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Sample records for advanced cluster analysis

  1. Cluster analysis

    CERN Document Server

    Everitt, Brian S; Leese, Morven; Stahl, Daniel

    2011-01-01

    Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

  2. Cluster analysis for applications

    CERN Document Server

    Anderberg, Michael R

    1973-01-01

    Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis.Comprised of 10 chapters, this book begins with an introduction to the subject o

  3. CLEAN: CLustering Enrichment ANalysis

    Directory of Open Access Journals (Sweden)

    Medvedovic Mario

    2009-07-01

    Full Text Available Abstract Background Integration of biological knowledge encoded in various lists of functionally related genes has become one of the most important aspects of analyzing genome-wide functional genomics data. In the context of cluster analysis, functional coherence of clusters established through such analyses have been used to identify biologically meaningful clusters, compare clustering algorithms and identify biological pathways associated with the biological process under investigation. Results We developed a computational framework for analytically and visually integrating knowledge-based functional categories with the cluster analysis of genomics data. The framework is based on the simple, conceptually appealing, and biologically interpretable gene-specific functional coherence score (CLEAN score. The score is derived by correlating the clustering structure as a whole with functional categories of interest. We directly demonstrate that integrating biological knowledge in this way improves the reproducibility of conclusions derived from cluster analysis. The CLEAN score differentiates between the levels of functional coherence for genes within the same cluster based on their membership in enriched functional categories. We show that this aspect results in higher reproducibility across independent datasets and produces more informative genes for distinguishing different sample types than the scores based on the traditional cluster-wide analysis. We also demonstrate the utility of the CLEAN framework in comparing clusterings produced by different algorithms. CLEAN was implemented as an add-on R package and can be downloaded at http://Clusteranalysis.org. The package integrates routines for calculating gene specific functional coherence scores and the open source interactive Java-based viewer Functional TreeView (FTreeView. Conclusion Our results indicate that using the gene-specific functional coherence score improves the reproducibility of the

  4. Fusion and fission of atomic clusters: recent advances

    DEFF Research Database (Denmark)

    Obolensky, Oleg I.; Solov'yov, Ilia; Solov'yov, Andrey V.

    2005-01-01

    We review recent advances made by our group in finding optimized geometries of atomic clusters as well as in description of fission of charged small metal clusters. We base our approach to these problems on analysis of multidimensional potential energy surface. For the fusion process we have...... developed an effective scheme of adding new atoms to stable cluster geometries of larger clusters in an efficient way. We apply this algorithm to finding geometries of metal and noble gas clusters. For the fission process the analysis of the potential energy landscape calculated on the ab initio level...... of theory allowed us to obtain very detailed information on energetics and pathways of the different fission channels for the Na^2+_10 clusters....

  5. Advances in Multiferroic Nanomaterials Assembled with Clusters

    Directory of Open Access Journals (Sweden)

    Shifeng Zhao

    2015-01-01

    Full Text Available As an entirely new perspective of multifunctional materials, multiferroics have attracted a great deal of attention. With the rapidly developing micro- and nano-electro-mechanical system (MEMS&NEMS, the new kinds of micro- and nanodevices and functionalities aroused extensive research activity in the area of multiferroics. As an ideal building block to assemble the nanostructure, cluster exhibits particular physical properties related to the cluster size at nanoscale, which is efficient in controlling the multiferroic properties for nanomaterials. This review focuses on our recent advances in multiferroic nanomaterials assembled with clusters. In particular, the single phase multiferroic films and compound heterostructured multiferroic films assembled with clusters were introduced detailedly. This technique presents a new and efficient method to produce the nanostructured multiferroic materials for their potential application in NEMS devices.

  6. Use of advanced cluster analysis to characterize fish consumption patterns and methylmercury dietary exposures from fish and other sea foods among pregnant women

    DEFF Research Database (Denmark)

    Pouzaud, Francois; Ibbou, Assia; Blanchemanche, Sandrine;

    2010-01-01

    % of the women reached the recommended intake of 500 mg/day n-3 PUFA. Cluster analysis of the fish consumption showed that they could be grouped in five major clusters that are largely predictable of the intake of both MeHg and n-3 PUFA. This study shows that a global increase in seafood consumption could lead......Hg) exposure in a sample of 161 French pregnant women consuming sea food, including fish, molluscs and crustaceans, and to explore the use of unsupervised statistical learning as an advanced type of cluster analysis to identify patterns of fish consumption that could predict exposure to MeHg and the coverage...... of the Recommended Daily Allowance for n-3 polyunsaturated fatty acid (PUFA). The proportion of about 5% of pregnant women exposed at levels higher than the tolerable weekly intake for MeHg is similar to that observed among women of childbearing age in earlier French studies. At the same time, only about 50...

  7. Integrative cluster analysis in bioinformatics

    CERN Document Server

    Abu-Jamous, Basel; Nandi, Asoke K

    2015-01-01

    Clustering techniques are increasingly being put to use in the analysis of high-throughput biological datasets. Novel computational techniques to analyse high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. This book details the complete pathway of cluster analysis, from the basics of molecular biology to the generation of biological knowledge. The book also presents the latest clustering methods and clustering validation, thereby offering the reader a comprehensive review o

  8. Advances in Significance Testing for Cluster Detection

    Science.gov (United States)

    Coleman, Deidra Andrea

    Over the past two decades, much attention has been given to data driven project goals such as the Human Genome Project and the development of syndromic surveillance systems. A major component of these types of projects is analyzing the abundance of data. Detecting clusters within the data can be beneficial as it can lead to the identification of specified sequences of DNA nucleotides that are related to important biological functions or the locations of epidemics such as disease outbreaks or bioterrorism attacks. Cluster detection techniques require efficient and accurate hypothesis testing procedures. In this dissertation, we improve upon the hypothesis testing procedures for cluster detection by enhancing distributional theory and providing an alternative method for spatial cluster detection using syndromic surveillance data. In Chapter 2, we provide an efficient method to compute the exact distribution of the number and coverage of h-clumps of a collection of words. This method involves defining a Markov chain using a minimal deterministic automaton to reduce the number of states needed for computation. We allow words of the collection to contain other words of the collection making the method more general. We use our method to compute the distributions of the number and coverage of h-clumps in the Chi motif of H. influenza.. In Chapter 3, we provide an efficient algorithm to compute the exact distribution of multiple window discrete scan statistics for higher-order, multi-state Markovian sequences. This algorithm involves defining a Markov chain to efficiently keep track of probabilities needed to compute p-values of the statistic. We use our algorithm to identify cases where the available approximation does not perform well. We also use our algorithm to detect unusual clusters of made free throw shots by National Basketball Association players during the 2009-2010 regular season. In Chapter 4, we give a procedure to detect outbreaks using syndromic

  9. Globular Clusters as Cradles of Life and Advanced Civilizations

    Science.gov (United States)

    Di Stefano, R.; Ray, A.

    2016-08-01

    Globular clusters are ancient stellar populations in compact dense ellipsoids. There is no star formation and there are no core-collapse supernovae, but several lines of evidence suggest that globular clusters are rich in planets. If so, and if advanced civilizations can develop there, then the distances between these civilizations and other stars would be far smaller than typical distances between stars in the Galactic disk, facilitating interstellar communication and travel. The potent combination of long-term stability and high stellar densities provides a globular cluster opportunity. Yet the very proximity that promotes interstellar travel also brings danger, as stellar interactions can destroy planetary systems. We find, however, that large portions of many globular clusters are “sweet spots,” where habitable-zone planetary orbits are stable for long times. Globular clusters in our own and other galaxies are, therefore, among the best targets for searches for extraterrestrial intelligence (SETI). We use the Drake equation to compare the likelihood of advanced civilizations in globular clusters to that in the Galactic disk. We also consider free-floating planets, since wide-orbit planets can be ejected to travel through the cluster. Civilizations spawned in globular clusters may be able to establish self-sustaining outposts, reducing the probability that a single catastrophic event will destroy the civilization. Although individual civilizations may follow different evolutionary paths, or even be destroyed, the cluster may continue to host advanced civilizations once a small number have jumped across interstellar space. Civilizations residing in globular clusters could therefore, in a sense, be immortal.

  10. Recent advances in coupled-cluster methods

    CERN Document Server

    Bartlett, Rodney J

    1997-01-01

    Today, coupled-cluster (CC) theory has emerged as the most accurate, widely applicable approach for the correlation problem in molecules. Furthermore, the correct scaling of the energy and wavefunction with size (i.e. extensivity) recommends it for studies of polymers and crystals as well as molecules. CC methods have also paid dividends for nuclei, and for certain strongly correlated systems of interest in field theory.In order for CC methods to have achieved this distinction, it has been necessary to formulate new, theoretical approaches for the treatment of a variety of essential quantities

  11. Globular Clusters as Cradles of Life and Advanced Civilizations

    CERN Document Server

    Di Stefano, R

    2016-01-01

    Globular clusters are ancient stellar populations with no star formation or core-collapse supernovae. Several lines of evidence suggest that globular clusters are rich in planets. If so, and if advanced civilizations can develop there, then the distances between these civilizations and other stars would be far smaller than typical distances between stars in the Galactic disk. The relative proximity would facilitate interstellar communication and travel. However, the very proximity that promotes interstellar travel also brings danger, since stellar interactions can destroy planetary systems. However, by modeling globular clusters and their stellar populations, we find that large regions of many globular clusters can be thought of as "sweet spots" where habitable-zone planetary orbits can be stable for long times. We also compute the ambient densities and fluxes in the regions within which habitable-zone planets can survive. Globular clusters are among the best targets for searches for extraterrestrial intellig...

  12. Globular Clusters as Cradles of Life and Advanced Civilizations

    Science.gov (United States)

    Di Stefano, Rosanne; Ray, Alak

    2016-01-01

    Globular clusters are bound groups of about a million stars and stellar remnants. They are old, largely isolated, and very dense. We consider what each of these special features can mean for the development of life, the evolution of intelligent life, and the long-term survival of technological civilizations. We find that, if they house planets, globular clusters provide ideal environments for advanced civilizations that can survive over long times. We therefore propose methods to search for planets in globular clusters. If planets are found and if our arguments are correct, searches for intelligent life are most likely to succeed when directed toward globular clusters. Globular clusters may be the first places in which distant life is identified in our own or in external galaxies.

  13. Advances in molecular vibrations and collision dynamics molecular clusters

    CERN Document Server

    Bacic, Zatko

    1998-01-01

    This volume focuses on molecular clusters, bound by van der Waals interactions and hydrogen bonds. Twelve chapters review a wide range of recent theoretical and experimental advances in the areas of cluster vibrations, spectroscopy, and reaction dynamics. The authors are leading experts, who have made significant contributions to these topics.The first chapter describes exciting results and new insights in the solvent effects on the short-time photo fragmentation dynamics of small molecules, obtained by combining heteroclusters with femtosecond laser excitation. The second is on theoretical work on effects of single solvent (argon) atom on the photodissociation dynamics of the solute H2O molecule. The next two chapters cover experimental and theoretical aspects of the energetics and vibrations of small clusters. Chapter 5 describes diffusion quantum Monte Carlo calculations and non additive three-body potential terms in molecular clusters. The next six chapters deal with hydrogen-bonded clusters, refle...

  14. Nonlinear analysis of EAS clusters

    CERN Document Server

    Zotov, M Yu; Fomin, Y A; Fomin, Yu. A.

    2002-01-01

    We apply certain methods of nonlinear time series analysis to the extensive air shower clusters found earlier in the data set obtained with the EAS-1000 Prototype array. In particular, we use the Grassberger-Procaccia algorithm to compute the correlation dimension of samples in the vicinity of the clusters. The validity of the results is checked by surrogate data tests and some additional quantities. We compare our conclusions with the results of similar investigations performed by the EAS-TOP and LAAS groups.

  15. Supermodel Analysis of Galaxy Clusters

    CERN Document Server

    Fusco-Femiano, R; Lapi, A

    2009-01-01

    [abridged] We present the analysis of the X-ray brightness and temperature profiles for six clusters belonging to both the Cool Core and Non Cool Core classes, in terms of the Supermodel (SM) developed by Cavaliere, Lapi & Fusco-Femiano (2009). Based on the gravitational wells set by the dark matter halos, the SM straightforwardly expresses the equilibrium of the IntraCluster Plasma (ICP) modulated by the entropy deposited at the boundary by standing shocks from gravitational accretion, and injected at the center by outgoing blastwaves from mergers or from outbursts of Active Galactic Nuclei. The cluster set analyzed here highlights not only how simply the SM represents the main dichotomy Cool vs. Non Cool Core clusters in terms of a few ICP parameters governing the radial entropy run, but also how accurately it fits even complex brightness and temperature profiles. For Cool Core clusters like A2199 and A2597, the SM with a low level of central entropy straightforwardly yields the characteristic peaked pr...

  16. Advances in theory and applications of fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The summarization and evaluation of the advances in fuzzy clustering theory are made in the aspects including the criterion functions, algorithm implementations, validity measurements and applications. Several important directions for a further study and the application prospects are also pointed out.

  17. Advanced analysis tool for X-ray photoelectron spectroscopy profiling: Cleaning of perovskite SrTiO{sub 3} oxide surface using argon cluster ion source

    Energy Technology Data Exchange (ETDEWEB)

    Aureau, D., E-mail: damien.aureau@uvsq.fr [Institut Lavoisier de Versailles, (UMR 8180) Université de Versailles-Saint-Quentin-en-Yvelines–CNRS, 45 Av. des États-Unis, 78035 Versailles (France); Ridier, K. [Institut Lavoisier de Versailles, (UMR 8180) Université de Versailles-Saint-Quentin-en-Yvelines–CNRS, 45 Av. des États-Unis, 78035 Versailles (France); Groupe d' Étude de la Matière Condensée (UMR 8635) Université de Versailles Saint-Quentin-en-Yvelines–CNRS, 45 Av. des États-Unis, 78035 Versailles (France); Bérini, B.; Dumont, Y.; Keller, N. [Groupe d' Étude de la Matière Condensée (UMR 8635) Université de Versailles Saint-Quentin-en-Yvelines–CNRS, 45 Av. des États-Unis, 78035 Versailles (France); Vigneron, J.; Bouttemy, M.; Etcheberry, A. [Institut Lavoisier de Versailles, (UMR 8180) Université de Versailles-Saint-Quentin-en-Yvelines–CNRS, 45 Av. des États-Unis, 78035 Versailles (France); Fouchet, A. [Groupe d' Étude de la Matière Condensée (UMR 8635) Université de Versailles Saint-Quentin-en-Yvelines–CNRS, 45 Av. des États-Unis, 78035 Versailles (France)

    2016-02-29

    This article shows the comparison between three different ionic bombardments during X-ray photoelectron spectroscopy (XPS) studies of single crystalline SrTiO{sub 3} (STO) substrates. The abrasion using a “cluster argon ion source” is compared with the standard “monoatomic Ar”. The influence of the energy of the monoatomic ions used is clearly demonstrated. While the chemically adsorbed species on the STO surface are removed, such bombardment strongly modifies the surface. A reduction of part of the titanium atoms and the appearance of a different chemical environment for surface strontium atoms are observed. Implantation of argon ions is also detected. Cluster ion etching is used on oxide surface and, in this case only, due to a much lower kinetic energy per atom compared to monoatomic ions, the possibility to remove surface contaminants at the surface without modification of the XP spectra is clearly demonstrated, ensuring that the stoichiometry of the surface is preserved. Such result is crucial for everybody working with oxide surfaces to obtain a non-modified XPS analysis. The progressive effect of this powerful tool allows the monitoring of the removal of surface contamination in the first steps of the bombardment which was not achievable with usual guns. - Highlights: • The effects of three argon etchings are studied as a function of time on SrTiO3 oxide. • A method for obtaining non-modified chemical analysis of oxides is presented. • The soft removal of adsorbed species thanks to argon cluster is demonstrated. • The damages induced on SrTiO3 surface by ionic bombardment are shown. • The influence of the kinetic energy of incoming Ar atoms is examined.

  18. Cluster analysis of obesity and asthma phenotypes.

    Directory of Open Access Journals (Sweden)

    E Rand Sutherland

    Full Text Available BACKGROUND: Asthma is a heterogeneous disease with variability among patients in characteristics such as lung function, symptoms and control, body weight, markers of inflammation, and responsiveness to glucocorticoids (GC. Cluster analysis of well-characterized cohorts can advance understanding of disease subgroups in asthma and point to unsuspected disease mechanisms. We utilized an hypothesis-free cluster analytical approach to define the contribution of obesity and related variables to asthma phenotype. METHODOLOGY AND PRINCIPAL FINDINGS: In a cohort of clinical trial participants (n = 250, minimum-variance hierarchical clustering was used to identify clinical and inflammatory biomarkers important in determining disease cluster membership in mild and moderate persistent asthmatics. In a subset of participants, GC sensitivity was assessed via expression of GC receptor alpha (GCRα and induction of MAP kinase phosphatase-1 (MKP-1 expression by dexamethasone. Four asthma clusters were identified, with body mass index (BMI, kg/m(2 and severity of asthma symptoms (AEQ score the most significant determinants of cluster membership (F = 57.1, p<0.0001 and F = 44.8, p<0.0001, respectively. Two clusters were composed of predominantly obese individuals; these two obese asthma clusters differed from one another with regard to age of asthma onset, measures of asthma symptoms (AEQ and control (ACQ, exhaled nitric oxide concentration (F(ENO and airway hyperresponsiveness (methacholine PC(20 but were similar with regard to measures of lung function (FEV(1 (% and FEV(1/FVC, airway eosinophilia, IgE, leptin, adiponectin and C-reactive protein (hsCRP. Members of obese clusters demonstrated evidence of reduced expression of GCRα, a finding which was correlated with a reduced induction of MKP-1 expression by dexamethasone CONCLUSIONS AND SIGNIFICANCE: Obesity is an important determinant of asthma phenotype in adults. There is heterogeneity in

  19. Pain Treatments for Nursing Home Residents with Advanced Dementia and Substantial Impaired Communication: A Cross-Sectional Analysis at Baseline of a Cluster Randomized Controlled Trial.

    Science.gov (United States)

    Liu, Justina Yat Wa; Leung, Doris Y P

    2016-09-28

    OBJECTIVES : This is a cross-sectional analysis at baseline of a cluster randomized controlled trial to identify factors associated with the use of pharmacological and nonpharmacological pain treatments by nursing home residents with dementia and impaired communication. METHODS : One hundred thirty-four residents with dementia and impaired communication were recruited. Nine of them were excluded because data on their pain treatments were missing, resulting in 125 for analysis. Hierarchical generalized estimating equations analyses controlling for the clustering effect of nursing homes were used to identify factors associated with the use of pharmacological and nonpharmacological pain treatments. RESULTS : Although all participants had a confirmed pain condition, only 23 (18.4%) and 45 (36%) had received pharmacological or nonpharmacological pain treatments, respectively. Participants with a higher ability to communicate (P = 0.031) and fewer pain locations were found to be more likely to receive pain medications, with the impact of communication ability being greater among participants with better cognitive status than among those with poor cognitive status. Participants who had been living in the home longer and who were more dependent were less likely to receive nonpharmacological treatments. CONCLUSION : Suboptimal pain management was common among this population. Severe impairment in the ability to communicate is a major reason for the underuse of pain medications. Staff may become desensitized and fail to perceive subtle changes in the residents' behavior as indicative of pain, leading to the underadministering of nonpharmacological treatments. To improve this situation, it is suggested that observational pain assessments be systematically carried out in nursing homes.

  20. The SMART CLUSTER METHOD - adaptive earthquake cluster analysis and declustering

    Science.gov (United States)

    Schaefer, Andreas; Daniell, James; Wenzel, Friedemann

    2016-04-01

    Earthquake declustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity with usual applications comprising of probabilistic seismic hazard assessments (PSHAs) and earthquake prediction methods. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation. Various methods have been developed to address this issue from other researchers. These have differing ranges of complexity ranging from rather simple statistical window methods to complex epidemic models. This study introduces the smart cluster method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal identification. Hereby, an adaptive search algorithm for data point clusters is adopted. It uses the earthquake density in the spatio-temporal neighbourhood of each event to adjust the search properties. The identified clusters are subsequently analysed to determine directional anisotropy, focussing on a strong correlation along the rupture plane and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010/2011 Darfield-Christchurch events, an adaptive classification procedure is applied to disassemble subsequent ruptures which may have been grouped into an individual cluster using near-field searches, support vector machines and temporal splitting. The steering parameters of the search behaviour are linked to local earthquake properties like magnitude of completeness, earthquake density and Gutenberg-Richter parameters. The method is capable of identifying and classifying earthquake clusters in space and time. It is tested and validated using earthquake data from California and New Zealand. As a result of the cluster identification process, each event in

  1. Cluster Analysis of Ranunculus Species

    Directory of Open Access Journals (Sweden)

    SURANTO

    2002-01-01

    Full Text Available The aim of the experiment was to examine whether the morphological characters of eleven species of Ranunculus collected from a number of populations were in agreement with the genetic data (isozyme. The method used in this study was polyacrilamide gel electrophoresis using peroxides, estarase, malate dehydrogenase, and acid phosphatase enzymes. The results showed that cluster analysis based on isozyme data have given a good support to classification of eleven species based on morphological groups. This study concluded that in certain species each morphological variation was profit to be genetically based.

  2. Effect of imbalance and intracluster correlation coefficient in cluster randomization trials with binary outcomes when the available number of clusters is fixed in advance.

    Science.gov (United States)

    Ahn, Chul; Hu, Fan; Skinner, Celette Sugg; Ahn, Daniel

    2009-07-01

    In some cluster randomization trials, the number of clusters cannot exceed a specified maximum value due to cost constraints or other practical reasons. Donner and Klar [Donner A, and Klar N. Design and analysis of cluster randomization trials in health research. Oxford University Press 2000] provided the sample size formula for the number of subjects required per cluster when the number of clusters cannot exceed a specified maximum value. The sample size formula of Donner and Klar assumes that the number of subjects is the same in each cluster. In practical situations, the number of subjects may be different among clusters. We conducted simulation studies to investigate the effect of the cluster size variability (kappa) and the intracluster correlation coefficient (rho) on the power of the study in which the number of available clusters is fixed in advance. For the balanced case (kappa=1.0), i.e., equal cluster size among clusters, the sample size formula yielded empirical powers close to the nominal level even when the number of available clusters per group (k*) is as small as 10. The sample size formula yielded empirical powers close to the nominal level when the number of available clusters per group (k*) is at least 20 and the imbalance parameter (kappa) is at least 0.8. Empirical powers were close to the nominal level when (rho or =0.8, and k*=10) or (rho< or =0.02, kappa=0.8, and k*=20).

  3. Comparative analysis of genomic signal processing for microarray data clustering.

    Science.gov (United States)

    Istepanian, Robert S H; Sungoor, Ala; Nebel, Jean-Christophe

    2011-12-01

    Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.

  4. Towards advanced structural analysis of iron oxide clusters on the surface of γ-Al{sub 2}O{sub 3} using EXAFS

    Energy Technology Data Exchange (ETDEWEB)

    Boubnov, Alexey [Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Kaiserstr. 12, D-76131 Karlsruhe (Germany); Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, D-76344 Eggenstein-Leopoldshafen (Germany); Roppertz, Andreas [Institute of Energy Process Engineering and Chemical Engineering, Chair of Reaction Engineering, Technical University of Freiberg, Fuchsmuehlenweg 9, D-09599 Freiberg (Germany); Kundrat, Matthew D. [Center for Functional Nanostructures and Institute of Physical Chemistry, Karlsruhe Institute of Technology, Kaiserstr. 12, D-76131 Karlsruhe (Germany); Mangold, Stefan [Institut für Beschleunigerphysik und Technologie (IBPT), Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, D-76344 Eggenstein-Leopoldshafen (Germany); Reznik, Boris [Institute of Applied Geosciences, Karlsruhe Institute of Technology, Kaiserstr. 12, D-76131 Karlsruhe (Germany); Jacob, Christoph R. [Center for Functional Nanostructures and Institute of Physical Chemistry, Karlsruhe Institute of Technology, Kaiserstr. 12, D-76131 Karlsruhe (Germany); Institute of Physical and Theoretical Chemistry, TU Braunschweig, Hans-Sommer-Str. 10, D-38106 Braunschweig (Germany); Kureti, Sven [Institute of Energy Process Engineering and Chemical Engineering, Chair of Reaction Engineering, Technical University of Freiberg, Fuchsmuehlenweg 9, D-09599 Freiberg (Germany); Grunwaldt, Jan-Dierk, E-mail: grunwaldt@kit.edu [Institute for Chemical Technology and Polymer Chemistry, Karlsruhe Institute of Technology, Kaiserstr. 12, D-76131 Karlsruhe (Germany); Institute of Catalysis Research and Technology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, D-76344 Eggenstein-Leopoldshafen (Germany)

    2016-11-15

    Highlights: • Analysis of isolated and oligomeric FeOx (x = 4, 5) on Al{sub 2}O{sub 3} by XANES and EXAFS. • Iron is trivalent and is mainly located at octahedral lattice sites of Al{sub 2}O{sub 3}. • Low Fe loading (0.1%) guarantees high dispersion of catalytically active iron sites. • Surface Fe-cluster on Al{sub 2}O{sub 3} and DFT-optimised Fe-doped Al{sub 2}O{sub 3} as input models for EXAFS. • Interactions of iron with support are well-accounted for using realistic models. - Abstract: Iron oxide centres are structurally investigated in 0.1% Fe/γ-Al{sub 2}O{sub 3}, which is known as highly active catalyst, for instance in the oxidation of CO. The sample was characterised by using X-ray absorption spectroscopy (XAS) in terms of X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS), Mössbauer spectroscopy, X-ray diffraction (XRD) and transmission electron microscopy (TEM). These analyses evidenced high dispersion of the iron oxide entities without significant presence of bulk-like aggregates associated with the low Fe content of the catalyst. A library of structural models of Al{sub 2}O{sub 3}-supported surface Fe was created as input for EXAFS fitting. Additionally, several model structures of Fe substituting Al ions in bulk γ-Al{sub 2}O{sub 3} were created with optimised geometry based on density-functional theory (DFT) calculations. From EXAFS refinement of the best 8 out of 24 models, it was found that the trivalent Fe ions are coordinated by 4–5 oxygen atoms and are located on octahedral lattice sites of the exposed surfaces of γ-Al{sub 2}O{sub 3}. These iron oxide species exist mainly as a mixture of monomeric and binuclear species and due to the low concentration represent suitable model systems as alternative to single crystal systems for structure-function relationships.

  5. Robust cluster analysis and variable selection

    CERN Document Server

    Ritter, Gunter

    2014-01-01

    Clustering remains a vibrant area of research in statistics. Although there are many books on this topic, there are relatively few that are well founded in the theoretical aspects. In Robust Cluster Analysis and Variable Selection, Gunter Ritter presents an overview of the theory and applications of probabilistic clustering and variable selection, synthesizing the key research results of the last 50 years. The author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides clear guidance for the varying needs of bot

  6. Cluster analysis for computer workload evaluation

    CERN Document Server

    Landau, K

    1976-01-01

    An introduction to computer workload analysis is given, showing its range of application in computer centre management, system and application programming. Cluster methods are discussed which can be used in conjunction with workload data and cluster algorithms are adapted to the specific set problem. Several samples of CDC 7600- accounting-data-collected at CERN, the European Organization for Nuclear Research-underwent a cluster analysis to determine job groups. The conclusions from resource usage of typical job groups in relation to computer workload analysis are discussed. (17 refs).

  7. ASteCA - Automated Stellar Cluster Analysis

    CERN Document Server

    Perren, Gabriel I; Piatti, Andrés E

    2014-01-01

    We present ASteCA (Automated Stellar Cluster Analysis), a suit of tools designed to fully automatize the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster's center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster's metallicity, age, extinction and distance values along with its unce...

  8. Broadband PLC for Clustered Advanced Metering Infrastructure (AMI Architecture

    Directory of Open Access Journals (Sweden)

    Augustine Ikpehai

    2016-07-01

    Full Text Available Advanced metering infrastructure (AMI subsystems monitor and control energy distribution through exchange of information between smart meters and utility networks. A key challenge is how to select a cost-effective communication system without compromising the performance of the applications. Current communication technologies were developed for conventional data networks with different requirements. It is therefore necessary to investigate how much of existing communication technologies can be retrofitted into the new energy infrastructure to cost-effectively deliver acceptable level of service. This paper investigates broadband power line communications (BPLC as a backhaul solution in AMI. By applying the disparate traffic characteristics of selected AMI applications, the network performance is evaluated. This study also examines the communication network response to changes in application configurations in terms of packet sizes. In each case, the network is stress-tested and performance is assessed against acceptable thresholds documented in the literature. Results show that, like every other communication technology, BPLC has certain limitations; however, with some modifications in the network topology, it indeed can fulfill most AMI traffic requirements for flexible and time-bounded applications. These opportunities, if tapped, can significantly improve fiscal and operational efficiencies in AMI services. Simulation results also reveal that BPLC as a backhaul can support flat and clustered AMI structures with cluster size ranging from 1 to 150 smart meters.

  9. Cluster Analysis of Adolescent Blogs

    Science.gov (United States)

    Liu, Eric Zhi-Feng; Lin, Chun-Hung; Chen, Feng-Yi; Peng, Ping-Chuan

    2012-01-01

    Emerging web applications and networking systems such as blogs have become popular, and they offer unique opportunities and environments for learners, especially for adolescent learners. This study attempts to explore the writing styles and genres used by adolescents in their blogs by employing content, factor, and cluster analyses. Factor…

  10. [Cluster analysis and its application].

    Science.gov (United States)

    Půlpán, Zdenĕk

    2002-01-01

    The study exploits knowledge-oriented and context-based modification of well-known algorithms of (fuzzy) clustering. The role of fuzzy sets is inherently inclined towards coping with linguistic domain knowledge also. We try hard to obtain from rich diverse data and knowledge new information about enviroment that is being explored.

  11. Using cluster analysis to explore survey data.

    Science.gov (United States)

    Spencer, Llinos; Roberts, Gwerfyl; Irvine, Fiona; Jones, Peter; Baker, Colin

    2007-01-01

    Llinos Haf Spencer reports on the use of the cluster analysis statistical technique in nursing research and uses data from the Welsh Language Awareness in Healthcare Provision in Wales survey as an exemplar She concludes that cluster analysis is a valuable tool to tease out patterns in data that are not initially evident in bivariate analyses and thus should be considered as a viable option for nursing research.

  12. Cluster Analysis of the Malaysian Hipposideros

    Science.gov (United States)

    Sazali, Siti Nurlydia; Laman, Charlie J.; Abdullah, M. T.

    2008-01-01

    A preliminary study on the morphometric variations among species in the genus Hipposideros was conducted using voucher specimens from the Universiti Malaysia Sarawak (UNIMAS) Zoological Museum and the Department of Wildlife and National Park (DWNP) Kuala Lumpur. A total of 24 individuals from six species of this genus were morphologically studied where all related measurements of body, skull and dental were measured and recorded. The statistical data subjected to the cluster analysis shows that the genus Hipposideros is divided into two major clusters where each species was clearly separated. The cluster analysis among Hipposideros species is useful for aiding in species identification.

  13. Cluster Analysis and Clinical Asthma Phenotypes

    Science.gov (United States)

    Shaw, Dominic E.; Berry, Michael A.; Thomas, Michael; Brightling, Christopher E.; Wardlaw, Andrew J.

    2014-01-01

    Rationale Heterogeneity in asthma expression is multidimensional, including variability in clinical, physiologic, and pathologic parameters. Classification requires consideration of these disparate domains in a unified model. Objectives To explore the application of a multivariate mathematical technique, k-means cluster analysis, for identifying distinct phenotypic groups. Methods We performed k-means cluster analysis in three independent asthma populations. Clusters of a population managed in primary care (n = 184) with predominantly mild to moderate disease, were compared with a refractory asthma population managed in secondary care (n = 187). We then compared differences in asthma outcomes (exacerbation frequency and change in corticosteroid dose at 12 mo) between clusters in a third population of 68 subjects with predominantly refractory asthma, clustered at entry into a randomized trial comparing a strategy of minimizing eosinophilic inflammation (inflammation-guided strategy) with standard care. Measurements and Main Results Two clusters (early-onset atopic and obese, noneosinophilic) were common to both asthma populations. Two clusters characterized by marked discordance between symptom expression and eosinophilic airway inflammation (early-onset symptom predominant and late-onset inflammation predominant) were specific to refractory asthma. Inflammation-guided management was superior for both discordant subgroups leading to a reduction in exacerbation frequency in the inflammation-predominant cluster (3.53 [SD, 1.18] vs. 0.38 [SD, 0.13] exacerbation/patient/yr, P = 0.002) and a dose reduction of inhaled corticosteroid in the symptom-predominant cluster (mean difference, 1,829 μg beclomethasone equivalent/d [95% confidence interval, 307–3,349 μg]; P = 0.02). Conclusions Cluster analysis offers a novel multidimensional approach for identifying asthma phenotypes that exhibit differences in clinical response to treatment algorithms. PMID:18480428

  14. Performance Analysis of Hierarchical Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    K.Ranjini

    2011-07-01

    Full Text Available Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters, so that the data in each subset (ideally share some common trait - often proximity according to some defined distance measure. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. This paper explains the implementation of agglomerative and divisive clustering algorithms applied on various types of data. The details of the victims of Tsunami in Thailand during the year 2004, was taken as the test data. Visual programming is used for implementation and running time of the algorithms using different linkages (agglomerative to different types of data are taken for analysis.

  15. A Geometric Analysis of Subspace Clustering with Outliers

    CERN Document Server

    Soltanolkotabi, Mahdi

    2011-01-01

    This paper considers the problem of clustering a collection of unlabeled data points assumed to lie near a union of lower dimensional planes. As is common in computer vision or unsupervised learning applications, we do not know in advance how many subspaces there are nor do we have any information about their dimensions. We develop a novel geometric analysis of an algorithm named {\\em sparse subspace clustering} (SSC) \\cite{Elhamifar09}, which significantly broadens the range of problems where it is provably effective. For instance, we show that SSC can recover multiple subspaces, each of dimension comparable to the ambient dimension. We also prove that SSC can correctly cluster data points even when the subspaces of interest intersect. Further, we develop an extension of SSC that succeeds when the data set is corrupted with possibly overwhelmingly many outliers. Underlying our analysis are clear geometric insights, which may bear on other sparse recovery problems. A numerical study complements our theoretica...

  16. Advanced defect detection algorithm using clustering in ultrasonic NDE

    Science.gov (United States)

    Gongzhang, Rui; Gachagan, Anthony

    2016-02-01

    A range of materials used in industry exhibit scattering properties which limits ultrasonic NDE. Many algorithms have been proposed to enhance defect detection ability, such as the well-known Split Spectrum Processing (SSP) technique. Scattering noise usually cannot be fully removed and the remaining noise can be easily confused with real feature signals, hence becoming artefacts during the image interpretation stage. This paper presents an advanced algorithm to further reduce the influence of artefacts remaining in A-scan data after processing using a conventional defect detection algorithm. The raw A-scan data can be acquired from either traditional single transducer or phased array configurations. The proposed algorithm uses the concept of unsupervised machine learning to cluster segmental defect signals from pre-processed A-scans into different classes. The distinction and similarity between each class and the ensemble of randomly selected noise segments can be observed by applying a classification algorithm. Each class will then be labelled as `legitimate reflector' or `artefacts' based on this observation and the expected probability of defection (PoD) and probability of false alarm (PFA) determined. To facilitate data collection and validate the proposed algorithm, a 5MHz linear array transducer is used to collect A-scans from both austenitic steel and Inconel samples. Each pulse-echo A-scan is pre-processed using SSP and the subsequent application of the proposed clustering algorithm has provided an additional reduction to PFA while maintaining PoD for both samples compared with SSP results alone.

  17. Advanced Analysis of Nontraditional Machining

    CERN Document Server

    Tsai, Hung-Yin

    2013-01-01

    Nontraditional machining utilizes thermal, chemical, electrical, mechanical and optical sources of energy to form and cut materials. Advanced Analysis of Nontraditional Machining explains in-depth how each of these advanced machining processes work, their machining system components, and process variables and industrial applications, thereby offering advanced knowledge and scientific insight. This book also documents the latest and frequently cited research results of a few key nonconventional machining processes for the most concerned topics in industrial applications, such as laser machining, electrical discharge machining, electropolishing of die and mold, and wafer processing for integrated circuit manufacturing. This book also: Fills the gap of the advanced knowledge of nonconventional machining between industry and research Documents latest and frequently cited research of key nonconventional machining processes for the most sought after topics in industrial applications Demonstrates advanced multidisci...

  18. Clustering analysis of telecommunication customers

    Institute of Scientific and Technical Information of China (English)

    REN Hong; ZHENG Yan; WU Ye-rong

    2009-01-01

    In this article, a clustering method based on genetic algorithm (GA) for telecommunication customer subdivision is presented. First, the features of telecommunication customers (such as the calling behavior and consuming behavior) are extracted. Second, the similarities between the multidimensional feature vectors of telecommunication customers are computed and mapped as the distance between samples on a two-dimensional plane. Finally, the distances are adjusted to approximate the similarities gradually by GA. One advantage of this method is the independent distribution of the sample space. The experiments demonstrate the feasibility of the proposed method.

  19. Advanced Economic Analysis

    Science.gov (United States)

    Greenberg, Marc W.; Laing, William

    2013-01-01

    An Economic Analysis (EA) is a systematic approach to the problem of choosing the best method of allocating scarce resources to achieve a given objective. An EA helps guide decisions on the "worth" of pursuing an action that departs from status quo ... an EA is the crux of decision-support.

  20. Advanced Electrochemistry of Individual Metal Clusters Electrodeposited Atom by Atom to Nanometer by Nanometer.

    Science.gov (United States)

    Kim, Jiyeon; Dick, Jeffrey E; Bard, Allen J

    2016-11-15

    Metal clusters are very important as building blocks for nanoparticles (NPs) for electrocatalysis and electroanalysis in both fundamental and applied electrochemistry. Attention has been given to understanding of traditional nucleation and growth of metal clusters and to their catalytic activities for various electrochemical applications in energy harvesting as well as analytical sensing. Importantly, understanding the properties of these clusters, primarily the relationship between catalysis and morphology, is required to optimize catalytic function. This has been difficult due to the heterogeneities in the size, shape, and surface properties. Thus, methods that address these issues are necessary to begin understanding the reactivity of individual catalytic centers as opposed to ensemble measurements, where the effect of size and morphology on the catalysis is averaged out in the measurement. This Account introduces our advanced electrochemical approaches to focus on each isolated metal cluster, where we electrochemically fabricated clusters or NPs atom by atom to nanometer by nanometer and explored their electrochemistry for their kinetic and catalytic behavior. Such approaches expand the dimensions of analysis, to include the electrochemistry of (1) a discrete atomic cluster, (2) solely a single NP, or (3) individual NPs in the ensemble sample. Specifically, we studied the electrocatalysis of atomic metal clusters as a nascent electrocatalyst via direct electrodeposition on carbon ultramicroelectrode (C UME) in a femtomolar metal ion precursor. In addition, we developed tunneling ultramicroelectrodes (TUMEs) to study electron transfer (ET) kinetics of a redox probe at a single metal NP electrodeposited on this TUME. Owing to the small dimension of a NP as an active area of a TUME, extremely high mass transfer conditions yielded a remarkably high standard ET rate constant, k(0), of 36 cm/s for outer-sphere ET reaction. Most recently, we advanced nanoscale

  1. Filtering Genes for Cluster and Network Analysis

    Directory of Open Access Journals (Sweden)

    Parkhomenko Elena

    2009-06-01

    Full Text Available Abstract Background Prior to cluster analysis or genetic network analysis it is customary to filter, or remove genes considered to be irrelevant from the set of genes to be analyzed. Often genes whose variation across samples is less than an arbitrary threshold value are deleted. This can improve interpretability and reduce bias. Results This paper introduces modular models for representing network structure in order to study the relative effects of different filtering methods. We show that cluster analysis and principal components are strongly affected by filtering. Filtering methods intended specifically for cluster and network analysis are introduced and compared by simulating modular networks with known statistical properties. To study more realistic situations, we analyze simulated "real" data based on well-characterized E. coli and S. cerevisiae regulatory networks. Conclusion The methods introduced apply very generally, to any similarity matrix describing gene expression. One of the proposed methods, SUMCOV, performed well for all models simulated.

  2. Clustering analysis of seismicity and aftershock identification.

    Science.gov (United States)

    Zaliapin, Ilya; Gabrielov, Andrei; Keilis-Borok, Vladimir; Wong, Henry

    2008-07-01

    We introduce a statistical methodology for clustering analysis of seismicity in the time-space-energy domain and use it to establish the existence of two statistically distinct populations of earthquakes: clustered and nonclustered. This result can be used, in particular, for nonparametric aftershock identification. The proposed approach expands the analysis of Baiesi and Paczuski [Phys. Rev. E 69, 066106 (2004)10.1103/PhysRevE.69.066106] based on the space-time-magnitude nearest-neighbor distance eta between earthquakes. We show that for a homogeneous Poisson marked point field with exponential marks, the distance eta has the Weibull distribution, which bridges our results with classical correlation analysis for point fields. The joint 2D distribution of spatial and temporal components of eta is used to identify the clustered part of a point field. The proposed technique is applied to several seismicity models and to the observed seismicity of southern California.

  3. Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis.

    Directory of Open Access Journals (Sweden)

    Nan Lin

    Full Text Available Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis.

  4. Handbook of Advanced Multilevel Analysis

    CERN Document Server

    Hox, Joop

    2010-01-01

    This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors have assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed.

  5. Cluster and constraint analysis in tetrahedron packings.

    Science.gov (United States)

    Jin, Weiwei; Lu, Peng; Liu, Lufeng; Li, Shuixiang

    2015-04-01

    The disordered packings of tetrahedra often show no obvious macroscopic orientational or positional order for a wide range of packing densities, and it has been found that the local order in particle clusters is the main order form of tetrahedron packings. Therefore, a cluster analysis is carried out to investigate the local structures and properties of tetrahedron packings in this work. We obtain a cluster distribution of differently sized clusters, and peaks are observed at two special clusters, i.e., dimer and wagon wheel. We then calculate the amounts of dimers and wagon wheels, which are observed to have linear or approximate linear correlations with packing density. Following our previous work, the amount of particles participating in dimers is used as an order metric to evaluate the order degree of the hierarchical packing structure of tetrahedra, and an order map is consequently depicted. Furthermore, a constraint analysis is performed to determine the isostatic or hyperstatic region in the order map. We employ a Monte Carlo algorithm to test jamming and then suggest a new maximally random jammed packing of hard tetrahedra from the order map with a packing density of 0.6337.

  6. Advances in Bayesian Model Based Clustering Using Particle Learning

    Energy Technology Data Exchange (ETDEWEB)

    Merl, D M

    2009-11-19

    implementation of Carvalho et al that allow us to retain the computational advantages of particle learning while improving the suitability of the methodology to the analysis of streaming data and simultaneously facilitating the real time discovery of latent cluster structures. Section 4 demonstrates our methodological enhancements in the context of several simulated and classical data sets, showcasing the use of particle learning methods for online anomaly detection, label generation, drift detection, and semi-supervised classification, none of which would be achievable through a standard MCMC approach. Section 5 concludes with a discussion of future directions for research.

  7. Secure and Faster Clustering Environment for Advanced Image Compression

    Directory of Open Access Journals (Sweden)

    D.Kesavaraja

    2010-11-01

    Full Text Available Cloud computing provides ample opportunity in many areas such as fastest image transmission, secure and efficient imaging as a service. In general users needs faster and secure service. Usually Image Compression Algorithms are not working faster. In spite of several ongoing researches, Conventional Compression and its Algorithms might not be able to run faster. So, we perform comparative study of three image compression algorithm and their variety of features and factors to choose best among them for cluster processing. After choosing a best one it can be applied for a cluster computing environment to run parallel image compression for faster processing. This paper is the real time implementation of a Distributed Image Compression in Clustering of Nodes. In cluster computing, security is also more important factor. So, we propose a Distributed Intrusion Detection System to monitors all the nodes in cluster . If an intrusion occur in node processing then take an prevention step based on RIC (Robust Intrusion Control Method. We demonstrate the effectiveness and feasibility of our method on a set of satellite images for defense forces. The efficiency ratio of this computation process is 91.20.

  8. MANNER OF STOCKS SORTING USING CLUSTER ANALYSIS METHODS

    Directory of Open Access Journals (Sweden)

    Jana Halčinová

    2014-06-01

    Full Text Available The aim of the present article is to show the possibility of using the methods of cluster analysis in classification of stocks of finished products. Cluster analysis creates groups (clusters of finished products according to similarity in demand i.e. customer requirements for each product. Manner stocks sorting of finished products by clusters is described a practical example. The resultants clusters are incorporated into the draft layout of the distribution warehouse.

  9. AMOEBA clustering revisited. [cluster analysis, classification, and image display program

    Science.gov (United States)

    Bryant, Jack

    1990-01-01

    A description of the clustering, classification, and image display program AMOEBA is presented. Using a difficult high resolution aircraft-acquired MSS image, the steps the program takes in forming clusters are traced. A number of new features are described here for the first time. Usage of the program is discussed. The theoretical foundation (the underlying mathematical model) is briefly presented. The program can handle images of any size and dimensionality.

  10. Mapping Cigarettes Similarities using Cluster Analysis Methods

    Directory of Open Access Journals (Sweden)

    Lorentz Jäntschi

    2007-09-01

    Full Text Available The aim of the research was to investigate the relationship and/or occurrences in and between chemical composition information (tar, nicotine, carbon monoxide, market information (brand, manufacturer, price, and public health information (class, health warning as well as clustering of a sample of cigarette data. A number of thirty cigarette brands have been analyzed. Six categorical (cigarette brand, manufacturer, health warnings, class and four continuous (tar, nicotine, carbon monoxide concentrations and package price variables were collected for investigation of chemical composition, market information and public health information. Multiple linear regression and two clusterization techniques have been applied. The study revealed interesting remarks. The carbon monoxide concentration proved to be linked with tar and nicotine concentration. The applied clusterization methods identified groups of cigarette brands that shown similar characteristics. The tar and carbon monoxide concentrations were the main criteria used in clusterization. An analysis of a largest sample could reveal more relevant and useful information regarding the similarities between cigarette brands.

  11. Equivalent damage validation by variable cluster analysis

    Science.gov (United States)

    Drago, Carlo; Ferlito, Rachele; Zucconi, Maria

    2016-06-01

    The main aim of this work is to perform a clustering analysis on the damage relieved in the old center of L'Aquila after the earthquake occurred on April 6, 2009 and to validate an Indicator of Equivalent Damage ED that summarizes the information reported on the AeDES card regarding the level of damage and their extension on the surface of the buildings. In particular we used a sample of 13442 masonry buildings located in an area characterized by a Macroseismic Intensity equal to 8 [1]. The aim is to ensure the coherence between the clusters and its hierarchy identified in the data of damage detected and in the data of the ED elaborated.

  12. Data Clustering Analysis Based on Wavelet Feature Extraction

    Institute of Scientific and Technical Information of China (English)

    QIANYuntao; TANGYuanyan

    2003-01-01

    A novel wavelet-based data clustering method is presented in this paper, which includes wavelet feature extraction and cluster growing algorithm. Wavelet transform can provide rich and diversified information for representing the global and local inherent structures of dataset. therefore, it is a very powerful tool for clustering feature extraction. As an unsupervised classification, the target of clustering analysis is dependent on the specific clustering criteria. Several criteria that should be con-sidered for general-purpose clustering algorithm are pro-posed. And the cluster growing algorithm is also con-structed to connect clustering criteria with wavelet fea-tures. Compared with other popular clustering methods,our clustering approach provides multi-resolution cluster-ing results,needs few prior parameters, correctly deals with irregularly shaped clusters, and is insensitive to noises and outliers. As this wavelet-based clustering method isaimed at solving two-dimensional data clustering prob-lem, for high-dimensional datasets, self-organizing mapand U-matrlx method are applied to transform them intotwo-dimensional Euclidean space, so that high-dimensional data clustering analysis,Results on some sim-ulated data and standard test data are reported to illus-trate the power of our method.

  13. Constructing storyboards based on hierarchical clustering analysis

    Science.gov (United States)

    Hasebe, Satoshi; Sami, Mustafa M.; Muramatsu, Shogo; Kikuchi, Hisakazu

    2005-07-01

    There are growing needs for quick preview of video contents for the purpose of improving accessibility of video archives as well as reducing network traffics. In this paper, a storyboard that contains a user-specified number of keyframes is produced from a given video sequence. It is based on hierarchical cluster analysis of feature vectors that are derived from wavelet coefficients of video frames. Consistent use of extracted feature vectors is the key to avoid a repetition of computationally-intensive parsing of the same video sequence. Experimental results suggest that a significant reduction in computational time is gained by this strategy.

  14. A PAC-Bayesian Analysis of Graph Clustering and Pairwise Clustering

    CERN Document Server

    Seldin, Yevgeny

    2010-01-01

    We formulate weighted graph clustering as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph clustering with other possible ways to model the graph. We adapt the PAC-Bayesian analysis of co-clustering (Seldin and Tishby, 2008; Seldin, 2009) to derive a PAC-Bayesian generalization bound for graph clustering. The bound shows that graph clustering should optimize a trade-off between empirical data fit and the mutual information that clusters preserve on the graph nodes. A similar trade-off derived from information-theoretic considerations was already shown to produce state-of-the-art results in practice (Slonim et al., 2005; Yom-Tov and Slonim, 2009). This paper supports the empirical evidence by providing a better theoretical foundation, suggesting formal generalization guarantees, and offering...

  15. Uplink multi-cluster scheduling with MU-MIMO for LTE-advanced with carrier aggregation

    DEFF Research Database (Denmark)

    Wang, Hua; Nguyen, Hung Tuan; Rosa, Claudio;

    2012-01-01

    -Advanced requirements and are being considered as part of LTE Release 10. In this paper, some of the physical layer enhancement techniques for LTE-Advanced have been studied including carrier aggregation (CA), uplink multi-cluster scheduling, and uplink multi-user multiple-input multiple-output (MU-MIMO) transmission......LTE-Advanced is the evolutionary path from LTE Release 8. It is designed to significantly enhance the performance of LTE Release 8 in terms of higher peak data rates, improved system capacity and coverage, and lower latency. These enhancements allow LTE-Advanced to meet or exceed the IMT...

  16. Advancing the Orang Asli through Malaysia's Clusters of Excellence Policy

    Directory of Open Access Journals (Sweden)

    Mohd Asri Mohd Noor

    2012-10-01

    Full Text Available Since gaining independence in 1957, the government of Malaysia has introduced various programmes to improve the quality of life of the Orang Asli (aboriginal people. The Ministry of Education, for example, is committed in providing education for all including the children of Orang Asli. However, whilst the number of Orang Asli children enrolled in primary and secondary schools has increased significantly over the last decade, the dropout rate among them is still high. This has been attributed to factors such as culture, school location, poverty, pedagogy and many more. The discussion in this article is drawn upon findings from fieldwork study at an Orang Asli village in Johor, Malaysia. This article discusses efforts in raising educational attainment of the Orang Asli through the implementation of the Clusters of Excellence Policy. In so doing it highlights the achievement of the policy and issues surrounding its implementation at the site.

  17. Advances in powder diffraction analysis

    Energy Technology Data Exchange (ETDEWEB)

    Louer, D. [Lab. de Chimie du Solide et Inorganique Moleculaire, Rennes (France). Groupe de Cristallochimie

    1998-11-01

    Powder diffraction offers a wide spectrum of applications to solid-state scientists. The method traditionally used for phase analysis and the study of structural imperfections has benefited, in the last twenty years, from great advances in the instrumentation and computer-based approaches for pattern indexing and modelling. The factors at the origin of the metamorphosis of the method are presented. The major modern applications reported include quantitative analysis and the extraction of three-dimensional structural and microstructural properties. The use of pattern-fitting techniques for the characterization of the microstructure is discussed through applications to nanocrystalline materials. Remarkable results achieved in the solution of crystal structures are presented, as well as the impact in solid-state chemistry of powder crystallography, particularly for elucidating the crystal chemistry of families of compounds for which only powders are available. New strategies for solving the phase problem have been introduced and new classes of solids are being studied, such as drugs, coordination and organic compounds. (orig.) 100 refs.

  18. Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

    CERN Document Server

    Emmons, Scott; Gallant, Mike; Börner, Katy

    2016-01-01

    Notions of community quality underlie network clustering. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms -- Blondel, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes. We find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 o...

  19. Cluster analysis of word frequency dynamics

    Science.gov (United States)

    Maslennikova, Yu S.; Bochkarev, V. V.; Belashova, I. A.

    2015-01-01

    This paper describes the analysis and modelling of word usage frequency time series. During one of previous studies, an assumption was put forward that all word usage frequencies have uniform dynamics approaching the shape of a Gaussian function. This assumption can be checked using the frequency dictionaries of the Google Books Ngram database. This database includes 5.2 million books published between 1500 and 2008. The corpus contains over 500 billion words in American English, British English, French, German, Spanish, Russian, Hebrew, and Chinese. We clustered time series of word usage frequencies using a Kohonen neural network. The similarity between input vectors was estimated using several algorithms. As a result of the neural network training procedure, more than ten different forms of time series were found. They describe the dynamics of word usage frequencies from birth to death of individual words. Different groups of word forms were found to have different dynamics of word usage frequency variations.

  20. Binary Black Hole Mergers from Globular Clusters: Implications for Advanced LIGO.

    Science.gov (United States)

    Rodriguez, Carl L; Morscher, Meagan; Pattabiraman, Bharath; Chatterjee, Sourav; Haster, Carl-Johan; Rasio, Frederic A

    2015-07-31

    The predicted rate of binary black hole mergers from galactic fields can vary over several orders of magnitude and is extremely sensitive to the assumptions of stellar evolution. But in dense stellar environments such as globular clusters, binary black holes form by well-understood gravitational interactions. In this Letter, we study the formation of black hole binaries in an extensive collection of realistic globular cluster models. By comparing these models to observed Milky Way and extragalactic globular clusters, we find that the mergers of dynamically formed binaries could be detected at a rate of ∼100 per year, potentially dominating the binary black hole merger rate. We also find that a majority of cluster-formed binaries are more massive than their field-formed counterparts, suggesting that Advanced LIGO could identify certain binaries as originating from dense stellar environments.

  1. Somatotyping using 3D anthropometry: a cluster analysis.

    Science.gov (United States)

    Olds, Tim; Daniell, Nathan; Petkov, John; David Stewart, Arthur

    2013-01-01

    Somatotyping is the quantification of human body shape, independent of body size. Hitherto, somatotyping (including the most popular method, the Heath-Carter system) has been based on subjective visual ratings, sometimes supported by surface anthropometry. This study used data derived from three-dimensional (3D) whole-body scans as inputs for cluster analysis to objectively derive clusters of similar body shapes. Twenty-nine dimensions normalised for body size were measured on a purposive sample of 301 adults aged 17-56 years who had been scanned using a Vitus Smart laser scanner. K-means Cluster Analysis with v-fold cross-validation was used to determine shape clusters. Three male and three female clusters emerged, and were visualised using those scans closest to the cluster centroid and a caricature defined by doubling the difference between the average scan and the cluster centroid. The male clusters were decidedly endomorphic (high fatness), ectomorphic (high linearity), and endo-mesomorphic (a mixture of fatness and muscularity). The female clusters were clearly endomorphic, ectomorphic, and the ecto-mesomorphic (a mixture of linearity and muscularity). An objective shape quantification procedure combining 3D scanning and cluster analysis yielded shape clusters strikingly similar to traditional somatotyping.

  2. A hybrid monkey search algorithm for clustering analysis.

    Science.gov (United States)

    Chen, Xin; Zhou, Yongquan; Luo, Qifang

    2014-01-01

    Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

  3. A Hybrid Monkey Search Algorithm for Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Xin Chen

    2014-01-01

    Full Text Available Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.

  4. Advanced Multi-Component Defect Cluster Oxide Doped Zirconia-Yttria Thermal Barrier Coatings

    Science.gov (United States)

    Zhu, Dongming; Miller, Robert A.

    2003-01-01

    The advantages of using ceramic thermal barrier coatings in gas turbine engine hot sections include increased fuel efficiency and improved engine reliability. However, current thermal barrier coatings will not have the low thermal conductivity and necessary sintering resistance under higher operating temperatures and thermal gradients required by future advanced ultra efficient and low emission aircraft engines. In this paper, a novel oxide defect cluster design approach is described for achieving low thermal conductivity and excellent thermal stability of the thermal barrier coating systems. This approach utilizes multi-component rare earth and other metal cluster oxide dopants that are incorporated in the zirconia-yttna based systems, thus significantly reducing coating thermal conductivity and sintering resistance by effectively promoting the formation of thermodynamically stable, essentially immobile defect clusters and/or nanoscale phases. The performance of selected plasma-sprayed cluster oxide thermal barrier coating systems has been evaluated. The advanced multi-component thermal barrier coating systems were found to have significantly lower initial and long-term thermal conductivities, and better high temperature stability. The effect of oxide cluster dopants on coating thermal conductivity, sintering resistance, oxide grain growth behavior and durability will be discussed.

  5. Instantaneous normal mode analysis of melting of finite dust clusters.

    Science.gov (United States)

    Melzer, André; Schella, André; Schablinski, Jan; Block, Dietmar; Piel, Alexander

    2012-06-01

    The experimental melting transition of finite two-dimensional dust clusters in a dusty plasma is analyzed using the method of instantaneous normal modes. In the experiment, dust clusters are heated in a thermodynamic equilibrium from a solid to a liquid state using a four-axis laser manipulation system. The fluid properties of the dust cluster, such as the diffusion constant, are measured from the instantaneous normal mode analysis. Thereby, the phase transition of these finite clusters is approached from the liquid phase. From the diffusion constants, unique melting temperatures have been assigned to dust clusters of various sizes that very well reflect their dynamical stability properties.

  6. Smartness and Italian Cities. A Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Flavio Boscacci

    2014-05-01

    Full Text Available Smart cities have been recently recognized as the most pleasing and attractive places to live in; due to this, both scholars and policy-makers pay close attention to this topic. Specifically, urban “smartness” has been identified by plenty of characteristics that can be grouped into six dimensions (Giffinger et al. 2007: smart Economy (competitiveness, smart People (social and human capital, smart Governance (participation, smart Mobility (both ICTs and transport, smart Environment (natural resources, and smart Living (quality of life. According to this analytical framework, in the present paper the relation between urban attractiveness and the “smart” characteristics has been investigated in the 103 Italian NUTS3 province capitals in the year 2011. To this aim, a descriptive statistics has been followed by a regression analysis (OLS, where the dependent variable measuring the urban attractiveness has been proxied by housing market prices. Besides, a Cluster Analysis (CA has been developed in order to find differences and commonalities among the province capitals.The OLS results indicate that living, people and economy are the key drivers for achieving a better urban attractiveness. Environment, instead, keeps on playing a minor role. Besides, the CA groups the province capitals a

  7. Advanced Automotive Fuels Research, Development, and Commercialization Cluster (OH)

    Energy Technology Data Exchange (ETDEWEB)

    Linkous, Clovis; Hripko, Michael; Abraham, Martin; Balendiran, Ganesaratnam; Hunter, Allen; Lovelace-Cameron, Sherri; Mette, Howard; Price, Douglas; Walker, Gary; Wang, Ruigang

    2013-08-31

    Technical aspects of producing alternative fuels that may eventually supplement or replace conventional the petroleum-derived fuels that are presently used in vehicular transportation have been investigated. The work was centered around three projects: 1) deriving butanol as a fuel additive from bacterial action on sugars produced from decomposition of aqueous suspensions of wood cellulose under elevated temperature and pressure; 2) using highly ordered, openly structured molecules known as metal-organic framework (MOF) compounds as adsorbents for gas separations in fuel processing operations; and 3) developing a photocatalytic membrane for solar-driven water decomposition to generate pure hydrogen fuel. Several departments within the STEM College at YSU contributed to the effort: Chemistry, Biology, and Chemical Engineering. In the butanol project, sawdust was blended with water at variable pH and temperature (150 – 250{degrees}C), and heated inside a pressure vessel for specified periods of time. Analysis of the extracts showed a wide variety of compounds, including simple sugars that bacteria are known to thrive upon. Samples of the cellulose hydrolysate were fed to colonies of Clostridium beijerinckii, which are known to convert sugars to a mixture of compounds, principally butanol. While the bacteria were active toward additions of pure sugar solutions, the cellulose extract appeared to inhibit butanol production, and furthermore encouraged the Clostridium to become dormant. Proteomic analysis showed that the bacteria had changed their genetic code to where it was becoming sporulated, i.e., the bacteria were trying to go dormant. This finding may be an opportunity, as it may be possible to genetically engineer bacteria that resist the butanol-driven triggering mechanism to stop further fuel production. Another way of handling the cellulosic hydrolysates was to simply add the enzymes responsible for butanol synthesis to the hydrolytic extract ex-vivo. These

  8. Using Cluster Analysis for Data Mining in Educational Technology Research

    Science.gov (United States)

    Antonenko, Pavlo D.; Toy, Serkan; Niederhauser, Dale S.

    2012-01-01

    Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through…

  9. A Survey of Popular R Packages for Cluster Analysis

    Science.gov (United States)

    Flynt, Abby; Dean, Nema

    2016-01-01

    Cluster analysis is a set of statistical methods for discovering new group/class structure when exploring data sets. This article reviews the following popular libraries/commands in the R software language for applying different types of cluster analysis: from the stats library, the kmeans, and hclust functions; the mclust library; the poLCA…

  10. ADVANCED POWER SYSTEMS ANALYSIS TOOLS

    Energy Technology Data Exchange (ETDEWEB)

    Robert R. Jensen; Steven A. Benson; Jason D. Laumb

    2001-08-31

    The use of Energy and Environmental Research Center (EERC) modeling tools and improved analytical methods has provided key information in optimizing advanced power system design and operating conditions for efficiency, producing minimal air pollutant emissions and utilizing a wide range of fossil fuel properties. This project was divided into four tasks: the demonstration of the ash transformation model, upgrading spreadsheet tools, enhancements to analytical capabilities using the scanning electron microscopy (SEM), and improvements to the slag viscosity model. The ash transformation model, Atran, was used to predict the size and composition of ash particles, which has a major impact on the fate of the combustion system. To optimize Atran key factors such as mineral fragmentation and coalescence, the heterogeneous and homogeneous interaction of the organically associated elements must be considered as they are applied to the operating conditions. The resulting model's ash composition compares favorably to measured results. Enhancements to existing EERC spreadsheet application included upgrading interactive spreadsheets to calculate the thermodynamic properties for fuels, reactants, products, and steam with Newton Raphson algorithms to perform calculations on mass, energy, and elemental balances, isentropic expansion of steam, and gasifier equilibrium conditions. Derivative calculations can be performed to estimate fuel heating values, adiabatic flame temperatures, emission factors, comparative fuel costs, and per-unit carbon taxes from fuel analyses. Using state-of-the-art computer-controlled scanning electron microscopes and associated microanalysis systems, a method to determine viscosity using the incorporation of grey-scale binning acquired by the SEM image was developed. The image analysis capabilities of a backscattered electron image can be subdivided into various grey-scale ranges that can be analyzed separately. Since the grey scale's intensity

  11. PERFORMANCE ANALYSIS OF CLUSTERED RADIO INTERFEROMETRIC CALIBRATION

    NARCIS (Netherlands)

    Kazemi, S.; Yatawatta, S.; Zaroubi, S.

    2012-01-01

    Subtraction of compact, bright sources is essential to produce high quality images in radio astronomy. It is recently proposed that 'clustered' calibration can perform better in subtracting fainter background sources. This is due to the fact that the effective power of a source cluster is greater th

  12. Investigating Subtypes of Child Development: A Comparison of Cluster Analysis and Latent Class Cluster Analysis in Typology Creation

    Science.gov (United States)

    DiStefano, Christine; Kamphaus, R. W.

    2006-01-01

    Two classification methods, latent class cluster analysis and cluster analysis, are used to identify groups of child behavioral adjustment underlying a sample of elementary school children aged 6 to 11 years. Behavioral rating information across 14 subscales was obtained from classroom teachers and used as input for analyses. Both the procedures…

  13. The smart cluster method - Adaptive earthquake cluster identification and analysis in strong seismic regions

    Science.gov (United States)

    Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

    2017-03-01

    Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

  14. Toward optimal cluster power spectrum analysis

    CERN Document Server

    Smith, Robert E

    2014-01-01

    The power spectrum of galaxy clusters is an important probe of the cosmological model. In this paper we determine the optimal weighting scheme for maximizing the signal-to-noise ratio for such measurements. We find a closed form analytic expression for the optimal weights. Our expression takes into account: cluster mass, finite survey volume effects, survey masking, and a flux limit. The implementation of this weighting scheme requires knowledge of the measured cluster masses, and analytic models for the bias and space-density of clusters as a function of mass and redshift. Recent studies have suggested that the optimal method for reconstruction of the matter density field from a set of clusters is mass-weighting (Seljak et al 2009, Hamaus et al 2010, Cai et al 2011). We compare our optimal weighting scheme with this approach and also with the original power spectrum scheme of Feldman et al (1994). We show that our optimal weighting scheme outperforms these approaches for both volume- and flux-limited cluster...

  15. Intelligent Hybrid Cluster Based Classification Algorithm for Social Network Analysis

    Directory of Open Access Journals (Sweden)

    S. Muthurajkumar

    2014-05-01

    Full Text Available In this paper, we propose an hybrid clustering based classification algorithm based on mean approach to effectively classify to mine the ordered sequences (paths from weblog data in order to perform social network analysis. In the system proposed in this work for social pattern analysis, the sequences of human activities are typically analyzed by switching behaviors, which are likely to produce overlapping clusters. In this proposed system, a robust Modified Boosting algorithm is proposed to hybrid clustering based classification for clustering the data. This work is useful to provide connection between the aggregated features from the network data and traditional indices used in social network analysis. Experimental results show that the proposed algorithm improves the decision results from data clustering when combined with the proposed classification algorithm and hence it is proved that of provides better classification accuracy when tested with Weblog dataset. In addition, this algorithm improves the predictive performance especially for multiclass datasets which can increases the accuracy.

  16. Bayesian model-based cluster analysis for predicting macrofaunal communities

    NARCIS (Netherlands)

    Braak, ter C.J.F.; Hoijtink, H.; Akkermans, W.; Verdonschot, P.F.M.

    2003-01-01

    To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster ana

  17. Hierarchical Cluster Analysis – Various Approaches to Data Preparation

    Directory of Open Access Journals (Sweden)

    Z. Pacáková

    2013-09-01

    Full Text Available The article deals with two various approaches to data preparation to avoid multicollinearity. The aim of the article is to find similarities among the e-communication level of EU states using hierarchical cluster analysis. The original set of fourteen indicators was first reduced on the basis of correlation analysis while in case of high correlation indicator of higher variability was included in further analysis. Secondly the data were transformed using principal component analysis while the principal components are poorly correlated. For further analysis five principal components explaining about 92% of variance were selected. Hierarchical cluster analysis was performed both based on the reduced data set and the principal component scores. Both times three clusters were assumed following Pseudo t-Squared and Pseudo F Statistic, but the final clusters were not identical. An important characteristic to compare the two results found was to look at the proportion of variance accounted for by the clusters which was about ten percent higher for the principal component scores (57.8% compared to 47%. Therefore it can be stated, that in case of using principal component scores as an input variables for cluster analysis with explained proportion high enough (about 92% for in our analysis, the loss of information is lower compared to data reduction on the basis of correlation analysis.

  18. Advancing Water and Water-Energy-Food Cluster Activities within Future Earth

    Science.gov (United States)

    Lawford, R. G.; Bhaduri, A.; Pahl-Wostl, C.

    2014-12-01

    In building its emerging program, Future Earth has encouraged former Earth System Science Partnership (ESSP) projects to redefine their objectives, priorities and problem approaches so they are aligned with those of Future Earth. These new projects will be characterized by more integrated applications of natural and social sciences as well as dialogue and science integrated across disciplinary boundaries to address a wide range of environmental and social issues. The Global Water System Project (GWSP) has had a heritage of integrating natural and social sciences, and recently started to also look at issues within the Water-Energy-Food (WEF) cluster using similar integrated approaches. As part of the growth of the scientific elements of this cluster, GWSP has approached Future Earth opportunities by addressing the sustainability for Water, Energy, and Food through integrated water information and improved governance.In this presentation the approaches being considered for promoting integration in both water and the WEF cluster will be discussed. In particular, potential contributions of Future Earth to research related to the use and management of water and to issues and science underpinning the W-E-F nexus deliberations will be identified. In both cases the increasing ability to utilize Earth observations and big data will advance this research agenda. In addition, the better understanding of the implications of governance structures in addressing these issues and the options for harmonizing the use of scientific knowledge and technological advances will be explored. For example, insights gained from water management studies undertaken within the GWSP are helping to focus plans for a "sustainable water futures" project and a WEF cluster within Future Earth. The potential role of the Sustainable Development Goals in bringing together the monitoring and science capabilities, and understanding of governance approaches, will be discussed as a framework for facilitating

  19. [Advanced data analysis and visualization for clinical laboratory].

    Science.gov (United States)

    Inada, Masanori; Yoneyama, Akiko

    2011-01-01

    This paper describes visualization techniques that help identify hidden structures in clinical laboratory data. The visualization of data is helpful for a rapid and better understanding of the characteristics of data sets. Various charts help the user identify trends in data. Scatter plots help prevent misinterpretations due to invalid data by identifying outliers. The representation of experimental data in figures is always useful for communicating results to others. Currently, flexible methods such as smoothing methods and latent structure analysis are available owing to the presence of advanced hardware and software. Principle component analysis, which is a well-known technique used to reduce multidimensional data sets, can be carried out on a personal computer. These methods could lead to advanced visualization with regard to exploratory data analysis. In this paper, we present 3 examples in order to introduce advanced data analysis. In the first example, a smoothing spline was fitted to a time-series from the control chart which is not in a state of statistical control. The trend line was clearly extracted from the daily measurements of the control samples. In the second example, principal component analysis was used to identify a new diagnostic indicator for Graves' disease. The multi-dimensional data obtained from patients were reduced to lower dimensions, and the principle components thus obtained summarized the variation in the data set. In the final example, a latent structure analysis for a Gaussian mixture model was used to draw complex density functions suitable for actual laboratory data. As a result, 5 clusters were extracted. The mixed density function of these clusters represented the data distribution graphically. The methods used in the above examples make the creation of complicated models for clinical laboratories more simple and flexible.

  20. Entropic Approach to Multiscale Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Antonio Insolia

    2012-05-01

    Full Text Available Recently, a novel method has been introduced to estimate the statistical significance of clustering in the direction distribution of objects. The method involves a multiscale procedure, based on the Kullback–Leibler divergence and the Gumbel statistics of extreme values, providing high discrimination power, even in presence of strong background isotropic contamination. It is shown that the method is: (i semi-analytical, drastically reducing computation time; (ii very sensitive to small, medium and large scale clustering; (iii not biased against the null hypothesis. Applications to the physics of ultra-high energy cosmic rays, as a cosmological probe, are presented and discussed.

  1. Advances in social media analysis

    CERN Document Server

    Cocea, Mihaela; Wiratunga, Nirmalie; Goker, Ayse

    2015-01-01

    This volume presents a collection of carefully selected contributions in the area of social media analysis. Each chapter opens up a number of research directions that have the potential to be taken on further in this rapidly growing area of research. The chapters are diverse enough to serve a number of directions of research with Sentiment Analysis as the dominant topic in the book. The authors have provided a broad range of research achievements from multimodal sentiment identification to emotion detection in a Chinese microblogging website. The book will be useful to research students, academics and practitioners in the area of social media analysis.  .

  2. Failure and damage analysis of advanced materials

    CERN Document Server

    Sadowski, Tomasz

    2015-01-01

    The papers in this volume present basic concepts and new developments in failure and damage analysis with focus on advanced materials such as composites, laminates, sandwiches and foams, and also new metallic materials. Starting from some mathematical foundations (limit surfaces, symmetry considerations, invariants) new experimental results and their analysis are shown. Finally, new concepts for failure prediction and analysis will be introduced and discussed as well as new methods of failure and damage prediction for advanced metallic and non-metallic materials. Based on experimental results the traditional methods will be revised.

  3. Advanced analysis methods in particle physics

    Energy Technology Data Exchange (ETDEWEB)

    Bhat, Pushpalatha C.; /Fermilab

    2010-10-01

    Each generation of high energy physics experiments is grander in scale than the previous - more powerful, more complex and more demanding in terms of data handling and analysis. The spectacular performance of the Tevatron and the beginning of operations of the Large Hadron Collider, have placed us at the threshold of a new era in particle physics. The discovery of the Higgs boson or another agent of electroweak symmetry breaking and evidence of new physics may be just around the corner. The greatest challenge in these pursuits is to extract the extremely rare signals, if any, from huge backgrounds arising from known physics processes. The use of advanced analysis techniques is crucial in achieving this goal. In this review, I discuss the concepts of optimal analysis, some important advanced analysis methods and a few examples. The judicious use of these advanced methods should enable new discoveries and produce results with better precision, robustness and clarity.

  4. Advanced Analysis Methods in Particle Physics

    Energy Technology Data Exchange (ETDEWEB)

    Bhat, Pushpalatha C. [Fermilab

    1900-01-01

    Each generation of high energy physics experiments is grander in scale than the previous – more powerful, more complex and more demanding in terms of data handling and analysis. The spectacular performance of the Tevatron and the beginning of operations of the Large Hadron Collider, have placed us at the threshold of a new era in particle physics. The discovery of the Higgs boson or another agent of electroweak symmetry breaking and evidence of new physics may be just around the corner. The greatest challenge in these pursuits is to extract the extremely rare signals, if any, from huge backgrounds arising from known physics processes. The use of advanced analysis techniques is crucial in achieving this goal. In this review, I discuss the concepts of optimal analysis, some important advanced analysis methods and a few examples. The judicious use of these advanced methods should enable new discoveries and produce results with better precision, robustness and clarity.

  5. Advanced calculus a transition to analysis

    CERN Document Server

    Dence, Thomas P

    2010-01-01

    Designed for a one-semester advanced calculus course, Advanced Calculus explores the theory of calculus and highlights the connections between calculus and real analysis -- providing a mathematically sophisticated introduction to functional analytical concepts. The text is interesting to read and includes many illustrative worked-out examples and instructive exercises, and precise historical notes to aid in further exploration of calculus. Ancillary list: * Companion website, Ebook- http://www.elsevierdirect.com/product.jsp?isbn=9780123749550 * Student Solutions Manual- To come * Instructor

  6. Analysis and comparison of very large metagenomes with fast clustering and functional annotation

    Directory of Open Access Journals (Sweden)

    Li Weizhong

    2009-10-01

    Full Text Available Abstract Background The remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand. Results The new metagenomic data analysis method Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline (RAMMCAP was developed using an ultra-fast sequence clustering algorithm, fast protein family annotation tools, and a novel statistical metagenome comparison method that employs a unique graphic interface. RAMMCAP processes extremely large datasets with only moderate computational effort. It identifies raw read clusters and protein clusters that may include novel gene families, and compares metagenomes using clusters or functional annotations calculated by RAMMCAP. In this study, RAMMCAP was applied to the two largest available metagenomic collections, the "Global Ocean Sampling" and the "Metagenomic Profiling of Nine Biomes". Conclusion RAMMCAP is a very fast method that can cluster and annotate one million metagenomic reads in only hundreds of CPU hours. It is available from http://tools.camera.calit2.net/camera/rammcap/.

  7. Visual verification and analysis of cluster detection for molecular dynamics.

    Science.gov (United States)

    Grottel, Sebastian; Reina, Guido; Vrabec, Jadran; Ertl, Thomas

    2007-01-01

    A current research topic in molecular thermodynamics is the condensation of vapor to liquid and the investigation of this process at the molecular level. Condensation is found in many physical phenomena, e.g. the formation of atmospheric clouds or the processes inside steam turbines, where a detailed knowledge of the dynamics of condensation processes will help to optimize energy efficiency and avoid problems with droplets of macroscopic size. The key properties of these processes are the nucleation rate and the critical cluster size. For the calculation of these properties it is essential to make use of a meaningful definition of molecular clusters, which currently is a not completely resolved issue. In this paper a framework capable of interactively visualizing molecular datasets of such nucleation simulations is presented, with an emphasis on the detected molecular clusters. To check the quality of the results of the cluster detection, our framework introduces the concept of flow groups to highlight potential cluster evolution over time which is not detected by the employed algorithm. To confirm the findings of the visual analysis, we coupled the rendering view with a schematic view of the clusters' evolution. This allows to rapidly assess the quality of the molecular cluster detection algorithm and to identify locations in the simulation data in space as well as in time where the cluster detection fails. Thus, thermodynamics researchers can eliminate weaknesses in their cluster detection algorithms. Several examples for the effective and efficient usage of our tool are presented.

  8. Logistics Enterprise Evaluation Model Based On Fuzzy Clustering Analysis

    Science.gov (United States)

    Fu, Pei-hua; Yin, Hong-bo

    In this thesis, we introduced an evaluation model based on fuzzy cluster algorithm of logistics enterprises. First of all,we present the evaluation index system which contains basic information, management level, technical strength, transport capacity,informatization level, market competition and customer service. We decided the index weight according to the grades, and evaluated integrate ability of the logistics enterprises using fuzzy cluster analysis method. In this thesis, we introduced the system evaluation module and cluster analysis module in detail and described how we achieved these two modules. At last, we gave the result of the system.

  9. Cancer incidence in men: a cluster analysis of spatial patterns

    Directory of Open Access Journals (Sweden)

    D'Alò Daniela

    2008-11-01

    Full Text Available Abstract Background Spatial clustering of different diseases has received much less attention than single disease mapping. Besides chance or artifact, clustering of different cancers in a given area may depend on exposure to a shared risk factor or to multiple correlated factors (e.g. cigarette smoking and obesity in a deprived area. Models developed so far to investigate co-occurrence of diseases are not well-suited for analyzing many cancers simultaneously. In this paper we propose a simple two-step exploratory method for screening clusters of different cancers in a population. Methods Cancer incidence data were derived from the regional cancer registry of Umbria, Italy. A cluster analysis was performed on smoothed and non-smoothed standardized incidence ratios (SIRs of the 13 most frequent cancers in males. The Besag, York and Mollie model (BYM and Poisson kriging were used to produce smoothed SIRs. Results Cluster analysis on non-smoothed SIRs was poorly informative in terms of clustering of different cancers, as only larynx and oral cavity were grouped, and of characteristic patterns of cancer incidence in specific geographical areas. On the other hand BYM and Poisson kriging gave similar results, showing cancers of the oral cavity, larynx, esophagus, stomach and liver formed a main cluster. Lung and urinary bladder cancers clustered together but not with the cancers mentioned above. Both methods, particularly the BYM model, identified distinct geographic clusters of adjacent areas. Conclusion As in single disease mapping, non-smoothed SIRs do not provide reliable estimates of cancer risks because of small area variability. The BYM model produces smooth risk surfaces which, when entered into a cluster analysis, identify well-defined geographical clusters of adjacent areas. It probably enhances or amplifies the signal arising from exposure of more areas (statistical units to shared risk factors that are associated with different cancers. In

  10. CLUSTERING ANALYSIS OF DEBRIS-FLOW STREAMS

    Institute of Scientific and Technical Information of China (English)

    Yuan-Fan TSAI; Huai-Kuang TSAI; Cheng-Yan KAO

    2004-01-01

    The Chi-Chi earthquake in 1999 caused disastrous landslides, which triggered numerous debris flows and killed hundreds of people. A critical rainfall intensity line for each debris-flow stream is studied to prevent such a disaster. However, setting rainfall lines from incomplete data is difficult, so this study considered eight critical factors to group streams, such that streams within a cluster have similar rainfall lines. A genetic algorithm is applied to group 377 debris-flow streams selected from the center of an area affected by the Chi-Chi earthquake. These streams are grouped into seven clusters with different characteristics. The results reveal that the proposed method effectively groups debris-flow streams.

  11. Cluster Analysis of Gene Expression Data

    CERN Document Server

    Domany, E

    2002-01-01

    The expression levels of many thousands of genes can be measured simultaneously by DNA microarrays (chips). This novel experimental tool has revolutionized research in molecular biology and generated considerable excitement. A typical experiment uses a few tens of such chips, each dedicated to a single sample - such as tissue extracted from a particular tumor. The results of such an experiment contain several hundred thousand numbers, that come in the form of a table, of several thousand rows (one for each gene) and 50 - 100 columns (one for each sample). We developed a clustering methodology to mine such data. In this review I provide a very basic introduction to the subject, aimed at a physics audience with no prior knowledge of either gene expression or clustering methods. I explain what genes are, what is gene expression and how it is measured by DNA chips. Next I explain what is meant by "clustering" and how we analyze the massive amounts of data from such experiments, and present results obtained from a...

  12. Lecture notes for Advanced Time Series Analysis

    DEFF Research Database (Denmark)

    Madsen, Henrik; Holst, Jan

    1997-01-01

    A first version of this notes was used at the lectures in Grenoble, and they are now extended and improved (together with Jan Holst), and used in Ph.D. courses on Advanced Time Series Analysis at IMM and at the Department of Mathematical Statistics, University of Lund, 1994, 1997, ......A first version of this notes was used at the lectures in Grenoble, and they are now extended and improved (together with Jan Holst), and used in Ph.D. courses on Advanced Time Series Analysis at IMM and at the Department of Mathematical Statistics, University of Lund, 1994, 1997, ...

  13. Advanced microtechnologies for cytogenetic analysis

    DEFF Research Database (Denmark)

    Kwasny, Dorota; Vedarethinam, Indumathi; Shah, Pranjul Jaykumar;

    2012-01-01

    Cytogenetic and molecular cytogenetic analyses, which aim to detect chromosome abnormalities, are routinely performed in cytogenetic laboratories all over the world. Traditional cytogenetic studies are performed by analyzing the banding pattern of chromosomes, and are complemented by molecular...... cytogenetic techniques such as fluorescent in situ hybridization (FISH). To improve FISH application in cytogenetic analysis the issues with long experimental time, high volumes of expensive reagents and requirement for trained technicians need to be addressed. The protocol has recently evolved towards...... to introduce automation in the cytogenetic laboratories at a microscale. We have developed membrane based micro perfusion systems capable of expansion of lymphocytes in a shorter time and at a smaller scale. The simulated and experimental results show very efficient exchange of the growth medium...

  14. A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis

    Directory of Open Access Journals (Sweden)

    Shaoning Li

    2017-01-01

    Full Text Available In the fields of geographic information systems (GIS and remote sensing (RS, the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC, is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters” if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.

  15. Cluster analysis of WIBS single particle bioaerosol data

    Directory of Open Access Journals (Sweden)

    N. H. Robinson

    2012-09-01

    Full Text Available Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial datasets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Waveband Integrated Bioaerosol Sensor (WIBS. The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL before being applied to two separate contemporaneous ambient WIBS datasets recorded in a forest site in Colorado, USA as part of the BEACHON-RoMBAS project. Cluster analysis results between both datasets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity to represent: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long term online PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics is improved.

  16. Cluster analysis of WIBS single particle bioaerosol data

    Science.gov (United States)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2012-09-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial datasets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Waveband Integrated Bioaerosol Sensor (WIBS). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS datasets recorded in a forest site in Colorado, USA as part of the BEACHON-RoMBAS project. Cluster analysis results between both datasets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long term online PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics is improved.

  17. Cluster analysis of clinical data identifies fibromyalgia subgroups.

    Directory of Open Access Journals (Sweden)

    Elisa Docampo

    Full Text Available INTRODUCTION: Fibromyalgia (FM is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. MATERIAL AND METHODS: 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. RESULTS: VARIABLES CLUSTERED INTO THREE INDEPENDENT DIMENSIONS: "symptomatology", "comorbidities" and "clinical scales". Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1, high symptomatology and comorbidities (Cluster 2, and high symptomatology but low comorbidities (Cluster 3, showing differences in measures of disease severity. CONCLUSIONS: We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment.

  18. Cluster analysis of Southeastern U.S. climate stations

    Science.gov (United States)

    Stooksbury, D. E.; Michaels, P. J.

    1991-09-01

    A two-step cluster analysis of 449 Southeastern climate stations is used to objectively determine general climate clusters (groups of climate stations) for eight southeastern states. The purpose is objectively to define regions of climatic homogeneity that should perform more robustly in subsequent climatic impact models. This type of analysis has been successfully used in many related climate research problems including the determination of corn/climate districts in Iowa (Ortiz-Valdez, 1985) and the classification of synoptic climate types (Davis, 1988). These general climate clusters may be more appropriate for climate research than the standard climate divisions (CD) groupings of climate stations, which are modifications of the agro-economic United States Department of Agriculture crop reporting districts. Unlike the CD's, these objectively determined climate clusters are not restricted by state borders and thus have reduced multicollinearity which makes them more appropriate for the study of the impact of climate and climatic change.

  19. Variable cluster analysis method for building neural network model

    Institute of Scientific and Technical Information of China (English)

    王海东; 刘元东

    2004-01-01

    To address the problems that input variables should be reduced as much as possible and explain output variables fully in building neural network model of complicated system, a variable selection method based on cluster analysis was investigated. Similarity coefficient which describes the mutual relation of variables was defined. The methods of the highest contribution rate, part replacing whole and variable replacement are put forwarded and deduced by information theory. The software of the neural network based on cluster analysis, which can provide many kinds of methods for defining variable similarity coefficient, clustering system variable and evaluating variable cluster, was developed and applied to build neural network forecast model of cement clinker quality. The results show that all the network scale, training time and prediction accuracy are perfect. The practical application demonstrates that the method of selecting variables for neural network is feasible and effective.

  20. Spatial Data Mining using Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Ch.N.Santhosh Kumar

    2012-09-01

    Full Text Available Data mining, which is refers to as Knowledge Discovery in Databases(KDD, means a process of nontrivialexaction of implicit, previously useful and unknown information such as knowledge rules, descriptions,regularities, and major trends from large databases. Data mining is evolved in a multidisciplinary field ,including database technology, machine learning, artificial intelligence, neural network, informationretrieval, and so on. In principle data mining should be applicable to the different kind of data and databasesused in many different applications, including relational databases, transactional databases, datawarehouses, object- oriented databases, and special application- oriented databases such as spatialdatabases, temporal databases, multimedia databases, and time- series databases. Spatial data mining, alsocalled spatial mining, is data mining as applied to the spatial data or spatial databases. Spatial data are thedata that have spatial or location component, and they show the information, which is more complex thanclassical data. A spatial database stores spatial data represents by spatial data types and spatialrelationships and among data. Spatial data mining encompasses various tasks. These include spatialclassification, spatial association rule mining, spatial clustering, characteristic rules, discriminant rules,trend detection. This paper presents how spatial data mining is achieved using clustering.

  1. Initial magnetization analysis of iron cluster assemblies

    Energy Technology Data Exchange (ETDEWEB)

    Michele, Oliver; Hesse, Juergen; Bremers, Heiko [Technische Universitaet Braunschweig, Institut fuer Metallphysik und Nukleare Festkoerperphysik, Mendelssohnstrasse 3, 38106 Braunschweig (Germany); Peng, Dong-Lian; Sumiyama, Kenji; Hihara, Takehiko; Yamamuro, Saeki [Department of Materials Science and Engineering, Nagoya Institute of Technology, Nagoya 466-8555 (Japan)

    2004-12-01

    Nearly monodispersed oxide-coated Fe cluster assemblies were prepared using a plasma-gas-condensation style cluster beam deposition apparatus (D. L. Peng et al. J. Appl. Phys. 92 3075 (2002)). The characterization of such assemblies is presented using SQUID magnetometry. The aim of this contribution is the interpretation of the initial magnetization curves instead of the usual presentation of hysteresis loops and coercivities. The description of the initial magnetization is based on a proposed vector model valid for Stoner-Wohlfarth particles. The model includes the particles' anisotropy and possible interactions regarding these influences as equivalent magnetic fields. The model is an extension of the one described by Michele et al. (J. Phys.: Condens. Matter 16 427 (2004)) regarding the fact that in a completely demagnetized state, in the sample consisting of a very large number of particles always equal anisotropy fields of opposite signs are present. We measured the initial magnetization curves for different temperatures and present the temperature dependence of the model's parameters. (Abstract Copyright [2004], Wiley Periodicals, Inc.)

  2. Advances in carbonate exploration and reservoir analysis

    Science.gov (United States)

    Garland, J.; Neilson, J.E.; Laubach, S.E.; Whidden, K.J.

    2012-01-01

    Carbonate reservoirs contain an increasingly important percentage of the world’s hydrocarbon reserves. This volume presents key recent advances in carbonate exploration and reservoir analysis. As well as a comprehensive overview of the trends in carbonate over the years, the volume focuses on four key areas:

  3. NATO Advanced Study Institute on Advances in Microlocal Analysis

    CERN Document Server

    1986-01-01

    The 1985 Castel vecchio-Pas coli NATO Advanced Study Institute is aimed to complete the trilogy with the two former institutes I organized : "Boundary Value Problem for Evolution Partial Differential Operators", Liege, 1976 and "Singularities in Boundary Value Problems", Maratea, 1980. It was indeed necessary to record the considerable progress realized in the field of the propagation of singularities of Schwartz Distri­ butions which led recently to the birth of a new branch of Mathema­ tical Analysis called Microlocal Analysis. Most of this theory was mainly built to be applied to distribution solutions of linear partial differential problems. A large part of this institute still went in this direction. But, on the other hand, it was also time to explore the new trend to use microlocal analysis In non linear differential problems. I hope that the Castelvecchio NATO ASI reached its purposes with the help of the more famous authorities in the field. The meeting was held in Tuscany (Italy) at Castelvecchio-P...

  4. Advancing Family Business Research Through Narrative Analysis

    DEFF Research Database (Denmark)

    Dawson, Alexandra; Hjorth, Daniel

    2012-01-01

    Despite advances in family business research, the field would benefit from greater methodological rigor. However, rigor does not mean convergence of methodologies. In this article, the authors adopt a novel approach, based on narrative analysis, to address the succession process in a family...... business. This interpretive perspective is appropriate for family business studies, which address multifaceted and complex social constructs that are performed by different actors in multiple contexts. The analysis highlights five key themes centering on leadership style and succession, trust...

  5. Multivariate analysis of the globular clusters in M87

    CERN Document Server

    Das, Sukanta; Davoust, Emmanuel

    2015-01-01

    An objective classification of 147 globular clusters in the inner region of the giant elliptical galaxy M87 is carried out with the help of two methods of multivariate analysis. First independent component analysis is used to determine a set of independent variables that are linear combinations of various observed parameters (mostly Lick indices) of the globular clusters. Next K-means cluster analysis is applied on the independent components, to find the optimum number of homogeneous groups having an underlying structure. The properties of the four groups of globular clusters thus uncovered are used to explain the formation mechanism of the host galaxy. It is suggested that M87 formed in two successive phases. First a monolithic collapse, which gave rise to an inner group of metal-rich clusters with little systematic rotation and an outer group of metal-poor clusters in eccentric orbits. In a second phase, the galaxy accreted low-mass satellites in a dissipationless fashion, from the gas of which the two othe...

  6. Identifying clinical course patterns in SMS data using cluster analysis

    DEFF Research Database (Denmark)

    Kent, Peter; Kongsted, Alice

    2012-01-01

    ABSTRACT: BACKGROUND: Recently, there has been interest in using the short message service (SMS or text messaging), to gather frequent information on the clinical course of individual patients. One possible role for identifying clinical course patterns is to assist in exploring clinically importa...... of cluster analysis. More research is needed, especially head-to-head studies, to identify which technique is best to use under what circumstances.......ABSTRACT: BACKGROUND: Recently, there has been interest in using the short message service (SMS or text messaging), to gather frequent information on the clinical course of individual patients. One possible role for identifying clinical course patterns is to assist in exploring clinically important...... by spline analysis. However, cluster analysis of SMS data in its original untransformed form may be simpler and offer other advantages. Therefore, the aim of this study was to determine whether cluster analysis could be used for identifying clinical course patterns distinct from the pattern of the whole...

  7. Advanced Interval Management: A Benefit Analysis

    Science.gov (United States)

    Timer, Sebastian; Peters, Mark

    2016-01-01

    This document is the final report for the NASA Langley Research Center (LaRC)- sponsored task order 'Possible Benefits for Advanced Interval Management Operations.' Under this research project, Architecture Technology Corporation performed an analysis to determine the maximum potential benefit to be gained if specific Advanced Interval Management (AIM) operations were implemented in the National Airspace System (NAS). The motivation for this research is to guide NASA decision-making on which Interval Management (IM) applications offer the most potential benefit and warrant further research.

  8. Bayesian Analysis of Two Stellar Populations in Galactic Globular Clusters III: Analysis of 30 Clusters

    CERN Document Server

    Wagner-Kaiser, R; Sarajedini, A; von Hippel, T; van Dyk, D A; Robinson, E; Stein, N; Jefferys, W H

    2016-01-01

    We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival ACS Treasury observations of 30 Galactic Globular Clusters to characterize two distinct stellar populations. A sophisticated Bayesian technique is employed to simultaneously sample the joint posterior distribution of age, distance, and extinction for each cluster, as well as unique helium values for two populations within each cluster and the relative proportion of those populations. We find the helium differences among the two populations in the clusters fall in the range of ~0.04 to 0.11. Because adequate models varying in CNO are not presently available, we view these spreads as upper limits and present them with statistical rather than observational uncertainties. Evidence supports previous studies suggesting an increase in helium content concurrent with increasing mass of the cluster and also find that the proportion of the first population of stars increases with mass as well. Our results are examined in the context of proposed g...

  9. Optoelectronic Devices Advanced Simulation and Analysis

    CERN Document Server

    Piprek, Joachim

    2005-01-01

    Optoelectronic devices transform electrical signals into optical signals and vice versa by utilizing the sophisticated interaction of electrons and light within micro- and nano-scale semiconductor structures. Advanced software tools for design and analysis of such devices have been developed in recent years. However, the large variety of materials, devices, physical mechanisms, and modeling approaches often makes it difficult to select appropriate theoretical models or software packages. This book presents a review of devices and advanced simulation approaches written by leading researchers and software developers. It is intended for scientists and device engineers in optoelectronics, who are interested in using advanced software tools. Each chapter includes the theoretical background as well as practical simulation results that help to better understand internal device physics. The software packages used in the book are available to the public, on a commercial or noncommercial basis, so that the interested r...

  10. Technology Clusters Exploration for Patent Portfolio through Patent Abstract Analysis

    Directory of Open Access Journals (Sweden)

    Gabjo Kim

    2016-12-01

    Full Text Available This study explores technology clusters through patent analysis. The aim of exploring technology clusters is to grasp competitors’ levels of sustainable research and development (R&D and establish a sustainable strategy for entering an industry. To achieve this, we first grouped the patent documents with similar technologies by applying affinity propagation (AP clustering, which is effective while grouping large amounts of data. Next, in order to define the technology clusters, we adopted the term frequency-inverse document frequency (TF-IDF weight, which lists the terms in order of importance. We collected the patent data of Korean electric car companies from the United States Patent and Trademark Office (USPTO to verify our proposed methodology. As a result, our proposed methodology presents more detailed information on the Korean electric car industry than previous studies.

  11. An Empirical Analysis of Rough Set Categorical Clustering Techniques

    Science.gov (United States)

    2017-01-01

    Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy. PMID:28068344

  12. Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means

    Directory of Open Access Journals (Sweden)

    Imianvan Anthony Agboizebeta

    2012-01-01

    Full Text Available Multiple sclerosis, often called MS, is a disease that affects the central nervous system (the brain and spinal cord. Myelin provides insulation for nerve cells improves the conduction of impulses along the nerves and is important for maintaining the health of the nerves. In multiple sclerosis, inflammation causes the myelin to disappear. Genetic factors, environmental issues and viral infection may also play a role in developing the disease. Ms is characterized by life threatening symptoms such as; loss of balance, hearing problem and depression. The application of Fuzzy Cluster Means (FCM or Fuzzy CMean analysis to the diagnosis of different forms of multiple sclerosis is the focal point of this paper. Application of cluster analysis involves a sequence of methodological and analytical decision steps that enhances the quality and meaning of the clusters produced. Uncertainties associated with analysis of multiple sclerosis test data are eliminated by the system

  13. Advanced Excel for scientific data analysis

    CERN Document Server

    De Levie, Robert

    2004-01-01

    Excel is by far the most widely distributed data analysis software but few users are aware of its full powers. Advanced Excel For Scientific Data Analysis takes off from where most books dealing with scientific applications of Excel end. It focuses on three areas-least squares, Fourier transformation, and digital simulation-and illustrates these with extensive examples, often taken from the literature. It also includes and describes a number of sample macros and functions to facilitate common data analysis tasks. These macros and functions are provided in uncompiled, computer-readable, easily

  14. Recent Advances in Morphological Cell Image Analysis

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

  15. Cluster Analysis of Metal Concentrations in River Kubanni Zaria, Nigeria

    Directory of Open Access Journals (Sweden)

    A.W. Butu

    2013-08-01

    Full Text Available The cluster analysis was used to assess the degree of association of the metal concentrations in river Kubanni Zaria, Nigeria. The main sources of data for the analysis were the sediment from four distinct locations along the long profile Kubanni River which were analyzed using Instrumental Nitrogen Activities Analysis (INAA techniques. The Nigerian Research Reactor-1(NIRR-1 which is Miniature Nitrogen Source Reactor (MNSR was used to analyze the data. The result of the laboratory analysis was subjected to cluster analysis. The analysis shows a stable clustering system where the metal concentrations in the four different locations were grouped into two main groups with one outlier. The level of concentration of elements that were sampled in the dry months were cluster in group I and those collected in the raining months were in group II. This strongly support that there is temporal variation in the levels of concentration of metal contaminants between wet and dry seasons in river Kubanni and also confirms the fact that the elements that were collected in the wet season are from the same source and those in the dry season are also from common source.

  16. System Level Analysis of LTE-Advanced

    DEFF Research Database (Denmark)

    Wang, Yuanye

    This PhD thesis focuses on system level analysis of Multi-Component Carrier (CC) management for Long Term Evolution (LTE)-Advanced. Cases where multiple CCs are aggregated to form a larger bandwidth are studied. The analysis is performed for both local area and wide area networks. In local area......, Time Division Duplexing (TDD) is chosen as the duplexing mode in this study. The performance with different network time synchronization levels is compared, and it is observed that achieving time synchronization significantly improves the uplink performance without penalizing much of the downlink.......e., some users can access all CCs (LTE-Advanced users), whereas some are restricted to operate within a single CC (release 8 users). First, load balancing across the multiple CCs is analyzed. Several known approaches are studied and the best one is identified. A cross-CC packet scheduler is afterwards...

  17. Examination of European Union economic cohesion: A cluster analysis approach

    Directory of Open Access Journals (Sweden)

    Jiri Mazurek

    2014-01-01

    Full Text Available In the past years majority of EU members experienced the highest economic decline in their modern history, but impacts of the global financial crisis were not distributed homogeneously across the continent. The aim of the paper is to examine a cohesion of European Union (plus Norway and Iceland in terms of an economic development of its members from the 1st of January 2008 to the 31st of December 2012. For the study five economic indicators were selected: GDP growth, unemployment, inflation, labour productivity and government debt. Annual data from Eurostat databases were averaged over the whole period and then used as an input for a cluster analysis. It was found that EU countries were divided into six different clusters. The most populated cluster with 14 countries covered Central and West Europe and reflected relative homogeneity of this part of Europe. Countries of Southern Europe (Greece, Portugal and Spain shared their own cluster of the most affected countries by the recent crisis as well as the Baltics and the Balkans states in another cluster. On the other hand Slovakia and Poland, only two countries that escaped a recession, were classified in their own cluster of the most successful countries

  18. Sun Protection Belief Clusters: Analysis of Amazon Mechanical Turk Data.

    Science.gov (United States)

    Santiago-Rivas, Marimer; Schnur, Julie B; Jandorf, Lina

    2016-12-01

    This study aimed (i) to determine whether people could be differentiated on the basis of their sun protection belief profiles and individual characteristics and (ii) explore the use of a crowdsourcing web service for the assessment of sun protection beliefs. A sample of 500 adults completed an online survey of sun protection belief items using Amazon Mechanical Turk. A two-phased cluster analysis (i.e., hierarchical and non-hierarchical K-means) was utilized to determine clusters of sun protection barriers and facilitators. Results yielded three distinct clusters of sun protection barriers and three distinct clusters of sun protection facilitators. Significant associations between gender, age, sun sensitivity, and cluster membership were identified. Results also showed an association between barrier and facilitator cluster membership. The results of this study provided a potential alternative approach to developing future sun protection promotion initiatives in the population. Findings add to our knowledge regarding individuals who support, oppose, or are ambivalent toward sun protection and inform intervention research by identifying distinct subtypes that may best benefit from (or have a higher need for) skin cancer prevention efforts.

  19. Bayesian analysis of two stellar populations in Galactic globular clusters- III. Analysis of 30 clusters

    Science.gov (United States)

    Wagner-Kaiser, R.; Stenning, D. C.; Sarajedini, A.; von Hippel, T.; van Dyk, D. A.; Robinson, E.; Stein, N.; Jefferys, W. H.

    2016-12-01

    We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival ACS Treasury observations of 30 Galactic globular clusters to characterize two distinct stellar populations. A sophisticated Bayesian technique is employed to simultaneously sample the joint posterior distribution of age, distance, and extinction for each cluster, as well as unique helium values for two populations within each cluster and the relative proportion of those populations. We find the helium differences among the two populations in the clusters fall in the range of ˜0.04 to 0.11. Because adequate models varying in carbon, nitrogen, and oxygen are not presently available, we view these spreads as upper limits and present them with statistical rather than observational uncertainties. Evidence supports previous studies suggesting an increase in helium content concurrent with increasing mass of the cluster and we also find that the proportion of the first population of stars increases with mass as well. Our results are examined in the context of proposed globular cluster formation scenarios. Additionally, we leverage our Bayesian technique to shed light on the inconsistencies between the theoretical models and the observed data.

  20. Cluster analysis of knowledge sources in standardized electrical engineering subfields

    Directory of Open Access Journals (Sweden)

    Blagojević Marija

    2016-01-01

    Full Text Available The paper presents a cluster analysis of innovation of knowledge sources based on the standards in the field of Electrical Engineering. Both local (SRPS and global (ISO knowledge sources have been analysed with the aim of innovating a Knowledge Base (KB. The results presented indicate a means/possibility of grouping the subfields within a cluster. They also point to a trend or intensity of knowledge source innovation for the purpose of innovating the KB that accompanies innovations. The study provides the possibility of predicting necessary financial resources in the forthcoming period by means of original mathematical relations. Furthermore, the cluster analysis facilitates the comparison of the innovation intensity in this and other (subfields. Future work relates to the monitoring of the knowledge source innovation by means of KB engineering and improvement of the methodology of prediction using neural networks.

  1. Cluster analysis of WIBS single-particle bioaerosol data

    Science.gov (United States)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2013-02-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

  2. Cluster analysis of WIBS single-particle bioaerosol data

    Directory of Open Access Journals (Sweden)

    N. H. Robinson

    2013-02-01

    Full Text Available Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs. The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

  3. Cluster Analysis of Clinical Data Identifies Fibromyalgia Subgroups

    Science.gov (United States)

    Docampo, Elisa; Collado, Antonio; Escaramís, Geòrgia; Carbonell, Jordi; Rivera, Javier; Vidal, Javier; Alegre, José

    2013-01-01

    Introduction Fibromyalgia (FM) is mainly characterized by widespread pain and multiple accompanying symptoms, which hinder FM assessment and management. In order to reduce FM heterogeneity we classified clinical data into simplified dimensions that were used to define FM subgroups. Material and Methods 48 variables were evaluated in 1,446 Spanish FM cases fulfilling 1990 ACR FM criteria. A partitioning analysis was performed to find groups of variables similar to each other. Similarities between variables were identified and the variables were grouped into dimensions. This was performed in a subset of 559 patients, and cross-validated in the remaining 887 patients. For each sample and dimension, a composite index was obtained based on the weights of the variables included in the dimension. Finally, a clustering procedure was applied to the indexes, resulting in FM subgroups. Results Variables clustered into three independent dimensions: “symptomatology”, “comorbidities” and “clinical scales”. Only the two first dimensions were considered for the construction of FM subgroups. Resulting scores classified FM samples into three subgroups: low symptomatology and comorbidities (Cluster 1), high symptomatology and comorbidities (Cluster 2), and high symptomatology but low comorbidities (Cluster 3), showing differences in measures of disease severity. Conclusions We have identified three subgroups of FM samples in a large cohort of FM by clustering clinical data. Our analysis stresses the importance of family and personal history of FM comorbidities. Also, the resulting patient clusters could indicate different forms of the disease, relevant to future research, and might have an impact on clinical assessment. PMID:24098674

  4. Probabilistic Durability Analysis in Advanced Engineering Design

    Directory of Open Access Journals (Sweden)

    A. Kudzys

    2000-01-01

    Full Text Available Expedience of probabilistic durability concepts and approaches in advanced engineering design of building materials, structural members and systems is considered. Target margin values of structural safety and serviceability indices are analyzed and their draft values are presented. Analytical methods of the cumulative coefficient of correlation and the limit transient action effect for calculation of reliability indices are given. Analysis can be used for probabilistic durability assessment of carrying and enclosure metal, reinforced concrete, wood, plastic, masonry both homogeneous and sandwich or composite structures and some kinds of equipments. Analysis models can be applied in other engineering fields.

  5. Advances in statistical models for data analysis

    CERN Document Server

    Minerva, Tommaso; Vichi, Maurizio

    2015-01-01

    This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.

  6. Transcriptional analysis of ESAT-6 cluster 3 in Mycobacterium smegmatis

    Directory of Open Access Journals (Sweden)

    Riccardi Giovanna

    2009-03-01

    Full Text Available Abstract Background The ESAT-6 (early secreted antigenic target, 6 kDa family collects small mycobacterial proteins secreted by Mycobacterium tuberculosis, particularly in the early phase of growth. There are 23 ESAT-6 family members in M. tuberculosis H37Rv. In a previous work, we identified the Zur- dependent regulation of five proteins of the ESAT-6/CFP-10 family (esxG, esxH, esxQ, esxR, and esxS. esxG and esxH are part of ESAT-6 cluster 3, whose expression was already known to be induced by iron starvation. Results In this research, we performed EMSA experiments and transcriptional analysis of ESAT-6 cluster 3 in Mycobacterium smegmatis (msmeg0615-msmeg0625 and M. tuberculosis. In contrast to what we had observed in M. tuberculosis, we found that in M. smegmatis ESAT-6 cluster 3 responds only to iron and not to zinc. In both organisms we identified an internal promoter, a finding which suggests the presence of two transcriptional units and, by consequence, a differential expression of cluster 3 genes. We compared the expression of msmeg0615 and msmeg0620 in different growth and stress conditions by means of relative quantitative PCR. The expression of msmeg0615 and msmeg0620 genes was essentially similar; they appeared to be repressed in most of the tested conditions, with the exception of acid stress (pH 4.2 where msmeg0615 was about 4-fold induced, while msmeg0620 was repressed. Analysis revealed that in acid stress conditions M. tuberculosis rv0282 gene was 3-fold induced too, while rv0287 induction was almost insignificant. Conclusion In contrast with what has been reported for M. tuberculosis, our results suggest that in M. smegmatis only IdeR-dependent regulation is retained, while zinc has no effect on gene expression. The role of cluster 3 in M. tuberculosis virulence is still to be defined; however, iron- and zinc-dependent expression strongly suggests that cluster 3 is highly expressed in the infective process, and that the cluster

  7. Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering

    Science.gov (United States)

    Yang, Bo; Fu, Xiao; Sidiropoulos, Nicholas D.

    2017-01-01

    Dimensionality reduction techniques play an essential role in data analytics, signal processing and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations that are well-suited for the intended task is more appealing. This paper proposes a joint factor analysis and latent clustering framework, which aims at learning cluster-aware low-dimensional representations of matrix and tensor data. The proposed approach leverages matrix and tensor factorization models that produce essentially unique latent representations of the data to unravel latent cluster structure -- which is otherwise obscured because of the freedom to apply an oblique transformation in latent space. At the same time, latent cluster structure is used as prior information to enhance the performance of factorization. Specific contributions include several custom-built problem formulations, corresponding algorithms, and discussion of associated convergence properties. Besides extensive simulations, real-world datasets such as Reuters document data and MNIST image data are also employed to showcase the effectiveness of the proposed approaches.

  8. A Cluster Analysis of Personality Style in Adults with ADHD

    Science.gov (United States)

    Robin, Arthur L.; Tzelepis, Angela; Bedway, Marquita

    2008-01-01

    Objective: The purpose of this study was to use hierarchical linear cluster analysis to examine the normative personality styles of adults with ADHD. Method: A total of 311 adults with ADHD completed the Millon Index of Personality Styles, which consists of 24 scales assessing motivating aims, cognitive modes, and interpersonal behaviors. Results:…

  9. Advanced Power Plant Development and Analysis Methodologies

    Energy Technology Data Exchange (ETDEWEB)

    A.D. Rao; G.S. Samuelsen; F.L. Robson; B. Washom; S.G. Berenyi

    2006-06-30

    Under the sponsorship of the U.S. Department of Energy/National Energy Technology Laboratory, a multi-disciplinary team led by the Advanced Power and Energy Program of the University of California at Irvine is defining the system engineering issues associated with the integration of key components and subsystems into advanced power plant systems with goals of achieving high efficiency and minimized environmental impact while using fossil fuels. These power plant concepts include 'Zero Emission' power plants and the 'FutureGen' H2 co-production facilities. The study is broken down into three phases. Phase 1 of this study consisted of utilizing advanced technologies that are expected to be available in the 'Vision 21' time frame such as mega scale fuel cell based hybrids. Phase 2 includes current state-of-the-art technologies and those expected to be deployed in the nearer term such as advanced gas turbines and high temperature membranes for separating gas species and advanced gasifier concepts. Phase 3 includes identification of gas turbine based cycles and engine configurations suitable to coal-based gasification applications and the conceptualization of the balance of plant technology, heat integration, and the bottoming cycle for analysis in a future study. Also included in Phase 3 is the task of acquiring/providing turbo-machinery in order to gather turbo-charger performance data that may be used to verify simulation models as well as establishing system design constraints. The results of these various investigations will serve as a guide for the U. S. Department of Energy in identifying the research areas and technologies that warrant further support.

  10. Outcome-Driven Cluster Analysis with Application to Microarray Data.

    Directory of Open Access Journals (Sweden)

    Jessie J Hsu

    Full Text Available One goal of cluster analysis is to sort characteristics into groups (clusters so that those in the same group are more highly correlated to each other than they are to those in other groups. An example is the search for groups of genes whose expression of RNA is correlated in a population of patients. These genes would be of greater interest if their common level of RNA expression were additionally predictive of the clinical outcome. This issue arose in the context of a study of trauma patients on whom RNA samples were available. The question of interest was whether there were groups of genes that were behaving similarly, and whether each gene in the cluster would have a similar effect on who would recover. For this, we develop an algorithm to simultaneously assign characteristics (genes into groups of highly correlated genes that have the same effect on the outcome (recovery. We propose a random effects model where the genes within each group (cluster equal the sum of a random effect, specific to the observation and cluster, and an independent error term. The outcome variable is a linear combination of the random effects of each cluster. To fit the model, we implement a Markov chain Monte Carlo algorithm based on the likelihood of the observed data. We evaluate the effect of including outcome in the model through simulation studies and describe a strategy for prediction. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program, revealing a clustering of the genes that are informed by the recovery outcome.

  11. Improving Cluster Analysis with Automatic Variable Selection Based on Trees

    Science.gov (United States)

    2014-12-01

    ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES by Anton D. Orr December 2014 Thesis Advisor: Samuel E. Buttrey Second Reader...DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE IMPROVING CLUSTER ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES 5. FUNDING NUMBERS 6...2006 based on classification and regression trees to address problems with determining dissimilarity. Current algorithms do not simultaneously address

  12. Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means

    Directory of Open Access Journals (Sweden)

    Imianvan Anthony Agboizebeta

    2012-02-01

    Full Text Available Multiple sclerosis, often called MS, is a disease that affects the central nervous system (the brain andspinal cord. Myelin provides insulation for nerve cells improves the conduction of impulses along thenerves and is important for maintaining the health of the nerves. In multiple sclerosis, inflammationcauses the myelin to disappear. Genetic factors, environmental issues and viral infection may alsoplay a role in developing the disease. Ms is characterized by life threatening symptoms such as; loss ofbalance, hearing problem and depression. The application of Fuzzy Cluster Means (FCM or Fuzzy CMeananalysis to the diagnosis of different forms of multiple sclerosis is the focal point of this paper.Application of cluster analysis involves a sequence of methodological and analytical decision stepsthat enhances the quality and meaning of the clusters produced. Uncertainties associated withanalysis of multiple sclerosis test data are eliminated by the system

  13. Deep Advanced Camera for Surveys Imaging in the Globular Cluster NGC 6397: the Cluster Color-Magnitude Diagram and Luminosity Function

    Science.gov (United States)

    Richer, Harvey B.; Dotter, Aaron; Hurley, Jarrod; Anderson, Jay; King, Ivan; Davis, Saul; Fahlman, Gregory G.; Hansen, Brad M. S.; Kalirai, Jason; Paust, Nathaniel; Rich, R. Michael; Shara, Michael M.

    2008-06-01

    We present the color-magnitude diagram (CMD) from deep Hubble Space Telescope imaging in the globular cluster NGC 6397. The Advanced Camera for Surveys (ACS) was used for 126 orbits to image a single field in two colors (F814W, F606W) 5' SE of the cluster center. The field observed overlaps that of archival WFPC2 data from 1994 and 1997 which were used to proper motion (PM) clean the data. Applying the PM corrections produces a remarkably clean CMD which reveals a number of features never seen before in a globular cluster CMD. In our field, the main-sequence stars appeared to terminate close to the location in the CMD of the hydrogen-burning limit predicted by two independent sets of stellar evolution models. The faintest observed main-sequence stars are about a magnitude fainter than the least luminous metal-poor field halo stars known, suggesting that the lowest-luminosity halo stars still await discovery. At the bright end the data extend beyond the main-sequence turnoff to well up the giant branch. A populous white dwarf cooling sequence is also seen in the cluster CMD. The most dramatic features of the cooling sequence are its turn to the blue at faint magnitudes as well as an apparent truncation near F814W = 28. The cluster luminosity and mass functions were derived, stretching from the turnoff down to the hydrogen-burning limit. It was well modeled with either a very flat power-law or a lognormal function. In order to interpret these fits more fully we compared them with similar functions in the cluster core and with a full N-body model of NGC 6397 finding satisfactory agreement between the model predictions and the data. This exercise demonstrates the important role and the effect that dynamics has played in altering the cluster initial mass function.

  14. DGA Clustering and Analysis: Mastering Modern, Evolving Threats, DGALab

    Directory of Open Access Journals (Sweden)

    Alexander Chailytko

    2016-05-01

    Full Text Available Domain Generation Algorithms (DGA is a basic building block used in almost all modern malware. Malware researchers have attempted to tackle the DGA problem with various tools and techniques, with varying degrees of success. We present a complex solution to populate DGA feed using reversed DGAs, third-party feeds, and a smart DGA extraction and clustering based on emulation of a large number of samples. Smart DGA extraction requires no reverse engineering and works regardless of the DGA type or initialization vector, while enabling a cluster-based analysis. Our method also automatically allows analysis of the whole malware family, specific campaign, etc. We present our system and demonstrate its abilities on more than 20 malware families. This includes showing connections between different campaigns, as well as comparing results. Most importantly, we discuss how to utilize the outcome of the analysis to create smarter protections against similar malware.

  15. Frailty phenotypes in the elderly based on cluster analysis

    DEFF Research Database (Denmark)

    Dato, Serena; Montesanto, Alberto; Lagani, Vincenzo

    2012-01-01

    genetic background on the frailty status is still questioned. We investigated the applicability of a cluster analysis approach based on specific geriatric parameters, previously set up and validated in a southern Italian population, to two large longitudinal Danish samples. In both cohorts, we identified...... groups of subjects homogeneous for their frailty status and characterized by different survival patterns. A subsequent survival analysis availing of Accelerated Failure Time models allowed us to formulate an operative index able to correlate classification variables with survival probability. From...... these models, we quantified the differential effect of various parameters on survival, and we estimated the heritability of the frailty phenotype by exploiting the twin pairs in our sample. These data suggest the presence of a genetic influence on the frailty variability and indicate that cluster analysis can...

  16. Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.

    Directory of Open Access Journals (Sweden)

    Jeban Ganesalingam

    Full Text Available BACKGROUND: Amyotrophic lateral sclerosis (ALS is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS: Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS: The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001. Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb and time from symptom onset to diagnosis (p<0.00001. CONCLUSION: The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research.

  17. Full text clustering and relationship network analysis of biomedical publications.

    Directory of Open Access Journals (Sweden)

    Renchu Guan

    Full Text Available Rapid developments in the biomedical sciences have increased the demand for automatic clustering of biomedical publications. In contrast to current approaches to text clustering, which focus exclusively on the contents of abstracts, a novel method is proposed for clustering and analysis of complete biomedical article texts. To reduce dimensionality, Cosine Coefficient is used on a sub-space of only two vectors, instead of computing the Euclidean distance within the space of all vectors. Then a strategy and algorithm is introduced for Semi-supervised Affinity Propagation (SSAP to improve analysis efficiency, using biomedical journal names as an evaluation background. Experimental results show that by avoiding high-dimensional sparse matrix computations, SSAP outperforms conventional k-means methods and improves upon the standard Affinity Propagation algorithm. In constructing a directed relationship network and distribution matrix for the clustering results, it can be noted that overlaps in scope and interests among BioMed publications can be easily identified, providing a valuable analytical tool for editors, authors and readers.

  18. The Productivity Analysis of Chennai Automotive Industry Cluster

    Science.gov (United States)

    Bhaskaran, E.

    2014-07-01

    Chennai, also called the Detroit of India, is India's second fastest growing auto market and exports auto components and vehicles to US, Germany, Japan and Brazil. For inclusive growth and sustainable development, 250 auto component industries in Ambattur, Thirumalisai and Thirumudivakkam Industrial Estates located in Chennai have adopted the Cluster Development Approach called Automotive Component Cluster. The objective is to study the Value Chain, Correlation and Data Envelopment Analysis by determining technical efficiency, peer weights, input and output slacks of 100 auto component industries in three estates. The methodology adopted is using Data Envelopment Analysis of Output Oriented Banker Charnes Cooper model by taking net worth, fixed assets, employment as inputs and gross output as outputs. The non-zero represents the weights for efficient clusters. The higher slack obtained reveals the excess net worth, fixed assets, employment and shortage in gross output. To conclude, the variables are highly correlated and the inefficient industries should increase their gross output or decrease the fixed assets or employment. Moreover for sustainable development, the cluster should strengthen infrastructure, technology, procurement, production and marketing interrelationships to decrease costs and to increase productivity and efficiency to compete in the indigenous and export market.

  19. Kinematic gait patterns in healthy runners: A hierarchical cluster analysis.

    Science.gov (United States)

    Phinyomark, Angkoon; Osis, Sean; Hettinga, Blayne A; Ferber, Reed

    2015-11-01

    Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (Pgait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners.

  20. Transcriptional analysis of exopolysaccharides biosynthesis gene clusters in Lactobacillus plantarum.

    Science.gov (United States)

    Vastano, Valeria; Perrone, Filomena; Marasco, Rosangela; Sacco, Margherita; Muscariello, Lidia

    2016-04-01

    Exopolysaccharides (EPS) from lactic acid bacteria contribute to specific rheology and texture of fermented milk products and find applications also in non-dairy foods and in therapeutics. Recently, four clusters of genes (cps) associated with surface polysaccharide production have been identified in Lactobacillus plantarum WCFS1, a probiotic and food-associated lactobacillus. These clusters are involved in cell surface architecture and probably in release and/or exposure of immunomodulating bacterial molecules. Here we show a transcriptional analysis of these clusters. Indeed, RT-PCR experiments revealed that the cps loci are organized in five operons. Moreover, by reverse transcription-qPCR analysis performed on L. plantarum WCFS1 (wild type) and WCFS1-2 (ΔccpA), we demonstrated that expression of three cps clusters is under the control of the global regulator CcpA. These results, together with the identification of putative CcpA target sequences (catabolite responsive element CRE) in the regulatory region of four out of five transcriptional units, strongly suggest for the first time a role of the master regulator CcpA in EPS gene transcription among lactobacilli.

  1. The Quantitative Analysis of Chennai Automotive Industry Cluster

    Science.gov (United States)

    Bhaskaran, Ethirajan

    2016-07-01

    Chennai, also called as Detroit of India due to presence of Automotive Industry producing over 40 % of the India's vehicle and components. During 2001-2002, the Automotive Component Industries (ACI) in Ambattur, Thirumalizai and Thirumudivakkam Industrial Estate, Chennai has faced problems on infrastructure, technology, procurement, production and marketing. The objective is to study the Quantitative Performance of Chennai Automotive Industry Cluster before (2001-2002) and after the CDA (2008-2009). The methodology adopted is collection of primary data from 100 ACI using quantitative questionnaire and analyzing using Correlation Analysis (CA), Regression Analysis (RA), Friedman Test (FMT), and Kruskall Wallis Test (KWT).The CA computed for the different set of variables reveals that there is high degree of relationship between the variables studied. The RA models constructed establish the strong relationship between the dependent variable and a host of independent variables. The models proposed here reveal the approximate relationship in a closer form. KWT proves, there is no significant difference between three locations clusters with respect to: Net Profit, Production Cost, Marketing Costs, Procurement Costs and Gross Output. This supports that each location has contributed for development of automobile component cluster uniformly. The FMT proves, there is no significant difference between industrial units in respect of cost like Production, Infrastructure, Technology, Marketing and Net Profit. To conclude, the Automotive Industries have fully utilized the Physical Infrastructure and Centralised Facilities by adopting CDA and now exporting their products to North America, South America, Europe, Australia, Africa and Asia. The value chain analysis models have been implemented in all the cluster units. This Cluster Development Approach (CDA) model can be implemented in industries of under developed and developing countries for cost reduction and productivity

  2. Subtyping demoralization in the medically ill by cluster analysis

    Directory of Open Access Journals (Sweden)

    Chiara Rafanelli

    2013-03-01

    Full Text Available Background and Objectives: There is increasing interest in the issue of demoralization, particularly in the setting of medical disease. The aim of this investigation was to use both DSM-IV comorbidity and the Diagnostic Criteria for Psychosomatic Research (DCPR in order to characterize demoralization in the medically ill. Methods: 1700 patients were recruited from 8 medical centers in the Italian Health System and 1560 agreed to participate. They all underwent a cross-sectional assessment with DSM-IV and DCPR structured interviews. 373 patients (23.9% received a diagnosis of demoralization. Data were submitted to cluster analysis. Results: Four clusters were identified: demoralization and comorbid depression; demoralization and comorbid somatoform/adjustment disorders; demoralization and comorbid anxiety; demoralization without any comorbid DSM disorder. The first cluster included 27.6% of the total sample and was characterized by the presence of DSM-IV mood disorders (mainly major depressive disorder. The second cluster had 18.2% of the cases and contained both DSM-IV somatoform (particularly, undifferentiated somatoform disorder and hypochondriasis and adjustment disorders. In the third cluster (24.7%, DSM-IV anxiety disorders in comorbidity with demoralization were predominant (particularly, generalized anxiety disorder, agoraphobia, panic disorder and obsessive-compulsive disorder. The fourth cluster had 29.5% of the patients and was characterized by the absence of any DSM-IV comorbid disorder. Conclusions: The findings indicate the need of expanding clinical assessment in the medically ill to include the various manifestations of demoralization as encompassed by the DCPR. Subtyping demoralization may yield improved targets for psychosomatic research and treatment trials.

  3. Bayesian Analysis of Multiple Populations in Galactic Globular Clusters

    Science.gov (United States)

    Wagner-Kaiser, Rachel A.; Sarajedini, Ata; von Hippel, Ted; Stenning, David; Piotto, Giampaolo; Milone, Antonino; van Dyk, David A.; Robinson, Elliot; Stein, Nathan

    2016-01-01

    We use GO 13297 Cycle 21 Hubble Space Telescope (HST) observations and archival GO 10775 Cycle 14 HST ACS Treasury observations of Galactic Globular Clusters to find and characterize multiple stellar populations. Determining how globular clusters are able to create and retain enriched material to produce several generations of stars is key to understanding how these objects formed and how they have affected the structural, kinematic, and chemical evolution of the Milky Way. We employ a sophisticated Bayesian technique with an adaptive MCMC algorithm to simultaneously fit the age, distance, absorption, and metallicity for each cluster. At the same time, we also fit unique helium values to two distinct populations of the cluster and determine the relative proportions of those populations. Our unique numerical approach allows objective and precise analysis of these complicated clusters, providing posterior distribution functions for each parameter of interest. We use these results to gain a better understanding of multiple populations in these clusters and their role in the history of the Milky Way.Support for this work was provided by NASA through grant numbers HST-GO-10775 and HST-GO-13297 from the Space Telescope Science Institute, which is operated by AURA, Inc., under NASA contract NAS5-26555. This material is based upon work supported by the National Aeronautics and Space Administration under Grant NNX11AF34G issued through the Office of Space Science. This project was supported by the National Aeronautics & Space Administration through the University of Central Florida's NASA Florida Space Grant Consortium.

  4. Applications of Cluster Analysis to the Creation of Perfectionism Profiles: A Comparison of two Clustering Approaches

    Directory of Open Access Journals (Sweden)

    Jocelyn H Bolin

    2014-04-01

    Full Text Available Although traditional clustering methods (e.g., K-means have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

  5. An Interpretation of the Boshier-Collins Cluster Analysis Testing Houle's Typology.

    Science.gov (United States)

    Furst, Edward J.

    1986-01-01

    This article speculates on an underlying order obscured by the details of the Boshier-Collins cluster analysis and the mapping of Houle's types onto it. A table illustrates an interpretation of cluster analysis on Boshier's Education Participation Scale. (CT)

  6. Segment clustering methodology for unsupervised Holter recordings analysis

    Science.gov (United States)

    Rodríguez-Sotelo, Jose Luis; Peluffo-Ordoñez, Diego; Castellanos Dominguez, German

    2015-01-01

    Cardiac arrhythmia analysis on Holter recordings is an important issue in clinical settings, however such issue implicitly involves attending other problems related to the large amount of unlabelled data which means a high computational cost. In this work an unsupervised methodology based in a segment framework is presented, which consists of dividing the raw data into a balanced number of segments in order to identify fiducial points, characterize and cluster the heartbeats in each segment separately. The resulting clusters are merged or split according to an assumed criterion of homogeneity. This framework compensates the high computational cost employed in Holter analysis, being possible its implementation for further real time applications. The performance of the method is measure over the records from the MIT/BIH arrhythmia database and achieves high values of sensibility and specificity, taking advantage of database labels, for a broad kind of heartbeats types recommended by the AAMI.

  7. Data Preprocessing in Cluster Analysis of Gene Expression

    Institute of Scientific and Technical Information of China (English)

    杨春梅; 万柏坤; 高晓峰

    2003-01-01

    Considering that the DNA microarray technology has generated explosive gene expression data and that it is urgent to analyse and to visualize such massive datasets with efficient methods, we investigate the data preprocessing methods used in cluster analysis, normalization or logarithm of the matrix, by using hierarchical clustering, principal component analysis (PCA) and self-organizing maps (SOMs). The results illustrate that when using the Euclidean distance as measuring metrics, logarithm of relative expression level is the best preprocessing method, while data preprocessed by normalization cannot attain the expected results because the data structure is ruined. If there are only a few principal components, the PCA is an effective method to extract the frame structure, while SOMs are more suitable for a specific structure.

  8. Supercomputer and cluster performance modeling and analysis efforts:2004-2006.

    Energy Technology Data Exchange (ETDEWEB)

    Sturtevant, Judith E.; Ganti, Anand; Meyer, Harold (Hal) Edward; Stevenson, Joel O.; Benner, Robert E., Jr. (.,; .); Goudy, Susan Phelps; Doerfler, Douglas W.; Domino, Stefan Paul; Taylor, Mark A.; Malins, Robert Joseph; Scott, Ryan T.; Barnette, Daniel Wayne; Rajan, Mahesh; Ang, James Alfred; Black, Amalia Rebecca; Laub, Thomas William; Vaughan, Courtenay Thomas; Franke, Brian Claude

    2007-02-01

    This report describes efforts by the Performance Modeling and Analysis Team to investigate performance characteristics of Sandia's engineering and scientific applications on the ASC capability and advanced architecture supercomputers, and Sandia's capacity Linux clusters. Efforts to model various aspects of these computers are also discussed. The goals of these efforts are to quantify and compare Sandia's supercomputer and cluster performance characteristics; to reveal strengths and weaknesses in such systems; and to predict performance characteristics of, and provide guidelines for, future acquisitions and follow-on systems. Described herein are the results obtained from running benchmarks and applications to extract performance characteristics and comparisons, as well as modeling efforts, obtained during the time period 2004-2006. The format of the report, with hypertext links to numerous additional documents, purposefully minimizes the document size needed to disseminate the extensive results from our research.

  9. Regional Innovation Clusters

    Data.gov (United States)

    Small Business Administration — The Regional Innovation Clusters serve a diverse group of sectors and geographies. Three of the initial pilot clusters, termed Advanced Defense Technology clusters,...

  10. Coupled Two-Way Clustering Analysis of Gene Microarray Data

    CERN Document Server

    Getz, G; Domany, E

    2000-01-01

    We present a novel coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task: we present an algorithm, based on iterative clustering, which performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.

  11. Coupled two-way clustering analysis of gene microarray data

    Science.gov (United States)

    Getz, Gad; Levine, Erel; Domany, Eytan

    2000-10-01

    We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.

  12. The Hubble Space Telescope advanced camera for surveys coma cluster survey. I. Survey objectives and design

    NARCIS (Netherlands)

    Carter, David; Goudfrooij, Paul; Mobasher, Bahram; Ferguson, Henry C.; Puzia, Thomas H.; Aguerri, Alfonso L.; Balcells, Marc; Batcheldor, Dan; Bridges, Terry J.; Davies, Jonathan I.; Erwin, Peter; Graham, Alister W.; Guzmán, Rafael; Hammer, Derek; Hornschemeier, Ann; Hoyos, Carlos; Hudson, Michael J.; Huxor, Avon; Jogee, Shardha; Komiyama, Yutaka; Lotz, Jennifer; Lucey, John R.; Marzke, Ronald O.; Merritt, David; Miller, Bryan W.; Miller, Neal A.; Mouhcine, Mustapha; Okamura, Sadanori; Peletier, Reynier F.; Phillipps, Steven; Poggianti, Bianca M.; Sharples, Ray M.; Smith, Russell J.; Trentham, Neil; Tully, R. Brent; Valentijn, Edwin; Verdoes Kleijn, Gijs

    2008-01-01

    We describe the HST ACS Coma Cluster Treasury survey, a deep two-passband imaging survey of one of the nearest rich clusters of galaxies, the Coma Cluster (Abell 1656). The survey was designed to cover an area of 740 arcmin2 in regions of different density of both galaxies and intergalactic medium w

  13. Diagnostics of subtropical plants functional state by cluster analysis

    Directory of Open Access Journals (Sweden)

    Oksana Belous

    2016-05-01

    Full Text Available The article presents an application example of statistical methods for data analysis on diagnosis of the adaptive capacity of subtropical plants varieties. We depicted selection indicators and basic physiological parameters that were defined as diagnostic. We used evaluation on a set of parameters of water regime, there are: determination of water deficit of the leaves, determining the fractional composition of water and detection parameters of the concentration of cell sap (CCS (for tea culture flushes. These settings are characterized by high liability and high responsiveness to the effects of many abiotic factors that determined the particular care in the selection of plant material for analysis and consideration of the impact on sustainability. On the basis of the experimental data calculated the coefficients of pair correlation between climatic factors and used physiological indicators. The result was a selection of physiological and biochemical indicators proposed to assess the adaptability and included in the basis of methodical recommendations on diagnostics of the functional state of the studied cultures. Analysis of complex studies involving a large number of indicators is quite difficult, especially does not allow to quickly identify the similarity of new varieties for their adaptive responses to adverse factors, and, therefore, to set general requirements to conditions of cultivation. Use of cluster analysis suggests that in the analysis of only quantitative data; define a set of variables used to assess varieties (and the more sampling, the more accurate the clustering will happen, be sure to ascertain the measure of similarity (or difference between objects. It is shown that the identification of diagnostic features, which are subjected to statistical processing, impact the accuracy of the varieties classification. Selection in result of the mono-clusters analysis (variety tea Kolhida; hazelnut Lombardsky red; variety kiwi Monty

  14. Advanced SWOT Analysis of E-Commerce

    Directory of Open Access Journals (Sweden)

    Muhammad Awais

    2012-03-01

    Full Text Available This research paper describes the invention and accessibility of internet connectivity and powerful online tools has resulted a new commerce era that is e-commerce, which has completely revolutionized the conventional concept of business. E-commerce deals with selling and purchasing of goods and services through internet and computer networks. E-commerce can enhance economic growth, increase business opportunities, competitiveness, better and profitable access to markets. E-Commerce is emerging as a new way of helping business enterprises to compete in the market and thus contributing to economic success. In this research paper we will discuss about advanced SWOT analysis of E-commerce which will comprise of strengths, weaknesses, opportunities and threats faced by e-commerce in current scenario.

  15. Using cluster analysis in measuring social domain of territorial brand

    Directory of Open Access Journals (Sweden)

    Zlata Stepanova

    2009-10-01

    Full Text Available Territorial brand has a social dimension reflected in the social equilibrium and measurable with social effectiveness indicators. The paper offers social effectiveness analysis of territory using investigation object “territorial and social systems (TSS” with their further classification according to social types based on cluster analysis. This method allows the authors to distinct four social types of TSS in Sverdlovsk region in accordance with such characteristics as financial activity, quality of life, social stability and ill-being levels. The results of investigation could be useful for brand policy of territorial authorities.

  16. Monitoring Customer Satisfaction in Service Industry: A Cluster Analysis Approach

    Directory of Open Access Journals (Sweden)

    Matúš Horváth

    2012-10-01

    Full Text Available One of the key performance indicators of quality management system of an organization is customer satisfaction. The process of monitoring customer satisfaction is therefore an important part of the measuring processes of the quality management system. This paper deals with new ways how to analyse and monitor customer satisfaction using the analysis of data containing how the customers use the organisation services and customer leaving rates. The article used cluster analysis in this process for segmentation of customers with the aim to increase the accuracy of the results and on these results based decisions. The aplication example was created as a part of bachelor thesis.

  17. Monitoring Customer Satisfaction in Service Industry: A Cluster Analysis Approach

    Directory of Open Access Journals (Sweden)

    Matúš Horváth

    2012-11-01

    Full Text Available One of the key performance indicators of quality management system of an organization is customer satisfaction. The process of monitoring customer satisfaction is therefore an important part of the measuring processes of the quality management system. This paper deals with new ways how to analyse and monitor customer satisfaction using the analysis of data containing how the customers use the organisation services and customer leaving rates. The article used cluster analysis in this process for segmentation of customers with the aim to increase the accuracy of the results and on these results based decisions. The aplication example was created as a part of bachelor thesis.

  18. Data Clustering

    Science.gov (United States)

    Wagstaff, Kiri L.

    2012-03-01

    On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

  19. Weighing the Giants I: Weak Lensing Masses for 51 Massive Galaxy Clusters - Project Overview, Data Analysis Methods, and Cluster Images

    CERN Document Server

    von der Linden, Anja; Applegate, Douglas E; Kelly, Patrick L; Allen, Steven W; Ebeling, Harald; Burchat, Patricia R; Burke, David L; Donovan, David; Morris, R Glenn; Blandford, Roger; Erben, Thomas; Mantz, Adam

    2012-01-01

    This is the first in a series of papers in which we measure accurate weak-lensing masses for 51 of the most X-ray luminous galaxy clusters known at redshifts 0.15cluster experiments. The primary aim is to improve the absolute mass calibration of cluster observables, currently the dominant systematic uncertainty for cluster count experiments. Key elements of this work are the rigorous quantification of systematic uncertainties, high-quality data reduction and photometric calibration, and the "blind" nature of the analysis to avoid confirmation bias. Our target clusters are drawn from RASS X-ray catalogs, and provide a versatile calibration sample for many aspects of cluster cosmology. We have acquired wide-field, high-quality imaging using the Subaru and CFHT telescopes for all 51 clusters, in at least three bands per cluster. For a subset of 27 clusters, we have data in at least five bands, allowing accurate photo-z estimates of...

  20. Analysis and Prediction of Crimes by Clustering and Classification

    Directory of Open Access Journals (Sweden)

    Rasoul Kiani

    2015-08-01

    Full Text Available Crimes will somehow influence organizations and institutions when occurred frequently in a society. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. The main objective of this paper is to classify clustered crimes based on occurrence frequency during different years. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. We applied a theoretical model based on data mining techniques such as clustering and classification to real crime dataset recorded by police in England and Wales within 1990 to 2011. We assigned weights to the features in order to improve the quality of the model and remove low value of them. The Genetic Algorithm (GA is used for optimizing of Outlier Detection operator parameters using RapidMiner tool.

  1. Cluster Analysis and Fuzzy Query in Ship Maintenance and Design

    Science.gov (United States)

    Che, Jianhua; He, Qinming; Zhao, Yinggang; Qian, Feng; Chen, Qi

    Cluster analysis and fuzzy query win wide-spread applications in modern intelligent information processing. In allusion to the features of ship maintenance data, a variant of hypergraph-based clustering algorithm, i.e., Correlation Coefficient-based Minimal Spanning Tree(CC-MST), is proposed to analyze the bulky data rooting in ship maintenance process, discovery the unknown rules and help ship maintainers make a decision on various device fault causes. At the same time, revising or renewing an existed design of ship or device maybe necessary to eliminate those device faults. For the sake of offering ship designers some valuable hints, a fuzzy query mechanism is designed to retrieve the useful information from large-scale complicated and reluctant ship technical and testing data. Finally, two experiments based on a real ship device fault statistical dataset validate the flexibility and efficiency of the CC-MST algorithm. A fuzzy query prototype demonstrates the usability of our fuzzy query mechanism.

  2. Analysis of breast cancer progression using principal component analysis and clustering

    Indian Academy of Sciences (India)

    G Alexe; G S Dalgin; S Ganesan; C DeLisi; G Bhanot

    2007-08-01

    We develop a new technique to analyse microarray data which uses a combination of principal components analysis and consensus ensemble -clustering to find robust clusters and gene markers in the data. We apply our method to a public microarray breast cancer dataset which has expression levels of genes in normal samples as well as in three pathological stages of disease; namely, atypical ductal hyperplasia or ADH, ductal carcinoma in situ or DCIS and invasive ductal carcinoma or IDC. Our method averages over clustering techniques and data perturbation to find stable, robust clusters and gene markers. We identify the clusters and their pathways with distinct subtypes of breast cancer (Luminal, Basal and Her2+). We confirm that the cancer phenotype develops early (in early hyperplasia or ADH stage) and find from our analysis that each subtype progresses from ADH to DCIS to IDC along its own specific pathway, as if each was a distinct disease.

  3. Advances in chemical physics, advancing theory for kinetics and dynamics of complex, many-dimensional systems clusters and proteins

    CERN Document Server

    Rice, Stuart A; Komatsuzaki, Tamiki; Berry, R Stephen; Leitner, David M; Rice, Stuart A; Berry, R Stephen Stephen; Leitner, David M M

    2011-01-01

    This series provides the chemical physics field with a forum for critical, authoritative evaluations of advances in every area of the discipline. Volume 145 in the series continues to report recent advances with significant, up-to-date chapters by internationally recognized researchers.

  4. [The hierarchical clustering analysis of hyperspectral image based on probabilistic latent semantic analysis].

    Science.gov (United States)

    Yi, Wen-Bin; Shen, Li; Qi, Yin-Feng; Tang, Hong

    2011-09-01

    The paper introduces the Probabilistic Latent Semantic Analysis (PLSA) to the image clustering and an effective image clustering algorithm using the semantic information from PLSA is proposed which is used for hyperspectral images. Firstly, the ISODATA algorithm is used to obtain the initial clustering result of hyperspectral image and the clusters of the initial clustering result are considered as the visual words of the PLSA. Secondly, the object-oriented image segmentation algorithm is used to partition the hyperspectral image and segments with relatively pure pixels are regarded as documents in PLSA. Thirdly, a variety of identification methods which can estimate the best number of cluster centers is combined to get the number of latent semantic topics. Then the conditional distributions of visual words in topics and the mixtures of topics in different documents are estimated by using PLSA. Finally, the conditional probabilistic of latent semantic topics are distinguished using statistical pattern recognition method, the topic type for each visual in each document will be given and the clustering result of hyperspectral image are then achieved. Experimental results show the clusters of the proposed algorithm are better than K-MEANS and ISODATA in terms of object-oriented property and the clustering result is closer to the distribution of real spatial distribution of surface.

  5. Cyber Profiling Using Log Analysis And K-Means Clustering

    Directory of Open Access Journals (Sweden)

    Muhammad Zulfadhilah

    2016-07-01

    Full Text Available The Activities of Internet users are increasing from year to year and has had an impact on the behavior of the users themselves. Assessment of user behavior is often only based on interaction across the Internet without knowing any others activities. The log activity can be used as another way to study the behavior of the user. The Log Internet activity is one of the types of big data so that the use of data mining with K-Means technique can be used as a solution for the analysis of user behavior. This study has been carried out the process of clustering using K-Means algorithm is divided into three clusters, namely high, medium, and low. The results of the higher education institution show that each of these clusters produces websites that are frequented by the sequence: website search engine, social media, news, and information. This study also showed that the cyber profiling had been done strongly influenced by environmental factors and daily activities.

  6. Clustered Numerical Data Analysis Using Markov Lie Monoid Based Networks

    Science.gov (United States)

    Johnson, Joseph

    2016-03-01

    We have designed and build an optimal numerical standardization algorithm that links numerical values with their associated units, error level, and defining metadata thus supporting automated data exchange and new levels of artificial intelligence (AI). The software manages all dimensional and error analysis and computational tracing. Tables of entities verses properties of these generalized numbers (called ``metanumbers'') support a transformation of each table into a network among the entities and another network among their properties where the network connection matrix is based upon a proximity metric between the two items. We previously proved that every network is isomorphic to the Lie algebra that generates continuous Markov transformations. We have also shown that the eigenvectors of these Markov matrices provide an agnostic clustering of the underlying patterns. We will present this methodology and show how our new work on conversion of scientific numerical data through this process can reveal underlying information clusters ordered by the eigenvalues. We will also show how the linking of clusters from different tables can be used to form a ``supernet'' of all numerical information supporting new initiatives in AI.

  7. Dynamical analysis of galaxy cluster merger Abell 2146

    CERN Document Server

    White, J A; King, L J; Lee, B E; Russell, H R; Baum, S A; Clowe, D I; Coleman, J E; Donahue, M; Edge, A C; Fabian, A C; Johnstone, R M; McNamara, B R; ODea, C P; Sanders, J S

    2015-01-01

    We present a dynamical analysis of the merging galaxy cluster system Abell 2146 using spectroscopy obtained with the Gemini Multi-Object Spectrograph on the Gemini North telescope. As revealed by the Chandra X-ray Observatory, the system is undergoing a major merger and has a gas structure indicative of a recent first core passage. The system presents two large shock fronts, making it unique amongst these rare systems. The hot gas structure indicates that the merger axis must be close to the plane of the sky and that the two merging clusters are relatively close in mass, from the observation of two shock fronts. Using 63 spectroscopically determined cluster members, we apply various statistical tests to establish the presence of two distinct massive structures. With the caveat that the system has recently undergone a major merger, the virial mass estimate is M_vir = 8.5 +4.3 -4.7 x 10 ^14 M_sol for the whole system, consistent with the mass determination in a previous study using the Sunyaev-Zeldovich signal....

  8. Covariance analysis of differential drag-based satellite cluster flight

    Science.gov (United States)

    Ben-Yaacov, Ohad; Ivantsov, Anatoly; Gurfil, Pini

    2016-06-01

    One possibility for satellite cluster flight is to control relative distances using differential drag. The idea is to increase or decrease the drag acceleration on each satellite by changing its attitude, and use the resulting small differential acceleration as a controller. The most significant advantage of the differential drag concept is that it enables cluster flight without consuming fuel. However, any drag-based control algorithm must cope with significant aerodynamical and mechanical uncertainties. The goal of the current paper is to develop a method for examination of the differential drag-based cluster flight performance in the presence of noise and uncertainties. In particular, the differential drag control law is examined under measurement noise, drag uncertainties, and initial condition-related uncertainties. The method used for uncertainty quantification is the Linear Covariance Analysis, which enables us to propagate the augmented state and filter covariance without propagating the state itself. Validation using a Monte-Carlo simulation is provided. The results show that all uncertainties have relatively small effect on the inter-satellite distance, even in the long term, which validates the robustness of the used differential drag controller.

  9. Coupled Two-Way Clustering Analysis of Breast Cancer and Colon Cancer Gene Expression Data

    CERN Document Server

    Getz, G; Kela, I; Domany, E; Notterman, D A; Getz, Gad; Gal, Hilah; Kela, Itai; Domany, Eytan; Notterman, Dan A.

    2003-01-01

    We present and review Coupled Two Way Clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis.

  10. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A;

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing...

  11. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.;

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  12. Clustering Analysis on E-commerce Transaction Based on K-means Clustering

    Directory of Open Access Journals (Sweden)

    Xuan HUANG

    2014-02-01

    Full Text Available Based on the density, increment and grid etc, shortcomings like the bad elasticity, weak handling ability of high-dimensional data, sensitive to time sequence of data, bad independence of parameters and weak handling ability of noise are usually existed in clustering algorithm when facing a large number of high-dimensional transaction data. Making experiments by sampling data samples of the 300 mobile phones of Taobao, the following conclusions can be obtained: compared with Single-pass clustering algorithm, the K-means clustering algorithm has a high intra-class dissimilarity and inter-class similarity when analyzing e-commerce transaction. In addition, the K-means clustering algorithm has very high efficiency and strong elasticity when dealing with a large number of data items. However, clustering effects of this algorithm are affected by clustering number and initial positions of clustering center. Therefore, it is easy to show the local optimization for clustering results. Therefore, how to determine clustering number and initial positions of the clustering center of this algorithm is still the important job to be researched in the future.

  13. Using Multilevel Factor Analysis with Clustered Data: Investigating the Factor Structure of the Positive Values Scale

    Science.gov (United States)

    Huang, Francis L.; Cornell, Dewey G.

    2016-01-01

    Advances in multilevel modeling techniques now make it possible to investigate the psychometric properties of instruments using clustered data. Factor models that overlook the clustering effect can lead to underestimated standard errors, incorrect parameter estimates, and model fit indices. In addition, factor structures may differ depending on…

  14. NATO Advanced Research Workshop on Physics and Chemistry of Finite Systems : from Clusters to Crystals

    CERN Document Server

    Khanna, S; Rao, B

    1992-01-01

    Recent innovations in experimental techniques such as molecular and cluster beam epitaxy, supersonic jet expansion, matrix isolation and chemical synthesis are increasingly enabling researchers to produce materials by design and with atomic dimension. These materials constrained by sire, shape, and symmetry range from clusters containing as few as two atoms to nanoscale materials consisting of thousands of atoms. They possess unique structuraI, electronic, magnetic and optical properties that depend strongly on their size and geometry. The availability of these materials raises many fundamental questions as weIl as technological possibilities. From the academic viewpoint, the most pertinent question concerns the evolution of the atomic and electronic structure of the system as it grows from micro clusters to crystals. At what stage, for example, does the cluster look as if it is a fragment of the corresponding crystal. How do electrons forming bonds in micro-clusters transform to bands in solids? How do the s...

  15. The cosmological analysis of X-ray cluster surveys; III. Bypassing cluster mass measurements

    CERN Document Server

    Pierre, M; Faccioli, L; Clerc, N; Gastaud, R; Koulouridis, E; Pacaud, F

    2016-01-01

    Despite strong theoretical arguments, the use of clusters as cosmological probes is, in practice, frequently questioned because of the many uncertainties impinging on cluster mass estimates. Our aim is to develop a fully self-consistent cosmological approach of X-ray cluster surveys, exclusively based on observable quantities, rather than masses. This procedure is justified given the possibility to directly derive the cluster properties via ab initio modelling, either analytically or by using hydrodynamical simulations. In this third paper, we evaluate the method on cluster toy-catalogues. We model the population of detected clusters in the count-rate -- hardness-ratio -- angular size -- redshift space and compare the corresponding 4-dimensional diagram with theoretical predictions. The best cosmology+physics parameter configuration is determined using a simple minimisation procedure; errors on the parameters are derived by scanning the likelihood hyper-surfaces with a wide range of starting values. The metho...

  16. Time series clustering analysis of health-promoting behavior

    Science.gov (United States)

    Yang, Chi-Ta; Hung, Yu-Shiang; Deng, Guang-Feng

    2013-10-01

    Health promotion must be emphasized to achieve the World Health Organization goal of health for all. Since the global population is aging rapidly, ComCare elder health-promoting service was developed by the Taiwan Institute for Information Industry in 2011. Based on the Pender health promotion model, ComCare service offers five categories of health-promoting functions to address the everyday needs of seniors: nutrition management, social support, exercise management, health responsibility, stress management. To assess the overall ComCare service and to improve understanding of the health-promoting behavior of elders, this study analyzed health-promoting behavioral data automatically collected by the ComCare monitoring system. In the 30638 session records collected for 249 elders from January, 2012 to March, 2013, behavior patterns were identified by fuzzy c-mean time series clustering algorithm combined with autocorrelation-based representation schemes. The analysis showed that time series data for elder health-promoting behavior can be classified into four different clusters. Each type reveals different health-promoting needs, frequencies, function numbers and behaviors. The data analysis result can assist policymakers, health-care providers, and experts in medicine, public health, nursing and psychology and has been provided to Taiwan National Health Insurance Administration to assess the elder health-promoting behavior.

  17. Reliability analysis of cluster-based ad-hoc networks

    Energy Technology Data Exchange (ETDEWEB)

    Cook, Jason L. [Quality Engineering and System Assurance, Armament Research Development Engineering Center, Picatinny Arsenal, NJ (United States); Ramirez-Marquez, Jose Emmanuel [School of Systems and Enterprises, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030 (United States)], E-mail: Jose.Ramirez-Marquez@stevens.edu

    2008-10-15

    The mobile ad-hoc wireless network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and, as such, only the mobile nodes comprise the network. Pairs of nodes communicate either directly or through other nodes. To do so, each node acts, in turn, as a source, destination, and relay of messages. The virtue of a MAWN is the flexibility this provides; however, the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of embedded mobility models. This paper joins both methods together and expands upon these works by modifying the problem formulation to address the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints on networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of a Monte Carlo simulation method through the analysis of several example networks.

  18. Advanced Materials and Solids Analysis Research Core (AMSARC)

    Science.gov (United States)

    The Advanced Materials and Solids Analysis Research Core (AMSARC), centered at the U.S. Environmental Protection Agency's (EPA) Andrew W. Breidenbach Environmental Research Center in Cincinnati, Ohio, is the foundation for the Agency's solids and surfaces analysis capabilities. ...

  19. IPC two-color analysis of x ray galaxy clusters

    Science.gov (United States)

    White, Raymond E., III

    1990-01-01

    The mass distributions were determined of several clusters of galaxies by using X ray surface brightness data from the Einstein Observatory Imaging Proportional Counter (IPC). Determining cluster mass distributions is important for constraining the nature of the dark matter which dominates the mass of galaxies, galaxy clusters, and the Universe. Galaxy clusters are permeated with hot gas in hydrostatic equilibrium with the gravitational potentials of the clusters. Cluster mass distributions can be determined from x ray observations of cluster gas by using the equation of hydrostatic equilibrium and knowledge of the density and temperature structure of the gas. The x ray surface brightness at some distance from the cluster is the result of the volume x ray emissivity being integrated along the line of sight in the cluster.

  20. Advances in face detection and facial image analysis

    CERN Document Server

    Celebi, M; Smolka, Bogdan

    2016-01-01

    This book presents the state-of-the-art in face detection and analysis. It outlines new research directions, including in particular psychology-based facial dynamics recognition, aimed at various applications such as behavior analysis, deception detection, and diagnosis of various psychological disorders. Topics of interest include face and facial landmark detection, face recognition, facial expression and emotion analysis, facial dynamics analysis, face classification, identification, and clustering, and gaze direction and head pose estimation, as well as applications of face analysis.

  1. Selections of data preprocessing methods and similarity metrics for gene cluster analysis

    Institute of Scientific and Technical Information of China (English)

    YANG Chunmei; WAN Baikun; GAO Xiaofeng

    2006-01-01

    Clustering is one of the major exploratory techniques for gene expression data analysis. Only with suitable similarity metrics and when datasets are properly preprocessed, can results of high quality be obtained in cluster analysis. In this study, gene expression datasets with external evaluation criteria were preprocessed as normalization by line, normalization by column or logarithm transformation by base-2, and were subsequently clustered by hierarchical clustering, k-means clustering and self-organizing maps (SOMs) with Pearson correlation coefficient or Euclidean distance as similarity metric. Finally, the quality of clusters was evaluated by adjusted Rand index. The results illustrate that k-means clustering and SOMs have distinct advantages over hierarchical clustering in gene clustering, and SOMs are a bit better than k-means when randomly initialized. It also shows that hierarchical clustering prefers Pearson correlation coefficient as similarity metric and dataset normalized by line. Meanwhile, k-means clustering and SOMs can produce better clusters with Euclidean distance and logarithm transformed datasets. These results will afford valuable reference to the implementation of gene expression cluster analysis.

  2. CLUSTER ANALYSIS OF NATURAL DISASTER LOSSES IN POLISH AGRICULTURE

    Directory of Open Access Journals (Sweden)

    Grzegorz STRUPCZEWSKI

    2015-04-01

    Full Text Available Agricultural production risk is of special nature due to a great number of hazards, relative weakness of production entities on the market and high ambiguity which is greater than in industrial production. Natural disasters occurring very frequently, at simultaneous low percentage of insured farmers, cause damage of such sizes that force the state to organise current financial aid (for instance in the form of preferential natural disaster loans. This aid is usually not sufficient. On the other hand, regional diversity of the risk level does not positively affect the development of insurance. From the perspective of insurance companies and policymakers it becomes highly important to investigate the spatial structure of losses in agriculture caused by natural disasters. The purpose of the research is to classify the 16 Polish voivodeships into clusters in order to show differences between them according to the criterion of level of damage in agricultural farms caused by natural disasters. On the basis of the cluster analysis it was demonstrated that 11 voivodeships form quite a homogeneous group in terms of size of damage in agriculture (the value of damage in cultivations and the acreage of destroyed cultivations are two most important factors determining affiliation to the cluster, however, the profile of loss occurring in other five voivodeships has a very individual course and requires separate handling in the actuarial sense. It was also proved that high value of losses in agriculture in the absolute sense in given voivodeships do not have to mean high vulnerability of agricultural farms from these voivodeships to natural risks.

  3. Principal Component Analysis and Cluster Analysis in Profile of Electrical System

    Science.gov (United States)

    Iswan; Garniwa, I.

    2017-03-01

    This paper propose to present approach for profile of electrical system, presented approach is combination algorithm, namely principal component analysis (PCA) and cluster analysis. Based on relevant data of gross domestic regional product and electric power and energy use. This profile is set up to show the condition of electrical system of the region, that will be used as a policy in the electrical system of spatial development in the future. This paper consider 24 region in South Sulawesi province as profile center points and use principal component analysis (PCA) to asses the regional profile for development. Cluster analysis is used to group these region into few cluster according to the new variable be produced PCA. The general planning of electrical system of South Sulawesi province can provide support for policy making of electrical system development. The future research can be added several variable into existing variable.

  4. The ACS Virgo Cluster Survey IV: Data Reduction Procedures for Surface Brightness Fluctuation Measurements with the Advanced Camera for Surveys

    CERN Document Server

    Mei, S; Tonry, J L; Jordan, A; Peng, E W; Côté, P; Ferrarese, L; Merritt, D; Milosavljevic, M; West, M J; Mei, Simona; Blakeslee, John P.; Tonry, John L.; Jordan, Andres; Peng, Eric W.; Cote, Patrick; Ferrarese, Laura; Merritt, David; Milosavljevic, Milos; West, Michael J.

    2005-01-01

    The Advanced Camera for Surveys (ACS) Virgo Cluster Survey is a large program to image 100 early-type Virgo galaxies using the F475W and F850LP bandpasses of the Wide Field Channel of the ACS instrument on the Hubble Space Telescope (HST). The scientific goals of this survey include an exploration of the three-dimensional structure of the Virgo Cluster and a critical examination of the usefulness of the globular cluster luminosity function as a distance indicator. Both of these issues require accurate distances for the full sample of 100 program galaxies. In this paper, we describe our data reduction procedures and examine the feasibility of accurate distance measurements using the method of surface brightness fluctuations (SBF) applied to the ACS Virgo Cluster Survey F850LP imaging. The ACS exhibits significant geometrical distortions due to its off-axis location in the HST focal plane; correcting for these distortions by resampling the pixel values onto an undistorted frame results in pixel correlations tha...

  5. MMPI profiles of males accused of severe crimes: a cluster analysis

    NARCIS (Netherlands)

    Spaans, M.; Barendregt, M.; Muller, E.; Beurs, E. de; Nijman, H.L.I.; Rinne, T.

    2009-01-01

    In studies attempting to classify criminal offenders by cluster analysis of Minnesota Multiphasic Personality Inventory-2 (MMPI-2) data, the number of clusters found varied between 10 (the Megargee System) and two (one cluster indicating no psychopathology and one exhibiting serious psychopathology)

  6. Online Cluster Analysis Supporting Real Time Anomaly Detection in Hyperspectral Imagery

    Science.gov (United States)

    2013-06-01

    algorithm is accomplished for this exercise by performing the principal component analysis on the entire image after the removal of the noise and...cluster completely without fully capturing the intended cluster is easily explained by referencing Figure 29. The tree cluster in green is an eccentric

  7. Investigating Faculty Familiarity with Assessment Terminology by Applying Cluster Analysis to Interpret Survey Data

    Science.gov (United States)

    Raker, Jeffrey R.; Holme, Thomas A.

    2014-01-01

    A cluster analysis was conducted with a set of survey data on chemistry faculty familiarity with 13 assessment terms. Cluster groupings suggest a high, middle, and low overall familiarity with the terminology and an independent high and low familiarity with terms related to fundamental statistics. The six resultant clusters were found to be…

  8. The Quintuplet Cluster II. Analysis of the WN stars

    CERN Document Server

    Liermann, A; Oskinova, L M; Todt, H; Butler, K; 10.1051/0004-6361/200912612

    2010-01-01

    Based on $K$-band integral-field spectroscopy, we analyze four Wolf-Rayet stars of the nitrogen sequence (WN) found in the inner part of the Quintuplet cluster. All WN stars (WR102d, WR102i, WR102hb, and WR102ea) are of spectral subtype WN9h. One further star, LHO110, is included in the analysis which has been classified as Of/WN? previously but turns out to be most likely a WN9h star as well. The Potsdam Wolf-Rayet (PoWR) models for expanding atmospheres are used to derive the fundamental stellar and wind parameters. The stars turn out to be very luminous, $\\log{(L/L_\\odot)} > 6.0$, with relatively low stellar temperatures, $T_* \\approx$ 25--35\\,kK. Their stellar winds contain a significant fraction of hydrogen, up to $X_\\mathrm{H} \\sim 0.45$ (by mass). We discuss the position of the Galactic center WN stars in the Hertzsprung-Russell diagram and find that they form a distinct group. In this respect, the Quintuplet WN stars are similar to late-type WN stars found in the Arches cluster and elsewhere in the Ga...

  9. Maximum-entropy clustering algorithm and its global convergence analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG; Zhihua

    2001-01-01

    [1]Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithm. New York: Plenum, 1981.[2]Krishnapuram, R., Keller, J., A possibilistic approach to clustering, IEEE Trans. on Fuzzy Systems, 1993, 1(2): 98.[3]Yair, E., Zeger, K., Gersho, A., Competitive learning and soft competition for vector quantizer design, IEEE Trans on Signal Processing, 1992, 40(2): 294.[4]Pal, N. R., Bezdek, J. C., Tsao, E. C. K., Generalized clustering networks and Kohonen's self-organizing scheme, IEEE Trans on Neural Networks, 1993, 4(4): 549.[5]Karayiannis, N. B., Bezdek, J. C., Pal, N. R. et al., Repair to GLVQ: a new family of competitive learning schemes, IEEE Trans on Neural Networks, 1996, 7(5): 1062.[6]Karayiannis, N. B., Pai, P. I., Fuzzy algorithms for learning vector quantization, IEEE Trans. on Neural Networks, 1996, 7(5): 1196.[7]Karayiannis, N. B., A methodology for constructing fuzzy algorithms for learning vector quantization, IEEE Trans. on Neural Networks, 1997, 8(3): 505.[8]Karayiannis, N. B., Bezdek, J. C., An integrated approach to fuzzy learning vector quantization and fuzzy C-Means clustering, IEEE Trans. on Fuzzy Systems, 1997, 5(4): 622.[9]Li Xing-si, An efficient approach to nonlinear minimax problems, Chinese Science Bulletin? 1992, 37(10): 802.[10]Li Xing-si, An efficient approach to a class of non-smooth optimization problems, Science in China, Series A,1994, 37(3): 323.[11]. Zangwill, W., Non-linear Programming: A Unified Approach, Englewood Cliffs: Prentice-Hall, 1969.[12]. Fletcher, R., Practical Methods of Optimization,2nd ed., New York: John Wiley & Sons, 1987.[13]. Zhang Zhihua, Zheng Nanning, Wang Tianshu, Behavioral analysis and improving of generalized LVQ neural network, Acta Automatica Sinica, 1999, 25(5): 582.[14]. Kirkpatrick, S., Gelatt, C. D., Vecchi, M. P., Optimization by simulated annealing, Science, 1983, 220(3): 671.[15]. Ross, K., Deterministic annealing for

  10. Unsupervised analysis of classical biomedical markers: robustness and medical relevance of patient clustering using bioinformatics tools.

    Directory of Open Access Journals (Sweden)

    Michal Markovich Gordon

    Full Text Available MOTIVATION: It has been proposed that clustering clinical markers, such as blood test results, can be used to stratify patients. However, the robustness of clusters formed with this approach to data pre-processing and clustering algorithm choices has not been evaluated, nor has clustering reproducibility. Here, we made use of the NHANES survey to compare clusters generated with various combinations of pre-processing and clustering algorithms, and tested their reproducibility in two separate samples. METHOD: Values of 44 biomarkers and 19 health/life style traits were extracted from the National Health and Nutrition Examination Survey (NHANES. The 1999-2002 survey was used for training, while data from the 2003-2006 survey was tested as a validation set. Twelve combinations of pre-processing and clustering algorithms were applied to the training set. The quality of the resulting clusters was evaluated both by considering their properties and by comparative enrichment analysis. Cluster assignments were projected to the validation set (using an artificial neural network and enrichment in health/life style traits in the resulting clusters was compared to the clusters generated from the original training set. RESULTS: The clusters obtained with different pre-processing and clustering combinations differed both in terms of cluster quality measures and in terms of reproducibility of enrichment with health/life style properties. Z-score normalization, for example, dramatically improved cluster quality and enrichments, as compared to unprocessed data, regardless of the clustering algorithm used. Clustering diabetes patients revealed a group of patients enriched with retinopathies. This could indicate that routine laboratory tests can be used to detect patients suffering from complications of diabetes, although other explanations for this observation should also be considered. CONCLUSIONS: Clustering according to classical clinical biomarkers is a robust

  11. Maximum-entropy clustering algorithm and its global convergence analysis

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Constructing a batch of differentiable entropy functions touniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximum-entropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hard C-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.

  12. Population analysis of open clusters: radii and mass segregation

    CERN Document Server

    Schilbach, E; Piskunov, A E; Röser, S; Scholz, R D

    2006-01-01

    Aims: Based on our well-determined sample of open clusters in the all-sky catalogue ASCC-2.5 we derive new linear sizes of some 600 clusters, and investigate the effect of mass segregation of stars in open clusters. Methods: Using statistical methods, we study the distribution of linear sizes as a function of spatial position and cluster age. We also examine statistically the distribution of stars of different masses within clusters as a function of the cluster age. Results: No significant dependence of the cluster size on location in the Galaxy is detected for younger clusters (< 200 Myr), whereas older clusters inside the solar orbit turned out to be, on average, smaller than outside. Also, small old clusters are preferentially found close to the Galactic plane, whereas larger ones more frequently live farther away from the plane and at larger Galactocentric distances. For clusters with (V - M_V) < 10.5, a clear dependence of the apparent radius on age has been detected: the cluster radii decrease by ...

  13. Patterns of Brucellosis Infection Symptoms in Azerbaijan: A Latent Class Cluster Analysis

    OpenAIRE

    Rita Ismayilova; Emilya Nasirova; Colleen Hanou; Rivard, Robert G.; Bautista, Christian T.

    2014-01-01

    Brucellosis infection is a multisystem disease, with a broad spectrum of symptoms. We investigated the existence of clusters of infected patients according to their clinical presentation. Using national surveillance data from the Electronic-Integrated Disease Surveillance System, we applied a latent class cluster (LCC) analysis on symptoms to determine clusters of brucellosis cases. A total of 454 cases reported between July 2011 and July 2013 were analyzed. LCC identified a two-cluster mo...

  14. Optimum Metallic-Bond Scheme: A Quantitative Analysis of Mass Spectra of Sodium Clusters

    Institute of Scientific and Technical Information of China (English)

    苏长荣; 李家明

    2001-01-01

    Based on the results of the optimum metallic-bond scheme for sodium clusters, we present a quantitative analysis of the detailed features of the mass spectra of sodium clusters. We find that, in the generation of sodium clusters with various abundances, the quasi-steady processes through adding or losing a sodium atom dominate. The quasi-steady processes through adding or losing a sodium dimer are also important to understand the detailed features of mass spectra for small clusters.

  15. Cluster randomized clinical trials in orthodontics: design, analysis and reporting issues.

    Science.gov (United States)

    Pandis, Nikolaos; Walsh, Tanya; Polychronopoulou, Argy; Eliades, Theodore

    2013-10-01

    Cluster randomized trials (CRTs) use as the unit of randomization clusters, which are usually defined as a collection of individuals sharing some common characteristics. Common examples of clusters include entire dental practices, hospitals, schools, school classes, villages, and towns. Additionally, several measurements (repeated measurements) taken on the same individual at different time points are also considered to be clusters. In dentistry, CRTs are applicable as patients may be treated as clusters containing several individual teeth. CRTs require certain methodological procedures during sample calculation, randomization, data analysis, and reporting, which are often ignored in dental research publications. In general, due to similarity of the observations within clusters, each individual within a cluster provides less information compared with an individual in a non-clustered trial. Therefore, clustered designs require larger sample sizes compared with non-clustered randomized designs, and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this article to highlight with relevant examples the important methodological characteristics of cluster randomized designs as they may be applied in orthodontics and to explain the problems that may arise if clustered observations are erroneously treated and analysed as independent (non-clustered).

  16. Sodium content as a predictor of the advanced evolution of globular cluster stars.

    Science.gov (United States)

    Campbell, Simon W; D'Orazi, Valentina; Yong, David; Constantino, Thomas N; Lattanzio, John C; Stancliffe, Richard J; Angelou, George C; Wylie-de Boer, Elizabeth C; Grundahl, Frank

    2013-06-13

    The asymptotic giant branch (AGB) phase is the final stage of nuclear burning for low-mass stars. Although Milky Way globular clusters are now known to harbour (at least) two generations of stars, they still provide relatively homogeneous samples of stars that are used to constrain stellar evolution theory. It is predicted by stellar models that the majority of cluster stars with masses around the current turn-off mass (that is, the mass of the stars that are currently leaving the main sequence phase) will evolve through the AGB phase. Here we report that all of the second-generation stars in the globular cluster NGC 6752--70 per cent of the cluster population--fail to reach the AGB phase. Through spectroscopic abundance measurements, we found that every AGB star in our sample has a low sodium abundance, indicating that they are exclusively first-generation stars. This implies that many clusters cannot reliably be used for star counts to test stellar evolution timescales if the AGB population is included. We have no clear explanation for this observation.

  17. Sodium content as a predictor of the advanced evolution of globular cluster stars

    CERN Document Server

    Campbell, Simon W; Yong, David; Constantino, Thomas N; Lattanzio, John C; Stancliffe, Richard J; Angelou, George C; Boer, Elizabeth C Wylie-de; Grundahl, Frank

    2013-01-01

    The asymptotic giant branch (AGB) phase is the final stage of nuclear burning for low-mass stars. Although Milky Way globular clusters are now known to harbour (at least) two generations of stars they still provide relatively homogeneous samples of stars that are used to constrain stellar evolution theory. It is predicted by stellar models that the majority of cluster stars with masses around the current turn-off mass (that is, the mass of the stars that are currently leaving the main sequence phase) will evolve through the AGB phase. Here we report that all of the second-generation stars in the globular cluster NGC 6752 -- 70 per cent of the cluster population -- fail to reach the AGB phase. Through spectroscopic abundance measurements, we found that every AGB star in our sample has a low sodium abundance, indicating that they are exclusively first-generation stars. This implies that many clusters cannot reliably be used for star counts to test stellar evolution timescales if the AGB population is included. ...

  18. Higgs Pair Production: Choosing Benchmarks With Cluster Analysis

    CERN Document Server

    Dall'Osso, Martino; Gottardo, Carlo A; Oliveira, Alexandra; Tosi, Mia; Goertz, Florian

    2015-01-01

    New physics theories often depend on a large number of free parameters. The precise values of those parameters in some cases drastically affect the resulting phenomenology of fundamental physics processes, while in others finite variations can leave it basically invariant at the level of detail experimentally accessible. When designing a strategy for the analysis of experimental data in the search for a signal predicted by a new physics model, it appears advantageous to categorize the parameter space describing the model according to the corresponding kinematical features of the final state. A multi-dimensional test statistic can be used to gauge the degree of similarity in the kinematics of different models; a clustering algorithm using that metric may then allow the division of the space into homogeneous regions, each of which can be successfully represented by a benchmark point. Searches targeting those benchmark points are then guaranteed to be sensitive to a large area of the parameter space. In this doc...

  19. Challenges for Cluster Analysis in a Virtual Observatory

    CERN Document Server

    Djorgovski, S G; Mahabal, A A; Williams, R; Granat, R; Stolorz, P

    2002-01-01

    There has been an unprecedented and continuing growth in the volume, quality, and complexity of astronomical data sets over the past few years, mainly through large digital sky surveys. Virtual Observatory (VO) concept represents a scientific and technological framework needed to cope with this data flood. We review some of the applied statistics and computing challenges posed by the analysis of large and complex data sets expected in the VO-based research. The challenges are driven both by the size and the complexity of the data sets (billions of data vectors in parameter spaces of tens or hundreds of dimensions), by the heterogeneity of the data and measurement errors, the selection effects and censored data, and by the intrinsic clustering properties (functional form, topology) of the data distribution in the parameter space of observed attributes. Examples of scientific questions one may wish to address include: objective determination of the numbers of object classes present in the data, and the membersh...

  20. Archetypal TRMM Radar Profiles Identified Through Cluster Analysis

    Science.gov (United States)

    Boccippio, Dennis J.

    2003-01-01

    It is widely held that identifiable 'convective regimes' exist in nature, although precise definitions of these are elusive. Examples include land / Ocean distinctions, break / monsoon beahvior, seasonal differences in the Amazon (SON vs DJF), etc. These regimes are often described by differences in the realized local convective spectra, and measured by various metrics of convective intensity, depth, areal coverage and rainfall amount. Objective regime identification may be valuable in several ways: regimes may serve as natural 'branch points' in satellite retrieval algorithms or data assimilation efforts; one example might be objective identification of regions that 'should' share a similar 2-R relationship. Similarly, objectively defined regimes may provide guidance on optimal siting of ground validation efforts. Objectively defined regimes could also serve as natural (rather than arbitrary geographic) domain 'controls' in studies of convective response to environmental forcing. Quantification of convective vertical structure has traditionally involved parametric study of prescribed quantities thought to be important to convective dynamics: maximum radar reflectivity, cloud top height, 30-35 dBZ echo top height, rain rate, etc. Individually, these parameters are somewhat deficient as their interpretation is often nonunique (the same metric value may signify different physics in different storm realizations). Individual metrics also fail to capture the coherence and interrelationships between vertical levels available in full 3-D radar datasets. An alternative approach is discovery of natural partitions of vertical structure in a globally representative dataset, or 'archetypal' reflectivity profiles. In this study, this is accomplished through cluster analysis of a very large sample (0[107) of TRMM-PR reflectivity columns. Once achieved, the rainconditional and unconditional 'mix' of archetypal profile types in a given location and/or season provides a description

  1. Application of Multi-SOM clustering approach to macrophage gene expression analysis.

    Science.gov (United States)

    Ghouila, Amel; Yahia, Sadok Ben; Malouche, Dhafer; Jmel, Haifa; Laouini, Dhafer; Guerfali, Fatma Z; Abdelhak, Sonia

    2009-05-01

    The production of increasingly reliable and accessible gene expression data has stimulated the development of computational tools to interpret such data and to organize them efficiently. The clustering techniques are largely recognized as useful exploratory tools for gene expression data analysis. Genes that show similar expression patterns over a wide range of experimental conditions can be clustered together. This relies on the hypothesis that genes that belong to the same cluster are coregulated and involved in related functions. Nevertheless, clustering algorithms still show limits, particularly for the estimation of the number of clusters and the interpretation of hierarchical dendrogram, which may significantly influence the outputs of the analysis process. We propose here a multi level SOM based clustering algorithm named Multi-SOM. Through the use of clustering validity indices, Multi-SOM overcomes the problem of the estimation of clusters number. To test the validity of the proposed clustering algorithm, we first tested it on supervised training data sets. Results were evaluated by computing the number of misclassified samples. We have then used Multi-SOM for the analysis of macrophage gene expression data generated in vitro from the same individual blood infected with 5 different pathogens. This analysis led to the identification of sets of tightly coregulated genes across different pathogens. Gene Ontology tools were then used to estimate the biological significance of the clustering, which showed that the obtained clusters are coherent and biologically significant.

  2. Adaptive Correction Forecasting Approach for Urban Traffic Flow Based on Fuzzy c-Mean Clustering and Advanced Neural Network

    Directory of Open Access Journals (Sweden)

    He Huang

    2013-01-01

    Full Text Available Forecasting of urban traffic flow is important to intelligent transportation system (ITS developments and implementations. The precise forecasting of traffic flow will be pretty helpful to relax road traffic congestion. The accuracy of traditional single model without correction mechanism is poor. Summarizing the existing prediction models and considering the characteristics of the traffic itself, a traffic flow prediction model based on fuzzy c-mean clustering method (FCM and advanced neural network (NN was proposed. FCM can improve the prediction accuracy and robustness of the model, while advanced NN can optimize the generalization ability of the model. Besides these, the output value of the model is calibrated by the correction mechanism. The experimental results show that the proposed method has better prediction accuracy and robustness than the other models.

  3. Characterizing Heterogeneity within Head and Neck Lesions Using Cluster Analysis of Multi-Parametric MRI Data.

    Directory of Open Access Journals (Sweden)

    Marco Borri

    Full Text Available To describe a methodology, based on cluster analysis, to partition multi-parametric functional imaging data into groups (or clusters of similar functional characteristics, with the aim of characterizing functional heterogeneity within head and neck tumour volumes. To evaluate the performance of the proposed approach on a set of longitudinal MRI data, analysing the evolution of the obtained sub-sets with treatment.The cluster analysis workflow was applied to a combination of dynamic contrast-enhanced and diffusion-weighted imaging MRI data from a cohort of squamous cell carcinoma of the head and neck patients. Cumulative distributions of voxels, containing pre and post-treatment data and including both primary tumours and lymph nodes, were partitioned into k clusters (k = 2, 3 or 4. Principal component analysis and cluster validation were employed to investigate data composition and to independently determine the optimal number of clusters. The evolution of the resulting sub-regions with induction chemotherapy treatment was assessed relative to the number of clusters.The clustering algorithm was able to separate clusters which significantly reduced in voxel number following induction chemotherapy from clusters with a non-significant reduction. Partitioning with the optimal number of clusters (k = 4, determined with cluster validation, produced the best separation between reducing and non-reducing clusters.The proposed methodology was able to identify tumour sub-regions with distinct functional properties, independently separating clusters which were affected differently by treatment. This work demonstrates that unsupervised cluster analysis, with no prior knowledge of the data, can be employed to provide a multi-parametric characterization of functional heterogeneity within tumour volumes.

  4. Cluster analysis application in research on pork quality determinants

    Science.gov (United States)

    Przybylski, W.; Wasiewicz, P.; Zieliński, P.; Gromadzka-Ostrowska, J.; Olczak, E.; Jaworska, D.; Niemyjski, S.; Santé-Lhoutellier, V.

    2010-09-01

    In this paper data mining methods were applied to investigate features determining high quality pork meat. The aim of the study was analysis of conditionality of the pork meat quality defined in coherence with HDL and LDL cholesterol concentration, plasma leptin, triglycerides, plasma glucose and serum. The research was carried out on 54 pigs. originated from crossbreeding of Naima sows with P76-PenArLan boars hybrids line. Meat quality parameters were evaluated in samples derived from the Longissimus (LD) muscle taken behind the last rib on the basis: the pH value, meat colour, drip loss, the RTN, intramuscular fat and glycolytic potential. The results of this study were elaborated by using R environment and show that cluster and regression analysis can be a useful tool for in-depth analysis of the determinants of the quality of pig meat in homogeneous populations of pigs. However, the question of determinants of the level of glycogen and fat in meat requires further research.

  5. A combined multidimensional scaling and hierarchical clustering view for the exploratory analysis of multidimensional data

    Science.gov (United States)

    Craig, Paul; Roa-Seïler, Néna

    2013-01-01

    This paper describes a novel information visualization technique that combines multidimensional scaling and hierarchical clustering to support the exploratory analysis of multidimensional data. The technique displays the results of multidimensional scaling using a scatter plot where the proximity of any two items' representations is approximate to their similarity according to a Euclidean distance metric. The results of hierarchical clustering are overlaid onto this view by drawing smoothed outlines around each nested cluster. The difference in similarity between successive cluster combinations is used to colour code clusters and make stronger natural clusters more prominent in the display. When a cluster or group of items is selected, multidimensional scaling and hierarchical clustering are re-applied to a filtered subset of the data, and animation is used to smooth the transition between successive filtered views. As a case study we demonstrate the technique being used to analyse survey data relating to the appropriateness of different phrases to different emotionally charged situations.

  6. ANALYSIS OF GENETIC PARAMETERS FOR BEAN PHYSICAL QUALITY CHARACTERS AND CLUSTERIZATIONS OF ELEVEN GENOTYPES OF ROBUSTA COFFEE (Coffea canephora

    Directory of Open Access Journals (Sweden)

    Rubiyo Rubiyo

    2013-10-01

    Full Text Available The genetic parameters of coffee related to their bean physical quality characters are important for breeder to improve the  bean quality. Eleven genotypes of robusta coffee were identified and their genetic relationship to the bean physical quality were characterized. The research was conducted at coffee plantation of the Association of Indonesian Coffee Exporters in West Lampung, altitude of 800 m above sea level, Latosol type of soil, and A type of climate, starting from 2010 to 2012. The objectives of this study were to estimate the genotypic coefficient of variation, heritability and genetic advance of the bean physical quality characters, and clusterization analysis of eleven genotypes of robusta coffee. A randomized complete block design with eleven treatments of coffee genotypes and three replications was used in this study. The results showed that the estimated values of genotypic coefficient of variation, heritability and genetic advance for small-size normal bean characters of robusta coffee were very high, so the genetic improvement for these characters has a high probability of success by direct selection. Clusterization of the genotypes resulted three clusters with their respective characteristics. The study implies that future breeding program especially for hybridization should be conducted between genotypes arising from different clusters to obtain the possible high heterosis effects.

  7. [Advances in clustered regularly interspaced short palindromic repeats--a review].

    Science.gov (United States)

    Wang, Lili; He, Jin; Wang, Jieping

    2011-08-01

    The recently discovered Clustered Regularly Interspaced Short Palindromic Repeat (CRISPRs) can protect bacteria and archaea with adaptive and heritable defense systems against the invasion of phage- and plasmid- associated mobile genetic elements. Here, we review the structure, diversity, mechanism of interference and self versus non-self discrimination of CRISPR systems. We also discuss the potential applications of this novel interference system.

  8. Classification of persons attempting suicide. A review of cluster analysis research

    Directory of Open Access Journals (Sweden)

    Wołodźko, Tymoteusz

    2014-08-01

    Full Text Available Aim: Review of conclusions from cluster analysis research on suicide risk factors published after the year 1993. Methods: Search and analysis of cluster analysis research papers on suicidal behaviour. Results: Following groups where distinguished: (1 persons with comorbid mental disorders or with severe symptoms, (2 persons without mental disorders or with mild symptoms, (3 persons with personality disorders and externalizing psychopathology, (4 socially withdrawn persons with a tendency to avoid social contacts, (5 depressive persons Conclusions: Analysis of studies on characteristics of suicide attempters, with the application of cluster analysis, has indicated the possibility of differentiation of several groups of persons with significantly increased risk of suicide attempt. The reviewed cluster analysis research had multiple methodological limitations. Studies employing cluster analysis on large, representative and homogeneous population are needed.

  9. Profiles of exercise motivation, physical activity, exercise habit, and academic performance in Malaysian adolescents: A cluster analysis

    OpenAIRE

    Hairul Anuar Hashim; Freddy Golok; Rosmatunisah Ali

    2011-01-01

    Objectives: This study examined Malaysian adolescents’ profiles of exercise motivation, exercise habit strength, academic performance, and levels of physical activity (PA) using cluster analysis.Methods: The sample (n = 300) consisted of 65.6% males and 34.4% females with a mean age of 13.40 ± 0.49. Statistical analysis was performed using cluster analysis.Results: Cluster analysis revealed three distinct cluster groups. Cluster 1 is characterized by a moderate level of PA, relatively high in...

  10. AVES: A Computer Cluster System approach for INTEGRAL Scientific Analysis

    Science.gov (United States)

    Federici, M.; Martino, B. L.; Natalucci, L.; Umbertini, P.

    The AVES computing system, based on an "Cluster" architecture is a fully integrated, low cost computing facility dedicated to the archiving and analysis of the INTEGRAL data. AVES is a modular system that uses the software resource manager (SLURM) and allows almost unlimited expandibility (65,536 nodes and hundreds of thousands of processors); actually is composed by 30 Personal Computers with Quad-Cores CPU able to reach the computing power of 300 Giga Flops (300x10{9} Floating point Operations Per Second), with 120 GB of RAM and 7.5 Tera Bytes (TB) of storage memory in UFS configuration plus 6 TB for users area. AVES was designed and built to solve growing problems raised from the analysis of the large data amount accumulated by the INTEGRAL mission (actually about 9 TB) and due to increase every year. The used analysis software is the OSA package, distributed by the ISDC in Geneva. This is a very complex package consisting of dozens of programs that can not be converted to parallel computing. To overcome this limitation we developed a series of programs to distribute the workload analysis on the various nodes making AVES automatically divide the analysis in N jobs sent to N cores. This solution thus produces a result similar to that obtained by the parallel computing configuration. In support of this we have developed tools that allow a flexible use of the scientific software and quality control of on-line data storing. The AVES software package is constituted by about 50 specific programs. Thus the whole computing time, compared to that provided by a Personal Computer with single processor, has been enhanced up to a factor 70.

  11. Advanced Modeling, Simulation and Analysis (AMSA) Capability Roadmap Progress Review

    Science.gov (United States)

    Antonsson, Erik; Gombosi, Tamas

    2005-01-01

    Contents include the following: NASA capability roadmap activity. Advanced modeling, simulation, and analysis overview. Scientific modeling and simulation. Operations modeling. Multi-special sensing (UV-gamma). System integration. M and S Environments and Infrastructure.

  12. Advanced Fingerprint Analysis Project Fingerprint Constituents

    Energy Technology Data Exchange (ETDEWEB)

    GM Mong; CE Petersen; TRW Clauss

    1999-10-29

    The work described in this report was focused on generating fundamental data on fingerprint components which will be used to develop advanced forensic techniques to enhance fluorescent detection, and visualization of latent fingerprints. Chemical components of sweat gland secretions are well documented in the medical literature and many chemical techniques are available to develop latent prints, but there have been no systematic forensic studies of fingerprint sweat components or of the chemical and physical changes these substances undergo over time.

  13. Progressive Failure Analysis of Advanced Composites

    Science.gov (United States)

    2008-07-25

    NASA – Langley Research Center 1 Joan Andreu Mayugo University of Girona 1 2 Contents 1 Introduction 11 2 UVARM subroutine 13 2.1 Overview...32] Turon A, Camanho PP, Costa J, Dávila CG. A damage model for the simulation of delamination in advanced composites under variable- mode loading...conference, New York, 1960. p. 63–78. [34] Turon A, Dávila CG, Camanho PP, Costa J. An engineering solution for using coarse meshes in the

  14. Significance analysis and statistical mechanics: an application to clustering.

    Science.gov (United States)

    Łuksza, Marta; Lässig, Michael; Berg, Johannes

    2010-11-26

    This Letter addresses the statistical significance of structures in random data: given a set of vectors and a measure of mutual similarity, how likely is it that a subset of these vectors forms a cluster with enhanced similarity among its elements? The computation of this cluster p value for randomly distributed vectors is mapped onto a well-defined problem of statistical mechanics. We solve this problem analytically, establishing a connection between the physics of quenched disorder and multiple-testing statistics in clustering and related problems. In an application to gene expression data, we find a remarkable link between the statistical significance of a cluster and the functional relationships between its genes.

  15. Study on Cluster Analysis Used with Laser-Induced Breakdown Spectroscopy

    Science.gov (United States)

    He, Li'ao; Wang, Qianqian; Zhao, Yu; Liu, Li; Peng, Zhong

    2016-06-01

    Supervised learning methods (eg. PLS-DA, SVM, etc.) have been widely used with laser-induced breakdown spectroscopy (LIBS) to classify materials; however, it may induce a low correct classification rate if a test sample type is not included in the training dataset. Unsupervised cluster analysis methods (hierarchical clustering analysis, K-means clustering analysis, and iterative self-organizing data analysis technique) are investigated in plastics classification based on the line intensities of LIBS emission in this paper. The results of hierarchical clustering analysis using four different similarity measuring methods (single linkage, complete linkage, unweighted pair-group average, and weighted pair-group average) are compared. In K-means clustering analysis, four kinds of choosing initial centers methods are applied in our case and their results are compared. The classification results of hierarchical clustering analysis, K-means clustering analysis, and ISODATA are analyzed. The experiment results demonstrated cluster analysis methods can be applied to plastics discrimination with LIBS. supported by Beijing Natural Science Foundation of China (No. 4132063)

  16. The Norma Cluster (ACO 3627): I. A Dynamical Analysis of the Most Massive Cluster in the Great Attractor

    CERN Document Server

    Woudt, P A; Lucey, J; Fairall, A P; Moore, S A W

    2007-01-01

    A detailed dynamical analysis of the nearby rich Norma cluster (ACO 3627) is presented. From radial velocities of 296 cluster members, we find a mean velocity of 4871 +/- 54 km/s and a velocity dispersion of 925 km/s. The mean velocity of the E/S0 population (4979 +/- 85 km/s) is offset with respect to that of the S/Irr population (4812 +/- 70 km/s) by `Delta' v = 164 km/s in the cluster rest frame. This offset increases towards the core of the cluster. The E/S0 population is free of any detectable substructure and appears relaxed. Its shape is clearly elongated with a position angle that is aligned along the dominant large-scale structures in this region, the so-called Norma wall. The central cD galaxy has a very large peculiar velocity of 561 km/s which is most probably related to an ongoing merger at the core of the cluster. The spiral/irregular galaxies reveal a large amount of substructure; two dynamically distinct subgroups within the overall spiral-population have been identified, located along the Nor...

  17. Multidimensional cluster stability analysis from a Brazilian Bradyrhizobium sp. RFLP/PCR data set

    Science.gov (United States)

    Milagre, S. T.; Maciel, C. D.; Shinoda, A. A.; Hungria, M.; Almeida, J. R. B.

    2009-05-01

    The taxonomy of the N2-fixing bacteria belonging to the genus Bradyrhizobium is still poorly refined, mainly due to conflicting results obtained by the analysis of the phenotypic and genotypic properties. This paper presents an application of a method aiming at the identification of possible new clusters within a Brazilian collection of 119 Bradyrhizobium strains showing phenotypic characteristics of B. japonicum and B. elkanii. The stability was studied as a function of the number of restriction enzymes used in the RFLP-PCR analysis of three ribosomal regions with three restriction enzymes per region. The method proposed here uses clustering algorithms with distances calculated by average-linkage clustering. Introducing perturbations using sub-sampling techniques makes the stability analysis. The method showed efficacy in the grouping of the species B. japonicum and B. elkanii. Furthermore, two new clusters were clearly defined, indicating possible new species, and sub-clusters within each detected cluster.

  18. Cluster Analysis as a Method of Recovering Types of Intraindividual Growth Trajectories: A Monte Carlo Study.

    Science.gov (United States)

    Dumenci, Levent; Windle, Michael

    2001-01-01

    Used Monte Carlo methods to evaluate the adequacy of cluster analysis to recover group membership based on simulated latent growth curve (LCG) models. Cluster analysis failed to recover growth subtypes adequately when the difference between growth curves was shape only. Discusses circumstances under which it was more successful. (SLD)

  19. Cluster Analysis of the Luria-Nebraska Neuropsychological Battery with Learning Disabled Adults.

    Science.gov (United States)

    McCue, Michael; And Others

    The study reports a cluster analysis of Luria-Nebraska Neuropsychological Battery sources of 25 learning disabled adults. The cluster analysis suggested the presence of three subgroups within this sample, one having high elevations on the Rhythm, Writing, Reading, and Arithmetic Rhythm scales, the second having an extremely high evelation on the…

  20. Tracking Undergraduate Student Achievement in a First-Year Physiology Course Using a Cluster Analysis Approach

    Science.gov (United States)

    Brown, S. J.; White, S.; Power, N.

    2015-01-01

    A cluster analysis data classification technique was used on assessment scores from 157 undergraduate nursing students who passed 2 successive compulsory courses in human anatomy and physiology. Student scores in five summative assessment tasks, taken in each of the courses, were used as inputs for a cluster analysis procedure. We aimed to group…

  1. Advanced Software Methods for Physics Analysis

    Science.gov (United States)

    Lista, L.

    2006-01-01

    Unprecedented data analysis complexity is experienced in modern High Energy Physics experiments. The complexity arises from the growing size of recorded data samples, the large number of data analyses performed by different users in each single experiment, and the level of complexity of each single analysis. For this reason, the requirements on software for data analysis impose a very high level of reliability. We present two concrete examples: the former from BaBar experience with the migration to a new Analysis Model with the definition of a new model for the Event Data Store, the latter about a toolkit for multivariate statistical and parametric Monte Carlo analysis developed using generic programming.

  2. Sequential Combination Methods forData Clustering Analysis

    Institute of Scientific and Technical Information of China (English)

    钱 涛; Ching Y.Suen; 唐远炎

    2002-01-01

    This paper proposes the use of more than one clustering method to improve clustering performance. Clustering is an optimization procedure based on a specific clustering criterion. Clustering combination can be regardedasatechnique that constructs and processes multiple clusteringcriteria.Sincetheglobalandlocalclusteringcriteriaarecomplementary rather than competitive, combining these two types of clustering criteria may enhance theclustering performance. In our past work, a multi-objective programming based simultaneous clustering combination algorithmhasbeenproposed, which incorporates multiple criteria into an objective function by a weighting method, and solves this problem with constrained nonlinear optimization programming. But this algorithm has high computationalcomplexity.Hereasequential combination approach is investigated, which first uses the global criterion based clustering to produce an initial result, then uses the local criterion based information to improve the initial result with aprobabilisticrelaxation algorithm or linear additive model.Compared with the simultaneous combination method, sequential combination haslow computational complexity. Results on some simulated data and standard test data arereported.Itappearsthatclustering performance improvement can be achieved at low cost through sequential combination.

  3. Multilevel Analysis Methods for Partially Nested Cluster Randomized Trials

    Science.gov (United States)

    Sanders, Elizabeth A.

    2011-01-01

    This paper explores multilevel modeling approaches for 2-group randomized experiments in which a treatment condition involving clusters of individuals is compared to a control condition involving only ungrouped individuals, otherwise known as partially nested cluster randomized designs (PNCRTs). Strategies for comparing groups from a PNCRT in the…

  4. Mesoscopic analysis of networks: applications to exploratory analysis and data clustering

    CERN Document Server

    Granell, Clara; Arenas, Alex

    2011-01-01

    We investigate the adaptation and performance of modularity-based algorithms, designed in the scope of complex networks, to analyze the mesoscopic structure of correlation matrices. Using a multi-resolution analysis we are able to describe the structure of the data in terms of clusters at different topological levels. We demonstrate the applicability of our findings in two different scenarios: to analyze the neural connectivity of the nematode {\\em Caenorhabditis elegans}, and to automatically classify a typical benchmark of unsupervised clustering, the Iris data set, with considerable success.

  5. MASSCLEAN - MASSive CLuster Evolution and ANalysis Package - Description and Tests

    CERN Document Server

    Popescu, Bogdan

    2008-01-01

    We present MASSCLEAN, a new, sophisticated and robust stellar cluster image and photometry simulation package. This package is able to create color-magnitude diagrams and standard FITS images in any of the traditional optical and near-infrared bands based on cluster characteristics input by the user, including but not limited to distance, age, mass, radius and extinction. At the limit of very distant, unresolved clusters, we have checked the integrated colors created in MASSCLEAN against those from other single stellar population models with consistent results. We have also tested models which provide a reasonable estimate of the field star contamination in images and color-magnitude diagrams. We demonstrate the package by simulating images and color-magnitude diagrams of well known massive Milky Way clusters and compare their appearance to real data. Because the algorithm populates the cluster with a discrete number of tenable stars, it can be used as part of a Monte Carlo Method to derive the probabilistic ...

  6. Boundaries, links and clusters: a new paradigm in spatial analysis?

    Science.gov (United States)

    Jacquez, Geoff M; Kaufmann, Andy; Goovaerts, Pierre

    2008-12-01

    This paper develops and applies new techniques for the simultaneous detection of boundaries and clusters within a probabilistic framework. The new statistic "little b" (written b(ij)) evaluates boundaries between adjacent areas with different values, as well as links between adjacent areas with similar values. Clusters of high values (hotspots) and low values (coldspots) are then constructed by joining areas abutting locations that are significantly high (e.g., an unusually high disease rate) and that are connected through a "link" such that the values in the adjoining areas are not significantly different. Two techniques are proposed and evaluated for accomplishing cluster construction: "big B" and the "ladder" approach. We compare the statistical power and empirical Type I and Type II error of these approaches to those of wombling and the local Moran test. Significance may be evaluated using distribution theory based on the product of two continuous (e.g., non-discrete) variables. We also provide a "distribution free" algorithm based on resampling of the observed values. The methods are applied to simulated data for which the locations of boundaries and clusters is known, and compared and contrasted with clusters found using the local Moran statistic and with polygon Womble boundaries. The little b approach to boundary detection is comparable to polygon wombling in terms of Type I error, Type II error and empirical statistical power. For cluster detection, both the big B and ladder approaches have lower Type I and Type II error and are more powerful than the local Moran statistic. The new methods are not constrained to find clusters of a pre-specified shape, such as circles, ellipses and donuts, and yield a more accurate description of geographic variation than alternative cluster tests that presuppose a specific cluster shape. We recommend these techniques over existing cluster and boundary detection methods that do not provide such a comprehensive description

  7. An Analysis of Particle Swarm Optimization with Data Clustering-Technique for Optimization in Data Mining

    Directory of Open Access Journals (Sweden)

    Amreen Khan,

    2010-07-01

    Full Text Available Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI has recently emerged that meets these requirements and has successfully been applied to a number ofreal world clustering problems. This paper looks into the use ofParticle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and also allows the particles to be robust to trace the changing environment.

  8. RELIABILITY ANALYSIS OF RING, AGENT AND CLUSTER BASED DISTRIBUTED SYSTEMS

    Directory of Open Access Journals (Sweden)

    R.SEETHALAKSHMI

    2011-08-01

    Full Text Available The introduction of pervasive devices and mobile devices has led to immense growth of real time distributed processing. In such context reliability of the computing environment is very important. Reliability is the probability that the devices, links, processes, programs and files work efficiently for the specified period of time and in the specified condition. Distributed systems are available as conventional ring networks, clusters and agent based systems. Reliability of such systems is focused. These networks are heterogeneous and scalable in nature. There are several factors, which are to be considered for reliability estimation. These include the application related factors like algorithms, data-set sizes, memory usage pattern, input-output, communication patterns, task granularity and load-balancing. It also includes the hardware related factors like processor architecture, memory hierarchy, input-output configuration and network. The software related factors concerning reliability are operating systems, compiler, communication protocols, libraries and preprocessor performance. In estimating the reliability of a system, the performance estimation is an important aspect. Reliability analysis is approached using probability.

  9. Fully Automated Operational Modal Analysis using multi-stage clustering

    Science.gov (United States)

    Neu, Eugen; Janser, Frank; Khatibi, Akbar A.; Orifici, Adrian C.

    2017-02-01

    The interest for robust automatic modal parameter extraction techniques has increased significantly over the last years, together with the rising demand for continuous health monitoring of critical infrastructure like bridges, buildings and wind turbine blades. In this study a novel, multi-stage clustering approach for Automated Operational Modal Analysis (AOMA) is introduced. In contrast to existing approaches, the procedure works without any user-provided thresholds, is applicable within large system order ranges, can be used with very small sensor numbers and does not place any limitations on the damping ratio or the complexity of the system under investigation. The approach works with any parametric system identification algorithm that uses the system order n as sole parameter. Here a data-driven Stochastic Subspace Identification (SSI) method is used. Measurements from a wind tunnel investigation with a composite cantilever equipped with Fiber Bragg Grating Sensors (FBGSs) and piezoelectric sensors are used to assess the performance of the algorithm with a highly damped structure and low signal to noise ratio conditions. The proposed method was able to identify all physical system modes in the investigated frequency range from over 1000 individual datasets using FBGSs under challenging signal to noise ratio conditions and under better signal conditions but from only two sensors.

  10. Functional Reconstitution of a Fungal Natural Product Gene Cluster by Advanced Genome Editing

    DEFF Research Database (Denmark)

    Weber, Jakob; Valiante, Vito; Nødvig, Christina Spuur

    2017-01-01

    Filamentous fungi produce varieties of natural products even in a strain dependent manner. However, the genetic basis of chemical speciation between strains is still widely unknown. One example is trypacidin, a natural product of the opportunistic human pathogen Aspergillus fumigatus, which...... for advanced molecular genetic studies in filamentous fungi, exploiting selectable markers separated from the edited locus....

  11. Advancing Alternative Analysis: Integration of Decision Science

    DEFF Research Database (Denmark)

    Malloy, Timothy F; Zaunbrecher, Virginia M; Batteate, Christina;

    2016-01-01

    Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate th...

  12. Cluster analysis in retail segmentation for credit scoring

    Directory of Open Access Journals (Sweden)

    Sanja Scitovski

    2014-12-01

    Full Text Available The aim of this paper is to segment retail clients by using adaptive Mahalanobis clustering in a way that each segment can be suitable for separate credit scoring development such that a better risk assessment of retail clients could be accomplished. A real data set on retail clients from a Croatian bank was used in the paper. Grouping of the data point set is carried out by using the adaptive Mahalanobis partitioning algorithm (see, e.g., [20]. It is an incremental algorithm, which recognizes ellipsoidal clusters with the main axes in the directions of eigenvectors of the corresponding covariance matrix of the data set. On the basis of the given data set, by using the well-known DIRECT algorithm for global optimization it is possible to search successively for an optimal partition with k=2, 3,... clusters. After that, a partition with the most appropriate number of clusters is determined by using various validity indexes. Based on the description of each cluster, banks could decide to develop a separate credit scoring model for each cluster as well as to create a business strategy customized to each cluster.

  13. Cluster Computing For Real Time Seismic Array Analysis.

    Science.gov (United States)

    Martini, M.; Giudicepietro, F.

    A seismic array is an instrument composed by a dense distribution of seismic sen- sors that allow to measure the directional properties of the wavefield (slowness or wavenumber vector) radiated by a seismic source. Over the last years arrays have been widely used in different fields of seismological researches. In particular they are applied in the investigation of seismic sources on volcanoes where they can be suc- cessfully used for studying the volcanic microtremor and long period events which are critical for getting information on the volcanic systems evolution. For this reason arrays could be usefully employed for the volcanoes monitoring, however the huge amount of data produced by this type of instruments and the processing techniques which are quite time consuming limited their potentiality for this application. In order to favor a direct application of arrays techniques to continuous volcano monitoring we designed and built a small PC cluster able to near real time computing the kinematics properties of the wavefield (slowness or wavenumber vector) produced by local seis- mic source. The cluster is composed of 8 Intel Pentium-III bi-processors PC working at 550 MHz, and has 4 Gigabytes of RAM memory. It runs under Linux operating system. The developed analysis software package is based on the Multiple SIgnal Classification (MUSIC) algorithm and is written in Fortran. The message-passing part is based upon the LAM programming environment package, an open-source imple- mentation of the Message Passing Interface (MPI). The developed software system includes modules devote to receiving date by internet and graphical applications for the continuous displaying of the processing results. The system has been tested with a data set collected during a seismic experiment conducted on Etna in 1999 when two dense seismic arrays have been deployed on the northeast and the southeast flanks of this volcano. A real time continuous acquisition system has been simulated by

  14. Advanced transport systems analysis, modeling, and evaluation of performances

    CERN Document Server

    Janić, Milan

    2014-01-01

    This book provides a systematic analysis, modeling and evaluation of the performance of advanced transport systems. It offers an innovative approach by presenting a multidimensional examination of the performance of advanced transport systems and transport modes, useful for both theoretical and practical purposes. Advanced transport systems for the twenty-first century are characterized by the superiority of one or several of their infrastructural, technical/technological, operational, economic, environmental, social, and policy performances as compared to their conventional counterparts. The advanced transport systems considered include: Bus Rapid Transit (BRT) and Personal Rapid Transit (PRT) systems in urban area(s), electric and fuel cell passenger cars, high speed tilting trains, High Speed Rail (HSR), Trans Rapid Maglev (TRM), Evacuated Tube Transport system (ETT), advanced commercial subsonic and Supersonic Transport Aircraft (STA), conventionally- and Liquid Hydrogen (LH2)-fuelled commercial air trans...

  15. Nonlinear analysis of nano-cluster doped fiber

    Institute of Scientific and Technical Information of China (English)

    LIU Gang; ZHANG Ru

    2007-01-01

    There are prominent nonlinear characteristics that we hope for the semiconductor nano-clusters doped fiber. Refractive index of fiber core can be effectively changed by adulteration. This technology can provide a new method for developing photons components. Because the semiconductor nano-cluster has quantum characteristics,Based on first-order perturbation theory and classical theory of fiber,we deduced refractive index expressions of fiber core,which was semiconductor nano-cluster doped fiber. Finally,third-order nonlinear coefficient equation was gained. Using this equation,we calculated SMF-28 fiber nonlinear coefficient. The equation shows that new third-order coefficient was greater.

  16. DNA splice site sequences clustering method for conservativeness analysis

    Institute of Scientific and Technical Information of China (English)

    Quanwei Zhang; Qinke Peng; Tao Xu

    2009-01-01

    DNA sequences that are near to splice sites have remarkable conservativeness,and many researchers have contributed to the prediction of splice site.In order to mine the underlying biological knowledge,we analyze the conservativeness of DNA splice site adjacent sequences by clustering.Firstly,we propose a kind of DNA splice site sequences clustering method which is based on DBSCAN,and use four kinds of dissimilarity calculating methods.Then,we analyze the conservative feature of the clustering results and the experimental data set.

  17. Cluster Forests

    CERN Document Server

    Yan, Donghui; Jordan, Michael I

    2011-01-01

    Inspired by Random Forests (RF) in the context of classification, we propose a new clustering ensemble method---Cluster Forests (CF). Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure $\\kappa$. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis shows that the $\\kappa$ criterion is shown to grow each local clustering in a desirable way---it is "noise-resistant." A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.

  18. Cluster Analysis in Patients with GOLD 1 Chronic Obstructive Pulmonary Disease.

    Directory of Open Access Journals (Sweden)

    Philippe Gagnon

    Full Text Available We hypothesized that heterogeneity exists within the Global Initiative for Chronic Obstructive Lung Disease (GOLD 1 spirometric category and that different subgroups could be identified within this GOLD category.Pre-randomization study participants from two clinical trials were symptomatic/asymptomatic GOLD 1 chronic obstructive pulmonary disease (COPD patients and healthy controls. A hierarchical cluster analysis used pre-randomization demographics, symptom scores, lung function, peak exercise response and daily physical activity levels to derive population subgroups.Considerable heterogeneity existed for clinical variables among patients with GOLD 1 COPD. All parameters, except forced expiratory volume in 1 second (FEV1/forced vital capacity (FVC, had considerable overlap between GOLD 1 COPD and controls. Three-clusters were identified: cluster I (18 [15%] COPD patients; 105 [85%] controls; cluster II (45 [80%] COPD patients; 11 [20%] controls; and cluster III (22 [92%] COPD patients; 2 [8%] controls. Apart from reduced diffusion capacity and lower baseline dyspnea index versus controls, cluster I COPD patients had otherwise preserved lung volumes, exercise capacity and physical activity levels. Cluster II COPD patients had a higher smoking history and greater hyperinflation versus cluster I COPD patients. Cluster III COPD patients had reduced physical activity versus controls and clusters I and II COPD patients, and lower FEV1/FVC versus clusters I and II COPD patients.The results emphasize heterogeneity within GOLD 1 COPD, supporting an individualized therapeutic approach to patients.www.clinicaltrials.gov. NCT01360788 and NCT01072396.

  19. Quality assessment of cortex cinnamomi by HPLC chemical fingerprint, principle component analysis and cluster analysis.

    Science.gov (United States)

    Yang, Jie; Chen, Li-Hong; Zhang, Qin; Lai, Mao-Xiang; Wang, Qiang

    2007-06-01

    HPLC fingerprint analysis, principle component analysis (PCA), and cluster analysis were introduced for quality assessment of Cortex cinnamomi (CC). The fingerprint of CC was developed and validated by analyzing 30 samples of CC from different species and geographic locations. Seventeen chromatographic peaks were selected as characteristic peaks and their relative peak areas (RPA) were calculated for quantitative expression of the HPLC fingerprints. The correlation coefficients of similarity in chromatograms were higher than 0.95 for the same species while much lower than 0.6 for different species. Besides, two principal components (PCs) have been extracted by PCA. PC1 separated Cinnamomum cassia from other species, capturing 56.75% of variance while PC2 contributed for their further separation, capturing 19.08% variance. The scores of the samples showed that the samples could be clustered reasonably into different groups corresponding to different species and different regions. The scores and loading plots together revealed different chemical properties of each group clearly. The cluster analysis confirmed the results of PCA analysis. Therefore, HPLC fingerprint in combination with chemometric techniques provide a very flexible and reliable method for quality assessment of traditional Chinese medicines.

  20. Critical clusters in interdependent economic sectors. A data-driven spectral clustering analysis

    Science.gov (United States)

    Oliva, Gabriele; Setola, Roberto; Panzieri, Stefano

    2016-10-01

    In this paper we develop a data-driven hierarchical clustering methodology to group the economic sectors of a country in order to highlight strongly coupled groups that are weakly coupled with other groups. Specifically, we consider an input-output representation of the coupling among the sectors and we interpret the relation among sectors as a directed graph; then we recursively apply the spectral clustering methodology over the graph, without a priori information on the number of groups that have to be obtained. In order to do this, we resort to the eigengap criterion, where a suitable number of groups is selected automatically based on the intensity and structure of the coupling among the sectors. We validate the proposed methodology considering a case study for Italy, inspecting how the coupling among clusters and sectors changes from the year 1995 to 2011, showing that in the years the Italian structure underwent deep changes, becoming more and more interdependent, i.e., a large part of the economy has become tightly coupled.

  1. Computational intelligence for big data analysis frontier advances and applications

    CERN Document Server

    Dehuri, Satchidananda; Sanyal, Sugata

    2015-01-01

    The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancements are supported with illustrative examples and its applications in handling real life problems. The applications are mostly undertaken from real life situations. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing. An elaborate bibliography is provided at the end of each chapter. The material in this book includes concepts, figures, graphs, and tables to guide researchers in the area of big data analysis and cloud computing.

  2. Cluster analysis of spontaneous preterm birth phenotypes identifies potential associations among preterm birth mechanisms

    Science.gov (United States)

    Esplin, M Sean; Manuck, Tracy A.; Varner, Michael W.; Christensen, Bryce; Biggio, Joseph; Bukowski, Radek; Parry, Samuel; Zhang, Heping; Huang, Hao; Andrews, William; Saade, George; Sadovsky, Yoel; Reddy, Uma M.; Ilekis, John

    2015-01-01

    Objective We sought to employ an innovative tool based on common biological pathways to identify specific phenotypes among women with spontaneous preterm birth (SPTB), in order to enhance investigators' ability to identify to highlight common mechanisms and underlying genetic factors responsible for SPTB. Study Design A secondary analysis of a prospective case-control multicenter study of SPTB. All cases delivered a preterm singleton at SPTB ≤34.0 weeks gestation. Each woman was assessed for the presence of underlying SPTB etiologies. A hierarchical cluster analysis was used to identify groups of women with homogeneous phenotypic profiles. One of the phenotypic clusters was selected for candidate gene association analysis using VEGAS software. Results 1028 women with SPTB were assigned phenotypes. Hierarchical clustering of the phenotypes revealed five major clusters. Cluster 1 (N=445) was characterized by maternal stress, cluster 2 (N=294) by premature membrane rupture, cluster 3 (N=120) by familial factors, and cluster 4 (N=63) by maternal comorbidities. Cluster 5 (N=106) was multifactorial, characterized by infection (INF), decidual hemorrhage (DH) and placental dysfunction (PD). These three phenotypes were highly correlated by Chi-square analysis [PD and DH (p<2.2e-6); PD and INF (p=6.2e-10); INF and DH (p=0.0036)]. Gene-based testing identified the INS (insulin) gene as significantly associated with cluster 3 of SPTB. Conclusion We identified 5 major clusters of SPTB based on a phenotype tool and hierarchal clustering. There was significant correlation between several of the phenotypes. The INS gene was associated with familial factors underlying SPTB. PMID:26070700

  3. Advance care planning - a multi-centre cluster randomised clinical trial

    DEFF Research Database (Denmark)

    Rietjens, Judith A C; Korfage, Ida J; Dunleavy, Lesley

    2016-01-01

    BACKGROUND: Awareness of preferences regarding medical care should be a central component of the care of patients with advanced cancer. Open communication can facilitate this but can occur in an ad hoc or variable manner. Advance care planning (ACP) is a formalized process of communication between...... patients, relatives and professional caregivers about patients' values and care preferences. It raises awareness of the need to anticipate possible future deterioration of health. ACP has the potential to improve current and future healthcare decision-making, provide patients with a sense of control....... If a patient dies within a year after inclusion, a relative will be asked to complete a questionnaire on end-of-life care. Use of medical care will be assessed by checking medical files. The primary endpoint is patients' quality of life at 2.5 months post-inclusion. Secondary endpoints are the extent to which...

  4. Geographic clustering of firms and urban form: a multivariate analysis

    Science.gov (United States)

    Maoh, Hanna; Kanaroglou, Pavlos

    2007-04-01

    This paper provides an empirical framework that applies spatial statistics methods to assess the relation between the change in the geographical clustering of firms and the emergence of urban form. We contend that where firms locate and eventually cluster give rise to the way commercial and industrial land uses are organized over space, which in turn defines the shape of urban form. Accordingly, the objectives of our work are twofold: (1) to identify the extent and shape of firm clustering and co-location at the intra-metropolitan level, and (2) examine how the change in the geographic clustering of different industries contributes to decentralization and the evolution of urban form. Spatial statistics methods and tools were vital and helped to fulfill these objectives.

  5. Clustering analysis of malware behavior using Self Organizing Map

    DEFF Research Database (Denmark)

    Pirscoveanu, Radu-Stefan; Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    Map, an unsupervised machine learning algorithm, for generating clusters that capture the similarities between malware behavior. A data set of approximately 270,000 samples was used to generate the behavioral profile of malicious types in order to compare the outcome of the proposed clustering...... accurate results based on the clusters created by competitive and cooperative algorithms like Self Organizing Map that better describe the behavioral profile of malware....... approach with the labels collected from 57 Antivirus vendors using VirusTotal. Upon evaluating the results, the paper concludes on shortcomings of relying on AV vendors for labeling malware samples. In order to solve the problem, a cluster-based classification is proposed, which should provide more...

  6. First PPMXL photometric analysis of open cluster Ruprecht 15

    Institute of Scientific and Technical Information of China (English)

    Ashraf Latif Tadross

    2012-01-01

    We present the first in a series studying the astrophysical parameters of open clusters using the PPMXL* database whose data are applied to study Ruprecht 15.The astrophysical parameters of Ruprecht 15 have been estimated for the first time.

  7. Advanced symbolic analysis for VLSI systems methods and applications

    CERN Document Server

    Shi, Guoyong; Tlelo Cuautle, Esteban

    2014-01-01

    This book provides comprehensive coverage of the recent advances in symbolic analysis techniques for design automation of nanometer VLSI systems. The presentation is organized in parts of fundamentals, basic implementation methods and applications for VLSI design. Topics emphasized include  statistical timing and crosstalk analysis, statistical and parallel analysis, performance bound analysis and behavioral modeling for analog integrated circuits . Among the recent advances, the Binary Decision Diagram (BDD) based approaches are studied in depth. The BDD-based hierarchical symbolic analysis approaches, have essentially broken the analog circuit size barrier. In particular, this book   • Provides an overview of classical symbolic analysis methods and a comprehensive presentation on the modern  BDD-based symbolic analysis techniques; • Describes detailed implementation strategies for BDD-based algorithms, including the principles of zero-suppression, variable ordering and canonical reduction; • Int...

  8. Advanced Color Image Processing and Analysis

    CERN Document Server

    2013-01-01

    This volume does much more than survey modern advanced color processing. Starting with a historical perspective on ways we have classified color, it sets out the latest numerical techniques for analyzing and processing colors, the leading edge in our search to accurately record and print what we see. The human eye perceives only a fraction of available light wavelengths, yet we live in a multicolor world of myriad shining hues. Colors rich in metaphorical associations make us “purple with rage” or “green with envy” and cause us to “see red.” Defining colors has been the work of centuries, culminating in today’s complex mathematical coding that nonetheless remains a work in progress: only recently have we possessed the computing capacity to process the algebraic matrices that reproduce color more accurately. With chapters on dihedral color and image spectrometers, this book provides technicians and researchers with the knowledge they need to grasp the intricacies of today’s color imaging.

  9. Development and optimization of SPECT gated blood pool cluster analysis for the prediction of CRT outcome

    Energy Technology Data Exchange (ETDEWEB)

    Lalonde, Michel, E-mail: mlalonde15@rogers.com; Wassenaar, Richard [Department of Physics, Carleton University, Ottawa, Ontario K1S 5B6 (Canada); Wells, R. Glenn; Birnie, David; Ruddy, Terrence D. [Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario K1Y 4W7 (Canada)

    2014-07-15

    Purpose: Phase analysis of single photon emission computed tomography (SPECT) radionuclide angiography (RNA) has been investigated for its potential to predict the outcome of cardiac resynchronization therapy (CRT). However, phase analysis may be limited in its potential at predicting CRT outcome as valuable information may be lost by assuming that time-activity curves (TAC) follow a simple sinusoidal shape. A new method, cluster analysis, is proposed which directly evaluates the TACs and may lead to a better understanding of dyssynchrony patterns and CRT outcome. Cluster analysis algorithms were developed and optimized to maximize their ability to predict CRT response. Methods: About 49 patients (N = 27 ischemic etiology) received a SPECT RNA scan as well as positron emission tomography (PET) perfusion and viability scans prior to undergoing CRT. A semiautomated algorithm sampled the left ventricle wall to produce 568 TACs from SPECT RNA data. The TACs were then subjected to two different cluster analysis techniques, K-means, and normal average, where several input metrics were also varied to determine the optimal settings for the prediction of CRT outcome. Each TAC was assigned to a cluster group based on the comparison criteria and global and segmental cluster size and scores were used as measures of dyssynchrony and used to predict response to CRT. A repeated random twofold cross-validation technique was used to train and validate the cluster algorithm. Receiver operating characteristic (ROC) analysis was used to calculate the area under the curve (AUC) and compare results to those obtained for SPECT RNA phase analysis and PET scar size analysis methods. Results: Using the normal average cluster analysis approach, the septal wall produced statistically significant results for predicting CRT results in the ischemic population (ROC AUC = 0.73;p < 0.05 vs. equal chance ROC AUC = 0.50) with an optimal operating point of 71% sensitivity and 60% specificity. Cluster

  10. Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation

    Directory of Open Access Journals (Sweden)

    Martinez Fernando J

    2010-03-01

    Full Text Available Abstract Background Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity. Methods We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster. Results We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1 post-bronchodilator FEV1 percent predicted, 2 percent bronchodilator responsiveness, and quantitative CT measurements of 3 apical emphysema and 4 airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1 emphysema predominant, 2 bronchodilator responsive, with higher FEV1; 3 discordant, with a lower FEV1 despite less severe emphysema and lower airway wall thickness, and 4 airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant was associated with TGFB1 SNP rs1800470. Conclusions Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.

  11. A Spitzer Survey of Young Stellar Clusters within One Kiloparsec of the Sun: Cluster Core Extraction and Basic Structural Analysis

    CERN Document Server

    Gutermuth, R A; Myers, P C; Allen, L E; Pipher, J L; Fazio, G G

    2009-01-01

    We present a uniform mid-infrared imaging and photometric survey of 36 young, nearby, star-forming clusters and groups using {\\it Spitzer} IRAC and MIPS. We have confidently identified and classified 2548 young stellar objects using recently established mid-infrared color-based methods. We have devised and applied a new algorithm for the isolation of local surface density enhancements from point source distributions, enabling us to extract the overdense cores of the observed star forming regions for further analysis. We have compiled several basic structural measurements of these cluster cores from the data, such as mean surface densities of sources, cluster core radii, and aspect ratios, in order to characterize the ranges for these quantities. We find that a typical cluster core is 0.39 pc in radius, has 26 members with infrared excess in a ratio of Class II to Class I sources of 3.7, is embedded in a $A_K$=0.8 mag cloud clump, and has a surface density of 60 pc$^{-2}$. We examine the nearest neighbor dista...

  12. Advances in microfluidics for environmental analysis.

    Science.gov (United States)

    Jokerst, Jana C; Emory, Jason M; Henry, Charles S

    2012-01-07

    During the past few years, a growing number of groups have recognized the utility of microfluidic devices for environmental analysis. Microfluidic devices offer a number of advantages and in many respects are ideally suited to environmental analyses. Challenges faced in environmental monitoring, including the ability to handle complex and highly variable sample matrices, lead to continued growth and research. Additionally, the need to operate for days to months in the field requires further development of robust, integrated microfluidic systems. This review examines recently published literature on the applications of microfluidic systems for environmental analysis and provides insight in the future direction of the field.

  13. The clustering of massive Primordial Black Holes as Dark Matter: measuring their mass distribution with Advanced LIGO

    CERN Document Server

    Clesse, Sebastien

    2016-01-01

    The recent detection by Advanced LIGO of gravitational waves (GW) from the merging of a binary black hole system sets new limits on the merging rates of massive primordial black holes (PBH) that could be a significant fraction or even the totality of the dark matter in the Universe. aLIGO opens the way to the determination of the distribution and clustering of such massive PBH. If PBH clusters have a similar density to the one observed in ultra-faint dwarf galaxies, we find merging rates comparable to aLIGO expectations. Massive PBH dark matter predicts the existence of thousands of those dwarf galaxies where star formation is unlikely because of gas accretion onto PBH, which would possibly provide a solution to the missing satellite and too-big-to-fail problems. Finally, we study the possibility of using aLIGO and future GW antennas to measure the abundance and mass distribution of PBH in the range [5 - 200] Msun to 10\\% accuracy.

  14. The clustering of massive Primordial Black Holes as Dark Matter: Measuring their mass distribution with advanced LIGO

    Science.gov (United States)

    Clesse, Sébastien; García-Bellido, Juan

    2017-03-01

    The recent detection by Advanced LIGO of gravitational waves (GW) from the merging of a binary black hole system sets new limits on the merging rates of massive primordial black holes (PBH) that could be a significant fraction or even the totality of the dark matter in the Universe. aLIGO opens the way to the determination of the distribution and clustering of such massive PBH. If PBH clusters have a similar density to the one observed in ultra-faint dwarf galaxies, we find merging rates comparable to aLIGO expectations. Massive PBH dark matter predicts the existence of thousands of those dwarf galaxies where star formation is unlikely because of gas accretion onto PBH, which would possibly provide a solution to the missing satellite and too-big-to-fail problems. Finally, we study the possibility of using aLIGO and future GW antennas to measure the abundance and mass distribution of PBH in the range [5-200] M⊙ to 10% accuracy.

  15. Modeling and analysis of advanced binary cycles

    Energy Technology Data Exchange (ETDEWEB)

    Gawlik, K.

    1997-12-31

    A computer model (Cycle Analysis Simulation Tool, CAST) and a methodology have been developed to perform value analysis for small, low- to moderate-temperature binary geothermal power plants. The value analysis method allows for incremental changes in the levelized electricity cost (LEC) to be determined between a baseline plant and a modified plant. Thermodynamic cycle analyses and component sizing are carried out in the model followed by economic analysis which provides LEC results. The emphasis of the present work is on evaluating the effect of mixed working fluids instead of pure fluids on the LEC of a geothermal binary plant that uses a simple Organic Rankine Cycle. Four resources were studied spanning the range of 265{degrees}F to 375{degrees}F. A variety of isobutane and propane based mixtures, in addition to pure fluids, were used as working fluids. This study shows that the use of propane mixtures at a 265{degrees}F resource can reduce the LEC by 24% when compared to a base case value that utilizes commercial isobutane as its working fluid. The cost savings drop to 6% for a 375{degrees}F resource, where an isobutane mixture is favored. Supercritical cycles were found to have the lowest cost at all resources.

  16. Marketing Mix Formulation for Higher Education: An Integrated Analysis Employing Analytic Hierarchy Process, Cluster Analysis and Correspondence Analysis

    Science.gov (United States)

    Ho, Hsuan-Fu; Hung, Chia-Chi

    2008-01-01

    Purpose: The purpose of this paper is to examine how a graduate institute at National Chiayi University (NCYU), by using a model that integrates analytic hierarchy process, cluster analysis and correspondence analysis, can develop effective marketing strategies. Design/methodology/approach: This is primarily a quantitative study aimed at…

  17. Identification and comparative analysis of the protocadherin cluster in a reptile, the green anole lizard.

    Directory of Open Access Journals (Sweden)

    Xiao-Juan Jiang

    Full Text Available BACKGROUND: The vertebrate protocadherins are a subfamily of cell adhesion molecules that are predominantly expressed in the nervous system and are believed to play an important role in establishing the complex neural network during animal development. Genes encoding these molecules are organized into a cluster in the genome. Comparative analysis of the protocadherin subcluster organization and gene arrangements in different vertebrates has provided interesting insights into the history of vertebrate genome evolution. Among tetrapods, protocadherin clusters have been fully characterized only in mammals. In this study, we report the identification and comparative analysis of the protocadherin cluster in a reptile, the green anole lizard (Anolis carolinensis. METHODOLOGY/PRINCIPAL FINDINGS: We show that the anole protocadherin cluster spans over a megabase and encodes a total of 71 genes. The number of genes in the anole protocadherin cluster is significantly higher than that in the coelacanth (49 genes and mammalian (54-59 genes clusters. The anole protocadherin genes are organized into four subclusters: the delta, alpha, beta and gamma. This subcluster organization is identical to that of the coelacanth protocadherin cluster, but differs from the mammalian clusters which lack the delta subcluster. The gene number expansion in the anole protocadherin cluster is largely due to the extensive gene duplication in the gammab subgroup. Similar to coelacanth and elephant shark protocadherin genes, the anole protocadherin genes have experienced a low frequency of gene conversion. CONCLUSIONS/SIGNIFICANCE: Our results suggest that similar to the protocadherin clusters in other vertebrates, the evolution of anole protocadherin cluster is driven mainly by lineage-specific gene duplications and degeneration. Our analysis also shows that loss of the protocadherin delta subcluster in the mammalian lineage occurred after the divergence of mammals and reptiles

  18. Bohai crude oil identification by gas chromatogram fingerprinting quantitative analysis coupled with cluster analysis

    Institute of Scientific and Technical Information of China (English)

    SUN Peiyan; BAO Mutai; GAO Zhenhui; LI Mei; ZHAO Yuhui; WANG Xinping; ZHOU Qing; WANG Xiulin

    2006-01-01

    By gas chromatogram, six crude oils fingerprinting distributed in four oilfields and four oil platforms were analyzed and the corresponding normal paraffin hydrocarbon (including pristane and phytane) concentration was obtained by the internal standard method. The normal paraffin hydrocarbon distribution patterns of six crude oils were built and compared. The cluster analysis on the normal paraffin hydrocarbon concentration was conducted for classification and some ratios of oils were used for oils comparison. The results indicated: there was a clear difference within different crude oils in different oil fields and a small difference between the crude oils in the same oil platform. The normal paraffin hydrocarbon distribution pattern and ratios, as well as the cluster analysis on the normal paraffin hydrocarbon concentration can have a better differentiation result for the crude oils with small difference than the original gas chromatogram.

  19. Advanced CMOS Radiation Effects Testing and Analysis

    Science.gov (United States)

    Pellish, J. A.; Marshall, P. W.; Rodbell, K. P.; Gordon, M. S.; LaBel, K. A.; Schwank, J. R.; Dodds, N. A.; Castaneda, C. M.; Berg, M. D.; Kim, H. S.; Phan, A. M.; Seidleck, C. M.

    2014-01-01

    Presentation at the annual NASA Electronic Parts and Packaging (NEPP) Program Electronic Technology Workshop (ETW). The material includes an update of progress in this NEPP task area over the past year, which includes testing, evaluation, and analysis of radiation effects data on the IBM 32 nm silicon-on-insulator (SOI) complementary metal oxide semiconductor (CMOS) process. The testing was conducted using test vehicles supplied by directly by IBM.

  20. Decision Analysis of Advanced Scout Helicopter Candidates

    Science.gov (United States)

    1980-01-01

    assist the ASH SSG by constructing a comprehensive ASH evaluation model utilizing multi-attribute utility assessment ( MAUA ) modeling. ~~UA is a forre...results are included as well. The output of the MAUA model is a numerical representation of the worth of each ASH candidate. These numbers are...instance of a methodology called Multi-Attribute Utility Analysis ( MAUA ). In general, MAUA is characterized by the represen- tation of outcomes in terms

  1. Identifying At-Risk Students in General Chemistry via Cluster Analysis of Affective Characteristics

    Science.gov (United States)

    Chan, Julia Y. K.; Bauer, Christopher F.

    2014-01-01

    The purpose of this study is to identify academically at-risk students in first-semester general chemistry using affective characteristics via cluster analysis. Through the clustering of six preselected affective variables, three distinct affective groups were identified: low (at-risk), medium, and high. Students in the low affective group…

  2. Cluster analysis of European Y-chromosomal STR haplotypes using the discrete Laplace method

    DEFF Research Database (Denmark)

    Andersen, Mikkel Meyer; Eriksen, Poul Svante; Morling, Niels

    2014-01-01

    method can be used for cluster analysis to further validate the discrete Laplace method. A very important practical fact is that the calculations can be performed on a normal computer. We identified two sub-clusters of the Eastern and Western European Y-STR haplotypes similar to results of previous...

  3. Applying Clustering to Statistical Analysis of Student Reasoning about Two-Dimensional Kinematics

    Science.gov (United States)

    Springuel, R. Padraic; Wittman, Michael C.; Thompson, John R.

    2007-01-01

    We use clustering, an analysis method not presently common to the physics education research community, to group and characterize student responses to written questions about two-dimensional kinematics. Previously, clustering has been used to analyze multiple-choice data; we analyze free-response data that includes both sketches of vectors and…

  4. The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS

    NARCIS (Netherlands)

    Zhou, Q.; Leng, F.; Leydesdorff, L.

    2015-01-01

    Purpose: To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach: We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare the

  5. Co-clustering : A versatile Tool for Data Analysis in Biomedical Informatics

    OpenAIRE

    Yoon, Sungroh; Benini, Luca; De Micheli, Giovanni

    2007-01-01

    Co-clustering has not been much exploited in biomedical in- formatics, despite its success in other domains. Most of the previous applications were limited to analyzing gene expression data. We performed co-clustering analysis on other types of data and obtained promising results, as summarized in this paper.

  6. Identification and structural analysis of a novel snoRNA gene cluster from Arabidopsis thaliana

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A Z2 snoRNA gene cluster,consisting of four antisense snoRNA genes, was identified from Arabidopsis thaliana. The sequence and structural analysis showed that the Z2 snoRNA gene cluster might be transcribed as a polycistronic precursor from an upstream promoter, and the intergenic spacers of the gene cluster encode the 'hairpin' structures similar to the processing recognition signals of yeast Saccharomyces cerevisiae polycistronic snoRNA precursor. The results also revealed that plant snoRNA gene with multiple copies is a characteristic in common, and provides a good system for further revealing the transcription and expression mechanism of plant snoRNA gene cluster.

  7. Analysis of cost data in a cluster-randomized, controlled trial: comparison of methods

    DEFF Research Database (Denmark)

    Sokolowski, Ineta; Ørnbøl, Eva; Rosendal, Marianne;

    in clusters of general practices.   There have been suggestions to apply different methods, e.g., the non-parametric bootstrap, to highly skewed data from pragmatic randomized trials without clusters, but there is very little information about how to analyse skewed data from cluster-randomized trials. Many...... studies have used non-valid analysis of skewed data. We propose two different methods to compare mean cost in two groups. Firstly, we use a non-parametric bootstrap method where the re-sampling takes place on two levels in order to take into account the cluster effect. Secondly, we proceed with a log...

  8. Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio

    Directory of Open Access Journals (Sweden)

    Elena ANDREI (DRAGOMIR

    2012-08-01

    Full Text Available Methods underlying cluster analysis are very useful in data analysis, especially when the processed volume of data is very large, so that it becomes impossible to extract essential information, unless specific instruments are used to summarize and structure the gross information. In this context, cluster analysis techniques are used particularly, for systematic information analysis. The aim of this article is to build an useful model for banking field, based on data mining techniques, by dividing the groups of borrowers into clusters, in order to obtain a profile of the customers (debtors and good payers. We assume that a class is appropriate if it contains members that have a high degree of similarity and the standard method for measuring the similarity within a group shows the lowest variance. After clustering, data mining techniques are implemented on the cluster with bad debtors, reaching a very high accuracy after implementation. The paper is structured as follows: Section 2 describes the model for data analysis based on a specific scoring model that we proposed. In section 3, we present a cluster analysis using K-means algorithm and the DM models are applied on a specific cluster. Section 4 shows the conclusions.

  9. Cluster Analysis of Customer Reviews Extracted from Web Pages

    Directory of Open Access Journals (Sweden)

    S. Shivashankar

    2010-01-01

    Full Text Available As e-commerce is gaining popularity day by day, the web has become an excellent source for gathering customer reviews / opinions by the market researchers. The number of customer reviews that a product receives is growing at very fast rate (It could be in hundreds or thousands. Customer reviews posted on the websites vary greatly in quality. The potential customer has to read necessarily all the reviews irrespective of their quality to make a decision on whether to purchase the product or not. In this paper, we make an attempt to assess are view based on its quality, to help the customer make a proper buying decision. The quality of customer review is assessed as most significant, more significant, significant and insignificant.A novel and effective web mining technique is proposed for assessing a customer review of a particular product based on the feature clustering techniques, namely, k-means method and fuzzy c-means method. This is performed in three steps : (1Identify review regions and extract reviews from it, (2 Extract and cluster the features of reviews by a clustering technique and then assign weights to the features belonging to each of the clusters (groups and (3 Assess the review by considering the feature weights and group belongingness. The k-means and fuzzy c-means clustering techniques are implemented and tested on customer reviews extracted from web pages. Performance of these techniques are analyzed.

  10. A Bayesian Analysis of the Ages of Four Open Clusters

    CERN Document Server

    Jeffery, Elizabeth J; van Dyk, David A; Stenning, David C; Robinson, Elliot; Stein, Nathan; Jefferys, W H

    2016-01-01

    In this paper we apply a Bayesian technique to determine the best fit of stellar evolution models to find the main sequence turn off age and other cluster parameters of four intermediate-age open clusters: NGC 2360, NGC 2477, NGC 2660, and NGC 3960. Our algorithm utilizes a Markov chain Monte Carlo technique to fit these various parameters, objectively finding the best-fit isochrone for each cluster. The result is a high-precision isochrone fit. We compare these results with the those of traditional "by-eye" isochrone fitting methods. By applying this Bayesian technique to NGC 2360, NGC 2477, NGC 2660, and NGC 3960, we determine the ages of these clusters to be 1.35 +/- 0.05, 1.02 +/- 0.02, 1.64 +/- 0.04, and 0.860 +/- 0.04 Gyr, respectively. The results of this paper continue our effort to determine cluster ages to higher precision than that offered by these traditional methods of isochrone fitting.

  11. Advanced analysis for structural steel building design

    Institute of Scientific and Technical Information of China (English)

    Wai Fah CHEN

    2008-01-01

    The 2005 AISC LRFD Specifications for Structural Steel Buildings are making it possible for designers to recognize explicitly the structural resistance provided within the elastic and inelastic ranges of beha-vior and up to the maximum load limit state. There is an increasing awareness of the need for practical second-order analysis approaches for a direct determination of overall structural system response. This paper attempts to present a simple, concise and reasonably comprehens-ive introduction to some of the theoretical and practical approaches which have been used in the traditional and modern processes of design of steel building structures.

  12. Advanced calculus an introduction to classical analysis

    CERN Document Server

    Brand, Louis

    2006-01-01

    A course in analysis that focuses on the functions of a real variable, this text is geared toward upper-level undergraduate students. It introduces the basic concepts in their simplest setting and illustrates its teachings with numerous examples, practical theorems, and coherent proofs.Starting with the structure of the system of real and complex numbers, the text deals at length with the convergence of sequences and series and explores the functions of a real variable and of several variables. Subsequent chapters offer a brief and self-contained introduction to vectors that covers important a

  13. Cluster-cluster clustering

    Science.gov (United States)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C. S.

    1985-01-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales.

  14. Cluster-cluster clustering

    Energy Technology Data Exchange (ETDEWEB)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.

    1985-08-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references.

  15. Analysis of protein profiles using fuzzy clustering methods

    DEFF Research Database (Denmark)

    Karemore, Gopal Raghunath; Ukendt, Sujatha; Rai, Lavanya

    clustering methods for their classification followed by various validation  measures.    The  clustering  algorithms  used  for  the  study  were  K-  means,  K- medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva.  The results presented in this study  conclude  that  the  protein  profiles  of  tissue......  samples  recorded  by  using  the  HPLC- LIF  system  and  the  data  analyzed  by  clustering  algorithms  quite  successfully  classifies them as belonging from normal and malignant conditions....

  16. Theoretical Analysis of Structures of Ga4N4 Clusters

    Institute of Scientific and Technical Information of China (English)

    宋斌; 曹培林

    2003-01-01

    The structures and energies of a Ga4N4 cluster have been calculated using a full-potential linear-muffin-tin-orbital molecular-dynamics (FP-LMTO MD) method. We obtained twenty-four structures for a Ga4N4 cluster. The most stable structure we obtained is a Cs three-dimensional structure, the energy of which is lower than that of the C2v symmetry structure proposed by Kandalam et al. [J. Phys. Chem. B 106 (2002) 1945] The calculated results show that the isomer with an N3 subunit is preferred, supporting the previous result made by Kandalam et al.We found that the most stable structure of Ga4N4 clusters presented semiconductor-like properties through the calculation of the density of states.

  17. Genetic Diversity among Parents of Hybrid Rice Based on Cluster Analysis of Morphological Traits and Simple Sequence Repeat Markers

    Institute of Scientific and Technical Information of China (English)

    WANG Sheng-jun; LU Zuo-mei; WAN Jian-min

    2006-01-01

    The genetic diversity of 41 parental lines popularized in commercial hybrid rice production in China was studied by using cluster analysis of morphological traits and simple sequence repeat (SSR) markers. Forty-one entries were assigned into two clusters (I.e. Early or medium-maturing cluster; medium or late-maturing cluster) and further assigned into six sub-clusters based on morphological trait cluster analysis. The early or medium-maturing cluster was composed of 15 maintainer lines, four early-maturing restorer lines and two thermo-sensitive genic male sterile lines, and the medium or late-maturing cluster included 16 restorer lines and 4 medium or late-maturing maintainer lines. Moreover, the SSR cluster analysis classified 41 entries into two clusters (I.e. Maintainer line cluster and restorer line cluster) and seven sub-clusters. The maintainer line cluster consisted of all 19 maintainer lines, two thermo-sensitive genic male sterile lines, while the restorer line cluster was composed of all 20 restorer lines. The SSR analysis fitted better with the pedigree information. From the views on hybrid rice breeding, the results suggested that SSR analysis might be a better method to study the diversity of parental lines in indica hybrid rice.

  18. Linking advanced fracture models to structural analysis

    Energy Technology Data Exchange (ETDEWEB)

    Chiesa, Matteo

    2001-07-01

    Shell structures with defects occur in many situations. The defects are usually introduced during the welding process necessary for joining different parts of the structure. Higher utilization of structural materials leads to a need for accurate numerical tools for reliable prediction of structural response. The direct discretization of the cracked shell structure with solid finite elements in order to perform an integrity assessment of the structure in question leads to large size problems, and makes such analysis infeasible in structural application. In this study a link between local material models and structural analysis is outlined. An ''ad hoc'' element formulation is used in order to connect complex material models to the finite element framework used for structural analysis. An improved elasto-plastic line spring finite element formulation, used in order to take cracks into account, is linked to shell elements which are further linked to beam elements. In this way one obtain a global model of the shell structure that also accounts for local flexibilities and fractures due to defects. An important advantage with such an approach is a direct fracture mechanics assessment e.g. via computed J-integral or CTOD. A recent development in this approach is the notion of two-parameter fracture assessment. This means that the crack tip stress tri-axiality (constraint) is employed in determining the corresponding fracture toughness, giving a much more realistic capacity of cracked structures. The present thesis is organized in six research articles and an introductory chapter that reviews important background literature related to this work. Paper I and II address the performance of shell and line spring finite elements as a cost effective tool for performing the numerical calculation needed to perform a fracture assessment. In Paper II a failure assessment, based on the testing of a constraint-corrected fracture mechanics specimen under tension, is

  19. Analysis of the Advantages of Creating Border Clusters

    Directory of Open Access Journals (Sweden)

    Liudmila Rosca-Sadurschi

    2015-08-01

    Full Text Available In a changing environment and rapid globalization, competitiveness of a country or region depends increasingly more effective in innovation. The main challenge for research and innovation is to facilitate the networking of companies and research laboratories. These networks can take the form of a highly integrated cross-border economic group, but may consist of action to facilitate business linkages and inter-laboratory, or cross-border clusters. The creation of these clusters requires performing several conditions but bring significant benefits to all stakeholders.

  20. Application of K-Means Algorithm for Cluster Analysis on Poverty of Provinces in Indonesia

    Directory of Open Access Journals (Sweden)

    Albert Verasius Dian Sano

    2016-06-01

    Full Text Available The objective of this study is to apply cluster analysis or also known as clustering on poverty data of provinces all over Indonesia.The problem is that the decision makers such as central government, local government and non-government organizations, which involve in poverty problems, need a tool to support decision-making process related to social welfare problems. The method used in the cluster analysis is k-means algorithm. The data used in this study were drawn from Badan Pusat Statistik (BPS or Central Bureau of Statistics on 2014.Cluster analysis in this study took characteristics of data such as absolute poverty of each province, relative number or percentage of poverty of each province, and the level of depth index poverty of each province in Indonesia. Results of cluster analysis in this study were presented in the form of grouping of clusters' members visually. Cluster analysis in the study could be used to identify more quickly and efficiently on poverty chart of all provinces all over Indonesia. The results of such identification can be used by policy makers who have interests of eradicating the problems associated with poverty and welfare distribution in Indonesia, ranging from government organizations, non-governmental organizations, and also private organizations.

  1. Multispectral laser imaging for advanced food analysis

    Science.gov (United States)

    Senni, L.; Burrascano, P.; Ricci, M.

    2016-07-01

    A hardware-software apparatus for food inspection capable of realizing multispectral NIR laser imaging at four different wavelengths is herein discussed. The system was designed to operate in a through-transmission configuration to detect the presence of unwanted foreign bodies inside samples, whether packed or unpacked. A modified Lock-In technique was employed to counterbalance the significant signal intensity attenuation due to transmission across the sample and to extract the multispectral information more efficiently. The NIR laser wavelengths used to acquire the multispectral images can be varied to deal with different materials and to focus on specific aspects. In the present work the wavelengths were selected after a preliminary analysis to enhance the image contrast between foreign bodies and food in the sample, thus identifying the location and nature of the defects. Experimental results obtained from several specimens, with and without packaging, are presented and the multispectral image processing as well as the achievable spatial resolution of the system are discussed.

  2. Advances in the environmental analysis of polychlorinated naphthalenes and toxaphene.

    Science.gov (United States)

    Kucklick, John R; Helm, Paul A

    2006-10-01

    Recent advances in the analysis of the chlorinated environmental pollutants polychlorinated naphthalenes (PCNs) and toxaphene are highlighted in this review. Method improvements have been realized for PCNs over the past decade in isomer-specific quantification, peak resolution, and the availability of mass-labeled standards. Toxaphene method advancements include the application of new capillary gas chromatographic (GC) stationary phases, mass spectrometry (MS), especially ion trap MS, and the availability of Standard Reference Materials that are value-assigned for total toxaphene and selected congener concentrations. An area of promise for the separation of complex mixtures such as PCNs and toxaphene is the development of multidimensional GC techniques. The need for continued advancements and efficiencies in the analysis of contaminants such as PCNs and toxaphene remains as monitoring requirements for these compound classes are established under international agreements.

  3. Advanced computational tools for 3-D seismic analysis

    Energy Technology Data Exchange (ETDEWEB)

    Barhen, J.; Glover, C.W.; Protopopescu, V.A. [Oak Ridge National Lab., TN (United States)] [and others

    1996-06-01

    The global objective of this effort is to develop advanced computational tools for 3-D seismic analysis, and test the products using a model dataset developed under the joint aegis of the United States` Society of Exploration Geophysicists (SEG) and the European Association of Exploration Geophysicists (EAEG). The goal is to enhance the value to the oil industry of the SEG/EAEG modeling project, carried out with US Department of Energy (DOE) funding in FY` 93-95. The primary objective of the ORNL Center for Engineering Systems Advanced Research (CESAR) is to spearhead the computational innovations techniques that would enable a revolutionary advance in 3-D seismic analysis. The CESAR effort is carried out in collaboration with world-class domain experts from leading universities, and in close coordination with other national laboratories and oil industry partners.

  4. The XMM Cluster Survey: X-ray analysis methodology

    CERN Document Server

    Lloyd-Davies, E J; Hosmer, Mark; Mehrtens, Nicola; Davidson, Michael; Sabirli, Kivanc; Mann, Robert G; Hilton, Matt; Liddle, Andrew R; Viana, Pedro T P; Campbell, Heather C; Collins, Chris A; Dubois, E Naomi; Freeman, Peter; Hoyle, Ben; Kay, Scott T; Kuwertz, Emma; Miller, Christopher J; Nichol, Robert C; Sahlen, Martin; Stanford, S Adam; Stott, John P

    2010-01-01

    The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM- Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling relations. In this paper we describe the data processing methodology applied to the 5776 XMM observations used to construct the current XCS source catalogue. A total of 3669 > 4-{\\sigma} cluster candidates with >50 background-subtracted X-ray counts are extracted from a total non-overlapping area suitable for cluster searching of 410 deg^2 . Of these, 1022 candidates are detected with >300 X-ray counts, and we demonstrate that robust temperature measurements can be obtained down to this count limit. We describe in detail the automated pipelines used to perform the spectral and surface brightness fitting for these sources, as well as to estimate redshifts from the X-ray data alone. A total of 517 (126) X-ray temperatures to a typical accuracy of <40 (<10) per cent have ...

  5. Dynamical analysis of the cluster pair: A3407 + A3408

    CERN Document Server

    Nascimento, R S; Trevisan, M; Carrasco, E R; Plana, H; Dupke, R

    2016-01-01

    We carried out a dynamical study of the galaxy cluster pair A3407 \\& A3408 based on a spectroscopic survey obtained with the 4 meter Blanco telescope at the CTIO, plus 6dF data, and ROSAT All-Sky-Survey. The sample consists of 122 member galaxies brighter than $m_R=20$. Our main goal is to probe the galaxy dynamics in this field and verify if the sample constitutes a single galaxy system or corresponds to an ongoing merging process. Statistical tests were applied to clusters members showing that both the composite system A3407 + A3408 as well as each individual cluster have Gaussian velocity distribution. A velocity gradient of $\\sim 847\\pm 114$ $\\rm km\\;s^{-1}$ was identified around the principal axis of the projected distribution of galaxies, indicating that the global field may be rotating. Applying the KMM algorithm to the distribution of galaxies we found that the solution with two clusters is better than the single unit solution at the 99\\% c.l. This is consistent with the X-ray distribution around ...

  6. Patterns of Brucellosis Infection Symptoms in Azerbaijan: A Latent Class Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Rita Ismayilova

    2014-01-01

    Full Text Available Brucellosis infection is a multisystem disease, with a broad spectrum of symptoms. We investigated the existence of clusters of infected patients according to their clinical presentation. Using national surveillance data from the Electronic-Integrated Disease Surveillance System, we applied a latent class cluster (LCC analysis on symptoms to determine clusters of brucellosis cases. A total of 454 cases reported between July 2011 and July 2013 were analyzed. LCC identified a two-cluster model and the Vuong-Lo-Mendell-Rubin likelihood ratio supported the cluster model. Brucellosis cases in the second cluster (19% reported higher percentages of poly-lymphadenopathy, hepatomegaly, arthritis, myositis, and neuritis and changes in liver function tests compared to cases of the first cluster. Patients in the second cluster had a severe brucellosis disease course and were associated with longer delay in seeking medical attention. Moreover, most of them were from Beylagan, a region focused on sheep and goat livestock production in south-central Azerbaijan. Patients in cluster 2 accounted for one-quarter of brucellosis cases and had a more severe clinical presentation. Delay in seeking medical care may explain severe illness. Future work needs to determine the factors that influence brucellosis case seeking and identify brucellosis species, particularly among cases from Beylagan.

  7. Polybrominated Diphenyl Ethers in Dryer Lint: An Advanced Analysis Laboratory

    Science.gov (United States)

    Thompson, Robert Q.

    2008-01-01

    An advanced analytical chemistry laboratory experiment is described that involves environmental analysis and gas chromatography-mass spectrometry. Students analyze lint from clothes dryers for traces of flame retardant chemicals, polybrominated diphenylethers (PBDEs), compounds receiving much attention recently. In a typical experiment, ng/g…

  8. Does published orthodontic research account for clustering effects during statistical data analysis?

    Science.gov (United States)

    Koletsi, Despina; Pandis, Nikolaos; Polychronopoulou, Argy; Eliades, Theodore

    2012-06-01

    In orthodontics, multiple site observations within patients or multiple observations collected at consecutive time points are often encountered. Clustered designs require larger sample sizes compared to individual randomized trials and special statistical analyses that account for the fact that observations within clusters are correlated. It is the purpose of this study to assess to what degree clustering effects are considered during design and data analysis in the three major orthodontic journals. The contents of the most recent 24 issues of the American Journal of Orthodontics and Dentofacial Orthopedics (AJODO), Angle Orthodontist (AO), and European Journal of Orthodontics (EJO) from December 2010 backwards were hand searched. Articles with clustering effects and whether the authors accounted for clustering effects were identified. Additionally, information was collected on: involvement of a statistician, single or multicenter study, number of authors in the publication, geographical area, and statistical significance. From the 1584 articles, after exclusions, 1062 were assessed for clustering effects from which 250 (23.5 per cent) were considered to have clustering effects in the design (kappa = 0.92, 95 per cent CI: 0.67-0.99 for inter rater agreement). From the studies with clustering effects only, 63 (25.20 per cent) had indicated accounting for clustering effects. There was evidence that the studies published in the AO have higher odds of accounting for clustering effects [AO versus AJODO: odds ratio (OR) = 2.17, 95 per cent confidence interval (CI): 1.06-4.43, P = 0.03; EJO versus AJODO: OR = 1.90, 95 per cent CI: 0.84-4.24, non-significant; and EJO versus AO: OR = 1.15, 95 per cent CI: 0.57-2.33, non-significant). The results of this study indicate that only about a quarter of the studies with clustering effects account for this in statistical data analysis.

  9. Recent Advances in Multidisciplinary Analysis and Optimization, part 3

    Science.gov (United States)

    Barthelemy, Jean-Francois M. (Editor)

    1989-01-01

    This three-part document contains a collection of technical papers presented at the Second NASA/Air Force Symposium on Recent Advances in Multidisciplinary Analysis and Optimization, held September 28-30, 1988 in Hampton, Virginia. The topics covered include: aircraft design, aeroelastic tailoring, control of aeroelastic structures, dynamics and control of flexible structures, structural design, design of large engineering systems, application of artificial intelligence, shape optimization, software development and implementation, and sensitivity analysis.

  10. Digital spectral analysis parametric, non-parametric and advanced methods

    CERN Document Server

    Castanié, Francis

    2013-01-01

    Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature.The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models.An entire chapter is devoted to the non-parametric methods most widely used in industry.High resolution methods a

  11. Ranking and clustering countries and their products; a network analysis

    CERN Document Server

    Caldarelli, Guido; Gabrielli, Andrea; Pietronero, Luciano; Scala, Antonio; Tacchella, Andrea

    2011-01-01

    In this paper we analyze the network of countries and products from UN data on country production. We define the country-country and product-product networks and we introduce a novel method of community detection based on elements similarity. As a result we find that country clustering reveals unexpected socio-geographic links among the most competing countries. On the same footings the products clustering can be efficiently used for a bottom-up classification of produced goods. Furthermore we define a procedure to rank different countries and their products over the global market. These analyses are a good proxy of country GDP and therefore could be possibly used to determine the robustness of a country economy.

  12. Functional Analysis of the Fusarielin Biosynthetic Gene Cluster

    Directory of Open Access Journals (Sweden)

    Aida Droce

    2016-12-01

    Full Text Available Fusarielins are polyketides with a decalin core produced by various species of Aspergillus and Fusarium. Although the responsible gene cluster has been identified, the biosynthetic pathway remains to be elucidated. In the present study, members of the gene cluster were deleted individually in a Fusarium graminearum strain overexpressing the local transcription factor. The results suggest that a trans-acting enoyl reductase (FSL5 assists the polyketide synthase FSL1 in biosynthesis of a polyketide product, which is released by hydrolysis by a trans-acting thioesterase (FSL2. Deletion of the epimerase (FSL3 resulted in accumulation of an unstable compound, which could be the released product. A novel compound, named prefusarielin, accumulated in the deletion mutant of the cytochrome P450 monooxygenase FSL4. Unlike the known fusarielins from Fusarium, this compound does not contain oxygenized decalin rings, suggesting that FSL4 is responsible for the oxygenation.

  13. ENVIRONMENTAL OBJECTIVE ANALYSIS, RANKING AND CLUSTERING OF HUNGARIAN CITIES

    Directory of Open Access Journals (Sweden)

    LÁSZLÓ MAKRA

    2008-12-01

    Full Text Available The aim of the study was to rank and classify Hungarian cities and counties according to their environmental quality and level of environmental awareness. Ranking of the Hungarian cities and counties are represented on their „Green Cities Index” and „Green Counties Index” values. According to the methodology shown in Part 1, cities and counties were grouped on different classification techniques and efficacy of the classification was analysed. However, they did not give acceptable results either for the cities, or for the counties. According to the parameters of the here mentioned three algorithms, reasonable structures were not found in any clustering. Clusters received applying algorithm fanny, though having weak structure, indicate large and definite regions in Hungary, which can be circumscribed by clear geographical objects.

  14. Advances in independent component analysis and learning machines

    CERN Document Server

    Bingham, Ella; Laaksonen, Jorma; Lampinen, Jouko

    2015-01-01

    In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t

  15. The DANCE Project: Dynamical Analysis of Nearby Clusters

    Science.gov (United States)

    Bouy, H.; Bertin, E.; Cuillandre, J. C.; Moraux, E.; Bouvier, J.; Arevalo Sánchez, M.; Barrado Y Navascués, D.

    We present the results of the DANCE project, a ground-based survey meant to prepare and complement Gaia i) down to the planetary mass regime; ii) in regions of high extinction. The DANCE project takes advantage of archival wide-field surveys to derive precise astrometry, and in particular proper motions, for millions of stars in young nearby associations. We present the first preliminary results obtained for the Pleiades cluster, as well as our immediate objectives for other associations.

  16. Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

    Energy Technology Data Exchange (ETDEWEB)

    Data Analysis and Visualization (IDAV) and the Department of Computer Science, University of California, Davis, One Shields Avenue, Davis CA 95616, USA,; nternational Research Training Group ``Visualization of Large and Unstructured Data Sets,' ' University of Kaiserslautern, Germany; Computational Research Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA; Genomics Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA; Life Sciences Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley CA 94720, USA,; Computer Science Division,University of California, Berkeley, CA, USA,; Computer Science Department, University of California, Irvine, CA, USA,; All authors are with the Berkeley Drosophila Transcription Network Project, Lawrence Berkeley National Laboratory,; Rubel, Oliver; Weber, Gunther H.; Huang, Min-Yu; Bethel, E. Wes; Biggin, Mark D.; Fowlkes, Charless C.; Hendriks, Cris L. Luengo; Keranen, Soile V. E.; Eisen, Michael B.; Knowles, David W.; Malik, Jitendra; Hagen, Hans; Hamann, Bernd

    2008-05-12

    The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors.

  17. SSR Cluster and Fertility Loci Analysis of GC13

    Institute of Scientific and Technical Information of China (English)

    NONG Bao-xuan; XIA Xiu-zhong; LIANG Yao-mao; LU Gang; ZHANG Zong-qiong; LI Dan-ting

    2011-01-01

    [Objective] The research aimed to clarify the genetic mechanism of special wide compatibility of GC13.[Method] The clustering analyses of GC13,five indica,five japonica and five wide compatibility varieties were carried out by using 70 SSR primers.[Result] GC13 was clustered into japonica group and had far genetic relationship with indica and wide compatibility variety.Two fertility loci were detected in GC13,in which one closely linked to RM225 on chromosome 6.According to the position on the chromosome,it speculated that this locus was allelic to S5.GC13 carried the allelic gene S5-n at this locus.The other locus closely linked to RM408 on chromosome 8 and was provisionally designated as Sg(t).At this locus,GC13 carried Sg(t)-i allelic gene,which was consistent with IR36.The effect of S5 locus was stronger than that of Sg(t).[Conclusion] The research laid the good foundation for using the wide compatibility line GC13 to breed the hybrid between subspecies.%[Objective] The research aimed to clarify the genetic mechanism of special wide compatibility of GC13.[Method] The clustering analyses of GC13,five indica,five japonica and five wide compatibility varieties were carried out by using 70 SSR primers.[Result

  18. Advanced Post-Irradiation Examination Capabilities Alternatives Analysis Report

    Energy Technology Data Exchange (ETDEWEB)

    Jeff Bryan; Bill Landman; Porter Hill

    2012-12-01

    An alternatives analysis was performed for the Advanced Post-Irradiation Capabilities (APIEC) project in accordance with the U.S. Department of Energy (DOE) Order DOE O 413.3B, “Program and Project Management for the Acquisition of Capital Assets”. The Alternatives Analysis considered six major alternatives: ? No Action ? Modify Existing DOE Facilities – capabilities distributed among multiple locations ? Modify Existing DOE Facilities – capabilities consolidated at a few locations ? Construct New Facility ? Commercial Partnership ? International Partnerships Based on the alternatives analysis documented herein, it is recommended to DOE that the advanced post-irradiation examination capabilities be provided by a new facility constructed at the Materials and Fuels Complex at the Idaho National Laboratory.

  19. FORMATION OF A INNOVATION REGIONAL CLUSTER MODEL

    Directory of Open Access Journals (Sweden)

    G. S. Merzlikina

    2015-01-01

    Full Text Available Summary. As a result of investigation of science and methodical approaches related problems of building and development of innovation clusters there were some issues in functional assignments of innovation and production clusters. Because of those issues, article’s authors differ conceptions of innovation cluster and production cluster, as they explain notion of innovation-production cluster. The main goal of this article is to reveal existing organizational issues in cluster building and its successful development. Based on regional clusters building analysis carried out there was typical practical structure of cluster members interaction revealed. This structure also have its cons, as following: absence cluster orientation to marketing environment, lack of members’ prolonged relations’ building and development system, along with ineffective management of information, financial and material streams within cluster, narrow competence difference and responsibility zones between cluster members, lack of transparence of cluster’s action, low environment changes adaptivity, hard to use cluster members’ intellectual property, and commercialization of hi-tech products. When all those issues listed above come together, it reduces life activity of existing models of innovative cluster-building along with practical opportunity of cluster realization. Because of that, authors offer an upgraded innovative-productive cluster building model with more efficient business processes management system, which includes advanced innovative cluster structure, competence matrix and subcluster responsibility zone. Suggested model differs from other ones by using unified innovative product development control center, which also controls production and marketing realization.

  20. How Teachers Use and Manage Their Blogs? A Cluster Analysis of Teachers' Blogs in Taiwan

    Science.gov (United States)

    Liu, Eric Zhi-Feng; Hou, Huei-Tse

    2013-01-01

    The development of Web 2.0 has ushered in a new set of web-based tools, including blogs. This study focused on how teachers use and manage their blogs. A sample of 165 teachers' blogs in Taiwan was analyzed by factor analysis, cluster analysis and qualitative content analysis. First, the teachers' blogs were analyzed according to six criteria…

  1. Advancing biopharmaceutical process development by system-level data analysis and integration of omics data.

    Science.gov (United States)

    Schaub, Jochen; Clemens, Christoph; Kaufmann, Hitto; Schulz, Torsten W

    2012-01-01

    Development of efficient bioprocesses is essential for cost-effective manufacturing of recombinant therapeutic proteins. To achieve further process improvement and process rationalization comprehensive data analysis of both process data and phenotypic cell-level data is essential. Here, we present a framework for advanced bioprocess data analysis consisting of multivariate data analysis (MVDA), metabolic flux analysis (MFA), and pathway analysis for mapping of large-scale gene expression data sets. This data analysis platform was applied in a process development project with an IgG-producing Chinese hamster ovary (CHO) cell line in which the maximal product titer could be increased from about 5 to 8 g/L.Principal component analysis (PCA), k-means clustering, and partial least-squares (PLS) models were applied to analyze the macroscopic bioprocess data. MFA and gene expression analysis revealed intracellular information on the characteristics of high-performance cell cultivations. By MVDA, for example, correlations between several essential amino acids and the product concentration were observed. Also, a grouping into rather cell specific productivity-driven and process control-driven processes could be unraveled. By MFA, phenotypic characteristics in glycolysis, glutaminolysis, pentose phosphate pathway, citrate cycle, coupling of amino acid metabolism to citrate cycle, and in the energy yield could be identified. By gene expression analysis 247 deregulated metabolic genes were identified which are involved, inter alia, in amino acid metabolism, transport, and protein synthesis.

  2. Analysis of basic clustering algorithms for numerical estimation of statistical averages in biomolecules.

    Science.gov (United States)

    Anandakrishnan, Ramu; Onufriev, Alexey

    2008-03-01

    In statistical mechanics, the equilibrium properties of a physical system of particles can be calculated as the statistical average over accessible microstates of the system. In general, these calculations are computationally intractable since they involve summations over an exponentially large number of microstates. Clustering algorithms are one of the methods used to numerically approximate these sums. The most basic clustering algorithms first sub-divide the system into a set of smaller subsets (clusters). Then, interactions between particles within each cluster are treated exactly, while all interactions between different clusters are ignored. These smaller clusters have far fewer microstates, making the summation over these microstates, tractable. These algorithms have been previously used for biomolecular computations, but remain relatively unexplored in this context. Presented here, is a theoretical analysis of the error and computational complexity for the two most basic clustering algorithms that were previously applied in the context of biomolecular electrostatics. We derive a tight, computationally inexpensive, error bound for the equilibrium state of a particle computed via these clustering algorithms. For some practical applications, it is the root mean square error, which can be significantly lower than the error bound, that may be more important. We how that there is a strong empirical relationship between error bound and root mean square error, suggesting that the error bound could be used as a computationally inexpensive metric for predicting the accuracy of clustering algorithms for practical applications. An example of error analysis for such an application-computation of average charge of ionizable amino-acids in proteins-is given, demonstrating that the clustering algorithm can be accurate enough for practical purposes.

  3. IMG-ABC: new features for bacterial secondary metabolism analysis and targeted biosynthetic gene cluster discovery in thousands of microbial genomes

    Science.gov (United States)

    Hadjithomas, Michalis; Chen, I-Min A.; Chu, Ken; Huang, Jinghua; Ratner, Anna; Palaniappan, Krishna; Andersen, Evan; Markowitz, Victor; Kyrpides, Nikos C.; Ivanova, Natalia N.

    2017-01-01

    Secondary metabolites produced by microbes have diverse biological functions, which makes them a great potential source of biotechnologically relevant compounds with antimicrobial, anti-cancer and other activities. The proteins needed to synthesize these natural products are often encoded by clusters of co-located genes called biosynthetic gene clusters (BCs). In order to advance the exploration of microbial secondary metabolism, we developed the largest publically available database of experimentally verified and predicted BCs, the Integrated Microbial Genomes Atlas of Biosynthetic gene Clusters (IMG-ABC) (https://img.jgi.doe.gov/abc/). Here, we describe an update of IMG-ABC, which includes ClusterScout, a tool for targeted identification of custom biosynthetic gene clusters across 40 000 isolate microbial genomes, and a new search capability to query more than 700 000 BCs from isolate genomes for clusters with similar Pfam composition. Additional features enable fast exploration and analysis of BCs through two new interactive visualization features, a BC function heatmap and a BC similarity network graph. These new tools and features add to the value of IMG-ABC's vast body of BC data, facilitating their in-depth analysis and accelerating secondary metabolite discovery. PMID:27903896

  4. Symptom Clusters in People Living with HIV Attending Five Palliative Care Facilities in Two Sub-Saharan African Countries: A Hierarchical Cluster Analysis.

    Directory of Open Access Journals (Sweden)

    Katrien Moens

    Full Text Available Symptom research across conditions has historically focused on single symptoms, and the burden of multiple symptoms and their interactions has been relatively neglected especially in people living with HIV. Symptom cluster studies are required to set priorities in treatment planning, and to lessen the total symptom burden. This study aimed to identify and compare symptom clusters among people living with HIV attending five palliative care facilities in two sub-Saharan African countries.Data from cross-sectional self-report of seven-day symptom prevalence on the 32-item Memorial Symptom Assessment Scale-Short Form were used. A hierarchical cluster analysis was conducted using Ward's method applying squared Euclidean Distance as the similarity measure to determine the clusters. Contingency tables, X2 tests and ANOVA were used to compare the clusters by patient specific characteristics and distress scores.Among the sample (N=217 the mean age was 36.5 (SD 9.0, 73.2% were female, and 49.1% were on antiretroviral therapy (ART. The cluster analysis produced five symptom clusters identified as: 1 dermatological; 2 generalised anxiety and elimination; 3 social and image; 4 persistently present; and 5 a gastrointestinal-related symptom cluster. The patients in the first three symptom clusters reported the highest physical and psychological distress scores. Patient characteristics varied significantly across the five clusters by functional status (worst functional physical status in cluster one, p<0.001; being on ART (highest proportions for clusters two and three, p=0.012; global distress (F=26.8, p<0.001, physical distress (F=36.3, p<0.001 and psychological distress subscale (F=21.8, p<0.001 (all subscales worst for cluster one, best for cluster four.The greatest burden is associated with cluster one, and should be prioritised in clinical management. Further symptom cluster research in people living with HIV with longitudinally collected symptom data to

  5. Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation

    Directory of Open Access Journals (Sweden)

    Shen Ying

    2015-08-01

    Full Text Available Three-dimensional (3D point analysis and visualization is one of the most effective methods of point cluster detection and segmentation in geospatial datasets. However, serious scattering and clotting characteristics interfere with the visual detection of 3D point clusters. To overcome this problem, this study proposes the use of 3D Voronoi diagrams to analyze and visualize 3D points instead of the original data item. The proposed algorithm computes the cluster of 3D points by applying a set of 3D Voronoi cells to describe and quantify 3D points. The decompositions of point cloud of 3D models are guided by the 3D Voronoi cell parameters. The parameter values are mapped from the Voronoi cells to 3D points to show the spatial pattern and relationships; thus, a 3D point cluster pattern can be highlighted and easily recognized. To capture different cluster patterns, continuous progressive clusters and segmentations are tested. The 3D spatial relationship is shown to facilitate cluster detection. Furthermore, the generated segmentations of real 3D data cases are exploited to demonstrate the feasibility of our approach in detecting different spatial clusters for continuous point cloud segmentation.

  6. Clinical heterogeneity in patients with early-stage Parkinson's disease: a cluster analysis

    Institute of Scientific and Technical Information of China (English)

    Ping LIU; Tao FENG; Yong-jun WANG; Xuan ZHANG; Biao CHEN

    2011-01-01

    The aim of this study was to investigate the clinical heterogeneity of Parkinson's disease (PD) among a cohort of Chinese patients in early stages.Clinical data on demographics,motor variables,motor phenotypes,disease progression,global cognitive function,depression,apathy,sleep quality,constipation,fatigue,and L-dopa complications were collected from 138 Chinese PD subjects in early stages (Hoehn and Yahr stages 1-3).The PD subject subtypes were classified using k-means cluster analysis according to the clinical data from five- to three-cluster consecutively.Kappa statistical analysis was performed to evaluate the consistency among different subtype solutions.The cluster analysis indicated four main subtypes:the non-tremor dominant subtype (NTD,n=28,20.3%),rapid disease progression subtype (RDP,n=7,5.1%),young-onset subtype (YO,n=50,36.2%),and tremor dominant subtype (TD,n=53,38.4%).Overall,78.3% (108/138) of subjects were always classified between the same three groups (52 always in TD,7 in RDP,and 49 in NTD),and 98.6% (136/138) between five- and four-cluster solutions.However,subjects classified as NTD in the four-cluster analysis were dispersed into different subtypes in the three-cluster analysis,with low concordance between four- and three-cluster solutions (kappa value=-0.139,P=0.001 ).This study defines clinical heterogeneity of PD patients in early stages using a data-driven approach.The subtypes generated by the four-cluster solution appear to exhibit ideal internal cohesion and external isolation.

  7. Heavy minerals clustering analysis in application of provenance analysis of Kong 2 Member in Kongnan area

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The main task of provenance analysis is to determine the source of sediments and the position of parent rocks. Provenance analysis may find out the relationship between erosion districts and sediment zone, between the uplift and the depression in the process of basin development. The authors use the method of heavy mineral clustering analysis and estimate the provenance direction of Huanghua Depression in the Paleogene Kong 2 Member. Research shows that there were five provenance areas of Kong 2 Member in Kongnan area.They are western (Shenusi), northwestern (Cangzhou), eastern (Ganhuatun), northeastern and southeastern. The main provenance areas were northwestern and western, while the southern provenance could not be ruled out. And these areas are consistent with the known provenance areas.

  8. Cluster analysis of particulate matter (PM10) and black carbon (BC) concentrations

    Science.gov (United States)

    Žibert, Janez; Pražnikar, Jure

    2012-09-01

    The monitoring of air-pollution constituents like particulate matter (PM10) and black carbon (BC) can provide information about air quality and the dynamics of emissions. Air quality depends on natural and anthropogenic sources of emissions as well as the weather conditions. For a one-year period the diurnal concentrations of PM10 and BC in the Port of Koper were analysed by clustering days into similar groups according to the similarity of the BC and PM10 hourly derived day-profiles without any prior assumptions about working and non-working days, weather conditions or hot and cold seasons. The analysis was performed by using k-means clustering with the squared Euclidean distance as the similarity measure. The analysis showed that 10 clusters in the BC case produced 3 clusters with just one member day and 7 clusters that encompasses more than one day with similar BC profiles. Similar results were found in the PM10 case, where one cluster has a single-member day, while 7 clusters contain several member days. The clustering analysis revealed that the clusters with less pronounced bimodal patterns and low hourly and average daily concentrations for both types of measurements include the most days in the one-year analysis. A typical day profile of the BC measurements includes a bimodal pattern with morning and evening peaks, while the PM10 measurements reveal a less pronounced bimodality. There are also clusters with single-peak day-profiles. The BC data in such cases exhibit morning peaks, while the PM10 data consist of noon or afternoon single peaks. Single pronounced peaks can be explained by appropriate cluster wind speed profiles. The analysis also revealed some special day-profiles. The BC cluster with a high midnight peak at 30/04/2010 and the PM10 cluster with the highest observed concentration of PM10 at 01/05/2010 (208.0 μg m-3) coincide with 1 May, which is a national holiday in Slovenia and has very strong tradition of bonfire parties. The clustering of

  9. Application of Cluster Analysis in Assessment of Dietary Habits of Secondary School Students

    Directory of Open Access Journals (Sweden)

    Zalewska Magdalena

    2014-12-01

    Full Text Available Maintenance of proper health and prevention of diseases of civilization are now significant public health problems. Nutrition is an important factor in the development of youth, as well as the current and future state of health. The aim of the study was to show the benefits of the application of cluster analysis to assess the dietary habits of high school students. The survey was carried out on 1,631 eighteen-year-old students in seven randomly selected secondary schools in Bialystok using a self-prepared anonymous questionnaire. An evaluation of the time of day meals were eaten and the number of meals consumed was made for the surveyed students. The cluster analysis allowed distinguishing characteristic structures of dietary habits in the observed population. Four clusters were identified, which were characterized by relative internal homogeneity and substantial variation in terms of the number of meals during the day and the time of their consumption. The most important characteristics of cluster 1 were cumulated food ration in 2 or 3 meals and long intervals between meals. Cluster 2 was characterized by eating the recommended number of 4 or 5 meals a day. In the 3rd cluster, students ate 3 meals a day with large intervals between them, and in the 4th they had four meals a day while maintaining proper intervals between them. In all clusters dietary mistakes occurred, but most of them were related to clusters 1 and 3. Cluster analysis allowed for the identification of major flaws in nutrition, which may include irregular eating and skipping meals, and indicated possible connections between eating patterns and disturbances of body weight in the examined population.

  10. Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient

    Directory of Open Access Journals (Sweden)

    Loraine Ann

    2008-06-01

    Full Text Available Abstract Background Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data. Results In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC, that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns. Conclusion

  11. MMPI-2: Cluster Analysis of Personality Profiles in Perinatal Depression—Preliminary Evidence

    Directory of Open Access Journals (Sweden)

    Valentina Meuti

    2014-01-01

    Full Text Available Background. To assess personality characteristics of women who develop perinatal depression. Methods. The study started with a screening of a sample of 453 women in their third trimester of pregnancy, to which was administered a survey data form, the Edinburgh Postnatal Depression Scale (EPDS and the Minnesota Multiphasic Personality Inventory 2 (MMPI-2. A clinical group of subjects with perinatal depression (PND, 55 subjects was selected; clinical and validity scales of MMPI-2 were used as predictors in hierarchical cluster analysis carried out. Results. The analysis identified three clusters of personality profile: two “clinical” clusters (1 and 3 and an “apparently common” one (cluster 2. The first cluster (39.5% collects structures of personality with prevalent obsessive or dependent functioning tending to develop a “psychasthenic” depression; the third cluster (13.95% includes women with prevalent borderline functioning tending to develop “dysphoric” depression; the second cluster (46.5% shows a normal profile with a “defensive” attitude, probably due to the presence of defense mechanisms or to the fear of stigma. Conclusion. Characteristics of personality have a key role in clinical manifestations of perinatal depression; it is important to detect them to identify mothers at risk and to plan targeted therapeutic interventions.

  12. Advanced Thermodynamic Analysis and Evaluation of a Supercritical Power Plant

    Directory of Open Access Journals (Sweden)

    George Tsatsaronis

    2012-06-01

    Full Text Available A conventional exergy analysis can highlight the main components having high thermodynamic inefficiencies, but cannot consider the interactions among components or the true potential for the improvement of each component. By splitting the exergy destruction into endogenous/exogenous and avoidable/unavoidable parts, the advanced exergy analysis is capable of providing additional information to conventional exergy analysis for improving the design and operation of energy conversion systems. This paper presents the application of both a conventional and an advanced exergy analysis to a supercritical coal-fired power plant. The results show that the ratio of exogenous exergy destruction differs quite a lot from component to component. In general, almost 90% of the total exergy destruction within turbines comes from their endogenous parts, while that of feedwater preheaters contributes more or less 70% to their total exergy destruction. Moreover, the boiler subsystem is proven to have a large amount of exergy destruction caused by the irreversibilities within the remaining components of the overall system. It is also found that the boiler subsystem still has the largest avoidable exergy destruction; however, the enhancement efforts should focus not only on its inherent irreversibilities but also on the inefficiencies within the remaining components. A large part of the avoidable exergy destruction within feedwater preheaters is exogenous; while that of the remaining components is mostly endogenous indicating that the improvements mainly depend on advances in design and operation of the component itself.

  13. Student academic performance analysis using fuzzy C-means clustering

    Science.gov (United States)

    Rosadi, R.; Akamal; Sudrajat, R.; Kharismawan, B.; Hambali, Y. A.

    2017-01-01

    Grade Point Average (GPA) is commonly used as an indicator of academic performance. Academic performance evaluations is a basic way to evaluate the progression of student performance, when evaluating student’s academic performance, there are occasion where the student data is grouped especially when the amounts of data is large. Thus, the pattern of data relationship within and among groups can be revealed. Grouping data can be done by using clustering method, where one of the methods is the Fuzzy C-Means algorithm. Furthermore, this algorithm is then applied to a set of student data form the Faculty of Mathematics and Natural Sciences, Padjadjaran University.

  14. Cluster analysis based on dimensional information with applications to feature selection and classification

    Science.gov (United States)

    Eigen, D. J.; Fromm, F. R.; Northouse, R. A.

    1974-01-01

    A new clustering algorithm is presented that is based on dimensional information. The algorithm includes an inherent feature selection criterion, which is discussed. Further, a heuristic method for choosing the proper number of intervals for a frequency distribution histogram, a feature necessary for the algorithm, is presented. The algorithm, although usable as a stand-alone clustering technique, is then utilized as a global approximator. Local clustering techniques and configuration of a global-local scheme are discussed, and finally the complete global-local and feature selector configuration is shown in application to a real-time adaptive classification scheme for the analysis of remote sensed multispectral scanner data.

  15. A critical cluster analysis of 44 indicators of author-level performance

    DEFF Research Database (Denmark)

    Wildgaard, Lorna Elizabeth

    2015-01-01

    This paper explores the relationship between author-level bibliometric indicators and the researchers the "measure", exemplified across five academic seniorities and four disciplines. Using cluster methodology, the disciplinary and seniority appropriateness of author-level indicators is examined....... Publication and citation data for 741 researchers across Astronomy, Environmental Science, Philosophy and Public Health was collected in Web of Science (WoS). Forty-four indicators of individual performance were computed using the data. A two-step cluster analysis using IBM SPSS version 22 was performed......, followed by a risk analysis and ordinal logistic regression to explore cluster membership. Indicator scores were contextualized using the individual researcher's curriculum vitae. Four different clusters based on indicator scores ranked researchers as low, middle, high and extremely high performers...

  16. Cluster analysis in kinetic modelling of the brain: A noninvasive alternative to arterial sampling

    DEFF Research Database (Denmark)

    Liptrot, Matthew George; Adams, K.H.; Martiny, L.

    2004-01-01

    In emission tomography, quantification of brain tracer uptake, metabolism or binding requires knowledge of the cerebral input function. Traditionally, this is achieved with arterial blood sampling. We propose a noninvasive alternative via the use of a blood vessel time-activity curve (TAC......) extracted directly from dynamic positron emission tomography (PET) scans by cluster analysis. Five healthy subjects were injected with the 5HT2A- receptor ligand [18F]-altanserin and blood samples were subsequently taken from the radial artery and cubital vein. Eight regions-of-interest (ROI) TACs were...... extracted from the PET data set. Hierarchical K-means cluster analysis was performed on the PET time series to extract a cerebral vasculature ROI. The number of clusters was varied from K = 1 to 10 for the second of the two-stage method. Determination of the correct number of clusters was performed...

  17. Development of the advanced PHWR technology -Design and analysis of CANDU advanced fuel-

    Energy Technology Data Exchange (ETDEWEB)

    Suk, Hoh Chun; Shim, Kee Sub; Byun, Taek Sang; Park, Kwang Suk; Kang, Heui Yung; Kim, Bong Kee; Jung, Chang Joon; Lee, Yung Wook; Bae, Chang Joon; Kwon, Oh Sun; Oh, Duk Joo; Im, Hong Sik; Ohn, Myung Ryong; Lee, Kang Moon; Park, Joo Hwan; Lee, Eui Joon [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1995-07-01

    This is the `94 annual report of the CANDU advanced fuel design and analysis project, and describes CANFLEX fuel design and mechanical integrity analysis, reactor physics analysis and safety analysis of the CANDU-6 with the CANFLEX-NU. The following is the R and D scope of this fiscal year : (1) Detail design of CANFLEX-NU and detail analysis on the fuel integrity, reactor physics and safety. (a) Detail design and mechanical integrity analysis of the bundle (b) CANDU-6 refueling simulation, and analysis on the Xe transients and adjuster system capability (c) Licensing strategy establishment and safety analysis for the CANFLEX-NU demonstration demonstration irradiation in a commercial CANDU-6. (2) Production and revision of CANFLEX-NU fuel design documents (a) Production and approval of CANFLEX-NU reference drawing, and revisions of fuel design manual and technical specifications (b) Production of draft physics design manual. (3) Basic research on CANFLEX-SEU fuel. 55 figs, 21 tabs, 45 refs. (Author).

  18. Insights into Quasar UV Spectra Using Unsupervised Clustering Analysis

    CERN Document Server

    Tammour, Aycha; Daley, Mark; Richards, Gordon T

    2016-01-01

    Machine learning can provide powerful tools to detect patterns in multi-dimensional parameter space. We use K-means -a simple yet powerful unsupervised clustering algorithm which picks out structure in unlabeled data- to study a sample of quasar UV spectra from the Quasar Catalog of the 10th Data Release of the Sloan Digital Sky Survey of Paris et al. (2014). Detecting patterns in large datasets helps us gain insights into the physical conditions and processes giving rise to the observed properties of quasars. We use K-means to find clusters in the parameter space of the equivalent width (EW), the blue- and red-half-width at half-maximum (HWHM) of the Mg II 2800 A line, the C IV 1549 A line, and the C III] 1908 A blend in samples of Broad Absorption-Line (BAL) and non-BAL quasars at redshift 1.6-2.1. Using this method, we successfully recover correlations well-known in the UV regime such as the anti-correlation between the EW and blueshift of the C IV emission line and the shape of the ionizing Spectra Energy...

  19. Information search behaviour among new car buyers: A two-step cluster analysis

    Directory of Open Access Journals (Sweden)

    S.M. Satish

    2010-03-01

    Full Text Available A two-step cluster analysis of new car buyers in India was performed to identify taxonomies of search behaviour using personality and situational variables, apart from sources of information. Four distinct groups were found—broad moderate searchers, intense heavy searchers, low broad searchers, and low searchers. Dealers can identify the members of each segment by measuring the variables used for clustering, and can then design appropriate communication strategies.

  20. Applying clustering to statistical analysis of student reasoning about two-dimensional kinematics

    Directory of Open Access Journals (Sweden)

    John R. Thompson

    2007-12-01

    Full Text Available We use clustering, an analysis method not presently common to the physics education research community, to group and characterize student responses to written questions about two-dimensional kinematics. Previously, clustering has been used to analyze multiple-choice data; we analyze free-response data that includes both sketches of vectors and written elements. The primary goal of this paper is to describe the methodology itself; we include a brief overview of relevant results.

  1. Advances in Mid-Infrared Spectroscopy for Chemical Analysis

    Science.gov (United States)

    Haas, Julian; Mizaikoff, Boris

    2016-06-01

    Infrared spectroscopy in the 3-20 μm spectral window has evolved from a routine laboratory technique into a state-of-the-art spectroscopy and sensing tool by benefitting from recent progress in increasingly sophisticated spectra acquisition techniques and advanced materials for generating, guiding, and detecting mid-infrared (MIR) radiation. Today, MIR spectroscopy provides molecular information with trace to ultratrace sensitivity, fast data acquisition rates, and high spectral resolution catering to demanding applications in bioanalytics, for example, and to improved routine analysis. In addition to advances in miniaturized device technology without sacrificing analytical performance, selected innovative applications for MIR spectroscopy ranging from process analysis to biotechnology and medical diagnostics are highlighted in this review.

  2. Advantages and Limitations of Cluster Analysis in Interpreting Regional GPS Velocity Fields in California and Elsewhere

    Science.gov (United States)

    Thatcher, W. R.; Savage, J. C.; Simpson, R.

    2012-12-01

    Regional Global Positioning System (GPS) velocity observations are providing increasingly precise mappings of actively deforming continental lithosphere. Cluster analysis, a venerable data analysis method, offers a simple, visual exploratory tool for the initial organization and investigation of GPS velocities (Simpson et al., 2012 GRL). Here we describe the application of cluster analysis to GPS velocities from three regions, the Mojave Desert and the San Francisco Bay regions in California, and the Aegean in the eastern Mediterranean. Our goal is to illustrate the strengths and shortcomings of the method in searching for spatially coherent patterns of deformation, including evidence for and against block-like behavior in these 3 regions. The deformation fields from dense regional GPS networks can often be concisely described in terms of relatively coherent blocks bounded by active faults, although the choice of blocks, their number and size, is subjective and usually guided by the distribution of known faults. Cluster analysis applied to GPS velocities provides a completely objective method for identifying groups of observations ranging in size from 10s to 100s of km in characteristic dimension based solely on the similarities of their velocity vectors. In the three regions we have studied, statistically significant clusters are almost invariably spatially coherent, fault bounded, and coincide with elastic, geologically identified structural blocks. Often, higher order clusters that are not statistically significant are also spatially coherent, suggesting the existence of additional blocks, or defining regions of other tectonic importance (e.g. zones of localized elastic strain accumulation near locked faults). These results can be used to both formulate tentative tectonic models with testable consequences and to suggest focused new measurements in under-sampled regions. Cluster analysis applied to GPS velocities has several potential limitations, aside from the

  3. Advanced approaches to failure mode and effect analysis (FMEA applications

    Directory of Open Access Journals (Sweden)

    D. Vykydal

    2015-10-01

    Full Text Available The present paper explores advanced approaches to the FMEA method (Failure Mode and Effect Analysis which take into account the costs associated with occurrence of failures during the manufacture of a product. Different approaches are demonstrated using an example FMEA application to production of drawn wire. Their purpose is to determine risk levels, while taking account of the above-mentioned costs. Finally, the resulting priority levels are compared for developing actions mitigating the risks.

  4. Advances in oriental document analysis and recognition techniques

    CERN Document Server

    Lee, Seong-Whan

    1999-01-01

    In recent years, rapid progress has been made in computer processing of oriental languages, and the research developments in this area have resulted in tremendous changes in handwriting processing, printed oriental character recognition, document analysis and recognition, automatic input methodologies for oriental languages, etc. Advances in computer processing of oriental languages can also be seen in multimedia computing and the World Wide Web. Many of the results in those domains are presented in this book.

  5. Estimating multi-phase pore-scale characteristics from X-ray tomographic data using cluster analysis-based segmentation

    DEFF Research Database (Denmark)

    Wildenschild, D.; Culligan, K.A.; Christensen, Britt Stenhøj Baun

    2006-01-01

    of individual pores and interfaces. However, separation of the various phases (fluids and solids) in the grey-scale tomographic images has posed a major problem to quantitative analysis of the data. We present an image processing technique that facilitates identification and separation of the various phases...... characterization. The results clearly illustrate the advantage of using X-ray tomography together with cluster analysis-based image processing techniques. We were able to obtain detailed information on pore scale distribution of air and water phases, as well as quantitative measures of air bubble size and air......Recent advances in experimental techniques have made it possible to characterize and distinguish such micro-scale entities as fluid phase ditributions and pore geometry in porous media. In particular, non-destructive synchrotron based X-ray computed microtomography allows 3D resolution...

  6. The application of cluster analysis in the intercomparison of loop structures in RNA.

    Science.gov (United States)

    Huang, Hung-Chung; Nagaswamy, Uma; Fox, George E

    2005-04-01

    We have developed a computational approach for the comparison and classification of RNA loop structures. Hairpin or interior loops identified in atomic resolution RNA structures were intercompared by conformational matching. The root-mean-square deviation (RMSD) values between all pairs of RNA fragments of interest, even if from different molecules, are calculated. Subsequently, cluster analysis is performed on the resulting matrix of RMSD distances using the unweighted pair group method with arithmetic mean (UPGMA). The cluster analysis objectively reveals groups of folds that resemble one another. To demonstrate the utility of the approach, a comprehensive analysis of all the terminal hairpin tetraloops that have been observed in 15 RNA structures that have been determined by X-ray crystallography was undertaken. The method found major clusters corresponding to the well-known GNRA and UNCG types. In addition, two tetraloops with the unusual primary sequence UMAC (M is A or C) were successfully assigned to the GNRA cluster. Larger loop structures were also examined and the clustering results confirmed the occurrence of variations of the GNRA and UNCG tetraloops in these loops and provided a systematic means for locating them. Nineteen examples of larger loops that closely resemble either the GNRA or UNCG tetraloop were found in the large ribosomal RNAs. When the clustering approach was extended to include all structures in the SCOR database, novel relationships were detected including one between the ANYA motif and a less common folding of the GAAA tetraloop sequence.

  7. Evaluation of socio-economic patterns of SHG members in Kerala using clustering analysis

    Directory of Open Access Journals (Sweden)

    Sajeev B. U

    2012-03-01

    Full Text Available Abstracts In the matter of social development, though Kerala stands ahead of all other states in India, the pattern of distribution of social and economic opportunities within the state is highly inequitable among different social groups. Self help groups (SHG are vehicles for social, political and financial intermediation of the state. Clustering analysis is one of the main analytical methods in data mining; the method of clustering algorithm will influence the clustering results directly. K-means and Fuzzy C-Means Algorithms are popular methods in cluster analysis. In this paper we have evaluated the socioeconomic developments of SHG in various districts in Kerala state using cluster analysis. The data were collected by field survey and interviews. The parameters considered for the study include the regularity of the members in attending meetings and training, social and economic benefits gained by the members in personal level, cluster level and society level, rate of employment and earning members in the family and literacy and educational level of SHG members.

  8. Crowd Analysis by Using Optical Flow and Density Based Clustering

    DEFF Research Database (Denmark)

    Santoro, Francesco; Pedro, Sergio; Tan, Zheng-Hua;

    2010-01-01

    , it is applied a crowd tracker in every frame, allowing us to detect and track the crowds. Our system gives the output as a graphic overlay, i.e it adds arrows and colors to the original frame sequence, in order to identify crowds and their movements. For the evaluation, we check when our system detect certains......In this paper, we present a system to detect and track crowds in a video sequence captured by a camera. In a first step, we compute optical flows by means of pyramidal Lucas-Kanade feature tracking. Afterwards, a density based clustering is used to group similar vectors. In the last step...... events on the crowds, such as merging, splitting and collision....

  9. Displacement of Building Cluster Using Field Analysis Method

    Institute of Scientific and Technical Information of China (English)

    Al Tinghua

    2003-01-01

    This paper presents a field based method to deal with the displacement of building cluster,which is driven by the street widening. The compress of street boundary results in the force to push the building moving inside and the force propagation is a decay process. To describe the phenomenon above, the field theory is introduced with the representation model of isoline. On the basis of the skeleton of Delaunay triangulation,the displacement field is built in which the propagation force is related to the adjacency degree with respect to the street boundary. The study offers the computation of displacement direction and offset distance for the building displacement. The vector operation is performed on the basis of grade and other field concepts.

  10. Brief notes in advanced DSP Fourier analysis with Matlab

    CERN Document Server

    Grigoryan, Artyom M

    2009-01-01

    Based on the authors' research in Fourier analysis, Brief Notes in Advanced DSP: Fourier Analysis with MATLAB® addresses many concepts and applications of digital signal processing (DSP). The included MATLAB® codes illustrate how to apply the ideas in practice.The book begins with the basic concept of the discrete Fourier transformation and its properties. It then describes lifting schemes, integer transformations, the discrete cosine transform, and the paired transform method for calculating the discrete Hadamard transform. The text also examines the decomposition of the 1D signal by so-calle

  11. Advanced Signal Analysis for Forensic Applications of Ground Penetrating Radar

    Energy Technology Data Exchange (ETDEWEB)

    Steven Koppenjan; Matthew Streeton; Hua Lee; Michael Lee; Sashi Ono

    2004-06-01

    Ground penetrating radar (GPR) systems have traditionally been used to image subsurface objects. The main focus of this paper is to evaluate an advanced signal analysis technique. Instead of compiling spatial data for the analysis, this technique conducts object recognition procedures based on spectral statistics. The identification feature of an object type is formed from the training vectors by a singular-value decomposition procedure. To illustrate its capability, this procedure is applied to experimental data and compared to the performance of the neural-network approach.

  12. Mapping informative clusters in a hierarchical [corrected] framework of FMRI multivariate analysis.

    Directory of Open Access Journals (Sweden)

    Rui Xu

    Full Text Available Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies.

  13. Cluster analysis for the probability of DSB site induced by electron tracks

    Science.gov (United States)

    Yoshii, Y.; Sasaki, K.; Matsuya, Y.; Date, H.

    2015-05-01

    To clarify the influence of bio-cells exposed to ionizing radiations, the densely populated pattern of the ionization in the cell nucleus is of importance because it governs the extent of DNA damage which may lead to cell lethality. In this study, we have conducted a cluster analysis of ionization and excitation events to estimate the number of double-strand breaks (DSBs) induced by electron tracks. A Monte Carlo simulation for electrons in liquid water was performed to determine the spatial location of the ionization and excitation events. The events were divided into clusters by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The algorithm enables us to sort out the events into the groups (clusters) in which a minimum number of neighboring events are contained within a given radius. For evaluating the number of DSBs in the extracted clusters, we have introduced an aggregation index (AI). The computational results show that a sub-keV electron produces DSBs in a dense formation more effectively than higher energy electrons. The root-mean square radius (RMSR) of the cluster size is below 5 nm, which is smaller than the chromatin fiber thickness. It was found that this size of clustering events has a high possibility to cause lesions in DNA within the chromatin fiber site.

  14. The distinction of 'psychosomatogenic family types' based on parents' self reported questionnaire information: a cluster analysis.

    Science.gov (United States)

    Rousseau, Sofie; Grietens, Hans; Vanderfaeillie, Johan; Ceulemans, Eva; Hoppenbrouwers, Karel; Desoete, Annemie; Van Leeuwen, Karla

    2014-06-01

    The theory of 'psychosomatogenic family types' is often used in treatment of somatizing adolescents. This study investigated the validity of distinguishing 'psychosomatogenic family types' based on parents' self-reported family features. The study included a Flemish general population sample of 12-year olds (n = 1428). We performed cluster analysis on 3 variables concerning parents' self-reported problems in family functioning. The distinguished clusters were examined for differences in marital problems, parental emotional problems, professional help for family members, demographics, and adolescents' somatization. Results showed the existence of 5 family types: 'chaotic family functioning,' 'average amount of family functioning problems,' 'few family functioning problems,' 'high amount of support and communication problems,' and 'high amount of sense of security problems' clusters. Membership of the 'chaotic family functioning' and 'average amount of family functioning problems' cluster was significantly associated with higher levels of somatization, compared with 'few family functioning problems' cluster membership. Among additional variables, only marital and parental emotional problems distinguished somatization relevant from non relevant clusters: parents in 'average amount of family functioning problems' and 'chaotic family functioning' clusters reported higher problems. The data showed that 'apparently perfect' or 'enmeshed' patterns of family functioning may not be assessed by means of parent report as adopted in this study. In addition, not only adolescents from 'extreme' types of family functioning may suffer from somatization. Further, professionals should be careful assuming that families in which parents report average to high amounts of family functioning problems also show different demographic characteristics.

  15. Profiling nurses' job satisfaction, acculturation, work environment, stress, cultural values and coping abilities: A cluster analysis.

    Science.gov (United States)

    Goh, Yong-Shian; Lee, Alice; Chan, Sally Wai-Chi; Chan, Moon Fai

    2015-08-01

    This study aimed to determine whether definable profiles existed in a cohort of nursing staff with regard to demographic characteristics, job satisfaction, acculturation, work environment, stress, cultural values and coping abilities. A survey was conducted in one hospital in Singapore from June to July 2012, and 814 full-time staff nurses completed a self-report questionnaire (89% response rate). Demographic characteristics, job satisfaction, acculturation, work environment, perceived stress, cultural values, ways of coping and intention to leave current workplace were assessed as outcomes. The two-step cluster analysis revealed three clusters. Nurses in cluster 1 (n = 222) had lower acculturation scores than nurses in cluster 3. Cluster 2 (n = 362) was a group of younger nurses who reported higher intention to leave (22.4%), stress level and job dissatisfaction than the other two clusters. Nurses in cluster 3 (n = 230) were mostly Singaporean and reported the lowest intention to leave (13.0%). Resources should be allocated to specifically address the needs of younger nurses and hopefully retain them in the profession. Management should focus their retention strategies on junior nurses and provide a work environment that helps to strengthen their intention to remain in nursing by increasing their job satisfaction.

  16. Fatigue Feature Extraction Analysis based on a K-Means Clustering Approach

    Directory of Open Access Journals (Sweden)

    M.F.M. Yunoh

    2015-06-01

    Full Text Available This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

  17. The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis

    CERN Document Server

    Chen, Chaomei; Hou, Jianhua

    2010-01-01

    A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA n...

  18. Cluster Analysis of the Newcastle Electronic Corpus of Tyneside English: A Comparison of Methods

    NARCIS (Netherlands)

    Moisl, Hermann; Jones, Val

    2005-01-01

    This article examines the feasibility of an empirical approach to sociolinguistic analysis of the Newcastle Electronic Corpus of Tyneside English using exploratory multivariate methods. It addresses a known problem with one class of such methods, hierarchical cluster analysis¿that different clusteri

  19. Standardized Effect Size Measures for Mediation Analysis in Cluster-Randomized Trials

    Science.gov (United States)

    Stapleton, Laura M.; Pituch, Keenan A.; Dion, Eric

    2015-01-01

    This article presents 3 standardized effect size measures to use when sharing results of an analysis of mediation of treatment effects for cluster-randomized trials. The authors discuss 3 examples of mediation analysis (upper-level mediation, cross-level mediation, and cross-level mediation with a contextual effect) with demonstration of the…

  20. Validation Database Based Thermal Analysis of an Advanced RPS Concept

    Science.gov (United States)

    Balint, Tibor S.; Emis, Nickolas D.

    2006-01-01

    Advanced RPS concepts can be conceived, designed and assessed using high-end computational analysis tools. These predictions may provide an initial insight into the potential performance of these models, but verification and validation are necessary and required steps to gain confidence in the numerical analysis results. This paper discusses the findings from a numerical validation exercise for a small advanced RPS concept, based on a thermal analysis methodology developed at JPL and on a validation database obtained from experiments performed at Oregon State University. Both the numerical and experimental configurations utilized a single GPHS module enabled design, resembling a Mod-RTG concept. The analysis focused on operating and environmental conditions during the storage phase only. This validation exercise helped to refine key thermal analysis and modeling parameters, such as heat transfer coefficients, and conductivity and radiation heat transfer values. Improved understanding of the Mod-RTG concept through validation of the thermal model allows for future improvements to this power system concept.

  1. Identifying patterns in treatment response profiles in acute bipolar mania: a cluster analysis approach

    Directory of Open Access Journals (Sweden)

    Houston John P

    2008-07-01

    Full Text Available Abstract Background Patients with acute mania respond differentially to treatment and, in many cases, fail to obtain or sustain symptom remission. The objective of this exploratory analysis was to characterize response in bipolar disorder by identifying groups of patients with similar manic symptom response profiles. Methods Patients (n = 222 were selected from a randomized, double-blind study of treatment with olanzapine or divalproex in bipolar I disorder, manic or mixed episode, with or without psychotic features. Hierarchical clustering based on Ward's distance was used to identify groups of patients based on Young-Mania Rating Scale (YMRS total scores at each of 5 assessments over 7 weeks. Logistic regression was used to identify baseline predictors for clusters of interest. Results Four distinct clusters of patients were identified: Cluster 1 (n = 64: patients did not maintain a response (YMRS total scores ≤ 12; Cluster 2 (n = 92: patients responded rapidly (within less than a week and response was maintained; Cluster 3 (n = 36: patients responded rapidly but relapsed soon afterwards (YMRS ≥ 15; Cluster 4 (n = 30: patients responded slowly (≥ 2 weeks and response was maintained. Predictive models using baseline variables found YMRS Item 10 (Appearance, and psychosis to be significant predictors for Clusters 1 and 4 vs. Clusters 2 and 3, but none of the baseline characteristics allowed discriminating between Clusters 1 vs. 4. Experiencing a mixed episode at baseline predicted membership in Clusters 2 and 3 vs. Clusters 1 and 4. Treatment with divalproex, larger number of previous manic episodes, lack of disruptive-aggressive behavior, and more prominent depressive symptoms at baseline were predictors for Cluster 3 vs. 2. Conclusion Distinct treatment response profiles can be predicted by clinical features at baseline. The presence of these features as potential risk factors for relapse in patients who have responded to treatment

  2. Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcomes: a simulation study

    Directory of Open Access Journals (Sweden)

    Ma Jinhui

    2013-01-01

    Full Text Available Abstracts Background The objective of this simulation study is to compare the accuracy and efficiency of population-averaged (i.e. generalized estimating equations (GEE and cluster-specific (i.e. random-effects logistic regression (RELR models for analyzing data from cluster randomized trials (CRTs with missing binary responses. Methods In this simulation study, clustered responses were generated from a beta-binomial distribution. The number of clusters per trial arm, the number of subjects per cluster, intra-cluster correlation coefficient, and the percentage of missing data were allowed to vary. Under the assumption of covariate dependent missingness, missing outcomes were handled by complete case analysis, standard multiple imputation (MI and within-cluster MI strategies. Data were analyzed using GEE and RELR. Performance of the methods was assessed using standardized bias, empirical standard error, root mean squared error (RMSE, and coverage probability. Results GEE performs well on all four measures — provided the downward bias of the standard error (when the number of clusters per arm is small is adjusted appropriately — under the following scenarios: complete case analysis for CRTs with a small amount of missing data; standard MI for CRTs with variance inflation factor (VIF 50. RELR performs well only when a small amount of data was missing, and complete case analysis was applied. Conclusion GEE performs well as long as appropriate missing data strategies are adopted based on the design of CRTs and the percentage of missing data. In contrast, RELR does not perform well when either standard or within-cluster MI strategy is applied prior to the analysis.

  3. Globular Cluster Abundances from High-Resolution, Integrated-Light Spectroscopy. II. Expanding the Metallicity Range for Old Clusters and Updated Analysis Techniques

    CERN Document Server

    Colucci, J E; McWilliam, A

    2016-01-01

    We present abundances of globular clusters in the Milky Way and Fornax from integrated light spectra. Our goal is to evaluate the consistency of the integrated light analysis relative to standard abundance analysis for individual stars in those same clusters. This sample includes an updated analysis of 7 clusters from our previous publications and results for 5 new clusters that expand the metallicity range over which our technique has been tested. We find that the [Fe/H] measured from integrated light spectra agrees to $\\sim$0.1 dex for globular clusters with metallicities as high as [Fe/H]=$-0.3$, but the abundances measured for more metal rich clusters may be underestimated. In addition we systematically evaluate the accuracy of abundance ratios, [X/Fe], for Na I, Mg I, Al I, Si I, Ca I, Ti I, Ti II, Sc II, V I, Cr I, Mn I, Co I, Ni I, Cu I, Y II, Zr I, Ba II, La II, Nd II, and Eu II. The elements for which the integrated light analysis gives results that are most similar to analysis of individual stellar ...

  4. Fault detection of flywheel system based on clustering and principal component analysis

    Directory of Open Access Journals (Sweden)

    Wang Rixin

    2015-12-01

    Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

  5. Fault detection of flywheel system based on clustering and principal component analysis

    Institute of Scientific and Technical Information of China (English)

    Wang Rixin; Gong Xuebing; Xu Minqiang; Li Yuqing

    2015-01-01

    Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of‘‘integrated power and attitude control”system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS) can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the rela-tionship of parameters in each operation through the principal component analysis (PCA) method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

  6. Feature-space clustering for fMRI meta-analysis

    DEFF Research Database (Denmark)

    Goutte, C.; Hansen, L.K.; Liptrot, Matthew George

    2001-01-01

    Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability...... of a clustering method applied to features extracted from the data. This approach is extremely versatile and encompasses previously published results [Goutte et al., 1999] as special cases. A typical application is in data reduction: as the increase in temporal resolution of fMRI experiments routinely yields f......-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular...

  7. Design and Analysis of SD_DWCA - A Mobility Based Clustering of Homogeneous MANETs

    Directory of Open Access Journals (Sweden)

    T.N. Janakiraman

    2011-05-01

    Full Text Available This paper deals with the design and analysis of the distributed weighted clustering algorithm SD_DWCAproposed for homogeneous mobile ad hoc networks. It is a connectivity, mobility and energy based clustering algorithm which is suitable for scalable ad hoc networks. The algorithm uses a new graph parameter called strong degree defined based on the quality of neighbours of a node. The parameters are so chosen to ensure high connectivity, cluster stability and energy efficient communication among nodes of high dynamic nature. This paper also includes the experimental results of the algorithm implementedusing the network simulator NS2. The experimental results show that the algorithm is suitable for highspeed networks and generate stable clusters with less maintenance overhead.

  8. Identification and structural analysis of a novel snoRNA gene cluster from Arabidopsis thaliana

    Institute of Scientific and Technical Information of China (English)

    周惠; 孟清; 屈良鹄

    2000-01-01

    A 22 snoRNA gene cluster, consisting of four antisense snoRNA genes, was identified from Arabidopsis thaliana. The sequence and structural analysis showed that the 22 snoRNA gene cluster might be transcribed as a polycistronic precursor from an upstream promoter, and the in-tergenic spacers of the gene cluster encode the ’hairpin’ structures similar to the processing recognition signals of yeast Saccharomyces cerevisiae polycistronic snoRNA precursor. The results also revealed that plant snoRNA gene with multiple copies is a characteristic in common, and provides a good system for further revealing the transcription and expression mechanism of plant snoRNA gene cluster.

  9. Design and Analysis of SD_DWCA - A Mobility based clustering of Homogeneous MANETs

    CERN Document Server

    Janakiraman, T N

    2011-01-01

    This paper deals with the design and analysis of the distributed weighted clustering algorithm SD_DWCA proposed for homogeneous mobile ad hoc networks. It is a connectivity, mobility and energy based clustering algorithm which is suitable for scalable ad hoc networks. The algorithm uses a new graph parameter called strong degree defined based on the quality of neighbours of a node. The parameters are so chosen to ensure high connectivity, cluster stability and energy efficient communication among nodes of high dynamic nature. This paper also includes the experimental results of the algorithm implemented using the network simulator NS2. The experimental results show that the algorithm is suitable for high speed networks and generate stable clusters with less maintenance overhead.

  10. PERFORMANCE EVALUATION OF CLUSTERING IN WEB-LOG ANALYSIS BASED ON AGENT

    Directory of Open Access Journals (Sweden)

    Himani

    2012-06-01

    Full Text Available Web mining is the use of data mining Technique toautomatically discover & extract information from webdocuments. When user searches for goods the managementagent receives order from graphical user interface.Management agent receives information, update agentinformation store house and feedback the mining result touser. Intelligent agent can help making computer systemeasier to use, enable finding & filtering information. Themining agent is the analytical center of whole agentsystem.It mainly adopts two kind of analytical method:related rule mining and cluster analysis. Cluster of objectsare formed so that objects with in a cluster have highsimilarity. The aim of this paper is to analyze the web logdata .To achieve this clustering tool is used. It performs intwo phases. First it captures the web-log data. Then itanalyzes the data& discovers the hidden pattern. Agentrequires an agent communication language to describe &process agent request. The future internet will use PERL toencode information with meaningful structure & semantics.

  11. Mismatch negativity/P3a complex in young people with psychiatric disorders: a cluster analysis.

    Directory of Open Access Journals (Sweden)

    Manreena Kaur

    Full Text Available BACKGROUND: We have recently shown that the event-related potential biomarkers, mismatch negativity (MMN and P3a, are similarly impaired in young patients with schizophrenia- and affective-spectrum psychoses as well as those with bipolar disorder. A data driven approach may help to further elucidate novel patterns of MMN/P3a amplitudes that characterise distinct subgroups in patients with emerging psychiatric disorders. METHODS: Eighty seven outpatients (16 to 30 years were assessed: 19 diagnosed with a depressive disorder; 26 with a bipolar disorder; and 42 with a psychotic disorder. The MMN/P3a complex was elicited using a two-tone passive auditory oddball paradigm with duration deviant tones. Hierarchical cluster analysis utilising frontal, central and temporal neurophysiological variables was conducted. RESULTS: Three clusters were determined: the 'globally impaired' cluster (n = 53 displayed reduced frontal and temporal MMN as well as reduced central P3a amplitudes; the 'largest frontal MMN' cluster (n = 17 were distinguished by increased frontal MMN amplitudes and the 'largest temporal MMN' cluster (n = 17 was characterised by increases in temporal MMN only. Notably, 55% of those in the globally impaired cluster were diagnosed with schizophrenia-spectrum disorder, whereas the three patient subgroups were equally represented in the remaining two clusters. The three cluster-groups did not differ in their current symptomatology; however, the globally impaired cluster was the most neuropsychologically impaired, compared with controls. CONCLUSIONS: These findings suggest that in emerging psychiatric disorders there are distinct MMN/P3a profiles of patient subgroups independent of current symptomatology. Schizophrenia-spectrum patients tended to show the most global impairments in this neurophysiological complex. Two other subgroups of patients were found to have neurophysiological profiles suggestive of quite different neurobiological (and

  12. Preliminary Cluster Analysis For Several Representatives Of Genus Kerivoula (Chiroptera: Vespertilionidae) in Borneo

    Science.gov (United States)

    Hasan, Noor Haliza; Abdullah, M. T.

    2008-01-01

    The aim of the study is to use cluster analysis on morphometric parameters within the genus Kerivoula to produce a dendrogram and to determine the suitability of this method to describe the relationship among species within this genus. A total of 15 adult male individuals from genus Kerivoula taken from sampling trips around Borneo and specimens kept at the zoological museum of Universiti Malaysia Sarawak were examined. A total of 27 characters using dental, skull and external body measurements were recorded. Clustering analysis illustrated the grouping and morphometric relationships between the species of this genus. It has clearly separated each species from each other despite the overlapping of measurements of some species within the genus. Cluster analysis provides an alternative approach to make a preliminary identification of a species.

  13. Application of cluster analysis to preventive maintenance scheme design of pavement

    Institute of Scientific and Technical Information of China (English)

    ZENG Feng; ZHANG Xiao-ning

    2009-01-01

    To quantitatively identify the maintenance demand for each highway segments in the pavement main-tenance scheme design, a mathematical model of uniform segment division was established and an approach of applying cluster analysis theory to the uniform segment division and evaluation of pavement maintenance demand was proposed.The actual maintenance project of a highway carried out in Guangdong province was cited as an example to demonstrate the validity of the proposed method.It is proved that the cluster analysis can eliminate human factors in classification without being constrained by the quantities of samples, considering muhiple pavement distress indexes and the continuity of samples.Thus it is evident that cluster analysis is an efficient analytical tool in uniform segment division and evaluation of maintenance demand.

  14. Parallelization and scheduling of data intensive particle physics analysis jobs on clusters of PCs

    CERN Document Server

    Ponce, S

    2004-01-01

    Summary form only given. Scheduling policies are proposed for parallelizing data intensive particle physics analysis applications on computer clusters. Particle physics analysis jobs require the analysis of tens of thousands of particle collision events, each event requiring typically 200ms processing time and 600KB of data. Many jobs are launched concurrently by a large number of physicists. At a first view, particle physics jobs seem to be easy to parallelize, since particle collision events can be processed independently one from another. However, since large amounts of data need to be accessed, the real challenge resides in making an efficient use of the underlying computing resources. We propose several job parallelization and scheduling policies aiming at reducing job processing times and at increasing the sustainable load of a cluster server. Since particle collision events are usually reused by several jobs, cache based job splitting strategies considerably increase cluster utilization and reduce job ...

  15. Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map.

    Science.gov (United States)

    Orsi, Rebecca

    2017-02-01

    Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research.

  16. Vertical Migrating and Cluster Analysis of Soil Mesofauna at Dongying Halophytes Garden in Yellow River Delta

    Institute of Scientific and Technical Information of China (English)

    He Fu-xia; Xie Tong-yin; Xie Gui-lin; Fu Rong-shu

    2014-01-01

    For the first time, we used Tullgren method made a study on vertical migrating and cluster analysis of the soil mesofauna in Dongying Halophytes Garden in the Yellow River Delta (YRD), Shandong Province. The results showed that the soil mesofauna tended to gather on soil surface in most samples at most times, but the vertical migrating greatly varied in different seasons or environment conditions. Acari was the dominant group. The index of diversity of the soil fauna was correlated with the index of evenness. The Acari's number of individuals infected other species and numbers. Dominant group-Acari made greater contribution to the result of cluster analysis, and there were significant differences between communities in different habitats by cluster analysis with both Bray-Curtis and Jaccard similarity coefficient.

  17. Fuzzy C-means clustering for chromatographic fingerprints analysis: A gas chromatography-mass spectrometry case study.

    Science.gov (United States)

    Parastar, Hadi; Bazrafshan, Alisina

    2016-03-18

    Fuzzy C-means clustering (FCM) is proposed as a promising method for the clustering of chromatographic fingerprints of complex samples, such as essential oils. As an example, secondary metabolites of 14 citrus leaves samples are extracted and analyzed by gas chromatography-mass spectrometry (GC-MS). The obtained chromatographic fingerprints are divided to desired number of chromatographic regions. Owing to the fact that chromatographic problems, such as elution time shift and peak overlap can significantly affect the clustering results, therefore, each chromatographic region is analyzed using multivariate curve resolution-alternating least squares (MCR-ALS) to address these problems. Then, the resolved elution profiles are used to make a new data matrix based on peak areas of pure components to cluster by FCM. The FCM clustering parameters (i.e., fuzziness coefficient and number of cluster) are optimized by two different methods of partial least squares (PLS) as a conventional method and minimization of FCM objective function as our new idea. The results showed that minimization of FCM objective function is an easier and better way to optimize FCM clustering parameters. Then, the optimized FCM clustering algorithm is used to cluster samples and variables to figure out the similarities and dissimilarities among samples and to find discriminant secondary metabolites in each cluster (chemotype). Finally, the FCM clustering results are compared with those of principal component analysis (PCA), hierarchical cluster analysis (HCA) and Kohonon maps. The results confirmed the outperformance of FCM over the frequently used clustering algorithms.

  18. Advances in the Chemical Analysis and Biological Activities of Chuanxiong

    Directory of Open Access Journals (Sweden)

    Jin-Ao Duan

    2012-09-01

    Full Text Available Chuanxiong Rhizoma (Chuan-Xiong, CX, the dried rhizome of Ligusticum chuanxiong Hort. (Umbelliferae, is one of the most popular plant medicines in the World. Modern research indicates that organic acids, phthalides, alkaloids, polysaccharides, ceramides and cerebrosides are main components responsible for the bioactivities and properties of CX. Because of its complex constituents, multidisciplinary techniques are needed to validate the analytical methods that support CX’s use worldwide. In the past two decades, rapid development of technology has advanced many aspects of CX research. The aim of this review is to illustrate the recent advances in the chemical analysis and biological activities of CX, and to highlight new applications and challenges. Emphasis is placed on recent trends and emerging techniques.

  19. Clustered frequency analysis of shear Alfven modes in stellarators

    Energy Technology Data Exchange (ETDEWEB)

    Spong, Donald A [ORNL; D' Azevedo, Ed F [ORNL; Todo, Yasushi [National Institute for Fusion Science, Toki, Japan

    2010-01-01

    The shear Alfven spectrum in three-dimensional configurations, such as stellarators and rippled tokamaks, is more densely populated due to the larger number of mode couplings caused by the variation in the magnetic field in the toroidal dimension. This implies more significant computational requirements that can rapidly become prohibitive as more resolution is requested. Alfven eigenfrequencies and mode structures are a primary point of contact between theory and experiment. A new algorithm based on the Jacobi-Davidson method is developed here and applied for a reduced magnetohydrodynamics model to several stellarator configurations. This technique focuses on finding a subset of eigenmodes clustered about a specified input frequency. This approach can be especially useful in modeling experimental observations, where the mode frequency can generally be measured with good accuracy and several different simultaneous frequency lines may be of interest. For cases considered in this paper, it can be a factor of 10{sup 2}-10{sup 3} times faster than more conventional methods.

  20. Analysis of cardiac tissue by gold cluster ion bombardment

    Science.gov (United States)

    Aranyosiova, M.; Chorvatova, A.; Chorvat, D.; Biro, Cs.; Velic, D.

    2006-07-01

    Specific molecules in cardiac tissue of spontaneously hypertensive rats are studied by using time-of-flight secondary ion mass spectrometry (TOF-SIMS). The investigation determines phospholipids, cholesterol, fatty acids and their fragments in the cardiac tissue, with special focus on cardiolipin. Cardiolipin is a unique phospholipid typical for cardiomyocyte mitochondrial membrane and its decrease is involved in pathologic conditions. In the positive polarity, the fragments of phosphatydilcholine are observed in the mass region of 700-850 u. Peaks over mass 1400 u correspond to intact and cationized molecules of cardiolipin. In animal tissue, cardiolipin contains of almost exclusively 18 carbon fatty acids, mostly linoleic acid. Linoleic acid at 279 u, other fatty acids, and phosphatidylglycerol fragments, as precursors of cardiolipin synthesis, are identified in the negative polarity. These data demonstrate that SIMS technique along with Au 3+ cluster primary ion beam is a good tool for detection of higher mass biomolecules providing approximately 10 times higher yield in comparison with Au +.

  1. Optimization of constitutive parameters of foundation soils k-means clustering analysis

    Institute of Scientific and Technical Information of China (English)

    Muge Elif Orakoglu; Cevdet Emin Ekinci

    2013-01-01

    The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Proctor tests and grain distribution tests of soils taken from three different types of foundation pits:raft foundations, partial raft foundations and strip foundations. k-means algorithm with clustering analysis was applied to determine the most appropriate foundation type given the un-confined compression strengths and other parameters of the different soils.

  2. Clustering of frequency spectrums from different bearing fault using principle component analysis

    Directory of Open Access Journals (Sweden)

    Yusof M.F.M.

    2017-01-01

    Full Text Available In studies associated with the defect in rolling element bearing, signal clustering are one of the popular approach taken in attempt to identify the type of defect. However, the noise interruption are one of the major issues which affect the degree of effectiveness of the applied clustering method. In this paper, the application of principle component analysis (PCA as a pre-processing method for hierarchical clustering analysis on the frequency spectrum of the vibration signal was proposed. To achieve the aim, the vibration signal was acquired from the operating bearings with different condition and speed. In the next stage, the principle component analysis was applied to the frequency spectrums of the acquired signals for pattern recognition purpose. Meanwhile the mahalanobis distance model was used to cluster the result from PCA. According to the results, it was found that the change in amplitude at the respective fundamental frequencies can be detected as a result from the application of PCA. Meanwhile, the application of mahalanobis distance was found to be suitable for clustering the results from principle component analysis. Uniquely, it was discovered that the spectrums from healthy and inner race defect bearing can be clearly distinguished from each other even though the change in amplitude pattern for inner race defect frequency spectrum was too small compared to the healthy one. In this work, it was demonstrated that the use of principle component analysis could sensitively detect the change in the pattern of the frequency spectrums. Likewise, the implementation of mahalanobis distance model for clustering purpose was found to be significant for bearing defect identification.

  3. Comprehensive behavioral analysis of cluster of differentiation 47 knockout mice.

    Directory of Open Access Journals (Sweden)

    Hisatsugu Koshimizu

    Full Text Available Cluster of differentiation 47 (CD47 is a member of the immunoglobulin superfamily which functions as a ligand for the extracellular region of signal regulatory protein α (SIRPα, a protein which is abundantly expressed in the brain. Previous studies, including ours, have demonstrated that both CD47 and SIRPα fulfill various functions in the central nervous system (CNS, such as the modulation of synaptic transmission and neuronal cell survival. We previously reported that CD47 is involved in the regulation of depression-like behavior of mice in the forced swim test through its modulation of tyrosine phosphorylation of SIRPα. However, other potential behavioral functions of CD47 remain largely unknown. In this study, in an effort to further investigate functional roles of CD47 in the CNS, CD47 knockout (KO mice and their wild-type littermates were subjected to a battery of behavioral tests. CD47 KO mice displayed decreased prepulse inhibition, while the startle response did not differ between genotypes. The mutants exhibited slightly but significantly decreased sociability and social novelty preference in Crawley's three-chamber social approach test, whereas in social interaction tests in which experimental and stimulus mice have direct contact with each other in a freely moving setting in a novel environment or home cage, there were no significant differences between the genotypes. While previous studies suggested that CD47 regulates fear memory in the inhibitory avoidance test in rodents, our CD47 KO mice exhibited normal fear and spatial memory in the fear conditioning and the Barnes maze tests, respectively. These findings suggest that CD47 is potentially involved in the regulation of sensorimotor gating and social behavior in mice.

  4. Identifying news clusters using Q-analysis and modularity

    OpenAIRE

    2013-01-01

    With online publication and social media taking the main role in dissemination of news, and with the decline of traditional printed media, it has become necessary to devise ways to automatically extract meaningful information from the plethora of sources available and to make that information readily available to interested parties. In this paper we present a method of automated analysis of the underlying structure of online newspapers based on Q-analysis and modularity. We show how the combi...

  5. Advanced spot quality analysis in two-colour microarray experiments

    Directory of Open Access Journals (Sweden)

    Vetter Guillaume

    2008-09-01

    Full Text Available Abstract Background Image analysis of microarrays and, in particular, spot quantification and spot quality control, is one of the most important steps in statistical analysis of microarray data. Recent methods of spot quality control are still in early age of development, often leading to underestimation of true positive microarray features and, consequently, to loss of important biological information. Therefore, improving and standardizing the statistical approaches of spot quality control are essential to facilitate the overall analysis of microarray data and subsequent extraction of biological information. Findings We evaluated the performance of two image analysis packages MAIA and GenePix (GP using two complementary experimental approaches with a focus on the statistical analysis of spot quality factors. First, we developed control microarrays with a priori known fluorescence ratios to verify the accuracy and precision of the ratio estimation of signal intensities. Next, we developed advanced semi-automatic protocols of spot quality evaluation in MAIA and GP and compared their performance with available facilities of spot quantitative filtering in GP. We evaluated these algorithms for standardised spot quality analysis in a whole-genome microarray experiment assessing well-characterised transcriptional modifications induced by the transcription regulator SNAI1. Using a set of RT-PCR or qRT-PCR validated microarray data, we found that the semi-automatic protocol of spot quality control we developed with MAIA allowed recovering approximately 13% more spots and 38% more differentially expressed genes (at FDR = 5% than GP with default spot filtering conditions. Conclusion Careful control of spot quality characteristics with advanced spot quality evaluation can significantly increase the amount of confident and accurate data resulting in more meaningful biological conclusions.

  6. A cluster analysis to investigating nurses' knowledge, attitudes, and skills regarding the clinical management system.

    Science.gov (United States)

    Chan, M F

    2007-01-01

    Nurses' knowledge, attitudes, and skills regarding the Clinical Management System are explored by identifying profiles of nurses working in Hong Kong. A total of 282 nurses from four hospitals completed a self-reported questionnaire during the period from December 2004 to May 2005. Two-step cluster analysis yielded two clusters. The first cluster (n = 159, 56.4%) was labeled "negative attitudes, less skillful, and average knowledge" group. The second cluster (n = 123, 43.6%) was labeled "positive attitudes, good knowledge, but less skillful." There was a positive correlation in cluster 1 for nurses' knowledge and attitudes (rs = 0.28) and in cluster 2 for nurses' skills and attitudes (rs = 0.25) toward computerization. The study showed that senior and more highly educated nurses generally held more positive attitudes to computerization, whereas the attitudes among younger and less well educated nurses generally were more negative. Such findings should be used to formulate strategies to encourage nurses to resolve actual problems following computer training and to increase the depth and breadth of nurses' computer knowledge and skills and improve their attitudes toward computerization.

  7. ARABIC TEXT SUMMARIZATION BASED ON LATENT SEMANTIC ANALYSIS TO ENHANCE ARABIC DOCUMENTS CLUSTERING

    Directory of Open Access Journals (Sweden)

    Hanane Froud

    2013-01-01

    Full Text Available Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the documents length, useful information on the documents is often accompanied by a large amount of noise, and therefore it is necessary to eliminate this noise while keeping useful information to boost the performance of Documents clustering. In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, Pearson Correlation Coefficient and Averaged Kullback-Leibler Divergence, for two times: without and with stemming. Our experimental results indicate that our proposed approach effectively solves the problems of noisy information and documents length, and thus significantly improve the clustering performance.

  8. Arabic Text Summarization Based on Latent Semantic Analysis to Enhance Arabic Documents Clustering

    Directory of Open Access Journals (Sweden)

    Hanane Froud

    2013-02-01

    Full Text Available Arabic Documents Clustering is an important task for obtaining good results with the traditional Information Retrieval (IR systems especially with the rapid growth of the number of online documents present in Arabic language. Documents clustering aim to automatically group similar documents in one cluster using different similarity/distance measures. This task is often affected by the documents length, useful information on the documents is often accompanied by a large amount of noise, and therefore it is necessary to eliminate this noise while keeping useful information to boost the performance of Documents clustering. In this paper, we propose to evaluate the impact of text summarization using the Latent Semantic Analysis Model on Arabic Documents Clustering in order to solve problems cited above, using five similarity/distance measures: Euclidean Distance, Cosine Similarity, Jaccard Coefficient, PearsonCorrelation Coefficient and Averaged Kullback-Leibler Divergence, for two times: without and with stemming. Our experimental results indicate that our proposed approach effectively solves the problems of noisy information and documents length, and thus significantly improve the clustering performance.

  9. Weighted Clustering

    DEFF Research Database (Denmark)

    Ackerman, Margareta; Ben-David, Shai; Branzei, Simina

    2012-01-01

    We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...... algorithms accordingly....

  10. Advanced spectral analysis of ionospheric waves observed with sparse arrays

    CERN Document Server

    Helmboldt, Joseph

    2014-01-01

    This paper presents a case study from a single, six-hour observing period to illustrate the application of techniques developed for interferometric radio telescopes to the spectral analysis of observations of ionospheric fluctuations with sparse arrays. We have adapted the deconvolution methods used for making high dynamic range images of cosmic sources with radio arrays to making comparably high dynamic range maps of spectral power of wavelike ionospheric phenomena. In the example presented here, we have used observations of the total electron content (TEC) gradient derived from Very Large Array (VLA) observations of synchrotron emission from two galaxy clusters at 330 MHz as well as GPS-based TEC measurements from a sparse array of 33 receivers located within New Mexico near the VLA. We show that these techniques provide a significant improvement in signal to noise (S/N) of detected wavelike structures by correcting for both measurement inaccuracies and wavefront distortions. This is especially true for the...

  11. Fine analysis on advanced detection of transient electromagnetic method

    Institute of Scientific and Technical Information of China (English)

    Wang Bo; Liu Shengdong; Yang Zhen; Wang Zhijun; Huang Lanying

    2012-01-01

    Fault fracture zones and water-bearing bodies in front of the driving head are the main disasters in mine laneways,thus it is important to perform their advanced detection and prediction in advance in order to provide reliable technical support for the excavation.Based on the electromagnetic induction theory,we analyzed the characteristics of primary and secondary fields with a positive and negative wave form of current,proposed the fine processing of the advanced detection with variation rate of apparent resistivity and introduced in detail the computational formulae and procedures.The result of physical simulation experiments illustrate that the tectonic interface of modules can be judged by first-order rate of apparent resistivity with a boundary error of 5%,and the position of water body determined by the fine analysis method agrees well with the result of borehole drilling.This shows that in terms of distinguishing structure and aqueous anomalies,the first-order rate of apparent resistivity is more sensitive than the secondorder rate of apparent resistivity.However,some remaining problems are suggested for future solutions.

  12. Coupled-cluster Green's function: Analysis of properties originating in the exponential parametrization of the ground-state wave function

    Science.gov (United States)

    Peng, Bo; Kowalski, Karol

    2016-12-01

    In this paper we derive basic properties of the Green's-function matrix elements stemming from the exponential coupled-cluster (CC) parametrization of the ground-state wave function. We demonstrate that all intermediates used to express the retarded (or, equivalently, ionized) part of the Green's function in the ω representation can be expressed only through connected diagrams. Similar properties are also shared by the first-order ω derivative of the retarded part of the CC Green's function. Moreover, the first-order ω derivative of the CC Green's function can be evaluated analytically. This result can be generalized to any order of ω derivatives. Through the Dyson equation, derivatives of the corresponding CC self-energy operator can be evaluated analytically. In analogy to the CC Green's function, the corresponding CC self-energy operator can be represented by connected terms. Our analysis can easily be generalized to the advanced part of the CC Green's function.

  13. Instructional Changes Adopted for an Engineering Course: Cluster Analysis on Academic Failure

    Science.gov (United States)

    Álvarez-Bermejo, José A.; Belmonte-Ureña, Luis J.; Martos-Martínez, África; Barragán-Martín, Ana B.; Simón-Márquez, María M.

    2016-01-01

    As first year students come from diverse backgrounds, basic skills should be accessible to everyone as soon as possible. Transferring such skills to these students is challenging, especially in highly technical courses. Ensuring that essential knowledge is acquired quickly promotes the student’s self-esteem and may positively influence failure rates. Metaphors can help do this. Metaphors are used to understand the unknown. This paper shows how we made a turn in student learning at the University of Almeria. Our hypothesis assumed that metaphors accelerate the acquisition of basic knowledge so that other skills built on that foundation are easily learned. With these goals in mind, we changed the way we teach by using metaphors and abstract concepts in a computer organization course, a technical course in the first year of an information technology engineering degree. Cluster analysis of the data on collective student performance after this methodological change clearly identified two distinct groups. These two groups perfectly matched the “before and after” scenarios of the use of metaphors. The study was conducted during 11 academic years (2002/2003 to 2012/2013). The 475 observations made during this period illustrate the usefulness of this change in teaching and learning, shifting from a propositional teaching/learning model to a more dynamic model based on metaphors and abstractions. Data covering the whole period showed favorable evolution of student achievement and reduced failure rates, not only in this course, but also in many of the following more advanced courses. The paper is structured in five sections. The first gives an introduction, the second describes the methodology. The third section describes the sample and the study carried out. The fourth section presents the results and, finally, the fifth section discusses the main conclusions. PMID:27895611

  14. Instructional changes adopted for an engineering course: cluster analysis on academic failure.

    Directory of Open Access Journals (Sweden)

    Jose Antonio Alvarez Bermejo

    2016-11-01

    Full Text Available As first-year students come from diverse backgrounds, basic skills should be accessible to everyone as soon as possible. Transferring such skills to these students is challenging, especially in highly technical courses. Ensuring that essential knowledge is acquired quickly promotes the student’s self-esteem and may positively influence failure rates. Metaphors can help do this. Metaphors are used to understand the unknown. This paper shows how we made a turn in student learning at the University of Almeria. Our hypothesis assumed that metaphors accelerate the acquisition of basic knowledge so that other skills built on that foundation are easily learned. With these goals in mind, we changed the way we teach by using metaphors and abstract concepts in a computer organisation course, a technical course in the first year of an information technology engineering degree. Cluster analysis of the data on collective student performance after this methodological change clearly identified two distinct groups. These two groups perfectly matched the before and after scenarios of the use of metaphors. The study was conducted during 11 academic years (2002/2003 to 2012/2013. The 475 observations made during this period illustrate the usefulness of this change in teaching and learning, shifting from a propositional teaching/learning model to a more dynamic model based on metaphors and abstractions. Data covering the whole period showed favourable evolution of student achievement and reduced failure rates, not only in this course, but also in many of the following more advanced courses.The paper is structured in five sections. The first gives an introduction, the second describes the methodology. The third section describes the sample and the study carried out. The fourth section presents the results and, finally, the fifth section discusses the main conclusions.

  15. Advanced Wireless Power Transfer Vehicle and Infrastructure Analysis (Presentation)

    Energy Technology Data Exchange (ETDEWEB)

    Gonder, J.; Brooker, A.; Burton, E.; Wang, J.; Konan, A.

    2014-06-01

    This presentation discusses current research at NREL on advanced wireless power transfer vehicle and infrastructure analysis. The potential benefits of E-roadway include more electrified driving miles from battery electric vehicles, plug-in hybrid electric vehicles, or even properly equipped hybrid electric vehicles (i.e., more electrified miles could be obtained from a given battery size, or electrified driving miles could be maintained while using smaller and less expensive batteries, thereby increasing cost competitiveness and potential market penetration). The system optimization aspect is key given the potential impact of this technology on the vehicles, the power grid and the road infrastructure.

  16. Life-cycle cost analysis of advanced design mixer pump

    Energy Technology Data Exchange (ETDEWEB)

    Hall, M.N., Westinghouse Hanford

    1996-07-23

    This analysis provides cost justification for the Advanced Design Mixer Pump program based on the cost benefit to the Hanford Site of 4 mixer pump systems defined in terms of the life-cycle cost.A computer model is used to estimate the total number of service hours necessary for each mixer pump to operate over the 20-year retrieval sequence period for single-shell tank waste. This study also considered the double-shell tank waste retrieved prior to the single-shell tank waste which is considered the initial retrieval.

  17. Mental State Talk Structure in Children’s Narratives: A Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Giuliana Pinto

    2017-01-01

    Full Text Available This study analysed children’s Theory of Mind (ToM as assessed by mental state talk in oral narratives. We hypothesized that the children’s mental state talk in narratives has an underlying structure, with specific terms organized in clusters. Ninety-eight children attending the last year of kindergarten were asked to tell a story twice, at the beginning and at the end of the school year. Mental state talk was analysed by identifying terms and expressions referring to perceptual, physiological, emotional, willingness, cognitive, moral, and sociorelational states. The cluster analysis showed that children’s mental state talk is organized in two main clusters: perceptual states and affective states. Results from the study confirm the feasibility of narratives as an outlet to inquire mental state talk and offer a more fine-grained analysis of mental state talk structure.

  18. Clustering of the Parameters of Rhythmographic Analysis of Man’s Electrocardiogram

    Directory of Open Access Journals (Sweden)

    Ekaterina A. Filippova

    2014-12-01

    Full Text Available The article considers the clustering of the parameters of man’s heart rate variability. The technique of parameters calculation and diagrams of rhythmographic analysis construction are presented. The algorithm of conceptual clustering Cobweb, modified for quantitative data, is used for parameters clustering. The results of the experiments prove the efficiency of the division of the learning range of electrocardiograms into the groups similar in terms of rhythmographic parameters. The practical application of the offered method as a part of the software support of electrocardiograms analysis will enable to provide operational evaluation of the rhythmographic nature of heart function in the course of screening examinations or in the emergency medicine for diagnosing and prediction.

  19. Advanced Analysis of Grid-connected PV System's Performance and Effect of Battery

    Science.gov (United States)

    Ueda, Yuzuru; Kurokawa, Kosuke; Itou, Takamitsu; Kitamura, Kiyoyuki; Akanuma, Katsumi; Yokota, Masaharu; Sugihara, Hiroyuki; Morimoto, Atsushi

    An advanced analysis method for grid connected PV systems is developed in this research. To investigate the issues which may happen in the clustered PV systems, “Demonstrative research on clustered PV systems" is being conducted from December, 2002, in Oota, Japan. More than 500 residential PV systems will be installed in the demonstrative research area, battery integrated PV systems are developed to avoid the restriction of output power due to the raising of grid voltage. Annual performance of commercial PV systems without battery is analyzed and resulted in around 80% of performance ratio on the average. Over voltage of power distribution line and snow are two major factors of very low performance ratio on daily basis. Effects of batteries are also analyzed, the results indicate that there will be some improvement for the energy loss due to the grid voltage but PCS's efficiency will be around 8% worse than that of the commercial PV systems. It is also found that the non-optimized operation of battery sometimes results in the fully-charged situation during the noontime and maximum reverse power flow may not be minimized in this situation.

  20. Empirical power and sample size calculations for cluster-randomized and cluster-randomized crossover studies.

    Science.gov (United States)

    Reich, Nicholas G; Myers, Jessica A; Obeng, Daniel; Milstone, Aaron M; Perl, Trish M

    2012-01-01

    In recent years, the number of studies using a cluster-randomized design has grown dramatically. In addition, the cluster-randomized crossover design has been touted as a methodological advance that can increase efficiency of cluster-randomized studies in certain situations. While the cluster-randomized crossover trial has become a popular tool, standards of design, analysis, reporting and implementation have not been established for this emergent design. We address one particular aspect of cluster-randomized and cluster-randomized crossover trial design: estimating statistical power. We present a general framework for estimating power via simulation in cluster-randomized studies with or without one or more crossover periods. We have implemented this framework in the clusterPower software package for R, freely available online from the Comprehensive R Archive Network. Our simulation framework is easy to implement and users may customize the methods used for data analysis. We give four examples of using the software in practice. The clusterPower package could play an important role in the design of future cluster-randomized and cluster-randomized crossover studies. This work is the first to establish a universal method for calculating power for both cluster-randomized and cluster-randomized clinical trials. More research is needed to develop standardized and recommended methodology for cluster-randomized crossover studies.

  1. Properties of Star Clusters -- III: Analysis of 13 FSR Clusters using UKIDSS-GPS and VISTA-VVV

    CERN Document Server

    Buckner, A S M

    2016-01-01

    Discerning the nature of open cluster candidates is essential for both individual and statistical analyses of cluster properties. Here we establish the nature of thirteen cluster candidates from the FSR cluster list using photometry from the 2MASS and deeper, higher resolution UKIDSS-GPS and VISTA-VVV surveys. These clusters were selected because they were flagged in our previous studies as expected to contain a large proportion of pre-main sequence members or are at unusually small/large Galactocentric distances. We employ a decontamination procedure of JHK photometry to identify cluster members. Cluster properties are homogeneously determined and we conduct a cross comparative study of our results with the literature (where available). Seven of the here studied clusters were confirmed to contain PMS stars, one of which is a newly confirmed cluster. Our study of FSR1716 is the deepest to date and is in notable disagreement with previous studies, finding that it has a distance of about 7.3kpc and age of 10-12...

  2. Hubble Frontier Fields: a high-precision strong-lensing analysis of the massive galaxy cluster Abell 2744 using ˜180 multiple images

    Science.gov (United States)

    Jauzac, M.; Richard, J.; Jullo, E.; Clément, B.; Limousin, M.; Kneib, J.-P.; Ebeling, H.; Natarajan, P.; Rodney, S.; Atek, H.; Massey, R.; Eckert, D.; Egami, E.; Rexroth, M.

    2015-09-01

    We present a high-precision mass model of galaxy cluster Abell 2744, based on a strong gravitational-lensing analysis of the Hubble Space Telescope Frontier Fields (HFF) imaging data, which now include both Advanced Camera for Surveys and Wide Field Camera 3 observations to the final depth. Taking advantage of the unprecedented depth of the visible and near-infrared data, we identify 34 new multiply imaged galaxies, bringing the total to 61, comprising 181 individual lensed images. In the process, we correct previous erroneous identifications and positions of multiple systems in the northern part of the cluster core. With the LENSTOOL software and the new sets of multiple images, we model the cluster using two cluster-scale dark matter haloes plus galaxy-scale haloes for the cluster members. Our best-fitting model predicts image positions with an rms error of 0.79 arcsec, which constitutes an improvement by almost a factor of 2 over previous parametric models of this cluster. We measure the total projected mass inside a 200 kpc aperture as (2.162 ± 0.005) × 1014 M⊙, thus reaching 1 per cent level precision for the second time, following the recent HFF measurement of MACSJ0416.1-2403. Importantly, the higher quality of the mass model translates into an overall improvement by a factor of 4 of the derived magnification factor. Together with our previous HFF gravitational lensing analysis, this work demonstrates that the HFF data enables high-precision mass measurements for massive galaxy clusters and the derivation of robust magnification maps to probe the early Universe.

  3. Cluster Analysis of Indonesian Province Based on Household Primary Cooking Fuel Using K-Means

    Science.gov (United States)

    Huda, S. N.

    2017-03-01

    Each household definitely provides installations for cooking. Kerosene, which is refined from petroleum products once dominated types of primary fuel for cooking in Indonesia, whereas kerosene has an expensive cost and small efficiency. Other household use LPG as their primary cooking fuel. However, LPG supply is also limited. In addition, with a very diverse environments and cultures in Indonesia led to diversity of the installation type of cooking, such as wood-burning stove brazier. The government is also promoting alternative fuels, such as charcoal briquettes, and fuel from biomass. The use of other fuels is part of the diversification of energy that is expected to reduce community dependence on petroleum-based fuels. The use of various fuels in cooking that vary from one region to another reflects the distribution of fuel basic use by household. By knowing the characteristics of each province, the government can take appropriate policies to each province according each character. Therefore, it would be very good if there exist a cluster analysis of all provinces in Indonesia based on the type of primary cooking fuel in household. Cluster analysis is done using K-Means method with K ranging from 2-5. Cluster results are validated using Silhouette Coefficient (SC). The results show that the highest SC achieved from K = 2 with SC value 0.39135818388151. Two clusters reflect provinces in Indonesia, one is a cluster of more traditional provinces and the other is a cluster of more modern provinces. The cluster results are then shown in a map using Google Map API.

  4. Applying of hierarchical clustering to analysis of protein patterns in the human cancer-associated liver.

    Directory of Open Access Journals (Sweden)

    Natalia A Petushkova

    Full Text Available There are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the training dataset is not available the task can be to mine for presence of meaningful groups (clusters of samples and to explore underlying data structure (unsupervised learning.We investigated the proteomic profiles of the cytosolic fraction of human liver samples using two-dimensional electrophoresis (2DE. Samples were resected upon surgical treatment of hepatic metastases in colorectal cancer. Unsupervised hierarchical clustering of 2DE gel images (n = 18 revealed a pair of clusters, containing 11 and 7 samples. Previously we used the same specimens to measure biochemical profiles based on cytochrome P450-dependent enzymatic activities and also found that samples were clearly divided into two well-separated groups by cluster analysis. It turned out that groups by enzyme activity almost perfectly match to the groups identified from proteomic data. Of the 271 reproducible spots on our 2DE gels, we selected 15 to distinguish the human liver cytosolic clusters. Using MALDI-TOF peptide mass fingerprinting, we identified 12 proteins for the selected spots, including known cancer-associated species.Our results highlight the importance of hierarchical cluster analysis of proteomic data, and showed concordance between results of biochemical and proteomic approaches. Grouping of the human liver samples and/or patients into differing clusters may provide insights into possible molecular mechanism of drug metabolism and creates a rationale for personalized treatment.

  5. Cluster Analysis of Velocity Field Derived from Dense GNSS Network of Japan

    Science.gov (United States)

    Takahashi, A.; Hashimoto, M.

    2015-12-01

    Dense GNSS networks have been widely used to observe crustal deformation. Simpson et al. (2012) and Savage and Simpson (2013) have conducted cluster analyses of GNSS velocity field in the San Francisco Bay Area and Mojave Desert, respectively. They have successfully found velocity discontinuities. They also showed an advantage of cluster analysis for classifying GNSS velocity field. Since in western United States, strike-slip events are dominant, geometry is simple. However, the Japanese Islands are tectonically complicated due to subduction of oceanic plates. There are many types of crustal deformation such as slow slip event and large postseismic deformation. We propose a modified clustering method of GNSS velocity field in Japan to separate time variant and static crustal deformation. Our modification is performing cluster analysis every several months or years, then qualifying cluster member similarity. If a GNSS station moved differently from its neighboring GNSS stations, the station will not belong to in the cluster which includes its surrounding stations. With this method, time variant phenomena were distinguished. We applied our method to GNSS data of Japan from 1996 to 2015. According to the analyses, following conclusions were derived. The first is the clusters boundaries are consistent with known active faults. For examples, the Arima-Takatsuki-Hanaore fault system and the Shimane-Tottori segment proposed by Nishimura (2015) are recognized, though without using prior information. The second is improving detectability of time variable phenomena, such as a slow slip event in northern part of Hokkaido region detected by Ohzono et al. (2015). The last one is the classification of postseismic deformation caused by large earthquakes. The result suggested velocity discontinuities in postseismic deformation of the Tohoku-oki earthquake. This result implies that postseismic deformation is not continuously decaying proportional to distance from its epicenter.

  6. Indentifying the major air pollutants base on factor and cluster analysis, a case study in 74 Chinese cities

    Science.gov (United States)

    Zhang, Jing; Zhang, Lan-yue; Du, Ming; Zhang, Wei; Huang, Xin; Zhang, Ya-qi; Yang, Yue-yi; Zhang, Jian-min; Deng, Shi-huai; Shen, Fei; Li, Yuan-wei; Xiao, Hong

    2016-11-01

    This article investigated the major air pollutants and its spatial and seasonal distribution in 74 Chinese cities. Factor analysis and Cluster analysis are employed to indentify major factors of air pollutants. The following results are obtained (1) major factors are obtained in spring, summer, autumn, and winter. The first factor in spring includes NO2, PM10, CO, and PM2.5; the first factor in summer and autumn includes PM10, PM2.5, CO and SO2; in winter, the first factor includes NO2, PM10, PM2.5, and SO2. (2) In spring, cities of cluster 5 are the severest polluted by emission sources of SO2, CO, PM10, and PM2.5; the emission sources of O3 would significantly influence the air quality in cities of cluster 2; the emission sources of NO2 could significantly influence the air quality in cities of cluster 3 and cluster 5. (3) In summer, cities of cluster 5 are the severest polluted by automotive emissions and coal flue gas. Cities of cluster 1 are the lightest polluted. Cities of cluster 3 and cluster 2 are polluted by emission sources of SO2 and O3. (4) In Autumn, cities of cluster 3 and 4 are the severest polluted by the emission sources of SO2, CO, PM10, and PM2.5; the emission sources of NO2 would significantly influence the air quality in cities of cluster 5; the emission sources of O3 could significantly influence the air quality in cities of cluster 1 and cluster 4. (5) In winter, cities of cluster 5 are the severest polluted by the emission sources of SO2, CO, PM10, PM2.5, and CO; the emission sources of O3 could significantly influence the air quality in cities of cluster 1 and cluster 5.

  7. Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks.

    Science.gov (United States)

    de Oña, Juan; López, Griselda; Mujalli, Randa; Calvo, Francisco J

    2013-03-01

    One of the principal objectives of traffic accident analyses is to identify key factors that affect the severity of an accident. However, with the presence of heterogeneity in the raw data used, the analysis of traffic accidents becomes difficult. In this paper, Latent Class Cluster (LCC) is used as a preliminary tool for segmentation of 3229 accidents on rural highways in Granada (Spain) between 2005 and 2008. Next, Bayesian Networks (BNs) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC. The results of these cluster-based analyses are compared with the results of a full-data analysis. The results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data. BN inference is used to obtain the variables that best identify accidents with killed or seriously injured. Accident type and sight distance have been identify in all the cases analysed; other variables such as time, occupant involved or age are identified in EDB and only in one cluster; whereas variables vehicles involved, number of injuries, atmospheric factors, pavement markings and pavement width are identified only in one cluster.

  8. Gene microarray data analysis using parallel point-symmetry-based clustering.

    Science.gov (United States)

    Sarkar, Anasua; Maulik, Ujjwal

    2015-01-01

    Identification of co-expressed genes is the central goal in microarray gene expression analysis. Point-symmetry-based clustering is an important unsupervised learning technique for recognising symmetrical convex- or non-convex-shaped clusters. To enable fast clustering of large microarray data, we propose a distributed time-efficient scalable approach for point-symmetry-based K-Means algorithm. A natural basis for analysing gene expression data using symmetry-based algorithm is to group together genes with similar symmetrical expression patterns. This new parallel implementation also satisfies linear speedup in timing without sacrificing the quality of clustering solution on large microarray data sets. The parallel point-symmetry-based K-Means algorithm is compared with another new parallel symmetry-based K-Means and existing parallel K-Means over eight artificial and benchmark microarray data sets, to demonstrate its superiority, in both timing and validity. The statistical analysis is also performed to establish the significance of this message-passing-interface based point-symmetry K-Means implementation. We also analysed the biological relevance of clustering solutions.

  9. Performance Analysis of a Cluster-Based MAC Protocol for Wireless Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Jesús Alonso-Zárate

    2010-01-01

    Full Text Available An analytical model to evaluate the non-saturated performance of the Distributed Queuing Medium Access Control Protocol for Ad Hoc Networks (DQMANs in single-hop networks is presented in this paper. DQMAN is comprised of a spontaneous, temporary, and dynamic clustering mechanism integrated with a near-optimum distributed queuing Medium Access Control (MAC protocol. Clustering is executed in a distributed manner using a mechanism inspired by the Distributed Coordination Function (DCF of the IEEE 802.11. Once a station seizes the channel, it becomes the temporary clusterhead of a spontaneous cluster and it coordinates the peer-to-peer communications between the clustermembers. Within each cluster, a near-optimum distributed queuing MAC protocol is executed. The theoretical performance analysis of DQMAN in single-hop networks under non-saturation conditions is presented in this paper. The approach integrates the analysis of the clustering mechanism into the MAC layer model. Up to the knowledge of the authors, this approach is novel in the literature. In addition, the performance of an ad hoc network using DQMAN is compared to that obtained when using the DCF of the IEEE 802.11, as a benchmark reference.

  10. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-07-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution

  11. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-11-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

  12. 2 x 2 Achievement Goals and Achievement Emotions: A Cluster Analysis of Students' Motivation

    Science.gov (United States)

    Jang, Leong Yeok; Liu, Woon Chia

    2012-01-01

    This study sought to better understand the adoption of multiple achievement goals at an intra-individual level, and its links to emotional well-being, learning, and academic achievement. Participants were 480 Secondary Two students (aged between 13 and 14 years) from two coeducational government schools. Hierarchical cluster analysis revealed the…

  13. Generating Geospatially Realistic Driving Patterns Derived From Clustering Analysis Of Real EV Driving Data

    DEFF Research Database (Denmark)

    Pedersen, Anders Bro; Aabrandt, Andreas; Østergaard, Jacob

    2014-01-01

    scales, which calls for a statistically correct, yet flexible model. This paper describes a method for modelling EV, based on non-categorized data, which takes into account the plug in locations of the vehicles. By using clustering analysis to extrapolate and classify the primary locations where...

  14. Improved Detection of Time Windows of Brain Responses in Fmri Using Modified Temporal Clustering Analysis

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    @@ Temporal clustering analysis (TCA) has been proposed recently as a method to detect time windows of brain responses in functional MRI (fMRI) studies when the timing and location of the activation are completely unknown. Modifications to the TCA technique are introduced in this report to further improve the sensitivity in detecting brain activation.

  15. Classification of shoulder complaints in general practice by means of cluster analysis

    NARCIS (Netherlands)

    Winters, JC; Groenier, KH; Sobel, JS; Arendzen, HH; Meyboom-de Jong, B

    1997-01-01

    Objective: To determine if a classification of shoulder complaints in general practice can be made with a cluster analysis of variables of medical history and physical examination. Method: One hundred one patients with shoulder complaints were examined upon inclusion (week 0) and after 2 weeks. Elev

  16. [Current service invention patents and growth pathways on basis of cluster analysis].

    Science.gov (United States)

    Yang, Xu-jie; Xiao, Shi-ying

    2012-09-01

    This study aims for enhancing quantity and quality of patents of traditional Chinese medicine compounds of traditional Chinese medicine enterprises, traditional Chinese medicine colleges and relevant institutions while building an efficient pathway for patent protection using simple statistics and cluster analysis, with service invention patent holders of traditional Chinese medicine compounds as the study object.

  17. Clustered Stomates in "Begonia": An Exercise in Data Collection & Statistical Analysis of Biological Space

    Science.gov (United States)

    Lau, Joann M.; Korn, Robert W.

    2007-01-01

    In this article, the authors present a laboratory exercise in data collection and statistical analysis in biological space using clustered stomates on leaves of "Begonia" plants. The exercise can be done in middle school classes by students making their own slides and seeing imprints of cells, or at the high school level through collecting data of…

  18. Cluster Analysis of Assessment in Anatomy and Physiology for Health Science Undergraduates

    Science.gov (United States)

    Brown, Stephen; White, Sue; Power, Nicola

    2016-01-01

    Academic content common to health science programs is often taught to a mixed group of students; however, content assessment may be consistent for each discipline. This study used a retrospective cluster analysis on such a group, first to identify high and low achieving students, and second, to determine the distribution of students within…

  19. Tool for Sizing Analysis of the Advanced Life Support System

    Science.gov (United States)

    Yeh, Hue-Hsie Jannivine; Brown, Cheryl B.; Jeng, Frank J.

    2005-01-01

    Advanced Life Support Sizing Analysis Tool (ALSSAT) is a computer model for sizing and analyzing designs of environmental-control and life support systems (ECLSS) for spacecraft and surface habitats involved in the exploration of Mars and Moon. It performs conceptual designs of advanced life support (ALS) subsystems that utilize physicochemical and biological processes to recycle air and water, and process wastes in order to reduce the need of resource resupply. By assuming steady-state operations, ALSSAT is a means of investigating combinations of such subsystems technologies and thereby assisting in determining the most cost-effective technology combination available. In fact, ALSSAT can perform sizing analysis of the ALS subsystems that are operated dynamically or steady in nature. Using the Microsoft Excel spreadsheet software with Visual Basic programming language, ALSSAT has been developed to perform multiple-case trade studies based on the calculated ECLSS mass, volume, power, and Equivalent System Mass, as well as parametric studies by varying the input parameters. ALSSAT s modular format is specifically designed for the ease of future maintenance and upgrades.

  20. Imaging spectroscopic analysis at the Advanced Light Source

    Energy Technology Data Exchange (ETDEWEB)

    MacDowell, A. A.; Warwick, T.; Anders, S.; Lamble, G.M.; Martin, M.C.; McKinney, W.R.; Padmore, H.A.

    1999-05-12

    One of the major advances at the high brightness third generation synchrotrons is the dramatic improvement of imaging capability. There is a large multi-disciplinary effort underway at the ALS to develop imaging X-ray, UV and Infra-red spectroscopic analysis on a spatial scale from. a few microns to 10nm. These developments make use of light that varies in energy from 6meV to 15KeV. Imaging and spectroscopy are finding applications in surface science, bulk materials analysis, semiconductor structures, particulate contaminants, magnetic thin films, biology and environmental science. This article is an overview and status report from the developers of some of these techniques at the ALS. The following table lists all the currently available microscopes at the. ALS. This article will describe some of the microscopes and some of the early applications.

  1. Advanced aerostatic analysis of long-span suspension bridges

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    As the span length of suspension bridges increases, the diameter of cables and thus the wind load acting on them, the nonlinear wind-structure interaction and the wind speed spatial non-uniformity all increase consequently, which may have unnegligible influence on the aerostatic behavior of long-span suspension bridges. In this work, a method of advanced aerostatic analysis is presented firstly by considering the geometric nonlinearity, the nonlinear wind-structures and wind speed spatial non-uniformity. By taking the Runyang Bridge over the Yangtze River as example, effects of the nonlinear wind-structure interaction, wind speed spatial non-uniformity, and the cable's wind load on the aerostatic behavior of the bridge are investigated analytically. The results showed that these factors all have important influence on the aerostatic behavior, and should be considered in the aerostatic analysis of long and particularly super long-span suspension bridges.

  2. Unraveling the dha cluster in Citrobacter werkmanii: comparative genomic analysis of bacterial 1,3-propanediol biosynthesis clusters.

    Science.gov (United States)

    Maervoet, Veerle E T; De Maeseneire, Sofie L; Soetaert, Wim K; De Mey, Marjan

    2014-04-01

    In natural 1,3-propanediol (PDO) producing microorganisms such as Klebsiella pneumoniae, Citrobacter freundii and Clostridium sp., the genes coding for PDO producing enzymes are grouped in a dha cluster. This article describes the dha cluster of a novel candidate for PDO production, Citrobacter werkmanii DSM17579 and compares the cluster to the currently known PDO clusters of Enterobacteriaceae and Clostridiaceae. Moreover, we attribute a putative function to two previously unannotated ORFs, OrfW and OrfY, both in C. freundii and in C. werkmanii: both proteins might form a complex and support the glycerol dehydratase by converting cob(I)alamin to the glycerol dehydratase cofactor coenzyme B12. Unraveling this biosynthesis cluster revealed high homology between the deduced amino acid sequence of the open reading frames of C. werkmanii DSM17579 and those of C. freundii DSM30040 and K. pneumoniae MGH78578, i.e., 96 and 87.5 % identity, respectively. On the other hand, major differences between the clusters have also been discovered. For example, only one dihydroxyacetone kinase (DHAK) is present in the dha cluster of C. werkmanii DSM17579, while two DHAK enzymes are present in the cluster of K. pneumoniae MGH78578 and Clostridium butyricum VPI1718.

  3. The mass of high-z massive galaxy cluster, SPT-CL J2106-5844 using weak-lensing analysis with HST observations

    Science.gov (United States)

    Kim, Jinhyub; Jee, James; Ko, Jongwan

    2017-01-01

    We present a weak-lensing analysis of the galaxy cluster SPT-CL J2106-5844 at z~1.132 using images from the Advanced Camera for Surveys (ACS) and Wide Field Camera 3 (WFC3) on-board on the Hubble Space Telescope (HST). This cluster discovered in the South Pole Telescope Sunyaev-Zel’dovich (SPT-SZ) survey is known to be the most massive system at z > 1 in the survey. Within the current ΛCDM hierarchical structure formation paradigm, the mass of the cluster at such a high redshift inferred by SZ, X-ray, and galaxy velocity dispersion data is somewhat unusual. The previous mass estimates, however, rely on assumptions on the dynamical state of the system, which may become questionable when the universe was young (about 40% of the current age). In this work, we present the first weak-lensing mass estimates of this interesting cluster. We describe how we derive a mass from the HST/ACS and HST/WFC3 deep imaging data and show a two-dimensional mass reconstruction. We find that the mass distribution of the cluster is unimodal with a centroid consistent (~1σ) with both galaxy luminosity and number density distributions. Based on tangential shear fitting with an NFW halo assumption, our weak-lensing mass estimates agree well with the previous estimates.

  4. Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Wei-Zhen [Department of Building and Construction, City University of Hong Kong (China); He, Hong-Di [Department of Building and Construction, City University of Hong Kong (China); Logistics Research Center, Shanghai Maritime University, Shanghai (China); Dong, Li-yun [Shanghai Institute of Applied Mathematics and Mechanics, Shanghai University, Shanghai (China)

    2011-03-15

    This study aims to evaluate the performance of two statistical methods, principal component analysis and cluster analysis, for the management of air quality monitoring network of Hong Kong and the reduction of associated expenses. The specific objectives include: (i) to identify city areas with similar air pollution behavior; and (ii) to locate emission sources. The statistical methods were applied to the mass concentrations of sulphur dioxide (SO{sub 2}), respirable suspended particulates (RSP) and nitrogen dioxide (NO{sub 2}), collected in monitoring network of Hong Kong from January 2001 to December 2007. The results demonstrate that, for each pollutant, the monitoring stations are grouped into different classes based on their air pollution behaviors. The monitoring stations located in nearby area are characterized by the same specific air pollution characteristics and suggested with an effective management of air quality monitoring system. The redundant equipments should be transferred to other monitoring stations for allowing further enlargement of the monitored area. Additionally, the existence of different air pollution behaviors in the monitoring network is explained by the variability of wind directions across the region. The results imply that the air quality problem in Hong Kong is not only a local problem mainly from street-level pollutions, but also a region problem from the Pearl River Delta region. (author)

  5. Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Lingli Jiang

    2011-01-01

    Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

  6. Advanced probabilistic risk analysis using RAVEN and RELAP-7

    Energy Technology Data Exchange (ETDEWEB)

    Rabiti, Cristian [Idaho National Lab. (INL), Idaho Falls, ID (United States); Alfonsi, Andrea [Idaho National Lab. (INL), Idaho Falls, ID (United States); Mandelli, Diego [Idaho National Lab. (INL), Idaho Falls, ID (United States); Cogliati, Joshua [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kinoshita, Robert [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2014-06-01

    RAVEN, under the support of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program [1], is advancing its capability to perform statistical analyses of stochastic dynamic systems. This is aligned with its mission to provide the tools needed by the Risk Informed Safety Margin Characterization (RISMC) path-lead [2] under the Department Of Energy (DOE) Light Water Reactor Sustainability program [3]. In particular this task is focused on the synergetic development with the RELAP-7 [4] code to advance the state of the art on the safety analysis of nuclear power plants (NPP). The investigation of the probabilistic evolution of accident scenarios for a complex system such as a nuclear power plant is not a trivial challenge. The complexity of the system to be modeled leads to demanding computational requirements even to simulate one of the many possible evolutions of an accident scenario (tens of CPU/hour). At the same time, the probabilistic analysis requires thousands of runs to investigate outcomes characterized by low probability and severe consequence (tail problem). The milestone reported in June of 2013 [5] described the capability of RAVEN to implement complex control logic and provide an adequate support for the exploration of the probabilistic space using a Monte Carlo sampling strategy. Unfortunately the Monte Carlo approach is ineffective with a problem of this complexity. In the following year of development, the RAVEN code has been extended with more sophisticated sampling strategies (grids, Latin Hypercube, and adaptive sampling). This milestone report illustrates the effectiveness of those methodologies in performing the assessment of the probability of core damage following the onset of a Station Black Out (SBO) situation in a boiling water reactor (BWR). The first part of the report provides an overview of the available probabilistic analysis capabilities, ranging from the different types of distributions available, possible sampling

  7. THE USE OF CLUSTER ANALYSIS TO EVALUATE SOCIO-ECONOMIC DEVELOPMENT OF REGIONS (EVIDENCE FROM THE YAROSLAVL REGION

    Directory of Open Access Journals (Sweden)

    Vera V. Zholudeva

    2014-01-01

    Full Text Available In the article the results of Cluster Analysis of the Central Federal District regions are presented. The use of cluster analysis methods for definition of the resources utilization degree and the improvement of socio-economic development of any region is considered. The article gives the index of socio-economic development of the Yaroslavl region.

  8. Module Cluster: IFE - 001.00 (GSC) Basic Terminology and Analysis of Writings Concerned with Educational Issues.

    Science.gov (United States)

    Zahn, R. D.

    This document is one of the module clusters developed for the Camden Teacher Corps project. The purpose of this module cluster is to enable students to define and use basic terminology in the discussion and analysis of educational issues, to use various approaches in studying an issue, and to apply critical analysis skills to written and spoken…

  9. Design and analysis of 19 pin annular fuel rod cluster for pressure tube type boiling water reactor

    Energy Technology Data Exchange (ETDEWEB)

    Deokule, A.P., E-mail: abhijit.deokule1986@gmail.com [Homi Bhabha National Institute, Trombay 400 085, Mumbai (India); Vishnoi, A.K.; Dasgupta, A.; Umasankari, K.; Chandraker, D.K.; Vijayan, P.K. [Bhabha Atomic Research Centre, Trombay 400 085, Mumbai (India)

    2014-09-15

    Highlights: • Development of 19 pin annular fuel rod cluster. • Reactor physics study of designed annular fuel rod cluster. • Thermal hydraulic study of annular fuel rod cluster. - Abstract: An assessment of 33 pin annular fuel rod cluster has been carried out previously for possible use in a pressure tube type boiling water reactor. Despite the benefits such as negative coolant void reactivity and larger heat transfer area, the 33 pin annular fuel rod cluster is having lower discharge burn up as compared to solid fuel rod cluster when all other parameters are kept the same. The power rating of this design cannot be increased beyond 20% of the corresponding solid fuel rod cluster. The limitation on the power is not due to physics parameters rather it comes from the thermal hydraulics side. In order to increase power rating of the annular fuel cluster, keeping same pressure tube diameter, the pin diameter was increased, achieving larger inside flow area. However, this reduces the number of annular fuel rods. In spite of this, the power of the annular fuel cluster can be increased by 30% compared to the solid fuel rod cluster. This makes the nineteen pin annular fuel rod cluster a suitable option to extract more power without any major changes in the existing design of the fuel. In the present study reactor physics and thermal hydraulic analysis carried out with different annular fuel rod cluster geometry is reported in detail.

  10. Investigating nurses' knowledge, attitudes, and skills patterns towards clinical management system: results of a cluster analysis.

    Science.gov (United States)

    Chan, M F

    2006-09-01

    To determine whether definable subtypes exist within a cohort of Hong Kong nurses as related to the clinical management system use in their clinical practices based on their knowledge, attitudes, skills, and background factors. Data were collected using a structured questionnaire. The sample of 242 registered nurses was recruited from three hospitals in Hong Kong. The study employs personal and demographic variables, knowledge, attitudes, and skills scale. A cluster analysis yielded two clusters. Each cluster represents a different profile of Hong Kong nurses on the clinical management system use in their clinical practices. The first group (Cluster 1) was labeled 'lower attitudes, less skilful and average knowledge' group, and represented 55.4% of the total respondents. The second group (Cluster 2) was labeled as 'positive attitudes, good knowledge but less skilful'. They comprised almost 44.6% of this nursing sample. Cluster 2 had more older nurses, the majority were educated to the baccalaureate or above level, with more than 10 years working experience, and they held a more senior ranking then Cluster 1. A clear profile of Hong Kong nurses may benefit healthcare professionals in making appropriate education or assistance to prompt the use of the clinical management system by nurses an officially recognized profession. The findings were useful in determining nurse-users' specific needs and their preferences for modification of the clinical management system. Such findings should be used to formulate strategies to encourage nurses to resolve actual problems following computer training and to increase the depth and breadth of nurses' knowledge, attitudes, and skills toward such system.

  11. Market segmentation for multiple option healthcare delivery systems--an application of cluster analysis.

    Science.gov (United States)

    Jarboe, G R; Gates, R H; McDaniel, C D

    1990-01-01

    Healthcare providers of multiple option plans may be confronted with special market segmentation problems. This study demonstrates how cluster analysis may be used for discovering distinct patterns of preference for multiple option plans. The availability of metric, as opposed to categorical or ordinal, data provides the ability to use sophisticated analysis techniques which may be superior to frequency distributions and cross-tabulations in revealing preference patterns.

  12. Clinical evaluation of nonsyndromic dental anomalies in Dravidian population: A cluster sample analysis

    OpenAIRE

    Yamunadevi, Andamuthu; Selvamani, M.; Vinitha, V.; Srivandhana, R.; Balakrithiga, M.; Prabhu, S; Ganapathy, N

    2015-01-01

    Aim: To record the prevalence rate of dental anomalies in Dravidian population and analyze the percentage of individual anomalies in the population. Methodology: A cluster sample analysis was done, where 244 subjects studying in a dental institution were all included and analyzed for occurrence of dental anomalies by clinical examination, excluding third molars from analysis. Results: 31.55% of the study subjects had dental anomalies and shape anomalies were more prevalent (22.1%), followed b...

  13. PERFORMANCE ANALYSIS OF MESSAGE PASSING INTERFACE COLLECTIVE COMMUNICATION ON INTEL XEON QUAD-CORE GIGABIT ETHERNET AND INFINIBAND CLUSTERS

    Directory of Open Access Journals (Sweden)

    Roswan Ismail

    2013-01-01

    Full Text Available The performance of MPI implementation operations still presents critical issues for high performance computing systems, particularly for more advanced processor technology. Consequently, this study concentrates on benchmarking MPI implementation on multi-core architecture by measuring the performance of Open MPI collective communication on Intel Xeon dual quad-core Gigabit Ethernet and InfiniBand clusters using SKaMPI. It focuses on well known collective communication routines such as MPI-Bcast, MPI-AlltoAll, MPI-Scatter and MPI-Gather. From the collection of results, MPI collective communication on InfiniBand clusters had distinctly better performance in terms of latency and throughput. The analysis indicates that the algorithm used for collective communication performed very well for all message sizes except for MPI-Bcast and MPI-Alltoall operation of inter-node communication. However, InfiniBand provides the lowest latency for all operations since it provides applications with an easy to use messaging service, compared to Gigabit Ethernet, which still requests the operating system for access to one of the server communication resources with the complex dance between an application and a network.

  14. A novel PPGA-based clustering analysis method for business cycle indicator selection

    Institute of Scientific and Technical Information of China (English)

    Dabin ZHANG; Lean YU; Shouyang WANG; Yingwen SONG

    2009-01-01

    A new clustering analysis method based on the pseudo parallel genetic algorithm (PPGA) is proposed for business cycle indicator selection. In the proposed method,the category of each indicator is coded by real numbers,and some illegal chromosomes are repaired by the identi-fication arid restoration of empty class. Two mutation op-erators, namely the discrete random mutation operator andthe optimal direction mutation operator, are designed to bal-ance the local convergence speed and the global convergence performance, which are then combined with migration strat-egy and insertion strategy. For the purpose of verification and illustration, the proposed method is compared with the K-means clustering algorithm and the standard genetic algo-rithms via a numerical simulation experiment. The experi-mental result shows the feasibility and effectiveness of the new PPGA-based clustering analysis algorithm. Meanwhile,the proposed clustering analysis algorithm is also applied to select the business cycle indicators to examine the status of the macro economy. Empirical results demonstrate that the proposed method can effectively and correctly select some leading indicators, coincident indicators, and lagging indi-cators to reflect the business cycle, which is extremely op-erational for some macro economy administrative managers and business decision-makers.

  15. 3D BUILDING MODELS SEGMENTATION BASED ON K-MEANS++ CLUSTER ANALYSIS

    Directory of Open Access Journals (Sweden)

    C. Zhang

    2016-10-01

    Full Text Available 3D mesh model segmentation is drawing increasing attentions from digital geometry processing field in recent years. The original 3D mesh model need to be divided into separate meaningful parts or surface patches based on certain standards to support reconstruction, compressing, texture mapping, model retrieval and etc. Therefore, segmentation is a key problem for 3D mesh model segmentation. In this paper, we propose a method to segment Collada (a type of mesh model 3D building models into meaningful parts using cluster analysis. Common clustering methods segment 3D mesh models by K-means, whose performance heavily depends on randomized initial seed points (i.e., centroid and different randomized centroid can get quite different results. Therefore, we improved the existing method and used K-means++ clustering algorithm to solve this problem. Our experiments show that K-means++ improves both the speed and the accuracy of K-means, and achieve good and meaningful results.

  16. Contour Cluster Shape Analysis for Building Damage Detection from Post-earthquake Airborne LiDAR

    Directory of Open Access Journals (Sweden)

    HE Meizhang

    2015-04-01

    Full Text Available Detection of the damaged building is the obligatory step prior to evaluate earthquake casualty and economic losses. It's very difficult to detect damaged buildings accurately based on the assumption that intact roofs appear in laser data as large planar segments whereas collapsed roofs are characterized by many small segments. This paper presents a contour cluster shape similarity analysis algorithm for reliable building damage detection from the post-earthquake airborne LiDAR point cloud. First we evaluate the entropies of shape similarities between all the combinations of two contour lines within a building cluster, which quantitatively describe the shape diversity. Then the maximum entropy model is employed to divide all the clusters into intact and damaged classes. The tests on the LiDAR data at El Mayor-Cucapah earthquake rupture prove the accuracy and reliability of the proposed method.

  17. Competitiveness Analysis of Processing Industry Cluster of Livestock Products in Inner Mongolia Based on "Diamond Model"

    Institute of Scientific and Technical Information of China (English)

    YANG Xing-long; REN Ya-tong

    2012-01-01

    Using Michael Porter’s "diamond model", based on regional development characteristics, we conduct analysis of the competitiveness of processing industry cluster of livestock products in Inner Mongolia from six aspects (the factor conditions, demand conditions, corporate strategy, structure and competition, related and supporting industries, government and opportunities). And we put forward the following rational recommendations for improving the competitiveness of processing industry cluster of livestock products in Inner Mongolia: (i) The government should increase capital input, focus on supporting processing industry of livestock products, and give play to the guidance and aggregation effect of financial funds; (ii) In terms of enterprises, it is necessary to vigorously develop leading enterprises, to give full play to the cluster effect of the leading enterprises.

  18. Clustering analysis of western North Pacific Tropical Cyclone tracks using the Self Organizing Map

    Science.gov (United States)

    Kim, H.; Seo, K.

    2013-12-01

    A cluster analysis using Self Organizing Map (SOM) is used to characterize tropical cyclone (TC) tracks over the western North Pacific. A False Discovery Rate (FDR) method is used to objectively determine an optimum cluster number. For 620 TC tracks over the WNP from June-October during 1979-2010, the five clusters for TC tracks are selected. These can further be categorized into three major patterns: straight-moving track, recurving track, and quasi-random pattern. Each pattern is characterized by land falling regions: near South and East China, East Asia, and off-shore of Japan. In addition, each pattern shows distinctive properties in its traveling distance, lifetime, intensity (mean minimum sea level pressure), and genesis location. It is revealed that these three patterns are associated with the large-scale dynamics such as variability of the western Pacific subtropical high and the Madden-Julian Oscillation. The impacts of El Nino and NAO will be discussed.

  19. External Defect classification of Citrus Fruit Images using Linear Discriminant Analysis Clustering and ANN classifiers

    Directory of Open Access Journals (Sweden)

    K.Vijayarekha

    2012-12-01

    Full Text Available Linear Discriminant Analysis (LDA is one technique for transforming raw data into a new feature space in which classification can be carried out more robustly. It is useful where the within-class frequencies are unequal. This method maximizes the ratio of between-class variance to the within-class variance in any particular data set and the maximal separability is guaranteed. LDA clustering models are used to classify object into different category. This study makes use of LDA for clustering the features obtained for the citrus fruit images taken in five different domains. Sub-windows of size 40x40 are cropped from the citrus fruit images having defects such as pitting, splitting and stem end rot. Features are extracted in four domains such as statistical features, fourier transform based features, discrete wavelet transform based features and stationary wavelet transform based features. The results of clustering and classification using LDA and ANN classifiers are reported

  20. Automation of Large-scale Computer Cluster Monitoring Information Analysis

    Science.gov (United States)

    Magradze, Erekle; Nadal, Jordi; Quadt, Arnulf; Kawamura, Gen; Musheghyan, Haykuhi

    2015-12-01

    High-throughput computing platforms consist of a complex infrastructure and provide a number of services apt to failures. To mitigate the impact of failures on the quality of the provided services, a constant monitoring and in time reaction is required, which is impossible without automation of the system administration processes. This paper introduces a way of automation of the process of monitoring information analysis to provide the long and short term predictions of the service response time (SRT) for a mass storage and batch systems and to identify the status of a service at a given time. The approach for the SRT predictions is based on Adaptive Neuro Fuzzy Inference System (ANFIS). An evaluation of the approaches is performed on real monitoring data from the WLCG Tier 2 center GoeGrid. Ten fold cross validation results demonstrate high efficiency of both approaches in comparison to known methods.

  1. A web-based system for near real-time surveillance and space-time cluster analysis of foot-and-mouth disease and other animal diseases.

    Science.gov (United States)

    Perez, Andres M; Zeng, Daniel; Tseng, Chun-ju; Chen, Hsinchun; Whedbee, Zachary; Paton, David; Thurmond, Mark C

    2009-09-01

    Considerable attention has been given lately to the need for global systems for animal disease surveillance that support real-time assessment of changing temporal-spatial risks. Until recently, however, prospects for development of such systems have been limited by the lack of informatics tools and an overarching collaboration framework to enable real-time data capturing, sharing, analysis, and related decision-making. In this paper, we present some of the tools of the FMD BioPortal System (www.fmd.ucdavis.edu/bioportal), which is a web-based system that facilitates near real-time information sharing, visualization, and advanced space-time cluster analysis for foot-and-mouth disease (FMD). Using this system, FMD information that is collected and maintained at various data acquisition and management sites around the world can be submitted to a data repository using various mutually agreed upon Extensible Markup Language (XML) formats, including Health Level Seven (HL7). FMD BioPortal makes available a set of advanced space-time cluster analysis techniques, including scan statistic-based methods and machine learning-based clustering methods. These techniques are aimed at identifying local clusters of disease cases in relation to the background risk. Data and analysis results can be displayed using a novel visualization environment, which supports multiple views including GIS, timeline, and periodical patterns. All FMD BioPortal functionalities are accessible through the Web and data confidentiality can be secured through user access control and computer network security techniques such as Secure Sockets Layer (SSL). FMD BioPortal is currently operational with limited data routinely collected by the Office International des Epizooties, the GenBank, the FMD World Reference Laboratory in Pirbright, and by the FMD Laboratory at the University of California in Davis. Here we describe technical attributes and capabilities of FMD BioPortal and illustrate its functionality

  2. The heterogeneity of headache patients who self-medicate: a cluster analysis approach.

    Science.gov (United States)

    Mehuys, Els; Paemeleire, Koen; Crombez, Geert; Adriaens, Els; Van Hees, Thierry; Demarche, Sophie; Christiaens, Thierry; Van Bortel, Luc; Van Tongelen, Inge; Remon, Jean-Paul; Boussery, Koen

    2016-07-01

    Patients with headache often self-treat their condition with over-the-counter analgesics. However, overuse of analgesics can cause medication-overuse headache. The present study aimed to identify subgroups of individuals with headache who self-medicate, as this could be helpful to tailor intervention strategies for prevention of medication-overuse headache. Patients (n = 1021) were recruited from 202 community pharmacies and completed a self-administered questionnaire. A hierarchical cluster analysis was used to group patients as a function of sociodemographics, pain, disability, and medication use for pain. Three patient clusters were identified. Cluster 1 (n = 498, 48.8%) consisted of relatively young individuals, and most of them suffered from migraine. They reported the least number of other pain complaints and the lowest prevalence of medication overuse (MO; 16%). Cluster 2 (n = 301, 29.5%) included older persons with mainly non-migraine headache, a low disability, and on average pain in 2 other locations. Prevalence of MO was 40%. Cluster 3 (n = 222, 21.7%) mostly consisted of patients with migraine who also report pain in many other locations. These patients reported a high disability and a severe limitation of activities. They also showed the highest rates of MO (73%).

  3. Dynamical analysis of NGC 110: cluster of fainter stars or data fluctuation?

    CERN Document Server

    Joshi, Gireesh C

    2016-01-01

    The stellar enhancement of the cluster NGC 110 is investigated in various optical and infrared (IR) bands. The radial density profile of the IR region does not show a stellar enhancement in the central region of the cluster. This stellar deficiency may be occurring by undetected fainter stars due to the contamination effect of massive stars. Since, our analysis is not indicating the stellar enhancement below 16.5 mag of I band, therefore the cluster is assumed to be a group of fainter stars. The proposed magnitude scatter factor would be an excellent tool to understand the characteristic of colour-scattering of stars. The most probable members do not coincide with the model isochronic fitting in the optical bands due to poor data quality of P P MXL catalogue. The different values of the mean proper motions are found for the fainter stars of the cluster and field regions, whereas similar values are obtained for radial zones of the cluster. The symmetrical distribution of fainter stars of the core are found aro...

  4. Global Analysis of miRNA Gene Clusters and Gene Families Reveals Dynamic and Coordinated Expression

    Directory of Open Access Journals (Sweden)

    Li Guo

    2014-01-01

    Full Text Available To further understand the potential expression relationships of miRNAs in miRNA gene clusters and gene families, a global analysis was performed in 4 paired tumor (breast cancer and adjacent normal tissue samples using deep sequencing datasets. The compositions of miRNA gene clusters and families are not random, and clustered and homologous miRNAs may have close relationships with overlapped miRNA species. Members in the miRNA group always had various expression levels, and even some showed larger expression divergence. Despite the dynamic expression as well as individual difference, these miRNAs always indicated consistent or similar deregulation patterns. The consistent deregulation expression may contribute to dynamic and coordinated interaction between different miRNAs in regulatory network. Further, we found that those clustered or homologous miRNAs that were also identified as sense and antisense miRNAs showed larger expression divergence. miRNA gene clusters and families indicated important biological roles, and the specific distribution and expression further enrich and ensure the flexible and robust regulatory network.

  5. Cluster-based analysis for personalized stress evaluation using physiological signals.

    Science.gov (United States)

    Xu, Qianli; Nwe, Tin Lay; Guan, Cuntai

    2015-01-01

    Technology development in wearable sensors and biosignal processing has made it possible to detect human stress from the physiological features. However, the intersubject difference in stress responses presents a major challenge for reliable and accurate stress estimation. This research proposes a novel cluster-based analysis method to measure perceived stress using physiological signals, which accounts for the intersubject differences. The physiological data are collected when human subjects undergo a series of task-rest cycles, incurring varying levels of stress that is indicated by an index of the State Trait Anxiety Inventory. Next, a quantitative measurement of stress is developed by analyzing the physiological features in two steps: 1) a k -means clustering process to divide subjects into different categories (clusters), and 2) cluster-wise stress evaluation using the general regression neural network. Experimental results show a significant improvement in evaluation accuracy as compared to traditional methods without clustering. The proposed method is useful in developing intelligent, personalized products for human stress management.

  6. A clustering analysis of eddies' spatial distribution in the South China Sea

    Directory of Open Access Journals (Sweden)

    J. Yi

    2012-11-01

    Full Text Available Spatial variation is important for studying the mesoscale eddies in the South China Sea (SCS. To investigate such spatial variations, this study made a clustering analysis on eddies' distribution using the K-means approach. Results showed that clustering tendency of anticyclonic eddies (AEs and cyclonic eddies (CEs were weak but not random, and the number of clusters were proved greater than four. Finer clustering results showed 10 regions where AEs densely populated and 6 regions for CEs in the SCS. Previous studies confirmed these partitions and possible generation mechanisms were related. Comparisons between AEs and CEs revealed that patterns of AE are relatively more aggregated than those of CE, and specific distinctions were summarized: (1 to the southwest of Luzon Island, AEs and CEs are generated spatially apart; AEs are likely located north of 14° N and closer to shore, while CEs are to the south and further offshore; (2 the Central SCS and Nansha Trough are mostly dominated by AEs; (3 along 112° E, clusters of AEs and CEs are located sequentially apart, and the pair off Vietnam represents the dipole eddies; (4 to the southwest of Dongsha Islands, AEs are concentrated to the east of CEs. Overlaps of AEs and CEs in the northeastern and Southern SCS were further examined considering seasonal variations. The northeastern overlap represented near-concentric distributions while the southern one was a mixed effect of seasonal variations, complex circulations and topography influences.

  7. A clustering analysis of eddies' spatial distribution in the South China Sea

    Directory of Open Access Journals (Sweden)

    J. Yi

    2013-02-01

    Full Text Available Spatial variation is important for studying the mesoscale eddies in the South China Sea (SCS. To investigate such spatial variations, this study made a clustering analysis on eddies' distribution using the K-means approach. Results showed that clustering tendency of anticyclonic eddies (AEs and cyclonic eddies (CEs were weak but not random, and the number of clusters were proved greater than four. Finer clustering results showed 10 regions where AEs densely populated and 6 regions for CEs in the SCS. Previous studies confirmed these partitions and possible generation mechanisms were related. Comparisons between AEs and CEs revealed that patterns of AE are relatively more aggregated than those of CE, and specific distinctions were summarized: (1 to the southwest of Luzon Island, AEs and CEs are generated spatially apart; AEs are likely located north of 14° N and closer to shore, while CEs are to the south and further offshore. (2 The central SCS and Nansha Trough are mostly dominated by AEs. (3 Along 112° E, clusters of AEs and CEs are located sequentially apart, and the pairs off Vietnam represent the dipole structures. (4 To the southwest of the Dongsha Islands, AEs are concentrated to the east of CEs. Overlaps of AEs and CEs in the northeastern and southern SCS were further examined considering seasonal variations. The northeastern overlap represented near-concentric distributions while the southern one was a mixed effect of seasonal variations, complex circulations and topography influences.

  8. Phenotype Clustering of Breast Epithelial Cells in Confocal Imagesbased on Nuclear Protein Distribution Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Long, Fuhui; Peng, Hanchuan; Sudar, Damir; Levievre, Sophie A.; Knowles, David W.

    2006-09-05

    Background: The distribution of the chromatin-associatedproteins plays a key role in directing nuclear function. Previously, wedeveloped an image-based method to quantify the nuclear distributions ofproteins and showed that these distributions depended on the phenotype ofhuman mammary epithelial cells. Here we describe a method that creates ahierarchical tree of the given cell phenotypes and calculates thestatistical significance between them, based on the clustering analysisof nuclear protein distributions. Results: Nuclear distributions ofnuclear mitotic apparatus protein were previously obtained fornon-neoplastic S1 and malignant T4-2 human mammary epithelial cellscultured for up to 12 days. Cell phenotype was defined as S1 or T4-2 andthe number of days in cultured. A probabilistic ensemble approach wasused to define a set of consensus clusters from the results of multipletraditional cluster analysis techniques applied to the nucleardistribution data. Cluster histograms were constructed to show how cellsin any one phenotype were distributed across the consensus clusters.Grouping various phenotypes allowed us to build phenotype trees andcalculate the statistical difference between each group. The resultsshowed that non-neoplastic S1 cells could be distinguished from malignantT4-2 cells with 94.19 percent accuracy; that proliferating S1 cells couldbe distinguished from differentiated S1 cells with 92.86 percentaccuracy; and showed no significant difference between the variousphenotypes of T4-2 cells corresponding to increasing tumor sizes.Conclusion: This work presents a cluster analysis method that canidentify significant cell phenotypes, based on the nuclear distributionof specific proteins, with high accuracy.

  9. Recent advances in (soil moisture) triple collocation analysis

    Science.gov (United States)

    Gruber, A.; Su, C.-H.; Zwieback, S.; Crow, W.; Dorigo, W.; Wagner, W.

    2016-03-01

    To date, triple collocation (TC) analysis is one of the most important methods for the global-scale evaluation of remotely sensed soil moisture data sets. In this study we review existing implementations of soil moisture TC analysis as well as investigations of the assumptions underlying the method. Different notations that are used to formulate the TC problem are shown to be mathematically identical. While many studies have investigated issues related to possible violations of the underlying assumptions, only few TC modifications have been proposed to mitigate the impact of these violations. Moreover, assumptions, which are often understood as a limitation that is unique to TC analysis are shown to be common also to other conventional performance metrics. Noteworthy advances in TC analysis have been made in the way error estimates are being presented by moving from the investigation of absolute error variance estimates to the investigation of signal-to-noise ratio (SNR) metrics. Here we review existing error presentations and propose the combined investigation of the SNR (expressed in logarithmic units), the unscaled error variances, and the soil moisture sensitivities of the data sets as an optimal strategy for the evaluation of remotely-sensed soil moisture data sets.

  10. Validation of hierarchical cluster analysis for identification of bacterial species using 42 bacterial isolates

    Science.gov (United States)

    Ghebremedhin, Meron; Yesupriya, Shubha; Luka, Janos; Crane, Nicole J.

    2015-03-01

    Recent studies have demonstrated the potential advantages of the use of Raman spectroscopy in the biomedical field due to its rapidity and noninvasive nature. In this study, Raman spectroscopy is applied as a method for differentiating between bacteria isolates for Gram status and Genus species. We created models for identifying 28 bacterial isolates using spectra collected with a 785 nm laser excitation Raman spectroscopic system. In order to investigate the groupings of these samples, partial least squares discriminant analysis (PLSDA) and hierarchical cluster analysis (HCA) was implemented. In addition, cluster analyses of the isolates were performed using various data types consisting of, biochemical tests, gene sequence alignment, high resolution melt (HRM) analysis and antimicrobial susceptibility tests of minimum inhibitory concentration (MIC) and degree of antimicrobial resistance (SIR). In order to evaluate the ability of these models to correctly classify bacterial isolates using solely Raman spectroscopic data, a set of 14 validation samples were tested using the PLSDA models and consequently the HCA models. External cluster evaluation criteria of purity and Rand index were calculated at different taxonomic levels to compare the performance of clustering using Raman spectra as well as the other datasets. Results showed that Raman spectra performed comparably, and in some cases better than, the other data types with Rand index and purity values up to 0.933 and 0.947, respectively. This study clearly demonstrates that the discrimination of bacterial species using Raman spectroscopic data and hierarchical cluster analysis is possible and has the potential to be a powerful point-of-care tool in clinical settings.

  11. The EtnaPlumeLab (EPL research cluster: advance the understanding of Mt. Etna plume, from source characterisation to downwind impact

    Directory of Open Access Journals (Sweden)

    Pasquale Sellitto

    2017-01-01

    Full Text Available In 2013, a multidisciplinary research cluster named EtnaPlumeLab (EPL was established, gathering experts from volcanology and atmospheric science communities. Target of EPL is to advance the understanding of Mt. Etna's gas and aerosol emissions and the related processes, from source to its regional climatic impact in the Mediterranean area. Here, we present the cluster and its three interacting modules: EPL-RADIO (Radioactive Aerosols and other source parameters for better atmospheric Dispersion and Impact estimatiOns, SMED (Sulfur MEditerranean Dispersion and Med-SuV (MEDiterranean SUpersite Volcanoes Work Package 5. Preliminary results have for the first time highlighted the relevance of Mt. Etna's plume impact at the Mediterranean regional scale. These results underline that further efforts need to be made to get insight into a synoptic volcanogenic-atmospheric chemistry/climatic understanding of volcanic plumes impact.

  12. Conceptual study of advanced PWR core design. Development of advanced PWR core neutronics analysis system

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Chang Hyo; Kim, Seung Cho; Kim, Taek Kyum; Cho, Jin Young; Lee, Hyun Cheol; Lee, Jung Hun; Jung, Gu Young [Seoul National University, Seoul (Korea, Republic of)

    1995-08-01

    The neutronics design system of the advanced PWR consists of (i) hexagonal cell and fuel assembly code for generation of homogenized few-group cross sections and (ii) global core neutronics analysis code for computations of steady-state pin-wise or assembly-wise core power distribution, core reactivity with fuel burnup, control rod worth and reactivity coefficients, transient core power, etc.. The major research target of the first year is to establish the numerical method and solution of multi-group diffusion equations for neutronics code development. Specifically, the following studies are planned; (i) Formulation of various numerical methods such as finite element method(FEM), analytical nodal method(ANM), analytic function expansion nodal(AFEN) method, polynomial expansion nodal(PEN) method that can be applicable for the hexagonal core geometry. (ii) Comparative evaluation of the numerical effectiveness of these methods based on numerical solutions to various hexagonal core neutronics benchmark problems. Results are follows: (i) Formulation of numerical solutions to multi-group diffusion equations based on numerical methods. (ii) Numerical computations by above methods for the hexagonal neutronics benchmark problems such as -VVER-1000 Problem Without Reflector -VVER-440 Problem I With Reflector -Modified IAEA PWR Problem Without Reflector -Modified IAEA PWR Problem With Reflector -ANL Large Heavy Water Reactor Problem -Small HTGR Problem -VVER-440 Problem II With Reactor (iii) Comparative evaluation on the numerical effectiveness of various numerical methods. (iv) Development of HEXFEM code, a multi-dimensional hexagonal core neutronics analysis code based on FEM. In the target year of this research, the spatial neutronics analysis code for hexagonal core geometry(called NEMSNAP-H temporarily) will be completed. Combination of NEMSNAP-H with hexagonal cell and assembly code will then equip us with hexagonal core neutronics design system. (Abstract Truncated)

  13. Globular Cluster Abundances from High-resolution, Integrated-light Spectroscopy. II. Expanding the Metallicity Range for Old Clusters and Updated Analysis Techniques

    Science.gov (United States)

    Colucci, Janet E.; Bernstein, Rebecca A.; McWilliam, Andrew

    2017-01-01

    We present abundances of globular clusters (GCs) in the Milky Way and Fornax from integrated-light (IL) spectra. Our goal is to evaluate the consistency of the IL analysis relative to standard abundance analysis for individual stars in those same clusters. This sample includes an updated analysis of seven clusters from our previous publications and results for five new clusters that expand the metallicity range over which our technique has been tested. We find that the [Fe/H] measured from IL spectra agrees to ∼0.1 dex for GCs with metallicities as high as [Fe/H] = ‑0.3, but the abundances measured for more metal-rich clusters may be underestimated. In addition we systematically evaluate the accuracy of abundance ratios, [X/Fe], for Na i, Mg i, Al i, Si i, Ca i, Ti i, Ti ii, Sc ii, V i, Cr i, Mn i, Co i, Ni i, Cu i, Y ii, Zr i, Ba ii, La ii, Nd ii, and Eu ii. The elements for which the IL analysis gives results that are most similar to analysis of individual stellar spectra are Fe i, Ca i, Si i, Ni i, and Ba ii. The elements that show the greatest differences include Mg i and Zr i. Some elements show good agreement only over a limited range in metallicity. More stellar abundance data in these clusters would enable more complete evaluation of the IL results for other important elements. This paper includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.

  14. CLUSTERING ANALYSIS OF OFFICER'S BEHAVIOURS IN LONDON POLICE FOOT PATROL ACTIVITIES

    Directory of Open Access Journals (Sweden)

    J. Shen

    2015-07-01

    Full Text Available In this small paper we aim at presenting a framework of conceptual representation and clustering analysis of police officers’ patrol pattern obtained from mining their raw movement trajectory data. This have been achieved by a model developed to accounts for the spatio-temporal dynamics human movements by incorporating both the behaviour features of the travellers and the semantic meaning of the environment they are moving in. Hence, the similarity metric of traveller behaviours is jointly defined according to the stay time allocation in each Spatio-temporal region of interests (ST-ROI to support clustering analysis of patrol behaviours. The proposed framework enables the analysis of behaviour and preferences on higher level based on raw moment trajectories. The model is firstly applied to police patrol data provided by the Metropolitan Police and will be tested by other type of dataset afterwards.

  15. Cluster analysis of midlatitude oceanic cloud regimes: mean properties and temperature sensitivity

    Directory of Open Access Journals (Sweden)

    N. D. Gordon

    2010-07-01

    Full Text Available Clouds play an important role in the climate system by reducing the amount of shortwave radiation reaching the surface and the amount of longwave radiation escaping to space. Accurate simulation of clouds in computer models remains elusive, however, pointing to a lack of understanding of the connection between large-scale dynamics and cloud properties. This study uses a k-means clustering algorithm to group 21 years of satellite cloud data over midlatitude oceans into seven clusters, and demonstrates that the cloud clusters are associated with distinct large-scale dynamical conditions. Three clusters correspond to low-level cloud regimes with different cloud fraction and cumuliform or stratiform characteristics, but all occur under large-scale descent and a relatively dry free troposphere. Three clusters correspond to vertically extensive cloud regimes with tops in the middle or upper troposphere, and they differ according to the strength of large-scale ascent and enhancement of tropospheric temperature and humidity. The final cluster is associated with a lower troposphere that is dry and an upper troposphere that is moist and experiencing weak ascent and horizontal moist advection.

    Since the present balance of reflection of shortwave and absorption of longwave radiation by clouds could change as the atmosphere warms from increasing anthropogenic greenhouse gases, we must also better understand how increasing temperature modifies cloud and radiative properties. We therefore undertake an observational analysis of how midlatitude oceanic clouds change with temperature when dynamical processes are held constant (i.e., partial derivative with respect to temperature. For each of the seven cloud regimes, we examine the difference in cloud and radiative properties between warm and cold subsets. To avoid misinterpreting a cloud response to large-scale dynamical forcing as a cloud response to temperature, we require horizontal and vertical

  16. Thermodynamic analysis of the advanced zero emission power plant

    Directory of Open Access Journals (Sweden)

    Kotowicz Janusz

    2016-03-01

    Full Text Available The paper presents the structure and parameters of advanced zero emission power plant (AZEP. This concept is based on the replacement of the combustion chamber in a gas turbine by the membrane reactor. The reactor has three basic functions: (i oxygen separation from the air through the membrane, (ii combustion of the fuel, and (iii heat transfer to heat the oxygen-depleted air. In the discussed unit hot depleted air is expanded in a turbine and further feeds a bottoming steam cycle (BSC through the main heat recovery steam generator (HRSG. Flue gas leaving the membrane reactor feeds the second HRSG. The flue gas consist mainly of CO2 and water vapor, thus, CO2 separation involves only the flue gas drying. Results of the thermodynamic analysis of described power plant are presented.

  17. Review on recent advances in the analysis of isolated organelles

    Energy Technology Data Exchange (ETDEWEB)

    Satori, Chad P. [Department of Chemistry, University of Minnesota, Minneapolis, MN 55455 (United States); Kostal, Vratislav [Department of Chemistry, University of Minnesota, Minneapolis, MN 55455 (United States); Institute of Analytical Chemistry, Academy of Sciences of the Czech Republic, Brno 616 00 (Czech Republic); Arriaga, Edgar A., E-mail: arriaga@umn.edu [Department of Chemistry, University of Minnesota, Minneapolis, MN 55455 (United States)

    2012-11-13

    Highlights: Black-Right-Pointing-Pointer Advancements in organelle release. Black-Right-Pointing-Pointer New approaches to fractionate organelles. Black-Right-Pointing-Pointer Updates on new techniques to characterize isolated organelles. - Abstract: The analysis of isolated organelles is one of the pillars of modern bioanalytical chemistry. This review describes recent developments on the isolation and characterization of isolated organelles both from living organisms and cell cultures. Salient reports on methods to release organelles focused on reproducibility and yield, membrane isolation, and integrated devices for organelle release. New developments on organelle fractionation after their isolation were on the topics of centrifugation, immunocapture, free flow electrophoresis, flow field-flow fractionation, fluorescence activated organelle sorting, laser capture microdissection, and dielectrophoresis. New concepts on characterization of isolated organelles included atomic force microscopy, optical tweezers combined with Raman spectroscopy, organelle sensors, flow cytometry, capillary electrophoresis, and microfluidic devices.

  18. Analysis of biofluids by paper spray MS: advances and challenges.

    Science.gov (United States)

    Manicke, Nicholas E; Bills, Brandon J; Zhang, Chengsen

    2016-03-01

    Paper spray MS is part of a cohort of ambient ionization or direct analysis methods that seek to analyze complex samples without prior sample preparation. Extraction and electrospray ionization occur directly from the paper substrate upon which a dried matrix spot is stored. Paper spray MS is capable of detecting drugs directly from dried blood, plasma and urine spots at the low ng/ml to pg/ml levels without sample preparation. No front end separation is performed, so MS/MS or high-resolution MS is required. Here, we discuss paper spray methodology, give a comprehensive literature review of the use of paper spray MS for bioanalysis, discuss technological advancements and variations on this technique and discuss some of its limitations.

  19. Thermodynamic analysis of the advanced zero emission power plant

    Science.gov (United States)

    Kotowicz, Janusz; Job, Marcin

    2016-03-01

    The paper presents the structure and parameters of advanced zero emission power plant (AZEP). This concept is based on the replacement of the combustion chamber in a gas turbine by the membrane reactor. The reactor has three basic functions: (i) oxygen separation from the air through the membrane, (ii) combustion of the fuel, and (iii) heat transfer to heat the oxygen-depleted air. In the discussed unit hot depleted air is expanded in a turbine and further feeds a bottoming steam cycle (BSC) through the main heat recovery steam generator (HRSG). Flue gas leaving the membrane reactor feeds the second HRSG. The flue gas consist mainly of CO2 and water vapor, thus, CO2 separation involves only the flue gas drying. Results of the thermodynamic analysis of described power plant are presented.

  20. Advanced analysis and design for fire safety of steel structures

    CERN Document Server

    Li, Guoqiang

    2013-01-01

    Advanced Analysis and Design for Fire Safety of Steel Structures systematically presents the latest findings on behaviours of steel structural components in a fire, such as the catenary actions of restrained steel beams, the design methods for restrained steel columns, and the membrane actions of concrete floor slabs with steel decks. Using a systematic description of structural fire safety engineering principles, the authors illustrate the important difference between behaviours of an isolated structural element and the restrained component in a complete structure under fire conditions. The book will be an essential resource for structural engineers who wish to improve their understanding of steel buildings exposed to fires. It is also an ideal textbook for introductory courses in fire safety for master’s degree programs in structural engineering, and is excellent reading material for final-year undergraduate students in civil engineering and fire safety engineering. Furthermore, it successfully bridges th...

  1. Systems analysis and futuristic designs of advanced biofuel factory concepts.

    Energy Technology Data Exchange (ETDEWEB)

    Chianelli, Russ; Leathers, James; Thoma, Steven George; Celina, Mathias Christopher; Gupta, Vipin P.

    2007-10-01

    The U.S. is addicted to petroleum--a dependency that periodically shocks the economy, compromises national security, and adversely affects the environment. If liquid fuels remain the main energy source for U.S. transportation for the foreseeable future, the system solution is the production of new liquid fuels that can directly displace diesel and gasoline. This study focuses on advanced concepts for biofuel factory production, describing three design concepts: biopetroleum, biodiesel, and higher alcohols. A general schematic is illustrated for each concept with technical description and analysis for each factory design. Looking beyond current biofuel pursuits by industry, this study explores unconventional feedstocks (e.g., extremophiles), out-of-favor reaction processes (e.g., radiation-induced catalytic cracking), and production of new fuel sources traditionally deemed undesirable (e.g., fusel oils). These concepts lay the foundation and path for future basic science and applied engineering to displace petroleum as a transportation energy source for good.

  2. Bayesian Analysis of Two Stellar Populations in Galactic Globular Clusters II: NGC 5024, NGC 5272, and NGC 6352

    CERN Document Server

    Wagner-Kaiser, R; Robinson, E; von Hippel, T; Sarajedini, A; van Dyk, D A; Stein, N; Jefferys, W H

    2016-01-01

    We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival ACS Treasury observations of Galactic Globular Clusters to find and characterize two stellar populations in NGC 5024 (M53), NGC 5272 (M3), and NGC 6352. For these three clusters, both single and double-population analyses are used to determine a best fit isochrone(s). We employ a sophisticated Bayesian analysis technique to simultaneously fit the cluster parameters (age, distance, absorption, and metallicity) that characterize each cluster. For the two-population analysis, unique population level helium values are also fit to each distinct population of the cluster and the relative proportions of the populations are determined. We find differences in helium ranging from $\\sim$0.05 to 0.11 for these three clusters. Model grids with solar $\\alpha$-element abundances ([$\\alpha$/Fe] =0.0) and enhanced $\\alpha$-elements ([$\\alpha$/Fe]=0.4) are adopted.

  3. Burnout prediction using advance image analysis coal characterization techniques

    Energy Technology Data Exchange (ETDEWEB)

    Edward Lester; Dave Watts; Michael Cloke [University of Nottingham, Nottingham (United Kingdom). School of Chemical Environmental and Mining Engineering

    2003-07-01

    The link between petrographic composition and burnout has been investigated previously by the authors. However, these predictions were based on 'bulk' properties of the coal, including the proportion of each maceral or the reflectance of the macerals in the whole sample. Combustion studies relating burnout with microlithotype analysis, or similar, remain less common partly because the technique is more complex than maceral analysis. Despite this, it is likely that any burnout prediction based on petrographic characteristics will become more accurate if it includes information about the maceral associations and the size of each particle. Chars from 13 coals, 106-125 micron size fractions, were prepared using a Drop Tube Furnace (DTF) at 1300{degree}C and 200 millisecond and 1% Oxygen. These chars were then refired in the DTF at 1300{degree}C 5% oxygen and residence times of 200, 400 and 600 milliseconds. The progressive burnout of each char was compared with the characteristics of the initial coals. This paper presents an extension of previous studies in that it relates combustion behaviour to coals that have been characterized on a particle by particle basis using advanced image analysis techniques. 13 refs., 7 figs.

  4. Advanced spectral analysis of ionospheric waves observed with sparse arrays

    Science.gov (United States)

    Helmboldt, J. F.; Intema, H. T.

    2014-02-01

    This paper presents a case study from a single, 6h observing period to illustrate the application of techniques developed for interferometric radio telescopes to the spectral analysis of observations of ionospheric fluctuations with sparse arrays. We have adapted the deconvolution methods used for making high dynamic range images of cosmic sources with radio arrays to making comparably high dynamic range maps of spectral power of wavelike ionospheric phenomena. In the example presented here, we have used observations of the total electron content (TEC) gradient derived from Very Large Array (VLA) observations of synchrotron emission from two galaxy clusters at 330MHz as well as GPS-based TEC measurements from a sparse array of 33 receivers located within New Mexico near the VLA. We show that these techniques provide a significant improvement in signal-to-noise ratio (S/N) of detected wavelike structures by correcting for both measurement inaccuracies and wavefront distortions. This is especially true for the GPS data when combining all available satellite/receiver pairs, which probe a larger physical area and likely have a wider variety of measurement errors than in the single-satellite case. In this instance, we found that the peak S/N of the detected waves was improved by more than an order of magnitude. The data products generated by the deconvolution procedure also allow for a reconstruction of the fluctuations as a two-dimensional waveform/phase screen that can be used to correct for their effects.

  5. 中国南瓜自交系的聚类分析%Cluster Analysis of Chinese Pumpkin Inbred Lines

    Institute of Scientific and Technical Information of China (English)

    杜晓华; 李小梅; 李新峥

    2008-01-01

    A cluster analysis was carried out based on Euclidean genetic distances through UPGMA method in Chinese pumpkin inbred lines. 7 important agronomic traits of 46 Chinese pumpkin inbred lines were investigated. The result indicated that 46 pumpkin inbred lines were clustered into 4 groups and the inter-groups distances was larger than that in intra-group. The genetic distances of parents were related to F1 performance and the results of cluster would increase effectiveness in the Chinese pumpkin crossing breeding.

  6. Study and Analysis of K-Means Clustering Algorithm Using Rapidminer

    Directory of Open Access Journals (Sweden)

    Abhinn Pandey

    2014-12-01

    Full Text Available Institution is a place where teacher explains and student just understands and learns the lesson. Every student has his own definition for toughness and easiness and there isn’t any absolute scale for measuring knowledge but examination score indicate the performance of student. In this case study, knowledge of data mining is combined with educational strategies to improve students’ performance. Generally, data mining (sometimes called data or knowledge discovery is the process of analysing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for data. It allows users to analyse data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational database. Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster are more similar (in some sense or another to each other than to those in other groups (clusters.This project describes the use of clustering data mining technique to improve the efficiency of academic performance in the educational institutions .In this project, a live experiment was conducted on students .By conducting an exam on students of computer science major using MOODLE(LMS and analysing that data generated using RapidMiner(Datamining Software and later by performing clustering on the data. This method helps to identify the students who need special advising or counselling by the teacher to give high quality of education.

  7. Cluster Mass Calibration at High Redshift: HST Weak Lensing Analysis of 13 Distant Galaxy Clusters from the South Pole Telescope Sunyaev-Zel'dovich Survey

    CERN Document Server

    Schrabback, T; Dietrich, J P; Hoekstra, H; Bocquet, S; Gonzalez, A H; von der Linden, A; McDonald, M; Morrison, C B; Raihan, S F; Allen, S W; Bayliss, M; Benson, B A; Bleem, L E; Chiu, I; Desai, S; Foley, R J; de Haan, T; High, F W; Hilbert, S; Mantz, A B; Massey, R; Mohr, J; Reichardt, C L; Saro, A; Simon, P; Stern, C; Stubbs, C W; Zenteno, A

    2016-01-01

    We present an HST/ACS weak gravitational lensing analysis of 13 massive high-redshift (z_median=0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V-I colour. Our estimate of the source redshift distribution is based on CANDELS data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing...

  8. Advanced Residuals Analysis for Determining the Number of PARAFAC Components in Dissolved Organic Matter.

    Science.gov (United States)

    Cuss, Chad W; Guéguen, Céline; Andersson, Per; Porcelli, Don; Maximov, Trofim; Kutscher, Liselott

    2016-02-01

    Parallel factor analysis (PARAFAC) has facilitated an explosion in research connecting the fluorescence properties of dissolved organic matter (DOM) to its functions and biogeochemical cycling in natural and engineered systems. However, the validation of robust PARAFAC models using split-half analysis requires an oft unrealistically large number (hundreds to thousands) of excitation-emission matrices (EEMs), and models with too few components may not adequately describe differences between DOM. This study used self-organizing maps (SOM) and comparing changes in residuals with the effects of adding components to estimate the number of PARAFAC components in DOM from two data sets: MS (110 EEMs from nine leaf leachates and headwaters) and LR (64 EEMs from the Lena River). Clustering by SOM demonstrated that peaks clearly persisted in model residuals after validation by split-half analysis. Plotting the changes to residuals was an effective method for visualizing the removal of fluorophore-like fluorescence caused by increasing the number of PARAFAC components. Extracting additional PARAFAC components via residuals analysis increased the proportion of correctly identified size-fractionated leaf leachates from 56.0 ± 0.8 to 75.2 ± 0.9%, and from 51.7 ± 1.4 to 92.9 ± 0.0% for whole leachates. Model overfitting was assessed by considering the correlations between components, and their distributions amongst samples. Advanced residuals analysis improved the ability of PARAFAC to resolve the variation in DOM fluorescence, and presents an enhanced validation approach for assessing the number of components that can be used to supplement the potentially misleading results of split-half analysis.

  9. WHY DO SOME NATIONS SUCCEED AND OTHERS FAIL IN INTERNATIONAL COMPETITION? FACTOR ANALYSIS AND CLUSTER ANALYSIS AT EUROPEAN LEVEL

    Directory of Open Access Journals (Sweden)

    Popa Ion

    2015-07-01

    Full Text Available As stated by Michael Porter (1998: 57, 'this is perhaps the most frequently asked economic question of our times.' However, a widely accepted answer is still missing. The aim of this paper is not to provide the BIG answer for such a BIG question, but rather to provide a different perspective on the competitiveness at the national level. In this respect, we followed a two step procedure, called “tandem analysis”. (OECD, 2008. First we employed a Factor Analysis in order to reveal the underlying factors of the initial dataset followed by a Cluster Analysis which aims classifying the 35 countries according to the main characteristics of competitiveness resulting from Factor Analysis. The findings revealed that clustering the 35 states after the first two factors: Smart Growth and Market Development, which recovers almost 76% of common variability of the twelve original variables, are highlighted four clusters as well as a series of useful information in order to analyze the characteristics of the four clusters and discussions on them.

  10. Interactive Parallel Data Analysis within Data-Centric Cluster Facilities using the IPython Notebook

    Science.gov (United States)

    Pascoe, S.; Lansdowne, J.; Iwi, A.; Stephens, A.; Kershaw, P.

    2012-12-01

    The data deluge is making traditional analysis workflows for many researchers obsolete. Support for parallelism within popular tools such as matlab, IDL and NCO is not well developed and rarely used. However parallelism is necessary for processing modern data volumes on a timescale conducive to curiosity-driven analysis. Furthermore, for peta-scale datasets such as the CMIP5 archive, it is no longer practical to bring an entire dataset to a researcher's workstation for analysis, or even to their institutional cluster. Therefore, there is an increasing need to develop new analysis platforms which both enable processing at the point of data storage and which provides parallelism. Such an environment should, where possible, maintain the convenience and familiarity of our current analysis environments to encourage curiosity-driven research. We describe how we are combining the interactive python shell (IPython) with our JASMIN data-cluster infrastructure. IPython has been specifically designed to bridge the gap between the HPC-style parallel workflows and the opportunistic curiosity-driven analysis usually carried out using domain specific languages and scriptable tools. IPython offers a web-based interactive environment, the IPython notebook, and a cluster engine for parallelism all underpinned by the well-respected Python/Scipy scientific programming stack. JASMIN is designed to support the data analysis requirements of the UK and European climate and earth system modeling community. JASMIN, with its sister facility CEMS focusing the earth observation community, has 4.5 PB of fast parallel disk storage alongside over 370 computing cores provide local computation. Through the IPython interface to JASMIN, users can make efficient use of JASMIN's multi-core virtual machines to perform interactive analysis on all cores simultaneously or can configure IPython clusters across multiple VMs. Larger-scale clusters can be provisioned through JASMIN's batch scheduling system

  11. Countries population determination to test rice crisis indicator at national level using k-means cluster analysis

    Science.gov (United States)

    Hidayat, Y.; Purwandari, T.; Sukono; Ariska, Y. D.

    2017-01-01

    This study aimed to obtain information on the population of the countries which is have similarities with Indonesia based on three characteristics, that is the democratic atmosphere, rice consumption and purchasing power of rice. It is useful as a reference material for research which tested the strength and predictability of the rice crisis indicators Unprecedented Restlessness (UR). The similarities countries with Indonesia were conducted using multivariate analysis that is non-hierarchical cluster analysis k-Means with 38 countries as the data population. This analysis is done repeatedly until the obtainment number of clusters which is capable to show the differentiator power of the three characteristics and describe the high similarity within clusters. Based on the results, it turns out with 6 clusters can describe the differentiator power of characteristics of formed clusters. However, to answer the purpose of the study, only one cluster which will be taken accordance with the criteria of success for the population of countries that have similarities with Indonesia that cluster contain Indonesia therein, there are countries which is sustain crisis and non-crisis of rice in 2008, and cluster which is have the largest member among them. This criterion is met by cluster 2, which consists of 22 countries, namely Indonesia, Brazil, Costa Rica, Djibouti, Dominican Republic, Ecuador, Fiji, Guinea-Bissau, Haiti, India, Jamaica, Japan, Korea South, Madagascar, Malaysia, Mali, Nicaragua, Panama, Peru, Senegal, Sierra Leone and Suriname.

  12. Principal component cluster analysis of ECG time series based on Lyapunov exponent spectrum

    Institute of Scientific and Technical Information of China (English)

    WANG Nai; RUAN Jiong

    2004-01-01

    In this paper we propose an approach of principal component cluster analysis based on Lyapunov exponent spectrum (LES) to analyze the ECG time series. Analysis results of 22 sample-files of ECG from the MIT-BIH database confirmed the validity of our approach. Another technique named improved teacher selecting student (TSS) algorithm is presented to analyze unknown samples by means of some known ones, which is of better accuracy. This technique combines the advantages of both statistical and nonlinear dynamical methods and is shown to be significant to the analysis of nonlinear ECG time series.

  13. Diagrammatic analysis of correlations in polymer fluids: Cluster diagrams via Edwards’ field theory

    Science.gov (United States)

    Morse, David C.

    2006-10-01

    Edwards' functional integral approach to the statistical mechanics of polymer liquids is amenable to a diagrammatic analysis in which free energies and correlation functions are expanded as infinite sums of Feynman diagrams. This analysis is shown to lead naturally to a perturbative cluster expansion that is closely related to the Mayer cluster expansion developed for molecular liquids by Chandler and co-workers. Expansion of the functional integral representation of the grand-canonical partition function yields a perturbation theory in which all quantities of interest are expressed as functionals of a monomer-monomer pair potential, as functionals of intramolecular correlation functions of non-interacting molecules, and as functions of molecular activities. In different variants of the theory, the pair potential may be either a bare or a screened potential. A series of topological reductions yields a renormalized diagrammatic expansion in which collective correlation functions are instead expressed diagrammatically as functionals of the true single-molecule correlation functions in the interacting fluid, and as functions of molecular number density. Similar renormalized expansions are also obtained for a collective Ornstein-Zernicke direct correlation function, and for intramolecular correlation functions. A concise discussion is given of the corresponding Mayer cluster expansion, and of the relationship between the Mayer and perturbative cluster expansions for liquids of flexible molecules. The application of the perturbative cluster expansion to coarse-grained models of dense multi-component polymer liquids is discussed, and a justification is given for the use of a loop expansion. As an example, the formalism is used to derive a new expression for the wave-number dependent direct correlation function and recover known expressions for the intramolecular two-point correlation function to first-order in a renormalized loop expansion for coarse-grained models of

  14. Analysis of Decision Trees in Context Clustering of Hidden Markov Model Based Thai Speech Synthesis

    Directory of Open Access Journals (Sweden)

    Suphattharachai Chomphan

    2011-01-01

    Full Text Available Problem statement: In Thai speech synthesis using Hidden Markov model (HMM based synthesis system, the tonal speech quality is degraded due to tone distortion. This major problem must be treated appropriately to preserve the tone characteristics of each syllable unit. Since tone brings about the intelligibility of the synthesized speech. It is needed to establish the tone questions and other phonetic questions in tree-based context clustering process accordingly. Approach: This study describes the analysis of questions in tree-based context clustering process of an HMM-based speech synthesis system for Thai language. In the system, spectrum, pitch or F0 and state duration are modeled simultaneously in a unified framework of HMM, their parameter distributions are clustered independently by using a decision-tree based context clustering technique. The contextual factors which affect spectrum, pitch and duration, i.e., part of speech, position and number of phones in a syllable, position and number of syllables in a word, position and number of words in a sentence, phone type and tone type, are taken into account for constructing the questions of the decision tree. All in all, thirteen sets of questions are analyzed in comparison. Results: In the experiment, we analyzed the decision trees by counting the number of questions in each node coming from those thirteen sets and by calculating the dominance score given to each question as the reciprocal of the distance from the root node to the question node. The highest number and dominance score are of the set of phonetic type, while the second, third highest ones are of the set of part of speech and tone type. Conclusion: By counting the number of questions in each node and calculating the dominance score, we can set the priority of each question set. All in all, the analysis results bring about further development of Thai speech synthesis with efficient context clustering process in

  15. A near-infrared surface compositional analysis of blue straggler stars in open cluster M67.

    Science.gov (United States)

    Seifert, Richard; Gosnell, Natalie M.; Sneden, Chris

    2017-01-01

    Blue straggler stars (BSSs) are stars whose evolutions have been directly impacted by binary system interactions. By obtaining additional mass from a companion, BSSs are able to live prolonged lives on the main sequence. BSSs bring confusions to studies that rely on a standard stellar evolutionary track when modeling stellar populations, since the presence of BSSs can make a population appear younger than it actually is. It is important to have a better understanding of the mechanisms that drive BSS formation so that BSSs may be correctly accounted for in future studies.What we know about BSS formation is that they form in one of two ways. Either from a close binary system in which one star accretes mass from its companion star or from a hierarchical trinary system in which a close inner binary merges as a result of perturbations from a farther-orbiting third star. What we don’t know are the relative frequencies of these two formation mechanisms. To investigate this problem, We obtained IGRINS near-IR (H- & K-band) high resolution spectra of 6 BSSs and 12 red giant stars in open cluster M67. Using a grid of synthetic spectra obtained from the line analysis code MOOG, we identified and fit abundances for absorption lines of iron, carbon, nitrogen, and oxygen. The latter three elements can be affected by internal hydrogen fusion, mixing, and binary mass transfer. In the BSS mass accretion mechanism, there should be enhanced abundances of these elements on the surfaces of BSSs. By analyzing the abundances of these elements in our BSS spectra, we determine the formation mechanism for each member of our BSS sample.Funding for this research comes from the John W. Cox endowment for the Advanced Studies in Astronomy. For support of this work we acknowledge NSF grants AST-1211585 and AST-1616040 to CS. The successful development of the IGRINS spectrograph has resulted from the combined efforts of teams at the University of Texas at Austin and the Korea Astronomy and Space

  16. Joint Analysis of Galaxy-Galaxy Lensing and Galaxy Clustering: Methodology and Forecasts for DES

    CERN Document Server

    Park, Y; Dodelson, S; Jain, B; Amara, A; Becker, M R; Bridle, S L; Clampitt, J; Crocce, M; Fosalba, P; Gaztanaga, E; Honscheid, K; Rozo, E; Sobreira, F; Sánchez, C; Wechsler, R H; Abbott, T; Abdalla, F B; Allam, S; Benoit-Lévy, A; Bertin, E; Brooks, D; Buckley-Geer, E; Burke, D L; Rosell, A Carnero; Kind, M Carrasco; Carretero, J; Castander, F J; da Costa, L N; DePoy, D L; Desai, S; Dietrich, J P; Gerdes, D W; Gruen, D; Gruendl, R A; Gutierrez, G; James, D J; Kent, S; Kuehn, K; Kuropatkin, N; Lima, M; Maia, M A G; Marshall, J L; Melchior, P; Miller, C J; Sanchez, E; Scarpine, V; Schubnell, M; Sevilla-Noarbe, I; Soares-Santos, M; Suchyta, E; Swanson, M E C; Tarle, G; Thaler, J; Vikram, V; Walker, A R; Weller, J; Zuntz, J

    2015-01-01

    The joint analysis of galaxy-galaxy lensing and galaxy clustering is a promising method for inferring the growth function of large scale structure. This analysis will be carried out on data from the Dark Energy Survey (DES), with its measurements of both the distribution of galaxies and the tangential shears of background galaxies induced by these foreground lenses. We develop a practical approach to modeling the assumptions and systematic effects affecting small scale lensing, which provides halo masses, and large scale galaxy clustering. Introducing parameters that characterize the halo occupation distribution (HOD), photometric redshift uncertainties, and shear measurement errors, we study how external priors on different subsets of these parameters affect our growth constraints. Degeneracies within the HOD model, as well as between the HOD and the growth function, are identified as the dominant source of complication, with other systematic effects sub-dominant. The impact of HOD parameters and their degen...

  17. Lock Acquisition and Sensitivity Analysis of Advanced LIGO Interferometers

    Science.gov (United States)

    Martynov, Denis

    Laser interferometer gravitational wave observatory (LIGO) consists of two complex large-scale laser interferometers designed for direct detection of gravitational waves from distant astrophysical sources in the frequency range 10Hz - 5kHz. Direct detection of space-time ripples will support Einstein's general theory of relativity and provide invaluable information and new insight into physics of the Universe. The initial phase of LIGO started in 2002, and since then data was collected during the six science runs. Instrument sensitivity improved from run to run due to the effort of commissioning team. Initial LIGO has reached designed sensitivity during the last science run, which ended in October 2010. In parallel with commissioning and data analysis with the initial detector, LIGO group worked on research and development of the next generation of detectors. Major instrument upgrade from initial to advanced LIGO started in 2010 and lasted until 2014. This thesis describes results of commissioning work done at the LIGO Livingston site from 2013 until 2015 in parallel with and after the installation of the instrument. This thesis also discusses new techniques and tools developed at the 40m prototype including adaptive filtering, estimation of quantization noise in digital filters and design of isolation kits for ground seismometers. The first part of this thesis is devoted to the description of methods for bringing the interferometer into linear regime when collection of data becomes possible. States of longitudinal and angular controls of interferometer degrees of freedom during lock acquisition process and in low noise configuration are discussed in details. Once interferometer is locked and transitioned to low noise regime, instrument produces astrophysics data that should be calibrated to units of meters or strain. The second part of this thesis describes online calibration technique set up in both observatories to monitor the quality of the collected data in

  18. Advanced High Temperature Reactor Systems and Economic Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Holcomb, David Eugene [ORNL; Peretz, Fred J [ORNL; Qualls, A L [ORNL

    2011-09-01

    The Advanced High Temperature Reactor (AHTR) is a design concept for a large-output [3400 MW(t)] fluoride-salt-cooled high-temperature reactor (FHR). FHRs, by definition, feature low-pressure liquid fluoride salt cooling, coated-particle fuel, a high-temperature power cycle, and fully passive decay heat rejection. The AHTR's large thermal output enables direct comparison of its performance and requirements with other high output reactor concepts. As high-temperature plants, FHRs can support either high-efficiency electricity generation or industrial process heat production. The AHTR analysis presented in this report is limited to the electricity generation mission. FHRs, in principle, have the potential to be low-cost electricity producers while maintaining full passive safety. However, no FHR has been built, and no FHR design has reached the stage of maturity where realistic economic analysis can be performed. The system design effort described in this report represents early steps along the design path toward being able to predict the cost and performance characteristics of the AHTR as well as toward being able to identify the technology developments necessary to build an FHR power plant. While FHRs represent a distinct reactor class, they inherit desirable attributes from other thermal power plants whose characteristics can be studied to provide general guidance on plant configuration, anticipated performance, and costs. Molten salt reactors provide experience on the materials, procedures, and components necessary to use liquid fluoride salts. Liquid metal reactors provide design experience on using low-pressure liquid coolants, passive decay heat removal, and hot refueling. High temperature gas-cooled reactors provide experience with coated particle fuel and graphite components. Light water reactors (LWRs) show the potentials of transparent, high-heat capacity coolants with low chemical reactivity. Modern coal-fired power plants provide design experience

  19. Quantitative Computed Tomography and image analysis for advanced muscle assessment

    Directory of Open Access Journals (Sweden)

    Kyle Joseph Edmunds

    2016-06-01

    Full Text Available Medical imaging is of particular interest in the field of translational myology, as extant literature describes the utilization of a wide variety of techniques to non-invasively recapitulate and quantity various internal and external tissue morphologies. In the clinical context, medical imaging remains a vital tool for diagnostics and investigative assessment. This review outlines the results from several investigations on the use of computed tomography (CT and image analysis techniques to assess muscle conditions and degenerative process due to aging or pathological conditions. Herein, we detail the acquisition of spiral CT images and the use of advanced image analysis tools to characterize muscles in 2D and 3D. Results from these studies recapitulate changes in tissue composition within muscles, as visualized by the association of tissue types to specified Hounsfield Unit (HU values for fat, loose connective tissue or atrophic muscle, and normal muscle, including fascia and tendon. We show how results from these analyses can be presented as both average HU values and compositions with respect to total muscle volumes, demonstrating the reliability of these tools to monitor, assess and characterize muscle degeneration.

  20. Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances.

    Science.gov (United States)

    Alarifi, Abdulrahman; Al-Salman, AbdulMalik; Alsaleh, Mansour; Alnafessah, Ahmad; Al-Hadhrami, Suheer; Al-Ammar, Mai A; Al-Khalifa, Hend S

    2016-05-16

    In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

  1. Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances

    Directory of Open Access Journals (Sweden)

    Abdulrahman Alarifi

    2016-05-01

    Full Text Available In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.

  2. Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances

    Science.gov (United States)

    Alarifi, Abdulrahman; Al-Salman, AbdulMalik; Alsaleh, Mansour; Alnafessah, Ahmad; Al-Hadhrami, Suheer; Al-Ammar, Mai A.; Al-Khalifa, Hend S.

    2016-01-01

    In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space. PMID:27196906

  3. Emergent team roles in organizational meetings: Identifying communication patterns via cluster analysis.

    OpenAIRE

    Lehmann-Willenbrock, N.K.; Beck, S.J.; Kauffeld, S.

    2016-01-01

    Previous team role taxonomies have largely relied on self-report data, focused on functional roles, and described individual predispositions or personality traits. Instead, this study takes a communicative approach and proposes that team roles are produced, shaped, and sustained in communicative behaviors. To identify team roles communicatively, 59 regular organizational meetings were videotaped and analyzed. Cluster analysis revealed five emergent roles: the solution seeker, the problem anal...

  4. A Study on Differences of China’s Regional Economic Development Level Based on Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Qi Yaoyuan

    2015-01-01

    Full Text Available An evaluation index system of regional economic development is established in this paper and STATA11.0 is used in the cluster analysis on samplings of 31 provincial regions. Results indicate that the economy of most regions is still in a backward stage except a few developed regions and the economic polarization of China is quite serious. This study provides a reference for the coordinated and rapid development of China’s economy.

  5. Dietary Patterns Derived by Cluster Analysis are Associated with Cognitive Function among Korean Older Adults

    OpenAIRE

    Jihye Kim; Areum Yu; Bo Youl Choi; Jung Hyun Nam; Mi Kyung Kim; Dong Hoon Oh; Yoon Jung Yang

    2015-01-01

    The objective of this study was to investigate major dietary patterns among older Korean adults through cluster analysis and to determine an association between dietary patterns and cognitive function. This is a cross-sectional study. The data from the Korean Multi-Rural Communities Cohort Study was used. Participants included 765 participants aged 60 years and over. A quantitative food frequency questionnaire with 106 items was used to investigate dietary intake. The Korean version of the M...

  6. Using cluster analysis for the estimation of efficiency of strategic management of the region enterprises

    Directory of Open Access Journals (Sweden)

    Feklistova Inessa

    2016-02-01

    Full Text Available The article presents methodical approach to the estimation of strategic man-agement efficiency of enterprises of the region with the use of cluster analysis, realized by means of the specially worked out application package. The necessity of its application in the analytical work of economic services of the region enterprises has been proved. It will allow to improve the quality of monitoring, and scientifically substantiate strategic administrative decisions

  7. A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS

    Directory of Open Access Journals (Sweden)

    W. Huang

    2016-06-01

    Full Text Available How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time to four dimensions (space, time and semantics. More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The

  8. Cluster analysis for identifying sub-groups and selecting potential discriminatory variables in human encephalitis

    Directory of Open Access Journals (Sweden)

    Crowcroft Natasha S

    2010-12-01

    Full Text Available Abstract Background Encephalitis is an acute clinical syndrome of the central nervous system (CNS, often associated with fatal outcome or permanent damage, including cognitive and behavioural impairment, affective disorders and epileptic seizures. Infection of the central nervous system is considered to be a major cause of encephalitis and more than 100 different pathogens have been recognized as causative agents. However, a large proportion of cases have unknown disease etiology. Methods We perform hierarchical cluster analysis on a multicenter England encephalitis data set with the aim of identifying sub-groups in human encephalitis. We use the simple matching similarity measure which is appropriate for binary data sets and performed variable selection using cluster heatmaps. We also use heatmaps to visually assess underlying patterns in the data, identify the main clinical and laboratory features and identify potential risk factors associated with encephalitis. Results Our results identified fever, personality and behavioural change, headache and lethargy as the main characteristics of encephalitis. Diagnostic variables such as brain scan and measurements from cerebrospinal fluids are also identified as main indicators of encephalitis. Our analysis revealed six major clusters in the England encephalitis data set. However, marked within-cluster heterogeneity is observed in some of the big clusters indicating possible sub-groups. Overall, the results show that patients are clustered according to symptom and diagnostic variables rather than causal agents. Exposure variables such as recent infection, sick person contact and animal contact have been identified as potential risk factors. Conclusions It is in general assumed and is a common practice to group encephalitis cases according to disease etiology. However, our results indicate that patients are clustered with respect to mainly symptom and diagnostic variables rather than causal agents

  9. Seismic clusters analysis in North-Eastern Italy by the nearest-neighbor approach

    Science.gov (United States)

    Peresan, Antonella; Gentili, Stefania

    2016-04-01

    The main features of earthquake clusters in the Friuli Venezia Giulia Region (North Eastern Italy) are explored, with the aim to get some new insights on local scale patterns of seismicity in the area. The study is based on a systematic analysis of robustly and uniformly detected seismic clusters of small-to-medium magnitude events, as opposed to selected clusters analyzed in earlier studies. To characterize the features of seismicity for FVG, we take advantage of updated information from local OGS bulletins, compiled at the National Institute of Oceanography and Experimental Geophysics, Centre of Seismological Research, since 1977. A preliminary reappraisal of the earthquake bulletins is carried out, in order to identify possible missing events and to remove spurious records (e.g. duplicates and explosions). The area of sufficient completeness is outlined; for this purpose, different techniques are applied, including a comparative analysis with global ISC data, which are available in the region for large and moderate size earthquakes. Various techniques are considered to estimate the average parameters that characterize the earthquake occurrence in the region, including the b-value and the fractal dimension of epicenters distribution. Specifically, besides the classical Gutenberg-Richter Law, the Unified Scaling Law for Earthquakes, USLE, is applied. Using the updated and revised OGS data, a new formal method for detection of earthquake clusters, based on nearest-neighbor distances of events in space-time-energy domain, is applied. The bimodality of the distribution, which characterizes the earthquake nearest-neighbor distances, is used to decompose the seismic catalog into sequences of individual clusters and background seismicity. Accordingly, the method allows for a data-driven identification of main shocks (first event with the largest magnitude in the cluster), foreshocks and aftershocks. Average robust estimates of the USLE parameters (particularly, b

  10. a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis

    Science.gov (United States)

    Huang, W.; Li, S.; Xu, S.

    2016-06-01

    How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the

  11. gamma-rays from annihilating dark matter in galaxy clusters: stacking vs single source analysis

    CERN Document Server

    Nezri, E; Combet, C; Maurin, D; Pointecouteau, E; Hinton, J A

    2012-01-01

    Clusters of galaxies are potentially important targets for indirect searches for dark matter annihilation. Here, we reassess the detection prospects for annihilation in massive halos, based on a statistical investigation of 1743 clusters from the recent MCXC meta-catalogue. We derive a new data-driven limit for the extra-galactic DM annihilation background Jextra-gal>JGal/5 and consider a source-stacking approach. The number of clusters scales with their brightness (boosted by DM substructures) to the power of -2 for an integration angle 0.1deg. It suggests that stacking may provide a significant improvement over a single target analysis for gamma-ray observations at high-energies where the angular resolution achievable is comparable to this angle. In our study the mean angle containing 80% of the dark-matter signal for the entire sample (assuming an NFW DM profile) is 0.15deg. It indicates that instruments with this angular resolution or better would be optimal for a cluster annihilation search based on stac...

  12. Tully-Fisher analysis of the multiple cluster system Abell 901/902

    CERN Document Server

    Bösch, Benjamin; Wolf, Christian; Aragón-Salamanca, Alfonso; Ziegler, Bodo L; Barden, Marco; Gray, Meghan E; Balogh, Michael; Meisenheimer, Klaus; Schindler, Sabine

    2013-01-01

    We derive rotation curves from optical emission lines of 182 disk galaxies (96 in the cluster and 86 in the field) in the region of Abell 901/902 located at $z\\sim 0.165$. We focus on the analysis of B-band and stellar-mass Tully-Fisher relations. We examine possible environmental dependencies and differences between normal spirals and "dusty red" galaxies, i.e. disk galaxies that have red colors due to relatively low star formation rates. We find no significant differences between the best-fit TF slope of cluster and field galaxies. At fixed slope, the field population with high-quality rotation curves (57 objects) is brighter by $\\Delta M_{B}=-0\\fm42\\pm0\\fm15$ than the cluster population (55 objects). We show that this slight difference is at least in part an environmental effect. The scatter of the cluster TFR increases for galaxies closer to the core region, also indicating an environmental effect. Interestingly, dusty red galaxies become fainter towards the core at given rotation velocity (i.e. total mas...

  13. Sequencing and transcriptional analysis of the biosynthesis gene cluster of putrescine-producing Lactococcus lactis.

    Science.gov (United States)

    Ladero, Victor; Rattray, Fergal P; Mayo, Baltasar; Martín, María Cruz; Fernández, María; Alvarez, Miguel A

    2011-09-01

    Lactococcus lactis is a prokaryotic microorganism with great importance as a culture starter and has become the model species among the lactic acid bacteria. The long and safe history of use of L. lactis in dairy fermentations has resulted in the classification of this species as GRAS (General Regarded As Safe) or QPS (Qualified Presumption of Safety). However, our group has identified several strains of L. lactis subsp. lactis and L. lactis subsp. cremoris that are able to produce putrescine from agmatine via the agmatine deiminase (AGDI) pathway. Putrescine is a biogenic amine that confers undesirable flavor characteristics and may even have toxic effects. The AGDI cluster of L. lactis is composed of a putative regulatory gene, aguR, followed by the genes (aguB, aguD, aguA, and aguC) encoding the catabolic enzymes. These genes are transcribed as an operon that is induced in the presence of agmatine. In some strains, an insertion (IS) element interrupts the transcription of the cluster, which results in a non-putrescine-producing phenotype. Based on this knowledge, a PCR-based test was developed in order to differentiate nonproducing L. lactis strains from those with a functional AGDI cluster. The analysis of the AGDI cluster and their flanking regions revealed that the capacity to produce putrescine via the AGDI pathway could be a specific characteristic that was lost during the adaptation to the milk environment by a process of reductive genome evolution.

  14. A Method for Traffic Congestion Clustering Judgment Based on Grey Relational Analysis

    Directory of Open Access Journals (Sweden)

    Yingya Zhang

    2016-05-01

    Full Text Available Traffic congestion clustering judgment is a fundamental problem in the study of traffic jam warning. However, it is not satisfactory to judge traffic congestion degrees using only vehicle speed. In this paper, we collect traffic flow information with three properties (traffic flow velocity, traffic flow density and traffic volume of urban trunk roads, which is used to judge the traffic congestion degree. We first define a grey relational clustering model by leveraging grey relational analysis and rough set theory to mine relationships of multidimensional-attribute information. Then, we propose a grey relational membership degree rank clustering algorithm (GMRC to discriminant clustering priority and further analyze the urban traffic congestion degree. Our experimental results show that the average accuracy of the GMRC algorithm is 24.9% greater than that of the K-means algorithm and 30.8% greater than that of the Fuzzy C-Means (FCM algorithm. Furthermore, we find that our method can be more conducive to dynamic traffic warnings.

  15. A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002

    Science.gov (United States)

    Saman, D.M.; Cole, H.P.; Odoi, A.; Myers, M.L.; Carey, D.I.; Westneat, S.C.

    2012-01-01

    Background: Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns. Methods: A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns. Results: The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55) and 10 counties in eastern Kentucky (RR = 1.97). Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002) and a greater percent of total tractors with less than 40 horsepower by county (ptractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions) with the intention of reducing tractor overturns in the highest risk counties in Kentucky. ?? 2012 Saman et al.

  16. The Heterogeneity of Early Parkinson’s Disease: A Cluster Analysis on Newly Diagnosed Untreated Patients

    Science.gov (United States)

    Amboni, Marianna; Picillo, Marina; Moccia, Marcello; Longo, Katia; Santangelo, Gabriella; De Rosa, Anna; Allocca, Roberto; Giordano, Flavio; Orefice, Giuseppe; De Michele, Giuseppe; Santoro, Lucio; Pellecchia, Maria Teresa; Barone, Paolo

    2013-01-01

    Background The variability in the clinical phenotype of Parkinson’s disease seems to suggest the existence of several subtypes of the disease. To test this hypothesis we performed a cluster analysis using data assessing both motor and non-motor symptoms in a large cohort of newly diagnosed untreated PD patients. Methods We collected data on demographic, motor, and the whole complex of non-motor symptoms from 100 consecutive newly diagnosed untreated outpatients. Statistical cluster analysis allowed the identification of different subgroups, which have been subsequently explored. Results The data driven approach identified four distinct groups of patients, we have labeled: 1) Benign Pure Motor; 2) Benign mixed Motor-Non-Motor; 3) Non-Motor Dominant; and 4) Motor Dominant. Conclusion Our results confirmed the existence of different subgroups of early PD patients. Cluster analysis revealed the presence of distinct subtypes of patients profiled according to the relevance of both motor and non-motor symptoms. Identification of such subtypes may have important implications for generating pathogenetic hypotheses and therapeutic strategies. PMID:23936396

  17. Combination of meta-analysis and graph clustering to identify prognostic markers of ESCC

    Directory of Open Access Journals (Sweden)

    Hongyun Gao

    2012-01-01

    Full Text Available Esophageal squamous cell carcinoma (ESCC is one of the most malignant gastrointestinal cancers and occurs at a high frequency rate in China and other Asian countries. Recently, several molecular markers were identified for predicting ESCC. Notwithstanding, additional prognostic markers, with a clear understanding of their underlying roles, are still required. Through bioinformatics, a graph-clustering method by DPClus was used to detect co-expressed modules. The aim was to identify a set of discriminating genes that could be used for predicting ESCC through graph-clustering and GO-term analysis. The results showed that CXCL12, CYP2C9, TGM3, MAL, S100A9, EMP-1 and SPRR3 were highly associated with ESCC development. In our study, all their predicted roles were in line with previous reports, whereby the assumption that a combination of meta-analysis, graph-clustering and GO-term analysis is effective for both identifying differentially expressed genes, and reflecting on their functions in ESCC.

  18. Time-clustering analysis of the 1978–2008 sub-crustal seismicity of Vrancea region

    Directory of Open Access Journals (Sweden)

    L. Telesca

    2011-08-01

    Full Text Available The analysis of time-clustering behaviour of the sub-crustal seismicity (depth larger than 60 km of the Vrancea region has been performed. The time span of the analyzed catalogue is from 1978 to 2008, and only the events with a magnitude of Mw ≥ 3 have been considered. The analysis, carried out on the full and aftershock-depleted catalogues, was performed using the Allan Factor (AF that allows the identificatiion and quantification of correlated temporal structures in temporal point processes. Our results, whose significance was analysed by means of two methods of generation of surrogate series, reveal the presence of time-clustering behaviour in the temporal distribution of seismicity data of the full catalogue. The analysis performed on the aftershock-depleted catalogue indicates that the time-clustering is associated mainly to the aftershocks generated by the two largest events occurred on 30 August 1986 (Mw = 7.1 and 30 May 1990 (Mw = 6.9.

  19. Combining multiobjective optimization and cluster analysis to study vocal fold functional morphology.

    Science.gov (United States)

    Palaparthi, Anil; Riede, Tobias; Titze, Ingo R

    2014-07-01

    Morphological design and the relationship between form and function have great influence on the functionality of a biological organ. However, the simultaneous investigation of morphological diversity and function is difficult in complex natural systems. We have developed a multiobjective optimization (MOO) approach in association with cluster analysis to study the form-function relation in vocal folds. An evolutionary algorithm (NSGA-II) was used to integrate MOO with an existing finite element model of the laryngeal sound source. Vocal fold morphology parameters served as decision variables and acoustic requirements (fundamental frequency, sound pressure level) as objective functions. A two-layer and a three-layer vocal fold configuration were explored to produce the targeted acoustic requirements. The mutation and crossover parameters of the NSGA-II algorithm were chosen to maximize a hypervolume indicator. The results were expressed using cluster analysis and were validated against a brute force method. Results from the MOO and the brute force approaches were comparable. The MOO approach demonstrated greater resolution in the exploration of the morphological space. In association with cluster analysis, MOO can efficiently explore vocal fold functional morphology.

  20. Combination of meta-analysis and graph clustering to identify prognostic markers of ESCC.

    Science.gov (United States)

    Gao, Hongyun; Wang, Lishan; Cui, Shitao; Wang, Mingsong

    2012-04-01

    Esophageal squamous cell carcinoma (ESCC) is one of the most malignant gastrointestinal cancers and occurs at a high frequency rate in China and other Asian countries. Recently, several molecular markers were identified for predicting ESCC. Notwithstanding, additional prognostic markers, with a clear understanding of their underlying roles, are still required. Through bioinformatics, a graph-clustering method by DPClus was used to detect co-expressed modules. The aim was to identify a set of discriminating genes that could be used for predicting ESCC through graph-clustering and GO-term analysis. The results showed that CXCL12, CYP2C9, TGM3, MAL, S100A9, EMP-1 and SPRR3 were highly associated with ESCC development. In our study, all their predicted roles were in line with previous reports, whereby the assumption that a combination of meta-analysis, graph-clustering and GO-term analysis is effective for both identifying differentially expressed genes, and reflecting on their functions in ESCC.

  1. Profiles of exercise motivation, physical activity, exercise habit, and academic performance in Malaysian adolescents: A cluster analysis

    Directory of Open Access Journals (Sweden)

    Hairul Anuar Hashim

    2011-06-01

    Full Text Available Objectives: This study examined Malaysian adolescents’ profiles of exercise motivation, exercise habit strength, academic performance, and levels of physical activity (PA using cluster analysis.Methods: The sample (n = 300 consisted of 65.6% males and 34.4% females with a mean age of 13.40 ± 0.49. Statistical analysis was performed using cluster analysis.Results: Cluster analysis revealed three distinct cluster groups. Cluster 1 is characterized by a moderate level of PA, relatively high in motivational indices and relative autonomy index (RAI, low in exercise habit, and moderate level of academic achievement. Cluster 2 has superior academic performance but is low in PA and all other measured variables. Cluster 3 is characterized by high levels of PA and all other variables but is lowest in academic performance. One way ANOVA revealed significant differences between cluster groups in total weekly MET, total minutes of weekly PA, academic performance, introjected regulation, and identified regulation.Conclusion: PA promotion with emphasis on external factors may be effective in instilling exercise habituation among adolescents in the present sample.

  2. Clustering Educational Digital Library Usage Data: A Comparison of Latent Class Analysis and K-Means Algorithms

    Science.gov (United States)

    Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei

    2013-01-01

    This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…

  3. Analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series based on unsupervised clustering methods

    Science.gov (United States)

    Lange, Oliver; Meyer-Baese, Anke; Wismuller, Axel; Hurdal, Monica

    2005-03-01

    We employ unsupervised clustering techniques for the analysis of dynamic contrast-enhanced perfusion MRI time-series in patients with and without stroke. "Neural gas" network, fuzzy clustering based on deterministic annealing, self-organizing maps, and fuzzy c-means clustering enable self-organized data-driven segmentation w.r.t.fine-grained differences of signal amplitude and dynamics, thus identifying asymmetries and local abnormalities of brain perfusion. We conclude that clustering is a useful extension to conventional perfusion parameter maps.

  4. A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data.

    Energy Technology Data Exchange (ETDEWEB)

    Mayo, Jackson R.; Kegelmeyer, W. Philip, Jr.; Wong, Matthew H.; Pebay, Philippe Pierre; Gentile, Ann C.; Thompson, David C.; Roe, Diana C.; De Sapio, Vincent; Brandt, James M.

    2010-08-01

    The monitoring and system analysis of high performance computing (HPC) clusters is of increasing importance to the HPC community. Analysis of HPC job data can be used to characterize system usage and diagnose and examine failure modes and their effects. This analysis is not straightforward, however, due to the complex relationships that exist between jobs. These relationships are based on a number of factors, including shared compute nodes between jobs, proximity of jobs in time, etc. Graph-based techniques represent an approach that is particularly well suited to this problem, and provide an effective technique for discovering important relationships in job queuing and execution data. The efficacy of these techniques is rooted in the use of a semantic graph as a knowledge representation tool. In a semantic graph job data, represented in a combination of numerical and textual forms, can be flexibly processed into edges, with corresponding weights, expressing relationships between jobs, nodes, users, and other relevant entities. This graph-based representation permits formal manipulation by a number of analysis algorithms. This report presents a methodology and software implementation that leverages semantic graph-based techniques for the system-level monitoring and analysis of HPC clusters based on job queuing and execution data. Ontology development and graph synthesis is discussed with respect to the domain of HPC job data. The framework developed automates the synthesis of graphs from a database of job information. It also provides a front end, enabling visualization of the synthesized graphs. Additionally, an analysis engine is incorporated that provides performance analysis, graph-based clustering, and failure prediction capabilities for HPC systems.

  5. Advancing sensitivity analysis to precisely characterize temporal parameter dominance

    Science.gov (United States)

    Guse, Björn; Pfannerstill, Matthias; Strauch, Michael; Reusser, Dominik; Lüdtke, Stefan; Volk, Martin; Gupta, Hoshin; Fohrer, Nicola

    2016-04-01

    Parameter sensitivity analysis is a strategy for detecting dominant model parameters. A temporal sensitivity analysis calculates daily sensitivities of model parameters. This allows a precise characterization of temporal patterns of parameter dominance and an identification of the related discharge conditions. To achieve this goal, the diagnostic information as derived from the temporal parameter sensitivity is advanced by including discharge information in three steps. In a first step, the temporal dynamics are analyzed by means of daily time series of parameter sensitivities. As sensitivity analysis method, we used the Fourier Amplitude Sensitivity Test (FAST) applied directly onto the modelled discharge. Next, the daily sensitivities are analyzed in combination with the flow duration curve (FDC). Through this step, we determine whether high sensitivities of model parameters are related to specific discharges. Finally, parameter sensitivities are separately analyzed for five segments of the FDC and presented as monthly averaged sensitivities. In this way, seasonal patterns of dominant model parameter are provided for each FDC segment. For this methodical approach, we used two contrasting catchments (upland and lowland catchment) to illustrate how parameter dominances change seasonally in different catchments. For all of the FDC segments, the groundwater parameters are dominant in the lowland catchment, while in the upland catchment the controlling parameters change seasonally between parameters from different runoff components. The three methodical steps lead to clear temporal patterns, which represent the typical characteristics of the study catchments. Our methodical approach thus provides a clear idea of how the hydrological dynamics are controlled by model parameters for certain discharge magnitudes during the year. Overall, these three methodical steps precisely characterize model parameters and improve the understanding of process dynamics in hydrological

  6. Understanding the Support Needs of People with Intellectual and Related Developmental Disabilities through Cluster Analysis and Factor Analysis of Statewide Data

    Science.gov (United States)

    Viriyangkura, Yuwadee

    2014-01-01

    Through a secondary analysis of statewide data from Colorado, people with intellectual and related developmental disabilities (ID/DD) were classified into five clusters based on their support needs characteristics using cluster analysis techniques. Prior latent factor models of support needs in the field of ID/DD were examined to investigate the…

  7. Cluster Mass Calibration at High Redshift: HST Weak Lensing Analysis of 13 Distant Galaxy Clusters from the South Pole Telescope Sunyaev-Zel'dovich Survey

    Energy Technology Data Exchange (ETDEWEB)

    Schrabback, T.; et al.

    2016-11-11

    We present an HST/ACS weak gravitational lensing analysis of 13 massive high-redshift (z_median=0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V-I colour. Our estimate of the source redshift distribution is based on CANDELS data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the mass-concentration relation using simulations. In combination with temperature estimates from Chandra we constrain the normalisation of the mass-temperature scaling relation ln(E(z) M_500c/10^14 M_sun)=A+1.5 ln(kT/7.2keV) to A=1.81^{+0.24}_{-0.14}(stat.) +/- 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c_200c=5.6^{+3.7}_{-1.8}.

  8. Analysis of Radiation Damage in Light Water Reactors: Comparison of Cluster Analysis Methods for the Analysis of Atom Probe Data.

    Science.gov (United States)

    Hyde, Jonathan M; DaCosta, Gérald; Hatzoglou, Constantinos; Weekes, Hannah; Radiguet, Bertrand; Styman, Paul D; Vurpillot, Francois; Pareige, Cristelle; Etienne, Auriane; Bonny, Giovanni; Castin, Nicolas; Malerba, Lorenzo; Pareige, Philippe

    2017-01-30

    Irradiation of reactor pressure vessel (RPV) steels causes the formation of nanoscale microstructural features (termed radiation damage), which affect the mechanical properties of the vessel. A key tool for characterizing these nanoscale features is atom probe tomography (APT), due to its high spatial resolution and the ability to identify different chemical species in three dimensions. Microstructural observations using APT can underpin development of a mechanistic understanding of defect formation. However, with atom probe analyses there are currently multiple methods for analyzing the data. This can result in inconsistencies between results obtained from different researchers and unnecessary scatter when combining data from multiple sources. This makes interpretation of results more complex and calibration of radiation damage models challenging. In this work simulations of a range of different microstructures are used to directly compare different cluster analysis algorithms and identify their strengths and weaknesses.

  9. Strong Lensing Analysis of the Galaxy Cluster MACS J1319.9+7003 and the Discovery of a Shell Galaxy

    Science.gov (United States)

    Zitrin, Adi

    2017-01-01

    We present a strong-lensing (SL) analysis of the galaxy cluster MACS J1319.9+7003 (z = 0.33, also known as Abell 1722), as part of our ongoing effort to analyze massive clusters with archival Hubble Space Telescope (HST) imaging. We spectroscopically measured with Keck/Multi-Object Spectrometer For Infra-Red Exploration (MOSFIRE) two galaxies multiply imaged by the cluster. Our analysis reveals a modest lens, with an effective Einstein radius of {θ }e(z=2)=12+/- 1\\prime\\prime , enclosing 2.1+/- 0.3× {10}13 M⊙. We briefly discuss the SL properties of the cluster, using two different modeling techniques (see the text for details), and make the mass models publicly available (ftp://wise-ftp.tau.ac.il/pub/adiz/MACS1319/). Independently, we identified a noteworthy, young shell galaxy (SG) system forming around two likely interacting cluster members, 20″ north of the brightest cluster galaxy. SGs are rare in galaxy clusters, and indeed, a simple estimate reveals that they are only expected in roughly one in several dozen, to several hundred, massive galaxy clusters (the estimate can easily change by an order of magnitude within a reasonable range of characteristic values relevant for the calculation). Taking advantage of our lens model best-fit, mass-to-light scaling relation for cluster members, we infer that the total mass of the SG system is ∼ 1.3× {10}11 {M}ȯ , with a host-to-companion mass ratio of about 10:1. Despite being rare in high density environments, the SG constitutes an example to how stars of cluster galaxies are efficiently redistributed to the intra-cluster medium. Dedicated numerical simulations for the observed shell configuration, perhaps aided by the mass model, might cast interesting light on the interaction history and properties of the two galaxies. An archival HST search in galaxy cluster images can reveal more such systems.

  10. Analysis of plasmaspheric plumes: CLUSTER and IMAGE observations and numerical simulations

    Science.gov (United States)

    Darouzet, Fabien; DeKeyser, Johan; Decreau, Pierrette; Gallagher, Dennis; Pierrard, Viviane; Lemaire, Joseph; Dandouras, Iannis; Matsui, Hiroshi; Dunlop, Malcolm; Andre, Mats

    2005-01-01

    Plasmaspheric plumes have been routinely observed by CLUSTER and IMAGE. The CLUSTER mission provides high time resolution four-point measurements of the plasmasphere near perigee. Total electron density profiles can be derived from the plasma frequency and/or from the spacecraft potential (note that the electron spectrometer is usually not operating inside the plasmasphere); ion velocity is also measured onboard these satellites (but ion density is not reliable because of instrumental limitations). The EUV imager onboard the IMAGE spacecraft provides global images of the plasmasphere with a spatial resolution of 0.1 RE every 10 minutes; such images acquired near apogee from high above the pole show the geometry of plasmaspheric plumes, their evolution and motion. We present coordinated observations for 3 plume events and compare CLUSTER in-situ data (panel A) with global images of the plasmasphere obtained from IMAGE (panel B), and with numerical simulations for the formation of plumes based on a model that includes the interchange instability mechanism (panel C). In particular, we study the geometry and the orientation of plasmaspheric plumes by using a four-point analysis method, the spatial gradient. We also compare several aspects of their motion as determined by different methods: (i) inner and outer plume boundary velocity calculated from time delays of this boundary observed by the wave experiment WHISPER on the four spacecraft, (ii) ion velocity derived from the ion spectrometer CIS onboard CLUSTER, (iii) drift velocity measured by the electron drift instrument ED1 onboard CLUSTER and (iv) global velocity determined from successive EUV images. These different techniques consistently indicate that plasmaspheric plumes rotate around the Earth, with their foot fully co-rotating, but with their tip rotating slower and moving farther out.

  11. Weighted Clustering

    CERN Document Server

    Ackerman, Margareta; Branzei, Simina; Loker, David

    2011-01-01

    In this paper we investigate clustering in the weighted setting, in which every data point is assigned a real valued weight. We conduct a theoretical analysis on the influence of weighted data on standard clustering algorithms in each of the partitional and hierarchical settings, characterising the precise conditions under which such algorithms react to weights, and classifying clustering methods into three broad categories: weight-responsive, weight-considering, and weight-robust. Our analysis raises several interesting questions and can be directly mapped to the classical unweighted setting.

  12. A climatology of surface ozone in the extra tropics: cluster analysis of observations and model results

    Directory of Open Access Journals (Sweden)

    O. A. Tarasova

    2007-08-01

    Full Text Available Important aspects of the seasonal variations of surface ozone are discussed. The underlying analysis is based on the long-term (1990–2004 ozone records of Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP and the World Data Center of Greenhouse Gases which do have a strong Northern Hemisphere bias. Seasonal variations are pronounced at most of the 114 locations for any time of the day. Seasonal-diurnal variability classification using hierarchical agglomeration clustering reveals 5 distinct clusters: clean/rural, semi-polluted non-elevated, semi-polluted semi-elevated, elevated and polar/remote marine types. For the cluster "clean/rural" the seasonal maximum is observed in April, both for night and day. For those sites with a double maximum or a wide spring-summer maximum, the one in spring appears both for day and night, while the one in summer is more pronounced for daytime and hence can be attributed to photochemical processes. For the spring maximum photochemistry is a less plausible explanation as no dependence of the maximum timing is observed. More probably the spring maximum is caused by dynamical/transport processes. Using data from the 3-D atmospheric chemistry general circulation model ECHAM5/MESSy1 covering the period of 1998–2005 a comparison has been performed for the identified clusters. For the model data four distinct classes of variability are detected. The majority of cases are covered by the regimes with a spring seasonal maximum or with a broad spring-summer maximum (with prevailing summer. The regime with winter–early spring maximum is reproduced by the model for southern hemispheric locations. Background and semi-polluted sites appear in the model in the same cluster. The seasonality in this model cluster is characterized by a pronounced spring (May maximum. For the model cluster that covers partly semi-elevated semi-polluted sites the role of the

  13. A climatology of surface ozone in the extra tropics: cluster analysis of observations and model results

    Directory of Open Access Journals (Sweden)

    O. A. Tarasova

    2007-12-01

    Full Text Available Important aspects of the seasonal variations of surface ozone are discussed. The underlying analysis is based on the long-term (1990–2004 ozone records of the Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP and the World Data Centre of Greenhouse Gases, which provide data mostly for the Northern Hemisphere. Seasonal variations are pronounced at most of the 114 locations at all times of the day. A seasonal-diurnal variations classification using hierarchical agglomeration clustering reveals 6 distinct clusters: clean background, rural, semi-polluted non-elevated, semi-polluted semi-elevated, elevated and polar/remote marine. For the "clean background" cluster the seasonal maximum is observed in March-April, both for night and day. For those sites with a double maximum or a wide spring-summer maximum, the spring maximum appears both for day and night, while the summer maximum is more pronounced for daytime and hence can be attributed to photochemical processes. The spring maximum is more likely caused by dynamical/transport processes than by photochemistry as it is observed in spring for all times of the day. We compare the identified clusters with corresponding data from the 3-D atmospheric chemistry general circulation model ECHAM5/MESSy1 covering the period of 1998–2005. For the model output as for the measurements 6 clusters are considered. The simulation shows at most of the sites a spring seasonal maximum or a broad spring-summer maximum (with higher summer mixing ratios. For southern hemispheric and polar remote locations the seasonal maximum in the simulation is shifted to spring, while the absolute mixing ratios are in good agreement with the measurements. The seasonality in the model cluster covering background locations is characterized by a pronounced spring (April–May maximum. For the model clusters which cover rural and semi-polluted sites the role of the

  14. Diversity of Xiphinema americanum-group Species and Hierarchical Cluster Analysis of Morphometrics.

    Science.gov (United States)

    Lamberti, F; Ciancio, A

    1993-09-01

    Of the 39 species composing the Xiphinema americanum group, 14 were described originally from North America and two others have been reported from this region. Many species are very similar morphologically and can be distinguished only by a difficult comparison of various combinations of some morphometric characters. Study of morphometrics of 49 populations, including the type populations of the 39 species attributed to this group, by principal component analysis and hierarchical cluster analysis placed the populations into five subgroups, proposed here as the X. brevicolle subgroup (seven species), the X. americanum subgroup (17 species), the X. taylori subgroup (two species), the X. pachtaicum subgroup (eight species), and the X. lambertii subgroup (five species).

  15. Study of cluster analysis used in explosives classification with laser-induced breakdown spectroscopy

    Science.gov (United States)

    Wang, Q. Q.; He, L. A.; Zhao, Y.; Peng, Z.; Liu, L.

    2016-06-01

    Supervised learning methods (such as partial least squares regression-discriminant analysis, SIMCA, etc) are widely used in explosives recognition. The correct classification rate may be lowered if a sample or substrate is not included in the training dataset. Unsupervised learning methods (such as hierarchical clustering analysis, K-means, etc) have the potential to solve this problem. In this paper we analyzed results of using as input variables the intensities of seven lines and then five intensity ratios of the seven lines. It was demonstrated that unsupervised learning methods had the ability to achieve a better classification result.

  16. AVES: A high performance computer cluster array for the INTEGRAL satellite scientific data analysis

    Science.gov (United States)

    Federici, Memmo; Martino, Bruno Luigi; Ubertini, Pietro

    2012-07-01

    In this paper we describe a new computing system array, designed, built and now used at the Space Astrophysics and Planetary Institute (IAPS) in Rome, Italy, for the INTEGRAL Space Observatory scientific data analysis. This new system has become necessary in order to reduce the processing time of the INTEGRAL data accumulated during the more than 9 years of in-orbit operation. In order to fulfill the scientific data analysis requirements with a moderately limited investment the starting approach has been to use a `cluster' array of commercial quad-CPU computers, featuring the extremely large scientific and calibration data archive on line.

  17. Expression Analysis of Genes in the Nif Cluster of Clostridium beijerinckii

    OpenAIRE

    2007-01-01

    The nif genes of Clostridium beijerinckii NRRL B593 occupy a region of about 16 kilobases. Besides the two glnB-like genes, five other genes are interspersed between the nifNB and the nifVw genes. An expression analysis of the nif genes in nitrogen-fixing and non-nitrogen-fixing cells with probes generated from various regions of the nif cluster by northern blot analysis revealed the presence of four different transcripts in nitrogen-fixing cells. Two of these transcripts had the predicted si...

  18. Economics of coal conversion processing. Advances in coal gasification: support research. Advances in coal gasification: process development and analysis

    Energy Technology Data Exchange (ETDEWEB)

    1978-01-01

    The fall meeting of the American Chemical Society, Division of Fuel Chemistry, was held at Miami Beach, Florida, September 10-15, 1978. Papers involved the economics of coal conversion processing and advances in coal gasification, especially support research and process development and analysis. Fourteen papers have been entered individually into EDB and ERA; three papers had been entered previously from other sources. (LTN)

  19. A Comprehensive Comparison of Different Clustering Methods for Reliability Analysis of Microarray Data

    Science.gov (United States)

    Kafieh, Rahele; Mehridehnavi, Alireza

    2013-01-01

    In this study, we considered some competitive learning methods including hard competitive learning and soft competitive learning with/without fixed network dimensionality for reliability analysis in microarrays. In order to have a more extensive view, and keeping in mind that competitive learning methods aim at error minimization or entropy maximization (different kinds of function optimization), we decided to investigate the abilities of mixture decomposition schemes. Therefore, we assert that this study covers the algorithms based on function optimization with particular insistence on different competitive learning methods. The destination is finding the most powerful method according to a pre-specified criterion determined with numerical methods and matrix similarity measures. Furthermore, we should provide an indication showing the intrinsic ability of the dataset to form clusters before we apply a clustering algorithm. Therefore, we proposed Hopkins statistic as a method for finding the intrinsic ability of a data to be clustered. The results show the remarkable ability of Rayleigh mixture model in comparison with other methods in reliability analysis task. PMID:24083134

  20. A Three-layered Self-Organizing Map Neural Network for Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Sheng-Chai Chi

    2003-12-01

    Full Text Available In the commercial world today, holding the effective information through information technology (IT and the internet is a very important indicator of whether an enterprise has competitive advantage in business. Clustering analysis, a technique for data mining or data analysis in databases, has been widely applied in various areas. Its purpose is to segment the individuals in the same population according to their characteristics. In this research, an enhanced three-layered self-organizing map neural network, called 3LSOM, is developed to overcome the drawback of the conventional two-layered SOM through sight-inspection after the mapping process. To further verify its feasibility, the proposed model is applied to two common problems: the identification of four given groups of work-part images and the clustering of a machine/part incidence matrix. The experimental results prove that the data that belong to the same group can be mapped to the same neuron on the output layer of the 3LSOM. Its performance in clustering accuracy is good and is also comparable with that of the FSOM, FCM and k-Means.