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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A deep learning approach to identify missing is-a relations in SNOMED CT

Abstract Objective SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT. Materials and Methods Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances to train a model predicting whether an is-a relation exists between 2 concepts with containment pattern. The model is a binary classifier leveraging concept name features, hierarchical features, enriched lexical attribute features, and logical definition features. We introduce a cross-validation inspired approach to identify missing is-a relations among all hierarchically unrelated containment concept-pairs. Results We trained and applied our model on the Clinical finding subhierarchy of SNOMED CT (September 2019 US edition). Our model (based on the validation sets) achieved a precision of 0.8164, recall of 0.8397, and F1 score of 0.8279. Applying the model to predict actual missing is-a relations, we obtained a total of 1661 potential candidates. Domain experts performed evaluation on randomly selected 230 samples and verified that 192 (83.48%) are valid. Conclusions The results showed that our deep learning approach is effective in uncovering missing is-a relations between containment concept-pairs in SNOMED CT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multivariate Testing of Sampling Techniques to Address Class Imbalance in Building Use Type Classification

This study addresses the challenges inherent in building use type classification, particularly focusing on the issue of class imbalance in the training datasets for machine learning classifiers. We comprehensively analyze the efficacy of various class-balancing sampling techniques. Employing Monte Carlo simulations and Bayesian optimization, we evaluated the performance of multiple sampling methods, including Random Oversampling, Random Undersampling, SMOTE, Borderline-SMOTE, and ADASYN, across a dataset encompassing nine southeastern coastal states of the United States. Our findings reveal that simple random over- and undersampling techniques outperform more sophisticated methods. Additionally, we show inherent value in creating an imbalance in training data to effectively train a machine learning classifier for distinguishing between residential and nonresidential buildings. This study provides valuable guidance for future research on building use type classification research and lays essential groundwork for developing attribute-rich building stock datasets.

Adams, Daniel↗
DOE OSTI2020

Neural MUSE Analysis

Researchers at Oak Ridge National Laboratory (ORNL) created data as part of the MUSE (Multi-Agency Urban Search Experiment Detector and Algorithm Test Bed) project simulating illicit nuclear materials located in various buildings along a road. In the simulation, a truck containing a radiation detector drives down the road gathering listmode data (counting the and energy of incident gamma radiation). Building materials, source shielding, driving speed, truck direction, truck location on the road, source type, and source placement are all varied between runs of the data set. This data was created using deterministic neutron transport and Monte Carlo methods through a combination of SCALE, MAVRIC, MCNP, and GADRAS. As part of a follow-on NA-22 project, two Kaggle competitions were created to determine the best algorithms for finding and identifying gamma sources in this simulated urban environment. The winning algorithm was neural network-based and had a test accuracy of 76.4% accuracy for source identification. This work seeks to build upon this work and improve the results through the application of novel machine learning techniques. As a first step, the data was classified by a simple Convolutional Neural Network (CNN) To accomplish this, the data was first preprocessed into “waterfall plots.” These plots are composed of energy vs count plots that are stacked vertically to show progression in time. The horizontal axis indicating the particle energy incorporated user defined bin spacing with options for in linear-, logarithmic-, square root-, and user-spaced bins. The z or color dimension showed the number of counts corresponding the energy-time combination. This data was then used to generate more data, by generating a local estimate of the mean of the distribution for a bin and then randomly re-sampling that bin from a Poisson distribution. Once all of this data was generated, it was fed into a well-known CNN architecture, ResNet50. The output layer of this model was removed and replaced with layers corresponding to the shape desired isotope outputs. The provided training data was used to train the classifier and the remaining testing data was used to evaluate the model. Results are soon to be forthcoming.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

The effects of 8-week complex training on lower-limb strength and power of Chinese elite female modern pentathlon athletes

Complex training (CT) is a combination training method that alternates between performing high-load resistance training (RT) and plyometric training within one single session. The study aimed to examine the effects of CT on lower-limb strength and power of elite female modern pentathlon athletes under the new modern pentathlon format and competition rules. Ten female participants (age: 23.55 ± 2.22 years, weight: 60.59 ± 3.87 kg, height: 169.44 ± 4.57 cm, and training experience: 6.90 ± 2.08 years) of the national modern pentathlon team completed 8 weeks of RT as followed by 8 weeks of CT, with 2 weeks of break. Then, the participants conducted 8 weeks of CT, which included RT combined with plyometric training (e.g., drop jump and continuous jump). All stages of training were designed by the linear strength training period theories, requiring participants to train twice for the first 4 weeks and three times for the second 4 weeks. The one-repetition maximum (1RM) of squat, isometric mid-thigh pull (IMTP), counter-movement jump (CMJ), squat jump (SJ), pre-stretch augmentation percentage (PSAP), and reaction strength index (RSI) were assessed before and after both RT and CT training. One-way repeated-measure ANOVA models revealed that the 1RM of squat was significantly improved ( p < 0.001) after RT as compared to pre-RT. No significant improvement in IMTP ( p = 0.055), CMJ ( p = 0.194), SJ ( p = 0.692), PSAP ( p = 0.087), and RSI ( p = 0.238) was not observed. After CT, 1RM of squat ( p < 0.001), IMTP ( p < 0.035), CMJ ( p < 0.001), SJ ( p < 0.008), RSI ( p < 0.006) were significant improved as compared to pre-RT, post-RT and pre-CT, while significant improvements in PSAP were observed as compared to pre-RT ( p = 0.003) and pre-CT ( p = 0.027), but not to post-RT ( p = 0.156). This pilot study showed the promise of CT following RT to improve lower-limb strength and power in elite female modern pentathlon athletes. The findings are worthwhile to be confirmed in future studies with larger sample size and randomized design.

Qiao, Zining↗
DOE OSTI2022

Characterization of Senecavirus A Isolates Collected From the Environment of U.S. Sow Slaughter Plants

Vesicular disease caused by Senecavirus A (SVA) is clinically indistinguishable from foot-and-mouth disease (FMD) and other vesicular diseases of swine. When a vesicle is observed in FMD-free countries, a costly and time-consuming foreign animal disease investigation (FADI) is performed to rule out FMD. Recently, there has been an increase in the number of FADIs and SVA positive samples at slaughter plants in the U.S. The objectives of this investigation were to: (1) describe the environmental burden of SVA in sow slaughter plants; (2) determine whether there was a correlation between PCR diagnostics, virus isolation (VI), and swine bioassay results; and (3) phylogenetically characterize the genetic diversity of contemporary SVA isolates. Environmental swabs were collected from three sow slaughter plants (Plants 1-3) and one market-weight slaughter plant (Plant 4) between June to December 2020. Of the 426 samples taken from Plants 1-3, 304 samples were PCR positive and 107 were VI positive. There was no detection of SVA by PCR or VI at Plant 4. SVA positive samples were most frequently found in the summer (78.3% June-September, vs. 59.4% October-December), with a peak at 85% in August. Eighteen PCR positive environmental samples with a range of C t values were selected for a swine bioassay: a single sample infected piglets ( n = 2). A random subset of the PCR positive samples was sequenced; and phylogenetic analysis demonstrated co-circulation and divergence of two genetically distinct groups of SVA. These data demonstrate that SVA was frequently found in the environment of sow slaughter plants, but environmental persistence and diagnostic detection was not indicative of whether a sampled was infectious to swine. Consequently, a more detailed understanding of the epidemiology of SVA and its environmental persistence in the marketing chain is necessary to reduce the number of FADIs and aide in the development of control measures to reduce the spread of SVA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Forward Global Photometric Calibration of the Dark Energy Survey

Many scientific goals for the Dark Energy Survey (DES) require the calibration of optical/NIR broadband b = grizY photometry that is stable in time and uniform over the celestial sky to one percent or better. It is also necessary to limit to similar accuracy systematic uncertainty in the calibrated broadband magnitudes due to uncertainty in the spectrum of the source. Here we present a "Forward Global Calibration Method (FGCM)" for photometric calibration of the DES, and we present results of its application to the first three years of the survey (Y3A1). The FGCM combines data taken with auxiliary instrumentation at the observatory with data from the broadband survey imaging itself and models of the instrument and atmosphere to estimate the spatial and time dependences of the passbands of individual DES survey exposures. "Standard" passbands that are typical of the passbands encountered during the survey are chosen. The passband of any individual observation is combined with an estimate of the source spectral shape to yield a magnitude in the standard system. This "chromatic correction" to the standard system is necessary to achieve subpercent calibrations and in particular, to resolve ambiguity between the broadband brightness of a source and the shape of its SED. The FGCM achieves a reproducible and stable photometric calibration of standard magnitudes of stellar sources over the multiyear Y3A1 data sample with residual random calibration errors of per exposure. The accuracy of the calibration is uniform across the DES footprint to within . The systematic uncertainties of magnitudes in the standard system due to the spectra of sources are less than for main-sequence stars with .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1978

Nationwide forestry applications program: Procedure 1 applicability to rangeland classification

The author has identified the following significant results. An assumption that short prairie grass and salt grass could be differentiated on aircraft photographs was inaccurate for the Weld County site. However, rangeland could be differentiated using procedure 1 from LACIE. Estimates derived from either random or systematic sampling were satisfactory. Level 1 features were separated and mapped, and proportions were estimated with accompanying confidence statements.

Reeves, C. A.↗
NASA NTRS1977

On the constancy of the lunar cratering flux over the past 3.3 billion yr

Utilizing a method that minimizes random fluctuations in sampling crater populations, it can be shown that the ejecta deposit of Tycho, the floor of Copernicus, and the region surrounding the Apollo 12 landing site have incremental crater size-frequency distributions that can be expressed as log-log linear functions over the diameter range from 0.1 to 1 km. Slopes are indistinguishable for the three populations, probably indicating that the surfaces are dominated by primary craters. Treating the crater populations of Tycho, the floor of Copernicus, and Apollo 12 as primary crater populations contaminated, but not overwhelmed, with secondaries, allows an attempt at calibration of the post-heavy bombardment cratering flux. Using the age of Tycho as 109 m.y., Copernicus as 800 m.y., and Apollo 12 as 3.26 billion yr, there is no basis for assuming that the flux has changed over the past 3.3 billion yr. This result can be used for dating intermediate aged surfaces by crater density.

Guinness, E. A.↗
NASA NTRS1980

Confidence regions of planar cardiac vectors

A method for plotting the confidence regions of vectorial data obtained in electrocardiology is presented. The 90%, 95% and 99% confidence regions of cardiac vectors represented in a plane are obtained in the form of an ellipse centered at coordinates corresponding to the means of a sample selected at random from a bivariate normal distribution. An example of such a plot for the frontal plane QRS mean electrical axis for 80 horses is also presented.

Dubin, S.↗
NASA NTRS1981

Development of alternative data analysis techniques for improving the accuracy and specificity of natural resource inventories made with remote sensing data

The applicability of digital LANDSAT data in updating the Minnesota Lake Land Management Information System and in assessing the trophic status of lakes was investigated. Data from various combinations of training, classification, and geometric correction techniques were compared to a photointerpreted reference data set consisting of ground-verified samples and six randomly selected photographs covering approximately one quarter of the study area. For a reconnaissance inventory, the training via polygons selected from aerial photographs with a canonical analysis minimum distance classifier is the most accurate and efficient analysis technique. The applications test project resulted in disappointing thematic classification results per se, but did contribute to the development of mechanisms for implementing a LANDSAT data processing capability in the State Planning Agency on a permanent basis.

Lillesand, T. M.↗
NASA NTRS1991

Expected antenna utilization and overload

The trade-offs between the number of antennas at Deep Space Network (DSN) Deep-Space Communications Complex and the fraction of continuous coverage provided to a set of hypothetical spacecraft, assuming random placement of the space craft passes during the day. The trade-offs are fairly robust with respect to the randomness assumption. A sample result is that a three-antenna complex provides an average of 82.6 percent utilization of facilities and coverage of nine spacecraft that each have 8-hour passes, whereas perfect phasing of the passes would yield 100 percent utilization and coverage. One key point is that sometimes fewer than three spacecraft are visible, so an antenna is idle, while at other times, there aren't enough antennas, and some spacecraft do without service. This point of view may be useful in helping to size the network or to develop a normalization for a figure of merit of DSN coverage.

Posner, Edward C.↗
NASA NTRS1996

Ulysses observations of double ion beams associated with coronal mass ejections

Coronal mass ejections (CME's) are thought to result from the loss of stability within a magnetically confined coronal structure leading to its radial expansion into interplanetary space. As the CME expands into the corona current sheets will form between the expanding CME and surrounding field lines in the ambient wind. This configuration may lead to reconnection between the CME and adjacemt field lines. Such reconnection may produce double ion beams as has been observed in the terrrestrial magnetosphere. We examine all 24 distinct signatures of CME's observed by Ulysses during the in-ecliptic portion of the mission. In 5 of these 24 cases the ion spectra were not clear and thus the presence of double ion beams could not be determined. In 13 of the remaining 19 CME's double ion beams were found on the leading and/or trailing edge of the CME but not in the interior of the CME. In 3 of the CME's double ion beams were found throughout the CME while in the remaining 3 CME's double ion beams were not present near or just inside of the CME. In contrast in a control sample of 19 randomly chosen intervals, double ion beams were present at the leading and/or trailing edges of the random intervals in only 3 of the 19 cases. There appears to be no correlation between probability of occurrence of double ion beams and a magnetic cloud or non-cloud configuration of the CME and no correlation between the presence of the double ion beams at the edges of the CME and the CME being a fast or slow CME.

Hammond, C. M.↗
NASA NTRS2004

Progressive Response Surfaces

Response surface functions are often used as simple and inexpensive replacements for computationally expensive computer models that simulate the behavior of a complex system over some parameter space. Progressive response surfaces are ones that are built up progressively as global information is added from new sample points in the parameter space. As the response surfaces are globally upgraded based on new information, heuristic indications of the convergence of the response surface approximation to the exact (fitted) function can be inferred. Sampling points can be incrementally added in a structured fashion, or in an unstructured fashion. Whatever the approach, at least in early stages of sampling it is usually desirable to sample the entire parameter space uniformly. At later stages of sampling, depending on the nature of the quantity being resolved, it may be desirable to continue sampling uniformly over the entire parameter space (Progressive response surfaces), or to switch to a focusing/economizing strategy of preferentially sampling certain regions of the parameter space based on information gained in early stages of sampling (Adaptive response surfaces). Here we consider Progressive response surfaces where a balanced indication of global response over the parameter space is desired.We use a variant of Moving Least Squares to fit and interpolate structured and unstructured point sets over the parameter space. On a 2-D test problem we compare response surface accuracy for three incremental sampling methods: Progressive Lattice Sampling; Simple-Random Monte Carlo; and Halton Quasi-Monte-Carlo sequences. We are ultimately after a system for constructing efficiently upgradable response surface approximations with reliable error estimates.

Romero, V. J.↗
NASA NTRS2004

Measures and Models for Estimating and Predicting Cognitive Fatigue

We analyzed EEG and ERPs in a fatiguing mental task and created statistical models for single subjects. Seventeen subjects (4 F, 18-38 y) viewed 4-digit problems (e.g., 3+5-2+7=15) on a computer, solved the problems, and pressed keys to respond (intertrial interval = 1 s). Subjects performed until either they felt exhausted or three hours had elapsed. Re- and post-task measures of mood (Activation Deactivation Adjective Checklist, Visual Analogue Mood Scale) confirmed that fatigue increased and energy decreased over time. We tested response times (RT); amplitudes of ERP components N1, P2, P300, readiness potentials; and amplitudes of frontal theta and parietal alpha rhythms for change as a function of time. For subjects who completed 3 h (n=9) we analyzed 12 15-min blocks. For subjects who completed at least 1.5 h (n=17), we analyzed the first-, middle-, and last 100 error-free trials. Mean RT rose from 6.7 s to 8.5 s over time. We found no changes in the amplitudes of ERP components. In both analyses, amplitudes of frontal theta and parietal alpha rose by 30% or more over time. We used 30-channel EEG frequency spectra to model the effects of time in single subjects using a kernel partial least squares classifier. We classified 3.5s EEG segments as being from the first 100 or the last 100 trials, using random sub-samples of each class. Test set accuracies ranged from 63.9% to 99.6% correct. Only 2 of 17 subjects had mean accuracies lower than 80%. The results suggest that EEG accurately classifies periods of cognitive fatigue in 90% of subjects.

Trejo, Leonard J.↗
NASA NTRS2011

Freeboard, Snow Depth and Sea-Ice Roughness in East Antarctica from In Situ and Multiple Satellite Data

In October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50 500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. The results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

Markus, Thorsten↗
NASA NTRS2013

Simultaneous Laser Ranging and Communication from an Earth-Based Satellite Laser Ranging Station to the Lunar Reconnaissance Orbiter in Lunar Orbit

We report a free space laser communication experiment from the satellite laser ranging (SLR) station at NASA Goddard Space Flight Center (GSFC) to the Lunar Reconnaissance Orbiter (LRO) in lunar orbit through the on board one-way Laser Ranging (LR) receiver. Pseudo random data and sample image files were transmitted to LRO using a 4096-ary pulse position modulation (PPM) signal format. Reed-Solomon forward error correction codes were used to achieve error free data transmission at a moderate coding overhead rate. The signal fading due to the atmosphere effect was measured and the coding gain could be estimated.

fee-space optical communication↗