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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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NASA NTRS2016

Photonic Integrated Circuit (PIC) Device Structures: Background, Fabrication Ecosystem, Relevance to Space Systems Applications, and Discussion of Related Radiation Effects

Electronic integrated circuits are considered one of the most significant technological advances of the 20th century, with demonstrated impact in their ability to incorporate successively higher numbers transistors and construct electronic devices onto a single CMOS chip. Photonic integrated circuits (PICs) exist as the optical analog to integrated circuits; however, in place of transistors, PICs consist of numerous scaled optical components, including such "building-block" structures as waveguides, MMIs, lasers, and optical ring resonators. The ability to construct electronic and photonic components on a single microsystems platform offers transformative potential for the development of technologies in fields including communications, biomedical device development, autonomous navigation, and chemical and atmospheric sensing. Developing on-chip systems that provide new avenues for integration and replacement of bulk optical and electro-optic components also reduces size, weight, power and cost (SWaP-C) limitations, which are important in the selection of instrumentation for specific flight projects. The number of applications currently emerging for complex photonics systems-particularly in data communications-warrants additional investigations when considering reliability for space systems development. This Body of Knowledge document seeks to provide an overview of existing integrated photonics architectures; the current state of design, development, and fabrication ecosystems in the United States and Europe; and potential space applications, with emphasis given to associated radiation effects and reliability.

Body of Knowledge (BOK)↗
NASA NTRSDate not supplied

Investigating Biological Responses to Deep Space Radiation for Missions Beyond Low Earth Orbit (LEO) using Yeast

To enable long-term spaceflight missions and establish habitation on the Moon and Mars, we require a comprehensive understanding of the effects of chronic deep space radiation exposure on humans. BioSentinel is NASA’s first biological CubeSat to venture beyond Low Earth Orbit (LEO). It utilizes Saccharomyces cerevisiae (budding yeast) as a model organism to study biological responses to deep space radiation. Yeast share significant genetic homology with humans, including basic cellular metabolism and DNA repair mechanisms. In addition, unlike human cell cultures, yeast can survive the duration and constraints of a deep space mission. BioSentinel measures biological responses using an optical system and alamarBlue oxidation-reduction (redox) dye. Two strains of yeast are studied - a wild-type and a rad51 mutant strain that is deficient in DNA repair. Changes in metabolism and growth are monitored throughout the nominal 6-month mission. Preliminary tests indicate a significant change in the alamarBlue response to low-dose ionizing radiation (IR). Additionally, rad51 cells have shown an IR dose-dependent decrease in glucose uptake and accumulation of oxidized NADH (NAD+). These biomolecules are involved in reactions responsible for basic cell processes, including growth and development, signaling, and respiration. The current study expanded upon previous data by exposing yeast to deep space-relevant radiation. Glucose and NADH/NAD+ assays were conducted on yeast subjected to varying dosages of high-energy Fe-56 and simulated galactic cosmic rays (GCRs). The resulting data was analyzed using Excel and GraphPad Prism. A particular focus was to identify biomolecules resulting from aerobic respiration, which requires the presence of oxygen, or anaerobic processes. As long-term spaceflight missions draw near, it is increasingly important to characterize biological processes affected by the conditions of deep space. Studying biomolecular damage caused by deep space radiation may enable the development of engineering controls or biomedical therapeutics that mitigate health complications for future astronauts.

Kyra Keenan↗
DOE OSTI2025

Physics-guided dual implicit neural representations for source separation

Significant challenges exist in efficient data analysis of most advanced experimental and observational techniques because the collected signals often include unwanted contributions, such as background and signal distortions, that can obscure the physically relevant information of interest. To address this, we have developed a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework that jointly trains two neural networks: one for approximating distortions of the physical signal of interest and the other for learning the effective background contribution. Our method learns directly from the raw data by minimizing a reconstruction-based loss function without requiring labeled data or pre-defined dictionaries. We demonstrate the effectiveness of our framework by considering a challenging case study involving large-scale simulated, as well as experimental, momentum-energy-dependent inelastic neutron scattering data in a four-dimensional parameter space, characterized by heterogeneous background contributions and unknown distortions to the target signal. The method is found to successfully separate physically meaningful signals from a complex or structured background even when the signal characteristics vary across all four dimensions of the parameter space. An analytical approach that informs the choice of the regularization parameter is presented. Our method offers a versatile framework for addressing source separation problems across diverse domains, ranging from superimposed signals in astronomical measurements to structural features in biomedical image reconstructions.

47 OTHER INSTRUMENTATION↗
NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗
NASA NTRS2016

GeneLab: Multi-Omics Investigation of Rodent Research-1 Bio-Banked Tissues

NASAs Rodent Research (RR) project is playing a critical role in advancing biomedical research on the physiological effects of space environments. Due to the limited resources for conducting biological experiments aboard the International Space Station (ISS), it is imperative to use crew time efficiently while maximizing high-quality science return. NASAs GeneLab project has as its primary objectives to 1) further increase the value of these experiments using a multi-omics, systems biology-based approach, and 2) disseminate these data without restrictions to the scientific community. The current investigation assessed viability of RNA, DNA, and protein extracted from archived RR-1 tissue samples for epigenomic, transcriptomic, and proteomic assays. During the first RR spaceflight experiment, a variety of tissue types were harvested from subjects, snap-frozen or RNAlater-preserved, and then stored at least a year at -80OC after return to Earth. They were then prioritized for this investigation based on likelihood of significant scientific value for spaceflight research. All tissues were made available to GeneLab through the bio-specimen sharing program managed by the Ames Life Science Data Archive and included mouse adrenal glands, quadriceps, gastrocnemius, tibialis anterior, extensor digitorum longus, soleus, eye, and kidney. We report here protocols for and results of these tissue extractions, and thus, the feasibility and value of these kinds of omics analyses. In addition to providing additional opportunities for investigation of spaceflight effects on the mouse transcriptome and proteome in new kinds of tissues, our results may also be of value to program managers for the prioritization of ISS crew time for rodent research activities. Support from the NASA Space Life and Physical Sciences Division and the International Space Station Program is gratefully acknowledged.

GeneLab↗
NASA NTRS1992

COSMOS 2044 Mission: Overview

The COSMOS 2044 spaceflight was the ninth Soviet-International joint mission dedicated to space biomedicine and the seventh in which the United States has participated. The unmanned Vostok vehicle carried 10 rats and two rhesus monkeys on its 14-day voyage. This spaceflight yielded an unprecedented bounty of data on physiological responses to the microgravity environment. The tissues studied and the numbers and types of studies performed by members of the international science community constituted a new record. Many of the results obtained by the approximately 80 American scientists who participated are reported in the series of COSMOS 2044 papers in this issue. Descriptions of the spaceflight and animal procedures are detailed elsewhere. The broad goals of the space biomedical program are threefold. The first is to characterize qualitatively and quantitatively the biological responses to the microgravity environment, be they adaptive or pathological. The second goal is to clarify the physiological-biochemical mechanisms mediating the responses to microgravity. The third goal of this program is to use the space environment as a tool to better understand adaptive and disease processes in terrestrial organisms.

Grindeland, R. E.↗
NASA NTRS2004

Malaria Modeling using Remote Sensing and GIS Technologies

Malaria has been with the human race since the ancient time. In spite of the advances of biomedical research and the completion of genomic mapping of Plasmodium falciparum, the exact mechanisms of how the various strains of parasites evade the human immune system and how they have adapted and become resistant to multiple drugs remain elusive. Perhaps because of these reasons, effective vaccines against malaria are still not available. Worldwide, approximately one to three millions deaths are attributed to malaria annually. With the increased availability of remotely sensed data, researchers in medical entomology, epidemiology and ecology have started to associate environmental and ecological variables with malaria transmission. In several studies, it has been shown that transmission correlates well with certain environmental and ecological parameters, and that remote sensing can be used to measure these determinants. In a NASA project, we have taken a holistic approach to examine how remote sensing and GIs can contribute to vector and malaria controls. To gain a better understanding of the interactions among the possible promoting factors, we have been developing a habitat model, a transmission model, and a risk prediction model, all using remote sensing data as input. Our objectives are: 1) To identify the potential breeding sites of major vector species and the locations for larvicide and insecticide applications in order to reduce costs, lessen the chance of developing pesticide resistance, and minimize the damage to the environment; 2) To develop a malaria transmission model characterizing the interactions among hosts, vectors, parasites, landcover and environment in order to identify the key factors that sustain or intensify malaria transmission, and 3) To develop a risk model to predict the occurrence of malaria and its transmission intensity using epidemiological data and satellite-derived or ground-measured environmental and meteorological data.

Kiang, Richard↗
NASA NTRS2014

Exposure to Microgravity for 30 Days Onboard Bion M1 Caused Muscle Atrophy and Decreased Regeneration in the Mouse Femoral Quadriceps

Mechanical unloading of muscle during spaceflight in microgravity is known to cause muscular atrophy, changes in muscle fiber type composition, gene expression, and reductions in regenerative muscle growth. Although limited data exists for long-term effects of microgravity in human muscle, these processes have mostly been studied in rodents for short periods of time. Here we report on how 30-day, long-term, mechanical unloading in microgravity affects mouse muscle of the femoral Quadriceps group. To conduct these studies we used muscle tissue from 6 mice from the NASA Biospecimen Sharing Program conducted in collaboration with the Institute for Biomedical Problems of the Russian Academy of Sciences, during the Russian Bion M1 biosatellite mission in 2013. Muscle morphology observed in histological sections shows signs of extensive atrophy and regenerative hypoplasia. Specifically, we observed a two-fold decrease in the number of myonuclei, their central location, low density of myofibers and myofibrils, in fragmentation and swelling of myofibers. Despite obvious atrophy, muscle regeneration nevertheless appears to have continued after 30 days in microgravity as evidenced by thin and short newly formed myofibers. Many of them however showed evidence of apoptotic, TUNEL positive cells and myofibrils degradation, suggesting long-term unloading in microgravity affects late stages of myofiber differentiation. Ground asynchronous and vivarium control animals showed normal, well-developed tissue structure with sufficient blood and nerve supply and evidence of regenerative formation of new myofibers free of apoptotic nuclei. Myonuclei stress response in spaceflight animals was detected by positive nuclear immunolocalization of c-jun and c-myc proteins. Regenerative activity of satellite cells in muscles is detected in mice of all animal groups, by pax7, MyoD, and myogenin immunostaining and myogenin PCR analysis. In summary, long-term spaceflight in microgravity causes significant atrophy and degeneration of the femoral Quadriceps muscle group, and it may interfere with muscle regenerative processes by inducing apoptosis in newly-formed myofibrils during their differentiation phase.

Grigoryan, E. N.↗
NASA NTRS1999

Cardiopulmonary baroreceptor control of muscle sympathetic nerve activity in heat-stressed humans

Whole body heating decreases central venous pressure (CVP) while increasing muscle sympathetic nerve activity (MSNA). In normothermia, similar decreases in CVP elevate MSNA, presumably via cardiopulmonary baroreceptor unloading. The purpose of this project was to identify whether increases in MSNA during whole body heating could be attributed to cardiopulmonary baroreceptor unloading coincident with the thermal challenge. Seven subjects were exposed to whole body heating while sublingual temperature, skin blood flow, heart rate, arterial blood pressure, and MSNA were monitored. During the heat stress, 15 ml/kg warmed saline was infused intravenously over 7-10 min to increase CVP and load the cardiopulmonary baroreceptors. We reported previously that this amount of saline was sufficient to return CVP to pre-heat stress levels. Whole body heating increased MSNA from 25 +/- 3 to 39 +/- 3 bursts/min (P < 0. 05). Central blood volume expansion via rapid saline infusion did not significantly decrease MSNA (44 +/- 4 bursts/min, P > 0.05 relative to heat stress period) and did not alter mean arterial blood pressure (MAP) or pulse pressure. To identify whether arterial baroreceptor loading decreases MSNA during heat stress, in a separate protocol MAP was elevated via steady-state infusion of phenylephrine during whole body heating. Increasing MAP from 82 +/- 3 to 93 +/- 4 mmHg (P < 0.05) caused MSNA to decrease from 36 +/- 3 to 15 +/- 4 bursts/min (P < 0.05). These data suggest that cardiopulmonary baroreceptor unloading during passive heating is not the primary mechanism resulting in elevations in MSNA. Moreover, arterial baroreceptors remain capable of modulating MSNA during heat stress.

NASA Discipline Cardiopulmonary↗
DOE OSTI2024

Quantum Annealing for Real-World Machine Learning Applications

Optimizing the training of a machine learning pipeline is important for reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications have shown interesting results especially under the conditions where the performance of classical machine learning techniques are limited such as limited training data and high dimensional features. This chapter explores the application of D-Wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, security, computational biology, biomedical sciences, and physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

Kumar nath, Rajdeep↗
NASA NTRS2003

A statistical model of diurnal variation in human growth hormone

The diurnal pattern of growth hormone (GH) serum levels depends on the frequency and amplitude of GH secretory events, the kinetics of GH infusion into and clearance from the circulation, and the feedback of GH on its secretion. We present a two-dimensional linear differential equation model based on these physiological principles to describe GH diurnal patterns. The model characterizes the onset times of the secretory events, the secretory event amplitudes, as well as the infusion, clearance, and feedback half-lives of GH. We illustrate the model by using maximum likelihood methods to fit it to GH measurements collected in 12 normal, healthy women during 8 h of scheduled sleep and a 16-h circadian constant-routine protocol. We assess the importance of the model components by using parameter standard error estimates and Akaike's Information Criterion. During sleep, both the median infusion and clearance half-life estimates were 13.8 min, and the median number of secretory events was 2. During the constant routine, the median infusion half-life estimate was 12.6 min, the median clearance half-life estimate was 11.7 min, and the median number of secretory events was 5. The infusion and clearance half-life estimates and the number of secretory events are consistent with current published reports. Our model gave an excellent fit to each GH data series. Our analysis paradigm suggests an approach to decomposing GH diurnal patterns that can be used to characterize the physiological properties of this hormone under normal and pathological conditions.

Non-NASA Center↗
NASA NTRS2001

Ultrasonic wave propagation in trabecular bone predicted by the stratified model

The objective of this study was to investigate ultrasound propagation in trabecular bone by considering the wave reflection and transmission in a multilayered medium. The use of ultrasound to identify those at risk of osteoporosis is a promising diagnostic method providing a measure of bone mineral density (BMD). A stratified model was proposed to study the effect of transmission and reflection of ultrasound wave within the trabecular architecture on the relationship between ultrasound and BMD. The results demonstrated that ultrasound velocity in trabecular bone was highly correlated with the bone apparent density (r=0.97). Moreover, a consistent pattern of the frequency dependence of ultrasound attenuation coefficient has been observed between simulation using this model and experimental measurement of trabecular bone. The normalized broadband ultrasound attenuation (nBUA) derived from the simulation results revealed that nBUA was nonlinear with respect to trabecular porosity and BMD. The curve of the relationship between nBUA and BMD was parabolic in shape, and the peak magnitude of nBUA was observed at approximately 60% of bone porosity. These results agreed with the published experimental data and demonstrated that according to the stratified model, reflection and transmission were important factors in the ultrasonic propagation through the trabecular bone.

Non-NASA Center↗
NASA NTRS1999

Bone Loss in Space: Shuttle/MIR Experience and Bed Rest Countermeasure Program

Loss of bone mineral during space flight was documented in the 1970's Skylab missions. The USSR space program made similar observations in the 1980's. The Institute of Biomedical Problems in Moscow and NASA JSC in 1989 began to collect pre- and post-flight bone mineral density (BMD) using Hologic QDR 1000 DEXA scanners transferred from JSC to Moscow and Star City. DEXA whole body, hip, and lumbar spine scans were performed prior to and during the first week after return from 4- to 6-month missions (plus one 8-month mission and one 14- month mission) on the Mir space station. These data documented the extent and regional nature of bone loss during long duration space flight. Of the 18 cosmonauts participating in this study between 1990 and 1995, seven flew two missions. BMD scans prior to the second flight compared to the first mission preflight scans indicated that recovery was possibly delayed or incomplete. Because of these findings, NASA and IBMP initiated the study "Bone Mineral Loss and Recovery After Shuttle/Mir Flights" in 1995 to evaluate bone recovery during a 3-year post-flight period. All of the 14 participants thus far evaluated lost bone in at least one region of the spine and lower extremities during flight. Of the 14, only one to date has exhibited full return to baseline BNM values in all regions. The current study will continue until the last participant has reached full bone recovery in all regions, has reached a plateau, or until three years after the flight (2001 for the last mission of the program). Bone mineral density losses in space and difficulty in returning to baseline indicate a need for countermeasure development. In late 1996 NASA JSC and Baylor College of Medicine were approved to conduct two countermeasure studies during 17 weeks of bed rest. In 1997 the studies were begun in the bed rest facility established by NASA, Baylor College of Medicine, and The Methodist Hospital in Houston. To date, three bed rest controls, five resistive exercisers, and four subjects taking alendronate (a bisphoshonate that inhibits osteoclastic resorption of bone) have completed 17 weeks bed rest. In contrast to information currently available from space flight (n=28) and bed rest (n= 12) in which all individuals experienced bone loss in at least one region, one of four subjects taking alendronate and one of five subjects performing heavy resistive exercise at bed rest fully maintained bone density in all regions of the spine and lower extremities. Overall results of both countermeasures which will be presented are encouraging. The study will be completed by mid to late 2000 with 10 subjects in each of three groups.

Shackelford, L. C.↗
NASA NTRS2004

Variations in Urine Calcium Isotope: Composition Reflect Changes in Bone Mineral Balance in Humans

Changes in bone mineral balance cause rapid and systematic changes in the calcium isotope composition of human urine. Urine from subjects in a 17 week bed rest study was analyzed for calcium isotopic composition. Comparison of isotopic data with measurements of bone mineral density and metabolic markers of bone metabolism indicates the calcium isotope composition of urine reflects changes in bone mineral balance. Urine calcium isotope composition probably is affected by both bone metabolism and renal processes. Calcium isotope. analysis of urine and other tissues may provide information on bone mineral balance that is in important respects better than that available from other techniques, and illustrates the usefulness of applying geochemical techniques to biomedical problems.

Skulan, Joseph↗
NASA NTRS1984

The APL image processing laboratory

The present and proposed capabilities of the Central Image Processing Laboratory, which provides a powerful resource for the advancement of programs in missile technology, space science, oceanography, and biomedical image analysis, are discussed. The use of image digitizing, digital image processing, and digital image output permits a variety of functional capabilities, including: enhancement, pseudocolor, convolution, computer output microfilm, presentation graphics, animations, transforms, geometric corrections, and feature extractions. The hardware and software of the Image Processing Laboratory, consisting of digitizing and processing equipment, software packages, and display equipment, is described. Attention is given to applications for imaging systems, map geometric correction, raster movie display of Seasat ocean data, Seasat and Skylab scenes of Nantucket Island, Space Shuttle imaging radar, differential radiography, and a computerized tomographic scan of the brain.

Jenkins, J. O.↗
NASA NTRS1994

Fiber optic probes for laser light scattering: Ground based evaluation for micgrogravity flight experimentation. Integrated coherent imaging fiber optic systems for laser light scattering and other applications

The research work presented in this report has established a new class of backscatter fiber optics probes for remote dynamic light scattering capability over a range of scattering angles from 94 degrees to 175 degrees. The fiber optic probes provide remote access to scattering systems, and can be utilized in either a noninvasive or invasive configuration. The fiber optics create an interference free data channel to inaccessible and harsh environments. Results from several studies of concentrated suspension, microemulsions, and protein systems are presented. The second part of the report describes the development of a new technology of wavefront processing within the optical fiber, that is, integrated fiber optics. Results have been very encouraging and the technology promises to have significant impact on the development of fiber optic sensors in a variety of fields ranging from environmental monitoring to optical recording, from biomedical sensing to photolithography.

Dhadwal, Harbans Singh↗
NASA NTRS2003

Investigating the interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance

The two-process model of sleep regulation has been applied successfully to describe, predict, and understand sleep-wake regulation in a variety of experimental protocols such as sleep deprivation and forced desynchrony. A non-linear interaction between the homeostatic and circadian processes was reported when the model was applied to describe alertness and performance data obtained during forced desynchrony. This non-linear interaction could also be due to intrinsic non-linearity in the metrics used to measure alertness and performance, however. Distinguishing these possibilities would be of theoretical interest, but could also have important implications for the design and interpretation of experiments placing sleep at different circadian phases or varying the duration of sleep and/or wakefulness. Although to date no resolution to this controversy has been found, here we show that the issue can be addressed with existing data sets. The interaction between the homeostatic and circadian processes of sleep-wake regulation was investigated using neurobehavioural performance data from a laboratory experiment involving total sleep deprivation. The results provided evidence of an actual non-linear interaction between the homeostatic and circadian processes of sleep-wake regulation for the prediction of waking neurobehavioural performance.

Non-NASA Center↗
NASA NTRS1992

Environmental monitoring and research at the John F. Kennedy Space Center

The Biomedical Operations and Research Office at the NASA John F. Kennedy Space Center has been supporting environmental monitoring and research since the mid-1970s. Program elements include monitoring of baseline conditions to document natural variability in the ecosystem, assessments of operations and construction of new facilities, and ecological research focusing on wildlife habitat associations. Information management is centered around development of a computerized geographic information system that incorporates remote sensing and digital image processing technologies along with traditional relational data base management capabilities. The proactive program is one in which the initiative is to anticipate potential environmental concerns before they occur and, by utilizing in-house expertise, develop impact minimization or mitigation strategies to reduce environmental risk.

Hall, C. R.↗