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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Effects of n-3PUFAs on autophagy and inflammation of hypothalamus and body weight in mice

Highlights: • The effect of fat-1 gene on body weight is achieved via regulating expression of inflammatory and autophagy in hypothalamus. • The body weight, body and the Lee’s index are decreased in fat-1 transgenic mice. • The autophagy increased and the inflammation decreased in hypothalamus of fat-1 transgenic mice. Fat-1 transgenic mice were used as a model to study the effect of endogenous n-3 polyunsaturated fatty acids (n-3PUFAs) on the body weight, inflammatory factors and autophagy proteins in hypothalamus to explore the mechanism of n-3PUFAs inhibiting obesity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Metabolic Imaging Biomarkers of Postradiotherapy Xerostomia

Purpose: Xerostomia is a major complication of head and neck radiotherapy (RT). Available xerostomia measures remain flawed. [{sup 18}F]fluorodeoxyglucose-labeled positron emission tomography-computed tomography (FDG-PET-CT) is routinely used for staging and response assessment of head and neck cancer. We investigated quantitative measurement of parotid gland FDG uptake as a potential biomarker for post-RT xerostomia. Methods and Materials: Ninety-eight locally advanced head and neck cancer patients receiving definitive RT underwent baseline and post-RT FDG-PET-CT on a prospective imaging trial. A separate validation cohort of 14 patients underwent identical imaging while prospectively enrolled in a second trial collecting sialometry and patient-reported outcomes. Radiation dose and pre- and post-RT standard uptake values (SUVs) for all voxels contained within parotid gland ROI were deformably registered. Results: Average whole-gland or voxel-by-voxel models incorporating parotid D{sub Met} (defined as the pretreatment parotid SUV weighted by dose) accurately predicted posttreatment changes in parotid FDG uptake (e.g., fractional parotid SUV). Fractional loss of parotid FDG uptake closely paralleled early parotid toxicity defined by posttreatment salivary output (p < 0.01) and Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer xerostomia scores (p < 0.01). Conclusions: In this pilot series, loss of parotid FDG uptake was strongly associated with acute clinical post-RT parotid toxicity. D{sub Met} may potentially be used to guide function-sparing treatment planning. Prospective validation of FDG-PET-CT as a convenient, quantifiable imaging biomarker of parotid function is warranted and ongoing.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

A multiscale modeling technique for bridging molecular dynamics with finite element method

In computational mechanics, molecular dynamics (MD) and finite element (FE) analysis are well developed and most popular on nanoscale and macroscale analysis, respectively. MD can very well simulate the atomistic behavior, but cannot simulate macroscale length and time due to computational limits. FE can very well simulate continuum mechanics (CM) problems, but has the limitation of the lack of atomistic level degrees of freedom. Multiscale modeling is an expedient methodology with a potential to connect different levels of modeling such as quantum mechanics, molecular dynamics, and continuum mechanics. This study proposes a new multiscale modeling technique to couple MD with FE. The proposed method relies on weighted average momentum principle. A wave propagation example has been used to illustrate the challenges in coupling MD with FE and to verify the proposed technique. Furthermore, 2-Dimensional problem has also been used to demonstrate how this method would translate into real world applications. -- Highlights: •A weighted averaging momentum method is introduced for bridging molecular dynamics (MD) with finite element (FE) method. •The proposed method shows excellent coupling results in 1-D and 2-D examples. •The proposed method successfully reduces the spurious wave reflection at the border of MD and FE regions. •Big advantages of the proposed method are simplicity and inexpensive computational cost of multiscale analysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

More high-impact atmospheric river-induced extreme precipitation events under warming in a high-resolution model

Extreme precipitation events, as occurred in Europe 2021, or western North America 2023, with an intensity of 75 mm/day or 100 mm/day, respectively, exert a catastrophic impact. Lower-resolution (~100 km) climate models cannot simulate the intensity, nor their response to greenhouse warming. Using a high-resolution (~25 km) model capable of simulating such events, here we show that frequency-weighted area of such events over western Europe and the west coast of North America likely expands by more than 80% under an approximately 4°C of global warming from the historical levels. Along the west coasts of Europe and North America, area impacted by atmospheric rivers-induced extreme precipitation events is projected to double, driven by intensified landfalling atmospheric rivers. Thermodynamic processes drive the increase, whereas dynamic processes reduce the intensity over western Europe but enhance it along the west coast of North America. Here our findings provide policy-relevant information for climate adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

EIF3H knockdown inhibits malignant melanoma through regulating cell proliferation, apoptosis and cell cycle

Highlights: • EIF3H is highly expressed in malignant melanoma and correlated with tumor stage and risk of lymphatic metastasis. • Knockdown of EIF3H inhibits cell proliferation, cell migration and promotes cell apoptosis of melanoma in vitro. • Knockdown of EIF3H suppresses tumor growth in vivo. Malignant melanoma (MM) causes 80% of skin cancer-related deaths and becomes the most lethal type of skin cancer. The molecular mechanism of MM is still not clear. This study aimed to reveal the relationship between MM and EIF3H. Clinical specimens were collected to preliminarily explore the role of EIF3H in MM. MM cell lines with EIF3H knockdown were constructed for investigating the effects of EIF3H on cell proliferation, apoptosis, cell cycle and cell motility. Mice xenograft model was constructed for verification in vivo. We found that EIF3H was obviously upregulated in MM tissues compared with normal skin tissues, which was correlated with tumor stage and risk of lymphatic metastasis. The in vitro results indicated that silencing EIF3H in MM cells could significantly suppress cell proliferation, promote cell apoptosis and induce cell cycle arrest. Moreover, EIF3H knockdown significantly restrained cell motility through regulating EMT-related proteins. The effects of EIF3H knockdown were also verified in mice xenograft model, which were represented by slower growth rate, smaller volume and lighter weight of tumors. Therefore, EIF3H was identified as a critical factor in the development and progression of MM which may be used as a novel therapeutic target in the treatment of MM.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Rapid and Simultaneous Characterization of Drug Conjugation in Heavy and Light Chains of a Monoclonal Antibody Revealed by High-Resolution Ion Mobility Separations in SLIM

Antibody-drug conjugates (ADCs) have recently gained traction in the biomedical community due to their promise for human therapeutics and an alternative to chemotherapy for cancer. Crucial metrics for ADC efficacy, safety, and selectivity are their drug-antibody ratios (DARs). However, DAR characterization (i.e., determining the average number of conjugated drugs on the antibody) through analytical methods remains challenging due to the heterogeneity of drug conjugation as well as the numerous post-translational modifications possible in the monoclonal antibody. In this work, we report on the use of high-resolution ion mobility spectrometry separations in our structures for lossless ion manipulations coupled to mass spectrometry (SLIM IMS-MS) platform for the rapid and simultaneous characterization of the drug load profile (i.e., stoichiometric distribution of the number of conjugated drugs present on the mAb), determination of the weighted average DAR in both the heavy and light chains of a model antibody-drug conjugate, and calculation of the overall DAR of the ADC. After chemical reduction of the ADC and a subsequent 31.5 meter SLIM IMS separation the various drug bound antibody species could be well resolved for both chains. Furthermore, we also show that higher resolution separations were possible with SLIM IMS as compared to ones performed on a commercially available (~1 meter) drift tube IMS-MS platform. Interestingly, additional gas-phase substructures become evident with longer path length SLIM IMS, potentially providing insight related to how drug conjugation may alter the structural properties of a monoclonal antibody and thus affect its overall potency and/or selectivity for a given antigen. We expect high-resolution SLIM IMS separations will augment the existing toolbox for ADC characterization, particularly for structural confirmation to enable the rapid optimization of DAR for a given ADC and thus better understand its potential toxicity and potency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Subsurface Structure of Long Valley Caldera Imaged With Seismic Scattering and Intrinsic Attenuation

Here we image seismic intrinsic (Q i -1 ) and scattering (Q s -1 ) attenuations in Long Valley Caldera, California, by analyzing more than 1,700 vertical component waveforms from local earthquakes. Observed energy envelopes are fit to the diffusion model and seismic attenuation images are produced using two-dimensional space weighting functions. Low intrinsic and low scattering attenuation anomalies in the center south of the caldera correspond to the location of an earthquake swarm in 2014. We identify high intrinsic and high scattering attenuation anomalies in the fluid-rich western and eastern areas of the caldera. From a comparison with other geophysical images (magnetotellurics and seismic tomography) we attribute these anomalies to a hydrothermal system (high attenuation). Average to high attenuation values are also observed at the adjacent Mammoth Mountain (southwest of the caldera) and may also have a hydrothermal origin. High intrinsic attenuation at low frequencies to the west of the Hartley Springs Fault may be produced by the magmatic system that produced the Inyo Craters. Finally, seismic attenuation imaging provides insights into subsurface structures that are complementary to velocity and conductivity images.

58 GEOSCIENCES↗
DOE OSTI2024

Evaluation of FluSight influenza forecasting in the 2021–22 and 2022–23 seasons with a new target laboratory-confirmed influenza hospitalizations

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2nd most accurate model measured by WIS in 2021–22 and the 5th most accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Design principles for lifelong learning AI accelerators

Lifelong learning-an agent's ability to learn throughout its lifetime-is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of novel AI applications, but this will also require the development of appropriate hardware accelerators, particularly if the models are to be deployed on edge platforms, which have strict size, weight and power constraints. Here we explore the design of lifelong learning AI accelerators that are intended for deployment in untethered environments. We identify key desirable capabilities for lifelong learning accelerators and highlight metrics to evaluate such accelerators. We then discuss current edge AI accelerators and explore the future design of lifelong learning accelerators, considering the role that different emerging technologies could play. The Perspective explores the future design of lifelong learning artificial intelligence (AI) accelerators that are intended for deployment in untethered environments, identifying key desirable capabilities for such edge AI accelerators and guidance on metrics to evaluate them.

42 ENGINEERING↗
DOE OSTI2025

A framework to evaluate machine learning crystal stability predictions

The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for machine learning energy models, here applied as pre-filters to first-principles computed data in a high-throughput search for stable inorganic crystals. We address the disconnect between (1) thermodynamic stability and formation energy and (2) retrospective and prospective benchmarking for materials discovery. Alongside this paper, we publish a Python package to aid with future model submissions and a growing online leaderboard with adaptive user-defined weighting of various performance metrics allowing researchers to prioritize the metrics they value most. To answer the question of which machine learning methodology performs best at materials discovery, our initial release includes random forests, graph neural networks, one-shot predictors, iterative Bayesian optimizers and universal interatomic potentials. We highlight a misalignment between commonly used regression metrics and more task-relevant classification metrics for materials discovery. Accurate regressors are susceptible to unexpectedly high false-positive rates if those accurate predictions lie close to the decision boundary at 0 eV per atom above the convex hull. The benchmark results demonstrate that universal interatomic potentials have advanced sufficiently to effectively and cheaply pre-screen thermodynamic stable hypothetical materials in future expansions of high-throughput materials databases.

Riebesell, Janosh↗
DOE OSTI2013

A hybrid stochastic-deconvolution model for large-eddy simulation of particle-laden flow

We develop a hybrid model for large-eddy simulation of particle-laden turbulent flow, which is a combination of the approximate deconvolution model for the resolved scales and a stochastic model for the sub-grid scales. The stochastic model incorporates a priori results of direct numerical simulation of turbulent channel flow, which showed that the parameters in the stochastic model are quite independent of Reynolds and Stokes number. In order to correctly predict the flux of particles towards the walls an extra term should be included in the stochastic model, which corresponds to the term related to the well-mixed condition in Langevin models for particle dispersion in inhomogeneous turbulent flow. The model predictions are compared with results of direct numerical simulation of channel flow at a frictional Reynolds number of 950. The inclusion of the stochastic forcing is shown to yield a significant improvement over the approximate deconvolution model for the particles alone when combined with a Stokes dependent weight-factor for the well-mixed term.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2012

THE EVOLUTION OF STELLAR VELOCITY DISPERSION DURING DISSIPATIONLESS GALAXY MERGERS

Using N-body simulations, we studied the detailed evolution of central stellar velocity dispersion, {sigma}{sub *}, during dissipationless binary mergers of galaxies. Stellar velocity dispersion was measured using the common mass-weighting method as well as a flux-weighting method designed to simulate the technique used by observers. A toy model for dust attenuation was introduced in order to study the effect of dust attenuation on measurements of {sigma}{sub *}. We found that there are three principal stages in the evolution of {sigma}{sub *} in such mergers: oscillation, phase mixing, and dynamical equilibrium. During the oscillation stage, {sigma}{sub *} undergoes damped oscillations of increasing frequency. The oscillation stage is followed by a phase mixing stage during which the amplitude of the variations in {sigma}{sub *} is smaller and more chaotic than in the oscillation stage. Upon reaching dynamical equilibrium, {sigma}{sub *} assumes a stable value. We used our data regarding the evolution of {sigma}{sub *} during mergers to characterize the scatter inherent in making measurements of {sigma}{sub *} in non-quiescent systems. In particular, we found that {sigma}{sub *} does not fall below 70% nor exceed 200% of its final, quiescent value during a merger and that a random measurement of {sigma}{sub *} in such a system is much more likely to fall near the equilibrium value than near an extremum. Our toy model of dust attenuation suggested that dust can systematically reduce observational measurements of {sigma}{sub *} and increase the scatter in {sigma}{sub *} measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

MAGNIFICATION BY GALAXY GROUP DARK MATTER HALOS

We report on the detection of gravitational lensing magnification by a population of galaxy groups, at a significance level of 4.9{sigma}. Using X-ray-selected groups in the COSMOS 1.64 deg{sup 2} field, and high-redshift Lyman break galaxies as sources, we measure a lensing-induced angular cross-correlation between the samples. After satisfying consistency checks that demonstrate we have indeed detected a magnification signal, and are not suffering from contamination by physical overlap of samples, we proceed to implement an optimally weighted cross-correlation function to further boost the signal to noise of the measurement. Interpreting this optimally weighted measurement allows us to study properties of the lensing groups. We model the full distribution of group masses using a composite-halo approach, considering both the singular isothermal sphere and Navarro-Frenk-White profiles, and find our best-fit values to be consistent with those recovered using the weak-lensing shear technique. We argue that future weak-lensing studies will need to incorporate magnification along with shear, both to reduce residual systematics and to make full use of all available source information, in an effort to maximize scientific yield of the observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Tensor renormalization group for fermions

Abstract We review the basic ideas of the tensor renormalization group method and show how they can be applied for lattice field theory models involving relativistic fermions and Grassmann variables in arbitrary dimensions. We discuss recent progress for entanglement filtering, loop optimization, bond-weighting techniques and matrix product decompositions for Grassmann tensor networks. The new methods are tested with two-dimensional Wilson–Majorana fermions and multi-flavor Gross–Neveu models. We show that the methods can also be applied to the fermionic Hubbard model in 1+1 and 2+1 dimensions.

Physics↗
DOE OSTI2022

Light-front holography model of the EMC effect

A new two-component model of the EMC effect based on Light-Front Holographic QCD (LFHQCD) is presented. The model suggests the EMC effect is the result of the nuclear potential breaking SU(6) symmetry. The model separates the $F_{2}^{A}$ nuclear structure function into two parts: a free contribution, involving the addition of proton and neutron structure functions weighted by the number of protons and neutrons respectively, and a nuclear/medium modified contribution that involves a universal function for all nuceli. Further, the model displays a connection with the correlation between the size of the EMC effect and the SRC pair density, a 2 - extracted from kinematic plateaus at around x > 1 in inclusive quasi-elastic (QE) scattering.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Unscented Kalman Filter-based Unbiased Minimum-Variance Estimation for Nonlinear Systems with Unknown Inputs

This paper proposes an unscented Kalman filter (UKF)-based unbiased minimum-variance estimation (UMV) method for the nonlinear system with unknown inputs. By utilizing the statistical linerization, the nonlinear system and measurement functions are transformed into a “linear-like” regression form. The latter preserves the nonlinearity of the system and the measurement models. To this end, the unknown inputs can be estimated by the weighted least-squares. This “linear-like” regression form also allows us to resort to the UMV state estimation framework for the development of new nonlinear filter to handle unknown inputs. Specifically, two approaches have been developed: 1) given the estimated inputs, we derive a filter by minimizing the trace of the state error covariance matrix; 2) without input estimation, we derive the filter by minimizing the trace of the state error covariance matrix subject to a constraint imposed on the gain matrix. We prove that these two approaches provide the same results. Numerical results validate the effectiveness of the proposed method.

Unscented Kalman filter, unbiased minimumvariance ↗
DOE OSTI2023

Towards Smart Grids Enhanced Situation Awareness: A Bi-Level Quasi-Static State Estimation Model

Smart Grid situational awareness is provided by Energy Management Systems. A core process of these systems is State Estimation. The great majority of state estimators model the Smart Grid through a set of nonlinear algebraic equations, named the measurement model. Problem formulation considers the Gauss solution. Several model improvements have been presented regarding the Gauss solution, aiming between others to provide measurement noise robustness to the state estimation process. While considerable effort has been focused on such developments, state estimation is still constrained by the implicit modelling error, and thus inevitably vulnerable to cyber-threats. In this work, a state estimation bi-level formal model is presented towards Smart Grids enhanced situational awareness considering the concepts of synthetic measurements and innovation. Comparative test results with the state-of-the-art on the IEEE 14-bus system are presented highlighting improved situational awareness to bad data. Easy-to-implement model, without hard-to-derive parameters, built-on the classic weighted least squares solution, highlight potential aspects for real-life implementation.

cyber security↗
DOE OSTI2011

3D automatic anatomy segmentation based on iterative graph-cut-ASM

Purpose: This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. Methods: The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al.[Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. Results: The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 deg. and 0.03, and over all foot bones are about 3.5709 mm, 0.35 deg. and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. Conclusions: The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.

62 RADIOLOGY AND NUCLEAR MEDICINE↗