Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “feature”

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.

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

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Analysis of nonlinear features associated with radio-frequency sheaths

An analytic study is conducted of nonlinear features that arise when radio-frequency (RF) sheath boundary conditions are solved self-consistently with the electromagnetic fields in a magnetized plasma. An instability associated with RF sheath–plasma interactions is identified and shown to be closely linked with sheath–plasma resonance and the occurrence of multiple solutions. It is proposed that this instability serves as a physical mechanism that enables jumping from one solution branch to another, providing a physical basis for the prediction of an RF sheath hysteresis in the time domain. It is further shown that this instability can lead to non-intuitive features such as spontaneous symmetry breaking in an otherwise completely symmetric problem. In plane-stratified geometry, two analytically solvable examples are considered for the cases of an unbounded half-space and a bounded domain

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

High-sensitivity electronic Stark spectrometer featuring a laser-driven light source

We report developmental details of a high-sensitivity Stark absorption spectrometer featuring a laser-driven light source. The light source exhibits intensity fluctuations of ~0.3% over timescales ranging from 1 min to 12 h, minimal drift (≤0.1%/h), and very little 1/f noise at frequencies greater than 200 Hz, which are comparable to or better than an arc-driven light source. Additional features of the spectrometer include balanced detection with multiplex sampling, which yielded lower noise in A, and constant wavelength or wavenumber (energy) spectral bandpass modes. We achieve noise amplitudes of ~7 × 10 –4 and ~6 × 10 –6 in measurements of single A and ΔA spectra (with 92 data points) taking ~7 and ~19 min, respectively.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Prevention efficacy of the broadly neutralizing antibody VRC01 depends on HIV-1 envelope sequence features

In the Antibody Mediated Prevention (AMP) trials (HVTN 704/HPTN 085 and HVTN 703/HPTN 081), prevention efficacy (PE) of the monoclonal broadly neutralizing antibody (bnAb) VRC01 (vs. placebo) against HIV-1 acquisition diagnosis varied according to the HIV-1 Envelope (Env) neutralization sensitivity to VRC01, as measured by 80% inhibitory concentration (IC80). Here, we performed a genotypic sieve analysis, a complementary approach to gaining insight into correlates of protection that assesses how PE varies with HIV-1 sequence features. We analyzed HIV-1 Env amino acid (AA) sequences from the earliest available HIV-1 RNA-positive plasma samples from AMP participants diagnosed with HIV-1 and identified Env sequence features that associated with PE. The strongest Env AA sequence correlate in both trials was VRC01 epitope distance that quantifies the divergence of the VRC01 epitope in an acquired HIV-1 isolate from the VRC01 epitope of reference HIV-1 strains that were most sensitive to VRC01-mediated neutralization. In HVTN 704/HPTN 085, the Env sequence-based predicted probability that VRC01 IC80 against the acquired isolate exceeded 1 µg/mL also significantly associated with PE. In HVTN 703/HPTN 081, a physicochemical-weighted Hamming distance across 50 VRC01 binding-associated Env AA positions of the acquired isolate from the most VRC01-sensitive HIV-1 strain significantly associated with PE. These results suggest that incorporating mutation scoring by BLOSUM62 and weighting by the strength of interactions at AA positions in the epitope:VRC01 interface can optimize performance of an Env sequence-based biomarker of VRC01 prevention efficacy. Future work could determine whether these results extend to other bnAbs and bnAb combinations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Structural analysis of the catalytic domain of Artemis endonuclease/SNM1C reveals distinct structural features

The endonuclease Artemis is responsible for opening DNA hairpins during V(D)J recombination and for processing a subset of pathological DNA double-strand breaks. Artemis is an attractive target for the development of therapeutics to manage various B cell and T cell tumors, because failure to open DNA hairpins and accumulation of chromosomal breaks may reduce the proliferation and viability of pre-T and pre-B cell derivatives. However, structure-based drug discovery of specific Artemis inhibitors has been hampered by a lack of crystal structures. Here, we report the structure of the catalytic domain of recombinant human Artemis. The catalytic domain displayed a polypeptide fold similar overall to those of other members in the DNA cross-link repair gene SNM1 family and in mRNA 3'-end-processing endonuclease CPSF-73, containing metallo-β-lactamase and β-CASP domains and a cluster of conserved histidine and aspartate residues capable of binding two metal atoms in the catalytic site. As in SNM1A, only one zinc ion was located in the Artemis active site. However, Artemis displayed several unique features. Unlike in other members of this enzyme class, a second zinc ion was present in the β-CASP domain that leads to structural reorientation of the putative DNA-binding surface and extends the substrate-binding pocket to a new pocket, pocket III. Moreover, the substrate-binding surface exhibited a dominant and extensive positive charge distribution compared with that in the structures of SNM1A and SNM1B, presumably because of the structurally distinct DNA substrate of Artemis. The structural features identified here may provide opportunities for designing selective Artemis inhibitors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Printed Targets with Micron-Scale Feature Patterns for the Study of Ablator Defects on OMEGA

As per present models, laser imprint and implosion symmetry are insufficient to account for observed performance degradation of direct-drive cryogenic fusion implosions. More and better data are needed on ablator defects as a source of hydrodynamic instability and mix. To investigate this, a series of OMEGA experimental campaigns is underway to study isolated target defects. Key requirements are systematic variation of the laser intensity and pulse shape at shot, with highly controlled defect type, geometry, and location. Here, given the need for sub-micron resolution and precise registration of multiple features, two-photon polymerization (TPP) printing was identified as an ideal method to fabricate these targets. TPP printing has enabled controlled formation of designed domes, divots, and vacuoles for studying the combined effect of size and proximity of these features on the hydro performance.

Two-photon polymerization printing↗
DOE OSTI2023

Designing polar textures with ultrafast neuromorphic features from atomistic simulations

This review summarizes recent works, all using a specific atomistic approach, that predict and explain the occurrence of key features for neuromorphic computing in three archetypical dipolar materials, when they are subject to THz excitations. The main ideas behind such atomistic approach are provided, and illustration of model relaxor ferroelectrics, antiferroelectrics, and normal ferroelectrics are given, highlighting the important potential of polar materials as candidates for neuromorphic computing. Some peculiar emphases are made in this Review, such as the connection between neuromorphic features and percolation theory, local minima in energy path, topological transitions and/or anharmonic oscillator model, depending on the material under investigation. By considering three different and main polar material families, this work provides a complete and innovative toolbox for designing polar-based neuromorphic systems.

THz pulses↗
DOE OSTI2025

Scaling Arctic landscape and permafrost features improves active layer depth modeling

Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD will respond to climate change and disturbances across tundra ecosystems. In this paper, we studied the fine-scale heterogeneity of ALD and its connections with land surface characteristics across spatial and spectral scales using a combination of ground, unoccupied aerial system, airborne, and satellite observations. We showed that airborne sensors such as AVIRIS-NG and medium-resolution satellite Earth observation systems like Sentinel-2 can capture the average ALD at the landscape scale. We found that the best observational scale for ALD modeling is heavily influenced by the vegetation and landform patterns occurring on the landscape. Landscapes characterized by small-scale permafrost features such as polygon tussock tundra require high-resolution observations to capture the intrinsic connections between permafrost and small-scale land surface and disturbance patterns. Conversely, in landscapes dominated by water tracks and shrubs, permafrost features manifest at a larger scale and our model results indicate the best performance at medium resolution (5 m), outperforming both higher (0.4 m) and lower resolution (10 m) models. This transcends our study to show that permafrost response to climate change may vary across dominant ecosystem types, driven by different above- and below-ground connections and the scales at which these connections are happening. We thus recommend tailoring observational scales based on landforms and characteristics for modeling permafrost distribution, thereby mitigating the influences of spatial-scale mismatches and improving the understanding of vegetation and permafrost changes for the Arctic region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

RCSB Protein Data Bank 1D3D module: displaying positional features on macromolecular assemblies

Mapping positional features from one-dimensional (1D) sequences onto three-dimensional (3D) structures of biological macromolecules is a powerful tool to show geometric patterns of biochemical annotations and provide a better understanding of the mechanisms underpinning protein and nucleic acid function at the atomic level. We present a new library designed to display fully customizable interactive views between 1D positional features of protein and/or nucleic acid sequences and their 3D structures as isolated chains or components of macromolecular assemblies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Poisson hurdle model-based method for clustering microbiome features

Abstract Motivation High-throughput sequencing technologies have greatly facilitated microbiome research and have generated a large volume of microbiome data with the potential to answer key questions regarding microbiome assembly, structure and function. Cluster analysis aims to group features that behave similarly across treatments, and such grouping helps to highlight the functional relationships among features and may provide biological insights into microbiome networks. However, clustering microbiome data are challenging due to the sparsity and high dimensionality. Results We propose a model-based clustering method based on Poisson hurdle models for sparse microbiome count data. We describe an expectation–maximization algorithm and a modified version using simulated annealing to conduct the cluster analysis. Moreover, we provide algorithms for initialization and choosing the number of clusters. Simulation results demonstrate that our proposed methods provide better clustering results than alternative methods under a variety of settings. We also apply the proposed method to a sorghum rhizosphere microbiome dataset that results in interesting biological findings. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PHclust. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning

ABSTRACT In this work, we explore the possibility of applying machine learning methods designed for 1D problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly steps, such as the point spread function deconvolution and the training and application of complex Convolutional Neural Networks of thousands or even millions of parameters. In our approach, we extract features from the galaxy images by analysing the elliptical isophotes in their light distribution and collect the information in a sequence. The sequences obtained with this method present definite features allowing a direct distinction between galaxy types. Then, we train and classify the sequences with machine learning algorithms, designed through the platform Modulos AutoML. As a demonstration of this method, we use the second public release of the Dark Energy Survey (DES DR2). We show that we are able to successfully distinguish between early-type and late-type galaxies, for images with signal-to-noise ratio greater than 300. This yields an accuracy of $86{{\ \rm per\ cent}}$ for the early-type galaxies and $93{{\ \rm per\ cent}}$ for the late-type galaxies, which is on par with most contemporary automated image classification approaches. The data dimensionality reduction of our novel method implies a significant lowering in computational cost of classification. In the perspective of future data sets obtained with e.g. Euclid and the Vera Rubin Observatory, this work represents a path towards using a well-tested and widely used platform from industry in efficiently tackling galaxy classification problems at the peta-byte scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

SN 2021fxy: mid-ultraviolet flux suppression is a common feature of Type Ia supernovae

ABSTRACT We present ultraviolet (UV) to near-infrared (NIR) observations and analysis of the nearby Type Ia supernova SN 2021fxy. Our observations include UV photometry from Swift/UVOT, UV spectroscopy from HST/STIS, and high-cadence optical photometry with the Swope 1-m telescope capturing intranight rises during the early light curve. Early B − V colours show SN 2021fxy is the first ‘shallow-silicon’ (SS) SN Ia to follow a red-to-blue evolution, compared to other SS objects which show blue colours from the earliest observations. Comparisons to other spectroscopically normal SNe Ia with HST UV spectra reveal SN 2021fxy is one of several SNe Ia with flux suppression in the mid-UV. These SNe also show blueshifted mid-UV spectral features and strong high-velocity Ca ii features. One possible origin of this mid-UV suppression is the increased effective opacity in the UV due to increased line blanketing from high velocity material, but differences in the explosion mechanism cannot be ruled out. Among SNe Ia with mid-UV suppression, SNe 2021fxy and 2017erp show substantial similarities in their optical properties despite belonging to different Branch subgroups, and UV flux differences of the same order as those found between SNe 2011fe and 2011by. Differential comparisons to multiple sets of synthetic SN Ia UV spectra reveal this UV flux difference likely originates from a luminosity difference between SNe 2021fxy and 2017erp, and not differing progenitor metallicities as suggested for SNe 2011by and 2011fe. These comparisons illustrate the complicated nature of UV spectral formation, and the need for more UV spectra to determine the physical source of SNe Ia UV diversity.

Astronomy & Astrophysics↗
DOE OSTI2024

Genomes OnLine Database (GOLD) v.10: new features and updates

The Genomes OnLine Database (GOLD; https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute is a comprehensive online metadata repository designed to catalog and manage information related to (meta)genomic sequence projects. GOLD provides a centralized platform where researchers can access a wide array of metadata from its four organization levels namely Study, Organism/Biosample, Sequencing Project and Analysis Project. GOLD continues to serve as a valuable resource and has seen significant growth and expansion since its inception in 1997. With its expanded role as a collaborative platform, it not only actively imports data from other primary repositories like National Center for Biotechnology Information but also supports contributions from researchers worldwide. This collaborative approach has enriched the database with diverse datasets, creating a more integrated resource to enhance scientific insights. As genomic research becomes increasingly integral to various scientific disciplines, more researchers and institutions are turning to GOLD for their metadata needs. To meet this growing demand, GOLD has expanded by adding diverse metadata fields, intuitive features, advanced search capabilities and enhanced data visualization tools, making it easier for users to find and interpret relevant information. This manuscript provides an update and highlights the new features introduced over the last 2 years.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Twin Polaritons: Classical versus Quantum Features in Polaritonic Spectra

Understanding whether a polaritonic phenomenon is fundamentally quantum or classical is essential for building accurate theoretical models and guiding experimental design. Here, in this work, we address this question in the context of polaritonic spectra and report an intriguing new feature: the twin polariton, an additional splitting beyond the primary resonant polariton splitting originating from vacuum field fluctuations. We show that the twin polariton persists in the many-molecule limit under permutationally symmetrical initial-state constraint and that it follows the same linear dependence on coupling strength as the primary polariton splitting. This establishes a novel mechanism by which a quantum feature (the twin polariton) can be tuned through a classical one (the primary polariton), offering new opportunities to probe and control the fundamental nature of polaritonic systems.

classical optics↗
DOE OSTI2023

Observing signals of spectral features in the cosmic-ray positrons and electrons from Milky Way pulsars

The Alpha Magnetic Spectrometer (AMS-02) has provided unprecedented precision measurements of the electron and positron cosmic-ray fluxes and the positron fraction spectrum. At the higher energies, sources as energetic local pulsars, may contribute to both cosmic-ray species. The discreteness of the source population, can result in features both on the positron fraction measurement and in the respective electron and positron spectra. For the latter, those would coincide in energy and would contrast predictions of smooth spectra as from particle dark matter. In this work, using a library of pulsar population models for the local part of the Milky Way, we perform a power-spectrum analysis on the cosmic-ray positron fraction. We also develop a technique to cross-correlate the electron and positron fluxes. We show that both such analyses, can be used to search statistically for the presence of spectral wiggles in the cosmic-ray data. For a significant fraction of our pulsar simulations, those techniques are already sensitive enough to give a signal for the presence of those features above the regular noise, with forthcoming observations making them even more sensitive. Lastly, by cross-correlating the AMS-02 electron and positron spectra, we find an intriguing first hint for a positive correlation between them, of the kind expected by a population of local pulsars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Introducing a multiscale feature integration network for inpainting with applications to enhanced CMB map reconstruction

We introduce a novel neural network, SkyReconNet, which combines the expanded receptive fields of dilated convolutional layers along with standard convolutions, to capture both the global and local features for reconstructing the missing information in an image. We implement our network to inpaint the masked regions in a full-sky cosmic microwave background (CMB) map. Inpainting CMB maps is a particularly formidable challenge when dealing with extensive and irregular masks, such as galactic masks which can obscure substantial fractions of the sky. The hybrid design of SkyReconNet leverages the strengths of standard and dilated convolutions to accurately predict CMB fluctuations in the masked regions by effectively utilizing the information from surrounding unmasked areas. During training, the network optimizes its weights by minimizing a composite loss function that combines the structural similarity index measure (SSIM) and mean squared error (MSE). SSIM preserves the essential structural features of the CMB, ensuring an accurate and coherent reconstruction of the missing CMB fluctuations, while MSE minimizes the pixelwise deviations, thus enhancing the overall accuracy of the predictions. The predicted CMB maps and their corresponding angular power spectra align closely with the targets, achieving the performance limited only by the fundamental uncertainty of cosmic variance. The network’s generic architecture enables application to other physics-based challenges involving data with missing or defective pixels, systematic artifacts, etc. In conclusion, our results demonstrate its effectiveness in addressing the challenges posed by large irregular masks, offering a significant inpainting tool not only for CMB analyses but also for image-based experiments across disciplines where such data imperfections are prevalent.

Cosmic microwave background↗
DOE OSTI2023

Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis

Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this paper, we propose a technique that addresses storage costs while improving post-analysis accuracy through spatiotemporal adaptive, error-controlled lossy compression. We investigate the trade-off between data precision and temporal output rates, revealing that reducing data precision and increasing timestep frequency lead to more accurate analysis outcomes. Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor. To evaluate our approach, we conduct experiments using the well-known E3SM climate simulation code and apply our method to compress variables used for cyclone tracking. Our results show a significant reduction in storage size while enhancing the quality of cyclone tracking analysis, both quantitatively and qualitatively, in comparison to the prevalent timestep decimation approach. Compared to three state-of-the-art lossy compressors lacking feature preservation capabilities, our adaptive compression framework improves perfectly matched cases in TC tracking by 26.4-51.3% at medium compression ratios and by 77.3-571.1% at large compression ratios, with a merely 5–11% computational overhead.

Gong, Qian↗
DOE OSTI2022

On Feature, Classifier and Detector Fusers for 235U Signatures Using Gamma Spectral Counts

Three types of information fusion strategies are studied to assess the performance of classifiers for detecting low-level 235 U radiation sources, using features obtained from gamma spectra of NaI detectors. These three strategies are based on using two spectral region features, fusing eight classifiers of diverse designs, and fusing multiple detectors located at different positions around the source. The inner, middle and outer groups of detectors, within a formation of two concentric circles and a spiral of 21 detectors, are identified based on their distance to the source, which is located at the center. This study provides two main qualitative insights into this classification task. First, the fusion of detectors leads to an overall improved classification performance, least in the inner group, most in the outer group, and in between for the middle group. Second, several classifiers and fusers achieve lower training error which does not translate to lower generalization error, indicating their over-fitting to training data.

Rao, Nageswara↗
DOE OSTI2023

Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison

Abstract Critical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time‐varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time‐varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large‐scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real‐world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.

97 MATHEMATICS AND COMPUTING↗