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NASA NTRS2000

Landsat TM as a Tool for Locating Habitat for Cerulean Warblers

I believe that I made significant strides in three areas between fall of 1997 and fall of 2000 when I concluded my participation in the JOVE program. First, I acquired skill in digital remote sensing. This was significant to me because it had been 20 years since I had done any work utilizing remote sensing. I used my new skills in two classroom settings (forest ecology and GIS). In addition, I will participate as an instructor of digital remote sensing in a workshop for secondary educators this coming spring. Second, I received funding from the Arkansas Game and Fish Commission and the U.S. Forest Service to supplement JOVE funds. Third, and most importantly, a students and I developed a technique using LandSAT TM for identifying habitat for cerulean warblers. We developed a habitat model using logistic regression to discriminate between pixels that had a high probability of representing good cerulean warbler habitat and pixels that had a low probability of representing cerulean warbler habitat. Using this model, we located five significant populations of cerulean warblers in the Ozark National Forest of Arkansas. These populations were unknown before the initiation of this research and further represent a significant proportion of the known cerulean warblers in Arkansas. Preliminary findings were presented at the Ornithological Societies of America meeting in August of 1999. I also presented findings at the Arkansas Game and Fish Commission Research Symposium held in June of 2000. Finally, one paper is in press: James, D. A., C.J. Kellner, J. Self, and J. Davis., 'Breeding season distribution of cerulean warblers in Arkansas in the 1990's'. In addition, one paper is under construction: 'Population fluctuation and habitat selection by cerulean warblers in upland forests of Arkansas,' and one paper is under consideration: 'LandSAT TM and Logistic regression for identification of cerulean warbler habitat in upland forests of Arkansas.'

Kellner, Chris↗
NASA NTRS2016

Post-Flight Back Pain Following International Space Station Missions: Evaluation of Spaceflight Risk Factors

INTRODUCTION Back pain during spaceflight has often been attributed to the lengthening of the spinal column due to the absence of gravity during both short and long-duration missions. Upon landing and re-adaptation to gravity, the spinal column reverts back to its original length thereby causing some individuals to experience pain and muscular spasms, while others experience no ill effects. With International Space Station (ISS) missions, cases of back pain and injury are more common post-flight, but little is known about the potential risk factors. Thus, the purpose of this project was to perform an initial evaluation of reported post-flight back pain and injury cases to relevant spaceflight risk factors in United States astronauts that have completed an ISS mission. METHODS All US astronauts who completed an ISS mission between Expeditions (EXP) 1 and 41 (2000-2015) were included in this evaluation. Forty-five astronauts (36 males and 9 females) completed 50 ISS missions during the study time period, as 5 astronauts completed 2 ISS missions. Researchers queried medical records of the 45 astronauts for occurrences of back pain and injury. A case was defined as any reported event of back pain or injury to the cervical, thoracic, lumbar, sacral, or coccyx spine regions. Data sources for the cases included the Flight Medicine Clinic's electronic medical record; Astronaut Strength, Conditioning and Rehabilitation electronic documentation; the Private Medical Conference tool; and the Space Medicine Operations Team records. Post-flight cases were classified as an early case if reported within 45 days of landing (R + 45) or a late case if reported from R + 46 to R + 365 days after landing (R + 1y). Risk factors in the astronaut population for back pain include age, sex, prior military service, and prior history of back pain. Additionally, spaceflight specific risk factors such as type of landing vehicle and onboard exercise countermeasures were included to evaluate their contribution to post-flight cases. Prior history of back pain included back pain recorded in the medical record within 3 years prior to launch. Landing vehicle was included in the model to discern if more astronauts experienced back pain or injury following a Shuttle or Soyuz landing. Onboard exercise countermeasures were noted for those astronauts who had a mission following 2009 deployment of the Advanced Resistive Exercise Device (aRED) (EXP 19 to 41). T-test and chi-squared tests were performed to evaluate the association between each individual risk factor and post-flight case. Logistic regression was used to evaluate the combined contribution of all the risk factors on post-flight cases. Separate models were calculated for cases reported by R + 45 and R + 1y. RESULTS During the study time period, there were 13 post-flight cases reported by R + 45 and an additional 5 reported by R + 1y. Most of these cases have been reported since EXP 19 with 10 cases by R + 45 and 4 by R + 1y. Individual risk factors of age, sex, landing vehicle, and prior military service were not significantly associated with post-flight cases identified at R + 45 or R + 1y (p greater than 0.05). Having back pain or injury within 3 years prior to launch significantly increased the likelihood of becoming a case by R + 1y (p = 0.041), but not at R+45 (p=0.204). Additionally, astronauts who experienced onboard exercise countermeasures that included aRED had a significantly increased risk of becoming a case at R + 45 (p = 0.024) and R + 1y (p=0.003). Multiple logistic regression evaluating all the risk factors for cases identified no significant risk factors at either the R + 45 or R + 1y time period (p greater than 0.05). Overall model fit was poor for both the R + 45 (R(exp 2) = 0.132) and R + 1y (R(exp 2) = 0.186) cases showing that there are risk factors not represented in our model. CONCLUSIONS Regardless of cause, post-flight cases are reported more often since aRED was deployed in 2009. This may reflect improved documentation or unidentified risk factors. No spaceflight risk factor explains the data fully. Post-flight cases are probably due to multi-faceted factors that are not easily elucidated in the medical data.

Laughlin, M. S.↗
NASA NTRS2006

Global Positioning System (GPS) Precipitable Water in Forecasting Lightning at Spaceport Canaveral

This paper evaluates the use of precipitable water (PW) from Global Positioning System (GPS) in lightning prediction. Additional independent verification of an earlier model is performed. This earlier model used binary logistic regression with the following four predictor variables optimally selected from a candidate list of 23 candidate predictors: the current precipitable water value for a given time of the day, the change in GPS-PW over the past 9 hours, the KIndex, and the electric field mill value. This earlier model was not optimized for any specific forecast interval, but showed promise for 6 hour and 1.5 hour forecasts. Two new models were developed and verified. These new models were optimized for two operationally significant forecast intervals. The first model was optimized for the 0.5 hour lightning advisories issued by the 45th Weather Squadron. An additional 1.5 hours was allowed for sensor dwell, communication, calculation, analysis, and advisory decision by the forecaster. Therefore the 0.5 hour advisory model became a 2 hour forecast model for lightning within the 45th Weather Squadron advisory areas. The second model was optimized for major ground processing operations supported by the 45th Weather Squadron, which can require lightning forecasts with a lead-time of up to 7.5 hours. Using the same 1.5 lag as in the other new model, this became a 9 hour forecast model for lightning within 37 km (20 NM)) of the 45th Weather Squadron advisory areas. The two new models were built using binary logistic regression from a list of 26 candidate predictor variables: the current GPS-PW value, the change of GPS-PW over 0.5 hour increments from 0.5 to 12 hours, and the K-index. The new 2 hour model found the following for predictors to be statistically significant, listed in decreasing order of contribution to the forecast: the 0.5 hour change in GPS-PW, the 7.5 hour change in GPS-PW, the current GPS-PW value, and the KIndex. The new 9 hour forecast model found the following five independent variables to be statistically significant, listed in decreasing order of contribution to the forecast: the current GPSPW value, the 8.5 hour change in GPS-PW, the 3.5 hour change in GPS-PW, the 12 hour change in GPS-PW, and the K-Index. In both models, the GPS-PW parameters had better correlation to the lightning forecast than the K-Index, a widely used thunderstorm index. Possible future improvements to this study are discussed.

Kehrer, Kristen C.↗
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 NTRSDate not supplied

The effect of modeling dose uncertainty on low-boom community noise dose-response curves

In logistic dose-response modeling, failing to account for uncertainty in estimated doses can cause an artificial flattening or attenuation of the slope of the summary curve. In Lee et al. [J. Acoust. Soc. Am. 147(4), pp. 2222-2234 (2020)], data from two NASA low-amplitude sonic boom community noise survey tests were modeled using a Bayesian multilevel logistic regression (MLR) statistical model that assumed there was no uncertainty in the noise dose estimates. However, in these community tests, the noise dose uncertainty was estimated by Page et al. [NASA/CR-2014-218180 and NASA/CR-2020-220589/Volume I] using a leave-one-out method. In the current work, a term was added to extend the Bayesian MLR model to account for the estimated noise dose uncertainty quantified in the Page et al. analyses. This uncertainty term was included in two ways, either as classical or as Berkson uncertainty, and yield similar results. When the uncertainty is accounted for in the Bayesian MLR model, the dose-response curves become 5-10% steeper, but the difference in the noise dose that elicits a 5% highly annoyed response is small (less than 1 dB). This result is encouraging for future X-59 community tests whose survey area will be sparsely populated with noise monitors.

X-59↗
NASA NTRS1993

Modeling and managing risk early in software development

In order to improve the quality of the software development process, we need to be able to build empirical multivariate models based on data collectable early in the software process. These models need to be both useful for prediction and easy to interpret, so that remedial actions may be taken in order to control and optimize the development process. We present an automated modeling technique which can be used as an alternative to regression techniques. We show how it can be used to facilitate the identification and aid the interpretation of the significant trends which characterize 'high risk' components in several Ada systems. Finally, we evaluate the effectiveness of our technique based on a comparison with logistic regression based models.

Briand, Lionel C.↗
NASA NTRS1998

Distribution and observed associations of orthostatic blood pressure changes in elderly general medicine outpatients

Factors associated with orthostatic blood pressure change in elderly outpatients were determined by surveying 398 medical clinical outpatients aged 65 years and older. Blood pressure was measured with random-zero sphygmomanometers after patients were 5 minutes in a supine and 5 minutes in a standing position. Orthostatic blood pressure changes were at normally distributed levels with systolic and diastolic pressures dropping an average of 4 mm Hg (standard deviation [SD]=15 mm Hg) and 2 mm Hg (SD=11 mm Hg), respectively. Orthostatic blood pressure changes were unassociated with age, race, sex, body mass, time since eating, symptoms, or other factors. According to multiple linear regression analysis, supine systolic pressure, chronic obstructive pulmonary disease (COPD), and diabetes mellitus were associated with a decrease in systolic pressure on standing. Hypertension, antiarthritic drugs, and abnormal heartbeat were associated with an increase in systolic pressure on standing. For orthostatic diastolic pressure changes, supine diastolic pressure and COPD were associated with a decrease in diastolic pressure on standing. Congestive heart failure was associated with an increase in standing diastolic pressure. Using logistic regression analysis, only supine systolic pressure was associated with a greater than 20-mm Hg drop in systolic pressure (n=53, prevalence=13%). Supine diastolic pressure and COPD were the only variables associated with a greater than 20-mm Hg drop in diastolic pressure (n=16, prevalence=4%). These factors may help physicians in identifying older persons at risk for having orthostatic hypotension.

Non-NASA Center↗
NASA NTRS2001

Prediction of coronary artery disease in patients undergoing operations for mitral valve degeneration

OBJECTIVES: We sought to develop and validate a model that estimates the risk of obstructive coronary artery disease in patients undergoing operations for mitral valve degeneration and to demonstrate its potential clinical utility. METHODS: A total of 722 patients (67% men; age, 61 +/- 12 years) without a history of myocardial infarction, ischemic electrocardiographic changes, or angina who underwent routine coronary angiography before mitral valve prolapse operations between 1989 and 1996 were analyzed. A bootstrap-validated logistic regression model on the basis of clinical risk factors was developed to identify low-risk (< or =5%) patients. Obstructive coronary atherosclerosis was defined as 50% or more luminal narrowing in one or more major epicardial vessels, as determined by means of coronary angiography. RESULTS: One hundred thirty-nine (19%) patients had obstructive coronary atherosclerosis. Independent predictors of coronary artery disease include age, male sex, hypertension, diabetes mellitus,and hyperlipidemia. Two hundred twenty patients were designated as low risk according to the logistic model. Of these patients, only 3 (1.3%) had single-vessel disease, and none had multivessel disease. The model showed good discrimination, with an area under the receiver-operating characteristic curve of 0.84. Cost analysis indicated that application of this model could safely eliminate 30% of coronary angiograms, corresponding to cost savings of $430,000 per 1000 patients without missing any case of high-risk coronary artery disease. CONCLUSION: A model with standard clinical predictors can reliably estimate the prevalence of obstructive coronary atherosclerosis in patients undergoing mitral valve prolapse operations. This model can identify low-risk patients in whom routine preoperative angiography may be safely avoided.

Non-NASA Center↗
NASA NTRS2011

Tissue Doppler Imaging Combined with Advanced 12-Lead ECG Analysis Might Improve Early Diagnosis of Hypertrophic Cardiomyopathy in Childhood

Optimization of early diagnosis of childhood hypertrophic cardiomyopathy (HCM) is essential in lowering the risk of HCM complications. Standard echocardiography (ECHO) has shown to be less sensitive in this regard. In this study, we sought to assess whether spatial QRS-T angle deviation, which has shown to predict HCM in adults with high sensitivity, and myocardial Tissue Doppler Imaging (TDI) could be additional tools in early diagnosis of HCM in childhood. Methods: Children and adolescents with familial HCM (n=10, median age 16, range 5-27 years), and without obvious hypertrophy but with heredity for HCM (n=12, median age 16, range 4-25 years, HCM or sudden death with autopsy-verified HCM in greater than or equal to 1 first-degree relative, HCM-risk) were additionally investigated with TDI and advanced 12-lead ECG analysis using Cardiax(Registered trademark) (IMED Co Ltd, Budapest, Hungary and Houston). Spatial QRS-T angle (SA) was derived from Kors regression-related transformation. Healthy age-matched controls (n=21) were also studied. All participants underwent thorough clinical examination. Results: Spatial QRS-T angle (Figure/ Panel A) and septal E/Ea ratio (Figure/Panel B) were most increased in HCM group as compared to the HCM-risk and control groups (p less than 0.05). Of note, these 2 variables showed a trend toward higher levels in HCM-risk group than in control group (p=0.05 for E/Ea and 0.06 for QRS/T by ANOVA). In a logistic regression model, increased SA and septal E/Ea ratio appeared to significantly predict both the disease (Chi-square in HCM group: 9 and 5, respectively, p less than 0.05 for both) and the risk for HCM (Chi-square in HCM-risk group: 5 and 4 respectively, p less than 0.05 for both), with further increased predictability level when these 2 variables were combined (Chi-square 10 in HCM group, and 7 in HCM-risk group, p less than 0.01 for both). Conclusions: In this small material, Tissue Doppler Imaging and spatial mean QRS-T angle deviation, particularly when combined, appear to be sensitive in predicting the risk for developing childhood HCM. Large-scale, prospective studies are needed to confirm this hypothesis.

Femlund, E.↗
NASA NTRS2004

NASA Experimental Program to Stimulate Competitive Research: South Carolina

The use of an appropriate relationship model is critical for reliable prediction of future urban growth. Identification of proper variables and mathematic functions and determination of the weights or coefficients are the key tasks for building such a model. Although the conventional logistic regression model is appropriate for handing land use problems, it appears insufficient to address the issue of interdependency of the predictor variables. This study used an alternative approach to simulation and modeling urban growth using artificial neural networks. It developed an operational neural network model trained using a robust backpropagation method. The model was applied in the Myrtle Beach region of South Carolina, and tested with both global datasets and areal datasets to examine the strength of both regional models and areal models. The results indicate that the neural network model not only has many theoretic advantages over other conventional mathematic models in representing the complex urban systems, but also is practically superior to the logistic model in its capability to predict urban growth with better - accuracy and less variation. The neural network model is particularly effective in terms of successfully identifying urban patterns in the rural areas where the logistic model often falls short. It was also found from the area-based tests that there are significant intra-regional differentiations in urban growth with different rules and rates. This suggests that the global modeling approach, or one model for the entire region, may not be adequate for simulation of a urban growth at the regional scale. Future research should develop methods for identification and subdivision of these areas and use a set of area-based models to address the issues of multi-centered, intra- regionally differentiated urban growth.

Sutton, Michael A.↗
NASA NTRS2004

Modeling Forest Understory Fires in an Eastern Amazonian Landscape

Forest understory fires are an increasingly important cause of forest impoverishment in Ammonia, but little is known of the landscape characteristics and climatic phenomena that determine their occurrence. We developed empirical functions relating the occurrence of understory fires to landscape features near Paragominas, a 35- yr-old ranching and logging center in eastern Ammonia. An historical sequence of maps of forest understory fire was created based on field interviews With local farmers and Landsat TM images. Several landscape features that might explain spatial variations in the occurrence of understory fires were also mapped and co-registered for each of the sample dates, including: forest fragment size and shape, forest impoverishment through logging and understory fires, source of ignition (settlements and charcoal pits), roads, forest edges, and others. The spatial relationship between forest understory fire and each landscape characteristic was tested by regression analyses. Fire probability models were then developed for various combinations of landscape characteristics. The analyses were conducted separately for years of the El Nino Southern Oscillation (ENSO), which are associated with severe drought in eastern Amazonia, and non-ENS0 years. Most (91 %) of the forest area that burned during the 10-yr sequence caught fire during ENSO years, when severe drought may have increased both forest flammability and the escape of agricultural management fires. Forest understory fires were associated with forest edges, as reported in previous studies from Ammonia. But the strongest predictor of forest fire was the percentage of the forest fragment that had been previously logged or burned. Forest fragment size, distance to charcoal pits, distance to agricultural settlement, proximity to forest edge, and distance to roads were also correlated with forest understory fire. Logistic regression models using information on fragment degradation and distance to ignition sources accurately predicted the location of lss than 80% of the forest fires observed during the ENSO event of 1997- 1998. In this Amazon landscape, forest understory fire is a complex function of several variables that influence both the flammability and ignition exposure of the forest.

Alencar, A. A. C.↗
NASA NTRS1990

Threshold altitude resulting in decompression sickness

A review of case reports, hypobaric chamber training data, and experimental evidence indicated that the threshold for incidence of altitude decompression sickness (DCS) was influenced by various factors such as prior denitrogenation, exercise or rest, and period of exposure, in addition to individual susceptibility. Fitting these data with appropriate statistical models makes it possible to examine the influence of various factors on the threshold for DCS. This approach was illustrated by logistic regression analysis on the incidence of DCS below 9144 m. Estimations using these regressions showed that, under a noprebreathe, 6-h exposure, simulated EVA profile, the threshold for symptoms occurred at approximately 3353 m; while under a noprebreathe, 2-h exposure profile with knee-bends exercise, the threshold occurred at 7925 m.

Kumar, K. V.↗
NASA NTRS1991

Ordinary chondrites - Multivariate statistical analysis of trace element contents

The contents of mobile trace elements (Co, Au, Sb, Ga, Se, Rb, Cs, Te, Bi, Ag, In, Tl, Zn, and Cd) in Antarctic and non-Antarctic populations of H4-6 and L4-6 chondrites, were compared using standard multivariate discriminant functions borrowed from linear discriminant analysis and logistic regression. A nonstandard randomization-simulation method was developed, making it possible to carry out probability assignments on a distribution-free basis. Compositional differences were found both between the Antarctic and non-Antarctic H4-6 chondrite populations and between two L4-6 chondrite populations. It is shown that, for various types of meteorites (in particular, for the H4-6 chondrites), the Antarctic/non-Antarctic compositional difference is due to preterrestrial differences in the genesis of their parent materials.

Lipschutz, Michael E.↗
NASA NTRS1992

Issues on human acceleration tolerance after long-duration space flights

This report reviewed the literature on human tolerance to acceleration at 1 G and changes in tolerance after exposure to hypogravic fields. It was found that human tolerance decreased after exposure to hypokinetic and hypogravic fields, but the magnitude of such reduction ranged from 0 to 30 percent for plateau G forces and 30 to 70 percent for time tolerance on sustained G forces. A logistic regression model of the probability of individuals with 25 percent reduction in +Gz tolerance after 1 to 41 days of hypogravic exposures was constructed. The estimated values from the model showed a good correlation with the observed data. A brief review of the need for in-flight centrifuge during long-duration missions was also presented. Review of the available data showed that the use of countermeasures (such as anti-G suits, periodic acceleration, and exercise) reduced the decrement in acceleration tolerance after long-duration space flights. Areas of further research include quantification of the effect of countermeasures on tolerance, and methods to augment tolerance during and after exposures to hypogravic fields. Such data are essential for planning long-duration human missions.

Kumar, K. Vasantha↗
NASA NTRS1993

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Wolf, S. F.↗
NASA NTRS1995

Chemical studies of H chondrites. 4: New data and comparison of Antarctic suites

We report data for the trace elements Au, Co, Sb, Ga, Rb, Ag, Se, Cs, Te, Zn, Cd, Bi, Ti, and In (ordered by putative volatility during nebular condensation and accretion) determined by neutron activation analysis in 13 H5 chondrites from Victoria Land and 20 H4-6 chondrites from Queen Maud Land, Antarctica. These and earlier results provide Antarctic sample suites of 34 chondrites from Victoria Land and 25 from Queen Maud Land. Treatment of data for the most volatile 10 elements (Rb to In) in these studies by multivariate statistical techniques more robust, as well as more conservative, than conventional linear discriminant analysis and logistic regression demonstrates that compositions differ at marginally significant levels. This difference cannot be explained by trivial (terrestrial) causes and becomes more significant, despite the smaller size of the database, when comparisons are limited to data from a single analyst and when all upper limits are eliminated from consideration. The Victoria Land and Queen Maud Land suites have different mean terrestrial ages (approximately 300 kyr and approximately 100 kyr, respectively) and age distributions, suggesting that a time-dependent variation of chondritic sources with different thermal histories is responsible. As a result, these two Antarctic suites are, on average, chemically distinguishable from each other. Since H chondrites serve as a paradigm for other meteorite classes, these results indicate that the near-Earth populations of planetary materials varied with time on the 10(exp 5)-year timescale.

Wolf, Stephen F.↗
NASA NTRS1995

Chemical studies of H chondrites. 6: Antarctic/non-Antarctic compositional differences revisited

We report data for the trace elements Au, Co, Sb, Ga, Rb, Ag, Se, Cs, Te, Zn, Cd, Bi, T1, and In (ordered by putative volatility during nebular condensation and accretion) determined by radiochemical neutron activation analysis of 14 additional H5 and H6 chondrite falls. Data for the 10 most volatile elements (Rb to In) treated by the multivariate techniques of linear discriminant analysis and logistic regression in these and 44 other falls are compared with those of 59 H4-6 chondrites from Antarctica. Various populations are tested by the multivariate techniques, using the previously developed method of randomization-simulation to assess significance levels. An earlier conclusion, based on fewer examples, that H4-6 chondrite falls are compositionally distinguishable from the Antarctic suite is verified by the additional data. This distinctiveness is highly significant because of the presence of samples from Victoria Land in the Antarctic population, which differ compositionally from falls beyond any reasonable doubt. However, it cannot be proven unequivocally that falls and Antarctic samples from Queen Maud Land are compositionally distinguishable. Trivial causes (e.g., analyst bias, weathering) cannot explain the Victoria Land (Antarctic)/non-Antarctic compositional difference for paradigmatic H4-6 chondrites. This seems to reflect a time-dependent variation of near-Earth meteoroid source regions differing in average thermal history.

Wolf, Stephen F.↗
NASA NTRS2003

Age affects severity of venous gas emboli on decompression from 14.7 to 4.3 psia

INTRODUCTION: Variables that define who we are, such as age, weight and fitness level influence the risk of decompression sickness (DCS) and venous gas emboli (VGE) from diving and aviation decompressions. We focus on age since astronauts that perform space walks are approximately 10 yr older than our test subjects. Our null hypothesis is that age is not statistically associated with the VGE outcomes from decompression to 4.3 psia. METHODS: Our data are from 7 different NASA tests where 188 men and 50 women performed light exercise at 4.3 psia for planned exposures no less than 4 h. Prebreathe (PB) time on 100% oxygen ranged from 150-270 min, including ascent time, with exercise of different intensity and length being performed during the PB in four of the seven tests with 150 min of PB. Subjects were monitored for VGE in the pulmonary artery using a Doppler ultrasound bubble detector for a 4-min period every 12 min. There were six design variables; the presence or absence of lower body adynamia and five PB variables; plus five concomitant variables on physical characteristics: age, weight height, body mass index, and gender that were available for logistic regression (LR). We used LR models for the probability of DCS and VGE, and multinomial logit (ML) models for the probability of Spencer VGE Grades 0-IV at exposure times of 61, 95, 131, 183 min, and for the entire exposure. RESULTS: Age was significantly associated with VGE in both the LR and ML models, so we reject the null hypothesis. Lower body adynamia was significant for all responses. CONCLUSIONS: Our selection of tests produced a wide range of the explanatory variables, but only age, lower body adynamia, height, and total PB time was helpful in various combinations to model the probability of DCS and VGE.

Clinical Trial↗