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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 37 records · Page 2

Simulated Transmission of the Dengue Virus Across the US-Mexico Border Using Remotely Sensed and Ground Based Weather Data

Incidence of dengue fever, caused by a mosquito transmitted virus, have increased in the Americas during recent decades. In the US, local transmission has been reported in southern Texas and Florida. However, despite its close proximity to dengue endemic areas in Mexico and the presence of a primary mosquito vector, there are no reports of local transmission in Arizona. Many studies have demonstrated that weather influences dengue virus transmission by regulating vector development rates, vector habitat availability, and the duration of the virus extrinsic incubation period (EIP). The EIP, the period between mosquito infection and the ability for it to retransmit the virus, is especially important given its high sensitivity to temperature and the short lifespan of mosquitoes. Other studies, however, have suggested that human related factors such as socioeconomic status and herd immunity may explain much of the disparity in dengue incidence in the US‐Mexico border region. Using a meteorologically driven model of vector population dynamics and virus transmission we compare simulations of dengue fever cases in southern Arizona and northern Mexico. A Monte Carlo approach is employed to select parameter values by evaluating simulations in Hermosillo Mexico with reported dengue fever case data. Simulations that replicate the case data best are retained and rerun using remotely sensed climate data from other Arizona and Mexico locations to determine the relative influence of weather on virus transmission. Although human and environmental factors undoubtedly influence dengue transmission in the US‐Mexico border regions, weather is a major facilitator of the transmission process.

Dengue↗

Space Motion Sickness - Analysis of Medical Debriefs Data for Incidence and Treatment

Astronauts use medications for the treatment of a variety of illnesses during space travel. Data mining efforts to assess minor clinical conditions occurring during Shuttle flights STS-1 through STS-94 revealed that space motion sickness (SMS) was the most common ailment during early flight days, occurring in approx.40% of crewmembers, followed by digestive system disturbances (9%) and infectious diseases, which most commonly involved the respiratory or urinary tracts. A more recent analysis of postflight medical debriefs data to examine trends with respect to medication use by astronauts during spaceflights indicated that ~37% of all prescriptions recorded was for pain followed by sleep (22%), SMS (18%), decongestion (14%), and all others (14%). Further analysis revealed that about 150 of 317 crewmembers experienced symptoms of SMS. Nearly all (132 of 150) crewmembers took medication for the treatment of symptoms with a total of 387 doses. Promethazine was taken most often (201 doses); in most cases this resulted in alleviation of symptoms with 130 crewmembers (65%) reporting feeling much or somewhat better. Although fewer total doses of the combination of promethazine and dextroamphetamine (Phen/Dex) were taken (45 doses), slightly more than half of these doses resulted in improvement. The combination of scopolamine and dextroamphetamine (Scop/Dex) was reported to be effective in only 37% of cases, with 36 of 97 total doses resulting in improvement. A higher percentage (24%) of Scop/Dex doses was reported to be ineffective compared with promethazine alone or as Phen/Dex (10% and 7%, respectively). Comparisons of the effectiveness of the different dosage forms of promethazine revealed that intramuscular injection was most effective in alleviating symptoms with 55% feeling much better, 16% feeling somewhat better, and only 7% feeling no effect or worse. Overall, it appears that promethazine alone was used more frequently during flight and was reported effective for the treatment of SMS.

Putcha, Lakshmi↗

User Acceptability of Physiological and Other Measures of Hazardous States of Awareness

Two studies explored user acceptance of devices that measure hazardous states of awareness. In the first study, critical incident data were collected in two workshops from 11 operators working as air traffic controllers or commercial pilots. These critical incident data were used to develop a survey of the acceptability of awareness measures. In the second study, the survey was administered to 100 people also working as air traffic controllers or commercial pilots. Results show that operators are open to the inclusion of technology to measure HSAs even if that technology is somewhat invasive as long as feedback about the HSAs is considered to be useful and helpful. Nonetheless, a major concern is the legal complications associated with being recorded, particularly for older and more experienced operators. Air traffic controllers emphasized the importance of sharing technology information with supervisors in order to receive backup or assistance under conditions of task overload, whereas pilots emphasized the influence of work schedules on problems with awareness. Recommendations are offered concerning the implementation of devices to measure hazardous states of awareness.

Dickinson, Terry L.↗

A traffic accident dataset for Chattanooga, Tennessee

This publication presents an annotated accident dataset which fuses traffic data from radar detection sensors, weather condition data, and light condition data with traffic accident data (as illustrated in Fig. 1) in a format that is easy to process using machine learning tools, databases, or data workflows. The purpose of this data is to analyze, predict, and detect traffic patterns when accidents occur. Each file contains a timeseries of traffic speeds, flows, and occupancies at the sensor nearest to the accident, as well as 5 neighboring sensors upstream and downstream. It also contains information about the accident type, date, and time. In addition to the accident data, we provide baseline data for typical traffic patterns during a given time of day. Overall, the dataset contains 6 months of annotated traffic data from November 2020 to April 2021. During this timeframe, and 361 accidents occurred in the monitored area around Chattanooga, Tennessee. This dataset served as the basis for a study on topology-aware automated accident detection for a companion publication [1].

97 MATHEMATICS AND COMPUTING↗

Characterization of the Elastic Constants of Unidirectional Laminates Using Oblique-Incidence Pulsed Data

From Intro: In this paper, wer apply the LLW (leaky Lamb Wave) technique to determine the stiffness constants of unnidirectional graphite/epoxy materials...In this paper, we describe a new technique which can be used to determine all five stiffness constants by analyzing the times-of-flight of the recorded reflected acoustic waves in a pulsed LLW experiment.

composites composite components ultrasonics ultras↗

Effect of front and rear incident proton irradiation on silicon solar cells

Four solar cell types of current manufacture were irradiated through the front and rear surfaces with protons in the energy range between 1 and 10 MeV. The solar cell parameters varied for this study were cell thickness and back surface field (BSF) vs. no BSF. Some cells were irradiated at normal incidence and an equal number were irradiated with simulated isotropic fluences. The solar cell electrical characteristics were measured under simulated AM0 illumination after each fluence. Using the normal incidence data, proton damage coefficients were computed for all four types of cells for both normal and omnidirectional radiation fields. These were found to compare well with the omnidirectional damage coefficients derived directly from the rear-incidence radiation data. Similarly, the rear-incidence omnidirectional radiation data were used to compute appropriate damage coefficients. A method for calculating the effect of a spectrum of energies is derived from these calculations. It is suitable for calculating the degradation of cells in space when they have minimal rear-surface shielding.

Anspaugh, Bruce↗

Lightning strike experience

A program implemented to report various lightning-strike incidents was reviewed. Strike incidence data was summarized according to aircraft altitude, flight path, meteorological conditions and atmospheric air temperature. Data was given indicating the incidence of reported lightning strikes to commercial and U.S. Air Force aircraft and an outline of pilot opinion in lightning avoidance was given.

Franklin A Fisher↗

Identification of Human Factors in Unmanned Aviation Via Pilot Incident Reports

There is a need for incident data relevant to the operation of civilian unmanned aircraft systems (UAS) in the National Air Space (NAS). Currently, very limited incident and accident data are available from military sources, and the tightly-restricted civilian UAS industry has produced very few incident reports that could shed light on design issues relevant to human factors. An exploratory study is being conducted to examine the feasibility of collecting voluntary critical incident reports from UAS pilots, and using the information to identify areas where human factors guidelines will be of assistance. Experienced UAS pilots are participating in small focus groups in which they are prompted to describe critical incidents that either reveal a system flaw, or highlight a case where the human operator contributed to system resilience or mission success. The de-identified incidents are being analyzed to identify contributing factors, with a focus on design issues that either hindered or assisted the pilot in dealing with the incident. Preliminary findings will be described.

human factors↗

Graph Representation Learning for Dengue Forecasting

In 2017, the largest recorded dengue outbreak in Sri Lanka’s history occurred. Since then, dengue has continued to threaten national health across Sri Lanka. The development of an effective Early Warning System (EWS) for dengue outbreaks is essential for Sri Lanka’s Ministry of Health to take preventative measures. We propose the use of Graph Neural Networks as EWS. Using earth observational data from NASAs global satellites and dengue incidence data from Sri Lanka s Ministry of Health, we developed a series of traditional and graph representation EWS to forecast Dengue cases across Sri Lanka’s 25 districts between 2013 and 2022. We demonstrate empirically that Graph Neural Networks which incorporate spatiotemporal relations significantly outperform traditional EWS such as Autoregressive Integrated Moving Average (ARIMA), Random Forest, and Long Short-Term Memory (LSTM). Our source code is available on GitHub and will be provided in the final submission.

Graph Neural Networks↗

Comparison of Flight Measured, Predicted and Wind Tunnel Measured Winglet Characteristics on a KC-135 Aircraft

One of the objectives of the KC-135 Winglet Flight Research and Demonstration Program was to obtain experimental flight test data to verify the theoretical and wind tunnel winglet aerodynamic performance prediction methods. Good agreement between analytic, wind tunnel and flight test performance was obtained when the known differences between the tests and analyses were accounted for. The flight test measured fuel mileage improvements for a 0.78 Mach number was 3.1 percent at 8 x 10(5) pounds W/delta and 5.5 percent at 1.05 x 10(6) pounds W/delta. Correcting the flight measured data for surface pressure differences between wind tunnel and flight resulted in a fuel mileage improvement of 4.4 percent at 8 x 10(5) pounds W/delta and 7.2 percent at 1.05 x 10(6) pounds W/delta. The performance improvement obtained was within the wind tunnel test data obtained from two different wind tunnel models. The buffet boundary data obtained for the baseline configuration was in good agreement with previous established data. Buffet data for the 15 deg cant/-4 deg incidence configuration showed a slight improvement, while the 15 deg cant/-2 deg incidence and 0 deg cant/-4 deg incidence data showed a slight deterioration.

Dodson, R. O., Jr.↗

Characterization of Evidence for Human System Risk Assessment

Understanding the kinds of evidence available and using the best evidence to answer a question is critical to evidenced-based decision-making, and it requires synthesis of evidence from a variety of sources. Categorization of human system risks in spaceflight, in particular, focuses on how well the integration and interpretation of all available evidence informs the risk statement that describes the relationship between spaceflight hazards and an outcome of interest. A mature understanding and categorization of these risks requires: 1) sufficient characterization of risk, 2) sufficient knowledge to determine an acceptable level of risk (i.e., a standard), 3) development of mitigations to meet the acceptable level of risk, and 4) identification of factors affecting generalizability of the evidence to different design reference missions. In the medical research community, evidence is often ranked by increasing confidence in findings gleaned from observational and experimental research (e.g., "levels of evidence"). However, an approach based solely on aspects of experimental design is problematic in assessing human system risks for spaceflight. For spaceflight, the unique challenges and opportunities include: (1) The independent variables in most evidence are the hazards of spaceflight, such as space radiation or low gravity, which cannot be entirely duplicated in terrestrial (Earth-based) analogs, (2) Evidence is drawn from multiple sources including medical and mission operations, Lifetime Surveillance of Astronaut Health (LSAH), spaceflight research (LSDA), and relevant environmental & terrestrial databases, (3) Risk metrics based primarily on LSAH data are typically derived from available prevalence or incidence data, which may limit rigorous interpretation, (4) The timeframe for obtaining adequate spaceflight sample size (n) is very long, given the small population, (5) Randomized controlled trials are unattainable in spaceflight, (6) Collection of personal and environmental data on the astronaut population may create opportunities for advanced analytics and human-environment modeling that goes beyond that achieved in isolated experimental designs; and (7) Translation of relevant research to operations is a complex, transdisciplinary enterprise in which the approach must apply across the physical, biological, behavioral, and social sciences. The approach to synthesizing evidence must address both source and fidelity of data, and reflect the most general attributes of quality of evidence in science and engineering: reliability and validity. The authors are developing a two-factor approach which includes the various kinds of evidence required to understand risks and for the integrated interpretation of all evidence that is essential to develop standards and countermeasures. A unified framework for aggregating and assessing different kinds of evidence provides a consistent, traceable, evidence-based decision-making process to translate research to operations in an environment where engineers, scientists, physicians, and managers all engage in analyzing the trade space of vehicle design, standards, requirements and solutions for spaceflight.

Steinberg, S. L.↗

Artificial Intelligence (AI) Methods for Automating the Impact Tool Evidence Library

INTRODUCTION: The development of the Evidence Library for use with the IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) probability risk assessment tool involved a multilayered, time intensive process of data collection and analysis by subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team to produce clinical findings forms (CliFFs) for 120 medical conditions. Artificial Intelligence Large Language Models (LLMs) can be leveraged to facilitate this process, thus reducing labor and time. TOPIC: CliFFs contain information about medical conditions as they pertain to spaceflight. This includes condition definitions, incidence data, crew task impairment estimates caused by conditions, treatment protocols and references to literature used for gathering condition evidence. Guided by the Evidence Library Methods document and the CliFF development instructions, a team has leveraged Microsoft Azure AI services and open-source documentation to construct an AI-assisted automated pipeline for CliFF development. This process is designed to search, retrieve, and evaluate the applicable data, and ultimately generate a completed CliFF. The LLM evaluates the relevance of each of the source materials to spaceflight, either as direct evidence or as an analog. The model extracts keywords and generates brief summaries to enhance search and retrieval in later stages of CliFF development. For instance, it can calculate epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. APPLICATION: Large Language Models (LLMs) can efficiently summarize large amounts of text. Leveraging this technology will automate data retrieval and evidence gathering for medical databases, like the IMPACT tool, by aiding in the labor-intensive process of analyzing large bodies of literature and organizing it into a formatted document like a CliFF. This added efficiency will enable expeditious expansion of the Evidence Library with additional medical conditions and update previous CLiFFs as new technology becomes available.

Ali Al↗

Assessing the Risk of Disc Heniation Related to Landing Impact Following Long-duration Spacecraft

Previous research has shown that crewmembers returning on the Space Shuttle have an increased incidence of herniated nucleus pulposus after spaceflight. This increased risk is thought to be related to disc volume expansion due to unloading and prolonged exposure to microgravity. Although there is an increased risk of disc herniation in Space Shuttle astronauts, it is unknown if dynamic landing loads further contribute to the risk of herniation. To determine if dynamic loads increase the risk of incidence, data from crewmembers (excluding cosmonauts) returning on the Soyuz spacecraft will be compared to Space Shuttle astronauts. These data will be obtained from the Lifetime Surveillance of Astronaut Health (LSAH) Project at NASA. Severity and incidence after spaceflight will be mined from the data, and statistical analyses will be used to determine if Soyuz crewmembers have a higher incidence of disc herniation than Space Shuttle crewmembers. The results are expected to show no difference between Space Shuttle and Soyuz crewmembers, indicating that higher dynamic loads on landing and long-duration spaceflight do not significantly increase the risk of disc herniation. If no difference is shown between the two crewmember populations, then disc volume expansion due to microgravity does not significantly increase the risk of injury due to dynamic loads for deconditioned crewmembers. Any risk associated with deconditioning would be primarily due to bone structure changes and resulting bone strength changes. This study is an important first step in determining whether the spinal disc plays a role in injury due to dynamic loads.

Somers, J. T.↗

Transmission and Incidence Losses for a Slotted Plate

The objective of this work is to find a model of the stagnation pressure loss resulting from flow through a slotted plate, which is effectively a cascade of flat plate airfoils, particularly at very large angles of incidence. Data from a published experiment is examined, and compared with control volume analysis, and CFD code calculations. An assumption that the loss can be separated into a transmission loss and an incidence loss seems to be justified by the data. Both the data and the CFD code results are consistent with an incidence loss model in which the flow component normal to the slot axis is lost. However, the experimental transmission loss is much larger than calculated values.

Wilson, Jack↗

Preliminary Analysis of Aircraft Loss of Control Accidents: Worst Case Precursor Combinations and Temporal Sequencing

Aircraft loss of control (LOC) is a leading cause of fatal accidents across all transport airplane and operational classes, and can result from a wide spectrum of hazards, often occurring in combination. Technologies developed for LOC prevention and recovery must therefore be effective under a wide variety of conditions and uncertainties, including multiple hazards, and their validation must provide a means of assessing system effectiveness and coverage of these hazards. This requires the definition of a comprehensive set of LOC test scenarios based on accident and incident data as well as future risks. This paper defines a comprehensive set of accidents and incidents over a recent 15 year period, and presents preliminary analysis results to identify worst-case combinations of causal and contributing factors (i.e., accident precursors) and how they sequence in time. Such analyses can provide insight in developing effective solutions for LOC, and form the basis for developing test scenarios that can be used in evaluating them. Preliminary findings based on the results of this paper indicate that system failures or malfunctions, crew actions or inactions, vehicle impairment conditions, and vehicle upsets contributed the most to accidents and fatalities, followed by inclement weather or atmospheric disturbances and poor visibility. Follow-on research will include finalizing the analysis through a team consensus process, defining future risks, and developing a comprehensive set of test scenarios with correlation to the accidents, incidents, and future risks. Since enhanced engineering simulations are required for batch and piloted evaluations under realistic LOC precursor conditions, these test scenarios can also serve as a high-level requirement for defining the engineering simulation enhancements needed for generating them.

Belcastro, Christine M.↗