Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Labeling”

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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Models and Processes to Extract Drug-like Molecules From Natural Language Text

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Sensing Techniques for Organochlorides through Intermolecular Interaction with Bicyclic Amidines

Toxic organochloride molecules are widely used in industry for various purposes. With their high volatility, the direct detection of organochlorides in environmental samples is challenging. Here, a new organochloride detection mechanism using 1,5-diazabicyclo[4.3.0]non-5-ene (DBN) is introduced to simplify a sensing method with higher detection sensitivity. Three types of organochloride compounds-trichloroethylene (TCE), dichloromethane (DCM), and dichlorodiphenyltrichloroethane (DDT)—were targeted to understand DCM conjugation chemistry by using nuclear magnetic resonance (NMR) and liquid chromatography with a mass spectrometer (LC-MS). 13C-NMR spectra and LC-MS data indicated that DBN can be labeled on these organochloride compounds by chlorine–nitrogen interaction. Furthermore, to demonstrate the organochloride sensing capability, the labeling yield and limit of detection were determined by a colorimetric assay as well as micellar electrokinetic chromatography (MEKC). The interaction with DBN was most appreciable for TCE, among other organochlorides. TCE was detected at picomolar levels, which is two orders of magnitude lower than the maximum contaminant level set by the United States Environmental Protection Agency. MEKC, in conjunction with this DBN-labeling method, enables us to develop a field-deployable sensing platform for detecting toxic organochlorides with high sensitivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Production, Purification, and Applications of a Potential Theranostic Pair: Cobalt-55 and Cobalt-58m

The emerging success of [68Ga/177Lu]Ga/Lu-DOTATATE as a theranostic pair has spurred interest in other isotopes as potential theranostic combinations. Here, we review cobalt-55 and cobalt-58m as a potential theranostic pair. Radionuclidically pure cobalt-55 and cobalt-58m have been produced on small cyclotrons with high molar activity. In vitro, DOTATOC labeled with cobalt has shown greater affinity for SSTR2 than DOTATOC labeled with gallium and yttrium. Similarly, [58mCo]Co-DOTATATE has shown improved cell-killing capabilities as compared to DOTATATE labeled with either indium-111 or lutetium-177. Finally, PET imaging with an isotope such as cobalt-55 allows for image acquisition at much later timepoints than gallium, allowing for an increased degree of biological clearance of non-bound radiotracer. We discuss the accelerator targetry and radiochemistry used to produce cobalt-55,58m, emphasizing the implications of these techniques to downstream radiotracers being developed for imaging and therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Deep-Learning-Based Segmentation of Keyhole in In-Situ X-ray Imaging of Laser Powder Bed Fusion

In laser powder bed fusion processes, keyholes are the gaseous cavities formed where laser interacts with metal, and their morphologies play an important role in defect formation and the final product quality. The in-situ X-ray imaging technique can monitor the keyhole dynamics from the side and capture keyhole shapes in the X-ray image stream. Keyhole shapes in X-ray images are then often labeled by humans for analysis, which increasingly involves attempting to correlate keyhole shapes with defects using machine learning. However, such labeling is tedious, time-consuming, error-prone, and cannot be scaled to large data sets. To use keyhole shapes more readily as the input to machine learning methods, an automatic tool to identify keyhole regions is desirable. In this paper, a deep-learning-based computer vision tool that can automatically segment keyhole shapes out of X-ray images is presented. The pipeline contains a filtering method and an implementation of the BASNet deep learning model to semantically segment the keyhole morphologies out of X-ray images. The presented tool shows promising average accuracy of 91.24% for keyhole area, and 92.81% for boundary shape, for a range of test dataset conditions in Al6061 (and one AliSi10Mg) alloys, with 300 training images/labels and 100 testing images for each trial. Prospective users may apply the presently trained tool or a retrained version following the approach used here to automatically label keyhole shapes in large image sets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Photocrosslinking Probes Proximity of Thymine Modifiers Tethering Excitonically Coupled Dye Aggregates to DNA Holliday Junction

A DNA Holliday junction (HJ) has been used as a versatile scaffold to create a variety of covalently templated molecular dye aggregates exhibiting strong excitonic coupling. In these dye-DNA constructs, one way to attach dyes to DNA is to tether them via single long linkers to thymine modifiers incorporated in the core of the HJ. Here, using photoinduced [2 + 2] cycloaddition (photocrosslinking) between thymines, we investigated the relative positions of squaraine-labeled thymine modifiers in the core of the HJ, and whether the proximity of thymine modifiers correlated with the excitonic coupling strength in squaraine dimers. Photocrosslinking between squaraine-labeled thymine modifiers was carried out in two distinct types of configurations: adjacent dimer and transverse dimer. The outcomes of the reactions in terms of relative photocrosslinking yields were evaluated by denaturing polyacrylamide electrophoresis. We found that for photocrosslinking to occur at a high yield, a synergetic combination of three parameters was necessary: adjacent dimer configuration, strong attractive dye–dye interactions that led to excitonic coupling, and an A-T neighboring base pair. The insight into the proximity of dye-labeled thymines in adjacent and transverse configurations correlated with the strength of excitonic coupling in the corresponding dimers. To demonstrate a utility of photocrosslinking, we created a squaraine tetramer templated by a doubly crosslinked HJ with increased thermal stability. These findings provide guidance for the design of HJ-templated dye aggregates exhibiting strong excitonic coupling for exciton-based applications such as organic optoelectronics and quantum computing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Deep Active Learning for Solvability Prediction in Power Systems

Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism and handle limited types of power flow models. In this letter, we propose a deep active learning framework for solvability prediction in power systems. Compared with passive learning where the training is performed after all instances are labeled, active learning selects most informative instances to be labeled and therefore significantly reduces the size of the labeled dataset for training. In the active learning framework, the acquisition functions, which correspond to different sampling strategies, are defined in terms of the on-the-fly posterior probability from the classifier. First, the IEEE 39-bus system is employed to validate the proposed framework, where a two-dimensional case is illustrated to visualize the effectiveness of the sampling method followed by the high-dimensional numerical experiments. Then, the Northeast Power Coordinating Council (NPCC) 140-bus system is used to validate the performance on large-scale power systems.

42 ENGINEERING↗
DOE OSTI2020

APOGEE Net: Improving the Derived Spectral Parameters for Young Stars through Deep Learning

Machine learning allows for efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of machine-learning approaches has been demonstrated for spectra covering a variety of wavelengths and spectral resolutions, but most often for main-sequence (MS) or evolved stars, where reliable synthetic spectra provide labels and data for training. Spectral models of young stellar objects (YSOs) and low-mass MS stars are less well-matched to their empirical counterparts, however, posing barriers to previous approaches to classify spectra of such stars. In this work, we generate labels for YSOs and low-mass MS stars through their photometry. We then use these labels to train a deep convolutional neural network to predict logg, T {sub eff}, and Fe/H for stars with Apache Point Observatory Galactic Evolution Experiment (APOGEE) spectra in the DR14 data set. This “APOGEE Net” has produced reliable predictions of logg for YSOs, with uncertainties of within 0.1 dex and a good agreement with the structure indicated by pre-MS evolutionary tracks, and it correlates well with independently derived stellar radii. These values will be useful for studying pre-MS stellar populations to accurately diagnose membership and ages.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

LCLS RF Station Phase Anomaly Candidate Dataset

A public anomaly detection dataset constructed from RF station faults for phase at SLAC's LCLS (Linac Coherent Light Source). We have compiled a dataset of the RF station diagnostic phase data and the beam-position monitor (BPM) signals, alongside the hand labels, for a labeled study period. The dataset consists of two HDF5 files (one for train and one for test) containing the raw data, two CSV files containing information about the candidates. The CSV file for the test dataset also contains the label.

Liang, Jia [Stanford Univ., CA (United States). In↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production by conducting a statistical analysis of historical wind data over a five-year period (2020-2025) from a single 1.5MW turbine manufactured by General Electric (GE) located at NLR’s Flatirons Campus, to generate an experimental test profile that was deployed on a 1.25-MW proton exchange membrane type MC250 electrolyzer system manufactured by Nel Hydrogen . [1] While the electrolyzer balance-of-plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. The historical wind data provided several metrics, however, the analysis particularly focused on the measured power output by the wind turbine. The power output time series of data for each day was categorized by total energy generation and standard deviation, and the day that represented the highest combination of these two metrics was chosen – December 25th, 2022. This process was then repeated for a moving four-hour window within this day to identify the most statistically variable period. Finally, this four-hour period was scaled by 65% to match the 1.25 MW electrolyzer. The electrolysis system controls hydrogen production by varying DC current applied to the stack, from a maximum of 3000 A to a minimum safe operation of 300 A, or 10%. Because the current – voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical wind profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1 Hz frequency. For more details on the statistical analysis process, see the presentation labeled “ Public Reference Data for Megawatt-Scale Hydrogen Electrolysis” provided with each data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}_{scaling factor}-{electrolyzer ramp rate in amperes/second} For instance, “wind-GE1.5MW_0.65-400.zip” represents the hour-long experiment using historical data from the wind-GE1.5MW turbine, scaled to 65%, with the electrolyzer power supply set to a maximum ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and wind power input. A PDF file detailing the historical wind data statistical analysis used to generate the wind profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_historical_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis [1] nelhydrogen.com/product/mc-series-electrolyser .

08 HYDROGEN↗
NASA NTRS1974

Automated microbial metabolism laboratory

The design and rationale of an advanced labeled release experiment based on single addition of soil and multiple sequential additions of media into each of four test chambers are outlined. The feasibility for multiple addition tests was established and various details of the methodology were studied. The four chamber battery of tests include: (1) determination of the effect of various atmospheric gases and selection of that gas which produces an optimum response; (2) determination of the effect of incubation temperature and selection of the optimum temperature for performing Martian biochemical tests; (3) sterile soil is dosed with a battery of C-14 labeled substrates and subjected to experimental temperature range; and (4) determination of the possible inhibitory effects of water on Martian organisms is performed initially by dosing with 0.01 ml and 0.5 ml of medium, respectively. A series of specifically labeled substrates are then added to obtain patterns in metabolic 14CO2 (C-14)O2 evolution.

Source record↗
NASA NTRS1974

Studies on propagation of microbes in the airborne state

An investigation was conducted to demonstrate whether airborne microbes could propagate. The procedure consisted of: (1) looking for dilution of a labelled base in DNA; (2) looking for labelling of DNA by mixing aerosols of the label and the cells; (3) examining changes in cell size; (4) testing the possibility of spore germination; and (5) seeking evidence of an increase in cell number. Results indicate that growth and propagation can occur under special conditions, principally at temperatures of approximately 30 C (87 F) and water activity equivalents of 0.95 to 0.98.

Dimmick, R. L.↗
NASA NTRS1976

Estimation of the probability of error without ground truth and known a priori probabilities

The probability of error or, alternatively, the probability of correct classification (PCC) is an important criterion in analyzing the performance of a classifier. Labeled samples (those with ground truth) are usually employed to evaluate the performance of a classifier. Occasionally, the numbers of labeled samples are inadequate, or no labeled samples are available to evaluate a classifier's performance; for example, when crop signatures from one area from which ground truth is available are used to classify another area from which no ground truth is available. This paper reports the results of an experiment to estimate the probability of error using unlabeled test samples (i.e., without the aid of ground truth).

Havens, K. A.↗
NASA NTRS1978

The biology instrument for the Viking Mars mission

Two Viking spacecraft have successfully soft landed on the surface of Mars. Each carries one biology laboratory with three different experiments designed to search for evidence of living microorganisms in material sampled from the Martian surface. This 15.5-kg biology instrument which occupies a volume of almost 28.3 dm is the first to carry out an in situ search for extraterrestrial life on a planet. The three experiments are called the pyrolytic release, labeled release, and gas exchange. The pyrolytic release experiment has the capability to measure the fixation of carbon dioxide or carbon monoxide into organic matter. The labeled release experiment detects metabolic processes by monitoring the production of volatile carbon compounds from a radioactively labeled nutrient mixture. The gas exchange experiment monitors the gas changes in the head space above a soil sample which is either incubated in a humid environment or supplied with a rich organic nutrient solution.

Brown, F. S.↗
NASA NTRS1979

Clustering algorithm evaluation and the development of a replacement for procedure 1

An efficient procedure which clusters data using a completely unsupervised clustering algorithm and then uses labeled pixels to label the resulting clusters or perform a stratified estimate using the clusters as strata is developed. Three clustering algorithms, CLASSY, AMOEBA, and ISOCLS, are compared for efficiency. Three stratified estimation schemes and three labeling schemes are also considered and compared.

Lennington, R. K.↗
NASA NTRS1980

Extremes of urine osmolality - Lack of effect on red blood cell survival

Rats were allowed a third of normal water intake for 20 days, and food consumption decreased. The reticulocyte count indicated a suppression of erythropoiesis. Urine osmolality increased from 2,000 mosmol/kg to 3,390 mosmol/kg. Random hemolysis and senescence of a cohort of red blood cell (RBC) previously labeled with (2-(C-14)) glycine was monitored via the production of (C-14)O. Neither hemolysis nor senescence was affected. Following water restriction, the polydipsic rats generated a hypotonic urine. Urine osmolality decreased to 1,300 mosmol/kg for at least 6 days; a reticulocytosis occurred, but RBC survival was unaffected. These results contradict those previously reported, which suggest that RBC survival is influenced by the osmotic stress imposed on the RBC by extremes of urine tonicity. This discrepancy, it is concluded, is due to differences in the methods employed for measuring RBC survival. The random-labeling technique employed previously assumes a steady state between RBC production and destruction. The cohort-labeling technique used here measures hemolysis and senescence independent of changes in RBC production, which is known to be depressed by fasting.

Leon, H. A.↗
NASA NTRS1980

As-built design specification for P1A software system modified display subsystem

This document contains the design of the proportional estimate processor which was written to satisfy the software requirement of Part A of the P1A experiment. The purposes of the project are: (1) to select the dots to be labelled; (2) to create tables of green numbers and brightness values for all selected dots per acquisition; (3) to create scatter plots of green numbers vs brightness for each acquisition for all selected dots. If labels have been provided then scatter plots of only categories of interest can be optionally produced; and (4) to produce trajectory plots of green number vs brightness at differing acquisition times for each dot. These plots need to be in the same order as the list of selected dots. When labels are provided only plots of dots of categories of interest are to be produced.

Horton, C. L.↗
NASA NTRS1980

Numerical trials of HISSE

The mathematical description and implementation of the statistical estimation procedure known as the Houston integrated spatial/spectral estimator (HISSE) is discussed. HISSE is based on a normal mixture model and is designed to take advantage of spectral and spatial information of LANDSAT data pixels, utilizing the initial classification and clustering information provided by the AMOEBA algorithm. The HISSE calculates parametric estimates of class proportions which reduce the error inherent in estimates derived from typical classify and count procedures common to nonparametric clustering algorithms. It also singles out spatial groupings of pixels which are most suitable for labeling classes. These calculations are designed to aid the analyst/interpreter in labeling patches with a crop class label. Finally, HISSE's initial performance on an actual LANDSAT agricultural ground truth data set is reported.

Peters, C.↗
NASA NTRS1981

Users manual for the US baseline corn and soybean segment classification procedure

A user's manual for the classification component of the FY-81 U.S. Corn and Soybean Pilot Experiment in the Foreign Commodity Production Forecasting Project of AgRISTARS is presented. This experiment is one of several major experiments in AgRISTARS designed to measure and advance the remote sensing technologies for cropland inventory. The classification procedure discussed is designed to produce segment proportion estimates for corn and soybeans in the U.S. Corn Belt (Iowa, Indiana, and Illinois) using LANDSAT data. The estimates are produced by an integrated Analyst/Machine procedure. The Analyst selects acquisitions, participates in stratification, and assigns crop labels to selected samples. In concert with the Analyst, the machine digitally preprocesses LANDSAT data to remove external effects, stratifies the data into field like units and into spectrally similar groups, statistically samples the data for Analyst labeling, and combines the labeled samples into a final estimate.

Horvath, R.↗