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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Redshift evolution of the H2/H i mass ratio in galaxies

ABSTRACT In this paper, we present an attempt to estimate the redshift evolution of the molecular to neutral gas mass ratio within galaxies (at fixed stellar mass). For a sample of five nearby grand design spirals located on the main-sequence (MS) of star-forming galaxies, we exploit maps at 500 pc resolution of stellar mass and star formation rate (M⋆ and SFR). For the same cells, we also have estimates of the neutral (MH i) and molecular ($M_{\rm H_2}$) gas masses. To compute the redshift evolution, we exploit two relations: (i) one between the molecular-to-neutral mass ratio and the total gas mass (Mgas), whose scatter shows a strong dependence with the distance from the spatially resolved MS, and (ii) the one between $\log (M_{\rm {H_2}}/M_{\star })$ and log (MH i/M⋆). For both methods, we and that $M_{\rm H_2}$/MH i within the optical radius slightly decreases with redshift, contrary to common expectations of galaxies becoming progressively more dominated by molecular hydrogen at high redshifts. We discuss possible implications of this trend on our understanding of the internal working of high-redshift galaxies.

Morselli, Laura (ORCID:0000000307532571)↗
DOE OSTI2022

Antiretroviral therapy for HIV and intrahepatic hepatitis C virus replication

HIV alters host responses to hepatitis C virus (HCV). However, the impact of antiretroviral therapy (ART) on HCV is rarely understood in relevant tissues and never before within individual hepatocytes. HIV and HCV kinetics were studied before and after ART initiation among 19 HIV/HCV co-infected persons. From five persons with the largest decline in plasma HCV RNA, liver tissues collected before and during ART, when plasma HIV RNA was undetectable, were studied. Here we used single-cell laser capture microdissection and quantitative PCR to assess intrahepatic HCV. Immunohistochemistry was performed to characterize intrahepatic immune cell populations. Plasma HCV RNA declined by 0.81 (0.52–1.60) log 10 IU/ml from a median (range) 7.26 (6.05–7.29) log 10 IU/ml and correlated with proportions of HCV-infected hepatocytes (r = 0.89, P = 2 × 10 -5 ), which declined from median (range) of 37% (6–49%) to 23% (0.5–52%) after plasma HIV clearance. Median (range) HCV RNA abundance within cells was unchanged in four of five participants. Liver T-cell abundance unexpectedly decreased, whereas natural killer (NK) and NK T-cell infiltration increased, correlating with changes in proportions of HCV-infected hepatocytes (r = -0.82 and r = -0.73, respectively). Hepatocyte expression of HLA-E, an NK cell restriction marker, correlated with proportions of HCV-infected hepatocytes (r = 0.79). These are the first data to show that ART control of HIV reduces the intrahepatic burden of HCV. Furthermore, our data suggest that HIV affects the pathogenesis of HCV infection by an NK/NK T-cell-mediated mechanism that may involve HLA-E and can be rescued, at least in part, by ART.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Block encoding of the three-dimensional heterogeneous Poisson equation with application to fracture flow

Quantum linear system (QLS) algorithms offer the potential to solve large-scale linear systems exponentially faster than classical methods. However, applying QLS algorithms to real-world problems remains challenging due to issues such as state preparation, data loading, and efficient information extraction. In this work, we study the feasibility of applying QLS algorithms to solve discretized three-dimensional (3D) heterogeneous Poisson equations, with specific examples relating to groundwater flow through geologic fracture networks. We explicitly construct a block encoding for the 3D heterogeneous Poisson matrix by leveraging the sparse local structure of the discretized operator. While classical solvers benefit from preconditioning, we show that block encoding the system matrix and preconditioner separately does not improve the effective condition number that dominates the QLS run-time. This differs from classical approaches where the preconditioner and the system matrix can often be implemented independently. Nevertheless, due to the structure of the problem in three dimensions, the quantum algorithm achieves a run-time of 𝑂⁡(𝑁 2/3 polylog 𝑁 ⋅log (1/𝜖)), outperforming the best classical methods (with run times of 𝑂⁡(𝑁⁢log 𝑁 ⋅log (1/𝜖))) and offering exponential memory savings. These results highlight both the promise and limitations of QLS algorithms for practical scientific computing, and point to effective condition-number reduction as a key barrier in achieving quantum advantages.

58 GEOSCIENCES↗
DOE OSTI2021

Scaling functions for eigenstate entanglement crossovers in harmonic lattices

For quantum matter, eigenstate entanglement entropies obey an area law or log-area law at low energies and small subsystem sizes and cross over to volume laws for high energies and large subsystems. This transition is captured by crossover functions, which assume a universal scaling form in quantum critical regimes. We demonstrate this for the harmonic lattice model, which describes quantized lattice vibrations and is a regularization for free scalar field theories, modeling, e.g., spin-0 bosonic particles. In one dimension, the ground-state entanglement obeys a log-area law. For dimensions d ≥ 2, it displays area laws, even at criticality. The distribution of excited-state entanglement entropies is found to be sharply peaked around subsystem entropies of corresponding thermodynamic ensembles in accordance with the eigenstate thermalization hypothesis. Numerically, we determine crossover scaling functions for the quantum critical regime of the model and do a large-deviation analysis. We show how infrared singularities of the system can be handled and how to access the thermodynamic limit using a perturbative trick for the covariance matrix. Eigenstates for quasi-free bosonic systems are not Gaussian. Here, we resolve this problem by considering appropriate squeezed states instead. For these, entanglement entropies can be evaluated efficiently.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Logarithmic criticality in transverse thermoelectric conductivity of the ferromagnetic topological semimetal CoMnSb

Here we report the results of our experimental studies on the magnetic, transport, and thermoelectric properties of the ferromagnetic metal CoMnSb. A sizable anomalous Hall conductivity $σ_{yx}$ and transverse thermoelectric conductivity $α_{yx}$ are found experimentally and comparable in size to the values estimated from first-principles calculation. Our experiment further reveals that CoMnSb exhibits -T log T critical behavior in $α_{yx}$ (T), deviating from Fermi liquid behavior $α_{yx}$ ~ T over a decade of temperature between 10 and 400 K, similar to ferromagnetic Weyl and nodal-line semimetals. Our theoretical calculation for CoMnSb also predicts the - T log T behavior when the Fermi energy locates near the Weyl nodes in momentum space.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ground-state β -decay spectroscopy of 187 Ta

We report beta-decay spectroscopy of the 187 Ta ground state was performed at the KEK Isotope Separation System. β-delayed γ rays corresponding to the previously reported in-beam transitions were observed. The β-decay half-life of the 187 Ta ground state was determined to be 283(10) s by analyzing a time spectrum of β-γ coincidence events. The β-decay branching ratio and log(ft) values were evaluated for the first time. Based on the newly evaluated log(ft) values of >6.0 and a decay scheme, spin-parity values of I π = 7/2 + originating from the odd-proton orbit π7/2[404] were assigned with high confidence, which is consistent with the systematics of neighboring odd-A nuclides.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Mechanisms and models of the turbulent boundary layers at transcritical conditions

Computational models for high-pressure transcritical turbulence in wall-modeled large-eddy simulation typically rely on wall-function models to overcome the need for resolving the boundary layer structures. However, the mechanisms and models of turbulent boundary layer at transcritical conditions remain poorly understood since the near-wall flow and heat transfer are significantly affected by the enhanced fluctuations and steep gradients in thermodynamic properties. Here, to address this issue, we study the mechanisms of transcritical turbulent boundary layers and wall-attached models at transcritical conditions. It is shown that the real-fluid variable-property effects are associated with the coupling between the near-wall cycle and the wall-normal coherent motions in the outer layer, resulting in the amplification of turbulent energy in the outer layer and noticeable energy transfer between the log-layer and the outer layer; hence, turbulence in the log-layer is modulated by the outer layer. Based on this underlying physical principle, we propose the characteristic velocity and length scales for the attached eddy at transcritical conditions, and extend the attached eddy model to transcritical turbulent boundary layers by introducing a mixed scaling that incorporates both inner and outer scalings. We show that the new characteristic scales and extended attached-eddy model perform well in characterizing the structures in transcritical turbulent boundary layers.

Li, Fangbo↗
DOE OSTI2025

Construction of the damped Ly⁢𝛼 absorber catalog for DESI DR2 Ly⁢𝛼 BAO

We present the Damped Ly⁢𝛼 Toolkit for automated detection and characterization of damped Ly⁢𝛼 absorbers (DLAs) in quasar spectra. Our method uses quasar spectral templates with and without absorption from intervening DLAs to reconstruct observed quasar forest regions. The best-fitting model determines whether a DLA is present while estimating the redshift and HI column density. With an optimized quality cut on detection significance (Δ⁢𝜒$^{2}_{𝑟}$ >0.03), the technique achieves an estimated 80% purity and 79% completeness when evaluated on simulated spectra with S/N>2 that are free of broad absorption lines (BALs). We provide a catalog containing candidate DLAs from the DLA Toolkit detected in DESI DR1 quasar spectra, of which 21 719 were found in S/N>2 spectra with predicted log 10 ⁡(𝑁 𝙷𝙸 )>20.3 and detection significance Δ⁢𝜒$^{2}_{𝑟}$ >0.03. We compare the Damped Ly⁢𝛼 Toolkit to two alternative DLA finders based on a convolutional neural network and Gaussian process models. We present a strategy for combining these three techniques to produce a high-fidelity DLA catalog from DESI DR2 for the Ly⁢𝛼 forest baryon acoustic oscillation measurement. The combined catalog contains 41 152 candidate DLAs with log 10 ⁡(𝑁 𝙷𝙸 )>20.3 from quasar spectra with S/N>2. We estimate this sample to be approximately 85% pure and 79% complete when BAL quasars are excluded.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Predicting Adaptively Chosen Observables in Quantum Systems

Recent advances have demonstrated that 𝒪⁡(log 𝑀) measurements suffice to predict 𝑀 properties of arbitrarily large quantum many-body systems. However, these remarkable findings assume that the properties to be predicted are chosen independently of the data. This assumption can be violated in practice, where scientists adaptively select properties after looking at previous predictions. This work investigates the adaptive setting for three classes of observables: local, Pauli, and bounded-Frobenius-norm observables. We prove that Ω⁡(√𝑀) samples of an arbitrarily large unknown quantum state are necessary to predict expectation values of 𝑀 adaptively chosen local and Pauli observables, where the system size scales exponentially and polynomially in 𝑀, respectively. We also present computationally efficient algorithms that achieve this information-theoretic lower bound. In contrast, for bounded-Frobenius-norm observables, we devise an algorithm requiring only 𝒪⁡(log 𝑀) samples, independent of system size. These results highlight the potential pitfalls of adaptivity in analyzing data from quantum experiments and provide algorithmic tools to safeguard against erroneous predictions in quantum experiments.

Machine learning↗
DOE OSTI2022

Robust Event Classification Using Imperfect Real-world PMU Data

Here, this paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

A Butterfly-Accelerated Volume Integral Equation Solver for Broad Permittivity and Large-Scale Electromagnetic Analysis

In this work, a butterfly-accelerated volume integral equation (VIE) solver is proposed for fast and accurate electromagnetic (EM) analysis of scattering from heterogeneous objects. The proposed solver leverages the hierarchical off-diagonal butterfly (HOD-BF) scheme to construct the system matrix and obtain its approximate inverse, used as a preconditioner. Complexity analysis and numerical experiments validate the O(N log 2 N) construction cost of the HOD-BF-compressed system matrix and O(N log 1.5 N) inversion cost for the preconditioner, where N is the number of unknowns in the high-frequency EM scattering problem. For many practical scenarios, the proposed VIE solver requires less memory and computational time to construct the system matrix and obtain its approximate inverse compared to a H matrix-accelerated VIE solver. The accuracy and efficiency of the proposed solver have been demonstrated via its application to the EM analysis of large-scale canonical and real-world structures comprising of broad permittivity values and involving millions of unknowns.

42 ENGINEERING↗
DOE OSTI2024

GraphCH: A Deep Framework for Assessing Cyber-Human Aspects in Insider Threat Detection

Insider threat is one of the most damaging cyber attacks that could cause the loss of intellectual property and enterprise data security breaches. Action sequence data such as host logs are used to investigate such threats and develop anomaly-based AI detectors. However, insider threat actions are similar to legitimate user activities, causing AI detectors to fail and suffer from high false alarm rates. Therefore, user cyber activity logs are inadequate to fully unfold insider threats. In this study, we adopt human psychological principles of risk-taking and impulsiveness along with host data to assess the influence and usefulness of human behavioral aspects in insider threat detection. Here, we hypothesize that individuals' impulsive and risk-taking behavior correlates with cyberspace activities. To validate our hypothesis, we conducted an IRB-approved study recruiting 35 participants who work in a large U.S. university and collected their cyber and psychological data for 90 days. Host and human-behavioral data analysis and mapping indicate that impulsive and risk-taking users trigger more system errors causing (un)intentional insider threats and are susceptible to attackers' social engineering and cognitive hacking. Utilizing cyber-human aspects, we introduce a Cyber-Human Graph Neural Network (GNN) based framework GraphCH to identify abnormal user behaviors and detect insider threats.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Investigating the Effects of Individual Neutron-Induced Defects in Bipolar Junction Transistors

Here, this study investigates neutron-induced displacement damage in Bipolar Junction Transistors (BJTs) using TCAD models informed by Deep-Level-Transient-Spectroscopy (DLTS) data. These models are calibrated and validated against experimental measurements performed at various neutron fluences. Both npn and pnp transistor configurations are studied to analyze the effects of individual traps on carrier recombination and base leakage currents. In npn transistors, deep traps (0.42 eV from the conduction band) dominate at low voltages, while shallow traps (0.17 eV from the conduction band) become prominent at higher voltages. Conversely, pnp transistors have base leakage current predominantly due to deep-level traps. The study observes a notable trend in trap density versus fluence, characterized by a linear relationship on a log-log scale. These insights into defect evolution under radiation conditions are crucial for optimizing semiconductor device reliability and performance in radiation-prone environments.

42 ENGINEERING↗
DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗
DOE OSTI2020

Melting temperature measurement of refractory oxide ceramics as a function of oxygen fugacity using containerless methods

An aerodynamic conical nozzle levitator (CNL) has been used to measure the melting temperatures of refractory ceramics. The well-established method of cooling traces has been adapted to allow measurements for air-sensitive samples using a controlled atmosphere, where the oxygen partial pressure can be adjusted and monitored in real time. In this study, we explain the details of a system that we developed for use at the Spallation Neutron Source (Oak Ridge National Laboratory). Measurements of the melting behavior of (Fe,Cr,Al) 3 O 4 spinels were made between log (PO 2 (atm)) of -0.7 and -5. The melting temperature of hercynite is found to be oxygen potential dependent, ranging between 1692 and 1767°C at log (PO 2 ) values from -4.42 to -0.70.

36 MATERIALS SCIENCE↗
DOE OSTI2020

HIV influences clustering and intracellular replication of hepatitis C virus

We report HCV and HIV coinfection is common and HIV leads to increased HCV viraemia and accelerated disease progression. However, the biological basis of this interaction remains poorly understood and little is known about the impact of HIV on HCV replication at the cellular level. We analysed HCV RNA, based on single-cell laser-capture microdissection, in liver biopsies from monoinfected (n = 4) and HCV/HIV-coinfected (n = 5) participants. HCV RNA was assayed in 3200 hepatocytes with information of spatial position. We compared HCV RNA levels and clustering properties of infection between mono- and coinfected participants, and developed a mathematical model of infection. Although the median plasma HCV RNA level and the fraction of infected cells were comparable in monoinfected (7.0 log 10 IU/mL and ~ 30%) and coinfected (7.3 log 10 IU/mL and ~ 40%) participants, the median HCV RNA per infected hepatocyte in monoinfected (2.8IU) was significantly lower than in coinfected (8.2IU) participants (p = .03). Clustering of infected cells was more prominent in monoinfected participants (91% of samples) than in coinfected participants (~48%), p = .0045, suggesting that spatial spread may be influenced by HIV coinfection. Interestingly, when clustering does occur, the size of clusters is similar in both types of infection. A mathematical model of infection suggested that HIV allows higher intracellular accumulation of HCV RNA by impeding the export of HCV RNA. Our observations show that HIV coinfection impacts intracellular accumulation of HCV RNA and the clustering of HCV-infected cells, but to a less extent the fraction of HCV-infected cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Global producer responsibility for plastic pollution

Brand names can be used to hold plastic companies accountable for their items found polluting the environment. We used data from a 5-year (2018–2022) worldwide (84 countries) program to identify brands found on plastic items in the environment through 1576 audit events. We found that 50% of items were unbranded, calling for mandated producer reporting. The top five brands globally were The Coca-Cola Company (11%), PepsiCo (5%), Nestlé (3%), Danone (3%), and Altria (2%), accounting for 24% of the total branded count, and 56 companies accounted for more than 50%. There was a clear and strong log-log linear relationship production (%) = pollution (%) between companies’ annual production of plastic and their branded plastic pollution, with food and beverage companies being disproportionately large polluters. Phasing out single-use and short-lived plastic products by the largest polluters would greatly reduce global plastic pollution.

Science & Technology - Other Topics↗
DOE OSTI2021

Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo

As an important Markov chain Monte Carlo (MCMC) method, the stochastic gradient Langevin dynamics (SGLD) algorithm has achieved great success in Bayesian learning and posterior sampling. Furthermore, SGLD typically suffers from a slow convergence rate due to its large variance caused by the stochastic gradient. In order to alleviate these drawbacks, we leverage the recently developed Laplacian smoothing technique and propose a Laplacian smoothing stochastic gradient Langevin dynamics (LS-SGLD) algorithm. We prove that for sampling from both log-concave and non-log-concave densities, LS-SGLD achieves strictly smaller discretization error in 2-Wasserstein distance, although its mixing rate can be slightly slower. Experiments on both synthetic and real datasets verify our theoretical results and demonstrate the superior performance of LS-SGLD on different machine learning tasks including posterior sampling, Bayesian logistic regression, and training Bayesian convolutional neural networks.

97 MATHEMATICS AND COMPUTING↗