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Results for “knowledge extraction”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

ECCE sensitivity studies for single hadron transverse single spin asymmetry measurements

Here, we performed feasibility studies for various single transverse spin measurements that are related to the Sivers effect, transversity and the tensor charge, and the Collins fragmentation function. The processes studied include semi-inclusive deep inelastic scattering (SIDIS) where single hadrons (pions and kaons) were detected in addition to the scattered DIS lepton. The data were obtained in pythia6 and geant4 simulated e+p collisions at 18 GeV on 275 GeV, 18 on 100, 10 on 100, and 5 on 41 that use the ECCE detector configuration. Typical DIS kinematics were selected, most notably Q 2 >1 GeV 2 , and cover the x range from 10 -4 to 1. The single spin asymmetries were extracted as a function of x and Q 2 , as well as the semi-inclusive variables z, which corresponds to the momentum fraction the detected hadron carries relative to the struck parton, and P T , which corresponds to the transverse momentum of the detected hadron relative to the virtual photon. They are obtained in azimuthal moments in combinations of the azimuthal angles of the hadron transverse momentum and transverse spin of the nucleon relative to the lepton scattering plane. In order to extract asymmetries, the initially unpolarized MonteCarlo was re-weighted in the true kinematic variables, hadron types and parton flavors based on global fits of fixed target SIDIS experiments and e + e – annihilation data. The expected statistical precision of such measurements is extrapolated to 10 fb –1 and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields. Similar neutron information is obtained by comparing the ECCE e+p pseudo-data with the same from the EIC Yellow Report and scaling the corresponding Yellow Report e+ 3 He pseudo-data uncertainties accordingly. The impact on the knowledge of the Sivers functions, transversity and tensor charges, and the Collins function has then been evaluated in the same phenomenological extractions as in the Yellow Report. Finally, the impact is found to be comparable to that obtained with the parametrized Yellow Report detector and shows that the ECCE detector configuration can fulfill the physics goals on these quantities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

An Integrated Energy Systems Prototype Human-System Interface for a Steam Extraction Loop System to Support Joint Electricity-Hydrogen Flexible Operations

Due to increasing economic competition from renewables and combined-cycle natural gas plants, nuclear power plants are looking toward flexible operations to enhance their cost competitiveness. The Integrated Energy Systems project under the Light Water Reactor Sustainability program of the U.S. Department of Energy focuses on joint electricity-hydrogen flexible operations. Joint electricity-hydrogen flexible operations entail the nuclear power plant diverting thermal energy via main steam to a hydrogen production plant located nearby. The steam serves to enhance the efficiency of the hydrogen production. Furthermore, high temperature electrolysis requires a large amount of electricity, which the plant can also provide. The plant provides steam and electricity to the hydrogen plant throughout the day, but during peak demand hours the nuclear power plant returns to solely providing electricity to meet the high demand. Through this flexible concept of operations, the plant can optimize the thermal energy it produces without having to maneuver the power of the plant. This report documents the human factors process to design and develop a prototype human-system interface for the steam extraction loop that serves as the conduit between the nuclear power plant and the adjacent hydrogen plant. The design process followed the human factors guidelines set by NUREG-0711, Human Factors Engineering Program Review Model (O’Hara, Higgins, & Fleger, 2012), and expanded upon by the Guideline for Operational Nuclear Usability and Knowledge Elicitation (GONUKE; Boring, Ulrich, Joe, & Lew, 2015; Boring, Lew, & Ulrich, 2016). The design process entailed operator interviews to determine the concept of operations for the steam extraction loop, a review and adaptation of digital interface design concepts developed by the team in prior projects, an iterative design process, and reviews conducted by both operators and human factors experts. Several versions of the prototype human-system interface were developed. Operators were interviewed to determine what design features they found useful and would like to see in the interface. The design underwent a review by human factors experts against NUREG-0700, Human Interface Design Review Guidelines (U.S. Nuclear Regulatory Commission, 2019), to ensure compliance with the latest human factors standards for digital interfaces in nuclear applications. The design was then prototyped as a functional windows-based application integrated with the Generic Pressurized Water Reactor simulator modified to include the steam extraction loop. The simulation is supported by the Human Systems Simulation Laboratory at Idaho National Laboratory, which supports operator-in-the-loop testing. This is an ongoing project and the next phase of the project entails performing an operator-in-the-loop usability study to evaluate the interface and examine the proposed concept of operations to extraction steam from the nuclear power plant for delivery to the coupled hydrogen production plant.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

A platform to measure isentropes from proton-heated warm dense matter on short pulse laser facilities

We describe the development of an experimental platform that measures the release isentrope of materials heated isochorically to temperatures of a few electron volts, using short-pulse laser-produced protons to heat the sample and long-pulse laser-produced x rays to perform streaked x-ray radiography. The density profiles derived from the radiography data are integrated to generate pressure–density isentropes, independent of prior knowledge of the equation of state of the sample material. In order to understand the sensitivities of isentrope extraction from radiography data, we analyze synthetic radiographs generated by a radiation hydrodynamics code. Noise reduction and high spatial resolution are critical for isentrope reconstruction, as demonstrated by the analysis of a proof-of-principle shot day on the OMEGA-EP facility. In conclusion, the data demonstrate the feasibility of the platform for characterizing isentropes, and we discuss the necessary improvements to enhance precision in differentiating between equation-of-state models.

Equations of state↗
DOE OSTI2024

Foundations of automatic feature extraction at LHC–point clouds and graphs

Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.

Bhardwaj, Akanksha↗
DOE OSTI2022

Basin-specific geochemistry to promote unconventional efficiency

This project focused on two strategic geochemistry-based research thrusts and provided new knowledge in the following areas: (i) mitigation of scale precipitation in shale reservoirs; and (ii) improvement of hydrocarbon extractions from tight matrix pore space. Additionally, we developed a new acoustic monitoring approach to nondestructively monitor shale alteration in-situ in the laboratory and subsurface.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Eucalyptus Wood Smoke Extract Elicits a Dose-Dependent Effect in Brain Endothelial Cells

The frequency, duration, and size of wildfires have been increasing, and the inhalation of wildfire smoke particles poses a significant risk to human health. Epidemiological studies have shown that wildfire smoke exposure is positively associated with cognitive and neurological dysfunctions. However, there is a significant gap in knowledge on how wildfire smoke exposure can affect the blood–brain barrier and cause molecular and cellular changes in the brain. Our study aims to determine the acute effect of smoldering eucalyptus wood smoke extract (WSE) on brain endothelial cells for potential neurotoxicity in vitro. Primary human brain microvascular endothelial cells (HBMEC) and immortalized human brain endothelial cell line (hCMEC/D3) were treated with different doses of WSE for 24 h. WSE treatment resulted in a dose-dependent increase in IL-8 in both HBMEC and hCMEC/D3. RNA-seq analyses showed a dose-dependent upregulation of genes involved in aryl hydrocarbon receptor (AhR) and nuclear factor erythroid 2-related factor 2 (NRF2) pathways and a decrease in tight junction markers in both HBMEC and hCMEC/D3. When comparing untreated controls, RNA-seq analyses showed that HBMEC have a higher expression of tight junction markers compared to hCMEC/D3. In summary, our study found that 24 h WSE treatment increases IL-8 production dose-dependently and decreases tight junction markers in both HBMEC and hCMEC/D3 that may be mediated through the AhR and NRF2 pathways, and HBMEC could be a better in vitro model for studying the effect of wood smoke extract or particles on brain endothelial cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Morphological Characterization of Fresh and 20-Yr-Old Fixed Nematode Specimens of Sauertylenchus maximus (Allen, 1955) Siddiqi, 2000 Deposited in the USDA Nematode Collection from Arlington National Cemetery, VA, USA

Sauertylenchus maximus was discovered during a survey conducted at the Arlington National Cemetery, Virginia, for the type specimens of Hoplolaimus galeatus. Besides the fresh material, the fixed specimens of S. maximus were also studied by molecular and morphological means. The morphological and morphometric characteristics of the recovered fresh material were consistent with the original and other description(s) of this species. The fixed specimens used in this study were preserved in a 3% formaldehyde and 2% glycerin solution for over 20 yr. Molecular analyses of the fresh and fixed specimens were performed using internal transcribed spacer, D2–D2 expansion segments of 28S large subunits, and 18S small subunit ribosomal DNA sequences. To our knowledge, this represents the first report of S. maximus from Virginia and the first report of a successful DNA extraction from fixed nematode specimens.

18S rDNA↗
DOE OSTI2019

Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection

Abstract Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for precipitate and matrix phases, in APT data has been obtained either by extracting iso-concentration surfaces based on a user-supplied concentration value or by manually perturbing the concentration value until the iso-concentration surface qualitatively matches the interface. These approaches are subjective, not scalable, and may lead to inconsistencies due to local composition inhomogeneities. We introduce a digital image segmentation approach based on deep neural networks that transfer learned knowledge from natural images to automatically segment the data obtained from APT into different phases. This approach not only provides an efficient way to segment the data and extract interfacial properties but does so without the need for expensive interface labeling for training the segmentation model. We consider here a system with a precipitate phase in a matrix and with three different interface modalities—layered, isolated, and interconnected—that are obtained for different relative geometries of the precipitate phase. We demonstrate the accuracy of our segmentation approach through qualitative visualization of the interfaces, as well as through quantitative comparisons with proximity histograms obtained by using more traditional approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Design strategies for ion-sieving charge mosaic membranes toward sustainable lithium extraction

Membrane-based technologies are essential for realizing sustainable ion-sieving processes such as lithium extraction. Much effort was devoted to designing new membrane materials, whereas the effect of charge distribution has been largely overlooked. Here, we filled this knowledge gap by providing a quantitative transport model for selective ion diffusion through a charge mosaic membrane (CMM). Composed of alternating regions of Li-selective ceramic and anion-selective polymeric materials, CMMs offered the unique advantage of promoting the transport of both Li + and anions while blocking other cations. As a result, the membrane achieved a high Li/Mg selectivity of 62 and a high permeation rate of 59 mmol·m −2 ·h −1 at a low feeding concentration of 30 mM, without any external driving force. Systematical experiments revealed the influence of brine Mg/Li ratio and membrane ceramic/polymer ratio on the overall extraction rate, which was consistent with the prediction of our transport model. In conclusion, the model developed in this work not only presented the design strategies of CMMs for Li extraction but also provided guidance for the development of other ion-sieving processes in general.

25 ENERGY STORAGE↗
DOE OSTI2020

F2 extraction from Inclusive Cross Section Data at Large Bjorken x

While we have made significant progress in probing nuclear structure at low Bjorken x and high Q 2 , we still have gaps in our knowledge regarding the high Bjorken x and intermediate Q 2 kinematic region. This is not an accident, but instead due to the diffculty in assessing the non-perturbative region of nuclear physics. For this reason, the E12-10-002 experiment measured the H(e,e') and D(e,e') inclusive cross section in the resonance region. This will allow us to study both perturbative and non-perturbative physics in a kinematic region that lacks precision measurements. Measurements were made up to a Bjorken x of 0.99 and Q 2 up to 17 GeV 2 . These measurements are made using the independent HMS and SHMS spectrometers at the upgraded beam energy of 11 GeV in Hall C at Jefferson Lab. Further, we extract F 2 structure functions from the measured data and study the effect of their inclusion in a global Parton Distribution Function analysis.

Araiza Gonzalez, Fernando↗
DOE OSTI2025

Systematic feature design for cycle life prediction of lithium-ion batteries during formation

Optimization of the formation step in lithium-ion battery manufacturing is challenging due to limited physical understanding of solid-electrolyte interphase formation and the long testing time (∼100 days) for cells to reach the end of life. We propose a systematic feature-design framework that requires minimal domain knowledge for accurate cycle life prediction during formation. By only using two simple Q (V) features designed from our framework, extracted from formation data without any additional diagnostic cycles, we achieved an average of 9.87% error for cycle life prediction. Here, the physics-based investigation guided by the two designed features shows that the voltage ranges identified by our framework capture the effects of formation temperature and microscopic-particle resistance heterogeneity. By designing highly predictive, robust, and interpretable features, our approach can accelerate industrial battery formation research, leveraging the interplay between data-driven feature design and mechanistic understanding.

25 ENERGY STORAGE↗
DOE OSTI2023

Pauli transfer matrix direct reconstruction: channel characterization without full process tomography

Abstract We present a tomographic protocol for the characterization of multiqubit quantum channels. We discuss a specific class of input states, for which the set of Pauli measurements at the output of the channel directly relates to its Pauli transfer matrix components. We compare our results to those of standard quantum process tomography, showing an exponential reduction in the number of different experimental configurations required by a single matrix element extraction, while keeping the same number of shots. This paves the way for more efficient experimental implementations, whenever a selective knowledge of the Pauli transfer matrix is needed. We provide several examples and simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

The endohyphal microbiome: current progress and challenges for scaling down integrative multi-omic microbiome research

Abstract As microbiome research has progressed, it has become clear that most, if not all, eukaryotic organisms are hosts to microbiomes composed of prokaryotes, other eukaryotes, and viruses. Fungi have only recently been considered holobionts with their own microbiomes, as filamentous fungi have been found to harbor bacteria (including cyanobacteria), mycoviruses, other fungi, and whole algal cells within their hyphae. Constituents of this complex endohyphal microbiome have been interrogated using multi-omic approaches. However, a lack of tools, techniques, and standardization for integrative multi-omics for small-scale microbiomes (e.g., intracellular microbiomes) has limited progress towards investigating and understanding the total diversity of the endohyphal microbiome and its functional impacts on fungal hosts. Understanding microbiome impacts on fungal hosts will advance explorations of how “microbiomes within microbiomes” affect broader microbial community dynamics and ecological functions. Progress to date as well as ongoing challenges of performing integrative multi-omics on the endohyphal microbiome is discussed herein. Addressing the challenges associated with the sample extraction, sample preparation, multi-omic data generation, and multi-omic data analysis and integration will help advance current knowledge of the endohyphal microbiome and provide a road map for shrinking microbiome investigations to smaller scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Multidimensional perspectives of geo-epidemiology: from interdisciplinary learning and research to cost–benefit oriented decision-making

Research typically promotes two types of outcomes (inventions and discoveries), which induce a virtuous cycle: something suspected or desired (not previously demonstrated) may become known or feasible once a new tool or procedure is invented and, later, the use of this invention may discover new knowledge. Research also promotes the opposite sequence—from new knowledge to new inventions. This bidirectional process is observed in geo-referenced epidemiology—a field that relates to but may also differ from spatial epidemiology. Geo-epidemiology encompasses several theories and technologies that promote inter/transdisciplinary knowledge integration, education, and research in population health. Based on visual examples derived from geo-referenced studies on epidemics and epizootics, this report demonstrates that this field may extract more (geographically related) information than simple spatial analyses, which then supports more effective and/or less costly interventions. Actual (not simulated) bio-geo-temporal interactions (never captured before the emergence of technologies that analyze geo-referenced data, such as geographical information systems) can now address research questions that relate to several fields, such as Network Theory. Thus, a new opportunity arises before us, which exceeds research: it also demands knowledge integration across disciplines as well as novel educational programs which, to be biomedically and socially justified, should demonstrate cost-effectiveness. Grounded on many bio-temporal-georeferenced examples, this report reviews the literature that supports this hypothesis: novel educational programs that focus on geo-referenced epidemic data may help generate cost-effective policies that prevent or control disease dissemination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Towards generic memory forensic framework for programmable logic controllers

A Programmable Logic Controller (PLC) is a microprocessor-based controller that is used to automate physical processes in critical infrastructure and various other industries and manufacturing sectors. Initially, PLCs were completely isolated from the Internet, and cyber security was not incorporated at the time of development. The introduction of industry 4.0 and the evolution of ICS systems to communicate over public IP addresses from the Internet enhanced productivity and efficiency, but Internet connectivity exposed the systems and their vulnerabilities, which led to an increase in cyber attacks. When a system is sabotaged/compromised, security analysts need to get to the root cause of the attack as quickly as possible to recover the system. To do so, memory forensic analysis is critical to provide a unique insight into the run-time memory activities and extract a reliable source of evidence. In this paper, we analyze the memory structure of the Schneider Electric Modicon M221 PLC. To build a memory profile, we reverse engineer the communication protocol and conduct differential analysis to gain knowledge about the structure of the memory and the low-level representation of control logic instructions. We then identify dynamic and static memory regions by modifying different project fields and conducting differential analysis, which allows us to identify boundaries of critical memory structures and extract important forensic artifacts that can be found in the memory. The Python implementation of the memory profile can help reduce the time and effort required for manual analysis in case of cyber incident or system failure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Role of boundary conditions in quantum computations of scattering observables

Quantum computing may offer the opportunity to simulate strongly interacting field theories, such as quantum chromodynamics, with physical time evolution. This would give access to Minkowski-signature correlators, in contrast to the Euclidean calculations routinely performed at present. However, as with present-day calculations, quantum computation strategies still require the restriction to a finite system size, including a finite, usually periodic, spatial volume. In this work, we investigate the consequences of this in the extraction of hadronic and Compton-like scattering amplitudes. Using the framework presented in Briceño et al., we estimate the volume effects for various 1+1D Minkowski-signature quantities and show that these can be a significant source of systematic uncertainty, even for volumes that are very large by the standards of present-day Euclidean calculations. We then present an improvement strategy, based in the fact that the finite volume has a reduced symmetry. This implies that kinematic points, which yield the same Lorentz invariants, may still be physically distinct in the periodic system. As we demonstrate, both numerically and analytically, averaging over such sets can significantly suppress the unwanted volume distortions and improve the extraction of the physical scattering amplitudes. As the improvement strategy is based only in kinematics, it can be applied without detailed knowledge of the system.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Bayesian Optimization of a Free-Electron Laser

The Linac Coherent Light Source X-ray free-electron laser is a complex scienti c apparatus which changes con gurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to maximizing X- ray laser pulse energy by controlling groups of quadrupole magnets. A Gaussian process model provides probabilistic predictions for the machine response with respect to control parameters, enabling a balance of exploration and exploitation in the search for the global optimum. We show that the model parameters can be learned from archived scans, and correlations between devices can be extracted from the beam transport. Furthermore, the result is a sample-e cient optimization routine, combining both historical data and knowledge of accelerator physics to signi cantly outperform existing optimizers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

LUCID Thrust 1 - Dataset Identification and Biodata Catalog Creation

The LUCID DOE consortium, part of the Department of Energy’s Biological and Environmental Research (BER) program, advances Low Dose Radiation (LDR) research through multidisciplinary efforts across seven key thrusts. This document focuses on Thrust 1, which centers on the creation of curated multimodal population health datasets and supports broader efforts within the LUCID program, including AI-based hypothesis generation, experimental design, and the study of LDR-induced health risks. Specifically, it describes the identification and cataloging of Thrust 1’s curated LDR datasets and biodata, emphasizing their critical role in supporting various research thrusts within the consortium, with potential applications in healthcare and public policy. In addition, the document includes an evaluation of three Large Language Models (LLMs)—GPT-4, SOLAR-10B, and Mixtral-8x7B—based on their ability to extract features from 25 LDR studies. The results indicate that GPT-4 performed the best, while Mixtral-8x7B demonstrated limited knowledge. Overall, this work advances understanding in radiation protection, risk assessment, and medical treatments, while providing valuable resources for researchers, educators, and policymakers.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗