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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation

Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Several tau PET tracers are available for neurodegenerative disease research, opening avenues for molecular diagnosis in vivo. However, few have been approved for clinical use. Understanding the neurobiological basis of PET signal validation remains problematic because it requires a large-scale, voxel-to-voxel correlation between PET and (immuno) histological signals. Large dimensionality of whole human brains, tissue deformation impacting co-registration, and computing requirements to process terabytes of information preclude proper validation. We developed a computational pipeline to identify and segment particles of interest in billion-pixel digital pathology images to generate quantitative, 3D density maps. The proposed convolutional neural network for immunohistochemistry samples, IHCNet, is at the pipeline's core. We have successfully processed and immunostained over 500 slides from two whole human brains with three phospho-tau antibodies (AT100, AT8, and MC1), spanning several terabytes of images. Our artificial neural network estimated tau inclusion from brain images, which performs with ROC AUC of 0.87, 0.85, and 0.91 for AT100, AT8, and MC1, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. We present an end-to-end pipeline to create terabytes-large 3D tau inclusion density maps co-registered to MRI as a means to facilitate validation of PET tracers.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Velocity Measurements in the Wake of the Swept Wing Flow Test Model at the National Transonic Facility

Femtosecond laser electronic excitation tagging (FLEET) was applied to obtain flowfield velocity data for the Swept Wing Flow Test (SWiFT) at the NASA Langley Research Center National Transonic Facility (NTF). Despite numerous challenges associated with performing flow velocimetry measurements within a large-scale cryogenic wind tunnel facility, the experimental campaign was conducted under various conditions, including dry air at 320 K, cool nitrogen at 240 K, and Mach numbers of 0.2 and 0.8. FLEET velocimetry measurements were performed in the downstream wake of the SWiFT model, providing a quantitative dataset. This dataset includes one-dimensional velocity profiles and two-dimensional velocity maps acquired at different angles of attack and Reynolds numbers. The two-dimensional single component velocity maps indicate relatively uniform flow across the 120-mm wide wake flow survey suggesting that the main flow features can be represented by single-position linear velocity profiles. The measured instantaneous velocity profiles at variable angles of attack are compared to model lift coefficient information obtained at Mach 0.2. At high Reynolds numbers, a sudden velocity decrease was observed in the FLEET measurements at the same time as stall in lift coefficient was observed. At a low Reynolds number, both the velocity profiles and the lift coefficient show a smoother transition, without a sudden stall. Furthermore, the two-dimensional, one component velocity map reveals a velocity deficit region at Mach 0.8 at various angles of attack. Both single shot and mean velocity measurements were acquired allowing assessment of flowfield fluctuations and measurement precisions. The uncertainties are within 4 m/s in mean measurements based on repeatability data and about 5 m/s in instantaneous single-shot measurements. Measurements are reported with ~4.8 mm spatial resolution with 32-pixel averaging used to reduce measurement errors.

femtosecond↗
NASA NTRSDate not supplied

Velocity Measurements in the Wake of the Swept Wing Flow Test (SWIFT) Model at the National Transonic Facility

Femtosecond laser electronic excitation tagging (FLEET) was applied to obtain flowfield velocity data for the Swept Wing Flow Test (SWiFT) at the NASA Langley Research Center National Transonic Facility (NTF). Despite numerous challenges associated with performing flow velocimetry measurements within a large-scale cryogenic wind tunnel facility, the experimental campaign was conducted under various conditions, including dry air at 320 K, cool nitrogen at 240 K, and Mach numbers of 0.2 and 0.8. FLEET velocimetry measurements were performed in the downstream wake of the SWiFT model, providing a quantitative dataset. This dataset includes one-dimensional velocity profiles and two-dimensional velocity maps acquired at different angles of attack and Reynolds numbers. The two-dimensional single component velocity maps indicate relatively uniform flow across the 120-mm wide wake flow survey suggesting that the main flow features can be represented by single-position linear velocity profiles. The measured instantaneous velocity profiles at variable angles of attack are compared to model lift coefficient information obtained at Mach 0.2. At high Reynolds numbers, a sudden velocity decrease was observed in the FLEET measurements at the same time as stall in lift coefficient was observed. At a low Reynolds number, both the velocity profiles and the lift coefficient show a smoother transition, without a sudden stall. Furthermore, the two-dimensional, one component velocity map reveals a velocity deficit region at Mach 0.8 at various angles of attack. Both single shot and mean velocity measurements were acquired allowing assessment of flowfield fluctuations and measurement precisions. The uncertainties are within 4 m/s in mean measurements based on repeatability data and about 5 m/s in instantaneous single-shot measurements. Measurements are reported with ~4.8 mm spatial resolution with 32-pixel averaging used to reduce measurement errors.

Transonic↗
DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗
NASA NTRS1985

Evaluation of space SAR as a land-cover classification

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

Brisco, B.↗
NASA NTRS2012

Predictive Sea State Estimation for Automated Ride Control and Handling - PSSEARCH

PSSEARCH provides predictive sea state estimation, coupled with closed-loop feedback control for automated ride control. It enables a manned or unmanned watercraft to determine the 3D map and sea state conditions in its vicinity in real time. Adaptive path-planning/ replanning software and a control surface management system will then use this information to choose the best settings and heading relative to the seas for the watercraft. PSSEARCH looks ahead and anticipates potential impact of waves on the boat and is used in a tight control loop to adjust trim tabs, course, and throttle settings. The software uses sensory inputs including IMU (Inertial Measurement Unit), stereo, radar, etc. to determine the sea state and wave conditions (wave height, frequency, wave direction) in the vicinity of a rapidly moving boat. This information can then be used to plot a safe path through the oncoming waves. The main issues in determining a safe path for sea surface navigation are: (1) deriving a 3D map of the surrounding environment, (2) extracting hazards and sea state surface state from the imaging sensors/map, and (3) planning a path and control surface settings that avoid the hazards, accomplish the mission navigation goals, and mitigate crew injuries from excessive heave, pitch, and roll accelerations while taking into account the dynamics of the sea surface state. The first part is solved using a wide baseline stereo system, where 3D structure is determined from two calibrated pairs of visual imagers. Once the 3D map is derived, anything above the sea surface is classified as a potential hazard and a surface analysis gives a static snapshot of the waves. Dynamics of the wave features are obtained from a frequency analysis of motion vectors derived from the orientation of the waves during a sequence of inputs. Fusion of the dynamic wave patterns with the 3D maps and the IMU outputs is used for efficient safe path planning.

Huntsberger, Terrance L.↗
NASA NTRS1970

Remote sensing for detection of soil limitations in agricultural areas

Automatic analysis of soil limitations studied by an automatic color TV density slicing system was accomplished. This system color codes the density range of the value component of color of an image. Maps of soil limitations or other similar soil groups are produced by photographing the color coded representation of an area. The planimeter feature of the density slicing system measures the area of each soil limitation providing information on the importance of a soil limitation in an area. The results of this study suggest that an automatic color TV density slicing system has great potential not only for identifying and mapping similar soil areas, but also for indicating the percentage composition of an area.

Frazee, C. J.↗
DOE OSTI2023

A simple, validated approach for design of two-dimensional periodic particle patterns via acoustophoresis

Two-dimensional patterning of microparticles enables a wide range of functional materials, including patterned energy storage electrodes, flexible electronics, and sensor arrays. Particle patterning via acoustics offers an attractive path to generate a wide variety of 2D periodic patterns that introduce tailorable hierarchical porosity, useful for controlling surface area, transport distances, and other properties. This method is most effective with micron scale particles and patterns of tens to hundreds of microns. To enable systematic exploration of the broad design space for such patterns, this work develops a model of 2D and 3D assembly of particles at high loadings and validates the obtained patterns against both experiments and more computationally intensive modeling techniques. Using this simple model, connections are mapped between input parameters (like actuation conditions, particle volume fraction, material properties) and output geometrical features (like void size and shape, pattern connectivity, and surface area) so that they can be tailored to given applications. The utility of this simple model is illustrated by predicting and then experimentally demonstrating new hierarchical patterns resulting from multiple waves of different frequencies interacting. These multiscale patterns offer the potential to lift the limits on surface area, diffusion distances, and other features.

42 ENGINEERING↗
DOE OSTI2022

Exploring nonequilibrium phases of photo-doped Mott insulators with generalized Gibbs ensembles

Many experiments show that strong excitations of correlated quantum materials can cause non-thermal phases without equilibrium analogues. Understanding the origin and properties of these nonequilibrium states has been challenging due to the limitations of theoretical methods for nonequilibrium strongly correlated systems. In this work, we introduce a generalized Gibbs ensemble description that enables a systematic analysis of the long-time behavior of photo-doped states in Mott insulators based on equilibrium methods. We demonstrate the power of the method by mapping out the nonequilibrium phase diagram of the one-dimensional extended Hubbard model, which features η -pairing and charge density wave phases in a wide photo-doping range. We furthermore clarify that the peculiar kinematics of photo-doped carriers, and the interaction between them, play an essential role in the formation of these non-thermal phases. Our results establish a new path for the systematic analysis of nonequilibrium strongly correlated systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Layer, Lukas↗
DOE OSTI2020

The Atacama Cosmology Telescope: DR4 maps and cosmological parameters

In this paper, we present new arcminute-resolution maps of the Cosmic Microwave Background temperature and polarization anisotropy from the Atacama Cosmology Telescope, using data taken from 2013–2016 at 98 and 150 GHz. The maps cover more than 17,000 deg 2 , the deepest 600 deg 2 with noise levels below 10µK-arcmin. We use the power spectrum derived from almost 6,000 deg 2 of these maps to constrain cosmology. The ACT data enable a measurement of the angular scale of features in both the divergence-like polarization and the temperature anisotropy, tracing both the velocity and density at last-scattering. From these one can derive the distance to the last-scattering surface and thus infer the local expansion rate, H 0 . By combining ACT data with large-scale information from WMAP we measure H 0 = 67.6±1.1 km/s/Mpc, at 68% confidence, in excellent agreement with the independently-measured Planck satellite estimate (from ACT alone we find H 0 = 67.9 ± 1.5 km/s/Mpc). The ΛCDM model provides a good fit to the ACT data, and we find no evidence for deviations: both the spatial curvature, and the departure from the standard lensing signal in the spectrum, are zero to within 1σ; the number of relativistic species, the primordial Helium fraction, and the running of the spectral index are consistent with ΛCDM predictions to within 1.5–2.2σ. We compare ACT, WMAP, and Planck at the parameter level and find good consistency; we investigate how the constraints on the correlated spectral index and baryon density parameters readjust when adding CMB large-scale information that ACT does not measure. The DR4 products presented here will be publicly released on the NASA Legacy Archive for Microwave Background Data Analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Excitation and tunneling spectra of a fractional quantum Hall system in the thin-cylinder limit

Here, the excitations of fractional quantum Hall effect (FQHE) states have been largely inaccessible to experimental probes until recently. New electron scanning tunneling microscopy (STM) results from Hu et al. [Nat. Phys. 21, 716 (2025)] show promise in detecting and identifying these excited states via the local density of states (LDOS) spectrum. On a torus, there exists a mapping from the lowest Landau level states to a 1D lattice with a Hamiltonian that features dipole moment conservation. In this work, we apply perturbation theory starting from the thin-cylinder limit (𝐿 𝑥 → ∞, 𝐿 𝑦 < 𝑙 𝐵 for torus dimensions 𝐿 𝑥 and 𝐿 𝑦 and magnetic length 𝑙 𝐵 ) to obtain an analytical approach to the low-lying neutral and charged excitations of the 𝜈 = 1/3 FQHE state. Notably, in the thin cylinder, we can systematically enumerate all the low-lying excitations by the patterns of “dipoles” formed by the electron occupation pattern on the 1D lattice. We find that the thin-cylinder limit predicts a significant dispersion of the low-lying neutral excitations but sharpness of the LDOS spectra, which measure charged excitations. We also discuss connections between our work and several different approaches to the FQHE STM spectra, including those using the composite fermion theory. Numerical exact diagonalization beyond the thin-cylinder limit suggests that the energies of charged excitations remain largely confined to a narrow range of energies, which in experiments might appear as a single peak.

Adhidewata, Jyesta M. [University of California, B↗
NASA NTRS1973

Mineral resources, geologic structure, and landform surveys

The use of ERTS-1 imagery for mineral resources, geologic structure, and landform surveys is discussed. Four categories of ERTS imagery application are defined and explained. The types of information obtained by the various multispectral band scanners are analyzed. Samples of land use maps and tectoning and metallogenic models are developed. It is stated that the most striking features visible on ERTS imagery are regional lineaments, or linear patterns in the topography, which reflect major fracture zones extending upward from the basement of the earth.

Lattman, L. H.↗
NASA NTRS1974

Water resources

ERTS-1 applications in snow and ice monitoring, surface water monitoring, including monitoring of wetland areas and flood inundated area mapping, and also watershed monitoring for runoff prediction are discussed. Results also indicate that geological features can be noted which relate to ground water. ERTS-1 data can be used successfully in operational situations by water resources management agencies.

Salomonson, V. V.↗
NASA NTRS1981

Remote sensing of precipitable water over the oceans from Nimbus-7 microwave measurements

Global maps of precipitable water over derived from scanning multichannel microwave radiometer (SMMR) data reveal salient features associated with ocean currents and the large scale general circulation in the atmosphere. Nimbus-7 SMMR brightness temperature measurements in the 21 and 18 GHz channels are used to sense the precipitable water in the atmospheric over oceans. The difference in the brightness temperature (T sub 21 -T sub 18), both in the horizontal and vertical polarization, is found to be essentially a function of the precipitable water in the atmosphere. An equation, based on the physical consideration of the radiative transfer in the microwave region, is developed to relate the precipitable water to (T sub 21 - T sub 18). It shows that the signal (T sub 21- T sub 18) does not suffer severely from the noise introduced by variations in the sea surface temperature, surface winds, and liquid water content in non rain clouds. The rms deviation between the estimated precipitable water from SMMR data and that given by the closely coincident ship radiosondes is about 0.25 g/ sq cm

Prabhakara, C.↗
NASA NTRS1983

Meteorological analysis of MAPS first shuttle flight

The data from the MAPS radiometer launched on board the first shuttle flight was analyzed. The meteorological data relevant to the MAPS measurements was also analyzed so that the in situ calibration measurements and any anomalous features within the data set can be evaluated in the context of the atmospheric circulation during the period of observation. The three dimensioned isentropic analysis scheme was used as the main analytical tool. A graphical output of the N and delta channel signals, black body reference temperatures, reduced CO concentrations and ground tracks for orbits 15 and 16 were provided. The insentropic trajectory methods was emphasized because it is a basic requirement that the analysis be extended to incorporate daily data within the tropics.

Doherty, M.↗
NASA NTRS1982

Radar images of the earth from space

The operational principles and imagery available from microwave SAR radars for earth observations from aircraft, the Shuttle, and Seasat are explored. Using microwave frequencies is noted to offer imagery access in day or night, all-weather conditions. SAR radar functions by obtaining a series of reflected signals over a single path, with data processing combining the echoes into an image corresponding to what may be obtained with a large antenna. A stable, reference signal is added to the incoming signals in order to establish the phase and amplitude of echoes. Because the wavelengths of the images can be precisely controlled, Doppler shifts can be detected, thus allowing point by point scattering analysis in two-dimensions. The Shuttle SIR-A and the Seasat radar feature a ground resolution of 25 m. Applications of the satellite systems to terrain, ice mapping, and for a Venus radar mapper mission are described.

Elachi, C.↗
NASA NTRS1984

Structural investigation of the Grenville Province by radar and other imaging and nonimaging sensors

The structural investigation of the Canadian Shield by orbital radar and LANDSAT, is outlined. The area includes parts of the central metasedimentary belt and the Ontario gneiss belt, and major structures as well-expressed topographically. The primary objective is to apply SIR-B data to the mapping of this key part of the Grenville orogen, specifically ductile fold structures and associated features, and igneous, metamorphic, and sedimentary rock (including glacial and recent sediments). Secondary objectives are to support the Canadian RADARSAT project by evaluating the baseline parameters of a Canadian imaging radar satellite planned for late in the decade. The baseline parameters include optimum incidence and azimuth angles. The experiment is to develop techniques for the use of multiple data sets.

Lowman, P. D., Jr.↗