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The Advanced Concepts Office needed human factors analyses on various hatches for future deep space modules. The current standard is the 32" hatch, and the goal of this analysis was to assess this hatch size compared to larger sizes for egress, logistics, and safety.
Purpose: Dynamic imaging (DI) provides additional diagnostic information in emission tomography in comparison to conventional static imaging at the cost of being computationally more challenging. Dynamic single photon emission computed tomography (SPECT) reconstruction is particularly difficult because of the limitations in the sampling geometry present in most existing scanners. We have developed an algorithm Spline Initialized Factor Analysis of Dynamic Structures (SIFADS) that is a matrix factorization method for reconstructing the dynamics of tracers in tissues and blood directly from the projections in dynamic cardiac SPECT, without first resorting to any 3D reconstruction. Methods: SIFADS is different from "pure" factor analysis in dynamic structures (FADS) in that it employs a dedicated spline-based pre-initialization. In this paper, we analyze the convergence properties of SIFADS and FADS using multiple metrics. The performances of the two approaches are evaluated for numerically simulated data and for real dynamic SPECT data from canine and human subjects. Results: For SIFADS, metrics analyzed for reconstruction algorithm convergence show better features of the metric curves vs iterations. In addition, SIAFDS provides better tissue segmentations than that from pure FADS. Measured computational times are also typically better for SIFADS implementations than those with pure FADS. Conclusion: The analysis supports the utility of the pre-initialization of a factorization algorithm for better dynamic SPECT image reconstruction.
This meta-analysis is examining the psychological effects of wintering-over in Antarctica. As an isolated, confined, and extreme (ICE) environment, Antarctica provides invaluable opportunities to experience stressors more common to spaceflight than to the average person s everyday life. Increased prevalence of psychological symptoms, syndromes, and psychiatric disorders, as well as positive effects, are expected to be associated with various demographic and environmental factors. Implications for spaceflight are discussed. Findings from statistical review of the Antarctic articles will be shared.
An analysis was conducted of the possible modes of thermal stress failure of brittle ceramics for potential use in point-focussing solar receivers. The pertinent materials properties which control thermal stress resistance were identified for conditions of steady-state and transient heat flow, convective and radiative heat transfer, thermal buckling and thermal fatigue as well as catastrophic crack propagation. Selection rules for materials with optimum thermal stress resistance for a particular thermal environment were identified. Recommendations for materials for particular components were made. The general requirements for a thermal shock testing program quantitatively meaningful for point-focussing solar receivers were outlined. Recommendations for follow-on theoretical analyses were made.
Airborne Imaging Spectrometer (AIS) data were collected 30 August 1985 from a desert shrub community in central Oregon. Spectra from artificial targets placed on the test site and from bare soil, big sagebrush (Artemesia tridentata wyomingensis), silver sagebrush (Artemesia cana bolander), and exposed volcanic rocks were studied. Spectral data from grating position 3 (tree mode) were selected from 25 ground positions for analysis by Principal Factor Analysis (PFA). In this grating position, as many as six factors were identified as significant in contributing to spectral structure. Channels 74 through 84 (tree mode) best characterized between-class differences. Other channels were identified as nondiscriminating and as associated with such errors as excessive atmospheric absorption and grating positin changes. The test site was relatively simple with the two species (A. tridentata and A. cana) representing nearly 95% of biomass and with only two mineral backgrounds, a montmorillonitic soil and volcanic rocks. If, as in this study, six factors of spectral structure can be extracted from a single grating position from data acquired over a simple vegetation community, then AIS data must be considered rich in information-gathering potential.
Lunar samples represent a heterogeneous assemblage of rocks with complex inter-relationships that are difficult to decipher using standard petrogenetic approaches. These inter-relationships reflect several distinct petrogenetic trends as well as thermomechanical mixing of distinct components. Additional complications arise from the unequal quality of chemical analyses and from the fact that many samples (e.g., breccia clasts) are too small to be representative of the system from which they derived. Polytopic vector analysis (PVA) is a multi-variate procedure used as a tool for exploratory data analysis. PVA allows the analyst to classify samples and clarifies relationships among heterogenous samples with complex petrogenetic histories. It differs from orthogonal factor analysis in that it uses non-orthogonal multivariate sample vectors to extract sample endmember compositions. The output from a Q-mode (sample based) factor analysis is the initial step in PVA. The Q-mode analysis, using criteria established by Miesch and Klovan and Miesch, is used to determine the number of endmembers in the data system. The second step involves determination of endmembers and mixing proportions with all output expressed in the same geochemical variable as the input. The composition of endmembers is derived by analysis of the variability of the data set. Endmembers need not be present in the data set, nor is it necessary for their composition to be known a priori. A set of any endmembers defines a 'polytope' or classification figure (triangle for a three component system, tetrahedron for a four component system, a 'five-tope' in four dimensions for five component system, et cetera).
We show how model-based estimation of the neural sources responsible for transient neuroelectric signals can be improved by the analysis of single trial data. Previously, we showed that a multiple component event-related potential (mcERP) algorithm can extract the responses of individual sources from recordings of a mixture of multiple, possibly interacting, neural ensembles. McERP also estimated single-trial amplitudes and onset latencies, thus allowing more accurate estimation of ongoing neural activity during an experimental trial. The mcERP algorithm is related to informax independent component analysis (ICA); however, the underlying signal model is more physiologically realistic in that a component is modeled as a stereotypic waveshape varying both in amplitude and onset latency from trial to trial. The result is a model that reflects quantities of interest to the neuroscientist. Here we demonstrate that the mcERP algorithm provides more accurate results than more traditional methods such as factor analysis and the more recent ICA. Whereas factor analysis assumes the sources are orthogonal and ICA assumes the sources are statistically independent, the mcERP algorithm makes no such assumptions thus allowing investigators to examine interactions among components by estimating the properties of single-trial responses.
Background and Motivation - Problem - Contemporary aircraft carry around structural mass so that the flutter instabilities lie well outside of the operational envelope. - Better methods of measuring the structural state could allow reduction of the extra structural weight - Modal filtering - Standard method in structural analysis - Deformations are a combination of mode shapes - Modal filtering is estimating these modal coordinates from data - Often ordinary least squares methods - Often applied to simpler test articles - Factor Analysis - Analysis method from psychology - Measurements are a combination of small number of unmeasurable variables. - Lessons from factor analysis can be adapted to improve the modal filtering methods
Statistical relationships between surface ozone (O3) concentration, precursor species and meteorological conditions in continental South Africa were examined from data obtained from measurement stations in north-eastern South Africa. Three multivariate statistical methods were applied in the investigation, i.e. multiple linear regression (MLR), principal component analysis (PCA) and –regression (PCR), and generalised additive model (GAM) analysis. The daily maximum 8-h moving average O3 concentrations were considered in these statistical models (dependent variable). MLR models indicated that meteorology and precursor species concentrations are able to explain ~50% of the variability in daily maximum O3 levels. MLR analysis revealed that atmospheric carbon monoxide (CO), temperature and relative humidity were the strongest factors affecting the daily O3 variability. In summer, daily O3 variances were mostly associated with relative humidity, while winter O3 levels were mostly linked to temperature and CO. PCA indicated that CO, temperature and relative humidity were not strongly collinear. GAM also identified CO, temperature and relative humidity as the strongest factors affecting the daily variation of O3. Partial residual plots found that temperature, radiation and nitrogen oxides most likely have a non-linear relationship with O3,while the relationship with relative humidity and CO is probably linear. An inter-comparison between O3 levels modelled with the three statistical models compared to measured O3 concentrations showed that the GAM model offered a slight improvement over the MLR model. These findings emphasise the critical role of regional-scale O3 precursors coupled with meteorological conditions in daily variances of O3 levels in continental South Africa.
The microfluidic mother machine platform has attracted much interest for its potential in studies of bacterial physiology, cellular organization, and cell mechanics. Despite numerous experiments and development of dedicated analysis software, differences in bacterial growth and morphology in narrow mother machine channels compared to typical liquid media conditions have not been systematically characterized. Here we determine changes in E. coli growth rates and cell dimensions in different sized dead-end microfluidic channels using high resolution optical microscopy. We find that E. coli adapt to the confined channel environment by becoming narrower and longer compared to the same strain grown in liquid culture. Cell dimensions decrease as the channel length increases and width decreases. These changes are accompanied by increases in doubling times in agreement with the universal growth law. In channels 100 μm and longer, cell doublings can completely stop as a result of frictional forces that oppose cell elongation. Before complete cessation of elongation, mechanical stresses lead to substantial deformation of cells and changes in their morphology. Lastly, our work shows that mechanical forces rather than nutrient limitation are the main growth limiting factor for bacterial growth in long and narrow channels.
Using virtual environments to assess complex large scale human tasks provides timely and cost effective results to evaluate designs and to reduce operational risks during assembly and integration of the Space Launch System (SLS). NASA's Marshall Space Flight Center (MSFC) uses a suite of tools to conduct integrated virtual analysis during the design phase of the SLS Program. Siemens Jack is a simulation tool that allows engineers to analyze human interaction with CAD designs by placing a digital human model into the environment to test different scenarios and assess the design's compliance to human factors requirements. Engineers at MSFC are using Jack in conjunction with motion capture and virtual reality systems in MSFC's Virtual Environments Lab (VEL). The VEL provides additional capability beyond standalone Jack to record and analyze a person performing a planned task to assemble the SLS at Kennedy Space Center (KSC). The VEL integrates Vicon Blade motion capture system, Siemens Jack, Oculus Rift, and other virtual tools to perform human factors assessments. By using motion capture and virtual reality, a more accurate breakdown and understanding of how an operator will perform a task can be gained. By virtual analysis, engineers are able to determine if a specific task is capable of being safely performed by both a 5% (approx. 5ft) female and a 95% (approx. 6'1) male. In addition, the analysis will help identify any tools or other accommodations that may to help complete the task. These assessments are critical for the safety of ground support engineers and keeping launch operations on schedule. Motion capture allows engineers to save and examine human movements on a frame by frame basis, while virtual reality gives the actor (person performing a task in the VEL) an immersive view of the task environment. This presentation will discuss the need of human factors for SLS and the benefits of analyzing tasks in NASA MSFC's VEL.
The Hydrologic Evaluation of Landfill Performance (HELP) model is used by Savannah River National Laboratory (SRNL) in conjunction with PORFLOW groundwater flow simulation software to make longterm predictions of the fate and transport of radionuclides in the environment at radiological waste sites. The work summarized in this report supports preparation of the planned 2018 Performance Assessment for the E-Area Low-Level Waste Facility (LLWF) at the Savannah River Site (SRS). More specifically, this project focused on conducting a sensitivity analysis of infiltration (i.e., the rate at which water travels vertically in soil) through the proposed E-Area LLWF closure cap. A sensitivity analysis was completed using HELP v3.95D to identify the cap design and material property parameters that most impact infiltration rates through the proposed closure cap for a 10,000-year simulation period. The results of the sensitivity analysis indicate that saturated hydraulic conductivity (Ksat) for select cap layers, precipitation rate, surface vegetation type, and geomembrane layer defect density are dominant factors limiting infiltration rate. Interestingly, calculated infiltration rates were substantially influenced by changes in the saturated hydraulic conductivity of the Upper Foundation and Lateral Drainage layers. For example, an order-of-magnitude decrease in Ksat for the Upper Foundation layer lowered the maximum infiltration rate from a base-case 11 inches per year to only two inches per year. Conversely, an order-of-magnitude increase in Ksat led to an increase in infiltration rate from 11 to 15 inches per year. This work and its results provide a framework for quantifying uncertainty in the radionuclide transport and dose models for the planned 2018 E-Area Performance Assessment. Future work will focus on the development of a nonlinear regression model for infiltration rate using Minitab 17® to facilitate execution of probabilistic simulations in the GoldSim® overall system model for the E-Area LLWF.
The NASA Docking System (NDS) is a 31.5-inch (800-millimeter)-diameter circular hatch for astronauts to pass through when docked to other pressurized elements in space or for surface egress. The NDS is utilized on the Orion Spacecraft and has been implemented as the International Docking System Standard (IDSS). The EV74 Human Factors Engineering (HFE) Team at NASA’s Marshall Space Flight Center (MSFC) conducted human factors analyses with various hatch shapes and sizes to accommodate for all astronaut anthropometries, task comfort, and task safety. The 32-inch hatch is too small and a bigger hatch size would better accommodate most astronauts and prove to be safer for daily pass-throughs. To conduct human factors analyses, four participants were gathered based on anthropometry: 1st female, 5th female, 95th male, and 99th male.
The NASA Docking System (NDS) is a 31.4961-inch (800 mm) diameter circular hatch for astronauts to pass through when docked to other pressurized elements in space or for entrance or egress on surface environments. The NDS is utilized on the Orion Spacecraft and has been implemented as the International Docking System Standard (IDSS). The EV74 Human Factors Engineering (HFE) Team at NASA’s Marshall Space Flight Center (MSFC) conducted human factors analyses with various hatch shapes and sizes to accommodate for all astronaut anthropometries and daily task comfort. It is believed that the hatch, approximately 32 inches, is too small, and a bigger hatch size would better accommodate most astronauts. In order to conduct human factors analyses, four participants were gathered based on anthropometry percentiles: 1st female, 5th female, 95th male, and 99th male.
Lunar soils returned from the Apollo 11 and 12 sites appear to consist of more than one source material. Investigators have suggested that the soil can be described by as few as two end members and possibly as many as five or six. In the present study Q-mode factor analysis is used to establish the end members more rigorously and suggests that major-element chemistry of Apollo 11 and 12 lunar soils can be adequately explained in terms of a three-component mixing system. The end members isolated by Q-mode factor analysis can be regarded as basaltic, anorthositic, and noritic (or KREEP) components. Three-component mixing models fitted by least squares to the eight major oxides indicate that six of the Apollo 12 soils investigated are dominated by the basaltic component, whereas the other five are dominated by the noritic or KREEP component. Anorthosite is present in all soils except sample 12033, which is a simple two-component mixture of basalt and KREEP. The Apollo 11 soil is also a three-component mixture but is more basaltic than the Apollo 12 soils. Anorthositic and KREEP materials are important components of the lunar lithosphere and may be distributed on the lunar surface areally in an independent manner.
‘White’ and ‘grey’ methods of data modeling have been employed to resolve the heterogeneous fluorescence from a fluorophore mixture of 9-cyanoanthracene (CNA), 10-chloro-9-cyanoanthracene (ClCNA) and 9,10-dicyanoanthracene (DCNA) into component individual fluorescence spectra. The three-component spectra of fluorescence quenching in methanol were recorded for increasing amounts of lithium bromide used as a quencher. The associated intensity decay profiles of differentially quenched fluorescence of single components were modeled on the basis of a linear Stern-Volmer plot. These profiles are necessary to initiate the fitting procedure in both ‘white’ and ‘grey’ modeling of the original data matrices. ‘White’ methods of data modeling, called also ‘hard’ methods, are based on chemical/physical laws expressed in terms of some well-known or generally accepted mathematical equations. The parameters of these models are not known and they are estimated by least squares curve fitting. ‘Grey’ approaches to data modeling, also known as hard-soft modeling techniques, make use of both hard-model and soft-model parts. In practice, the difference between ‘white’ and ‘grey’ methods lies in the way in which the ‘crude’ fluorescence intensity decays of the mixture components are estimated. In the former case they are given in a functional form while in the latter as digitized curves which, in general, can only be obtained by using dedicated techniques of factor analysis. In the paper, the initial values of the Stern-Volmer constants of pure components were evaluated by both ‘point-by-point’ and ‘matrix’ versions of the method making use of the concept of wavelength dependent intensity fractions as well as by the rank annihilation factor analysis applied to the data matrices of the difference fluorescence spectra constructed in two ways: from the spectra recorded for a few excitation lines at the same concentration of a fluorescence quencher or classically from a series of the spectra measured for one selected excitation line but for increasing concentration of the quencher. The results of multiple curve resolution obtained by all types of the applied methods have been scrutinized and compared. In addition, the effect of inadequacy of sample preparation and increasing instrumental noise on the shape of the resolved spectral profiles has been studied on several datasets mimicking the measured data matrices. .
The Cascade Distillation Subsystem (CDS) is a rotary multistage distiller being developed to serve as the primary processor for wastewater recovery during long-duration space missions. The CDS could be integrated with a system similar to the International Space Station Water Processor Assembly to form a complete water recovery system for future missions. A preliminary chemical process simulation was previously developed using Aspen Custom Modeler® (ACM), but it could not simulate thermal startup and lacked detailed analysis of several key internal processes, including heat transfer between stages. This paper describes modifications to the ACM simulation of the CDS that improve its capabilities and the accuracy of its predictions. Notably, the modified version can be used to model thermal startup and predicts the total energy consumption of the CDS. The simulation has been validated for both NaC1 solution and pretreated urine feeds and no longer requires retuning when operating parameters change. The simulation was also used to predict how internal processes and operating conditions of the CDS affect its performance. In particular, it is shown that the coefficient of performance of the thermoelectric heat pump used to provide heating and cooling for the CDS is the largest factor in determining CDS efficiency. Intrastage heat transfer affects CDS performance indirectly through effects on the coefficient of performance.
Staphylococcus aureus is a major human pathogen of the skin. The global burden of diabetes is high, with S. aureus a major complication of diabetic wound infections. We investigated how the diabetic environment influences S. aureus skin infection and observed an increased susceptibility to infection in mouse models of both type I and type II diabetes. A dual gene expression approach was taken to investigate transcriptional alterations in both the host and bacterium after infection. While analysis of the host response revealed only minor changes between infected control and diabetic mice, we observed that S. aureus isolated from diabetic mice had significant increases in genes associated with translation, posttranslational modification and chaperones, and reductions in genes associated with amino acid transport and metabolism. One family of genes upregulated in S. aureus isolated from diabetic lesions encoded the Clp proteases associated with the misfolded protein response. The Clp proteases were found to be partially glucose regulated as well as influencing the hemolytic activity of S. aureus . Strains lacking the Clp proteases, ClpX, ClpC and ClpP were significantly attenuated in our animal model of skin infection, with significant reductions observed in dermonecrosis and bacterial burden. In particular, mutations in clpP and clpX were significantly attenuated and remained attenuation in both normal and diabetic mice. As a result, our data suggests that the diabetic environment also causes changes to occur in invading pathogens and one of these virulence determinants is the Clp protease system.