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At least 811 records · Page 45

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NASA NTRS2002

Simultaneous dual-task performance reveals parallel response selection after practice

E. H. Schumacher, T. L. Seymour, J. M. Glass, D. E. Kieras, and D. E. Meyer (2001) reported that dual-task costs are minimal when participants are practiced and give the 2 tasks equal emphasis. The present research examined whether such findings are compatible with the operation of an efficient response selection bottleneck. Participants trained until they were able to perform both tasks simultaneously without interference. Novel stimulus pairs produced no reaction time costs, arguing against the development of compound stimulus-response associations (Experiment 1). Manipulating the relative onsets (Experiments 2 and 4) and durations (Experiments 3 and 4) of response selection processes did not lead to dual-task costs. The results indicate that the 2 tasks did not share a bottleneck after practice.

Practice (Psychology)↗

NASA NTRS2011

Molecular Mechanisms of Circadian Regulation During Spaceflight

Disruption of the regular environmental circadian cues in addition to stringent and demanding operational schedules are two main factors that undoubtedly impact sleep patterns and vigilant performance in the astronaut crews during spaceflight. Most research is focused on the behavioral aspects of the risk of circadian desynchronization, characterized by fatigue and health and performance decrement. A common countermeasure for circadian re-entrainment utilizes blue-green light to entrain the circadian clock and mitigate this risk. However, an effective countermeasure targeting the photoreceptor system requires that the basic circadian molecular machinery remains intact during spaceflight. The molecular clock consists of sets of proteins that perform different functions within the clock machinery: circadian oscillators (genes whose expression levels cycle during the day, keep the pass of cellular time and regulate downstream effector genes), the effector or output genes (those which impact the physiology of the tissue or organism), and the input genes (responsible for sensing the environmental cues that allow circadian entrainment). The main environmental cue is light. As opposed to the known photoreceptors (rods and cones), the non-visual light stimulus is received by a subset of the population of retinal ganglion cells called intrinsically photosensitive retinal ganglion cells (ipRGC) that express melanopsin (opsin 4 -Opn4-) as the photoreceptor. We hypothesize that spaceflight may affect ipRGC and melanopsin expression, which may be a contributing cause of circadian disruption during spaceflight. To answer this question, eyes from albino Balb/cJ mice aboard STS-133 were collected for histological analysis and gene expression profiling of the retina at 1 and 7 days after landing. Both vivarium and AEM (animal enclosure module) mice were used as ground controls. Opn4 expression was analyzed by real time RT/qPCR and retinal sections were stained for Opn4 immunofluorescence. Opn4 was decreased (abrogated in one case) in retinas that concurrently showed higher evidence of oxidative stress. We propose that oxidative stress can lead to a decrease in melanopsin expression, likely via ipRGC loss or impairment, and thus, it can be a contributing factor to circadian disruption during spaceflight. Countermeasures contemplating the use of light should therefore be complemented with melanopsin expression maintenance and/or reduction in oxidative stress.

Zanello, Susana↗

NASA NTRSDate not supplied

Flow Regime Transitions and Pressure Drop in Gas-Liquid Flows through Packed Beds in Microgravity

The Packed Bed Reactor Experiment (PBRE) focused on the hydrodynamics of gas-liquid flow in porous media, specifically on flow regime transition and pressure drop correlation. The reactor included a cylindrical test section of 60 cm in length, 5 cm in diameter and filled with 2 mm spherical glass beads. Liquid water and Nitrogen gas were flowed into a centrifugal phase separator after exiting the test section. Five absolute pressure transducers and two high-speed video cameras spanned the test section to measure the pressure drop and capture flow regime behavior. Video observations, analysis of pressure traces, and pressure drop data were used to identify flow patterns. It was found that the slope of pressure gradient data when plotted versus the gas or liquid Reynolds numbers changed when the flow regime changed. This observation was used to identify the flow regime boundaries. At high liquid flow rates (high interaction regime), dispersed bubble and pulse flow regimes were detected. At low liquid flow rates (low interaction regime), large bubble and gas channeling regimes were observed. The transition from the big bubble regime to the gas channeling regime occurred at a critical gas flow rate independent of the liquid flow rate. Similarly, the transition from the low interaction to the high interaction regime occurred at a critical liquid flow rate. For these different flow regimes, different two-phase flow friction factors and pressure gradient correlations were fitted as a function of the gas and liquid modified Reynolds numbers and Suratman number. It was found that in all the flow regimes the capillary contribution to the pressure drop is significant and can be as high as 90% of the total pressure drop.

Brian Motil↗

NASA NTRSDate not supplied

Natural Fiber Simulations and Analysis for Composites

As the U.S. government and the aviation industry strive to achieve net-zero carbon emissions by 2050, the Sustainable Manufacturing of Aircraft (SUMAC) project, supported by the Convergent Aeronautics Solutions (CAS) program of the National Aeronautics and Space Administration (NASA), aims to develop sustainably derived thermoplastic composites and manufacturing technologies for future aviation. A multidisciplinary team of experts drives this initiative through a collective effort. The team’s key focus areas are sustainably derived thermoplastic resins, natural fiber reinforcement, composite manufacturing, structural health monitoring, computational materials modeling, and systems analysis. This presentation will give an overview of the SUMAC project. Fiber-scale microscopy analysis characterized fiber cross sections finding that hemp and flax fibers are very deformable and non-circular (circularities of 0.2 were observed), and diameters range from submicron to 20 μm. Fiber shapes range from flattened, with aspect ratios Rlong/Rshort up to 5, to round, Rlong/Rshort =1. And Fiber core perimeters, which likely drives deformability, were calculated Pcore=7 μm. Explicit-fiber yarn simulations, parameterized from single-fiber measurements, resolved and identified slack as a cause for yarn strain-hardening tensile behavior. The bonded network model of PLA, PHA and PA11 resins captures tensile behavior, and can model an infused weave. Computational models of the weave unit cells matched the pattern (including plain, twill and triaxial braid), yarn ply (1to 2), and yarn twist from optical microscopy.

thermoplastic↗

NASA NTRSDate not supplied

Natural Fiber Simulations and Analysis for Composites

thermoplastic↗

DOE OSTI2015

Structural, dielectric and magnetic behavior of nanocrystalline zinc substituted magnesium ferrite

Zinc substituted magnesium ferrites Zn{sub 0.2}Mg{sub 0.8}Fe{sub 2}O{sub 4} and Zn{sub 0.4}Mg{sub 0.6}Fe{sub 2}O{sub 4} were prepared by sol-gel auto combustion method. Rietveld profile refinement of the XRD patterns confirms the formation of a cubic spinel structure in single phase. The dielectric properties viz. dielectric constant and dielectric loss tangent tanδ increase with increasing temperature. The dielectric behavior is explained by using the mechanism of polarization process, which is correlated to that of electron exchange interaction. The saturation magnetization, coercivity and remanent magnetization decreases appreciably with increase in Zn which could be attributed to change in cation distribution.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Effect of CdTe Addition on the Electrical Properties and AC Susceptibility of YBa{sub 2}Cu{sub 3}O{sub 7−δ} Superconductor

The effect of CdTe addition on YBa{sub 2}Cu{sub 3}O{sub 7−δ}(CdTe){sub x} (x = 0–0.10) has been studied. XRD patterns showed the presence of impurities including CdTe for x ≥ 0.05 wt%. The resistance versus temperature curves showed metallic behavior for all samples. The onset temperature T{sub c onset} for all samples is between 90 and 92 K. The superconducting transition width, ΔT{sub c} = 4 K for all samples except for x = 0.1 wt% where ΔT{sub c} = 6 K. The superconducting transition determined by AC susceptibility measurement also showed little change in T{sub cχ{sup ′}} (between 89 and 92 K). However, the peak temperature T{sub p} of the imaginary part of the susceptibility χ″, showed a drastic decrease for samples with x > 0.05 wt%. This indicated that the superconducting grains were strongly decoupled for x > 0.05 wt% due to the impurities as observed in the XRD pattern. The intergrain critical current density, J{sub c} at T{sub p} for the x = 0 sample is J{sub c} (82 K) = 17 A cm{sup −2}. These results can be useful in the fabrication of semiconductor/YBCO superconductor hybrid systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2015

A Wavelet Analysis Approach for Categorizing Air Traffic Behavior

In this paper two frequency domain techniques are applied to air traffic analysis. The Continuous Wavelet Transform (CWT), like the Fourier Transform, is shown to identify changes in historical traffic patterns caused by Traffic Management Initiatives (TMIs) and weather with the added benefit of detecting when in time those changes take place. Next, with the expectation that it could detect anomalies in the network and indicate the extent to which they affect traffic flows, the Spectral Graph Wavelet Transform (SGWT) is applied to a center based graph model of air traffic. When applied to simulations based on historical flight plans, it identified the traffic flows between centers that have the greatest impact on either neighboring flows, or flows between centers many centers away. Like the CWT, however, it can be difficult to interpret SGWT results and relate them to simulations where major TMIs are implemented, and more research may be warranted in this area. These frequency analysis techniques can detect off-nominal air traffic behavior, but due to the nature of air traffic time series data, so far they prove difficult to apply in a way that provides significant insight or specific identification of traffic patterns.

wavelet analysis↗

DOE OSTI2020

In Situ Synchrotron X-ray Diffraction Investigations of the Nonlinear Deformation Behavior of a Low Modulus β -Type Ti36Nb5Zr Alloy

The low modulus β-type Ti alloys usually have peculiar deformation behaviors due to their low phase stability. However, the study of the underlying mechanisms is challenging since some physical mechanisms are fully reversible after the release of the load. In this paper, the deformation behavior of a low modulus β-type Ti36Nb5Zr alloy was investigated with the aid of in situ synchrotron X-ray diffraction (SXRD) during tensile loading. The evolution of lattice strains and relative integrated diffraction peak intensities of both the β and α” phases were analyzed to determine the characteristics of the potential deformation mechanisms. Upon loading, the α” diffraction spots appeared at specific azimuth angles of the two-dimensional SXRD patterns due to the <110> fiber texture of original β grains and the selection of favorable martensitic variants. The nonlinear deformation behavior originated from a reversible stress-induced martensitic transformation (SIMT). However, the SIMT contributed a little to the large recoverable strain of over 2.0%, which was dominated by the elastic deformation of the β phase. Various deformation mechanisms were activated successively at different applied strains, including elastic deformation, SIMT and plastic deformation. Our investigations provide in-depth understandings of the deformation mechanisms in β-type Ti alloys with low elastic modulus.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗

DOE OSTI2020

Emulating climate extreme indices

We use simple pattern scaling and time-shift to emulate changes in a set of climate extreme indices under future scenarios, and evaluate the emulators' accuracy. We propose a metric for the error in emulation in the context of initial condition ensembles, to specifically characterize the role of internal variability in the emulation performance. Our metric separates systematic emulation errors from unavoidable discrepancies between emulated and target values due to internal variability. We compute the metricis at grid-point scale, and we show geographically resolved results, or aggregate them at global scale. We demonstrate the use of our error metric in the emulation of a suite of temperature and precipitation extreme indices. We test and compare simple pattern scaling and time-shift using a range of trajectories spanning targets inspired by the Paris agreement -- warming to 1.5C and 2.0C from the pre-industrial baseline -- and two of the longer-established trajectories, RCP4.5 and RCP8.5. With this suite of scenarios we can test the effects on the performance of the size of the temperature gap between emulation origin and target. We find that for most indices emulation the dominant source of discrepancy is internal variability. For at least one index, however, counting exceedances of a high temperature threshold, significant portions of the globally aggregated discrepancy and its regional pattern originate from the systematic emulation error. This error exceeds internal variability of both the target and the emulated quantities in large coherent regions at low latitudes, and the explanation can be found in the differential behavior of temperature distributions across latitudes. The metric also highlights a fundamental difference in the two methods related to the simulation of internal variability, which is dampened significantly by simple pattern scaling. This aspect is of consequence when using these methods for specific applications, where preserving variability for uncertainty quantification is deemed important. With this study we offer our metric as a diagnostic tool, facilitating the formulation of scientific hypotheses on the reasons for the error. In the meantime, we show that for many impact relevant indices by now traditional emulation techniques can be accurate within the variations unavoidably introduced by internal variability, establishing the fundamental condition for using their emulation in impact modeling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

SU-F-T-91: Development of Real Time Abdominal Compression Force (ACF) Monitoring System

Purpose: Hard-plate based abdominal compression is known to be effective, but no explicit method exists to quantify abdominal compression force (ACF) and maintain the proper ACF through the whole procedure. In addition, even with compression, it is necessary to do 4D CT to manage residual motion but, 4D CT is often not possible due to reduced surrogating sensitivity. In this study, we developed and evaluated a system that both monitors ACF in real time and provides surrogating signal even under compression. The system can also provide visual-biofeedback. Methods: The system developed consists of a compression plate, an ACF monitoring unit and a visual-biofeedback device. The ACF monitoring unit contains a thin air balloon in the size of compression plate and a gas pressure sensor. The unit is attached to the bottom of the plate thus, placed between the plate and the patient when compression is applied, and detects compression pressure. For reliability test, 3 volunteers were directed to take several different breathing patterns and the ACF variation was compared with the respiratory flow and external respiratory signal to assure that the system provides corresponding behavior. In addition, guiding waveform were generated based on free breathing, and then applied for evaluating the effectiveness of visual-biofeedback. Results: We could monitor ACF variation in real time and confirmed that the data was correlated with both respiratory flow data and external respiratory signal. Even under abdominal compression, in addition, it was possible to make the subjects successfully follow the guide patterns using the visual biofeedback system. Conclusion: The developed real time ACF monitoring system was found to be functional as intended and consistent. With the capability of both providing real time surrogating signal under compression and enabling visual-biofeedback, it is considered that the system would improve the quality of respiratory motion management in radiation therapy. This research was supported by the Mid-career Researcher Program through NRF funded by the Ministry of Science, ICT & Future Planning of Korea (NRF-2014R1A2A1A10050270) and by the Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Heterothermic Migration Strategies in Flying Vertebrates

Migration is a widespread and highly variable trait among animals. Population-level patterns arise from individual-level decisions, including physiological and energetic constraints. Many aspects of migration are influenced by behaviors and strategies employed during periods of stopover, where migrants may encounter variable or unpredictable conditions. Thermoregulation can be a major cost for homeotherms which largely encounter ambient temperatures below the lower critical temperature during migration, especially during the rest phase of the daily cycle. In this review we describe the empirical evidence, theoretical models, and potential implications of bats and birds that use heterothermy to reduce thermoregulatory costs during migration. Torpor-assisted migration is a strategy described for migrating temperate insectivorous bats, whereby torpor can be used during periods of inactivity to drastically reduce thermoregulatory costs and increase net refueling rate, leading to shorter stopover duration, reduced fuel load requirement, and potential consequences for broad-scale movement patterns and survival. Hummingbirds can adopt a similar strategy, but most birds are not capable of torpor. However, there is an increasing recognition of the use of more shallow heterothermic strategies by diverse bird species during migration, with similarly important implications for migration energetics. A growing body of published literature and preliminary data from ongoing research indicate that heterothermic migration strategies in birds may be more common than traditionally appreciated. We further take a broad evolutionary perspective to consider heterothermy as an alternative to migration in some species, or as a conceptual link to consider alternatives to seasonal resource limitations. There is a growing body of evidence related to heterothermic migration strategies in bats and birds, but many important questions related to the broader implications of this strategy remain.

bats↗

NASA NTRS2018

Pattern Recognition Control Design

Spacecraft control algorithms must know the expected vehicle response to any command to the available control effectors, such as reaction thrusters or torque devices. Spacecraft control system design approaches have traditionally relied on the estimated vehicle mass properties to determine the desired force and moment, as well as knowledge of the effector performance to efficiently control the spacecraft. A pattern recognition approach was used to investigate the relationship between the control effector commands and spacecraft responses. Instead of supplying the approximated vehicle properties and the thruster performance characteristics, a database of information relating the thruster ring commands and the desired vehicle response was used for closed-loop control. A Monte Carlo simulation data set of the spacecraft dynamic response to effector commands was analyzed to establish the influence a command has on the behavior of the spacecraft. A tool developed at NASA Johnson Space Center to analyze flight dynamics Monte Carlo data sets through pattern recognition methods was used to perform this analysis. Once a comprehensive data set relating spacecraft responses with commands was established, it was used in place of traditional control methods and gains set. This pattern recognition approach was compared with traditional control algorithms to determine the potential benefits and uses.

Gambone, Elisabeth A.↗

DOE OSTI2024

Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels

Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.

Gokhale, Swapna↗

DOE OSTI2016

Storyline Visualizations of Eye Tracking of Movie Viewing

Storyline visualizations offer an approach that promises to capture the spatio-temporal characteristics of individual observers and simultaneously illustrate emerging group behaviors. We develop a visual analytics approach to parsing, aligning, and clustering fixation sequences from eye tracking data. Visualization of the results captures the similarities and differences across a group of observers performing a common task. We apply our storyline approach to visualize gaze patterns of people watching dynamic movie clips. Storylines mitigate some of the shortcomings of existent spatio-temporal visualization techniques and, importantly, continue to highlight individual observer behavioral dynamics.

dynamic time warping↗

DOE OSTI2022

The Effect of Acceleration on the Separation Force in Constrained-Surface Stereolithography

Constrained-surface-based stereolithography has recently attracted much attention from both academic and industrial communities. Despite numerous experimental, numerical and theoretical efforts, the fundamental need to reduce the separation force between the newly cured part and constrained surface has not yet been completely solved. In this paper, we develop a fluid dynamics approach, proposed in our previous work, to theoretically model the separation force in 3D printing of a cylindrical part for flat and patterned windows. We demonstrate the possibility of separation force reduction with an accelerated movement of the printing platform. In particular, we investigate behaviors of transient parameter, its reduction rate, and separation force reduction with respect to elevation speed and time. The proposed approach involves deceleration and acceleration stages and allows to achieve the force reduction for the entire printing process. Finally, we provide implicit analytical solutions for time moments when switching between the stages can be done without noticeable increase of separation force and explicit expression for separation force in case of patterned window.

Gritsenko, Dmitry (ORCID:0000000248181512)↗

DOE OSTI2026

NREL OpenPATH: An Open-Source, Extensible Platform for Instrumenting Travel Behavior Data

NREL OpenPATH is an open-source, extensible platform that allows communities to instrument their own travel behavior data. The platform consists of a smartphone app, server and analysis pipeline, and enables collection of opt-in, multi-modal, end-to-end travel diaries. It makes the aggregate statistics available via a public dashboard, and allows deployers to download and visualize trip and trajectory data through the admin dashboard. It also allows for customization of the initial demographic survey and the trip-level qualitative information collected. Our goal is to provide an easy-to-use tool that can democratize travel behavior data collection by empower communities of all sizes to recruit participants and obtain a holistic picture of their travel patterns. The platform has been used by close to 40 partners, to collect data from thousands of participants. Upon signing a simple MOU, it is currently available for free to universities, non-profits and public agencies in the United States.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗