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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Diversity, evolution, and classification of virophages uncovered through global metagenomics

BACKGROUND: Virophages are small viruses with double-stranded DNA genomes that replicate along with giant viruses and co-infect eukaryotic cells. Due to the paucity of virophage reference genomes, a collective understanding of the global virophage diversity, distribution, and evolution is lacking. RESULTS: Here we screened a public collection of over 14,000 metagenomes using the virophage-specific major capsid protein (MCP) as "bait." We identified 44,221 assembled virophage sequences, of which 328 represent high-quality (complete or near-complete) genomes from diverse habitats including the human gut, plant rhizosphere, and terrestrial subsurface. Comparative genomic analysis confirmed the presence of four core genes in a conserved block. We used these genes to establish a revised virophage classification including 27 clades with consistent genome length, gene content, and habitat distribution. Moreover, for eight high-quality virophage genomes, we computationally predicted putative eukaryotic virus hosts. CONCLUSION: Overall, our approach has increased the number of known virophage genomes by 10-fold and revealed patterns of genome evolution and global virophage distribution. We anticipate that the expanded diversity presented here will provide the backbone for further virophage studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Benders Cut Classification via Support Vector Machines for Solving Two-Stage Stochastic Programs

In this work, we consider Benders decomposition for solving two-stage stochastic programs with complete recourse based on finite samples of the uncertain parameters. We define the Benders cuts binding at the final optimal solution or the ones significantly improving bounds over iterations as valuable cuts. We propose a learning-enhanced Benders decomposition (LearnBD) algorithm, which adds a cut classification step in each iteration to selectively generate cuts that are more likely to be valuable cuts. The LearnBD algorithm includes two phases: (i) sampling cuts and collecting information from training problems and (ii) solving testing problems with a support vector machine (SVM) cut classifier. We run the LearnBD algorithm on instances of capacitated facility location and multicommodity network design under uncertain demand. Our results show that SVM cut classifier works effectively for identifying valuable cuts, and the LearnBD algorithm reduces the total solving time of all instances for different problems with various sizes and complexities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

A topological approach for protein classification

Here, protein function and dynamics are closely related to its sequence and structure. However, prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done through measuring the similarity between proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Reference Shapefiles and Pre-trained Random Forest Classification Models for Detecting Aufeis on the North Slope of Alaska in Landsat Imagery

This dataset provides shapefiles and trained machine learning models used for aufeis detection at four sites on the North Slope of Alaska. It includes reference data for evaluating Landsat-based detection methods, supporting research on remote sensing approaches for identifying aufeis. The ReferenceData folder contains ArcGIS shapefiles of semi-automated land cover classifications for 217 Landsat Collection 2 images, categorizing pixels into six classes: aufeis, snow, ground, none, water, and cloud. The SiteBuffers.zip file includes 10-kilometer buffer shapefiles defining regions of interest around four aufeis fields (Canning21, FH1, Firth, and Kuparuk), used to test three detection techniques. Additionally, the TrainedRFModels folder contains six pre-trained Scikit-Learn Random Forest classifiers (100 trees, max depth = 30) designed to predict aufeis presence in Landsat Collection 2 Surface Reflectance images using Red, Blue, SWIR2, NDVI, and NDWI bands. This dataset supports the development and validation of remote sensing methods for mapping aufeis in Arctic environments.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Deep Learning Based Superconducting Radio-Frequency Cavity Fault Classification at Jefferson Laboratory

This work investigates the efficacy of deep learning (DL) for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a large, high-power continuous wave recirculating linac that utilizes 418 SRF cavities to accelerate electrons up to 12 GeV. Recent upgrades to CEBAF include installation of 11 new cryomodules (88 cavities) equipped with a low-level RF system that records RF time-series data from each cavity at the onset of an RF failure. Typically, subject matter experts (SME) analyze this data to determine the fault type and identify the cavity of origin. This information is subsequently utilized to identify failure trends and to implement corrective measures on the offending cavity. Manual inspection of large-scale, time-series data, generated by frequent system failures is tedious and time consuming, and thereby motivates the use of machine learning (ML) to automate the task. This study extends work on a previously developed system based on traditional ML methods (Tennant and Carpenter and Powers and Shabalina Solopova and Vidyaratne and Iftekharuddin, Phys. Rev. Accel. Beams, 2020, 23, 114601), and investigates the effectiveness of deep learning approaches. The transition to a DL model is driven by the goal of developing a system with sufficiently fast inference that it could be used to predict a fault event and take actionable information before the onset (on the order of a few hundred milliseconds). Because features are learned, rather than explicitly computed, DL offers a potential advantage over traditional ML. Specifically, two seminal DL architecture types are explored: deep recurrent neural networks (RNN) and deep convolutional neural networks (CNN). We provide a detailed analysis on the performance of individual models using an RF waveform dataset built from past operational runs of CEBAF. In particular, the performance of RNN models incorporating long short-term memory (LSTM) are analyzed along with the CNN performance. Furthermore, comparing these DL models with a state-of-the-art fault ML model shows that DL architectures obtain similar performance for cavity identification, do not perform quite as well for fault classification, but provide an advantage in inference speed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Classification Analysis of Southwest Pacific Tropical Cyclone Intensity Changes Prior to Landfall

This study evaluates the ability of a random forest classifier to identify tropical cyclone (TC) intensification or weakening prior to landfall over the western region of the Southwest Pacific Ocean (SWPO) basin. For both Australia mainland and SWPO island cases, when a TC first crosses land after spending ≥24 h over the ocean, the closest hour prior to the intersection is considered as the landfall hour. If the maximum wind speed (V max ) at the landfall hour increased or remained the same from the 24-h mark prior to landfall, the TC is labeled as intensifying and if the V max at the landfall hour decreases, the TC is labeled as weakening. Geophysical and aerosol variables closest to the 24 h before landfall hour were collected for each sample. The random forest model with leave-one-out cross validation and the random oversampling example technique was identified as the best-performing classifier for both mainland and island cases. The model identified longitude, initial intensity, and sea skin temperature as the most important variables for the mainland and island landfall classification decisions. Incorrectly classified cases from the test data were analyzed by sorting the cases by their initial intensity hour, landfall hour, monthly distribution, and 24-h intensity changes. TC intensity changes near land strongly impact coastal preparations such as wind damage and flood damage mitigations; hence, this study will contribute to improve identifying and prioritizing prediction of important variables contributing to TC intensity change before landfall.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integration of Air Classification and Hydrothermal Carbonization to Enhance Energy Recovery of Corn Stover

Air classification (AC) is a cost-effective technology that separates the energy-dense light ash fraction (LAF) from the inorganic-rich high ash fraction (HAF) of corn stover. HAF could be upgraded into energy-dense solid fuel by hydrothermal carbonization (HTC). However, HTC is a high-temperature, high-pressure process, which requires additional energy to operate. In this study, three different scenarios (i.e., AC only, HTC only, and integrated AC–HTC) were investigated for the energy recovery of corn stover. AC was performed on corn stover at an 8 Hz fan speed, which yielded 84.4 wt. % LAF, 12.8 wt. % HAF, and 2.8 wt. % below screen particles. About 27 wt. % ash was reduced from LAF by the AC process. Furthermore, HTC was performed on raw corn stover and the HAF of corn stover at 200, 230, and 260 °C for 30 min. To evaluate energy recovery, solid products were characterized in terms of mass yield, ash yield, ultimate analysis, proximate analyses, and higher heating value (HHV). The results showed that the energy density was increased with the increase in HTC temperature, meanwhile the mass yield and ash yield were decreased with the increase in HTC temperature. Proximate analysis showed that fixed carbon increased 18 wt. % for original char and 27 wt. % for HAF char at 260 °C, compared to their respective feedstocks. Finally, the hydrochar resulting from HAF was mixed with LAF and pelletized at 180 bar and 90 °C to densify the energy content. An energy balance of the integrated AC–HTC process was performed, and the results shows that integrated AC with HTC performed at 230 °C resulted in an additional 800 MJ/ton of energy recovery compared to the AC-only scenario.

09 BIOMASS FUELS↗
DOE OSTI2021

CyBERT: Cybersecurity Claim Classification by Fine-Tuning the BERT Language Model

We introduce CyBERT, a cybersecurity feature claims classifier based on bidirectional encoder representations from transformers and a key component in our semi-automated cybersecurity vetting for industrial control systems (ICS). To train CyBERT, we created a corpus of labeled sequences from ICS device documentation collected across a wide range of vendors and devices. This corpus provides the foundation for fine-tuning BERT’s language model, including a prediction-guided relabeling process. We propose an approach to obtain optimal hyperparameters, including the learning rate, the number of dense layers, and their configuration, to increase the accuracy of our classifier. Fine-tuning all hyperparameters of the resulting model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original architecture to 94.4% obtained with CyBERT. Furthermore, we evaluated CyBERT for the impact of randomness in the initialization, training, and data-sampling phases. CyBERT demonstrated a standard deviation of ±0.6% during validation across 100 random seed values. Finally, we also compared the performance of CyBERT to other well-established language models including GPT2, ULMFiT, and ELMo, as well as neural network models such as CNN, LSTM, and BiLSTM. The results showed that CyBERT outperforms these models on the validation accuracy and the F1 score, validating CyBERT’s robustness and accuracy as a cybersecurity feature claims classifier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Two-Stage Wildlife Event Classification for Edge Deployment

Camera-based wildlife monitoring is often overwhelmed by non-target triggers and slowed by manual review or cloud-dependent inference, which can prevent timely intervention for high stakes human–wildlife conflicts. Our key contribution is a deployable, fully offline edge vision sensor that achieves near-real-time, highly accurate wildlife event classification by combining detector-based empty-image suppression with a lightweight classifier trained with a staged transfer-learning curriculum. Specifically, Stage 1 uses a pretrained You Only Look Once (YOLO)-family detector for permissive animal localization and empty-trigger suppression, and Stage 2 uses a lightweight EfficientNet-based binary classifier to confirm puma on detector crops and gate downstream actions. Our design is robust to low-quality nighttime monochrome imagery (motion blur, low contrast, illumination artifacts, and partial-body captures) and operates using commercially available components in connectivity-limited settings. In field deployments running since May 2025, end-to-end latency from camera trigger to action command is approximately 4 s. Ablation studies using a dataset of labeled wildlife images (pumas, not pumas) show that the two-stage approach substantially reduces false alarms in identifying pumas relative to a full-image classifier while maintaining high recall. On the held-out test set (N = 1434 events), the proposed two-stage cascade achieves precision 0.983, recall 0.975, F1 0.979, accuracy 0.986, and balanced accuracy 0.983, with only 8 false positives and 12 false negatives. The system can be easily adapted for other species, as demonstrated by rapid retraining of the second stage to classify ringtails. Downstream responses (e.g., notifications and optional audio/light outputs) provide flexible actuation capabilities that can be configured to support intervention.

58 GEOSCIENCES↗
DOE OSTI2016

OPTICAL SPECTROSCOPY OF SDSS J004054.65-0915268: THREE POSSIBLE SCENARIOS FOR THE CLASSIFICATION. A z ∼ 5 BL LACERTAE, A BLUE FSRQ, OR A WEAK EMISSION LINE QUASAR

The haunt of high-redshift BL Lacerate objects is day by day more compelling to firmly understand their intrinsic nature and evolution. SDSS J004054.65-0915268 is, at the moment, one of the most distant BL Lac candidates, at z ∼ 5. We present a new optical-near-IR spectrum obtained with ALFOSC-NOT with a new, custom designed dispersive grating aimed to detect broad emission lines that could disprove this classification. In the obtained spectra, we do not detect any emission features and we provide an upper limit to the luminosity of the C iv broad emission line. Therefore, the nature of the object is then discussed, building the overall spectral energy distribution (SED) and fitting it with three different models. Our fits, based on SED modeling with different possible scenarios, cannot rule out the possibility that this source is indeed a BL Lac object, though the absence of optical variability and the lack of strong radio flux seem to suggest that the observed optical emission originates from a thermalized accretion disk.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SN REFSDAL: CLASSIFICATION AS A LUMINOUS AND BLUE SN 1987A-LIKE TYPE II SUPERNOVA

We have acquired Hubble Space Telescope (HST) and Very Large Telescope near-infrared spectra and images of supernova (SN) Refsdal after its discovery as an Einstein cross in fall 2014. The HST light curve of SN Refsdal has a shape consistent with the distinctive, slowly rising light curves of SN 1987A-like SNe, and we find strong evidence for a broad H α P-Cygni profile and Na I D absorption in the HST grism spectrum at the redshift ( z = 1.49) of the spiral host galaxy. SNe IIn, largely powered by circumstellar interaction, could provide a good match to the light curve of SN Refsdal, but the spectrum of a SN IIn would not show broad and strong H α and Na I D absorption. From the grism spectrum, we measure an H α expansion velocity consistent with those of SN 1987A-like SNe at a similar phase. The luminosity, evolution, and Gaussian profile of the H α emission of the WFC3 and X-shooter spectra, separated by ∼2.5 months in the rest frame, provide additional evidence that supports the SN 1987A-like classification. In comparison with other examples of SN 1987A-like SNe, photometry of SN Refsdal favors bluer B - V and V - R colors and one of the largest luminosities for the assumed range of potential magnifications. The evolution of the light curve at late times will provide additional evidence about the potential existence of any substantial circumstellar material. Using MOSFIRE and X-shooter spectra, we estimate a subsolar host-galaxy metallicity (8.3 ± 0.1 dex and <8.4 dex, respectively) near the explosion site.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

The K2 Ecliptic Plane Input Catalog (EPIC) and stellar classifications of 138,600 targets in campaigns 1–8

The K2 Mission uses the Kepler spacecraft to obtain high-precision photometry over ≈80 day campaigns in the ecliptic plane. The Ecliptic Plane Input Catalog (EPIC) provides coordinates, photometry, and kinematics based on a federation of all-sky catalogs to support target selection and target management for the K2 mission. We describe the construction of the EPIC, as well as modifications and shortcomings of the catalog. Kepler magnitudes (Kp) are shown to be accurate to ≈0.1 mag for the Kepler field, and the EPIC is typically complete to Kp ≈ 17 (Kp ≈ 19 for campaigns covered by Sloan Digital Sky Survey). We furthermore classify 138,600 targets in Campaigns 1–8 (≈88% of the full target sample) using colors, proper motions, spectroscopy, parallaxes, and galactic population synthesis models, with typical uncertainties for G-type stars of ≈3% in T{sub eff}, ≈0.3 dex in log g, ≈40% in radius, ≈10% in mass, and ≈40% in distance. Our results show that stars targeted by K2 are dominated by K–M dwarfs (≈41% of all selected targets), F–G dwarfs (≈36%), and K giants (≈21%), consistent with key K2 science programs to search for transiting exoplanets and galactic archeology studies using oscillating red giants. However, we find significant variation of the fraction of cool dwarfs with galactic latitude, indicating a target selection bias due to interstellar reddening and increased contamination by giant stars near the galactic plane. We discuss possible systematic errors in the derived stellar properties, and differences with published classifications for K2 exoplanet host stars. The EPIC is hosted at the Mikulski Archive for Space Telescopes (MAST): http://archive.stsci.edu/k2/epic/search.php.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

An Optical Overview of Blazars with LAMOST. II. Gamma-Ray Blazar Candidates and Updated Classifications

Blazars represent the dominant class of associated γ-ray sources detected by the Fermi Large Area Telescope (LAT). However, in the more recent release of the Fourth Fermi-LAT Point Source Catalog (4FGL), ∼25% of the sources associated with lower-energy counterparts show a multifrequency behavior similar to that of blazars, but lacks an optical spectroscopic confirmation of their nature and are therefore classified as Blazar Candidates of Uncertain Type (BCUs). A particularly challenging task in blazar studies is to classify these BCUs and, when possible to estimate their redshifts, in particular for BL Lac objects, characterized by almost featureless optical spectra with only weak emission lines. Continuing our study of blazars with Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) optical spectral data, we perform an extensive search for optical spectra available in the LAMOST Data Release 6 archive. Our aim is confirming the blazar nature of BCUs and to test if new data can allow us to get a redshift estimate for BL Lac objects that lack measurement, as well as to search for and discover changing-look blazars. We selected sources out of the 4FGL catalog, the list of targets from our follow-up spectroscopic campaign of unidentified and/or unassociated γ-ray sources, and the Roma-BZCAT multifrequency catalog of blazars, finding a total of 42 sources with available LAMOST DR6 spectra. We confirmed the blazar-like nature of four blazar candidates . For the remaining 37 sources we confirm their previous classification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Evaluating Optical Classification for Fermi Blazar Candidates with a Statistical Method Using Broadband Spectral Indices

We aim to test whether a blazar candidate of uncertain type (BCU) in the third Fermi catalog of active galactic nuclei (3LAC) can be potentially classified as a BL Lac object or a flat-spectrum radio quasar (FSRQ) by performing a statistical analysis of its broadband spectral properties. We find that 34% of the radio-selected BCUs (583 BCUs) are BL Lac-like and 20% are FSRQ-like, at a 90% level of confidence. Similarly, 77.3% of the X-ray-selected BCUs (176 BCUs) are evaluated as BL Lac-like and 6.8% may be FSRQ-like sources. And 88.7% of the BL Lac-like BCUs that have synchrotron peak frequencies available are high synchrotron peaked BL Lacs in the X-ray-selected BCUs. The percentages are accordingly 62% and 7.3% in the sample of 124 optically selected BCUs. The high ratio of the number of BL Lac-like sources to the number of FSRQ-like BCUs in the X-ray-selected and optically selected BCU samples is due to selection effects. Examining the consistency between our evaluation and spectroscopic identification case by case with a sample of 78 radio-selected BCUs, it is found that the statistical analysis and its resulting classifications agree with the results of the optical follow-up spectroscopic observations. Our observation campaign for high-|ρ{sub s}| BCUs selected with our method, i.e., |ρ{sub s}|>0.8, is ongoing.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Unblinded Data for PLAsTiCC Classification Challenge

For classification challenge (https://plasticc.org), the unblinded data files are included here. See PDF note above for more information. The original challenge (Sep 28, 2018 - Dec 17, 2018) was hosted at https://www.kaggle.com/c/PLAsTiCC-2018 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

ANN↗