Results for “sequence determinants” — Page 40

NASA NTRS1974

The detection of fatigue cracks by nondestructive testing methods

X-radiographic penetrant, ultrasonic, eddy current, holographic, and acoustic emission techniques were optimized and applied to the evaluation of 2219-T87 aluminum alloy test specimens. One hundred eighteen specimens containing a total of 328 fatigue cracks were evaluated. The cracks ranged in length from 0.500 inch (1.27 cm) to 0.007 inch (0.018 cm) and in depth from 0.178 inch (0.451 cm) and 0.001 inch (0.003 cm). Specimen thicknesses were nominally 0.060 inch (0.152 cm) and 0.210 inch (0.532 cm) and surface finishes were nominally 32 and 125 rms and 64 and 200 rms respectively. Specimens were evaluated in the as-milled surface condition, in the chemically milled surface condition and, after proof loading, in a randomized inspection sequence. Results of the nondestructive test (NDT) evaluations were compared with actual crack size obtained by measurement of the fractured specimens. Inspection data was then analyzed to provide a statistical basis for determinating the threshold crack detection sensitivity (the largest crack size that would be missed) for each of the inspection techniques at a 95% probability and 95% confidence level.

Rummel, W. D.↗

NASA NTRS1978

An infrared study of the Ophiuchus dark cloud

A near-infrared survey has been conducted of nearly 18 square degrees of the Ophiuchus dark cloud complex. Additional observations have been made of selected objects found in this survey and of the brighter objects found by Grasdalen et al. and by Vrba et al. in this region in order to identify and study the young stars associated with the cloud. These observations show that very recent star formation has been largely restricted to a small region at the center of the dark cloud complex, no more than a few parsecs in extent. Most of the young stars do not appear to be main-sequence stars. At least three of these objects appear to be surrounded by infrared reflection nebulae. Many of the objects studied are background K and M giants which can be used to determine the near-infrared extinction due to the cloud.

Elias, J. H.↗

NASA NTRS1981

Ayame/PAM-D apogee kick motor nozzle failure analysis

The failure of two communication satellites during firing sequence were examined. The correlation/comparison of the circumstances of the Ayame incidents and the failure of the STAR 48 (DM-2) motor are reviewed. The massive nozzle failure of the AKM to determine the impact on spacecraft performance is examined. It is recommended that a closer watch is kept on systems techniques,

Source record↗

NASA NTRS1984

Quantification of waste morphology in Martian fretted terrain

Qualitative and quantitative analyses are performed on the northern fretted terrain of the Martian Isnenius Lacus quadrangle to determine the formation processes. The fretted terrain lies between 35-50 deg N latitude and 305-350 deg W longitude and was mapped with Viking orbiter instrumentation. The dominant landforms are a sequence of heavily cratered terrain, scattered flat-floored, fretted valleys, linear waste-covered valleys between flat-topped mesas, circum-mesa debris areas, a transition from mesas to knobby terrain, fewer upland forms, and low-relief, mantled, polar plains. The features spread out from a cratered terrain boundary in a succession of shapes. A principal components analysis characterizes definable spatial variations in the surface morphology and suggests that the features are youngest in the southwest direction. Relationships are also found between neighboring features. The principal components analysis technique is concluded to be a valuable tool for explaining the geomorphic evolution of the Martian surface.

Kochel, R. C.↗

NASA NTRS1989

Characteristics and preliminary observations of the influence of electromyostimulation on the size and function of human skeletal muscle during 30 days of simulated microgravity

The effect of transcutaneous electromyostimulation (EMS) on the development of atrophy and the loss of strength in lower limb musculature in humans exposed to microgravity was determined in three subjects who received EMS twice daily in a 3-d on/1-d off cycle on their dominant leg during 30 days of bedrest. The output waveform from the stimulator was sequenced to the knee extensors, knee flexors, ankle extensors, and ankle flexors, and caused three isometric contractions of each muscle group per minute. It was found that, in the dominant leg, EMS acted to attenuate the changes caused by bedrest, such as reductions in the leg volume, muscle compartment size, cross-sectional area of slow- and fast-twitch fibers, strength, and aerobic enzyme activities, and an increase in leg compliance.

Duvoisin, Marc R.↗

NASA NTRS1992

Near-optimal energy transitions for energy-state trajectories of hypersonic aircraft

A problem of the instantaneous energy transition that occurs in energy-state approximation is considered. The transitions are modeled as a sequence of two load-factor bounded paths (either climb-dive or dive-climb). The boundary-layer equations associated with the energy-state dynamic model are analyzed to determine the precise location of the transition.

Ardema, M. D.↗

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 NTRS2014

Characterization of the Inner Knot of the Crab: The Site of the Gamma-Ray Flares?

Subsequent to the detections AGILE and Fermi/LAT of the gamma-ray flares from the Crab Nebula in the fall of 2010, this team has been monitoring the X-Ray emission from the Crab on a regular basis. Initially X-Ray observations took place once per month when viewing constraints allowed. More recently observations with Chandra and HST have taken place four times per year. There have been notable exceptions, e.g. in April of 2011 and March 2013 when we initiated a set of Chandra Target of opportunity observations in conjunction with bright gamma-ray flares. Often Keck observations were obtained. The aim of this program to characterize, in depth, the X-ray, optical, and infrared variations that take place in the nebula, and, by so doing, determine the regions which contribute to the harder X-ray variations and, if possible, determine the precise location within the Nebula of the origin of the gamma-ray flares. As part of this project members of the team have applied Singular Value Decomposition techniques to sequences of images in order to more accurately characterize features and their behavior. The current status of the project will be discussed highlighting studies of the inner knot and possible correlations with the gamma-ray flares.

Chandra↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

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.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

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.

Machine Learning↗

NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

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.

Machine Learning↗

NASA NTRS1975

Determination of discontinuities in the derivative of a piecewise-smooth function on the basis of the results of measurements

A piecewise-smooth function with discontinuity in the first derivative on a given interval is considered. The values of the function are measured at a sequence of points in the interval and a random error is included in the measurements. A method is proposed to estimate the position of the discontinuity in the derivative. Regression lines are associated with each measurement point and account for k - 1 points preceding or following the point. The estimate for the position of the discontinuity is the measurement point with the largest angle between the regression lines. The error in the estimate is analyzed and the results are verified.

Gurin, L. S.↗

NASA NTRS2009

Determining Aliasing in Isolated Signal Conditioning Modules

The basic concept of aliasing is this: Converting analog data into digital data requires sampling the signal at a specific rate, known as the sampling frequency. The result of this conversion process is a new function, which is a sequence of digital samples. This new function has a frequency spectrum, which contains all the frequency components of the original signal. The Fourier transform mathematics of this process show that the frequency spectrum of the sequence of digital samples consists of the original signal s frequency spectrum plus the spectrum shifted by all the harmonics of the sampling frequency. If the original analog signal is sampled in the conversion process at a minimum of twice the highest frequency component contained in the analog signal, and if the reconstruction process is limited to the highest frequency of the original signal, then the reconstructed signal accurately duplicates the original analog signal. It is this process that can give birth to aliasing.

Source record↗

NASA NTRS1999

Determining divergence times with protein clocks

No abstract available

NASA Discipline Exobiology↗

NASA NTRS1991

Event-scale relationships between surface velocity, temperature and chlorophyll in the coastal ocean, as seen by satellite

The overall goal of this project was to increase our understanding of processes which determine the temporally varying distributions of surface chlorophyll pigment concentration and surface temperature in the California Current System (CCS) on the time-scale of 'events', i.e., several days to several weeks. We also proposed to investigate seasonal and regional differences in these events. Additionally, we proposed to evaluate methods of estimating surface velocities and horizontal transport of pigment and heat from sequences of AVHRR and CZCS images. The four specific objectives stated in the original proposal were to: (1) test surface current estimates made from sequences of both SST and color images using variations of the statistical method of Emery et al. (1986) and estimate the uncertainties in these satellite-derived surface currents; (2) characterize the spatial and temporal relationships of chlorophyll and temperature in rapidly evolving features for which adequate imagery exist and evaluate the contribution of these events to monthly and seasonal averages; (3) use the methods tested in (1) to determine the nature of the velocity fields in the CCS; and (4) compare the currents, temperature, and currents in different seasons and in different geographic regions.

Strub, P. Ted↗

NASA NTRS2014

Calculation of Operations Efficiency Factors for Mars Surface Missions

For planning of Mars surface missions, to be operated on a sol-by-sol basis by a team on Earth (where a "sol" is a Martian day), activities are described in terms of "sol types" that are strung together to build a surface mission scenario. Some sol types require ground decisions based on a previous sol's results to feed into the activity planning ("ground in the loop"), while others do not. Due to the differences in duration between Earth days and Mars sols, for a given Mars local solar time, the corresponding Earth time "walks" relative to the corresponding times on the prior sol/day. In particular, even if a communication window has a fixed Mars local solar time, the Earth time for that window will be approximately 40 minutes later each succeeding day. Further complexity is added for non-Mars synchronous communication relay assets, and when there are multiple control centers in different Earth time zones. The solution is the development of "ops efficiency factors" that reflect the efficiency of a given operations configuration (how many and location of control centers, types of communication windows, synchronous or non-synchronous nature of relay assets, sol types, more-or-less sustainable operations schedule choices) against a theoretical "optimal" operations configuration for the mission being studied. These factors are then incorporated into scenario models in order to determine the surface duration (and therefore minimum spacecraft surface lifetime) required to fulfill scenario objectives. The resulting model is used to perform "what-if" analyses for variations in scenario objectives. The ops efficiency factor is the ratio of the figure of merit for a given operations factor to the figure of merit for the theoretical optimal configuration. The current implementation is a pair of models in Excel. The first represents a ground operations schedule for 500 sols in each operations configuration for the mission being studied (500 sols was chosen as being a long enough time to capture variations in relay asset interactions, Earth/Mars time phasing, and seasonal variations in holidays). This model is used to estimate the ops efficiency factor for each operations configuration. The second model in a separate Excel spreadsheet is a scenario model, which uses the sol types to rack up the total number of "scenario sols" for that scenario (in other words, the ideal number of sols it would take to perform the scenario objectives). Then, the number of sols requiring ground in the loop is calculated based on the soil types contained in the given scenario. Next, the scenario contains a description of what sequence of operations configurations is used, for how many days each, and this is used with the corresponding ops efficiency factors for each configuration to calculate the "ops duration" corresponding to that scenario. Finally, a margin is applied to determine the minimum surface lifetime required for that scenario. Typically, this level of analysis has not been performed until much later in the mission, and has not been able to influence mission design. Further, the notion of moving to sustainable operations during Prime Mission - and the effect that that move would have on surface mission productivity and mission objective choices - has not been encountered until the most recent rover missions (MSL and Mars 2018).

Layback, Sharon L.↗

NASA NTRS2006

Discovering Atypical Flights in Sequences of Discrete Flight Parameters

This paper describes the results of a novel research and development effort conducted at the NASA Ames Research Center for discovering anomalies in discrete parameter sequences recorded from flight data. Many of the discrete parameters that are recorded during the flight of a commercial airliner correspond to binary switches inside the cockpit. The inputs to our system are records from thousands of flights for a given class of aircraft and destination. The system delivers a list of potentially anomalous flights as well as reasons why the flight was tagged as anomalous. This output can be analyzed by safety experts to determine whether or not the anomalies are indicative of a problem that could be addressed with a human factors intervention. The final goal of the system is to help safety experts discover significant human factors issues such as pilot mode confusion, i.e., a flight in which a pilot has lost situational awareness as reflected in atypicality of the sequence of switches that he or she throws during descent compared to a population of similar flights. We view this work as an extension of Integrated System Health Management (ISHM) where the goal is to understand and evaluate the combined health of a class of aircraft ar a given destination.

Budalakoti, Suratna↗

NASA NTRS1980

Impact cratering in viscous targets - Laboratory experiments

To determine the effects of target yield strength and viscosity on the formation and morphology of Martian multilobed, slosh and rampart-type impact craters, 75 experiments in which target properties and impact energies were varied were carried out for high-speed motion picture observation in keeping with the following sequence: (1) projectile initial impact; (2) crater excavation and rise of ejecta plume; (3) formation of a transient central mound which generates a surge of material upon collapse that can partly override the plume deposit; and (4) oscillation of the central mound with progressively smaller surges of material leaving the crater. A dimensional analysis of the experimental results indicates that the dimensions of the central mound are proportional to (1) the energy of the impacting projectile and (2) to the inverse of both the yield strength and viscosity of the target material, and it is determined that extrapolation of these results to large Martian craters requires an effective surface layer viscosity of less than 10 to the 10th poise. These results may also be applicable to impacts on outer planet satellites composed of ice-silicate mixtures.

Greeley, R.↗