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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Velocity structure and evolution of the moon

Seismic data from the Apollo Passive Seismic Network stations are analyzed to determine the velocity structure and to infer the composition and physical properties of the lunar interior. Data from artificial impacts (SIBV booster and LM-ascent stage) cover a distance range of 9 to 1750 km. Travel times and amplitudes, as well as theoretical seismograms, are used to derive a velocity model for the outer 150 km of the moon. The P-wave velocity model confirms an earlier report of a lunar crust in the eastern part of Oceanus Procellarum. The crust is about 60 km thick and may consist of two layers in the mare regions. Possible values for the P-wave velocity in the uppermost mantle are between 7.6 and 9.0 km/sec. The 9 km/sec velocity represents either a localized heterogeneous unit, or a thin layer less than about 40 km in thickness. The elastic properties of the deep interior, as inferred from the seismograms of natural events (meteoroid impacts and moonquakes) occurring at great distances, indicate that there is an increase in attenuation and a possible decrease of velocity at depths below about 1000 km.

Toksoz, M. N.↗
DOE OSTI2021

A crystal plasticity finite element model embedding strain-rate sensitivities inherent to deformation mechanisms: Application to alloy AZ31

We report the fundamental power-law relationship representing the flow rule in crystal visco-plasticity ensures uniqueness in the selection of slip systems accommodating imposed plastic strain-rates. The power-law relationship also introduces an artificially high strain-rate sensitivity in crystal plasticity simulations, unless a high value of the power-law exponent is used. However, the use of high values for the exponent is limited by numerical tractability. This paper presents a numerical method implemented in a crystal plasticity finite element (CPFE) model for embedding any value of the power-law exponent reflecting the true material strain-rate sensitivity. Importantly, the method does not increase computation time involved in the simulations. The enhanced CPFE model is used to interpret and predict a complex strain-rate sensitive response and microstructural evolution of AZ31 Mg alloy. Measured values of strain-rate sensitivity for slip and twinning modes are used in the simulations. Calculations show that the model successfully captures the phenomena pertaining to the effect of changing applied strain-rate on the mechanical response including flow stress and evolution of texture and twinning for a broad range of strain-rates ranging from 10 -3 s -1 to 10 3 s -1 and loading orientations in tension and compression. It is shown that such predictions are a consequence of not only relative amounts of slip and twinning activities driven by a set of accurately characterized hardening law parameters but also values of the strain-rate sensitivities inherent to individual deformation mechanisms. Besides, the model validates the measured strain-rate dependency of deformation mechanisms while accurately reproducing the mechanical data. Hence, the model can be used to verify and further refine or infer measured strain-rate sensitivity per deformation mechanism by reproducing experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Abstract Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A computational pipeline to generate a synthetic dataset of metal ion sorption to oxides for AI/ML exploration

The charged mineral/electrolyte interfaces are ubiquitous in the surface and subsurface–including the surroundings of the geological disposal sites for radioactive waste. Therefore, understanding how ions interact with charged surfaces is critically important for predicting radionuclide mobility in the case of waste leakage. At present, the Surface Complexation Models (SCMs) are the most successful thermodynamic frameworks to describe ion retention by mineral surfaces. SCMs are interfacial speciation models that account for the effect of the electric field generated by charged surfaces on sorption equilibria. These models have been successfully used to analyze and interpret a broad range of experimental observations including potentiometric and electrokinetic titrations or spectroscopy. Unfortunately, many of the current procedures to solve and fit SCM to experimental data are not optimal, which leads to a non-transferable or non-unique description of interfacial electrostatics and consequently of the strength and extent of ion retention by mineral surfaces. Recent developments in Artificial Intelligence (AI) offer a new avenue to replace SCM solvers and fitting algorithms with trained AI surrogates. Unfortunately, there is a lack of a standardized dataset covering a wide range of SCM parameter values available for AI exploration and training–a gap filled by this study. Here, we described the computational pipeline to generate synthetic SCM data and discussed approaches to transform this dataset into AI-learnable input. First, we used this pipeline to generate a synthetic dataset of electrostatic properties for a broad range of the prototypical oxide/electrolyte interfaces. The next step is to extend this dataset to include complex radionuclide sorption and complexation, and finally, to provide trained AI architectures able to infer SCMs parameter values rapidly from experimental data. Here, we illustrated the AI-surrogate development using the ensemble learning algorithms, such as Random Forest and Gradient Boosting. These surrogate models allow a rapid prediction of the SCM model parameters, do not rely on an initial guess, and guarantee convergence in all cases.

Li, Chunhui↗
NASA NTRS1981

The Viking seismometry

Efforts were made to determine the seismicity of Mars as well as define its internal structure by detecting vibrations generated by marsquakes and meteoroid impacts. The lack of marsquakes recognized in the Viking data made it impossible to make any direct inferences about the interior of Mars and only allowed the setting of upper bounds on the seismic activity of the planet. After obtaining more than 2100 hours worth of data during the quite periods at rates of one sample per second or higher, the Viking 2 seismometer was turned off as a consequence of a landing system failure. During the periods when adequate data were obtained, one event of possible seismic or meteoroid impact origin was recognized; however, there is a significant probability that this event was generated by a wind gust.

Lazarewicz, A. R.↗
NASA NTRS1986

Precise GPS orbit determination results from 1985 field tests

Data from three different receiver types have been used to obtain precise orbits for the satellites of the Global Positioning System (GPS). The data were collected during the 1985 March-April GPS experiment to test and validate GPS techniques for precision orbit determination and geodesy. A new software package developed at the Jet Propulsion Laboratory (JPL), GIPSY (GPS Inferred Positioning SYstem), was used to process the data. To assess orbit accuracy, solutions are compared using integrated doppler data from various different receiver types, different fiducial sites, and independent data arcs, including one spanning six days. From these intercomparisons, orbit accuracy for a well-tracked GPS satellite of three meters in altitude and about five meters in each of down and cross-track components are inferred.

Lichten, S. M.↗
NASA NTRS2000

Sunyaev - Zel'dovich Effect Imaging of Massive Clusters of Galaxies at Redshift > 0.8

We present Sunyaev-Zel'dovich Effect (SZE) imaging observations of three distant (z greater than 0.8) and highly X-ray luminous clusters of galaxies, C1J1226+33, C1J0152-13 and MS1054-03. Two of the clusters, C1J1226+33 and C1J0152-13, were recently discovered in the WARPS X-ray survey. Their high X-ray luminosity suggests they are massive systems and, if confirmed, would provide strong constraints on the cosmological parameters of structure formation models. Our Sunyaev-Zel'dovich Effect data provide confirmation that they are massive clusters similar to the well studied cluster MS1054-03. Assuming the clusters have the same gas mass fraction derived from SZE measurements of eighteen known massive clusters, we are able to infer their mass and electron temperatures from the SZE data. The derived electron temperatures are 10.0, 8.5, and 10.3 KeV, respectively, and we infer total masses of approximately 2 x 10(circumflex) 14 h(circumflex) -l M - sun within a radius of 65" for all three clusters. For C1J0152-13 and MS1054-03 we find good agreement between our SZE derived temperatures and those derived from X-ray spectroscopic measurements. No X-ray derived temperatures are available for C1J1226+33, and thus the SZE data provide the first confirmation that it is indeed a massive system. The demonstrated ability to determine cluster temperatures and masses from SZE observations without access to X-ray data illustrates the power of using deep SZE surveys to probe the distant universe.

Joy, Marshall↗
DOE OSTI2022

Integrating multimodal data through interpretable heterogeneous ensembles

Motivation: Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results: We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Tencoder: tensor-product encoder-decoder architecture for predicting solutions of PDEs with variable boundary data

It is widely hoped that artificial intelligence will boost data-driven surrogate models in science and engineering. However, fundamental spatial aspects of AI surrogate models remain under-studied. We investigate the ability of neural-network surrogate models to predict solutions to PDEs under variable boundary values. We do not wish to retrain the model when the boundary values change but to make them inputs to the model and infer the solution of the PDE under those boundary conditions. Such a capability is essential to making AI-based surrogate models practically useful. While simple feedforward networks are used for one-dimensional (1D) Poisson equation, an encoder-decoder architecture with a tensor-product layer is developed for the two-dimensional Poisson equation posed on a rectangular domain. We show that it is indeed possible to infer solutions to PDEs from variable boundary data using neural networks in this relatively simple setting, and point to future directions.

Kashi, Aditya↗
NASA NTRS1981

Inference of sea surface temperature, near surface wind, and atmospheric water by Fourier analysis of Scanning Multichannel Microwave Radiometer data

The Scanning Multichannel Microwave Radiometer measures thermal microwave emission from the earth in both polarizations at wavelengths of 0.8, 1.4, 1.7, 2.8 and 4.6 cm. Similar instruments were launched on Nimbus 7 and Seasat. Both spatial resolution on the earth and relative sensitivity to different geophysical parameters change with wavelength. Therefore, spatial Fourier components of geophysical parameters are inferred from the corresponding Fourier components of the radiometer measurements, taking into account the different dependence of signal-to-noise ratio on spatial frequency for each radiometer wavelength. The geophysical parameters are sea surface temperature, near-surface wind speed, integrated water vapor mass, integrated liquid water mass, and the product of rainfall rate with height of the rain layer. The capabilities and limitations of the inversion method are illustrated by means of data from the North Atlantic and from tropical storms.

Rosenkranz, P. W.↗
NASA NTRS1974

Apollo laser altimetry and inferences as to lunar structure

Weighted mean laser altimetry data from Apollo 15, 16, and 17 tracks were analyzed, yielding a mean lunar radius of 1737.7 km and an offset of center-of mass from center of figure of 2.55 km toward 24 deg E. Weighted mean elevations with respect to a 1738 km radius sphere for various terrain types are: (1) farside terrae +1.8 km, (2) nearside terrae -1.4 km, (3) ringed maria -4.0 km, and (4) other maria -2.3 km. Comparison of gravity and topography data indicates that there is a variation in density in the outer parts of the moon and that the moon has a crust which is equivalent to at least 60 km of material of 2.95 grams per cu cm density. This result and moment-of-inertia data are consistent with a lunar interior model with a uniform density gradient in the mantle to the bottom of the lithosphere, constant density in the asthenosphere, and no core.

Kaula, W. M.↗
NASA NTRS1977

Detection and interpretation of ocean roughness variations across the Gulf Stream inferred from radar cross section observations

Radar cross section data shows that the Gulf Stream has a higher cross section per unit area (interpreted here as a greater roughness) than the water on the continental shelf. A steep gradient in cross section was often seen at the expected location of the western boundary. There were also longer-scale (10-20 km) gradual fluctuations within the stream of significant magnitude. These roughness variations are correlated with the surface shear stress that the local wind imposes on the sea. Using the available surface-truth information concerning the wind speed and direction, an assumed Gulf Stream velocity profile, and high-resolution ocean-surface temperature data obtained by the VHRR onboard a NOAA-NESS polar-orbiting satellite, the present study demonstrates that the computed surface stress variation bears a striking resemblance to the measured radar cross-section variations.

Weissman, D. E.↗
DOE OSTI2023

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13 C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13 C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13 C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13 C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13 C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P- 13 C MOMA and P- 13 C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Toward Forecasting Geomagnetic Storms

We present a selection of attempts to infer geomagnetic storm properties from time series data. Initially, we unsuccessfully attempt to predict the duration of a geomagnetic storm based on data preceding the onset of a geomagnetic storm. Similar techniques are also used to attempt to predict the maximum depth of a geomagnetic storm. Additionally, we present a case study in Gaussian Process regression, where we are able to accurately interpolate between geomagnetic storm parameters, but are unable to predict future storm behavior. Lastly, we describe attempts at using random forest regression with sliding windows to infer geomagnetic storm behavior at future times. These results did not provide robust forecasts of geomagnetic storm behavior, but did occasionally correctly predict a storm within the margin of error. Techniques presented in this paper include machine learning classification and regression, Gaussian Process regression, and machine learning regression with sliding windows. Many of the attempts presented in this document did not result in meaningful predictions of geomagnetic storm behavior. We hope this document can be used as a guide for future readers attempting to predict geomagnetic storm properties.

58 GEOSCIENCES↗
DOE OSTI2023

Transmission risk of Oropouche fever across the Americas

Abstract Background Vector-borne diseases (VBDs) are important contributors to the global burden of infectious diseases due to their epidemic potential, which can result in significant population and economic impacts. Oropouche fever, caused by Oropouche virus (OROV), is an understudied zoonotic VBD febrile illness reported in Central and South America. The epidemic potential and areas of likely OROV spread remain unexplored, limiting capacities to improve epidemiological surveillance. Methods To better understand the capacity for spread of OROV, we developed spatial epidemiology models using human outbreaks as OROV transmission-locality data, coupled with high-resolution satellite-derived vegetation phenology. Data were integrated using hypervolume modeling to infer likely areas of OROV transmission and emergence across the Americas. Results Models based on one-support vector machine hypervolumes consistently predicted risk areas for OROV transmission across the tropics of Latin America despite the inclusion of different parameters such as different study areas and environmental predictors. Models estimate that up to 5 million people are at risk of exposure to OROV. Nevertheless, the limited epidemiological data available generates uncertainty in projections. For example, some outbreaks have occurred under climatic conditions outside those where most transmission events occur. The distribution models also revealed that landscape variation, expressed as vegetation loss, is linked to OROV outbreaks. Conclusions Hotspots of OROV transmission risk were detected along the tropics of South America. Vegetation loss might be a driver of Oropouche fever emergence. Modeling based on hypervolumes in spatial epidemiology might be considered an exploratory tool for analyzing data-limited emerging infectious diseases for which little understanding exists on their sylvatic cycles. OROV transmission risk maps can be used to improve surveillance, investigate OROV ecology and epidemiology, and inform early detection.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2015

Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (𝑁), plunge force (𝐹𝑧), and welding speed (𝑉) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (𝑁,𝑉,𝐹𝑧,𝐸𝐹𝐼) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

Mohammad W Dewan↗
NASA NTRS1987

Regional sources and sinks of dust on Mars: Viking observations of Cerberus, Solis Planum and Syrtis Major

A study of seasonal variations of albedo features in the Cerberus, Solis Planum, and Syrtis Major regions was based on Viking Orbiter data obtained over more than one complete Martian year. Contour maps of Lambert albedo and single-point thermal inertia were constructed from the Infrared Thermal Mapper (IRTM) experiment data, and Orbiter images were used to determine the pattern and variability of regional winds (inferred from wind streak orientations). Coupled with ground-based radar data, these data allow the regional sediment transport direction, surface properties (texture, morphology, and roughness), and the implications of the observed seasonal and longer term dust redistribution to be investigated. Results are outlined.

Lee, Steven W.↗
DOE OSTI2024

Neural network denoising of x-ray images from high-energy-density experiments

Noise is a consistent problem for x-ray transmission images of High-Energy-Density (HED) experiments because it can significantly affect the accuracy of inferring quantitative physical properties from these images. We consider experiments that use x-ray area backlighting to image a thin layer of opaque material within a physics package to observe its hydrodynamic evolution. The spatial variance of the x-ray transmission across the system due to changing opacity serves as an analog for measuring density in this evolving layer. The noise in these images adds nonphysical variations in measured intensity, which can significantly reduce the accuracy of our inferred densities, particularly at small spatial scales. Denoising these images is thus necessary to improve our quantitative analysis, but any denoising method also affects the underlying information in the image. In this paper, we present a method for denoising HED x-ray images via a deep convolutional neural network model with a modified DenseNet architecture. In our denoising framework, we estimate the noise present in the real (data) images of interest and apply the inferred noise distribution to a set of natural images. These synthetic noisy images are then used to train a neural network model to recognize and remove noise of that character. We show that our trained denoiser network significantly reduces the noise in our experimental images while retaining important physical features.

47 OTHER INSTRUMENTATION↗