SEARCH · Search NASA

Results for “Feature Extraction”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,279 records · Page 71

All sources NASA NTRS DOE OSTI

DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗

DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Surprisingly selective sulfate extraction by a simple monofunctional di(imino)guanidinium micelle-forming anion receptor

Here, we report a novel di(imino)guanidinium anion extractant with unparalleled selectivity for sulfate in a liquid–liquid separation system. In addition to a 4.4 order-of-magnitude enhancement in affinity compared to a standard benchmark, our alkylated di(imino)guanidinium receptor is economically synthesized and features good compatibility with application-relevant aliphatic solvents. Small-angle X-ray scattering results reveal the formation of reverse-micelles, which together with the significant organic-phase water content challenge traditional notions of selectivity in extraction of superhydrophilic anions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1978

Models for extracting vertical crustal movements from leveling data

Various adjustment strategies are being used in North America to obtain vertical crustal movements from repeated leveling. The more successful models utilize polynomials or multiquadric analysis to describe elevation change with a velocity surface. Other features permit determination of nonlinear motions, motions associated with earthquakes or episodes, and vertical motions of blocks where boundaries are prespecified. The preferred models for estimating crustal motions permit the use of detached segments of releveling to govern the shape of a velocity surface and allow for input from nonleveling sources such as tide gages and paired lake gages. Some models for extracting vertical crustal movements from releveling data are also excellent for adjusting leveling networks, and permit mixing old and new data in areas exhibiting vertical motion. The new adjustment techniques are more general than older static models and will undoubtedly be used routinely in the future as the constitution of level networks becomes mainly relevelings.

Holdahl, S. H.↗

DOE OSTI2026

UV–Vis–NIR Reflectance Spectroscopy and Chemometrics for Monitoring Pu Directly on an Ion Exchange Column

Here, we present a fiber-optic UV–vis–NIR reflectance spectroscopy method for direct, noninvasive monitoring of Pu(IV) in a glass ion exchange column during dynamic loading and elution in a glovebox. A movable probe enables spatially resolved spectral acquisition along the column axis, capturing distinct features associated with Pu(IV) nitrate complexes during loading and free ions during elution. Principal component analysis was applied to extract the dominant spectral variance and resolve relative concentration profiles without requiring precise knowledge of optical penetration depth or species identity. This in situ approach reveals spatial gradients and speciation dynamics in real time, which provides actionable insight into Pu(IV) ion migration, resin saturation, and breakthrough behavior under evolving flow conditions. The method offers a practical, fiber-compatible strategy to monitor glass column–based separations for Pu and other lanthanides or actinides and to characterize metal–resin interactions in flow-through systems.

actinide↗

DOE OSTI2025

A bi-channel aided stitching of atomic force microscopy images

Microscopy is an essential tool in scientific research, enabling the visualization of structures at micro- and nanoscale resolutions. However, the field of microscopy often encounters limitations in field-of-view (FOV), restricting the amount of sample that can be imaged in a single capture. To overcome this limitation, image stitching techniques have been developed to seamlessly merge multiple overlapping images into a single, high-resolution composite. The images collected from microscope need to be optimally stitched before accurate physical information can be extracted from post analysis. However, the existing stitching tools either struggle to stitch images together when the microscopy images are feature sparse or cannot address all the transformations of images when performing image stitching. To address these issues, we propose a bi-channel aided feature-based image stitching method and demonstrate its use on Atomic Force Microscopy (AFM) generated Pantoea sp. YR343 biofilm and PTO thin film sample images as experimental data. The topographical channel image of AFM data captures the morphological details of the sample, and a stitched topographical image is desired for researchers. We utilize the amplitude and phase channels of AFM data to maximize the matching features and to estimate the position of the original topographical images and show that the proposed bi-channel aided stitching method outperforms the traditional direct stitching approach in AFM topographical image stitching task. Here, we demonstrated the application on AFM, but similar approaches could be employed of optical microscopy with brightfield and fluorescence channels. We believe this proposed workflow can serve as a valuable augmentation strategy for microscopy image stitching tasks and will benefit the experimentalist to avoid erroneous analysis and discovery due to incorrect stitching.

Atomic force microscopy↗

DOE OSTI2021

Enhanced orbital anisotropy through the proximity to a SrTiO 3 layer in the perovskite iridate superlattices

We have used angle-dependent soft x-ray absorption spectroscopy (XAS) at the O K edge and first-principles calculations to investigate the electronic structures of iridate-based superlattices ( SrIr O 3 ) m / ( SrTi O 3 ) ( m = 1 , 2, 3, and ∞). We focus on the pre-edge Ir 5 d t 2 g – O 2 p orbital hybridization feature in the XAS spectra. By varying the measurement geometry relative to the incident photon polarization, we are able to extract the dichroic contrast and observe the systematic increase in the anisotropy of Ir 5 d orbitals as m decreases. First-principles calculations elucidate the orbital anisotropy coming mainly from the enhanced out-of-plane compression of Ir O 6 octahedra in the SrIr O 3 layers that are adjacent to the inserted SrTi O 3 layers. As m decreases, the increased volume fraction of these interfacial SrIr O 3 layers and their contact with the SrTi O 3 layers within the ( SrIr O 3 ) m / ( SrTi O 3 ) supercell lead to enhanced orbital anisotropy. Furthermore, the tilt and rotation of Ir O 6 octahedra are shown to be essential to understand the subtle orbital anisotropy in these superlattices, and constraining these degrees of freedom will give an incorrect trend. Overall, our results demonstrate that the structural constraint from the inserted SrTi O 3 layers, in addition to other electronic means such as polar interface and charge transfer, can serve as a knob to control the orbital degree of freedom in these iridate-based superlattices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection

Abstract Background Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. Methods We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. Results We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations inTP53,PLXNB3, Zinc finger genes, and fewer mutations inSDC2,CDH1,PIK3CA,SVIL, andPTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA–protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, includingKIAA1522,DCK,FOXO3,MYO9B,ARID1A,EPRS,ZC3HAV1, andRBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. Conclusions This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Oncology↗

DOE OSTI2020

Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy

Optical two-dimensional (2D) coherent spectroscopy excels in studying coupling and dynamics in complex systems. The dynamical information can be learned from lineshape analysis to extract the corresponding linewidth. However, it is usually challenging to fit a 2D spectrum, especially when the homogeneous and inhomogeneous linewidths are comparable. We implemented a machine learning algorithm to analyze 2D spectra to retrieve homogeneous and inhomogeneous linewidths. The algorithm was trained using simulated 2D spectra with known linewidth values. The trained algorithm can analyze both simulated (not used in training) and experimental spectra to extract the homogeneous and inhomogeneous linewidths. Finally, this approach can be potentially applied to 2D spectra with more sophisticated spectral features.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1977

Resampling study

The author has identified the following significant results. The nearest neighbor and cubic convolution resampling algorithms were applied to a variety of images extracted from LANDSAT MSS data. A comparison of the results demonstrated that (1) cubic convolution can cause spreading of small features and can introduce noticeable overshoot (ringing) into the data; (2) cubic convolution attenuates the high spatial frequencies compared to the original and nearest neighbor resampled data; and (3) cubic convolution generally produces photographic products of superior visual quality. The effects of the resampling algorithms on multispectral classification were not conclusively determined due to the small number of images tested.

Ferneyhough, D. G.↗

NASA NTRS1992

The Keck keyword layer

Each Keck instrument presents a consistent software view to the user interface programmer. The view consists of a small library of functions, which are identical for all instruments, and a large set of keywords, that vary from instrument to instrument. All knowledge of the underlying task structure is hidden from the application programmer by the keyword layer. Image capture software uses the same function library to collect data for the image header. Because the image capture software and the instrument control software are built on top of the same keyword layer, a given observation can be 'replayed' by extracting keyword-value pairs from the image header and passing them back to the control system. The keyword layer features non-blocking as well as blocking I/O. A non-blocking keyword write operation (such as setting a filter position) specifies a callback to be invoked when the operation is complete. A non-blocking keyword read operation specifies a callback to be invoked whenever the keyword changes state. The keyword-callback style meshes well with the widget-callback style commonly used in X window programs. The first keyword library was built for the two Keck optical instruments. More recently, keyword libraries have been developed for the infrared instruments and for telescope control. Although the underlying mechanisms used for inter-process communication by each of these systems vary widely (Lick MUSIC, Sun RPC, and direct socket I/O, respectively), a basic user interface has been written that can be used with any of these systems. Since the keyword libraries are bound to user interface programs dynamically at run time, only a single set of user interface executables is needed. For example, the same program, 'xshow', can be used to display continuously the telescope's position, the time left in an instrument's exposure, or both values simultaneously. Less generic tools that operate on specific keywords, for example an X display that controls optical instrument exposures, have also been written using the keyword layer.

Conrad, A. R.↗

NASA NTRS1999

Textural-Contextual Labeling and Metadata Generation for Remote Sensing Applications

Despite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from cooccurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed.

Kiang, Richard K.↗

DOE OSTI2023

Protein3D: Enabling analysis and extraction of metal‐containing sites from the Protein Data Bank with molSimplify

Abstract Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non‐heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high‐throughput study of metal‐containing active sites in proteins.

Edholm, Freya↗

DOE OSTI2024

Three-chamber electrochemical reactor for selective lithium extraction from brine

Efficient lithium recovery from geothermal brines is crucial for the battery industry. Current electrochemical separation methods struggle with the simultaneous presence of Na + , K + , Mg 2+ , and Ca 2+ because these cations are similar to Li + , making it challenging to separate effectively. We address these challenges with a three-chamber reactor featuring a polymer porous solid electrolyte in the middle layer. This design improves the transference number of Li + (t Li+ ) by 2.1 times compared to the two-chamber reactor and also reduces the chlorine evolution reaction, a common side reaction in electrochemical lithium extraction, to only 6.4% in Faradaic Efficiency. Employing a lithium-ion conductive glass ceramic (LICGC) membrane, the reactor achieved high t Li+ of 97.5% in LiOH production from simulated brine, while the concentrations of Na + K + , Mg 2+ , and Ca 2+ are below the detection limit. Electrochemical experiments and surface analysis elucidated the cation transport mechanism, highlighting the impact of Na + on Li + migration at the LICGC interface.

Science & Technology - Other Topics↗

DOE OSTI2020

Landau diagrams in AdS and S-matrices from conformal correlators

Quantum field theories in AdS generate conformal correlation functions on the boundary, and in the limit where AdS is nearly flat one should be able to extract an S-matrix from such correlators. We discuss a particularly simple position-space procedure to do so. It features a direct map from boundary positions to (on-shell) momenta and thereby relates cross ratios to Mandelstam invariants. This recipe succeeds in several examples, includes the momentum-conserving delta functions, and can be shown to imply the two proposals in based on Mellin space and on the OPE data. Interestingly the procedure does not always work: the Landau singularities of a Feynman diagram are shown to be part of larger regions, to be called ‘bad regions’, where the flat-space limit of the Witten diagram diverges. To capture these divergences we introduce the notion of Landau diagrams in AdS. As in flat space, these describe on-shell particles propagating over large distances in a complexified space, with a form of momentum conservation holding at each bulk vertex. As an application we recover the anomalous threshold of the four-point triangle diagram at the boundary of a bad region.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Investigation of process history and underlying phenomena associated with the synthesis of plutonium oxides using Vector Quantizing Variational Autoencoder

Accurate, high throughput, and unbiased analysis of plutonium oxide particles is needed for analysis of the phenomenology associated with process parameters in their synthesis. Compared to qualitative and taxonomic descriptors, quantitative descriptors of particle morphology through scanning electron microscopy (SEM) have shown success in analyzing process parameters of uranium oxides. Among other candidates, a neural network called a Vector Quantizing Variational Autoencoder (VQ-VAE) has shown the ability to quantitatively describe particle morphology to attain >85% accuracy in identifying uranium oxide processing routes. We utilize a VQ-VAE to quantitatively describe plutonium dioxide (PuO 2 ) particles created in a designed experiment and investigate their phenomenology and prediction of their process parameters. PuO 2 was calcined from Pu(III) oxalates that were precipitated under varying synthetic conditions that related to concentrations, temperature, addition and digestion times, precipitant feed, and strike order; the surface morphology of the resulting PuO 2 powders were analyzed by SEM. A pipeline was developed to extract and quantify useful image representations for individual particles with the VQ-VAE, then further reduce the dimensionality of the feature space using a bottlenecking neural network fit to perform multiple classification tasks simultaneously. The reduced feature space could predict process parameters with greater than 80% accuracies for some parameters with a single particle. They also showed utility for grouping particles with similar surface morphology characteristics together. Both the clustering and classification results reveal valuable information regarding which chemical process parameters chiefly influence the PuO 2 particle morphologies: strike order and oxalic acid feedstock. Doing the same analysis with multiple particles was shown to improve the classification accuracy on each process parameter over the use of a single particle, with statistically significant results generally seen with as few as four particles in a sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Geochemical Phosphorus Sequestration in Tundra Soils Impedes Delivery of Bioavailable Phosphorus to the Kuparuk River, Alaska, USA: Implications for the Broader Arctic Region

Long-term river monitoring of the Kuparuk River (North Slope, Alaska, USA) confirms significant increases in solutes that are indicative of active layer thickening due to thawing permafrost. However, there is no evidence of an increase in total dissolved phosphorus (TDP) or soluble reactive phosphorus (SRP), the nutrient that limits primary production in this and similar rivers in the region. Here, we show that Mehlich-3 extractable iron (Fe) and aluminum (Al) in active layer soils impart high P geochemical sorption capacities across a range of landscape features that we would expect to promote lateral movement of water and solutes to headwater streams in our study watershed. Reanalysis of a recently published pan-Arctic soils database that includes active layer and permafrost soil samples suggests that this high P sorption capacity could be common in other parts of the Arctic region. We conclude that soil minerals enhance P retention on hillslopes and propose pedogenic secondary Fe and Al minerals may continue to retain P in these soils and limit biological productivity in the adjacent river even as active layer thickening increases potential P mobility in the watershed. We suggest that similar interactions may occur in other areas of the Arctic where comparable geochemical conditions prevail.

Sutor, Frederick W. [Univ. of Vermont, Burlington,↗

DOE OSTI2024

Tracking the topology of neural manifolds across populations

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and in vivo experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds.

97 MATHEMATICS AND COMPUTING↗