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NASA NTRS1989

Managerial leadership assessment - Personality correlates of and sex differences in ratings by leaders, peers, and followers

A performance appraisal was conducted at a Fortune 500 airline. Evaluations of each manager were taken from his or her management, peers and subordinates. These ratings were related to personality clusters revealing patterns for males similar to those found between personality and performance in pilot populations. A case is made that piloting aircraft requires similar skills to managing other complex enterprises and that similar profiles predict success in each.

Gibson, Robert H.↗

NASA NTRS1993

Fast parallel algorithms that compute transitive closure of a fuzzy relation

The notion of a transitive closure of a fuzzy relation is very useful for clustering in pattern recognition, for fuzzy databases, etc. The original algorithm proposed by L. Zadeh (1971) requires the computation time O(n(sup 4)), where n is the number of elements in the relation. In 1974, J. C. Dunn proposed a O(n(sup 2)) algorithm. Since we must compute n(n-1)/2 different values s(a, b) (a not equal to b) that represent the fuzzy relation, and we need at least one computational step to compute each of these values, we cannot compute all of them in less than O(n(sup 2)) steps. So, Dunn's algorithm is in this sense optimal. For small n, it is ok. However, for big n (e.g., for big databases), it is still a lot, so it would be desirable to decrease the computation time (this problem was formulated by J. Bezdek). Since this decrease cannot be done on a sequential computer, the only way to do it is to use a computer with several processors working in parallel. We show that on a parallel computer, transitive closure can be computed in time O((log(sub 2)(n))2).

Kreinovich, Vladik YA.↗

NASA NTRS1998

ADP-glucose pyrophosphorylase is localized to both the cytoplasm and plastids in developing pericarp of tomato fruit

The intracellular location of ADP-glucose pyrophosphorylase (AGP) in developing pericarp of tomato (Lycopersicon esculentum Mill) has been investigated by immunolocalization. With the use of a highly specific anti-tomato fruit AGP antibody, the enzyme was localized in cytoplasm as well as plastids at both the light and electron microscope levels. The immunogold particles in plastids were localized in the stroma and at the surface of the starch granule, whereas those in the cytoplasm occurred in cluster-like patterns. Contrary to the fruit, the labeling in tomato leaf cells occurred exclusively in the chloroplasts. These data demonstrate that AGP is localized to both the cytoplasm and plastids in developing pericarp cells of tomato.

Non-NASA Center↗

NASA NTRS2017

Classification of Meteorological Influences Surrounding Extreme Precipitation Events in the United States using the MERRA-2 Reanalysis

Extreme precipitation events can have a large impact on society through flooding that can result in property destruction, crop losses, economic losses, the spread of water-borne diseases, and fatalities. Observations indicate there has been a statistically significant increase in extreme precipitation events over the past 15 years in the Northeastern United States and other localized regions of the country have become crippled with record flooding events, for example, the flooding that occurred in the Southeast United States associated with Hurricane Matthew in October 2016. Extreme precipitation events in the United States can be caused by various meteorological influences such as extratropical cyclones, tropical cyclones, mesoscale convective complexes, general air mass thunderstorms, upslope flow, fronts, and the North American Monsoon. Reanalyses, such as the Modern Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), have become a pivotal tool to study the meteorology surrounding extreme precipitation events. Using days classified as an extreme precipitation events based on a combination of observational gauge and radar data, two techniques for the classification of these events are used to gather additional information that can be used to determine how events have changed over time using atmospheric data from MERRA-2. The first is self organizing maps, which is an artificial neural network that uses unsupervised learning to cluster like patterns and the second is an automated detection technique that searches for characteristics in the atmosphere that define a meteorological phenomena. For example, the automated detection for tropical cycles searches for a defined area of suppressed sea level pressure, alongside thickness anomalies aloft, indicating the presence of a warm core. These techniques are employed for extreme precipitation events in preselected regions that were chosen based an analysis of the climatology of precipitation.

Monsoon↗

NASA NTRS1982

A bootstrap approach to bump hunting

An important question in cluster analysis and pattern recognition is the determination of the number of clusters into which a given population should be divided. Frequently, particularly when certain specific clustering methods are being used, the number of clusters is taken to be equal to the number of modes, or local maxima, in the probability density function underlying the given data set. The use of kernal density estimates in mode estimation is discussed. The test statistic to be used is defined and a bootstrap technique for assessing significance is given. An illustrative application is followed by an examination of the asymptotic behavior of the test statistic.

Silverman, B. W.↗

NASA NTRS1992

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Keller, James M.↗

NASA NTRS1993

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, the clustering problem was cast into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data was constructed, and the membership and prototype update equations from necessary conditions for minimization of our criterion function were derived. The ability of this approach to detect linear and quartic curves in the presence of considerable noise is shown.

Keller, James M.↗

NASA NTRS2000

Structural Characterization of Crystalline Ice Nanoclusters

Water ice nanoclusters are useful analogs for studying a variety of processes that occur within icy grains in the extraterrestrial environment. The surface of ice nanoclusters prepared in the laboratory is similar to the surface of interstellar ice grains. In cold molecular clouds, the silicate cores of interstellar grains are typically approx. 100 nm in diameter and have a coating of impure amorphous water ice. Depositional, thermal and radiolytic processes leave the surface and subsurface molecules in a disordered state. In this state, structural defects become mobile and reactions of trapped gases and small molecules can occur. The large surface area of nanocluster deposits relative to their bulk allows for routine observation of such surface-mediated processes. Furthermore, the disordered surface and subsurface layers in nanocluster deposits mimic the structure of amorphous ice rinds found on interstellar dust grains. Transmission Electron Microscopy (TEM has been used tn characterize the crystallinity, growth mechanism, and size distribution of nanoclusters formed from a mixture of water vapor with an inert carrier gas that has been rapidly cooled to 77K. E M imaging reveals a Gaussian size distribution around a modal diameter that increases from approx. 15 to 30 nm as the percentage of water vapor within the mixture increases from 0.5 to 2.007, respectively . TEM bright and dark field imaging also reveals the crystalline nature of the clusters. h4any of the clusters show a mosaic structure in which crystalline domains originate at the center Other images show mirror planes that are separated by approx. 10 nm. Electron diffraction patterns of these clusters show that the clusters are composed of cubic ice with only a small hexagonal component. Further, the crystalline domain size is approximately the same as the modal diameter suggesting that the clusters are single crystals.

Blake, David↗

NASA NTRS1998

Method and system for data clustering for very large databases

Multi-dimensional data contained in very large databases is efficiently and accurately clustered to determine patterns therein and extract useful information from such patterns. Conventional computer processors may be used which have limited memory capacity and conventional operating speed, allowing massive data sets to be processed in a reasonable time and with reasonable computer resources. The clustering process is organized using a clustering feature tree structure wherein each clustering feature comprises the number of data points in the cluster, the linear sum of the data points in the cluster, and the square sum of the data points in the cluster. A dense region of data points is treated collectively as a single cluster, and points in sparsely occupied regions can be treated as outliers and removed from the clustering feature tree. The clustering can be carried out continuously with new data points being received and processed, and with the clustering feature tree being restructured as necessary to accommodate the information from the newly received data points.

Zhang, Tian↗

NASA NTRSDate not supplied

Detecting and Characterizing Patterns of Failure in Complex Systems: An Ontology Development and Clustering Approach

While the causes of failures in complex engineered systems are often clear in hindsight, it can be challenging to predict failures proactively during the design of novel engineered products or systems. Identifying patterns can be useful for capturing common characteristics that may lead to failure. In this paper, we present a methodology for identifying patterns of failure from NASA’s publicly available Lessons Learned Information System (LLIS). We apply an ontology development and clustering approach to identify representative patterns leading to failures in historical lessons learned. A joint inductive-deductive approach reveals the key themes in lessons that lead to failure, which are formalized and recorded as an ontology of complex systems failure causes. Documents from the LLIS are manually tagged with relevant characteristics from the ontology. From the tagged set, clustering is used to capture co-occurring sets of characteristics that lead to failure. The primary contribution of this work is a method for extracting a set of generic failure patterns in complex engineered systems and characteristics for these patterns that can be identified at design time, knowledge of which can be used to plan mitigation strategies.

Systems Engineering↗

NASA NTRS1991

Fractal dynamics of bioconvective patterns

Biologically generated cellular patterns, sometimes called bioconvective patterns, are found to cluster into aggregates which follow fractal growth dynamics akin to diffusion-limited aggregation (DLA) models. The pattern formed is self-similar with fractal dimension of 1.66 +/-0.038. Bioconvective DLA branching results from thermal roughening which shifts the balance between ordering viscous forces and disordering cell motility and random diffusion. The phase diagram for pattern morphology includes DLA, boundary spokes, random clusters, and reverse clusters.

Noever, David A.↗

NASA NTRS2019

The Omics of Stem Cell Mediated Regeneration: A Pilot Single Cell RNA-Seq Study of Mechanotransduction

Mechanical forces are potent modulators of stem cell based tissue regenerative mechanisms, inducing cell fate decisions and tissue specific commitment. A unique platform for investigating mechanotransduction is spaceflight, where microgravity and altered fluid mechanics provide a loading-null experimental condition. Seminal investigations of regenerative capacity in a wholly regenerative species, the newt model, and in a variety of totipotent and adult stem cell populations have demonstrated the detrimental effects of unloading on maintenance of stem cell based regeneration. Of particular interest is the observation that unloading interferes with the transition of stem cell pools from proliferative state to differentiation commitment. In this work we sought to test the hypothesis that gravity mechanotransduction regulates stem cell tissue regenerative processes by modulating stem cell proliferation and differentiation fates at specific cell cycle stages. To do this, clonally-derived ESCs were plated on a collagen matrix and expanded for 36 hours before re-plating on a non-adherent culture dish in the absence of leukemia inhibitory factor (LIF) to form spheroid aggregate EBs. After formation, the EBs were transferred to a collagen matrix coated culture dishes and given 4 days to allow implantation and outgrowth. In parallel, totipotent ESCs were plated 24 hours before mechanical stimulation on collagen matrix culture dishes in the presence of LIF to maintain totipotency and serve as un-differentiation committed controls. The EBs and ESCs were then subjected to either a 60 minute pulse of gravity (static loading) or 60 minutes of cyclic stretch (dynamic loading) mechanotransduction. Six hours post-stimulation, we used a 10X Genomics Single Cell controller to generate bar-coded single cell Illumina libraries and sequenced expressomes for 5,000 static loaded cells, representative of a change in gravity mechanotransduction, 5,000 dynamic loaded cells, representative of tissue loading associate with physiologic function, and 5,000 unstimulated 1g control cells. The comparison of these 3 libraries by cluster assignment based on like gene expression patterns show substantial alteration in cluster geometry due to mechanical loading. Specifically the mechanically loaded EB outgrowth cells to retain potency markers (PAX6, SOX2, CD34) and suppress early commitment markers (Dhh, VCAN, Igf1). Whereas the EBs cultured under the non-stimulated conditions display clear departure from the ESC expressome with lineage commitment markers upregulated and several tissue specific markers being expressed (BMP "early musculoskeletal development, Mesp1" early cardiovascular cell lineage). These markers are not seen in the mechano-stimulated cultures or the totipotent ESC cultures. Comparison of like clusters between our experimental conditions revealed an array of regenerative and stem cell genes are significantly mechano-regulated. Of particular importance CDKN1a/p21, a gene shown by previous investigation of our research team to be significantly upregulated in unloading, was suppressed in the static and dynamic loaded EBS. In addition to CDKN1a/p21 many genes related to cell cycle and transitory differentiation markers had elevated expression in the mechano-stimulated EBs, but surprisingly these trends were not observed in the ESC cultures. This study is the first of its kind investigating for mechano-signaling and mechano-regulated pathways, and has alre

Single cell RNA-sequecing↗

NASA NTRSDate not supplied

Detecting and Characterizing Patterns of Failure in Complex Engineered Systems: an Ontology Development and Clustering Approach

Systems Engineering↗

NASA NTRS1975

Air pollution source identification

The techniques available for source identification are reviewed: remote sensing, injected tracers, and pollutants themselves as tracers. The use of the large number of trace elements in the ambient airborne particulate matter as a practical means of identifying sources is discussed. Trace constituents are determined by sensitive, inexpensive, nondestructive, multielement analytical methods such as instrumental neutron activation and charged particle X-ray fluorescence. The application to a large data set of pairwise correlation, the more advanced pattern recognition-cluster analysis approach with and without training sets, enrichment factors, and pollutant concentration rose displays for each element is described. It is shown that elemental constituents are related to specific source types: earth crustal, automotive, metallurgical, and more specific industries. A field-ready source identification system based on time and wind direction resolved sampling is described.

Fordyce, J. S.↗

NASA NTRS1974

Satellite detection of air pollutants

NASA's ERTS-1 satellite, with its high resolution and multispectral capabilities, has been found useful in the detection and analysis of smoke from large point sources (power plants, steel mills, etc.), and widespread atmospheric turbidity associated with atmospheric stagnations. Smoke plumes from the Chicago-Northern Indiana industrial complex have been tracked over Lake Michigan for over 100 km. Experience has shown that smoke plumes are relatively easy to detect over water (in the 0.6-0.7 micrometer band) but much more difficult over land surfaces. Pattern recognition techniques (cluster analysis) were applied to the digital ERTS data, and it was found that the smoke plumes indeed had a unique spectral signature. Studies are currently underway to use measured plume geometries to obtain quantitative estimates of diffusion over water surfaces.

Lyons, W. A.↗

NASA NTRS2011

Atmospheric Blocking and Atlantic Multi-Decadal Ocean Variability

Based on the 20th century atmospheric reanalysis, winters with more frequent blocking, in a band of blocked latitudes from Greenland to Western Europe, are found to persist over several decades and correspond to a warm North Atlantic Ocean, in-phase with Atlantic multi-decadal ocean variability. Atmospheric blocking over the northern North Atlantic, which involves isolation of large regions of air from the westerly circulation for 5 days or more, influences fundamentally the ocean circulation and upper ocean properties by impacting wind patterns. Winters with clusters of more frequent blocking between Greenland and western Europe correspond to a warmer, more saline subpolar ocean. The correspondence between blocked westerly winds and warm ocean holds in recent decadal episodes (especially, 1996-2010). It also describes much longer-timescale Atlantic multidecadal ocean variability (AMV), including the extreme, pre-greenhouse-gas, northern warming of the 1930s-1960s. The space-time structure of the wind forcing associated with a blocked regime leads to weaker ocean gyres and weaker heat-exchange, both of which contribute to the warm phase of AMV.

Haekkinen, Sirpa↗

NASA NTRS2011

Atmospheric Blocking and Atlantic Multidecadal Ocean Variability

Atmospheric blocking over the northern North Atlantic, which involves isolation of large regions of air from the westerly circulation for 5 days or more, influences fundamentally the ocean circulation and upper ocean properties by affecting wind patterns. Winters with clusters of more frequent blocking between Greenland and western Europe correspond to a warmer, more saline subpolar ocean. The correspondence between blocked westerly winds and warm ocean holds in recent decadal episodes (especially 1996 to 2010). It also describes much longer time scale Atlantic multidecadal ocean variability (AMV), including the extreme pre-greenhouse-gas northern warming of the 1930s to 1960s. The space-time structure of the wind forcing associated with a blocked regime leads to weaker ocean gyres and weaker heat exchange, both of which contribute to the warm phase of AMV.

Hakkinen, Sirpa↗

NASA NTRS1997

The Universe at Moderate Redshift

The report covers the work done in the past year and a wide range of fields including properties of clusters of galaxies; topological properties of galaxy distributions in terms of galaxy types; patterns of gravitational nonlinear clustering process; development of a ray tracing algorithm to study the gravitational lensing phenomenon by galaxies, clusters and large-scale structure, one of whose applications being the effects of weak gravitational lensing by large-scale structure on the determination of q(0); the origin of magnetic fields on the galactic and cluster scales; the topological properties of Ly(alpha) clouds the Ly(alpha) optical depth distribution; clustering properties of Ly(alpha) clouds; and a determination (lower bound) of Omega(b) based on the observed Ly(alpha) forest flux distribution. In the coming year, we plan to continue the investigation of Ly(alpha) clouds using larger dynamic range (about a factor of two) and better simulations (with more input physics included) than what we have now. We will study the properties of galaxies on 1 - 100h(sup -1) Mpc scales using our state-of-the-art large scale galaxy formation simulations of various cosmological models, which will have a resolution about a factor of 5 (in each dimension) better than our current, best simulations. We will plan to study the properties of X-ray clusters using unprecedented, very high dynamic range (20,000) simulations which will enable us to resolve the cores of clusters while keeping the simulation volume sufficiently large to ensure a statistically fair sample of the objects of interest. The details of the last year's works are now described.

Cen, Renyue↗