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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

GeneLab for High Schools – Bioinformatic Training For Students And Educators

Modern biological sciences are increasingly based on high-throughput molecular techniques, including genomics, transcriptomics, and proteomics. NASA’s GeneLab program has collected extensive data from ‘omics’ studies, curated them into an accessible platform and provided data analysis/visualization tools to facilitate the generation of new hypotheses and research directions. GeneLab for High Schools (GL4HS), launched in 2017, has endeavored to utilize this database and provide tools for students to understand and analyze omics datasets whilst also learning about spaceflight research. The GL4HS program ran in person at Ames from 2017-2019 and has run virtually since 2020. Each year fifteen high school students are trained to analyze and interpret GeneLab transcriptomic data. Additionally, in the last several years we have expanded our “teacher training program” to include 10 teachers total in an effort to enable this program to be utilized in classrooms across the USA. Teachers also join the NASA GeneLab Education Working Group (EWG) enabling support as they implement custom GL4HS modules into their classrooms. The GL4HS program consists of three main components – (1) core learning modules, (2) networking and teamwork, and (3) an independent learning project. Students are also taught critical networking and science communication skills facilitating their ability to ‘sell their science’ in innovative and creative ways. This program has enabled students to learn about biology in space and to have a glimpse into the world of research for the first time. Many of the students in this program shared that the course was transformative to their perception about biological sciences and how it linked to other areas of STEM. The ultimate and long-term goal of GL4HS is to expand the program to multiple locations thereby facilitating the reach of NASA Space Biology beyond NASA-centric regions.

GeneLab↗
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
NASA NTRS2024

Crew State and Risk Model Development to Predict Hydration Status During Extravehicular Activity Training Events

Introduction: Hydration is critical for optimal human health and performance and dehydration can lead to impaired cardiovascular function, thermal dysregulation, decreased blood plasma volume, and cognitive impacts, particularly during physical activity. Prolonged and repeated extravehicular activities (EVA) without sufficiently available drinking water may increase risk for dehydration, which could impair crew health and impact mission success. Understanding hydration needs and potential effects on health and performance are necessary to optimize crew well-being and enable successful EVA objectives. This study aims to develop a model of hydration status during EVA using water balance techniques. Methods: Water balance measures were collected on 15 healthy astronauts who performed ≈6-hour simulated microgravity extravehicular activity (EVA) training in the NASA Neutral Buoyancy Laboratory (NBL). Data collected included pre-and post-EVA nude body weight (BW), maximum absorption garment (MAG) weight, Disposable In-suit Drink Bag (DIDB) weight, urine specific gravity (USG), and extra pre-EVA intake (W). Variables were combined to create the water balance model as pre-EVA (Hn)= BWn+ MAGn+ DIDBn+ Wnand post EVA (Hn+1) = BWn+1+ MAGn+1+ DIDBn+1. Urine specific gravity values were used to refine water balance measures into hydration categories: Hydrated, Marginally Hydrated, and Dehydrated. Results: Pre-EVA modeling indicated53% of crew were hydrated, 20% were marginally hydrated, and 27% were dehydrated. Alternately, Hn+1 showed 13% of crew remained hydrated, 47% were marginally hydrated, and 40% were dehydrated at the end of the EVA. Furthermore, 75% of the crewmembers who started sufficiently hydrated finished the run marginally hydrated or dehydrated. According to USG indices presented by Casa and Lawrence, et al. (2000), only 25% of the crew started and remained hydrated throughout the EVA, and those who were dehydrated at the outset stayed dehydrated. Conclusion: Model outcomes assessing hydration status during 6-hour simulated microgravity EVAs demonstrate the necessity to further address hydration requirements for optimal human performance during spaceflight and EVA. This study enables additional baseline development of the Crew State and Risk Model Hydration, Nutrition, and Waste Management component that aims to provide individualized crew state and risk predictions during EVAs. Reference: Casa, D. J., Armstrong, L. E., et al. (2000). National Athletic Trainers’ Association Position Statement: Fluid Replacement for Athletes. Journal of Athletic Training, 35:212-224.

L Cooper↗
NASA NTRSDate not supplied

AMSR-2 Daily Snow Depth Data Product Using a Neural Network Algorithm Trained by Collocated ICESat-2 Measurements

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu 1 et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu 2 et al. (2022,2024) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, a neural network (NN) algorithm, employing several channels from AMSR-2 and the humidity vertical profiles GMAO GEOS-IT, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained NN snow-depth was applied to 2014-2020 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. This paper used AMSR-2 data, a passive microwave instrument to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
NASA NTRSDate not supplied

EVA Planning: Using Neutral Buoyancy Laboratory (NBL) Training to Predict in-Flight Energy Expenditure

Metabolic rate (“met rate”) is the amount of energy expended over a period of time and is influenced by many factors including body composition, level of physical activity, resting metabolic rate, sex, age, and food intake. Met rate is measured during Extravehicular Activity (EVA) training at the Neutral Buoyancy Laboratory (NBL) and during in-flight EVAs through indirect calorimetry, calculating energy expenditure from respiratory measurements of O 2 consumption and/or CO 2 production. During Extravehicular Activity (EVA) planning, metabolic cost is important to consider and is used to inform EVA duration based on spacesuit consumables associated with life support systems. Currently, NBL and previous ISS EVA met rate data for specified crewmembers are utilized to predict in-flight EVA metabolic costs based on a proposed EVA timeline. Timeline data collected during training is used to relate met rates to specific EVA activities, which are in turn assigned to more generalized EVA task categories, categorizing by both task type and restraint type. EVA task categories include EVA Setup/Cleanup, Worksite Setup/Cleanup, Cable Routing, Bolts, Fluid Connectors, Electrical Connectors, R&R Work, Miscellaneous Work, Incapacitated Crew Rescue (being rescued or performing), Assisted Crew Rescue (being assisted or performing), and Translation. Restraint types consist of Free-Float, Body Restraint Tether (BRT), Articulating Portable Foot Restraint (APFR), and Space Station Remote Manipulator System (SSRMS). From a crewmember’s historical data, individualized 10th, 50th and 90th percentile met rate estimates are generated for each task category and used to estimate the proposed EVA timeline metabolic cost. In-flight metabolic data (“As-Executed”) from recent ISS US EVAs 85-88 (totaling eight EVA crewmember met rates) was compared with their predicted metabolic cost (“As-Planned”) to evaluate the accuracy of the current met rate estimation method. Across the four EVAs, As-Executed Cumulative EVA Total Metabolic Cost (M = 5893.78 BTU, SD = 816.60) was not significantly different compared to As-Planned Cumulative EVA Total Metabolic Cost (M = 6106.60 BTU, SD = 826.62; t(7) = 0.751 , p = .477). Though not a significant difference, generally, As-Planned total estimates were slightly higher than As-Executed total metabolic cost. Relative Error for Cumulative EVA Total Metabolic Cost ranged from -33% to 14.7%, depending on the crewmember and EVA. When comparing As-Planned to A-Executed EVA task categories for Bolts, Electrical Connectors, EVA Cleanup, EVA Setup, Miscellaneous Work, Translation, Worksite Cleanup, and Worksite Setup during these EVAs, no significant differences were observed, however, there was a significant difference in As-Planned (M = 783.71 BTU, SD = 408.88) compared to As-Executed (M = 608.87 BTU, SD = 425.29) metabolic cost for the task category of Repair-and-Replace (R&R) Work (t(17) = 3.21 , p = .005). Looking closer within the R&R Work task category, As-Executed R&R Work with Free-Float restraint type (M = 706.17 BTU, SD = 352.18) was significantly less than As-Planned R&R Work with Free-Float restraint type (M= 887.96 BTU, SD = 381.42; t(12) = 2.59, p < .024). As-Executed R&R Work with SSRMS Restraint type (M = 355.89 BTU, SD = 534.65) was not significantly different from As-Planned values (M = 512.69 BTU, SD = 383.34; t(4) = 1.88, p = 0.132). These findings suggest that the energy expended performing R&R Work (Free-Float) is lower in flight than predicted. Accurate predictions of the metabolic cost of EVA are essential for planning and executing successful ISS EVAs. Overall, the current met rate prediction method is similar to actual in-flight values, slightly erring on the side of overestimation. Future work includes analysis of more historical in-flight EVA data to increase the power of the analysis, evaluating the NBL-ISS met rate conversion factor between NBL and ISS tasks, as well as exploring methods of substitution when crewmembers are missing prior task category data.

Lauren Cox↗
DOE OSTI2010

Self-Consistant Numerical Modeling of E-Cloud Driven Instability of a Bunch Train in the CERN SPS

The simulation package WARP-POSINST was recently upgraded for handling multiple bunches and modeling concurrently the electron cloud buildup and its effect on the beam, allowing for direct self-consistent simulation of bunch trains generating, and interacting with, electron clouds. We have used the WARP-POSINST package on massively parallel supercomputers to study the growth rate and frequency patterns in space-time of the electron cloud driven transverse instability for a proton bunch train in the CERN SPS accelerator. Results suggest that a positive feedback mechanism exists between the electron buildup and the e-cloud driven transverse instability, leading to a net increase in predicted electron density. Comparisons to selected experimental data are also given. Electron clouds have been shown to trigger fast growing instabilities on proton beams circulating in the SPS and other accelerators. So far, simulations of electron cloud buildup and their effects on beam dynamics have been performed separately. This is a consequence of the large computational cost of the combined calculation due to large space and time scale disparities between the two processes. We have presented the latest improvements of the simulation package WARP-POSINST for the simulation of self-consistent ecloud effects, including mesh refinement, and generation of electrons from gas ionization and impact at the pipe walls. We also presented simulations of two consecutive bunches interacting with electrons clouds in the SPS, which included generation of secondary electrons. The distribution of electrons in front of the first beam was initialized from a dump taken from a preceding buildup calculation using the POSINST code. In this paper, we present an extension of this work where one full batch of 72 bunches is simulated in the SPS, including the entire buildup calculation and the self-consistent interaction between the bunches and the electrons. Comparisons to experimental data are also given.

43 PARTICLE ACCELERATORS↗
DOE OSTI2011

Direct Numerical Modeling of E-Cloud Driven Instability of a Bunch Train in the CERN SPS

The simulation package WARP-POSINST was recently upgraded for handling multiple bunches and modeling concurrently the electron cloud buildup and its effect on the beam, allowing for direct self-consistent simulation of bunch trains generating, and interacting with, electron clouds. We have used the WARP-POSINST package on massively parallel supercomputers to study the buildup and interaction of electron clouds with a proton bunch train in the CERN SPS accelerator. Results suggest that a positive feedback mechanism exists between the electron buildup and the e-cloud driven transverse instability, leading to a net increase in predicted electron density. Electron clouds have been shown to trigger fast growing instabilities on proton beams circulating in the SPS and other accelerators. So far, simulations of electron cloud buildup and their effects on beam dynamics have been performed separately. This is a consequence of the large computational cost of the combined calculation due to large space and time scale disparities between the two processes. We have presented the latest improvements of the simulation package WARP-POSINST for the simulation of self-consistent ecloud effects, including mesh refinement, and generation of electrons from gas ionization and impact at the pipe walls. We also presented simulations of two consecutive bunches interacting with electrons clouds in the SPS, which included generation of secondary electrons. The distribution of electrons in front of the first beam was initialized from a dump taken from a preceding buildup calculation using the POSINST code. In this paper, we present an extension of this work where one full batch of 72 bunches is simulated in the SPS, including the entire buildup calculation and the self-consistent interaction between the bunches and the electrons.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

ORNL Automated-In-Motion Evaluation Environment (AIMVEE) User Training and Testing Materials - U.S. Copyright TXu 1-797-273

The AIMVEE/WIM system electronically retrieves deployment information, identifies vehicle automatically, and determines total weight, individual wheel weight, individual axle weights, axle spacing, and center-of-balance for any wheeled vehicle in motion. The AIMVEE/WIM system can also perform these functions statically for both wheel vehicles and cargo with information. The AIMVEE/WIM system incorporates digital images and applies cubing algorithms to determine length, width, height for cubic dimensions of both vehicle and cargo. Once all this information is stored, it electronically links to data collection and dissemination systems to provide actual weight and measurement information for planning, deployment, and in-transit visibility. The Static Scale Conversion (SSC) system is an unique enhancement to the AIMVEE/WIM system. It enables a SSC to weigh and measure vehicles and cargo dynamically (i.e., as they pass over the large scale and is included in the AIMVEE computer code base. The material copyrighted is the ORNL Automated-In-Motion Vehicle Evaluation Environment (AIMVEE)/Weigh-In-Motion User Training and Testing material. It includes instructional material in the set-up, operation and tear-down of the AIMVEE/WIM system. It also includes a final exam associated with the training.

Automated-in-Motion Evaluation Environment (AIMVEE↗
DOE OSTI2014

Adaptive model training system and method

An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Adaptive model training system and method

An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Solar Training Network and Solar Ready Vets

In 2016, the White House announced the Solar Ready Vets program, funded under DOE's SunShot initiative would be administered by The Solar Foundation to connect transitioning military personnel to solar training and employment as they separate from service. This presentation is geared to informing and recruiting employer partners for the Solar Ready Vets program, and the Solar Training Network. It describes the programs, and the benefits to employers that choose to connect to the programs.

14 SOLAR ENERGY↗
DOE OSTI2015

Four key areas for training the next-generation of global change researchers

Anthropogenic activities are changing the global environment in numerous ways, including increasing concentration of atmospheric carbon dioxide, alteration of biogeochemical cycles and changing land-cover types. These changes are increasing stresses on ecosystem goods and services, driving climate change and thus creating a few grand challenges for sustainability of the human society. Improving our understanding of these profound changes and grand challenges will help us in developing better predictive capabilities for the future. As these changes are expected to be long-lasting and we still do not have a complete understanding of their causes and consequences, the next generation of scientists needs to be properly trained in knowledge, methods and techniques in order to meet the research needs to tackle future challenges in global change ecology research. This guest editorial discusses the challenges and opportunities in preparing the next-generation global change researchers according to four key aspects of training.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Method and system for training a visual prosthesis

A method for training a visual prosthesis includes presenting a non-visual reference stimulus corresponding to a reference image to a visual prosthesis patient. Training data sets are generated by presenting a series of stimulation patterns to the patient through the visual prosthesis. Each stimulation pattern in the series is determined at least in part on a received user perception input and a fitness function optimization algorithm. The presented stimulation patterns and the user perception inputs are stored and presented to a neural network off-line to determine a vision solution.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

ESPC ESA Training Series: Photovoltaic (PV) Project Considerations

This training covers technical, economic and other photovoltaic (PV) project considerations. Technical topics include cost-effectiveness, resilience, cybersecurity, and electrical considerations. In addition, other topics, such as compatibility with agency mission, site plans, coordination with the serving utility, the National Environmental Policy Act, and agency approval requirements. Considerations specific to third-party owned/third-party financed PV projects are also covered, as well as available resources and templates for project development. The training also includes a recap of the first session in the Energy Savings Performance Contracts Energy Sales Agreements (ESPC ESA) Webinar Series.

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

Confronting Domain Shift in Trained Neural Networks

Neural networks (NNs) are known as universal function approximators and can interpolate nonlinear functions between observed data points. However, when the target domain for deployment shifts from the training domain and NNs must extrapolate, the results are notoriously poor. Prior work Martinez et al. (2019) has shown that NN uncertainty estimates can be used to correct binary predictions in shifted domains without retraining the model. We hypothesize that this approach can be extended to correct real-valued time series predictions. As an exemplar, we consider two mechanical systems with nonlinear dynamics. The first system consists of a spring-mass system where the stiffness changes abruptly, and the second is a real experimental system with a frictional joint that is an open challenge for structural dynamicists to model efficiently. Our experiments will test whether 1) NN uncertainty estimates can identify when the input domain has shifted from the training domain and 2) whether the information used to calculate uncertainty estimates can be used to correct the NN’s time series predictions. While the method as proposed did not significantly improve predictions, our results did show potential for modifications that could improve models’ predictions and play a role in structural health monitoring systems that directly impact public safety.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Designing alloys with process-mapping AI pre-trained on empirical knowledge

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Accelerated materials design should match the recent trends in the product development cycles. Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. However, it faces a challenge of scarce data available for training ML models. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. Artificial Intelligence (AI) was used in this study to incorporate such knowledge in the domain-specific computational tool, pyroMind. The tool provides not only novel design ideas but also their interpretation via physics and engineering concepts.</span>

Romanov, Vyacheslav↗
DOE OSTI2020

Radiation Signature Training Device Gamma Equivalent Spheres

Radiation signature training device sources are a continuation of a previous effort to produce a highly enriched uranium equivalent sphere. The original research was designed and motivated to produce a Department of Transportation special form source that emulated the passive gamma signature of a solid metal highly enriched uranium sphere. In addition, the mass reduction (i.e., of the 235 U) minimized logistical costs (security, criticality safety, radiological safety and transportation requirements). Radiation signature training device sources expand on the original concept, redesigning the encapsulation in a modular fashion that allows a variety of loadings. The result is a reconfigurable special nuclear material source set that can vary greatly in the magnitude of the gamma flux and the isotopic distribution. Use of the source to test a variety of standards (N42.34-2015, N42.38-2015, N42.43-2015, N42.53-2018, N42.48-2018, DHS-TCS Backpack Based Radiation Detection Systems, DHS-TCS Handheld Instruments Used for the Detection and Identification of Radionuclides, DHS-TCS Vehicle Mounted Mobile Systems) will be discussed.

Blessinger, Christopher S.↗
DOE OSTI2023

Pulsed Power Supply for Magnet Quench Training

Superconducting magnets typically go through aseries of spontaneous “training” quenches as their current isincreased towards its design value. To decrease the number ofthese quenches, a device has been designed to boost the magnetcurrent at the moment a quench has been detected and beforemagnet protection has been initiated. The aim of the boost is todrive the magnet coil further along towards its maximum currentwith each training quench, and hence reduce the number ofquenches required.

43 PARTICLE ACCELERATORS↗