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104 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

MINOS Infrasound Analysis Synopsis

This report was written as a guide to working with infrasound data collected as part of the Multi-Informatics for Nuclear Operations Scenarios (MINOS)project, an NA-22 funded venture. The main purpose of overall MINOS project is the combination of multiple, disparate data modalities to characterize the operations at a nuclear facility, specifically instrumenting and studying the High-Flux Isotope Reactor (HFIR) and Radiochemical Engineering Development Center (REDC) locate at Oak Ridge National Laboratory in Oak Ridge, TN. HFIR is an 85 MW research reactor and is used primarily for production of medical radioisotopes, material irradiation experiments, neutron activation, and neutron scattering. Targets for the reactor are constructed, processed, and dissolved at REDC. REDC also hosts other glove-box and hot-cell type activities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis

We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.

Scanning Electron Microscopy, Vector Quantizing Va↗
DOE OSTI2026

Magnetic flux distribution, quasiparticle spectroscopy, and quality factors in Nb films for superconducting qubits

Niobium is a practical material platform for superconducting microwave circuits; however, device-level performance can vary significantly depending on film growth and processing conditions. We compare three epitaxial Nb films grown on $c-$plane sapphire substrates under nominally identical conditions, except for the deposition temperature. To correlate internal quality factors, $Q_{\mathrm {i}}$, with material properties, we combine magneto-optical imaging of magnetic flux distribution with quasiparticle spectroscopy via measurements of the London penetration depth, $λ(T)$. In the low-$Q_{\mathrm i}$ film, there is a lesser ability to screen the magnetic field and an irregular temperature variation of $λ(T)$, implying the existence of localized in-gap states. High $Q_{\mathrm i}$ films show the opposite trend. We conclude that our measurements provide an efficient method for characterizing and optimizing superconducting films for quantum informatics applications.

Datta, Amlan [Ames Lab; Iowa State U.]↗
NASA NTRS2013

Advanced Curation of Current and Future Extraterrestrial Samples

Curation of extraterrestrial samples is the critical interface between sample return missions and the international research community. Curation includes documentation, preservation, preparation, and distribution of samples. The current collections of extraterrestrial samples include: Lunar rocks / soils collected by the Apollo astronauts Meteorites, including samples of asteroids, the Moon, and Mars "Cosmic dust" (asteroid and comet particles) collected by high-altitude aircraft Solar wind atoms collected by the Genesis spacecraft Comet particles collected by the Stardust spacecraft Interstellar dust collected by the Stardust spacecraft Asteroid particles collected by the Hayabusa spacecraft These samples were formed in environments strikingly different from that on Earth. Terrestrial contamination can destroy much of the scientific significance of many extraterrestrial materials. In order to preserve the research value of these precious samples, contamination must be minimized, understood, and documented. In addition the samples must be preserved - as far as possible - from physical and chemical alteration. In 2011 NASA selected the OSIRIS-REx mission, designed to return samples from the primitive asteroid 1999 RQ36 (Bennu). JAXA will sample C-class asteroid 1999 JU3 with the Hayabusa-2 mission. ESA is considering the near-Earth asteroid sample return mission Marco Polo-R. The Decadal Survey listed the first lander in a Mars sample return campaign as its highest priority flagship-class mission, with sample return from the South Pole-Aitken basin and the surface of a comet among additional top priorities. The latest NASA budget proposal includes a mission to capture a 5-10 m asteroid and return it to the vicinity of the Moon as a target for future sampling. Samples, tools, containers, and contamination witness materials from any of these missions carry unique requirements for acquisition and curation. Some of these requirements represent significant advances over methods currently used. New analytical and screening techniques will increase the value of current sample collections. Improved web-based tools will make information on all samples more accessible to researchers and the public. Advanced curation of current and future extraterrestrial samples includes: Contamination Control - inorganic / organic Temperature of preservation - subfreezing / cryogenic Non-destructive preliminary examination - X-ray tomography / XRF mapping / Raman mapping Microscopic samples - handling / sectioning / transport Special samples - unopened lunar cores Informatics - online catalogs / community-based characterization.

Allen, Carlton C.↗
NASA NTRS2018

From Apollo to the Future, the NASA Curation Model for Engaging the Sample Science Community Maximizes Science on Extraterrestrial Samples

The Astromaterials Acquisition and Curation Office at Johnson Space Center (JSC) has enjoyed a long-term partnership (50 years!) with a broad community of planetary sample scientists. This partnership has enabled the curators of planetary samples to plan for and enact evolving requirements for preservation of sample scientific integrity and for handling and long-term storage. The basis for this relationship is a standing peer review advisory committee composed of leading scientists who are recognized for achievements in sample analysis. The committee and its descendants have brought familiarity with the most relevant scientific investigations and the associated analytical and contamination challenges. Beginning with Apollo, the review committee was charged with oversight of curatorial operations and with ensuring fair access to samples. As additional samples from other planetary bodies were acquired, the committee evolved, taking on new responsibilities, reflected in committee name changes. However, oversight of curatorial operations and fair allocation of samples remain basic responsibilities. Committee recommendations are sent to the NASA Headquarters Discipline Scientist for approval. To minimize conflict of interest and maximize fair access, the rules governing the make-up of the committee is structured. Systematic rotation of leadership and staggered terms of membership allow the committee to retain expertise while bringing in fresh ideas. The first peer review committee was called the Lunar Sample Analysis and Planning Team (LSAPT) and was formalized in early 1968 with about 15 members. Their function was to review a) the equipment and procedures used in the new Lunar Receiving Laboratory (LRL); b) the proficiency and capability of the LRL staff; c) the sequence of sample analysis and allocation after quarantine release; and d) the findings of the Preliminary Examination Team (PET). According to LSAPT member Gerald Wasserburg, one of the first issues they faced was deciding whether to have most of the sample analyses performed in house at the LRL or to distribute samples to members of the scientific community. LSAPT concluded that the major scientific investigations should be carried out externally to the LRL by scientists chosen for their expertise in specific disciplines. Further they recommended that the PET's basic characterization of samples be circulated to the broad scientific community. LSAPT set its own agenda, paid attention to facility details, closely monitored the move of samples from the LRL to the interim curatorial facility in 1973, and was active in inspecting curation facilities. Between 1975 and 1979, a Facility Subcommittee of LSAPT oversaw the design and construction of a permanent facility for preservation of lunar samples. The result was an outstanding facility still in use today. In 1977, a separate peer review committee, the Meteorite Working Group (MWG), was formed to evaluate requests for new meteorites then being collected in Antarctica under what would in 1980 become a 3-agency agreement (National Science Foundation, NASA, Smithsonian Institution). By 1979, after lunar samples were moved into the new permanent facility, the vacated gloveboxes and laboratory were prepared for meteorite curation. Recognizing that LSAPT had been helpful in setting up the JSC curatorial facility for Antarctic meteorites, JSC recommended the review committee be given expanded duties, including advice on curation and analysis of materials from other planetary bodies and the name be changed to Lunar and Planetary Sample Team (LAPST). In 1993, LAPST was renamed the Curation and Analysis Planning Team for Extraterrestrial Materials (CAPTEM) to reflect additional functions. CAPTEM is chartered to be (1) a community-based, interdisciplinary forum for discussion and analysis of matters concerning the collection and curation of extraterrestrial samples, including planning future sample return missions and (2) a standing review panel, charged with evaluating proposals requesting allocation of all extraterrestrial samples contained in NASA collections. Efficiency and flexibility are gained through use of subcommittees, both ad hoc and standing. Transition of the MWG to a subcommittee of CAPTEM was completed in 2017. Today subcommittees review allocation requests for lunar samples, Antarctic meteorites, cosmic dust, Stardust cometary samples, Genesis solar wind samples, and samples returned from asteroids. Other subcommittees address facilities, informatics, and micro-cratered substrates. Planetary samples have been sent to research teams in over 30 countries world-wide. The expertise in the care and fair distribution of astromaterials by NASA using this model spans generations of planetary sample scientists and is a valuable resource to be tapped for future sample returns - OSIRIS-REx, Hayabusa 2, and Mars 2020.

Allton, Judith↗
DOE OSTI2021

Editorial: Multi-Omics Technologies for Optimizing Synthetic Biomanufacturing

Industrial manufacturing endures as an essential human activity yielding a variety of useful products; it plays a significant role in the global economy with huge impacts in everyday life. However, the manufacturing process requires consumption of various raw materials (especially petroleum derivatives), generates a variety of harmful waste products, causes pollution, and is energetically inefficient. Biological manufacturing from sustainable, affordable, and scalable feedstocks potentially enables the displacement of the entire portfolio of currently available products produced by industrial processes, enabling the manufacturing of renewable and eco-friendly products (Clomburg et al., 2017). Thus, successful development of a robust biomanufacturing strategy and technology platform, based on the latest advances in synthetic biology and chemical catalysis, will decrease both the cost and production time compared with previous manufacturing processes. Development of biomanufacturing processes using a synthetic biology platform requires the multidisciplinary efforts of science and engineering fields including molecular biology, microbiology, genetic engineering, informatics, metabolic modeling and chemical or process engineering (El Karoui et al., 2019).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Mondo: integrating disease terminology across communities

Precision medicine aims to enhance diagnosis, treatment, and prognosis by integrating multimodal data at the point of care. However, challenges arise due to the vast number of diseases, differing methods of classification, and conflicting terminological coding systems and practices used to represent molecular definitions of disease. This lack of interoperability artificially constrains the potential for diagnosis, clinical decision support, care outcome analysis, as well as data linkage across research domains to support the development or repurposing of therapeutics. There is a clear and pressing need for a unified system for managing disease entities⁠—including identifiers, synonyms, and definitions. To address these issues, we created the Mondo disease ontology—a community-driven, open-source, unified disease classification system that harmonizes diverse terminologies into a consistent, computable framework. Mondo integrates key medical and biomedical terminologies, including Online Mendelian Inheritance in Man (OMIM), Orphanet, Medical Subject Headings (MeSH), National Cancer Institute Thesaurus (NCIt), and more, to provide a comprehensive and accurate representation of disease concepts with fully provenanced and attributed links back to the sources. Mondo can be used as the handle for curation of gene–disease associations utilized in diagnostic applications, research applications such as computational phenotyping, and in clinical coding systems in clinical decision support by pointing the clinician to the numerous knowledge resources linked to the Mondo identifier. Mondo's community-centric approach, stewarded by the Monarch Initiative's expertise in ontologies, ensures that the ontology remains adaptable to the evolving needs of biomedical research and clinical communities, as well as the knowledge providers.

biomedical informatics↗
DOE OSTI2021

Utilization of machine learning to accelerate colloidal synthesis and discovery

Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Guide to the Distributed Energy Resource Risk Management Framework

The emergence of distributed energy resources (DERs) has transformed the electric power sector and will likely have even more profound impacts on the future evolution of the United States energy sector as it modernizes and becomes more reliant upon complex informatics programming and systems to ensure that our power grid remains safe from malicious interference. To mitigate risks associated with the increased and diversified use of DERs, the Distributed Energy Resource Cybersecurity Framework (DER-CF) was developed in 2019. The National Renewable Energy Laboratory extended the scope of the DER-CF to include the RMF. To address the challenges faced by federal energy managers and energy system stakeholders in applying the RMF to DER systems, the Distributed Energy Resource Risk Manager (DER-RM) is a six-step process to proactively manage cybersecurity risk in a methodical manner. The DER-RM is independent of the DER-CF's existing assessment, allowing users to focus specifically on the RMF steps. The tools are targeted to different processes - DER-CF enables organizations to perform self-assessments to improve their cybersecurity posture, while DER-RM assists organizations in achieving compliance with specific requirements. This document provides an overview of the DER-RM. The RMF process outlined in this report serves as a guide to diagnose information and operational system threats, gather required materials to comply with industry standards, and document plans for achieving Authority to Operate. Using the DER-RM, federal agencies and other organizations can easily and intuitively follow the RMF process, manage the risks to their grid-edge infrastructure through the integration of their on-site DERs, and comply with appropriate requirements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Annular Metallic Nuclear Fuel Informatics at 50 nm Resolution

U-10wt.% Zr (U-10Zr) based metallic fuel is the leading candidate for next-generation sodium cooled fast reactor in United States. Advanced post-irradiation characterization (from sub-nanometer to micrometer) helps to understand fuel microstructure and property change during irradiation, benefiting fuel qualification for commercial application. With high velocity image data generating method, an automatic way to extract the microstructural information quantitively can better serve the needs from post irradiation characterization. A trained machine learning model, named Decision Tree, is employed to categorize pores caused by fission gas release and to aid phase identification. This work presents a showcase of this approach on different irradiated U-10Zr metallic fuels. This quantitative data offers insights into the fission product migration and potentially thermal conductivity degradation. This information from machine learning will be fed into fuel design code for better prediction of fuel performance.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

A deep learning approach to identify missing is-a relations in SNOMED CT

Abstract Objective SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT. Materials and Methods Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances to train a model predicting whether an is-a relation exists between 2 concepts with containment pattern. The model is a binary classifier leveraging concept name features, hierarchical features, enriched lexical attribute features, and logical definition features. We introduce a cross-validation inspired approach to identify missing is-a relations among all hierarchically unrelated containment concept-pairs. Results We trained and applied our model on the Clinical finding subhierarchy of SNOMED CT (September 2019 US edition). Our model (based on the validation sets) achieved a precision of 0.8164, recall of 0.8397, and F1 score of 0.8279. Applying the model to predict actual missing is-a relations, we obtained a total of 1661 potential candidates. Domain experts performed evaluation on randomly selected 230 samples and verified that 192 (83.48%) are valid. Conclusions The results showed that our deep learning approach is effective in uncovering missing is-a relations between containment concept-pairs in SNOMED CT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Roadmap for Photonics with 2D Materials

Triggered by advances in atomic-layer exfoliation and growth techniques, along with the identification of a wide range of extraordinary physical properties in self-standing films consisting of one or a few atomic layers, two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), and other van der Waals (vdW) crystals now constitute a broad research field expanding in multiple directions through the combination of layer stacking and twisting, nanofabrication, surface-science methods, and integration into nanostructured environments. Photonics encompasses a multidisciplinary subset of those directions, where 2D materials contribute remarkable nonlinearities, long-lived and ultraconfined polaritons, strong excitons, topological and chiral effects, susceptibility to external stimuli, accessibility, robustness, and a completely new range of photonic materials based on layer stacking, gating, and the formation of moiré patterns. These properties are being leveraged to develop applications in electro-optical modulation, light emission and detection, imaging and metasurfaces, integrated optics, sensing, and quantum physics across a broad spectral range extending from the far-infrared to the ultraviolet, as well as enabling hybridization with spin and momentum textures of electronic band structures and magnetic degrees of freedom. The rapid expansion of photonics with 2D materials as a dynamic research arena is yielding breakthroughs, which this Roadmap summarizes while identifying challenges and opportunities for future goals and how to meet them through a wide collection of topical sections prepared by leading practitioners.

2D materials↗
DOE OSTI2026

A sorghum pangenome reference improves global crop trait discovery

Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops, like that of the grain and bioenergy crop sorghum, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breeding and trait discovery and provide a framework for similar applications in other crops.

agricultural genetics↗