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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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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Quirky Circling Behavior in Mice Informs Research on Humans in Space

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. We previously conducted a detailed phenotypic analysis of mouse behavior during long duration (33-day) spaceflight in the NASA Rodent Habitat (Rodent Research-1; RR1 mission). Notably, we documented the emergence within the 8-10 days of launch of spontaneous ambulatory behavior in the form of circling or ‘race-tracking’ behavior in spaceflight but not in an identical ground control condition. Circling is unique to the NASA RH not having been reported to occur in other mouse habitats that provide limited opportunity for grasping, and/or are characterized by smaller habitable volumes viz., Italian Mice Drawer System (MDS) flown on ISS; Russian Block Obespecheniya Soderzhaniya (BOS) flown on Bion M-1, or JAXA Habitat Cage Unit (HCU) flown in the ISS Kibo module. Over time, circling became the primary dark cycle activity of FLT mice, occurring in individuals then as a coordinated group activity. Here we discuss possible interpretations of circling behavior including: (1) Stereotypic or abnormal repetitive behaviors (ARBs) that are unvarying, and apparently functionless behavior patterns documented numerous species in laboratory and zoo settings, possibility related to insufficient environmental enrichment and stress, (2) Rewarding effects of physical activity that are well-documented in terrestrial studies of rodents given the opportunity to run in wheels, and (3) Vestibular self-stimulation, i.e., the generation biologically-relevant amounts of vestibular sensory input to reduce the effects of microgravity. Affording mice the opportunity to grab and run in the RH resembles physical activities that the crew participate in routinely. Our approach is yielding an interesting analogue for better understanding human responses to spaceflight, and providing the opportunity to begin to address how physical movement influences responses to microgravity.

spaceflight↗
NASA NTRS2014

Wetware, Hardware, or Software Incapacitation: Observational Methods to Determine When Autonomy Should Assume Control

Control-theoretic modeling of human operator's dynamic behavior in manual control tasks has a long, rich history. There has been significant work on techniques used to identify the pilot model of a given structure. This research attempts to go beyond pilot identification based on experimental data to develop a predictor of pilot behavior. Two methods for pre-dicting pilot stick input during changing aircraft dynamics and deducing changes in pilot behavior are presented This approach may also have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot. With this ability to detect changes in piloting behavior, the possibility now exists to mediate human adverse behaviors, hardware failures, and software anomalies with autono-my that may ameliorate these undesirable effects. However, appropriate timing of when au-tonomy should assume control is dependent on criticality of actions to safety, sensitivity of methods to accurately detect these adverse changes, and effects of changes in levels of auto-mation of the system as a whole.

Trujillo, Anna C.↗
NASA NTRSDate not supplied

Sex differences and deep space stressors: effects of 5-ion gcrsim, simulated microgravityand social isolation on immune function, brain, and behavior in mice

This project is testing the hypothesis that Ionizing Radiation (IR), microgravity and social isolation combine synergistically to trigger an oxidative stress response that alters immune homeostasis, brain structure/function, and neurobehavioral/cognitive performance. Specific Aims for this project are to: (1) Determine dose-response curves for acute ‘Five-Ion GCR Simulation’ exposure for immune, brain and performance responses in crew age-matched adult male and female mice; (2) Determine effects of acute ‘Five-Ion GCR Simulation’ exposure singly and in combination with simulated microgravity and social isolation, on immune, brain and performance responses in crew age-matched male and female mice mimicking deep space missions; and (3) Determine efficacy of the dietary antioxidant, Nicotinamide Mononucleotide (NMN), a key intermediate in nicotinamide adenine dinucleotide (NAD+) biosynthesis. Here we report findings from our studies of mature (24-week-old) male and female mice exposed to simulated 5-Ion GCRsim (0, 5, 15, or 50cGy) at the NASA Space Radiation Laboratory (NSRL) followed by combinatorial exposures to 15cGy, simulated microgravity via head-down tilt (hindlimb unloading) and social isolation. Immune, brain and behavioral (sensorimotor, risk-taking & cognitive) measures were acquired at ‘Acute’ (IR+24hrs, IR+72hrs), ‘Intermediate’ (IR+14 days) and ‘Delayed’ (IR+28 to IR+124 days) to inform biological responses anticipated during a transit to Mars. This project addresses NASA’s efforts to characterize risks and identify appropriate countermeasures in both women and men in anticipation of future deep space missions. Ensuring crew health and performance during extended transits necessitates that sensorimotor and cognitive abilities remain strong to avoid potentially catastrophic health and safety outcomes. Supported by the NASA Human Research Program (HRP) Human Factors Behavioral Performance Element Grant 18 18FLAG 2 0028.

behavioral sciences↗
DOE OSTI2024

Eco-driving Profile Optimization by Dynamic Programming for Battery Electric Vehicles

Although full automation has not yet been achieved, automated vehicles are a valid research area. Not only would automated vehicles provide ultimate driver convenience, but they would maximize energy efficiency by eliminating undesired human driving behaviors and optimally controlling the powertrain. From the perspective of control related to energy saving, speed profile optimization is important for improving system efficiency and satisfying passenger demands. This study employs Dynamic Programming (DP) to solve the constrained optimal problem for travel time, distance, and speed limit by exploring all possible control options. The solutions obtained by DP demonstrate consistent control patterns combining four control modes-acceleration, cruising, coasting, and braking, with cruising or coasting being selective depending on the boundary conditions. Further, this study introduces DP-based simulation results and attempts to provide comprehensive interpretations of the optimal policy by analyzing the essential factors that affect the control problem, including boundary conditions, road load, and powertrain characteristics. Based on these interpretations, the control concepts can be explained as the optimal policy selecting the best control option based on system efficiency and boundary conditions. The results of DP are compared with a human-like driver model to show that the optimal speed profiles can effectively reduce energy consumption.

Autonomous vehicles↗
DOE OSTI2023

Human Dimensions of Energy Systems Workshop - September 6-7, 2022: Findings and Next Steps

Advanced energy technologies and informed policies are necessary but not sufficient to accelerate the energy transition at the speed required to meet our shared climate and energy resilience goals. To fully understand and implement integrated energy systems, we need to be able to model and analyze the complete system-of-systems, including the behavior of people interacting with and being impacted by energy systems. The purpose of this workshop is: Understand how human behavior and decisions affect the performance of energy systems with a focus on resilience and human well-being; Identify opportunities to improve energy system design to explicitly consider human behavior and well-being; Enhance our capability to model and predict human actions. Develop the ability to stress test these integrated systems; Identify or develop requirements and tools for more robust system designs that can be used for human-in-the-loop exercises and training; Identify opportunities for joint research, joint appointments, and collaboration; and Guide internal investments and strategic hires.

analyze↗
NASA NTRS2002

Central and peripheral regulation of feeding and nutrition by the mammalian circadian clock: implications for nutrition during manned space flight

Circadian clocks have evolved to predict and coordinate physiologic processes with the rhythmic environment on Earth. Space studies in non-human primates and humans have suggested that this clock persists in its rhythmicity in space but that its function is altered significantly in long-term space flight. Under normal circumstances, the clock is synchronized by the light-dark cycle via the retinohypothalamic tract and the suprachiasmatic nucleus. It is also entrained by restricted feeding regimes via a suprachiasmatic nucleus-independent circadian oscillator. The site of this suboscillator (or oscillators) is not known, but new evidence has suggested that peripheral tissues in the liver and viscera may express circadian clock function when forced to do so by restricted feeding schedules or other homeostatic disruptions. New research on the role of the circadian clock in the control of feeding on Earth and in space is warranted.

NASA Discipline Regulatory Physiology↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
DOE OSTI2023

Behaviourally-Informed Peer Referral Programmes can Increase the Reach of Low-Income Energy Policies

Low-income solar adopters are more likely to refer others to a fully subsidized solar programme when referral rewards are combined with an appeal to reciprocity and a simplified referral process, leading to five times as many solar contracts as when referral rewards are used alone. The findings highlight behavioural science strategies that administrators of low-income energy assistance programmes can use to cost-effectively accelerate programme uptake.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2022

Strym: A Python Package for Real-time CAN Data Logging, Analysis and Visualization to Work with USB-CAN Interface

In this report, we describe a data analysis tool developed for decoding and analyzing vehicle data obtained from a passenger vehicle’s onboard controller area network (CAN) bus. The tool developed in this paper provides a timeseries framework to perform domain-specific analysis at scale when interpreting data from a vehicle or a collection of vehicles in light of how to design intelligent vehicle applications. The tool, called Strym, exploits the CAN bus mechanism of modern vehicles to capture data using commercially available CAN-to-USB hardware Comma.ai Panda devices, managed through open-source software Libpanda. Strym permits the decoding of vendor-specific CAN messages in a vehicle-agnostic manner. Through this, a researcher can characterize data throughput, assess data quality, and perform analyses. Such analyses are useful in a number of research such as studying human driving behavior in mixed-autonomy, new driver models, rare-event detection, traffic flow estimation, and custom control of vehicles.

Performance evaluation, Smart cities, Intelligent ↗
DOE OSTI2025

Traffic Control via Connected and Automated Vehicles (CAVs): An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. Also called “phantom jams” or “stop-and-go waves,” these instabilities are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system, referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment, the MegaVanderTest (MVT), leveraged a heterogeneous fleet of 100 longitudinally controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this article. The MegaController is a hierarchical control architecture that consists of two main layers. The upper layer is called the Speed Planner and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock onboard sensors. The Speed Planner ingests live data feeds provided by third parties as well as data from our own control vehicles and uses both to perform the speed assignment. The architecture of the Speed Planner allows for the modular use of standard control techniques, such as optimal control, model predictive control (MPC), kernel methods, and others. The architecture of the local controller allows for the flexible implementation of local controllers. Corresponding techniques include deep reinforcement learning (RL), MPC, and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers or only some. Likewise, control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars to electronic selection of adaptive cruise control (ACC) setpoints in others. The proposed architecture technically allows for the combination of all possible settings proposed previously, that is {Speed Planner algorithms} × {local Vehicle Controller algorithms} × {full or partial sensing} × {torque or speed control}. As a result, most configurations were tested throughout the ramp up to the MegaVandertest (MVT).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Data-Driven Forgetting and Discount Factors for Vehicle Speed Forecasting in Ecological Adaptive Cruise Control

Abstract This paper investigates temporal correlations in human driving behavior using real-world driving to improve speed forecasting accuracy. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discount factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control system, and its influence is analyzed on the prediction accuracy and the performance of the ecological adaptive cruise control in an electric vehicle powertrain model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4.7% compared to a baseline least-square-based polynomial regression. This is a 10% improvement over the constant speed/acceleration model, a conventional speed predictor.

Automation & Control Systems↗
DOE OSTI2024

Structural basis for antibody recognition of the proximal MUC16 ectodomain

Mucin 16 (MUC16) overexpression is linked with cancer progression, metastasis, and therapy resistance in high grade serous ovarian cancer and other malignancies. The cleavage of MUC16 forms independent bimodular fragments, the shed tandem repeat sequence which circulates as a protein bearing the ovarian cancer biomarker (CA125) and a proximal membrane-bound component which is critical in MUC16 oncogenic behavior. A humanized, high affinity antibody targeting the proximal ectodomain represents a potential therapeutic agent against MUC16 with lower antigenic potential and restricted human tissue expression. Here, we demonstrate the potential therapeutic versatility of the humanized antibody as a monoclonal antibody, antibody drug conjugate, and chimeric antigen receptor. We report the crystal structures of 4H11-scFv, derived from an antibody specifically targeting the MUC16 C-terminal region, alone and in complex with a 26-amino acid MUC16 segment resolved at 2.36 Å and 2.47 Å resolution, respectively. The scFv forms a robust interaction with an epitope consisting of two consecutive β-turns and a β-hairpin stabilized by 2 hydrogen bonds. The V H -V L interface within the 4H11-scFv is stabilized through an intricate network of 11 hydrogen bonds and a cation-π interaction. Together, our studies offer insight into antibody-MUC16 ectodomain interaction and advance our ability to design agents with potentially improved therapeutic properties over anti-CA125 moiety antibodies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

wmpy-power: A Python package for process-based regional hydropower simulation

Hydropower is an important source of renewable energy in many parts of the world. The generation potential for a hydropower facility can vary greatly due to fluctuations in precipitation and snowmelt patterns impacting streamflow and reservoir storage. Human activities such as irrigation, manufacturing, and hydration can also influence water availability at nearby and downstream facilities. wmpy-power--the hydropower model described in this work--is process-based, leveraging explicit reservoir storage and release data to address impacts on hydropower from climate change and human adaptive behaviors to inform long-term planning and resource-adequacy considerations.

13 HYDRO ENERGY↗
DOE OSTI2020

Boosting Energy Efficiency of Heterogeneous Connected Automated Vehicle (CAV) Fleets via Anticipative and Cooperative Vehicle Guidance

In 2017, the Department of Energy funded a team at Clemson University and Argonne National Laboratory to develop collaborative perception and anticipative/predictive vehicle guidance schemes for Connected and Automated Vehicles (CAVs) and to quantify the energy saving potential of this technology in large scale traffic microsimulations at different levels of technology penetration and also experimentally. The project goal was demonstrating up to a 10% energy saving potential from different aspects of the implementation with a focus on reducing unnecessary braking events by anticipatory speed and lane selection. This project introduced novel anticipative car following and lane selection schemes for Connected and Automated Vehicles (CAVs). Our control schemes benefited from prediction of human driver behavior, information exchange between CAVs, and sometimes from collaboration to save energy, reduce braking events, and harmonize traffic. The energy savings was first demonstrated by traffic micro-simulations and then via a novel Vehicle-In-the-Loop (VIL) experimental testbed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

CRCNS US-France Research Proposal: Collaborative Research: Encoding reward expectation in Drosophilia

The fruit fly Drosophila melanogaster has been a valuable model for investigating the genetic and neural bases that underlie learning and memory. Early and most current studies use basic behavior conditioning protocols to study learning in controlled laboratory settings. More recently, the ability to transgenically manipulate many of the brain neurons in the fruit fly with exquisite specificity, and the recent knowledge of the synaptic ‘connectome’ of the fruit fly brain, makes these animals almost unique as a comprehensive model for studies of learning, memory and motivated behavior. In fact, the connectome has revealed many types of new connections that had until now been overlooked. Within this context, the thesis of this proposal is that studies of learning and memory will be greatly enhanced by using more sophisticated means for evaluating memory representations, such as have been developed in vertebrates, and combining those studies with information from the connectome guided by computational modelling. We propose to push beyond the boundaries of existing conditioning protocols for fruit flies to investigate more complex memory representations. In particular, we will investigate the function of reinforcement pathways in relation to the absence of expected reinforcement. More specifically, we propose a series of experiments designed to investigate the memory representations in fruit flies when an expected consequence of a Conditioned Stimulus (CS) fails to occur. Although studies have evaluated how this failure can establish extinction memory for the CS, our studies will go beyond studying extinction. Specifically, we predict that in Drosophila when a CS is associated with a failed expectation of an appetitive food reinforcement it will acquire aversive value, and vice versa for a failed expectation of an aversive reinforcer. We combine these studies with manipulations of reinforcement pathways in the CNS inspired from the connectome, iteratively knitted in with established computational models. Intellectual Merit: The concept of reinforcement expectation and incentive contrast have been influential in the development of studies of associative learning in mammals. These questions are particularly challenging to answer in vertebrates because they require exquisite cellular, temporal, and genetic specificity of experimental manipulations. The recent development of work with identified neurons and their connectomes makes the larval and adult fly brains ripe as models for pushing our understanding of neural bases for these higher- order conditioning phenomena. Broader Impacts: Public health: These analyses and the conceptual framework of prediction error processing underlying them have a profound impact on our understanding of reinforcement-related behavior in humans, including monetary rewards and the mnemonic consequences of traumatic experiences, and for pathologies of the dopamine reinforcement system. Educational: This project will provide interdisciplinary training for postdoctoral researchers, Ph.D. and undergraduate students. The PIs will act as co-supervisors or mentors of students working in the different labs via face-to-face and internet-based technologies. We will also work with ASU’s award-winning Ask- A-Biologist program. This is an online science program designed to enrich the learning experiences of students of all ages and to provide classroom material for use by K-12 teachers. We will develop an extension of a game developed under a prior NSF award, and the new game will include modules to teach K-12 students about how insects learn. We will also integrate into the AAB site a program developed by a collaborator (B Gerber) at the Leibniz Institut für Neurobiologie, Magdeburg, and now in use in schools in Germany, to teach K-12 students how to train animals using the fruit fly larval learning paradigm. Underrepresented groups: All PIs will work with their university offices of Academic Diversity and Equal Opportunity for reaching underrepresented students.

59 BASIC BIOLOGICAL SCIENCES↗