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All sourcesNASA NTRSDOE OSTI
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 OSTI2024

Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing

Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.

Mandelli, Diego [Idaho National Laboratory (INL), ↗
DOE OSTI2022

Assessment of Sandia's 2021 Pilot Program for Research Traineeships to Broaden and Diversify Fusion Energy Science: Development and Rapid Screening of Refractory Multi-Principal Elemental Composites for Plasma Facing Components

The Fusion Energy Sciences office supported “A Pilot Program for Research Traineeships to Broaden and Diversify Fusion Energy Sciences” at Sandia National Laboratories during the summer of 2021. This pilot project was motivated in part by the Fusion Energy Sciences Advisory Committee report observation that “The multidisciplinary workforce needed for fusion energy and plasma science requires that the community commit to the creation and maintenance of a healthy climate of diversity, equity, and inclusion, which will benefit the community as a whole and the mission of FES”. The pilot project was designed to work with North Carolina A&T (NCAT) University and leverage SNL efforts in FES to engage underrepresented students in developing and accessing advanced material solutions for plasma facing components in fusion systems. The intent was to create an environment conducive to the development of a sense of belonging amongst participants, foster a strong sense of physics identity among the participants, and provide financial support to enable students to advance academically while earning money. The purpose of this assessment is to review what worked well and lessons that can be learned. We reviewed implementation and execution of the pilot, describe successes and areas for improvement and propose a no-cost extension of the pilot project to apply these lessons and continue engagement activities in the summer of 2022.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1987

Space experiment development process

Described is a process for developing space experiments utilizing the Space Shuttle. The role of the Principal Investigator is described as well as the Principal Investigator's relation with the project development team. Described also is the sequence of events from an early definition phase through the steps of hardware development. The major interactions between the hardware development program and the Shuttle integration and safety activities are also shown. The presentation is directed to people with limited Shuttle experiment experience. The objective is to summarize the development process, discuss the roles of major participants, and list some lessons learned. Two points should be made at the outset. First, no two projects are the same so the process varies from case to case. Second, the emphasis here is on Code EN/Microgravity Science and Applications Division (MSAD).

Depauw, James F.↗
NASA NTRS2002

A "Sweet 16" of Rules About Teamwork

The following "Sweet 16" rules included in this paper derive from a longer paper by APPL Director Dr. Edward Hoffman and myself entitled " 99 Rules for Managing Faster, Better, Cheaper Projects." Our sources consisted mainly of "war stories" told by master project managers in my book Simultaneous Management: Managing Projects in a Dynamic Environment (AMACOM, The American Management Association, 1996). The Simultaneous Management model was a result of 10 years of intensive research and testing conducted with the active participation of master project managers from leading private organizations such as AT&T, DuPont, Exxon, General Motors, IBM, Motorola and Procter & Gamble. In a more recent study, led by Dr. Hoffman, we learned that master project managers in leading public organizations employ most of these rules as well. Both studies, in private and public organizations, found that a dynamic environment calls for dynamic management, and that is especially clear in how successful project managers think about their teams.

Laufer, Alexander↗
NASA NTRS2002

An Intelligent System for Monitoring the Microgravity Environment Quality On-Board the International Space Station

An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen's self-organizing feature map, learning vector quantization, and back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of confidence associated with each vibrating source activation detected by the system.

Lin, Paul P.↗
NASA NTRS2007

Human Factors in the Design of the Crew Exploration Vehicle (CEV)

NASA s Space Exploration vision for humans to venture to the moon and beyond provides interesting human factors opportunities and challenges. The Human Engineering group at NASA has been involved in the initial phases of development of the Crew Exploration Vehicle (CEV), Orion. Getting involved at the ground level, Human Factors engineers are beginning to influence design; this involvement is expected to continue throughout the development lifecycle. The information presented here describes what has been done to date, what is currently going on, and what is expected in the future. During Phase 1, prior to the contract award to Lockheed Martin, the Human Engineering group was involved in generating requirements, conducting preliminary task analyses based on interviews with subject matter experts in all vehicle systems areas, and developing preliminary concepts of operations based on the task analysis results. In addition, some early evaluations to look at CEV net habitable volume were also conducted. The program is currently in Phase 2, which is broken down into design cycles, including System Readiness Review, Preliminary Design Review, and Critical Design Review. Currently, there are ongoing Human Engineering Technical Interchange Meetings being held with both NASA and Lockheed Martin in order to establish processes, desired products, and schedules. Multiple design trades and quick-look evaluations (e.g. display device layout and external window size) are also in progress. Future Human Engineering activities include requirement verification assessments and crew/stakeholder evaluations of increasing fidelity. During actual flights of the CEV, the Human Engineering group is expected to be involved in in-situ testing and lessons learned reporting, in order to benefit human space flight beyond the initial CEV program.

Whitmore, Mihriban↗
NASA NTRS2008

Ares Project Overview - Quality in Design

This presentation introduces the audience to the overall goals of the Ares Project, which include providing human access to low-Earth orbit, the Moon, and beyond. The presentation also provides an overview of with the vehicles that will execute those goals and progress made on the vehicles to date. The briefing will provide an introduction to Lean, Six Sigma, and Kaizen practices Ares will use to improve the overall effectiveness and quality of its efforts. Finally, the briefing includes a summary of Safety and Mission Assurance practices being implemented within[Ares to ensure safety and quality early in the design process. Integrating Safety and Mission Assurance in Design: This presentation describes how the Ares Projects are learning from the successes and failures of previous launch systems in order to maximize safety and reliability while maintaining fiscal responsibility. The Ares Projects are integrating Safer T and Mission Assurance into design activities and embracing independent assessments by Quality experts in thorough reviews of designs and processes. Incorporating Lean thinking into the design process, Ares is also streamlining existing processes and future manufacturing flows which will yield savings during production. Understanding the value of early involvement of Quality experts, the Ares Projects are leading launch vehicle development into the 21st century.

Cianciola, Chris↗
NASA NTRS2010

Advances in Measuring the Apparent Optical Properties (AOPs) of Optically Complex Waters

This report documents new technology used to measure the apparent optical properties (AOPs) of optically complex waters. The principal objective is to be prepared for the launch of next-generation ocean color satellites with the most capable commercial off-the-shelf (COTS) instrumentation. An enhanced COTS radiometer was the starting point for designing and testing the new sensors. The follow-on steps were to apply the lessons learned towards a new in-water profiler based on a kite-shaped backplane for mounting the light sensors. The next level of sophistication involved evaluating new radiometers emerging from a development activity based on so-called microradiometers. The exploitation of microradiometers resulted in an in-water profiling system, which includes a sensor networking capability to control ancillary sensors like a shadowband or global positioning system (GPS) device. A principal advantage of microradiometers is their flexibility in producing, interconnecting, and maintaining instruments. The full problem set for collecting sea-truth data--whether in coastal waters or the open ocean-- involves other aspects of data collection that were improved for instruments measuring both AOPs and inherent optical properties (IOPs), if the uncertainty budget is to be minimized. New capabilities associated with deploying solar references were developed as well as a compact solution for recovering in-water instrument systems from small boats.

Morrow, John H.↗
NASA NTRS2016

Engineering Design Tools for Shape Memory Alloy Actuators: CASMART Collaborative Best Practices and Case Studies

The primary goal of the Consortium for the Advancement of Shape Memory Alloy Research and Technology (CASMART) is to enable the design of revolutionary applications based on shape memory alloy (SMA) technology. In order to help realize this goal and reduce the development time and required experience for the fabrication of SMA actuation systems, several modeling tools have been developed for common actuator types and are discussed herein along with case studies, which highlight the capabilities and limitations of these tools. Due to their ability to sustain high stresses and recover large deformations, SMAs have many potential applications as reliable, lightweight, solid-state actuators. Their advantage over classical actuators can also be further improved when the actuator geometry is modified to fit the specific application. In this paper, three common actuator designs are studied: wires, which are lightweight, low-profile, and easily implemented; springs, which offer actuation strokes upwards of 200 at reduced mechanical loads; and torque tubes, which can provide large actuation forces in small volumes and develop a repeatable zero-load actuation response (known as the two-way shape memory effect). The modeling frameworks, which have been implemented in the design tools, are developed for each of these frequently used SMA actuator types. In order to demonstrate the versatility and flexibility of the presented design tools, as well as validate their modeling framework, several design challenges were completed. These case studies include the design and development of an active hinge for the deployment of a solar array or foldable space structure, an adaptive solar array deployment and positioning system, a passive air temperature controller for regulation flow temperatures inside of a jet engine, and a redesign of the Corvette active hatch, which allows for pressure equalization of the car interior. For each of the presented case studies, a prototype or proof-of-concept was fabricated and the experimental results and lessons learned are discussed. This analysis presents a collection of CASMART collaborative best practices in order to allow readers to utilize the available design tools and understand their modeling principles. These design tools, which are based on engineering models, can provide first-order optimal designs and are a basic and efficient method for either demonstrating design feasibility or refining design parameters. Although the design and integration of an SMA-based actuation system always requires application- and environment-specific engineering considerations, common modeling tools can significantly reduce the investment required for actuation system development and provide valuable engineering insight.

Design tools↗
NASA NTRS2021

Significant Incidents in Extravehicular Activity

Dr. Jonathan B. Clark is an adjunct associate professor of Neurology and Space Medicine at Baylor College of Medicine. From 1997 to 2005, he worked at NASA as a six-time Space Shuttle crew surgeon and was chief of the Medical Operations Branch at the Johnson Space Center. During this event, Dr. Clark delivered a historic review of Extravehicular Activity (EVA) events with an emphasis on EVA system hazards. This is one event of a medical series delivered to the SKC Program by Dr. Clark. This series focuses on spacesuit medical lessons learned and medical safety aspects of the suit.

Jonathan B. Clark↗
NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗
DOE OSTI2023

Battery state-of-health diagnostics during fast cycling using physics-informed deep-learning

Rapid, in-situ Li-ion battery state-of-health (SOH) quantification is challenging. Li-ion battery aging can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. As a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation by tracking critical aging modes such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. This manuscript describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. The framework uses a physically based Li-ion battery model to produce synthetic high-rate responses at aged states. The aging model is calibrated against experimental data from cells with different electrode loadings and cycled under a variety of fast-charging conditions (1 h, 15 min, 10 min, and 7 min charging). The synthetically generated high-rate responses at aged states are then used to train a deep-learning model to identify real cell state-of-health from fast charge/discharge battery voltage responses. The synthetically trained deep-learning model performance is validated by comparing to standard incremental capacity analysis and half-cell measurements. Finally, the framework demonstrates the benefits of using high-rate physics-based models to generate synthetic data for training deep-learning models.

25 ENERGY STORAGE↗
DOE OSTI2024

Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation

Monitoring the status of a high throughput computing cluster running computationally intensive production jobs is a crucial yet challenging system administration task due to the complexity of such systems. To this end, we train autoencoders using the Linux kernel CPU metrics of the cluster. Additionally, we explore assisting these models with graph neural networks to share information across threads within a compute node. The models are compared in terms of their ability to: 1) Produce a compressed latent representation that captures the salient features of the input, 2) Detect anomalous activity, and 3) Make distinction between different kinds of jobs run at Jefferson Lab. The goal is to have a robust encoder whose compressed embeddings are used for several downstream tasks. We extend this study further by deploying these models in a human-in-the-loop production-based setting for the anomaly detection task and discuss the associated implementation aspects such as continual learning and the criterion to generate alarms. This study represents a first step in the endeavor towards building self-supervised large-scale foundation models for computing centers.

Mohammed, Ahmed↗
DOE OSTI2022

A Review on the State of the Art of Machine Learning and Satellite Imaging: Detecting Scene Changes in Selected Nuclear Fuel Cycle Datasets

The timely detection of clandestine nuclear facilities is one of the greatest challenges faced by the International Atomic Energy Agency’s (IAEA). Idaho National Laboratory is currently applying machine learning (ML) to existing satellite imagery (SI) datasets to find facilities within the nuclear fuel life cycle, with primary focus placed on identifying critical predecessor (i.e., fuel fabrication and fuel enrichment) and successor (i.e., nuclear power plants) facilities. This could provide a satellite image methodology that the IAEA could leverage to discover clandestine facilities. The work presented in this paper describes the evolution of a workflow developed by this team for object detection related to critical infrastructure by expanding that workflow for the purpose of identifying nuclear fuel cycle components and automating dependency assessments. This will be done using two methods housed within a single pipeline. The first method involves implementing a DenseNet161 convolutional neural network to classify the images and explain the results using Local Interpretable Model-Agnostic Explanations (LIME). The second method implements You Only Look Once version 5 (YoloV5), to detect objects within images, provide a probability for the detection, and provide a bounding box that corresponds to the object of interest. The results of this work are anticipated to provide a clear picture of this portion of the nuclear fuel cycle and perform as a stand-alone tool for image assessment that can be expanded to additional fuel cycle components and implemented in international safeguards and national security domains. This capability addresses the IAEA’s need to detect undeclared nuclear materials and activities within a state while encompassing the entire nuclear fuel cycle.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Spitzer Space Telescope Sequencing Operations Software, Strategies, and Lessons Learned

The Space Infrared Telescope Facility (SIRTF) was launched in August, 2003, and renamed to the Spitzer Space Telescope in 2004. Two years of observing the universe in the wavelength range from 3 to 180 microns has yielded enormous scientific discoveries. Since this magnificent observatory has a limited lifetime, maximizing science viewing efficiency (ie, maximizing time spent executing activities directly related to science observations) was the key operational objective. The strategy employed for maximizing science viewing efficiency was to optimize spacecraft flexibility, adaptability, and use of observation time. The selected approach involved implementation of a multi-engine sequencing architecture coupled with nondeterministic spacecraft and science execution times. This approach, though effective, added much complexity to uplink operations and sequence development. The Jet Propulsion Laboratory (JPL) manages Spitzer s operations. As part of the uplink process, Spitzer s Mission Sequence Team (MST) was tasked with processing observatory inputs from the Spitzer Science Center (SSC) into efficiently integrated, constraint-checked, and modeled review and command products which accommodated the complexity of non-deterministic spacecraft and science event executions without increasing operations costs. The MST developed processes, scripts, and participated in the adaptation of multi-mission core software to enable rapid processing of complex sequences. The MST was also tasked with developing a Downlink Keyword File (DKF) which could instruct Deep Space Network (DSN) stations on how and when to configure themselves to receive Spitzer science data. As MST and uplink operations developed, important lessons were learned that should be applied to future missions, especially those missions which employ command-intensive operations via a multi-engine sequence architecture.

missions operations↗
NASA NTRS2012

NASA Resources for Educators and Public

A variety of NASA Classroom Activities, Educator Guides, Lithographs, Posters and more are available to Pre ]service and In ]service Educators through Professional Development Workshops. We are here for you to engage, demonstrate, and facilitate the use of educational technologies, the NASA Website, NASA Education Homepage and more! We are here for you to inspire you by providing in-service and pre- service training utilizing NASA curriculum support products. We are here for you to partner with your local, state, and regional educational organizations to better educate ALL! NASA AESP specialists are experienced professional educators, current on education issues and familiar with the curriculum frameworks, educational standards, and systemic architecture of the states they service. These specialists provide engaging and inspiring student presentations and teacher training right at YOUR school at no cost to you! Experience free out-of-this-world interactive learning with NASA's Digital Learning Network. Students of all ages can participate in LIVE events with NASA Experts and Education Specialists. The Exploration Station provides NASA educational programs that introduce the application of Science, Technology, Engineering, & Mathematics, to students. Students participate in a variety of hands-on activities that compliment related topics taught by the classroom teacher. NASA KSC ERC can create Professional Development Workshops for teachers in groups of fifteen or more. Education/Information Specialists also assist educators in developing lessons to meet Sunshine State and national curriculum standards.

Morales, Lester↗
DOE OSTI2025

Lessons Learned: Summary of Insights From the HERO WEC and Waves to Water Deployments Between 2022 and 2024

In 2020, a team from the National Renewable Energy Laboratory (NREL) partnered with the East Carolina University Coastal Studies Institute to develop a small, modular, wave-powered point absorber desalination prototype. That prototype, known as the hydraulic and electric reverse osmosis wave energy converter (HERO WEC), was intended to de-risk the installation activities planned for the Waves to Water Prize sponsored by the Water Power Technologies Office. The NREL team was given approximately 18 months to design, build, bench test, and deploy the HERO WEC prototype. Since the initial HERO WEC development, it has been used for two in-lab test programs and three ocean deployments. The first in-lab test program focused on ensuring that the overall operation of the device occurred as expected and on validating the operation of safety systems such as pressure relief valves or electrical breakers for load mitigation. The second in-lab test program was the first time the NREL team leveraged NREL’s large-amplitude motion platform to characterize the performance of the HERO WEC. Of the three in-water deployments, the first deployment was effectively an installation and recovery practice with no meaningful wave activity during the time that it was installed. The second deployment was a 2-week effort that enabled both the electric and hydraulic configurations of the device to be in the water for approximately 5 days each. This deployment was also the first time that the team had the opportunity to perform a drivetrain swap on the Coastal Studies Institute research vessel. The third deployment had to be split into two separate installations due to minor damage incurred from the WEC spinning during the initial installation. This report summarizes the deployments, the challenges encountered with each deployment, and the lessons learned for future work.

16 TIDAL AND WAVE POWER↗