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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A robust derivation of the tight relationship of radio core dominance to inclination angle in high redshift 3CRR sources

It is believed that, in radio-loud active galactic nuclei (AGNs), the core radio flux density can be normalized to the flux density of the extended lobe emission to infer the orientation of a radio source. However, very little is known about the reliability and precision of this method, and we are unaware of any robust conversion recipe to infer the inclination from the core dominance. Investigating whether or not the radio core dominance parameter R separates the quasars from the radio galaxies in the z ⩾ 1 3CRR catalog, we found excellent agreement of R with optical type, infrared flux ratios, and optical polarization. This indicates that probably both R and optical classification are very good orientation indicators, and the unified model is strongly predictive for these objects. The relative number densities indicate half-opening angles close to 60°, as expected from large surveys. The separations of optical types according to radio core dominance as well as near-infrared/MIR ratios, which are essentially perfect, mean that there can be only a small dispersion of torus half-opening angles. Also, even though torus dust is thought to be clumpy, there is an almost zero probability to see a type-1 source at high inclination. Finally, using only the Copernican Principle, i.e., the assumption that solid angle is filled uniformly with source axis orientations, we estimated a semi-empirical relation between core dominance and AGN inclination. This makes it possible to use R to infer the inclination of a source to an accuracy of ∼10° or less, at least for this type of object.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Inferring Viral Transmission Time from Phylogenies for Known Transmission Pairs

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source’s infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model—which make use of different information within a tree—suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Scaling kinetic Monte-Carlo simulations of grain growth with combined convolutional and graph neural networks

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we suggest a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160 3 ), our method reduces memory usage and runtime in inference by 117× and 115×, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder’s ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. Training data are generated from stochastic grain growth simulations, providing realistic variability for learning robust microstructure evolution. Comprehensive system validation confirms that the model is accurate, robust, and scalable.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Systematic network assessment of the carcinogenic activities of cadmium

Cadmium has been defined as type I carcinogen for humans, but the underlying mechanisms of its carcinogenic activity and its influence on protein-protein interactions in cells are not fully elucidated. The aim of the current study was to evaluate, systematically, the carcinogenic activity of cadmium with systems biology approaches. From a literature search of 209 studies that performed with cellular models, 208 proteins influenced by cadmium exposure were identified. All of these were assessed by Western blotting and were recognized as key nodes in network analyses. The protein-protein functional interaction networks were constructed with NetBox software and visualized with Cytoscape software. These cadmium-rewired genes were used to construct a scale-free, highly connected biological protein interaction network with 850 nodes and 8770 edges. Of the network, nine key modules were identified and 60 key signaling pathways, including the estrogen, RAS, PI3K-Akt, NF-κB, HIF-1α, Jak-STAT, and TGF-β signaling pathways, were significantly enriched. With breast cancer, colorectal and prostate cancer cellular models, we validated the key node genes in the network that had been previously reported or inferred form the network by Western blotting methods, including STAT3, JNK, p38, SMAD2/3, P65, AKT1, and HIF-1α. These results suggested the established network was robust and provided a systematic view of the carcinogenic activities of cadmium in human. - Highlights: • A cadmium-influenced network with 850 nodes and 8770 edges was established. • The cadmium-rewired gene network was scale-free and highly connected. • Nine modules were identified, and 60 key signaling pathways related to cadmium-induced carcinogenesis were found. • Key mediators in the network were validated in multiple cellular models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants

The growth of human immunodeficiency virus (HIV) sequence databases resulting from drug resistance testing has motivated efforts using phylogenetic methods to assess how HIV spreads. Such inference is potentially both powerful and useful for tracking the epidemiology of HIV and the allocation of resources to prevention campaigns. We recently used simulation and a small number of illustrative cases to show that certain phylogenetic patterns are associated with different types of epidemiological linkage. Our original approach was later generalized for large next-generation sequencing datasets and implemented as a free computational pipeline. Previous work has claimed that direction and directness of transmission could not be established from phylogeny because one could not be sure that there were no intervening or missing links involved. We address this issue by investigating phylogenetic patterns from 272 previously identified HIV transmission chains with 955 transmission pairs representing diverse geography, risk groups, subtypes, and genomic regions. These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases. Here, we show that the resulting phylogeny inferred from real HIV genetic sequences indeed reveals distinct patterns associated with direct transmission contra transmissions from a common source. Thus, our results establish how to interpret phylogenetic trees based on HIV sequences when tracking who-infected-whom, when and how genetic information can be used for improved tracking of HIV spread. We also investigate limitations that stem from limited sampling and genetic time-trends in the donor and recipient HIV populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Pushered single shell implosions for mix and radiation trapping studies using high-Z layers on National Ignition Facility

Pushered Single Shells (PSSs) are another approach to Inertial Confinement Fusion implosions that employ high-Z materials in the innermost capsule layer (pusher) as a means to enhance radiation trapping and lower core ignition requirements. However, adding high-Z materials can also increase losses due to mix, provide extra tamping, and make the capsule emission opaque to x-ray diagnostics. The first PSS implosions performed on the National Ignition Facility use plastic ablators with a germanium (Ge) dopant as a high-Z surrogate in the pusher to isolate the effects of high-Z mix and radiation trapping without changing tamping. Using a 2-shock laser pulse, the PSS implosions are designed and symmetrized to reach 3.7 keV core temperatures. A low concentration (2.8%) Ge dopant is added to the innermost layer, and the resulting effects on mix and x-ray opacity are observed. The method of separated reactants is used to infer information about mixing between the deuterated plastic pusher and the capsule fill gas (25% tritium) from the resulting nuclear DT reactions. Radiation transport is studied via capsule emission x-ray spectroscopy and imaging. Both nuclear and x-ray data corroborate the hypothesis that the addition of Ge strongly affects the mix region through radiation losses but has a minimal effect on the core and the warm, unmixed regions. Simulations using diffusive and turbulent mix models agree qualitatively with data, but quantitative agreement may require hybrid mix models that can model the transitional regime between turbulence and diffusion. Simulations matching the observables show increased core radiation trapping when Ge is added.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Inferring Quantum Network Topology Using Local Measurements

Statistical correlations that can be generated across the nodes in a quantum network depend crucially on its topology. However, this topological information might not be known a priori, or it may need to be verified. In this paper, we propose an efficient protocol for distinguishing and inferring the topology of a quantum network. We leverage entropic quantities-namely, the von Neumann entropy and the measured mutual information-as well as measurement covariance to uniquely characterize the topology. We show that the entropic quantities are sufficient to distinguish two networks that prepare GHZ states. Moreover, if qubit measurements are available, both entropic quantities and covariance can be used to infer the network topology without state-preparation assumptions. We show that the protocol can be entirely robust to noise and can be implemented via quantum variational optimization. Numerical experiments on both classical simulators and quantum hardware show that covariance is generally more reliable for accurately and efficiently inferring the topology, whereas entropy-based methods are often better at identifying the absence of entanglement in the low-shot regime.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems

We formulate, and present a numerical method for solving, an inverse problem for inferring parameters of a deterministic model from stochastic observational data on quantities of interest. The solution, given as a probability measure, is derived using a Bayesian updating approach for measurable maps that finds a posterior probability measure, that when propagated through the deterministic model produces a push-forward measure that exactly matches the observed probability measure on the data. Our approach for finding such posterior measures, which we call consistent Bayesian inference or push-forward based inference, is simple and only requires the computation of the push-forward probability measure induced by the combination of a prior probability measure and the deterministic model. We establish existence and uniqueness of observation-consistent posteriors and present both stability and error analyses. We also discuss the relationships between consistent Bayesian inference, classical/statistical Bayesian inference, and a recently developed measure-theoretic approach for inference. Finally, analytical and numerical results are presented to highlight certain properties of the consistent Bayesian approach and the differences between this approach and the two aforementioned alternatives for inference.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Comparative Genomic Analysis of the Class Epsilonproteobacteria and Proposed Reclassification to Epsilonbacteraeota (phyl. nov.)

The Epsilonproteobacteria is the fifth validly described class of the phylum Proteobacteria, known primarily for clinical relevance and for chemolithotrophy in various terrestrial and marine environments, including deep-sea hydrothermal vents. As 16S rRNA gene repositories have expanded and protein marker analysis become more common, the phylogenetic placement of this class has become less certain. A number of recent analyses of the bacterial tree of life using both 16S rRNA and concatenated marker gene analyses have failed to recover the Epsilonproteobacteria as monophyletic with all other classes of Proteobacteria. In order to address this issue, we investigated the phylogenetic placement of this class in the bacterial domain using 16S and 23S rRNA genes, as well as 120 single-copy marker proteins. Singleand concatenated-marker trees were created using a data set of 4,170 bacterial representatives, including 98 Epsilonproteobacteria. Phylogenies were inferred under a variety of tree building methods, with sequential jackknifing of outgroup phyla to ensure robustness of phylogenetic affiliations under differing combinations of bacterial genomes. Based on the assessment of nearly 300 phylogenetic tree topologies, we conclude that the continued inclusion of Epsilonproteobacteria within the Proteobacteria is not warranted, and that this group should be reassigned to a novel phylum for which we propose the name Epsilonbacteraeota (phyl. nov.). We further recommend the reclassification of the order Desulfurellales (Deltaproteobacteria) to a novel class within this phylum and a number of subordinate changes to ensure consistency with the genome-based phylogeny. Phylogenomic analysis of 658 genomes belonging to the newly proposed Epsilonbacteraeota suggests that the ancestor of this phylum was an autotrophic, motile, thermophilic chemolithotroph that likely assimilated nitrogen from ammonium taken up from the environment or generated from environmental nitrate and nitrite by employing a variety of functional redox modules. The emergence of chemoorganoheterotrophic lifestyles in several Epsilonbacteraeota families is the result of multiple independent losses of various ancestral chemolithoautotrophic pathways. Our proposed reclassification of this group resolves an important anomaly in bacterial systematics and ensures that the taxonomy of Proteobacteria remains robust, specifically as genome-based taxonomies become more common.

evolution↗
DOE OSTI2023

Revealing the Galaxy–Halo Connection through Machine Learning

Abstract Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of star formation, feedback from supernovae, and the radiative transfer of ionizing photons can capture the processes relevant for establishing these connections. The complexity of these physics can prove difficult to disentangle and obfuscate how mass-dependent trends in the galaxy population originate. Here, we train a machine-learning method called Explainable Boosting Machines (EBMs) to infer how the stellar mass and star formation rate of nearly 6 million galaxies simulated by the Cosmic Reionization on Computers project depend on the physical properties of halo mass, the peak circular velocity of the galaxy during its formation history v peak , cosmic environment, and redshift. The resulting EBM models reveal the relative importance of these properties in setting galaxy stellar mass and star formation rate, with v peak providing the most dominant contribution. Environmental properties provide substantial improvements for modeling the stellar mass and star formation rate in only ≲10% of the simulated galaxies. We also provide alternative formulations of EBM models that enable low-resolution simulations, which cannot track the interior structure of dark matter halos, to predict the stellar mass and star formation rate of galaxies computed by high-resolution simulations with detailed baryonic physics.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1978

Seasonal variation of the vertical distribution of stratospheric ozone as observed with the Umkehr and BUV methods

Month-to-month variations evidenced by ozone profiles inferred from the classical Umkehr observations and from the back-scattered ultraviolet (BUV) satellite observations made from the Nimbus 4 satellite are examined. Upper stratospheric ozone profiles derived from BUV and Umkehr data display similar seasonal variations of about the same phase and magnitude for the 38 km to 50 km region. Between 28 km and 38 km, the seasonal variations are less marked, but the same rough picture emerges for both data sets. If both data sets indicate an increasing (or decreasing) trend over a period of years, it is not possible to conclude that a trend exists unless separate means exists for monitoring stratospheric dust. Because of the stratosphere well above the Junge layer seems less likely to be affected by volcanic debris, BUV data should be superior to Umkehr data for monitoring trends in the 38 km to 50 km range, provided that the calibration problems of flying such a monitoring instrument in space can be overcome.

Mateer, C. L.↗
NASA NTRS1985

The planetary distribution of heat sources and sinks during FGGE

Heating distributions from analysis of the National Meteorological Center and European Center for Medium Range Weather Forecasts data sets; methods used and problems involved in the inference of diabatic heating; the relationship between differential heating and energy transport; and recommendations on the inference of heat soruces and heat sinks for the planetary show are discussed.

Johnson, D. R.↗
NASA NTRS1986

An evaluation of errors observed in the measurement of low wind velocities

Measurements of low wind velocities (the absolute value of V sub H is approx. equal to 6 m/s) with a VHF wind profiler can be difficult if ground clutter or other biases in the system dominate in altering the position of the perceived peak in the calculated power spectrum. A variety of methods for ground clutter suppression are used in profiler systems today (Cornish, 1983). An editing method called zero suppression takes the spectral value of selectable number of points (N) on each side of 0 velocity (one point on either side, in this study) and sets them equal to the mean value of the points exterior to the specified N points on either side of 0. Analysis done with the PSU VHF(1) radar, shows that this zero-suppression method can systematically bias horizontal wings V sub H below 6 m/s. With the zero suppression, an artificial increase in absolute wind velocities occurs when the spectral peaks fall within the plus or minus N points of the FFT (personal communication, Strauch, 1985). It was also established that the method artificially decreases the absolute wind velocities inferred from spectral peaks that are outside but near the suppressed region. Comparisons of wind profiles observed with and without zero suppression are given. The range of the biased velocities extends to about plus or minus 6 m/s. Biases have been deduced to be as much as 2 m/s, but more commonly they are on the order of 1.0 m/s.

Williams, S. R.↗
NASA NTRS1987

Prediction and causal reasoning in planning

Nonlinear planners are often touted as having an efficiency advantage over linear planners. The reason usually given is that nonlinear planners, unlike their linear counterparts, are not forced to make arbitrary commitments to the order in which actions are to be performed. This ability to delay commitment enables nonlinear planners to solve certain problems with far less effort than would be required of linear planners. Here, it is argued that this advantage is bought with a significant reduction in the ability of a nonlinear planner to accurately predict the consequences of actions. Unfortunately, the general problem of predicting the consequences of a partially ordered set of actions is intractable. In gaining the predictive power of linear planners, nonlinear planners sacrifice their efficiency advantage. There are, however, other advantages to nonlinear planning (e.g., the ability to reason about partial orders and incomplete information) that make it well worth the effort needed to extend nonlinear methods. A framework is supplied for causal inference that supports reasoning about partially ordered events and actions whose effects depend upon the context in which they are executed. As an alternative to a complete but potentially exponential-time algorithm, researchers provide a provably sound polynomial-time algorithm for predicting the consequences of partially ordered events.

Dean, T.↗
NASA NTRS1988

Magnetospheric ULF waves observed during the major magnetospheric compression of November 1, 1984

The magnetospheric ULF waves observed during the magnetospheric compression event of November 1, 1984, were investigated using the magnetic field and medium-energy ion data obtained by the AMPTE Charge Composition Explorer. The electric field of ULF waves was inferred from the particle data, using a method developed on the basis of the gyration acceleration mechanism. Three types of waves were found: (1) a 10-15-min wave with perturbations in the magnetic field intensity and in the flux of ions; (2) a 3-5-min standing Alfven wave with perturbations in the azimuthal magnetic field component and in the radial component of the electric field; and (3) a 2-5-min irregular disturbance near the magnetopause, which involves all components of the magnetic field and the intensity of the ion flux. The origins of these waves are discussed.

Takahashi, K.↗
NASA NTRS1987

Efficient reordering of Prolog programs

Redundant search often makes Prolog programs inefficient. Prolog execution corresponds to a depth-first traversal of an AND/OR graph; often a large subgraph will be searched successfully, with failure resulting from a subsequent small one. It is shown how reordering of Prolog clauses and especially goals can prevent unnecessary search. The restrictions on reordering are characterized and how they may be detected is shown. A new system of calling modes for Prolog, geared to reordering, and a system for inferring them automatically are designed. An improved method for determining a good goal order for Prolog clauses is presented, and it is used as the basis for a reordering system, showing how it can be guided by information about modes and restrictions to generate reordered Prolog that behaves correctly.

Gooley, Markian M.↗
NASA NTRS1994

Understanding the relation between wind- and pressure-driven sea level variability

Sea surface adjustment to combined wind and pressure forcing is examined using numerical solutions to the shallow water equations. The experiments use coastal geometry and bottom topography representative of the North Atlantic and are forced by realistic barometric pressure and wind stress fields. The repsonse to pressure is essentially static or close to the inverted barometer solution at periods longer than a few days and dominates the sea level variability, with wind-driven sea level signals being relatively small. With regard to the dynamic signals, wind-driven fluctuations dominate at long periods, as expected from quasi-geostrophic theory. Pressure becomes more important than wind stress as a source of dynamic signals only at periods shorter than approximately three days. Wind- and pressure-driven sea level fluctuations are anticorrelated over most regions. Hence, regressions of sea level on barometric pressure yield coefficients generally smaller than expected for the inverted barometer response known to be the case in the model. In the regions of significant wind-pressure correlation effects, to infer the correct pressure reponse using statistical methods, input fields must include winds as well as pressure. Because of the nonlocal character of the wind response, multivariate statistical models with local wind driving as input are not very successful. Inclusion of nonlocal wind variability over extensive regions is necessary to extract the correct pressure response. Implications of these results to the interpretation of sea level observations are discussed.

Ponte, Rui M.↗
NASA NTRS2003

Quantification of Ice Accretions for Icing Scaling Evaluations

The comparison of ice accretion characteristics is an integral part of aircraft icing research. It is often necessary to compare an ice accretion obtained from a flight test or numerical simulation to one produced in an icing wind tunnel or for validation of an icing scaling method. Traditionally, this has been accomplished by overlaying two-dimensional tracings of ice accretion shapes. This paper addresses the basic question of how to compare ice accretions using more quantitative methods. For simplicity, geometric characteristics of the ice accretions are used for the comparison. One method evaluated is a direct comparison of the percent differences of the geometric measurements. The second method inputs these measurements into a fuzzy inference system to obtain a single measure of the goodness of the comparison. The procedures are demonstrated by comparing ice shapes obtained in the Icing Research Tunnel at NASA Glenn Research Center during recent icing scaling tests. The results demonstrate that this type of analysis is useful in quantifying the similarity of ice accretion shapes and that the procedures should be further developed by expanding the analysis to additional icing data sets.

Ruff, Gary A.↗