 75 mg of doxorubicin per HepaSphere vial. Baseline: Barcelona Clinic Liver Cancer BCLC A/B was 49.3%/50.7%, and median diameter 6.1 cm (mean 6.7 ± 2.0). Liver function, local response (mRECIST), liver time to progression (LTTP), progression-free survival (PFS), overall survival (OS) and adverse events (AEs) were recorded. Results: Final analysis included 142 patients with median follow-up of 46.8 months (range 4–72) without grade 4/5 AEs, and 30-day mortality was 0%. Mean number of scheduled treatments was 2.6 (range 1–3) and on demand 3 (range 1–8). Complete response for single tumor ≤ 5 cm was 75.0% and 66.7% for Child A and Child B, while for > 5 cm was 28.6% and 11.8%, respectively. OS was 31.0 months (mean 33.3 ± 15.2; range 8–69), notably for BCLC A 41 months (mean 41.1 ± 15.3; range 13–69) and for BCLC B 26.0 (mean 26.0 ± 10.5; range 8–51). OS at 1, 3 and 5 years: 95.8%, 75.7% and 21.4% for BCLC A, and 94.4%, 36.1% and 2.7% for BCLC B. Median LTTP for BCLC A was 11 months (mean 11.9 ± 4.7; range 3–24) and 7.5 for BCLC B (mean 7.9 ± 2.9). Local response was significant for OS and LTTP (p < 0.0001), while size and lesion number affected LPFS and OS (p < 0.001). Conclusions: HepaSphere 30–60 μm loaded with doxorubicin provides a safe and effective treatment option for patients with HCC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Frictionless knowledge injection for few-shot learning

Cutting-edge machine learning methods often require large volumes of curated training data, precluding their use in national security problems with rare events in massive datasets. We present a method for incorporating abstract knowledge into models tailored for sparse data. A subject matter expert defines salient concepts using data examples, which are encoded in the model’s embedding space. Models are then trained to respect these concepts. This method enables knowledge injection, yielding effective models with limited labeled data and the ability to assess model sensitivity for subject matter expertise across the nonproliferation mission space, as demonstrated with Raman spectra analysis.

Stomps, Jordan [ORNL] (ORCID:0000000178114479)↗
DOE OSTI2022

A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

Here, we present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.

42 ENGINEERING↗
DOE OSTI2016

The Materials Data Facility: Data Services to Advance Materials Science Research

With increasingly strict data management requirements from funding agencies and institutions, expanding focus on the challenges of research replicability, and growing data sizes and heterogeneity, new data needs are emerging in the materials community. The materials data facility (MDF) operates two cloudhosted services, data publication and data discovery, with features to promote open data sharing, self-service data publication and curation, and encourage data reuse, layered with powerful data discovery tools. The data publication service simplifies the process of copying data to a secure storage location, assigning data a citable persistent identifier, and recording custom (e.g., material, technique, or instrument specific)and automatically-extractedmetadata in a registrywhile the data discovery service will provide advanced search capabilities (e.g., faceting, free text range querying, and full text search) against the registered data and metadata. TheMDF services empower individual researchers, research projects, and institutions to (I) publish research datasets, regardless of size, from local storage, institutional data stores, or cloud storage, without involvement of thirdparty publishers; (II) build, share, and enforce extensible domain-specific custom metadata schemas; (III) interact with published data and metadata via representational state transfer (REST) application program interfaces (APIs) to facilitate automation, analysis, and feedback; and (IV) access a data discovery model that allows researchers to search, interrogate, and eventually build on existing published data. We describe MDF’s design, current status, and future plans.

data management↗
DOE OSTI2019

Direct health-care cost of head and neck cancers: a population-based study in north-eastern Italy

Improvements in prognosis of head-and-neck squamous cell carcinoma (HNSCC) have paralleled with an increase in health-care costs, so that an economic evaluation is of growing importance. Presently, most of the evidence is from insurance-based studies in the USA. Between 2007 and 2010, 879 HNSCC patients were identified through the population-based cancer registry of the Friuli Venezia Giulia region, including 266 oral, 187 oropharyngeal, 136 hypopharyngeal, and 290 laryngeal cancers. Health-care costs from diagnosis to treatment initiation and in the following 2 years were retrieved through a record linkage with the regional health data warehouse. This database collected comprehensive health information on all resident citizens. Generalized linear models with a gamma distribution and log-link function were applied to model costs. The average health-care cost from diagnosis up to 2 years after treatment initiation was €20,184 (95% confidence interval: €19,634 − 20,733). Heterogeneity emerged according to cancer site, elective treatment, and retreatment for cancer persistence/recurrence (no: €13,896; yes: €24,599; p < 0.001). An advanced stage was associated with increased costs stage (I: €12,969; II: €18,276; III: €26,229; IV: €25,574; p < 0.001) as the result of treatment complexity and elevated frequency of patients retreatment due to recurrence. These findings further support strategies to diagnose patients at an earlier cancer stage and the accurate definition of diagnostic and treatment pathways, to start treating patients when radical unimodal approach is still feasible. Besides the advantage in prognosis due to timely curative treatments, this would reduce the economic burden of cancer treatment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Recurrent prostate cancer after radical prostatectomy: restaging performance of 18F-choline hybrid PET/MRI

To evaluate the diagnostic performance of a whole-body 18F-choline (FCH) hybrid PET/MRI for prostate cancer patients at biochemical relapse after radical prostatectomy (RP) compared to pelvic multiparametric MRI (mpMRI), one of the standard imaging modality for this patient population. From 2010 to 2016, 58 whole-body FCH PET/MRI studies with mpMRI acquisitions were performed in 53 prostate cancer patients relapsing after curative RP. Median PSA and PSA doubling time (PSA DT) at PET study were 1.5 ng/ml and 6.5 months, respectively. The overall positivity rate of FCH PET/MRI was 58.6% (n = 34), dropping to 44% in patients with a PSA ≤ 2 ng/ml (n = 36). Median PSA values in positive and negative PET/MRI studies were 2.2 ng/ml and 0.8 ng/ml, respectively, with no differences in PSA DT (6.5 vs. 6.6 months). A PSA value ≥ 1.5 ng/ml was a significant predictor of positivity on PET/MRI studies. Compared to PET, mpMRI identified more local relapses (17 vs. 14, p = 0.453) while PET outperformed whole-body Dixon MRI for regional (16 vs. 9, p = 0.016) and distant (12 vs. 6, p = 0.031) metastases. Compared to pelvic mpMRI, the treatment approach turned out to be influenced more frequently using whole-body FCH hybrid PET/MRI studies (58.6% vs. 38%). In prostate cancer patients with biochemical recurrence after RP, whole-body FCH PET/MRI achieved a higher detection rate of nodal/distant metastases compared to pelvic mpMRI alone, increasing the change of treatment strategy by more than 20%.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Storages Are Not Forever

Not unlike the concern over diminishing fossil fuel, information technology is bringing its own share of future worries. Here, we chose to look closely into one concern in this paper, namely the limited amount of data storage. By a simple extrapolatory analysis, it is shown that we are on the way to exhaust our storage capacity in less than two centuries with current technology and no recycling. This can be taken as a note of caution to expand research initiative in several directions: firstly, bringing forth innovative data analysis techniques to represent, learn, and aggregate useful knowledge while filtering out noise from data; secondly, tap onto the interplay between storage and computing to minimize storage allocation; thirdly, explore ingenious solutions to expand storage capacity. Throughout this paper, we delve deeper into the state-of-the-art research and also put forth novel propositions in all of the abovementioned directions, including space- and time-efficient data representation, intelligent data aggregation, in-memory computing, extra-terrestrial storage, and data curation. The main aim of this paper is to raise awareness on the storage limitation we are about to face if current technology is adopted and the storage utilization growth rate persists. In the manuscript, we propose some storage solutions and a better utilization of storage capacity through a global DIKW hierarchy.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

A novel data science workflow is developed and demonstrated to extract process-structure linkages (i.e., reduced-order model) for microstructure evolution problems when the final microstructure depends on (simulation or experimental) processing parameters. Our workflow consists of four main steps: data pre-processing, microstructure quantification, dimensionality reduction, and extraction/validation of process-structure linkages. These methods that can be employed within each step vary based on the type and amount of available data. In this paper, this data-driven workflow is applied to a set of synthetic additive manufacturing microstructures obtained using the Potts-kinetic Monte Carlo (kMC) approach. Additive manufacturing techniques inherently produce complex microstructures that can vary significantly with processing conditions. Using the developed workflow, a low-dimensional data-driven model was established to correlate process parameters with the predicted final microstructure. In addition, the modular workflows developed and presented in this work facilitate easy dissemination and curation by the broader community.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Correlating real-world incidents with vessel traffic off the coast of Hawaii, 2017–2020

Abstract Objectives Because of the high-risk nature of emergencies and illegal activities at sea, it is critical that algorithms designed to detect anomalies from maritime traffic data be robust. However, there exist no publicly available maritime traffic data sets with real-world expert-labeled anomalies. As a result, most anomaly detection algorithms for maritime traffic are validated without ground truth. Data description We introduce the HawaiiCoast_GT data set, the first ever publicly available automatic identification system (AIS) data set with a large corresponding set of true anomalous incidents. This data set—cleaned and curated from raw Bureau of Ocean Energy Management (BOEM) and National Oceanic and Atmospheric Administration (NOAA) automatic identification system (AIS) data—covers Hawaii’s coastal waters for four years (2017–2020) and contains 88,749,176 AIS points for a total of 2622 unique vessels. This includes 208 labeled tracks corresponding to 154 rigorously documented real-world incidents.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

A data-centric weak supervised learning for highway traffic incident detection

Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models-random forest, k-nearest neighbors, and a support vector machine ensemble-and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Finally, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Active learning for the design of polycrystalline textures using conditional normalizing flows

Generative modeling has opened new avenues for solving previously intractable materials design problems. However, these new opportunities are accompanied by a drastic increase in the required amount of training data. This is in stark juxtaposition to the high expense and difficulty in curating such large materials datasets. In this work, we propose a novel framework for integrating generative models within an active learning loop. Further, this enables the training of generative models with datasets significantly smaller than what has previously been demonstrated, providing a direct route for their application in data constrained environments. The functionality of this framework is then demonstrated by addressing the challenge of designing polycrystalline textures associated with target anisotropic mechanical properties. The developed protocol exhibited a cost reduction between 14 to 18 times over a randomly sampled experimental design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Physics-coupled data-driven design of high-temperature alloys

We present a materials design loop, which streamlines physics-coupled machine learning (ML) surrogate models to discover new alloy chemistries with improved properties. The efficacy is demonstrated by discovering a high-temperature alumina-forming austenitic (AFA) stainless steel with enhanced creep, followed by experimental validation. The ML models have been trained using a well-curated, highly consistent experimental dataset augmented with synthetic microstructural features from a computational thermodynamic approach. We have populated a large number of hypothetical AFA alloys to explore the high-dimensional composition space and have predicted their creep properties by providing the same synthetic input features obtained from the trained ML models. Uncertainties from the ML training were taken as thresholds for truncating predicted results to identify alloys with improved or deteriorated creep. Individual elemental compositions have been determined via probability density distribution analysis from the group of alloys at the top and bottom of the predicted creep values for further virtual and experimental validations. In conclusion, we anticipate that this workflow can be applied to screen desired conditions, such as chemistry and processing parameters, in high-dimensional space through physics-guided data analytics.

Alloy design↗
DOE OSTI2020

Automated detection of part quality during two-photon lithography via deep learning

Two-photon lithography (TPL) is an additive manufacturing technique for fabricating three-dimensional objects with nanoscale features. A main challenge of TPL is the routine and labor-intensive task of finding suitable light dosage parameters, i.e. writing speed and laser intensity that induce photo-polymerization within a wide variety of candidate photo-curing polymers. Another challenge is the monitoring required during fabrication. In this work, we apply machine learning (ML) models to accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. We curate TPL videos of different parts fabricated under a range of light dosage parameters using different resins and train spatial-temporal ML models on this data. Our results show that ML models can detect TPL part quality with a 95.1% accuracy in milliseconds. We also evaluate classification failures and identify two operating modes: parameter optimization and part quality detection. Last but not least, we publicly release this labelled dataset so that it may serve as a useful benchmark to the community. Our approach to process optimization and part quality detection addresses important aspects of TPL industrialization, is applicable beyond TPL and should benefit other additive manufacturing techniques with similar barriers to operating at industrial scale.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rapid Annotation of Photosynthetic Systems (RAPS): automated algorithm to generate genome-scale metabolic networks from algal genomes

Algae have mounting potential to serve as platform strains for engineering efforts, but tools for these species lag behind traditional model systems like E. coli and yeast. Metabolic models are one such tool that would enhance our ability to rationally engineer algae but current automated algorithms to build models do not perform well for algal genomes. We introduce a novel software pipeline, Rapid Annotation of Photosynthetic Systems (RAPS), which leverages manual curation efforts of published models to create high quality first draft metabolic networks for new species. We compared models produced by our pipeline to published models and found that these models are able to capture more genes than the published models and are more predictive of experimentally determined growth rate. We used RAPS to automate the creation of 8 first draft metabolic models of new species to enable the further study of metabolism in these species.

09 BIOMASS FUELS↗
DOE OSTI2018

Brick : Metadata schema for portable smart building applications

Buildings account for 32% of worldwide energy usage. A new regime of exciting new “applications” that span a distributed fabric of sensors, actuators and humans has emerged to improve building energy efficiency and operations management. These applications leverage the technological advances in embedded sensing, processing, networking and methods by which they can be coupled with supervisory control and data acquisition systems deployed in modern buildings and with users on mobile wireless platforms. There are, however, several technical challenges to confront before such a vision of smart building applications and cyber-physical systems can be realized. The sensory data produced by these systems need significant curation before it can be used meaningfully. This is largely a manual, cost-prohibitive task and hence such solutions rarely experience widespread adoption due to the lack of a common descriptive schema. Recent attempts have sought to address this through data standards and metadata schemata but fall short in capturing the richness of relationships required by applications. Here, this paper describes Brick, a uniform metadata schema for representing buildings that builds upon recent advances in the area. Our schema defines a concrete ontology for sensors, subsystems and the relationships between them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight unmodified energy efficiency applications on these buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗