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We perform nadir retrievals of ozone using simulated radiances from ozone-sondes over Bermuda from April 14 to May 25, 1993. Using a novel two-step retrieval strategy, we characterize the sensitivity of TES nadir retrievals to time variations of ozone. 02000 Optical Society of America.
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The effort and results of a program to determine and study the critical process variables associated with the manufacture of aerospace, hermetically-sealed, nickel-cadmium cells are reported. During the period, the impregnation/polarization process variable study was brought to a close with the completion of a series of related experiments. The results of the experiments are summarized. During this period, a general characterization of cell separator materials was initiated. The major conclusions resulting from the characterization of materials are included.
The determination of variability of the emitted and reflected components of outgoing radiation from the earth-atmosphere system is discussed. The effects of variability on climate and weather are considered, and meteorological and climate variables to be correlated with radiation budget measurements are determined.
Human space exploration is inherently hazardous, particularly for lon g duration (LD) missions (22 days or longer). Maintenance of cognitive functioning is essential, but flight environments pose numerous pote ntial risks to the brain and cognitive performance (eg, radiation, to xins, chronic stress, sleep deprivation, hypercarbia, fluid shifts, h ormone imbalances, and injury). There have been persistent anecdotal reports of cognitive deficits during missions, but an up?-to-date rev iew of the evidence for such changes has remained unavailable. Method s: We identified and reviewed English language publications found via electronic searches in PubMed, PsycInfo, Inspec, the NASA Technical Report Server, and the Defense Technical Information Center, plus rec ursive searches of publication bibliographies. Search terms included the word cognition, cognitive, or performance along with spaceflight, flight, mission, or closely related terms. Results: Inter?-study variability precluded meta?-analysis. Some 32 published studies involving cognitive assessment during spaceflight were identified, involving a total of 110 participants (mean: 3.4 participants per study). The lo ngest?-duration study spanned 438 days, with six additional studies i nvolving flight durations of 90 days, and 11 more studies involved fl ight durations exceeding 21 days. The available evidence failed to st rongly support or refute the existence of cognitive deficits in LD sp aceflight, in part due to inadequate power or control conditions. Evi dence of increased variability in cognitive performance during spacef light, both within and between individuals, was common. Discussion: T hese results represent a negative finding based on small numbers of s ubjects for any given cognitive function. The increased variability within and (particularly) between individuals highlights the potential danger of generalizing from case studies. A mismatch therefore remain s between anecdotal reports describing generalized cognitive slowing, attention and memory problems during missions and the experimental e vidence supporting such deficits. Since a major justification for man ned spaceflight rests with the cognitive flexibility of humans, addit ional studies and further analysis of existing operational data appea rs warranted.
Spectral modeling and variability studies are reported in this paper, including fitting with relativistically blurred ionized-disk models designed to reproduce the radiation spectrum from the inner accretion disk around a black hole. PG 1404+226 is highly variable in flux and has a steep X-ray spectrum that varies over the course of the observation. Its strong observed soft X-ray excess can be well fitted with an ionized-disk model. The parameters derived from this model indicate that the central black hole is rotating, since the inner radius of the accretion disk lies within the last stable orbit for a non-rotating black hole. The harder spectrum of PG 1244426 also shows a soft X-ray excess, but it is better represented by a more conventional two-component phenomenonological model of a power-law and thermal emission.
Stroemgren's classification of photometric systems is used to survey wideband, medium, and narrow-band systems for studying variable stars. Multicolor photometry of variable stars is used to obtain curves of brightness change and to study the physical properties of the variables as they change during alteration of brightness.
The results of a multigroup, diffusion theory study of spherical gaseous-core cavity reactors are presented in this report. The reactor cavity of gaseous U235 is enclosed by a region of hydrogen gas and is separated from an external D2O moderator-reflector by a zirconium structural shell. Some cylindrical reactors are also investigated. A parametric study of spherical reactors indicates that, for the range of variables studied, critical mass increases as: (1) Fuel region is compressed within the reactor cavity, (2) moderator thickness is decreased, (3) structural shell thickness is increased, and (4) moderator temperature is increased. A buckling analogy is used to estimate the critical mass of fully reflected cylindrical reactors from spherical results without fuel compression. For a reactor cavity of a 120-centimeter radius uniformly filled with fuel, no structural shell, a moderator temperature of 70 F, and a moderator thickness of 100 centimeters, the critical mass of a spherical reactor is 3.1 kilograms while that of a cylinder with a length-to-diameter ratio of 1.0 (L/D = 1) is approximately 3.8 kilograms and, with L/D = 2, 5.9 kilograms. For the range of variables considered for U235-D2O gaseous-core cavity reactors, the systems are characterized by 95 to 99 percent thermal absorptions, with the flux reaching a maximum in the moderator about 10 to 15 centimeters from the reactor cavity.
The results are summarized of an investigation of discrete component bonding reliability and a fundamental study of new thick film resistor materials. The component bonding study examined several types of solder bonded components with some processing variable studies to determine their influence upon bonding reliability. The bonding reliability was assessed using the thermal cycle: 15 minutes at room temperature, 15 minutes at +125 C 15 minutes at room temperature, and 15 minutes at -55 C. The thick film resistor materials examined were of the transition metal oxide-phosphate glass family with several elemental metal additions of the same transition metal. These studies were conducted by preparing a paste of the subject composition, printing, drying, and firing using both air and reducing atmospheres. The resulting resistors were examined for adherence, resistance, thermal coefficient of resistance, and voltage coefficient of resistance.
Terrestrial far ultraviolet (FUV) airglow emissions have been suggested as a means for remote sensing the structure of the upper atmosphere. The energy which leads to the excitation of FUV airglow emissions is solar irradiance at extreme ultraviolet (EUV) and soft x-ray wavelengths. Solar irradiance at these wavelengths is known to be highly variable; studies of nitric oxide (NO) in the lower thermosphere have suggested a variability of more than an order of magnitude in the solar soft x-ray irradiance. To properly interpret the FUV airflow, the magnitude of the solar energy deposition must be known. Previous analyses have used the electron impact excited Lyman-Birge-Hopfield (LBH) bands of N2 to infer the flux of photoelectrons in the atmosphere and thus to infer the magnitude of the solar irradiance. This dissertation presents the first simultaneous measurements of the FUV airglow, the major atmospheric constituent densities, and the solar EUV and soft x-ray irradiances. The measurements were made on three flights of an identical sounding rocket payload at different levels of solar activity. The linear response in brightness of the LBH bands to variations in solar irradiance is demonstrated. In addition to the N2 LBH bands, atomic oxygen lines at 135.6 and 130.4 nm are also studied. Unlike the LBH bands, these emissions undergo radiative transfer effects in the atmosphere. The OI emission at 135.6 nm is found to be well modeled using a radiative transfer calculation and the known excitation processes. Unfortunately, the assumed processes leading to OI 130.4 nm excitation are found to be insufficient to reproduce the observed variability of this emission. Production of NO in the atmosphere is examined; it is shown that a lower than previously reported variability in the solar soft x-ray irradiance is required to explain the variability of NO.
Preliminary design studies were performed on four continuously variable transmission (CVT) concepts for use with a flywheel equipped electric vehicle of 1700 kg gross weight. Requirements of the CVT's were a maximum torque of 450 N-m (330 lb-ft), a maximum output power of 75 kW (100 hp), and a flywheel speed range of 28,000 to 14,000 rpm. Efficiency, size, weight, cost, reliability, maintainability, and controls were evaluated for each of the four concepts which included a steel V-belt type, a flat rubber belt type, a toroidal traction type, and a cone roller traction type. All CVT's exhibited relatively high calculated efficiencies (68 percent to 97 percent) over a broad range of vehicle operating conditions. Estimated weight and size of these transmissions were comparable to or less than equivalent automatic transmission. The design of each concept was carried through the design layout stage.
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.
Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.
Vortex wake alleviation studies were conducted in a wind tunnel and a water towing tank using a multisegmented wing model which provided controlled and measured variations in span load. Fourteen model configurations are tested at a Reynolds number of one million and a lift coefficient of 0.6 in the Langley 4- by 7-Meter Tunnel and the Hydronautics Ship Model Basin water tank at Hydronautics, Inc., Laurel, Md. Detailed measurements of span load and wake velocities at one semispan downstream correlate well with each other, with inviscid predictions of span load and wake roll up, and with peak trailing-wing rolling moments measured in the far wake. Average trailing-wing rolling moments are found to be an unreliable indicator of vortex wake intensity because vortex meander does not scale between test facilities and free-air conditions. A tapered-span-load configuration, which exhibits little or no drag penalty, is shown to offer significant downstream wake alleviation to a small trailing wing. The greater downstream wake alleviation achieved with the addition of spoilers to a flapped-wing configuration is shown to result directly from the high incremental drag and turbulence associated with the spoilers and not from the span load alteration they cause.
A two-blade rotor having a diameter of 4 feet and a solidity of 0.037 was subjected to sharp-edge vertical gusts while being operated at various forward speeds to study the effect of the gusts on the blade periodic bending moments and flapping angles. Variables studied included gust velocity, collective pitch angle, flapping hinge offset, and tip-speed ratio. Dimensionless coefficients are derived for the periodic components of the incremental changes in blade flapping angles and bending moments which arise when a rotor blade penetrates a sharp-edge gust. Mental changes in both the flapping angles and bending moments are essentially proportional to gust velocity, and the coefficients express the ratio of these increments to gust velccity. The results show that the flapping coefficient usually increases with an increase in collective pitch angle, is generally dependent on tip-speed ratio, and is essentially independent of the amount of flapping hinge offset. The bending-moment coefficient is also dependent on collective pitch angle and tip-speed ratio. Expected reductions in bending moments are realized by the use of flapping hinges, and further reductions in bending moments are achieved as the amount of flapping hinge offset is increased. Comparison of the experimental results of this investigation with limited available theoretical results shows substantial agreement but indicates that the assumption that the response of the rotor to a sharp-edge gust is independent of the collective pitch angle prior to gust entry is probably inadequate.
In order to provide information that might lead to the development of better propeller section, 13 related symmetrical airfoils were tested in the NACA high-speed wind tunnel for a study of the effect of thickness form on the aerodynamic characteristics. The thickness-form variables studies were the value of the maximum thickness, the position along the chord at which the maximum thickness occurs, and the value of the leading-edge radius. The tests were conducted through the low angle-of-attack range for speeds extending from 35 percent of that of sound to slightly in excess of the speed at which a compressibility burble, or breakdown of flow, occurs. The corresponding Reynolds number range is 350,000 to 750,000.