Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “science data processing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, the re-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA’s Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related ‘omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata ‘omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data re-use, resulting in 38 additional publications derived from the original 67 publication over the past four years. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA “Open Science Data Repositories (OSDR)” and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Fluorescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to “big data” from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology. Several other talks will cover these topics in this conference.

life sciences↗
DOE OSTI2016

Segmentation of nanotomographic cortical bone images for quantitative characterization of the osteoctyte lacuno-canalicular network

A newly developed data processing method able to characterize the osteocytes lacuno-canalicular network (LCN) is presented. Osteocytes are the most abundant cells in the bone, living in spaces called lacunae embedded inside the bone matrix and connected to each other with an extensive network of canals that allows for the exchange of nutrients and for mechanotransduction functions. The geometrical three-dimensional (3D) architecture is increasingly thought to be related to the macroscopic strength or failure of the bone and it is becoming the focus for investigating widely spread diseases such as osteoporosis. To obtain 3D LCN images non-destructively has been out of reach until recently, since tens-of-nanometers scale resolution is required. Ptychographic tomography was validated for bone imaging in [1], showing clearly the LCN. The method presented here was applied to 3D ptychographic tomographic images in order to extract morphological and geometrical parameters of the lacuno-canalicular structures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2012

The Race To Understand A Changing Planet

The Earth's climate is changing rapidly. In some respects, the rate of change is outpacing the predictions of only a few years ago. The challenge to Earth Science is to put forward credible projections of possible future climates so that the public and policy makers can make science-based decisions about energy development strategies. Models, observations and experiments all play strong roles in improving knowledge and increasing confidence in our predictions. The models have progressed from simple, coarse-resolution descriptions of atmospheric dynamics and physics only twenty years ago, to full-up Earth System models (ESMs) that include complete descriptions of the oceans and cryosphere. It has been convincingly argued that such complexity - the construction of realistic "toy" Earth's - is necessary to address the complex processes involved in climate change, including not only the physical atmosphere, oceans and cryosphere, but also the carbon cycle - both its natural and anthropogenic components - and the biosphere. Observations, particularly satellite observations, have more or less kept pace with the demands of the modelers, being able to observe progressively more and different facets of the Earth system, but the global satellite fleet is in need of an overhaul very soon. Lastly, field experiments and process studies confront the models with facts and allow us to develop more sophisticated and accurate satellite data algorithms. The challenges facing our relatively small Earth and planetary science communities are considerable and the stakes are significant. The stakeholders, now numbering 7 billion but soon to be 10 billion, will be relying on our results and capabilitie's to guide them into the future.

Sellers, Piers J.↗
NASA NTRS1999

An Overview of Future NASA Missions, Concepts, and Technologies Related to Imaging of the World's Land Areas

In the near term NASA is entering into the peak activity period of the Earth Observing System (EOS). The EOS AM-1 /"Terra" spacecraft is nearing launch and operation to be followed soon by the New Millennium Program (NMP) Earth Observing (EO-1) mission. Other missions related to land imaging and studies include EOS PM-1 mission, the Earth System Sciences Program (ESSP) Vegetation Canopy Lidar (VCL) mission, the EOS/IceSat mission. These missions involve clear advances in technologies and observational capability including improvements in multispectral imaging and other observing strategies, for example, "formation flying". Plans are underway to define the next era of EOS missions, commonly called "EOS Follow-on" or EOS II. The programmatic planning includes concepts that represent advances over the present Landsat-7 mission that concomitantly recognize the advances being made in land imaging within the private sector. The National Polar Orbiting Environmental Satellite Series (NPOESS) Preparatory Project (NPP) is an effort that will help to transition EOS medium resolution (herein meaning spatial resolutions near 500 meters), multispectral measurement capabilities such as represented by the EOS Moderate Resolution Imaging Spectroradiometer (MODIS) into the NPOESS operational series of satellites. Developments in Synthetic Aperture Radar (SAR) and passive microwave land observing capabilities are also proceeding. Beyond these efforts the Earth Science Enterprise Technology Strategy is embarking efforts to advance technologies in several basic areas: instruments, flight systems and operational capability, and information systems. In the case of instruments architectures will be examined that offer significant reductions in mass, volume, power and observational flexibility. For flight systems and operational capability, formation flying including calibration and data fusion, systems operation autonomy, and mechanical and electronic innovations that can reduce spacecraft and subsystem resource requirements. The efforts in information systems will include better approaches for linking multiple data sets, extracting and visualizing information, and improvements in collecting, compressing, transmitting, processing, distributing and archiving data from multiple platforms. Overall concepts such as sensor webs, constellations of observing systems, and rapid and tailored data availability and delivery to multiple users comprise and notions Earth Science Vision for the future.

Salomonson, Vincent V.↗
NASA NTRSDate not supplied

Repurposing Drilling Control Diagnostics for Subsurface Edge Detection and Boundary Advisement During Planetary Drilling

Informed decision-making during lunar drilling and sampling missions will require data monitoring tools and specialized ground data systems. Accurate and updated situational awareness, with ongoing data monitoring, is critical for timely responses by to incoming science data. Traverse plans and scheduled activities may need to be flexibly changed in order to react to unexpected data or situations. Unlike (for example) Mars missions, the relative lightspeed closeness of the Moon allows for near-real-time ground processing of incoming mission and instrument data. An Apollo-class lunar regolith drill will in a sense “travel” a meter or two vertically at a given subsurface characterization site. As the drill penetrates into lunar regolith, it is likely to encounter a range of material densities, orientations, fracture toughness, and (perhaps) ice percentages. Lunar drill telemetry can provide science teams with a valuable first look into the subsurface structure, the regolith bulk properties, and constituents at each drilled site. Real-time AI-based recognition and reaction to downhole situations has been developed for automated deeper drilling on Mars and beyond. We can leverage the same knowledge bases and pattern-matching as areal-time interpreter of the subsurface, a situational awareness tool during drilling operations. We recently (Sept. 2019) demonstrated this AI drilling monitoring and analysis capability, in control of in-situ drilling and sampling operations, mounted on a KREX-2 rover in Chile’s Atacama Desert. Terrestrial automated drilling log analyses in oil exploration have used similar machine learning techniques in classifying and identifying features in drilling logs –but these typically are designed assuming a drilling fluid influencing downhole measurements and data (permeability, resistivity). Drilling models and existing AI software designed to detect and respond to drilling faults and hard materials can be repurposed, for near-real-time (ground-based) interpretation of drilling telemetry –a potentially valuable advisory tool for strata boundaries and changes in drilling parameters. On the Moon, this approach could be used to study the structure and to some extent the composition of lunar regolith vs. borehole depth, based on recognizable variations in fracture hardness, drilling energy and penetration rates while actively drilling. Since the early 2000s, a series of increasingly-capable real-time drilling telemetry interpretation and characterization software tools have been developed. These subsurface models and software tools have monitored the real-time drilling data received, and automatically identified changes in drill behavior (e.g., encountering a harder target layer, bit inclusions, drill choking due to infall downhole, and others) correlating these with subsurface structures and features. We discuss the mappings between drill borehole parameters, faults or events detected, and modeled changes in rock layer boundaries, in examples drawn from field testing at analog sites in an Arctic impact crater, Rio Tinto, and Chile’s Atacama Desert. These demonstrate how subsurface structural boundaries led to fault detections and responses by the software.

drilling advisor↗
DOE OSTI2019

Distributions of Ag, Bi, and Sb as Minor Elements between Iron-Silicate Slag and Copper in Equilibrium with Tridymite in the Cu-Fe-O-Si System at T = 1250 °C and 1300 °C (1523 K and 1573 K)

New experimental data on the distributions of silver (Ag), bismuth (Bi), and antimony (Sb) between liquid iron-silicate slag and liquid copper metal in equilibrium with tridymite in the Cu-Fe-O-Si system at T = 1250 °C and 1300 °C (1523 K and 1573 K) have been obtained. The experimental methodology involved high-temperature equilibration, rapid quenching of the equilibrated phases, followed by direct measurement of the equilibrium phases with electron probe X-ray microanalysis (EPMA) for measurement of major element concentrations and laser ablation inductively coupled plasma mass spectrometry (LAICPMS) for the measurement of minor element concentrations. The 4-point test approach was employed to confirm the achievement of chemical equilibrium in the current study. Open-system equilibration on quartz substrates in controlled gas atmospheres (CO/CO{sub 2}/Ar) and closed-system equilibration in sealed quartz ampoules were used. The new experimental data resolve significant discrepancies found in previous studies and can be used as input for the development of thermodynamic databases for copper-making processes.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-I-12: Flexible Geometry Computed Tomography

Purpose: The concept separates the mechanical connection between the radiation source and detector. This design allows the trajectory and orientation of the radiation source/detector to be customized to the object that is being imaged. This is in contrast to the formulaic rotation-translation image acquisition of conventional computed tomography(CT).Background/significance:CT devices that image a full range of: anatomy, patient populations, and imaging procedures are large. The root cause of the expanding size of comprehensive CT is due to the commitment to helical geometry that is hardwired into the image reconstruction. FGCT extends the application of alternative reconstruction techniques, i.e. tomosynthesis, by separating the two main components— radiation source and detector— and allow for 6 degrees of freedom motion for radiation source, detector, or both. The image acquisition geometry is then tailored to how the patient/object is positioned. This provides greater flexibility on the position and location that the patient/object is being imaged. Additionally, removing the need of a rotating gantry reduces the footprint so that CT is more mobile and more available to move to where the patient/object is at, instead of the other way around. Methods: As proof-of-principle, a reconstruction algorithm is designed to produce FGCT images. Using simulated detector data, voxels intersecting a line drawn between the radiation source and an individual detector are traced and modified using the detector signal. The detector signal is modified to compensate for changes in the source to detector distance. Adjacent voxels are modified in proportion to the detector signal, providing a simple image filter. Results: Image-quality from the proposed FGCT reconstruction technique is proving to be a challenge, producing hardily recognizable images from limited projections angles. Conclusion: Preliminary assessment of the reconstruction technique demonstrates the inevitable tradeoff of producing meaningful image-quality verses the practicality of how to obtain sufficient image data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-A-12A-10: Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Purpose: Recent advances in medical imaging technologies provide opportunities to quantify the tumor phenotype throughout the course of treatment non-invasively. The emerging field of Radiomics addresses this by converting medical images into minable data by applying a large number of quantitative imaging algorithms. Accurate tumor segmentation is one of the main challenges of Radiomics. It has been shown that semiautomatic segmentation approaches efficiently reduce inter-observer variability as compared to the time consuming manual delineations. In this study, a semiautomatic volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for Radiomics features quantification. Methods: Fifty-six 3D-Radiomics features, quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These Radiomics features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Results: Radiomics features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC= 0.85 0.15, p=0.0009) compared to the features extracted from the manual segmentations (ICC= 0.77 0.17). Furthermore, the features extracted from 3D-Slicer based segmentations, spread over significantly smaller range across observers as compared to those of the manual delineations (p= 3.819e-07). Moreover, the features derived from 3D-Slicer segmentations overlapped in range with those of the manual delineations, as the lower(higher) limit(s) being significantly higher(lower) for the 3D-Slicer features (p= 0.007, p= 5.863e-06). Conclusion: 3D-Slicer based semiautomatic segmentation significantly improves the robustness of radiomics features and thus could serve as a potential alternative to the time consuming manual segmentation process. So 3D-Slicer can have a large application in Radiomics, and be employed for high-throughput data mining research of medical imaging in clinical oncology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Adjusted Graphite Thermal Scattering Data Applied to Fluoride-Salt-Cooled Reactors

Thermal scattering data is a key component of fission reactor simulation. While most materials can be reasonably modeled with a free gas approximation, the moderators in thermal systems require a more rigorous treatment to sufficiently capture their specific neutron interaction physics. Graphite does not typically feature as a principle moderator in power reactors in service today, but remains an attractive prospect for a number of new reactor concepts currently in development. In comparison to light water, a material nearly ubiquitous in thermal reactors, graphite offers a better neutron economy by way of a small absorption cross section, high thermodynamic stability and can be incorporated into the structure of the core itself. Graphite in its purest form is a crystalline material composed of stacked layers of graphene, a hexagonal planar lattice of carbon atoms. In-plane, each carbon atom is covalently bonded to 3 neighbouring carbon atoms, creating a strong bond, while between planes only a weak van der Waals force exists. Pure graphite has a density of 2.26 g/cm{sup 3}. However, reactor-grade contains a significant amount of carbon binder, leading to a porous bulk medium with randomly oriented micro crystals. Densities of 1.5 - 1.9 g/cm{sup 3} are typical for reactor-grade graphite, corresponding to porosities on the order of 10-30%. This deviation from pure crystalline graphite should be accounted for in the thermal scattering data. When an incident neutron is in or near the thermal region, its de Broglie wavelength is the same order as the molecular bond lengths, leading to elastic scattering. At similar energies, the dynamics of the moderator lattice (in the case of solid moderators) can cause inelastic scattering. For these moderators, special cross sections should be calculated and used in reactor simulations. To generate this thermal scattering data, parameters pertaining to the properties and structure of the material in question are fed into a nuclear data processing code, such as NJOY. Within NJOY is a module called LEAPR which is used to generate the thermal scattering law S(α,β), which is in turn is fed into another module THERMR which is used to generate thermal cross sections that factor in the molecular-scale scattering phenomena. The core of the thermal scattering law is the phonon spectrum ρ(β) which describes the modes of excitation of the material lattice. An investigation into the phonon frequency spectrum was conducted at North Carolina State University (NCSU) for reactor-grade graphite which accounts for porosity. This phonon spectrum was calculated using the ab initio density functional theory (DFT) code VASP. The inelastic cross section calculated using this spectrum was found to have good agreement with relevant experimental data. The resulting spectra differed from the benchmark spectrum produced by Young and Koppel, the same data used in current nuclear data evaluations. The latter uses the lattice dynamics of perfect crystalline graphite with four force constants fitted to the model from thermodynamics data from reactor-grade (porous) graphite, which yields a reasonable approximation to the phonon spectrum of reactor-grade graphite.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The next era in human space exploration is rapidly approaching and will require the use of countermeasures to deep space health hazards. The development of countermeasures (or, there-purposing of existing agents) will be highly dependent on our understanding of basic biological responses to space stressors (e.g. ionizing radiation, altered gravitational fields, altered day-night cycles, confinement, isolation, hostile-closed environments, distance-duration from Earth, exposure to celestial regolith, etc.). The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, imaging, whole organism and behavior). We will discuss here several strategies that NASA's Biological and Physical Science Division has put in place to maximize the return on investment for spaceflight bioscience data. Open Science, as a scientific philosophy, is the concept that the more people who have access to the data, the more knowledge will be gained from it. This guiding principle led NASA to develop GeneLab in 2015. GeneLab houses spaceflight and relevant ground-based multi-omics data, and has grown to ~400 transcriptomatic, proteomic, metabolomic and epigenomic datasets from plant, rodent, small animal, and microbial space experiments. GeneLab provides users with various tools for data analysis and a visualization portal that allows users to interact with gene expression data from space-related 'omics experiments. Open Science is also about building scientific communities, and with this spirit in mind, GeneLab has spawned several Analysis Working Groups (AWGs), comprised of more than 200 volunteer scientists. The AWGs initially provided feedback on the processing pipeline and metadata 'omics standards for GeneLab. Over the last few years, they have become a community-driven science enterprise, engaging in large meta-analysis of GeneLab datasets, resulting in 10 publications (beyond the originally submitted research). Overall, the Open Science nature of GeneLab has resulted in a high degree of data-use, resulting in 40 enabled publications by open data. The enormous success and knowledge gained from GeneLab has led to a collection of sister NASA "Open Science Data Repositories (OSDR)" and research support groups. These include the NASA Ames Life Sciences Data Archive (ALSDA), the NASA Biological Institutional Scientific Collection (NBISC), and the Biospecimen Sharing Program (BSP). All are adopting the GeneLab data architecture system to maximize open-access, find-ability, accessibility, interoperability, and reusability (FAIR). ALSDA collects and curates phenotypic-physiological bioimaging-behavioral data from space and space-relevant non-human experiments, oftentimes coming from the same omics-associated experimental datasets found in GeneLab. Since 2021, a community of ~100 researchers have rallied around ALSDA, to provide feedback in a new ALSDA AWG focused on phenotypic-physiological investigation-sample-assay metadata standards (e.g., Micro-Computed Tomography, Light/Flourescence Microscopy, Western Blot, Flow Cytometry, Novel Object Recognition, Elevated Plus Maze, etc. of ~50 assays collected). These standards are part of a new single point-of-entry data submission portal for all non-human Space Biology and Human Research Program principal investigators, to submit, curate, and share their research data. With open-access space biological data now collected and curated together with rich metadata, and with the potential for linkage to "big data" from the international biological and medical communities (NIH, EBI, etc.), the artificial intelligence and machine learning (AI/ML) era has started for Space Biology.

omics↗
NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗
DOE OSTI2015

A Novel Front-End ASIC With Post Digital Filtering and Calibration for CZT-Based PET Detector

This paper presents a novel front-end electronics based on a front-end ASIC with post digital filtering and calibration dedicated to CZT detectors for PET imaging. A cascade amplifier based on split-leg topology is selected to realize the charge-sensitive amplifier (CSA) for the sake of low noise performances and the simple scheme of the power supplies. The output of the CSA is connected to a variable-gain amplifier to generate the compatible signals for the A/D conversion. A multi-channel single-slope ADC is designed to sample multiple points for the digital filtering and shaping. The digital signal processing algorithms are implemented by a FPGA. To verify the proposed scheme, a front-end readout prototype ASIC is designed and implemented in 0.35 μm CMOS process. In a single readout channel, a CSA, a VGA, a 10-bit ADC and registers are integrated. Two dummy channels, bias circuits, and time controller are also integrated. The die size is 2.0 mm x 2.1 mm. The input range of the ASIC is from 2000 e{sup -} to 100000 e{sup -}, which is suitable for the detection of the X-and gamma ray from 11.2 keV to 550 keV. The linearity of the output voltage is less than 1 %. The gain of the readout channel is 40.2 V/pC. The static power dissipation is about 10 mW/channel. The above tested results show that the electrical performances of the ASIC can well satisfy PET imaging applications. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

It's not just the food you eat: Environmental factors in the development of food allergies

The dramatic rise in the prevalence of food allergy and food allergy-associated anaphylaxis in the past few decades has fueled investigative interest into understanding this puzzling trend. Here, we review the question as to whether important external environmental determinants beyond dietary habits and exposure to food allergens are involved. This review will summarize our current understanding of these environment determinants, derived from the latest experimental and epidemiological research. Specifically, we will review the role of exposures that affect skin barrier function, development of a diverse microbiome, and food processing. Additional exposures of concern are insufficient sunlight, endocrine disrupting chemicals and pesticides, and use of specific pharmaceutical agents that may drive or modify the risk for food allergy. Despite limitations in the quantity and quality of research to date, many new epidemiological associations and experimental data in support of this paradigm have emerged.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Automated annotation of scientific texts for ML-based keyphrase extraction and validation

Advanced omics technologies and facilities generate a wealth of valuable data daily; however, the data often lack the essential metadata required for researchers to find, curate, and search them effectively. The lack of metadata poses a significant challenge in the utilization of these data sets. Machine learning (ML)–based metadata extraction techniques have emerged as a potentially viable approach to automatically annotating scientific data sets with the metadata necessary for enabling effective search. Text labeling, usually performed manually, plays a crucial role in validating machine-extracted metadata. However, manual labeling is time-consuming and not always feasible; thus, there is a need to develop automated text labeling techniques in order to accelerate the process of scientific innovation. This need is particularly urgent in fields such as environmental genomics and microbiome science, which have historically received less attention in terms of metadata curation and creation of gold-standard text mining data sets. In this paper, we present two novel automated text labeling approaches for the validation of ML-generated metadata for unlabeled texts, with specific applications in environmental genomics. Our techniques show the potential of two new ways to leverage existing information that is only available for select documents within a corpus to validate ML models, which can then be used to describe the remaining documents in the corpus. The first technique exploits relationships between different types of data sources related to the same research study, such as publications and proposals. The second technique takes advantage of domain-specific controlled vocabularies or ontologies. In this paper, we detail applying these approaches in the context of environmental genomics research for ML-generated metadata validation. Our results show that the proposed label assignment approaches can generate both generic and highly specific text labels for the unlabeled texts, with up to 44% of the labels matching with those suggested by a ML keyword extraction algorithm.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

WHONDRS Surface Water and Sediment Non-Purgeable Organic Carbon and FTICR-MS across Glacial Features in Svalbard 2021 (v3)

This dataset supports a broader study examining land-atmosphere gas exchange associated with permafrost and glaciers in Svalbard. The dataset provides geochemistry and organic matter characterization data generated from surface water and sediment. Samples were collected from 8 spatially diverse sites across central Svalbard and also from a more comprehensive temporal and spatial study within one glacial catchment area throughout one summer melt season. Related data were collected and will be published separately in collaboration with Yde and Kleber.This data package was originally published in September 2022. It was updated in March 2023 (v2; new and modified files), and July 2025 (v3; modified files). See the change history section in the readme for details.For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) dissolved organic carbon (DOC; measure as non-purgeable organic carbon; NPOC); (5) surface water sampling protocol; (6) sediment extraction protocol; (7) readme; (8) methods codes; (9) international generic sample number (IGSN) mapping file; and (10) folder of high resolution characterization of organic matter via 7 and 12 Tesla (7T and 12T) Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). The FTICR folder contains two subfolders containing the 7T and 12T .xml data files and another subfolder containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml.

54 ENVIRONMENTAL SCIENCES↗
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↗
NASA NTRS2022

The Psyche Planning Software Subsystem: Creating a Robust Toolset for a Discovery-class Mission

Psyche is a Discovery-class mission to the small metal-rich asteroid (16) Psyche, and is slated to launch in 2022. Psyche, like many missions, requires low-cost activity planning and sequence generation that serves as the backbone to overall uplink design. Such tools must be maintainable over long periods of operations, and powerful enough to solve complex issues that deep-space one-off missions encounter. In this paper we introduce cost-effective solutions that leverage inner- and open-source principles to meet a variety of common and novel use cases.The uplink process that was designed to meet these challenges is presented, as well as the data-flow through the high-level architecture of the planning software subsystem. The user-facing planning tools are described, particularly the Science Opportunity Analyzer, the Plan Editor, Psyche’s planning automation in the Blackbird framework, and Psyche Simulation Reports. All these applications are either new or have been substantially revamped to meet Psyche’s concept of operations. In particular, ensuring the entire toolchain can correctly process epoch-relative activities is discussed. Underlying the main applications are a common set of dependencies developed and maintained by a new cross-mission association of planning developers. In this way, Psyche can inherit well-tested functionality which saves effort and ensures its developers can focus on solving domain challenges. Quality control of the applications and libraries is ensured with a code-review and unit-test based novel ‘CM lite’ process. Collaboration with international industry and academia using the open-source modules is already occurring.The planning and scheduling software is designed to maximize operator awareness of the integrated plan at every step of the process and use common interfaces and file formats to easily transfer information. Design choices plus the team’s test-driven development process enables more expansive capabilities compared to the decentralized planning and sequence generation functions typical of Discovery-class orbiters without significant development cost increases. Benefits and drawbacks of Psyche’s approach are discussed, including comparison to other missions and tools where appropriate.

Ramanathan, Keshav↗
DOE OSTI2019

Tracking materials science data lineage to manage millions of materials experiments and analyses

Abstract In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research.

Soedarmadji, Edwin↗