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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Preventing Failures By Dataset Shift Detection in Safety-Critical Graph Applications

Dataset shift refers to the problem where the input data distribution may change over time (e.g., between training and test stages). Since this can be a critical bottleneck in several safety-critical applications such as healthcare, drug-discovery, etc., dataset shift detection has become an important research issue in machine learning. Though several existing efforts have focused on image/video data, applications with graph-structured data have not received sufficient attention. Therefore, in this paper, we investigate the problem of detecting shifts in graph structured data through the lens of statistical hypothesis testing. Specifically, we propose a practical two-sample test based approach for shift detection in large-scale graph structured data. Our approach is very flexible in that it is suitable for both undirected and directed graphs, and eliminates the need for equal sample sizes. Using empirical studies, we demonstrate the effectiveness of the proposed test in detecting dataset shifts. We also corroborate these findings using real-world datasets, characterized by directed graphs and a large number of nodes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Aggregation and data analysis of corrosion studies in molten chloride and fluoride salts

Corrosion studies in molten fluoride and chloride salts were surveyed, and key data were aggregated into a single dataset. Studies were graphed by salt purity, temperature, sample material, container material, and experimental method (loop, capsule, crucible). To elucidate and quantify the factors that affect corrosion in molten salts, a data analysis using a newly-defined corrosion resistance score and a correlation analysis using techniques borrowed from machine learning were performed. It is shown that salt purity had the strongest correlation with corrosion rates in molten chlorides and fluorides. As a result, data analysis across varied works was inconsistent due to the lack of standardization among molten salt corrosion studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Dynamic Graph Sequence Data from Simulated Neutron Reflectometry Measurements

This dataset comprises dynamic graph sequences derived from simulated in-situ neutron reflectometry measurements, capturing the gradual evolution of a layer structure over time. Each graph sequence represents a synthetic sample, with node features detailing the scattering vector and corresponding reflectivity measurements, while adjacency matrices have corresponding reference material parameters attached as metadata. The dataset spans multiple sets, each with a different number of sequences, offering a comprehensive basis for training models that handle dynamic input sequences with embedded physics. This dataset is particularly suited for tackling inverse problems with hidden physical states that evolve over time, challenges that are typically difficult to address using conventional iterative fitting methods.

36 MATERIALS SCIENCE↗
NASA NTRS1977

Inexpensive portable drug detector

Inexpensive, easy-to-use, self-scanning, self-calibrating, portable unit automatically graphs fluorescence spectrum of drug sample. Device also measures rate of movement through chromatographic column for forensic and medical testing.

Dimeff, J.↗
NASA NTRS1983

Cardiovascular responses to hypogravic environments

The cardiovascular deconditioning observed during and after space flight is characterized in a review of human space and simulation studies and animal simulations. The various simulation techniques (horizontal bed rest, head-down tilt, and water immersion in man, and immobilization of animals) are examined, and sample results are presented in graphs. Countermeasures such as exercise regimens, fluid replacement, drugs, venous pooling, G-suits, oscillating beds, electrostimulation of muscles, lower-body negative pressure, body-surface cooling, and hypoxia are reviewed and found to be generally ineffective or unreliable. The need for future space experimentation in both humans and animals is indicated.

Sandler, H.↗
NASA NTRS1984

A rapid method for obtaining frequency-response functions for multiple input photogrammetric data

A two-digital-camera photogrammetric technique for measuring the motion of a vibrating spacecraft structure or wing surface and an applicable data-reduction algorithm are presented. The 3D frequency-response functions are obtained by coordinate transformation from averaged cross and autopower spectra derived from the 4D camera coordinates by Fourier transformation. Error sources are investigated analytically, and sample results are shown in graphs.

Kroen, M. L.↗
NASA NTRS1983

Establishing homologies in protein sequences

Computer-based statistical techniques used to determine homologies between proteins occurring in different species are reviewed. The technique is based on comparison of two protein sequences, either by relating all segments of a given length in one sequence to all segments of the second or by finding the best alignment of the two sequences. Approaches discussed include selection using printed tabulations, identification of very similar sequences, and computer searches of a database. The use of the SEARCH, RELATE, and ALIGN programs (Dayhoff, 1979) is explained; sample data are presented in graphs, diagrams, and tables and the construction of scoring matrices is considered.

Dayhoff, M. O.↗
NASA NTRS1989

Effect of mean load on the non-linear behavior of spur gear noise source

An analytical technique for estimating noise generation by spur-gear pairs with backlash is developed using the results obtained by Comparin and Singh (1989). The derivation of the governing equations is outlined, and numerical results for sample problems are presented in graphs. Good agreement with published experimental data (Munro, 1962) is demonstrated.

Kahraman, Ahmet↗
NASA NTRS2010

Thermal Control Materials on MISSE-6 with Comparison to Earlier Flight Data

This slide presentation reviews the results of exposing thermal control materials as studied during Materials International Space Station Experiment-6 (MISSE-6). It discusses the location of the materials, and the type of environmental exposure. It then reviews the effect on the various types of coatings, showing the results with graphs and photos of the samples after the experimental exposure to the low earth orbital environment for comparison with the control sample and with earlier flights.

Finckenor, Miria M.↗
DOE OSTI2024

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

Abstract Rivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Barnes, Richard↗
NASA NTRS1975

Ecology and thermal inactivation of microbes in and on interplanetary space vehicle components

A series of experiments was conducted to determine the dry heat resistance of microorganisms in soil obtained from Denver Colorado, Pasadena California, Kennedy Space Center Florida, and Cincinnati Ohio. The results of the KSC terminal sterilization cycle experiment are given in graphs. The average number of viable organisms per m1 was calculated for 18 replicate soil samples for each sample area and points plotted equivalent to 30 hr exposure at 112 C. The result showed a reduction of 3 logs from the initial population for both KSC and Cincinnati soil samples. Results from other areas are given in graphs.

Reyes, A. L.↗
DOE OSTI2019

Continuous-time quantum walks on dynamic graphs

Continuous-time quantum walks (CTQWs) on static graphs provide efficient methods for search and sampling as well as a model for universal quantum computation. We consider an extension of CTQWs to the case of dynamic graphs, in which an ordered sequence of graphs governs free evolution of the quantum walk. We then consider how perfect state transfer during the quantum walk can be used to design dynamic graphs that implement a universal set of quantum logic gates. We give explicit examples for a complete logical basis, and we validate implementations using numerical simulations for quantum teleportation and addition circuits. Lastly, we discuss the potential for realizing CTQWs on dynamic graphs using actively controlled quantum optical waveguides.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Benchmark Analysis on Magnetic and Photoluminescence Properties of Selective Metal Ions Doped ZnS Nanoparticles

The creation of diluted magnetic semiconductors (DMSs) at lower dimensions that exhibit room temperature ferromagnetism (RTFM) has been given immense significance for the fabrication of a new class of spintronic devices through utilizing spin degrees of freedom besides charge nature of electrons. In this view, nanocrystals of ZnS doped with 4% concentration of Fe, Co, Ni, Cr, Mn, Sr, Cu, and Ce have been synthesized at room temperature (RT) by chemical co-precipitation method. The samples were examined by various characterization techniques like energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), and photoluminescence (PL). EDS spectra revealed the existence of parent elements in the prepared samples. XRD characterization disclosed hexagonal structures of nanocrystals, and no secondary phases were observed in all the samples except for Cu and Ni doped ZnS. FTIR data confirmed the proper substitution of dopants in the host lattice as all the graphs have almost same stretchings. The magnetic nature of each sample was evaluated from M-H graphs of VSM, and it corroborated the transition of magnetic properties due to doping. Pristine ZnS showed diamagnetic nature, while Fe and Sr doped ZnS evinced strong ferromagnetic and anti-ferromagnetic properties, respectively. Photoluminescence studies on Ce, Sr and Mn doped ZnS samples revealed strong emission peaks with enhanced luminescence properties compared to bare ZnS. Cu, Cr and Mn doped ZnS exhibited a red shift in emission wavelengths, whereas Co doped ZnS showed emission peak shifted towards the blue region as compared to the host lattice.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Hydrogeological Model Selection Among Complex Spatial Priors

Hydrogeological field studies rely often on a single conceptual representation of the subsurface. This is problematic since the impact of a poorly chosen conceptual model on predictions might be significantly larger than the one caused by parameter uncertainty. Furthermore, conceptual models often need to incorporate geological concepts and patterns in order to provide meaningful uncertainty quantification and predictions. Consequently, several geologically realistic conceptual models should ideally be considered and evaluated in terms of their relative merits. Here, we propose a full Bayesian methodology based on Markov chain Monte Carlo to enable model selection among 2-D conceptual models that are sampled using training images and concepts from multiple-point statistics. More precisely, power posteriors for the different conceptual subsurface models are sampled using sequential geostatistical resampling and Graph Cuts. To demonstrate the methodology, we compare and rank five alternative conceptual geological models that have been proposed in the literature to describe aquifer heterogeneity at the MAcroDispersion Experiment site in Mississippi, USA. We consider a small-scale tracer test for which the spatial distribution of hydraulic conductivity impacts multilevel solute concentration data observed along a 2-D transect. The thermodynamic integration and the stepping-stone sampling methods were used to compute the evidence and associated Bayes factors using the computed power posteriors. We find that both methods are compatible with multiple-point statistics-based inversions and provide a consistent ranking of the competing conceptual models considered.

58 GEOSCIENCES↗
DOE OSTI2024

Active causal learning for decoding chemical complexities with targeted interventions

Abstract Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task—finding molecules with a large dipole moment—our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1986

Effect of texture on magnetoacoustic stress measurement in steel

The effect of texture on the general stress measurements of a heavily cold rolled steel plate is analyzed. Changes in the acoustic natural velocity for plate samples with their long axes perpendicular, parallel, and in a 40 degree direction with respect to the rolling axis were measured; graphs of the velocity variations for the three samples are provided. It is observed that the negative minimum change in the acoustic natural velocity under residual compression for the perpendicular sample is about half that of the parallel and 40 degree samples indicating that the functional form of the velocity curve is influenced by the structural properties of the sample; however, the stress measurements are unaffected by the texture.

Namkung, M.↗
DOE OSTI2023

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.

97 MATHEMATICS AND COMPUTING↗