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Sample records for advanced cluster analysis

  1. Advanced Heat Map and Clustering Analysis Using Heatmap3

    OpenAIRE

    Shilin Zhao; Yan Guo; Quanhu Sheng; Yu Shyr

    2014-01-01

    Heat maps and clustering are used frequently in expression analysis studies for data visualization and quality control. Simple clustering and heat maps can be produced from the “heatmap” function in R. However, the “heatmap” function lacks certain functionalities and customizability, preventing it from generating advanced heat maps and dendrograms. To tackle the limitations of the “heatmap” function, we have developed an R package “heatmap3” which significantly improves the original “heatmap”...

  2. Clustering analysis

    International Nuclear Information System (INIS)

    Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K-mean method' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods

  3. 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

  4. Pharmacokinetic analysis and k-means clustering of DCEMR images for radiotherapy outcome prediction of advanced cervical cancers

    International Nuclear Information System (INIS)

    Introduction. Pharmacokinetic analysis of dynamic contrast enhanced magnetic resonance images (DCEMRI) allows for quantitative characterization of vascular properties of tumors. The aim of this study is twofold, first to determine if tumor regions with similar vascularization could be labeled by clustering methods, second to determine if the identified regions can be associated with local cancer relapse. Materials and methods. Eighty-one patients with locally advanced cervical cancer treated with chemoradiotherapy underwent DCEMRI with Gd-DTPA prior to external beam radiotherapy. The median follow-up time after treatment was four years, in which nine patients had primary tumor relapse. By fitting a pharmacokinetic two-compartment model function to the temporal contrast enhancement in the tumor, two pharmacokinetic parameters, Ktrans and ue, were estimated voxel by voxel from the DCEMR-images. Intratumoral regions with similar vascularization were identified by k-means clustering of the two pharmacokinetic parameter estimates over all patients. The volume fraction of each cluster was used to evaluate the prognostic value of the clusters. Results. Three clusters provided a sufficient reduction of the cluster variance to label different vascular properties within the tumors. The corresponding median volume fraction of each cluster was 38%, 46% and 10%. The second cluster was significantly associated with primary tumor control in a log-rank survival test (p-value: 0.042), showing a decreased risk of treatment failure for patients with high volume fraction of voxels. Conclusions. Intratumoral regions showing similar vascular properties could successfully be labeled in three distinct clusters and the volume fraction of one cluster region was associated with primary tumor control

  5. Graph partitioning advance clustering technique

    CERN Document Server

    Madhulatha, T Soni

    2012-01-01

    Clustering is a common technique for statistical data analysis, Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. Often, distance measures are used. Clustering is an unsupervised learning technique, where interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. This paper also considers the partitioning of m-dimensional lattice graphs using Fiedler's approach, which requires the determination of the eigenvector belonging to the second smallest Eigenvalue of the Laplacian with K-means partitioning algorithm.

  6. 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

  7. ADVANCED CLUSTER BASED IMAGE SEGMENTATION

    Directory of Open Access Journals (Sweden)

    D. Kesavaraja

    2011-11-01

    Full Text Available This paper presents efficient and portable implementations of a useful image segmentation technique which makes use of the faster and a variant of the conventional connected components algorithm which we call parallel Components. In the Modern world majority of the doctors are need image segmentation as the service for various purposes and also they expect this system is run faster and secure. Usually Image segmentation Algorithms are not working faster. In spite of several ongoing researches in Conventional Segmentation and its Algorithms might not be able to run faster. So we propose a cluster computing environment for parallel image Segmentation to provide faster result. This paper is the real time implementation of Distributed Image Segmentation in Clustering of Nodes. We demonstrate the effectiveness and feasibility of our method on a set of Medical CT Scan Images. Our general framework is a single address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient cluster process which uses a novel approach for parallel merging. Our experimental results are consistent with the theoretical analysis and practical results. It provides the faster execution time for segmentation, when compared with Conventional method. Our test data is different CT scan images from the Medical database. More efficient implementations of Image Segmentation will likely result in even faster execution times.

  8. 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....

  9. Weak Lensing Analysis of the z~0.8 cluster CL 0152-1357 with the Advanced Camera for Surveys

    CERN Document Server

    Jee, M J; Benítez, N; Ford, H C; Blakeslee, J P; Rosati, P; Demarco, R; Illingworth, G D

    2004-01-01

    We present a weak lensing analysis of the X-ray luminous cluster CL 0152-1357 at z~0.84 using HST/ACS observations. The unparalleled resolution and sensitivity of ACS enable us to measure weakly distorted, faint background galaxies to the extent that the number density reaches ~175 arcmin^-2. The PSF of ACS has a complicated shape that also varies across the field. We construct a PSF model for ACS from an extensive investigation of 47 Tuc stars in a modestly crowded region. We show that this model PSF excellently describes the PSF variation pattern in the cluster observation when a slight adjustment of ellipticity is applied. The high number density of source galaxies and the accurate removal of the PSF effect through moment-based deconvolution allow us to restore the dark matter distribution of the cluster in great detail. The direct comparison of the mass map with the X-ray morphology from Chandra observations shows that the two peaks of intracluster medium traced by X-ray emission are lagging behind the co...

  10. 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

    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...

  11. [Cluster analysis in biomedical researches].

    Science.gov (United States)

    Akopov, A S; Moskovtsev, A A; Dolenko, S A; Savina, G D

    2013-01-01

    Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research. PMID:24640781

  12. Advanced Low Energy Adaptive Clustering Hierarchy

    Directory of Open Access Journals (Sweden)

    Ezzati Abdellah,

    2010-10-01

    Full Text Available The use of Wireless Sensor Networks (WSNs is anticipated to bring enormous changes in data gathering, processing and dissemination for different environments and applications. However, a WSN is a power constrained system, since nodes run on limited power batteries which shorten its lifespan. Prolonging the network lifetime depends on efficient management of sensing node energy resource. Hierarchicalrouting protocols are best known in regard to energy efficiency. By using a clustering technique hierarchical routing protocols greatly minimize energy consumed in collecting and disseminating data. Low Energy Adaptive Clustering Hierarchy (LEACH is one of the undamental protocols in this class. In this paper we propose Advanced LEACH (A-LEACH, a heterogeneous-energy protocol to decrease probability of failure nodes and to prolong the time interval before the death of the first node (we refer to as stability period and increasing the lifetime in heterogeneous WSNs, which is crucial for many applications.

  13. Integrating cluster formation and cluster evaluation in interactive visual analysis

    OpenAIRE

    Turkay, C.; Parulek, J.; Reuter, N.; Hauser, H.

    2011-01-01

    Cluster analysis is a popular method for data investigation where data items are structured into groups called clusters. This analysis involves two sequential steps, namely cluster formation and cluster evaluation. In this paper, we propose the tight integration of cluster formation and cluster evaluation in interactive visual analysis in order to overcome the challenges that relate to the black-box nature of clustering algorithms. We present our conceptual framework in the form of an interac...

  14. Clustering analysis using Swarm Intelligence

    OpenAIRE

    Farmani, Mohammad Reza

    2016-01-01

    This thesis is concerned with the application of the swarm intelligence methods in clustering analysis of datasets. The main objectives of the thesis are ∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony, to improve the capability of K-means in finding global optimum clusters in nonlinear partitional clustering problems. ∙ Consider partitional clustering as an optimization problem and an improved antbased algorithm, named Opposition-Based A...

  15. Combination Clustering Analysis Method and its Application

    OpenAIRE

    Bang-Chun Wen; Li-Yuan Dong; Qin-Liang Li; Yang Liu

    2013-01-01

    The traditional clustering analysis method can not automatically determine the optimal clustering number. In this study, we provided a new clustering analysis method which is combination clustering analysis method to solve this problem. Through analyzed 25 kinds of automobile data samples by combination clustering analysis method, the correctness of the analysis result was verified. It showed that combination clustering analysis method could objectively determine the number of clustering firs...

  16. 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

  17. Advanced Analysis Environments - Summary

    International Nuclear Information System (INIS)

    This is a summary of the panel discussion on Advanced Analysis Environments. Rene Brun, Tony Johnson, and Lassi Tuura shared their insights about the trends and challenges in analysis environments. This paper contains the initial questions, a summary of the speakers' presentation, and the questions asked by the audience

  18. EM Clustering Analysis of Diabetes Patients Basic Diagnosis Index

    OpenAIRE

    Wu, Cai; Steinbauer, Jeffrey R.; Kuo, Grace M

    2005-01-01

    Cluster analysis can group similar instances into same group. Partitioning cluster assigns classes to samples without known the classes in advance. Most common algorithms are K-means and Expectation Maximization (EM). EM clustering algorithm can find number of distributions of generating data and build “mixture models”. It identifies groups that are either overlapping or varying sizes and shapes. In this project, by using EM in Machine Learning Algorithm in JAVA (WEKA) syste...

  19. 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

  20. Analysis of Various Clustering Algorithms

    OpenAIRE

    Asst Prof. Sunila Godara,; Ms. Amita Verma,

    2013-01-01

    Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper reviews four types of clustering techniques- k-Means Clustering, Farther first clustering, Density Based Clustering, Filtered clusterer. These clustering techniques are implemented and analyzed using a clustering tool WEKA. Performance of the 4 techniques are presented and compared.

  1. Tools for Advanced Hadoop Cluster Control

    OpenAIRE

    Cimerman, Gregor

    2013-01-01

    Hadoop is a platform for storing and processing big data. This kind of platform that stretches over multiple servers is difficult to manage. Traditional management systems for computer grids do not allow complete management over Hadoop services because the dynamic and elastic properties. Because the complexity of Hadoop services, the combination of management systems are necessary for complete management. In this thesis we describe this combination of tools for Hadoop cluster management. We d...

  2. 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

  3. 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...

  4. 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.

  5. 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...

  6. Survey and Analysis of University Clustering

    Directory of Open Access Journals (Sweden)

    Srinatha Karur

    2013-07-01

    Full Text Available This paper gives on Clustering of Universities in the world with respect to their country policies OR local polices OR continent level polices with sub aims. So clustering method can generally apply when objective is specifically mentioned. For general objectives clusters are available in the form of logical or physical groups without networks. In this paper we emphasis on only University Clusters directly or University Clusters with some other clusters. Data miming methods are used for useful for Sampling Analysis and Clustering of Universities and Colleges with respect to local clusters [1] pp 1.

  7. 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.

  8. Missing data treatment method on cluster analysis

    OpenAIRE

    Elsiddig Elsadig Mohamed Koko; Amin Ibrahim Adam Mohamed

    2015-01-01

    The missing data in household health survey was challenged for the researcher because of incomplete analysis. The statistical tool cluster analysis methodology implemented in the collected data of Sudan's household health survey in 2006. Current research specifically focuses on the data analysis as the objective is to deal with the missing values in cluster analysis. Two-Step Cluster Analysis is applied in which each participant is classified into one of the identified pattern and the opt...

  9. Cluster analysis for portfolio optimization

    CERN Document Server

    Tola, V; Gallegati, M; Mantegna, R N; Tola, Vincenzo; Lillo, Fabrizio; Gallegati, Mauro; Mantegna, Rosario N.

    2005-01-01

    We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio composition of the selected portfolio for a given value of the portfolio return are also investigated for each considered filtering method.

  10. Digital Fourier analysis advanced techniques

    CERN Document Server

    Kido, Ken'iti

    2015-01-01

    This textbook is a thorough, accessible introduction to advanced digital Fourier analysis for advanced undergraduate and graduate students. Assuming knowledge of the Fast Fourier Transform, this book covers advanced topics including the Hilbert transform, cepstrum analysis, and the two-dimensional Fourier transform. Saturated with clear, coherent illustrations, "Digital Fourier Analysis - Advanced Techniques" includes practice problems and thorough Appendices. As a central feature, the book includes interactive applets (available online) that mirror the illustrations. These user-friendly applets animate concepts interactively, allowing the user to experiment with the underlying mathematics. The applet source code in Visual Basic is provided online, enabling advanced students to tweak and change the programs for more sophisticated results. A complete, intuitive guide, "Digital Fourier Analysis - Advanced Techniques" is an essential reference for students in science and engineering.

  11. PUNJABI TEXT CLUSTERING BY SENTENCE STRUCTURE ANALYSIS

    Directory of Open Access Journals (Sweden)

    Saurabh Sharma

    2012-10-01

    Full Text Available Punjabi Text Document Clustering is done by analyzing the sentence structure of similar documents sharing same topics and grouping them into clusters. The prevalent algorithms in this field utilize the vector space model which treats the documents as a bag of words. The meaning in natural language inherently depends on the word sequences which are overlooked and ignored while clustering. The current paper deals with a new Punjabi text clustering algorithm named Clustering by Sentence Structure Analysis(CSSA which has been carried out on 221 Punjabi news articles available on news sites. The phrases are extracted for processing by a meticulous analysis of the structure of a sentence by applying the basic grammatical rules of Karaka. Sequences formed from phrases, are used to find the topic and for finding similarities among all documents which results in the formation of meaningful clusters.

  12. New Developments in Fuzzy Cluster Analysis

    Czech Academy of Sciences Publication Activity Database

    Řezanková, H.; Húsek, Dušan

    Praha: Nakladatelství Oeconomica, 2009 - (Fischer, J.), s. 403-416 ISBN 978-80-245-1600-4. [AMSE 2009. International Conference on Mathematics and Statistics in Economy /12./. Uherské Hradiště (CZ), 26.08.2009-28.08.2009] R&D Projects: GA ČR GA205/09/1079; GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy cluster analysis * ensembles of fuzzy clustering * relationships between clusters and variables * cluster number determination Subject RIV: BB - Applied Statistics, Operational Research

  13. 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...

  14. Exact WKB analysis and cluster algebras

    International Nuclear Information System (INIS)

    We develop the mutation theory in the exact WKB analysis using the framework of cluster algebras. Under a continuous deformation of the potential of the Schrödinger equation on a compact Riemann surface, the Stokes graph may change the topology. We call this phenomenon the mutation of Stokes graphs. Along the mutation of Stokes graphs, the Voros symbols, which are monodromy data of the equation, also mutate due to the Stokes phenomenon. We show that the Voros symbols mutate as variables of a cluster algebra with surface realization. As an application, we obtain the identities of Stokes automorphisms associated with periods of cluster algebras. The paper also includes an extensive introduction of the exact WKB analysis and the surface realization of cluster algebras for nonexperts. This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Cluster algebras in mathematical physics’. (paper)

  15. 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.

  16. 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...

  17. 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.

  18. Towards optimal cluster power spectrum analysis

    Science.gov (United States)

    Smith, Robert E.; Marian, Laura

    2016-04-01

    The power spectrum of galaxy clusters is an important probe of the cosmological model. In this paper, we develop a formalism to compute the optimal weights for the estimation of the matter power spectrum from cluster power spectrum measurements. We find a closed-form analytic expression for the optimal weights, which takes into account: the cluster mass, finite survey volume effects, survey masking, and a flux limit. The optimal weights are w(M,χ ) ∝ b(M,χ )/[1+bar{n}_h(χ ) overline{b^2}(χ )overline{P}(k)], where b(M, χ) is the bias of clusters of mass M at radial position χ(z), bar{n}_h(χ ) and overline{b^2}(χ ) are the expected space density and bias squared of all clusters, and overline{P}(k) is the matter power spectrum at wavenumber k. This result is analogous to that of Percival et al. We compare our optimal weighting scheme with mass weighting and also with the original power spectrum scheme of Feldman et al. We show that our optimal weighting scheme outperforms these approaches for both volume- and flux-limited cluster surveys. Finally, we present a new expression for the Fisher information matrix for cluster power spectrum analysis. Our expression shows that for an optimally weighted cluster survey the cosmological information content is boosted, relative to the standard approach of Tegmark.

  19. 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...

  20. 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.

  1. Advanced biomedical image analysis

    CERN Document Server

    Haidekker, Mark A

    2010-01-01

    "This book covers the four major areas of image processing: Image enhancement and restoration, image segmentation, image quantification and classification, and image visualization. Image registration, storage, and compression are also covered. The text focuses on recently developed image processing and analysis operators and covers topical research"--Provided by publisher.

  2. 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.

  3. Using Cluster Analysis to Examine Husband-Wife Decision Making

    Science.gov (United States)

    Bonds-Raacke, Jennifer M.

    2006-01-01

    Cluster analysis has a rich history in many disciplines and although cluster analysis has been used in clinical psychology to identify types of disorders, its use in other areas of psychology has been less popular. The purpose of the current experiments was to use cluster analysis to investigate husband-wife decision making. Cluster analysis was…

  4. 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.

  5. Clustering and classification

    CERN Document Server

    Arabie, Phipps

    1996-01-01

    At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review

  6. Analysis of delocalization of clusters in linear-chain $\\alpha$-cluster states with entanglement entropy

    OpenAIRE

    Kanada-En'yo, Yoshiko

    2015-01-01

    I investigate entanglement entropy of one dimension (1D) cluster states to discuss the delocalization of clusters in linear-chain $3\\alpha$- and $4\\alpha$-cluster states. In analysis of entanglement entropy of 1D Tohsaki-Horiuchi-Schuck-R\\"opke (THSR) and Brink-Bloch cluster wave functions, I show clear differences in the entanglement entropy between localized cluster wave functions and delocalized cluster wave functions. In order to clarify spatial regions where the entanglement entropy is g...

  7. 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.

  8. Research on competition diffusion of the multiple-advanced manufacturing mode in a cluster environment

    OpenAIRE

    C G Xue; Liu, J.J.; H W Cao

    2013-01-01

    This paper deals with competition diffusion of multiple-advanced manufacturing modes in a cluster environment, to reveal the competition diffusion rules of the advanced manufacturing mode. First, the influencing factors on advanced manufacturing mode diffusion in a cluster environment are analysed. Second, the diffusion properties and the diffusion mechanism are analysed, and the competition diffusion model of multiple-modes is established. Third, the model is analysed and the qualitative res...

  9. Hierarchical genetic clusters for phenotypic analysis

    Directory of Open Access Journals (Sweden)

    Luiza Barbosa da Matta

    2015-10-01

    Full Text Available Methods to obtain phenotypic information were evaluated to help breeders choosing the best methodology for analysis of genetic diversity in backcross populations. Phenotypes were simulated for 13 characteristics generated in 10 populations with 100 individuals each. Genotypic information was generated from 100 loci of which 20 were taken at random to determine the characteristics expressing two alleles. Dissimilarity measures were calculated, and genetic diversity was analyzed through hierarchical clustering and graphic projection of the distances. A backcross was performed from the two most divergent populations. A set of characteristics with variable heritability was taken into account. The environmental effect was simulated assuming . For hierarchical clusters, the following methods were used: Gower Method, average linkage within the cluster, average linkage among clusters, the furthest neighbor method, the nearest neighbor method, Ward’s method, and the median method. The environmental effect and heritability of the analyzed variables had an influence on the pattern of hierarchical clustering populations according to the backcrossed generations. The nearest neighbor method was the most efficient in reconstructing the system of backcrossing, and it presented the highest cophenetic correlation. The efficiency of the nearest neighbor method was the highest when the analysis involved characteristics of high heritability.

  10. Implementation and experimental analysis of consensus clustering

    OpenAIRE

    Perc, Domen

    2011-01-01

    Consensus clustering is a machine learning tehnique for class discovery and clustering validation. The method uses various clustering algorithms in conjunction with different resampling tehniques for data clustering. It is based on multiple runs of clustering and sampling algorithm. Data gathered in these runs is used for clustering and for visual representation of clustering. Visual representation helps us to understand clustering results. In this thesis we compare consensus clustering with ...

  11. Semi-supervised consensus clustering for gene expression data analysis

    OpenAIRE

    Wang, Yunli; Pan, Youlian

    2014-01-01

    Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

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

    OpenAIRE

    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 generaliza...

  13. Clustering

    Directory of Open Access Journals (Sweden)

    Jinfei Liu

    2013-04-01

    Full Text Available DBSCAN is a well-known density-based clustering algorithm which offers advantages for finding clusters of arbitrary shapes compared to partitioning and hierarchical clustering methods. However, there are few papers studying the DBSCAN algorithm under the privacy preserving distributed data mining model, in which the data is distributed between two or more parties, and the parties cooperate to obtain the clustering results without revealing the data at the individual parties. In this paper, we address the problem of two-party privacy preserving DBSCAN clustering. We first propose two protocols for privacy preserving DBSCAN clustering over horizontally and vertically partitioned data respectively and then extend them to arbitrarily partitioned data. We also provide performance analysis and privacy proof of our solution..

  14. 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.

  15. CLUSTERING-BASED ANALYSIS OF TEXT SIMILARITY

    OpenAIRE

    Bovcon , Borja

    2013-01-01

    The focus of this thesis is comparison of analysis of text-document similarity using clustering algorithms. We begin by defining main problem and then, we proceed to describe the two most used text-document representation techniques, where we present words filtering methods and their importance, Porter's algorithm and tf-idf term weighting algorithm. We then proceed to apply all previously described algorithms on selected data-sets, which vary in size and compactness. Fallowing this, we ...

  16. Intelligent Pattern Mining and Data Clustering for Pattern Cluster Analysis using Cancer Data

    Directory of Open Access Journals (Sweden)

    G.Raj Kumar

    2010-12-01

    Full Text Available Data mining techniques are used for the knowledge discovery process under the large data set environment. Clustering techniques are used to group up the relevant data sets. Hierarchical and partitioned clustering techniques are used for the clustering process. The clustering process is the complex task with high process time. The pattern extraction scheme is applied to find frequent item sets. Association rule mining techniques are applied to carry out the pattern extraction process. The pattern extraction scheme and the clustering scheme are integrated in the simultaneous pattern extraction and clustering scheme. The clustering process is improved with pattern comparison and transaction transfer process. The simultaneous clustering scheme is implemented to analyze the cancer patient diagnosis reports. The system is implemented as four major modules data set management, pattern extraction, clustering process and performance analysis. The data sets are preprocessed before the pattern extraction process. The patterns are used in the simultaneous clustering process. The performance analysis is done with the comparison of the data clustering scheme and pattern clustering schemes. The process time and memory factors are used in the performance analysis process. The cluster accuracy is represented using the fitness values. The system is enhanced with the K-means clustering algorithm.

  17. ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network.

    Science.gov (United States)

    Wang, Jianxin; Zhong, Jiancheng; Chen, Gang; Li, Min; Wu, Fang-xiang; Pan, Yi

    2015-01-01

    Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks. PMID:26357321

  18. CO MPARATIVE STUDY OF CLUSTERING TECHNIQUES IN MULTIVARIATE DATA ANALYSIS

    OpenAIRE

    Sabba Ruhi; Md. Shamim Reza

    2015-01-01

    In present, Clustering techniques is a standard tool in several exploratory pattern - analysis, grouping, decision making, and machine - learning situations; including data mining, document retrieval, image segmentation, pattern recognition and in the field of artificial intelligenc e. In this study we have compared five different types of clustering techniques such as Fuzzy clustering, K - Means clustering, Hierarc...

  19. Cluster Analysis in Rapeseed (Brassica Napus L.)

    International Nuclear Information System (INIS)

    With widening edible deficit, Kenya has become increasingly dependent on imported edible oils. Many oilseed crops (e.g. sunflower, soya beans, rapeseed/mustard, sesame, groundnuts etc) can be grown in Kenya. But oilseed rape is preferred because it very high yielding (1.5 tons-4.0 tons/ha) with oil content of 42-46%. Other uses include fitting in various cropping systems as; relay/inter crops, rotational crops, trap crops and fodder. It is soft seeded hence oil extraction is relatively easy. The meal is high in protein and very useful in livestock supplementation. Rapeseed can be straight combined using adjusted wheat combines. The priority is to expand domestic oilseed production, hence the need to introduce improved rapeseed germplasm from other countries. The success of any crop improvement programme depends on the extent of genetic diversity in the material. Hence, it is essential to understand the adaptation of introduced genotypes and the similarities if any among them. Evaluation trials were carried out on 17 rapeseed genotypes (nine Canadian origin and eight of European origin) grown at 4 locations namely Endebess, Njoro, Timau and Mau Narok in three years (1992, 1993 and 1994). Results for 1993 were discarded due to severe drought. An analysis of variance was carried out only on seed yields and the treatments were found to be significantly different. Cluster analysis was then carried out on mean seed yields and based on this analysis; only one major group exists within the material. In 1992, varieties 2,3,8 and 9 didn't fall in the same cluster as the rest. Variety 8 was the only one not classified with the rest of the Canadian varieties. Three European varieties (2,3 and 9) were however not classified with the others. In 1994, varieties 10 and 6 didn't fall in the major cluster. Of these two, variety 10 is of Canadian origin. Varieties were more similar in 1994 than 1992 due to favorable weather. It is evident that, genotypes from different geographical

  20. A Review on Clustering and Outlier Analysis Techniques in Datamining

    Directory of Open Access Journals (Sweden)

    S. Koteeswaran

    2012-01-01

    Full Text Available Problem statement: The modern world is based on using physical, biological and social systems more effectively using advanced computerized techniques. A great amount of data being generated by such systems; it leads to a paradigm shift from classical modeling and analyses based on basic principles to developing models and the corresponding analyses directly from data. The ability to extract useful hidden knowledge in these data and to act on that knowledge is becoming increasingly important in today's competitive world. Approach: The entire process of applying a computer-based methodology, including new techniques, for discovering knowledge from data is called data mining. There are two primary goals in the data mining which are prediction and classification. The larger data involved in the data mining requires clustering and outlier analysis for reducing as well as collecting only useful data set. Results: This study is focusing the review of implementation techniques, recent research on clustering and outlier analysis. Conclusion: The study aims for providing the review of clustering and outlier analysis technique and the discussion on the study will guide the researcher for improving their research direction.

  1. Chaotic map clustering algorithm for EEG analysis

    Science.gov (United States)

    Bellotti, R.; De Carlo, F.; Stramaglia, S.

    2004-03-01

    The non-parametric chaotic map clustering algorithm has been applied to the analysis of electroencephalographic signals, in order to recognize the Huntington's disease, one of the most dangerous pathologies of the central nervous system. The performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic annealing, and supervised multi-layer perceptron. While supervised neural networks need a training phase, performed by means of data tagged by the genetic test, and the parametric methods require a prior choice of the number of classes to find, the chaotic map clustering gives a natural evidence of the pathological class, without any training or supervision, thus providing a new efficient methodology for the recognition of patterns affected by the Huntington's disease.

  2. Clustering Analysis within Text Classification Techniques

    Directory of Open Access Journals (Sweden)

    Madalina ZURINI

    2011-01-01

    Full Text Available The paper represents a personal approach upon the main applications of classification which are presented in the area of knowledge based society by means of methods and techniques widely spread in the literature. Text classification is underlined in chapter two where the main techniques used are described, along with an integrated taxonomy. The transition is made through the concept of spatial representation. Having the elementary elements of geometry and the artificial intelligence analysis, spatial representation models are presented. Using a parallel approach, spatial dimension is introduced in the process of classification. The main clustering methods are described in an aggregated taxonomy. For an example, spam and ham words are clustered and spatial represented, when the concepts of spam, ham and common and linkage word are presented and explained in the xOy space representation.

  3. 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.

  4. 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.

  5. 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.

  6. Adaptive Fuzzy Consensus Clustering Framework for Clustering Analysis of Cancer Data.

    Science.gov (United States)

    Yu, Zhiwen; Chen, Hantao; You, Jane; Liu, Jiming; Wong, Hau-San; Han, Guoqiang; Li, Le

    2015-01-01

    Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research works which perform tumor clustering, few of them considers how to incorporate fuzzy theory together with an optimization process into a consensus clustering framework to improve the performance of clustering analysis. In this paper, we first propose a random double clustering based cluster ensemble framework (RDCCE) to perform tumor clustering based on gene expression data. Specifically, RDCCE generates a set of representative features using a randomly selected clustering algorithm in the ensemble, and then assigns samples to their corresponding clusters based on the grouping results. In addition, we also introduce the random double clustering based fuzzy cluster ensemble framework (RDCFCE), which is designed to improve the performance of RDCCE by integrating the newly proposed fuzzy extension model into the ensemble framework. RDCFCE adopts the normalized cut algorithm as the consensus function to summarize the fuzzy matrices generated by the fuzzy extension models, partition the consensus matrix, and obtain the final result. Finally, adaptive RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and improve the performance of RDCFCE further by adopting a self-evolutionary process (SEPP) for the parameter set. Experiments on real cancer gene expression profiles indicate that RDCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms. PMID:26357330

  7. Advances in Plasmaspheric Wave Research with CLUSTER and IMAGE Observations

    Czech Academy of Sciences Publication Activity Database

    Masson, A.; Santolík, Ondřej; Carpenter, D. L.; Darrouzet, F.; Décréau, P. M. E.; Mazouz, F. El-L.; Green, J. L.; Grimald, S.; Moldwin, M. B.; Němec, František; Sonwalkar, V. S.

    2009-01-01

    Roč. 145, 1-2 (2009), s. 137-191. ISSN 0038-6308 R&D Projects: GA AV ČR IAA301120601 Grant ostatní: GA MŠk(CZ) ME 842 Institutional research plan: CEZ:AV0Z30420517 Keywords : Plasmasphere * CLUSTER * IMAGE * Wave s Subject RIV: BL - Plasma and Gas Discharge Physics Impact factor: 4.589, year: 2009 http://www.springerlink.com/content/b20518u541127044/fulltext.pdf

  8. 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...

  9. Cluster analysis of word frequency dynamics

    International Nuclear Information System (INIS)

    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

  10. 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.

  11. Lung scintigraphy clustering by texture analysis

    International Nuclear Information System (INIS)

    The efficiency of texture analysis parameters, describing the organization of grey level variations of an image, was studied for lung scintigraphic data classification. Twenty one patients received a99mTC-MAA perfusion scan and 81mKr and 127Xe ventilation scans. Scans were scaled to 64 grey levels and 100 k events for inter subject comparison. The texture index was the average of the absolute difference between a pixel and its neighbors. Energy, entropy, correlation, local homogeneity and inertia were computed using co-occurrence matrices. A principal component analysis was carried out on each parameter for each type of scan and the first principal components were selected as clustering indices. Validation was achieved by simulating 2 series of 20 increasingly heterogenous perfusion and ventilation scans. For most of the texture parameters, one principal component could summarize the patients data since it corresponded to the relative variances of 67%-88% for perfusion scans, 53%-99% for 81mKr scans and 38%-97% for 127Xe scans. The simulated series demonstrated a linear relationship between the heterogeneity and the first principal component for texture index, energy, entropy and inertia. This was not the case for correlation and local Homogeneity. We conclude that heterogeneity of lung scans may be quantified by texture analysis. The texture index is the easiest to compute and provides the most efficient results for clinical purpose. (orig.)

  12. Semantic Analysis of Web Pages using Cluster Analysis and Nonnegative Matrix Factorization

    Czech Academy of Sciences Publication Activity Database

    Snášel, V.; Řezanková, H.; Húsek, Dušan; Kudělka, M.; Lehečka, O.

    Berlin: Springer, 2007 - (Wegrzyn-Wolska, K.; Szczepaniak, P.), s. 328-336. (Advances in Soft Computing. 43). ISBN 978-3-540-72574-9. [AWIC 2007. Atlantic Web Intelligence Conference /5./. Fontainbleau (FR), 25.06.2007-27.06.2007] R&D Projects: GA ČR GA201/05/0079; GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : semantic analysis * web pages * cluster analysis * matrix factorization Subject RIV: BB - Applied Statistics, Operational Research

  13. An analysis of hospital brand mark clusters.

    Science.gov (United States)

    Vollmers, Stacy M; Miller, Darryl W; Kilic, Ozcan

    2010-07-01

    This study analyzed brand mark clusters (i.e., various types of brand marks displayed in combination) used by hospitals in the United States. The brand marks were assessed against several normative criteria for creating brand marks that are memorable and that elicit positive affect. Overall, results show a reasonably high level of adherence to many of these normative criteria. Many of the clusters exhibited pictorial elements that reflected benefits and that were conceptually consistent with the verbal content of the cluster. Also, many clusters featured icons that were balanced and moderately complex. However, only a few contained interactive imagery or taglines communicating benefits. PMID:20582849

  14. 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

  15. 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…

  16. Simultaneous Two-Way Clustering of Multiple Correspondence Analysis

    Science.gov (United States)

    Hwang, Heungsun; Dillon, William R.

    2010-01-01

    A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is applied…

  17. 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…

  18. 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. PMID:26274407

  19. Analysis of delocalization of clusters in linear-chain $\\alpha$-cluster states with entanglement entropy

    CERN Document Server

    Kanada-En'yo, Yoshiko

    2015-01-01

    I investigate entanglement entropy of one dimension (1D) cluster states to discuss the delocalization of clusters in linear-chain $3\\alpha$- and $4\\alpha$-cluster states. In analysis of entanglement entropy of 1D Tohsaki-Horiuchi-Schuck-R\\"opke (THSR) and Brink-Bloch cluster wave functions, I show clear differences in the entanglement entropy between localized cluster wave functions and delocalized cluster wave functions. In order to clarify spatial regions where the entanglement entropy is generated by the delocalization of clusters, I analyze the spatial distribution of entanglement entropy. In the linear-chain $3\\alpha$ cluster state, the delocalization occurs dominantly in a low-density tail region while it is relatively suppressed in an inner region because of Pauli blocking effect between clusters. In the linear-chain 4$\\alpha$ state having a larger system size than the linear-chain $3\\alpha$ state, the delocalization occurs in the whole system. The entanglement entropy is found to be a measure of the d...

  20. TOURISM DESTINATION MAPPING THROUGH CLUSTER ANALYSIS

    Directory of Open Access Journals (Sweden)

    Ion DONA

    2013-01-01

    Full Text Available The concept of tourism destination appeared in theory and practice after the development of mass tourism and tourism marketing. They are theoretically “travel market units” or areas that are capable “to exist independently and efficiently in the tourism market according to the principles of marketing and the policy of tourism product”. However the main idea of which we start this paper is that the most of tourism destinations are not born naturally, they were created by implementing an efficient development management of attractions, accessibility and amenities at a specific area level. We consider that the stakeholders can intervene in an area with touristic potential to support the development of rural tourism and implement measures that can transform it in a touristic destination. With this purpose in mind we present in this paper a methodology to map the areas with rural tourism development potential by utilising cluster analysis. The case studies are the villages from Gorj County with touristic potential that have a proximity access to high value natural and/or anthropic touristic resources. The main results of our research is that in this county exists five areas where can be implemented tourism destination management plans and through which can be assured a better promotion and valorisation of rural tourism.

  1. Clustering and Feature Selection using Sparse Principal Component Analysis

    OpenAIRE

    Luss, Ronny; d'Aspremont, Alexandre

    2007-01-01

    In this paper, we study the application of sparse principal component analysis (PCA) to clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to interpret the clusters in terms of a reduced set of variables. We begin with a brief int...

  2. 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...

  3. 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

  4. The Sensitivity of Atmospheric Trajectory Cluster Analysis Results to Clustering Methods Using Trajectories to the PICO-NARE Station

    Science.gov (United States)

    Owen, R. C.; Honrath, R. E.; Merrill, J.

    2003-12-01

    The use of cluster analysis to group atmospheric trajectories according to similar flow paths has become a common tool in atmospheric studies. Many methods are available to conduct a cluster analysis. However, the dependence of the resulting clusters upon the specific clustering method chosen has not been fully characterized. Specifically, the use of hierarchical versus non-hierarchical clustering algorithms has received little focus. This study presents the results of two cluster analyses: one using the hierarchical clustering algorithm average linkage, and one using the non-hierarchical clustering algorithm k-means. These results demonstrate the sensitivity of this cluster analysis to the use of a hierarchical method versus a non-hierarchical method. In addition, this study analyzes methods for dealing with the vertical component of trajectories during the clustering process. The analyses were performed using a 40-year set of trajectories to the PICO-NARE station, located atop Pico Mountain in the Azores Islands in the central North Atlantic.

  5. Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis

    OpenAIRE

    Cohen, Mitchell J; Grossman, Adam D; Morabito, Diane; Knudson, M. Margaret; Butte, Atul J; Manley, Geoffrey T.

    2010-01-01

    Introduction Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enabl...

  6. 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.  .

  7. Advances in digital terrain analysis

    CERN Document Server

    Zhou, Qiming; Tang, Guo-An

    2008-01-01

    Terrain analysis has been an active study field for years and attracted research studies from geographers, surveyors, engineers and computer scientists. With the rapid growth of Geographical Information System (GIS) technology, particularly the establishment of high resolution Digital Elevation Models (DEM) at national level, the challenge is now focused on delivering justifiable socio-economical and environmental benefits. The contributions in this book represent the state-of-the-art of terrain analysis methods and techniques in areas of digital representation, morphological and hydrological models, uncertainty and applications of terrain analysis.

  8. Why do creative industries cluster? An analysis of the determinants of clustering of creative industries

    OpenAIRE

    Lazzeretti, Luciana; Boix, Rafael; Capone, Francesco

    2009-01-01

    Creative industries tend to concentrate mainly around large- and medium-sized cities, forming creative local production systems. The text analyses the forces behind clustering of creative industries to provide the first empirical explanation of the determinants of creative employment clustering following a multidisciplinary approach based on cultural and creative economics, evolutionary geography and urban economics. A comparative analysis has been performed for Italy and Spain. The results s...

  9. Obstructive Sleep Apnea: A Cluster Analysis at Time of Diagnosis

    Science.gov (United States)

    Grillet, Yves; Richard, Philippe; Stach, Bruno; Vivodtzev, Isabelle; Timsit, Jean-Francois; Lévy, Patrick; Tamisier, Renaud; Pépin, Jean-Louis

    2016-01-01

    Background The classification of obstructive sleep apnea is on the basis of sleep study criteria that may not adequately capture disease heterogeneity. Improved phenotyping may improve prognosis prediction and help select therapeutic strategies. Objectives: This study used cluster analysis to investigate the clinical clusters of obstructive sleep apnea. Methods An ascending hierarchical cluster analysis was performed on baseline symptoms, physical examination, risk factor exposure and co-morbidities from 18,263 participants in the OSFP (French national registry of sleep apnea). The probability for criteria to be associated with a given cluster was assessed using odds ratios, determined by univariate logistic regression. Results: Six clusters were identified, in which patients varied considerably in age, sex, symptoms, obesity, co-morbidities and environmental risk factors. The main significant differences between clusters were minimally symptomatic versus sleepy obstructive sleep apnea patients, lean versus obese, and among obese patients different combinations of co-morbidities and environmental risk factors. Conclusions Our cluster analysis identified six distinct clusters of obstructive sleep apnea. Our findings underscore the high degree of heterogeneity that exists within obstructive sleep apnea patients regarding clinical presentation, risk factors and consequences. This may help in both research and clinical practice for validating new prevention programs, in diagnosis and in decisions regarding therapeutic strategies. PMID:27314230

  10. OCAAT: automated analysis of star cluster colour-magnitude diagrams for gauging the local distance scale

    Science.gov (United States)

    Perren, Gabriel I.; Vázquez, Ruben A.; Piatti, Andrés E.; Moitinho, André

    2014-05-01

    Star clusters are among the fundamental astrophysical objects used in setting the local distance scale. Despite its crucial importance, the accurate determination of the distances to the Magellanic Clouds (SMC/LMC) remains a fuzzy step in the cosmological distance ladder. The exquisite astrometry of the recently launched ESA Gaia mission is expected to deliver extremely accurate statistical parallaxes, and thus distances, to the SMC/LMC. However, an independent SMC/LMC distance determination via main sequence fitting of star clusters provides an important validation check point for the Gaia distances. This has been a valuable lesson learnt from the famous Hipparcos Pleiades distance discrepancy problem. Current observations will allow hundreds of LMC/SMC clusters to be analyzed in this light. Today, the most common approach for star cluster main sequence fitting is still by eye. The process is intrinsically subjective and affected by large uncertainties, especially when applied to poorly populated clusters. It is also, clearly, not an efficient route for addressing the analysis of hundreds, or thousands, of star clusters. These concerns, together with a new attitude towards advanced statistical techniques in astronomy and the availability of powerful computers, have led to the emergence of software packages designed for analyzing star cluster photometry. With a few rare exceptions, those packages are not publicly available. Here we present OCAAT (Open Cluster Automated Analysis Tool), a suite of publicly available open source tools that fully automatises cluster isochrone fitting. The code will be applied to a large set of hundreds of open clusters observed in the Washington system, located in the Milky Way and the Magellanic Clouds. This will allow us to generate an objective and homogeneous catalog of distances up to ~ 60 kpc along with its associated reddening, ages and metallicities and uncertainty estimates.

  11. 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.

  12. Bayesian Analysis of Two Stellar Populations in Galactic Globular Clusters. II. NGC 5024, NGC 5272, and NGC 6352

    Science.gov (United States)

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

    2016-07-01

    We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival Advanced Camera for Surveys 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 ˜0.05 to 0.11 for these three clusters. Model grids with solar α-element abundances ([α/Fe] = 0.0) and enhanced α-elements ([α/Fe] = 0.4) are adopted.

  13. Bayesian Analysis of Two Stellar Populations in Galactic Globular Clusters. II. NGC 5024, NGC 5272, and NGC 6352

    Science.gov (United States)

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

    2016-07-01

    We use Cycle 21 Hubble Space Telescope (HST) observations and HST archival Advanced Camera for Surveys 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 ∼0.05 to 0.11 for these three clusters. Model grids with solar α-element abundances ([α/Fe] = 0.0) and enhanced α-elements ([α/Fe] = 0.4) are adopted.

  14. 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.

  15. 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.

  16. Advanced Power System Analysis Capabilities

    Science.gov (United States)

    1997-01-01

    As a continuing effort to assist in the design and characterization of space power systems, the NASA Lewis Research Center's Power and Propulsion Office developed a powerful computerized analysis tool called System Power Analysis for Capability Evaluation (SPACE). This year, SPACE was used extensively in analyzing detailed operational timelines for the International Space Station (ISS) program. SPACE was developed to analyze the performance of space-based photovoltaic power systems such as that being developed for the ISS. It is a highly integrated tool that combines numerous factors in a single analysis, providing a comprehensive assessment of the power system's capability. Factors particularly critical to the ISS include the orientation of the solar arrays toward the Sun and the shadowing of the arrays by other portions of the station.

  17. Advanced microtechnologies for cytogenetic analysis

    DEFF Research Database (Denmark)

    Kwasny, Dorota; Vedarethinam, Indumathi; Shah, Pranjul Jaykumar; Dimaki, Maria; Silahtaroglu, Asli; Tumer, Zeynep; Svendsen, Winnie Edith

    2012-01-01

    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 to the...... hypotonic solution and fixative. These are commonly used solutions required for proper preparation of a metaphase chromosomes analysis. Further we developed a microfluidic chip for preparation of metaphase chromosome spreads and their analysis by metaphase FISH on chip. All developed devices are capable of...

  18. Advanced Calculus An Introduction to Linear Analysis

    CERN Document Server

    Richardson, Leonard F

    2008-01-01

    Features an introduction to advanced calculus and highlights its inherent concepts from linear algebra. Advanced Calculus reflects the unifying role of linear algebra in an effort to smooth readers' transition to advanced mathematics. The book fosters the development of complete theorem-proving skills through abundant exercises while also promoting a sound approach to the study. The traditional theorems of elementary differential and integral calculus are rigorously established, presenting the foundations of calculus in a way that reorients thinking toward modern analysis. Following an introdu

  19. 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

  20. 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/.

  1. 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.

  2. Cluster analysis for anomaly detection in accounting data : an audit approach

    OpenAIRE

    Thiprungsri, Sutapat; Vasarhelyi, Miklos A.

    2011-01-01

    This study examines the application of cluster analysis in the accounting domain, particularly discrepancy detection in audit. Cluster analysis groups data so that points within a single group or cluster are similar to one another and distinct from points in other clusters. Clustering has been shown to be a good candidate for anomaly detection. The purpose of this study is to examine the use of clustering technology to automate fraud filtering during an audit. We use cluster analysis to help ...

  3. Atlas-guided cluster analysis of large tractography datasets.

    Science.gov (United States)

    Ros, Christian; Güllmar, Daniel; Stenzel, Martin; Mentzel, Hans-Joachim; Reichenbach, Jürgen Rainer

    2013-01-01

    Diffusion Tensor Imaging (DTI) and fiber tractography are important tools to map the cerebral white matter microstructure in vivo and to model the underlying axonal pathways in the brain with three-dimensional fiber tracts. As the fast and consistent extraction of anatomically correct fiber bundles for multiple datasets is still challenging, we present a novel atlas-guided clustering framework for exploratory data analysis of large tractography datasets. The framework uses an hierarchical cluster analysis approach that exploits the inherent redundancy in large datasets to time-efficiently group fiber tracts. Structural information of a white matter atlas can be incorporated into the clustering to achieve an anatomically correct and reproducible grouping of fiber tracts. This approach facilitates not only the identification of the bundles corresponding to the classes of the atlas; it also enables the extraction of bundles that are not present in the atlas. The new technique was applied to cluster datasets of 46 healthy subjects. Prospects of automatic and anatomically correct as well as reproducible clustering are explored. Reconstructed clusters were well separated and showed good correspondence to anatomical bundles. Using the atlas-guided cluster approach, we observed consistent results across subjects with high reproducibility. In order to investigate the outlier elimination performance of the clustering algorithm, scenarios with varying amounts of noise were simulated and clustered with three different outlier elimination strategies. By exploiting the multithreading capabilities of modern multiprocessor systems in combination with novel algorithms, our toolkit clusters large datasets in a couple of minutes. Experiments were conducted to investigate the achievable speedup and to demonstrate the high performance of the clustering framework in a multiprocessing environment. PMID:24386292

  4. 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

  5. Development of advanced PWR system analysis technology

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, Y. D.; Kim, S. O.; Jung, B. D.; Kim, Y. I.; Chang, M. H.; Lee, Y. J.; Yun, J. H.

    1997-12-31

    The scope of this project is to establish the basic analysis technologies for the advanced designed with the passive and inherent safety concepts. The scope is extended to the application of these technologies to the performance and safety analysis of the passive reactor. Since the different design concepts are applied depending on the reactor power, the study is conducted for the small and medium sized integral reactor as well as the large scale passive reactors by focusing on the analysis technology development for the passive components. The design concepts which can be applied for the safety enhancement of the domestic advanced reactor are developed through evaluating the technical information of the overseas advanced reactor concepts.

  6. Using cluster analysis to organize and explore regional GPS velocities

    Science.gov (United States)

    Simpson, Robert W.; Thatcher, Wayne; Savage, James C.

    2012-01-01

    Cluster analysis offers a simple visual exploratory tool for the initial investigation of regional Global Positioning System (GPS) velocity observations, which are providing increasingly precise mappings of actively deforming continental lithosphere. 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, can be subjective and is often guided by the distribution of known faults. To illustrate our method, we apply cluster analysis to GPS velocities from the San Francisco Bay Region, California, to search for spatially coherent patterns of deformation, including evidence of block-like behavior. The clustering process identifies four robust groupings of velocities that we identify with four crustal blocks. Although the analysis uses no prior geologic information other than the GPS velocities, the cluster/block boundaries track three major faults, both locked and creeping.

  7. 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

  8. Statistical fractal analysis of 25 young star clusters

    CERN Document Server

    Gregorio-Hetem, J; Santos-Silva, T; Fernandes, B

    2015-01-01

    A large sample of young stellar groups is analysed aiming to investigate their clustering properties and dynamical evolution. A comparison of the Q statistical parameter, measured for the clusters, with the fractal dimension estimated for the projected clouds shows that 52% of the sample has substructures and tends to follow the theoretically expected relation between clusters and clouds, according to calculations for artificial distribution of points. The fractal statistics was also compared to structural parameters revealing that clusters having radial density profile show a trend of parameter s increasing with mean surface stellar density. The core radius of the sample, as a function of age, follows a distribution similar to that observed in stellar groups of Milky Way and other galaxies. They also have dynamical age, indicated by their crossing time that is similar to unbound associations. The statistical analysis allowed us to separate the sample into two groups showing different clustering characteristi...

  9. Entropic Approach to Multiscale Clustering Analysis

    OpenAIRE

    Antonio Insolia; Manlio De Domenico

    2012-01-01

    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, med...

  10. 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...

  11. 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, ...

  12. A Comparative Analysis of Density Based Clustering Techniques for Outlier Mining

    OpenAIRE

    R.Prabahari*,; Dr.V.Thiagarasu

    2014-01-01

    Density based Clustering Algorithms such as Density Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and DENsity based CLUstering (DENCLUE) are designed to discover clusters of arbitrary shape. DBSCAN grows clusters according to a density based connectivity analysis. OPTICS, which is an extension of DBSCAN used to produce clusters ordering obtained by setting range of parameter. DENCLUE clusters object ...

  13. 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.

  14. 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.

  15. 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.

  16. Performance Analysis of Enhanced Clustering Algorithm for Gene Expression Data

    CERN Document Server

    Chandrasekhar, T; Elayaraja, E

    2011-01-01

    Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this paper we applied K-Means with Automatic Generations of Merge Factor for ISODATA- AGMFI. Though AGMFI has been applied for clustering of Gene Expression Data, this proposed Enhanced Automatic Generations of Merge Factor for ISODATA- EAGMFI Algorithms overcome the drawbacks of AGMFI in terms of specifying the optimal number of clusters and initialization of good cluster centroids. Experimental results on Gene Expression Data show that the proposed EAGMFI algorithms could identify compact clus...

  17. 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.

  18. Prospective assessment of the prognostic value of circulating tumor cells and their clusters in patients with advanced-stage breast cancer.

    Science.gov (United States)

    Mu, Zhaomei; Wang, Chun; Ye, Zhong; Austin, Laura; Civan, Jesse; Hyslop, Terry; Palazzo, Juan P; Jaslow, Rebecca; Li, Bingshan; Myers, Ronald E; Jiang, Juntao; Xing, Jinliang; Yang, Hushan; Cristofanilli, Massimo

    2015-12-01

    The enumeration of circulating tumor cells (CTCs) provides important prognostic values in patients with metastatic breast cancer. Recent studies indicate that individual CTCs form clusters and these CTC-clusters play an important role in tumor metastasis. We aimed to assess whether quantification of CTC-clusters provides additional prognostic value over quantification of individual CTCs alone. In 115 prospectively enrolled advanced-stage (III and IV) breast cancer patients, CTCs and CTC-clusters were counted in 7.5 ml whole blood using the CellSearch system at baseline before first-line therapy. The individual and joint effects of CTC and CTC cluster counts on patients' progression-free survival (PFS) were analyzed using Cox proportional hazards modeling. Of the 115 patients, 36 (31.3 %) had elevated baseline CTCs (≥5 CTCs/7.5 ml) and 20 (17.4 %) had CTC-clusters (≥2 CTCs/7.5 ml). Patients with elevated CTCs and CTC-clusters both had worse PFS with a hazard ratio (HR) of 2.76 [95 % confidence interval (CI) 1.57-4.86, P log-rank = 0.0005] and 2.83 (1.48-5.39, P log-rank = 0.001), respectively. In joint analysis, compared with patients with IBC), the most aggressive form of breast cancer with the poorest survival. Baseline counts of both individual CTCs and CTC-clusters were associated with PFS in advanced-stage breast cancer patients. CTC-clusters might provide additional prognostic value compared with CTC enumeration alone, in patients with elevated CTCs. PMID:26573830

  19. Polyhydrido Copper Clusters: Synthetic Advances, Structural Diversity, and Nanocluster-to-Nanoparticle Conversion.

    Science.gov (United States)

    Dhayal, Rajendra S; van Zyl, Werner E; Liu, C W

    2016-01-19

    Metal hydride clusters have historically been studied to unravel their aesthetically pleasing molecular structures and interesting properties, especially toward hydrogen related applications. Central to this work is the hydride ligand, H¯, the smallest closed-shell spherical anion known. Two new developments in polyhydrido nanocluster chemistry include the determination of heretofore unknown hydride coordination modes and novel structural constructs, and conversion from the molecular entities to rhombus-shaped copper nanoparticles (CuNPs). These advances, together with hydrogen evolution and catalysis, have provided both experimentalists and theorists with a rich scientific directive to further explore. The isolation of hexameric [{(Ph3P)CuH}6] (Stryker reagent) could be regarded as the springboard for the recent emergence of polyhydrido copper cluster chemistry due to its utilization in a variety of organic chemical transformations. The stability of clusters of various nuclearity was improved through phosphine, pyridine, and carbene type ligands. Our focus lies with the isolation of novel copper (poly)hydride clusters using mostly the phosphor-1,1-dithiolato type ligands. We found such chalcogen-stabilized clusters to be exceptionally air and moisture stable over a wide range of nuclearities (Cu7 to Cu32). In this Account, we (i) report on state-of-the-art copper hydride cluster chemistry, especially with regards to the diverse and novel structural types generally, and newly discovered hydride coordination modes in particular, (ii) demonstrate the indispensable power of neutron diffraction for the unambiguous assignment and location of hydride ligand(s) within a cluster, and (iii) prove unique transformations that can occur not only between well characterized high nuclearity clusters, but also how such clusters can transform to uniquely shaped nanoparticles of several nanometers in diameter through copper hydride reduction. The increase in the number of low- to

  20. Three Systems of Insular Functional Connectivity Identified with Cluster Analysis

    OpenAIRE

    Deen, Ben; Pitskel, Naomi B.; Kevin A. Pelphrey

    2010-01-01

    Despite much research on the function of the insular cortex, few studies have investigated functional subdivisions of the insula in humans. The present study used resting-state functional connectivity magnetic resonance imaging (MRI) to parcellate the human insular lobe based on clustering of functional connectivity patterns. Connectivity maps were computed for each voxel in the insula based on resting-state functional MRI (fMRI) data and segregated using cluster analysis. We identified 3 ins...

  1. An Ontological-Fuzzy Approach to Advance Reservation in Multi-Cluster Grids

    International Nuclear Information System (INIS)

    Advance reservation is an important mechanism for a successful utilization of available resources in distributed multi-cluster environments. This mechanism allows, for example, a user to provide parameters aiming to satisfy requirements related to applications' execution time and temporal dependence. This predictability can lead the system to reach higher levels of QoS. However, the support for advance reservation has been restricted due to the complexity of large scale configurations and also dynamic changes verified in these systems. In this research work it is proposed an advance reservation method, based on a ontology-fuzzy approach. It allows a user to reserve a wide variety of resources and enable large jobs to be reserved among different nodes. In addition, it dynamically verifies the possibility of reservation with the local RMS, avoiding future allocation conflicts. Experimental results of the proposal, through simulation, indicate that the proposed mechanism reached a successful level of flexibility for large jobs and more appropriated distribution of resources in a distributed multi-cluster configuration.

  2. 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.

  3. Fuzzy clustering analysis to study geomagnetic coastal effects

    Directory of Open Access Journals (Sweden)

    M. Sridharan

    2005-06-01

    Full Text Available The utility of fuzzy set theory in cluster analysis and pattern recognition has been evolving since the mid 1960s, in conjunction with the emergence and evolution of computer technology. The classification of objects into categories is the subject of cluster analysis. The aim of this paper is to employ Fuzzy-clustering technique to examine the interrelationship of geomagnetic coastal and other effects at Indian observatories. Data from the observatories used for the present studies are from Alibag on the West Coast, Visakhapatnam and Pondicherry on the East Coast, Hyderabad and Nagpur as central inland stations which are located far from either of the coasts; all the above stations are free from the influence of the daytime equatorial electrojet. It has been found that Alibag and Pondicherry Observatories form a separate cluster showing anomalous variations in the vertical (Z-component. H- and D-components form different clusters. The results are compared with the graphical method. Analytical technique and the results of Fuzzy-clustering analysis are discussed here.

  4. Multivariate Analysis of the Globular Clusters in M87

    Science.gov (United States)

    Das, Sukanta; Chattopadhayay, Tanuka; Davoust, Emmanuel

    2015-11-01

    An objective classification of 147 globular clusters (GCs) 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 (ICA) is used to determine a set of independent variables that are linear combinations of various observed parameters (mostly Lick indices) of the GCs. Next, K-means cluster analysis (CA) is applied on the independent components (ICs), to find the optimum number of homogeneous groups having an underlying structure. The properties of the four groups of GCs 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 other groups of GCs formed. Evidence is given for a blue stellar population in the more metal rich clusters, which we interpret by Helium enrichment. Finally, it is found that the clusters of M87 differ in some of their chemical properties (NaD, TiO1, light-element abundances) from GCs in our Galaxy and M31.

  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. 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...

  7. Towards eliminating bias in cluster analysis of TB genotyped data.

    Science.gov (United States)

    van Schalkwyk, Cari; Cule, Madeleine; Welte, Alex; van Helden, Paul; van der Spuy, Gian; Uys, Pieter

    2012-01-01

    The relative contributions of transmission and reactivation of latent infection to TB cases observed clinically has been reported in many situations, but always with some uncertainty. Genotyped data from TB organisms obtained from patients have been used as the basis for heuristic distinctions between circulating (clustered strains) and reactivated infections (unclustered strains). Naïve methods previously applied to the analysis of such data are known to provide biased estimates of the proportion of unclustered cases. The hypergeometric distribution, which generates probabilities of observing clusters of a given size as realized clusters of all possible sizes, is analyzed in this paper to yield a formal estimator for genotype cluster sizes. Subtle aspects of numerical stability, bias, and variance are explored. This formal estimator is seen to be stable with respect to the epidemiologically interesting properties of the cluster size distribution (the number of clusters and the number of singletons) though it does not yield satisfactory estimates of the number of clusters of larger sizes. The problem that even complete coverage of genotyping, in a practical sampling frame, will only provide a partial view of the actual transmission network remains to be explored. PMID:22479534

  8. Cluster analysis of radionuclide concentrations in beach sand

    NARCIS (Netherlands)

    de Meijer, R.J.; James, I.; Jennings, P.J.; Keoyers, J.E.

    2001-01-01

    This paper presents a method in which natural radionuclide concentrations of beach sand minerals are traced along a stretch of coast by cluster analysis. This analysis yields two groups of mineral deposit with different origins. The method deviates from standard methods of following dispersal of rad

  9. 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

    , and improve their quality of life. METHODS/DESIGN: We will study the effects of the ACP program Respecting Choices on the quality of life of patients with advanced lung or colorectal cancer. In a phase III multicenter cluster randomised controlled trial, 22 hospitals in 6 countries will be randomised...... of their disease trajectory, is an important next step in an era of increased focus on patient centered healthcare and shared decision-making. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number: ISRCTN63110516 . Date of registration: 10/3/2014....

  10. Using ICD for structural analysis of clusters: a case study on NeAr clusters

    International Nuclear Information System (INIS)

    We present a method to utilize interatomic Coulombic decay (ICD) to retrieve information about the mean geometric structures of heteronuclear clusters. It is based on observation and modelling of competing ICD channels, which involve the same initial vacancy, but energetically different final states with vacancies in different components of the cluster. Using binary rare gas clusters of Ne and Ar as an example, we measure the relative intensity of ICD into (Ne+)2 and Ne+Ar+ final states with spectroscopically well separated ICD peaks. We compare in detail the experimental ratios of the Ne–Ne and Ne–Ar ICD contributions and their positions and widths to values calculated for a diverse set of possible structures. We conclude that NeAr clusters exhibit a core–shell structure with an argon core surrounded by complete neon shells and, possibly, further an incomplete shell of neon atoms for the experimental conditions investigated. Our analysis allows one to differentiate between clusters of similar size and stochiometric Ar content, but different internal structure. We find evidence for ICD of Ne 2s−1, producing Ar+ vacancies in the second coordination shell of the initial site. (paper)

  11. Performance Analysis of Enhanced Clustering Algorithm for Gene Expression Data

    Directory of Open Access Journals (Sweden)

    T. Chandrasekhar

    2011-11-01

    Full Text Available Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this paper we applied K-Means with Automatic Generations of Merge Factor for ISODATA- AGMFI. Though AGMFI has been applied for clustering of Gene Expression Data, this proposed Enhanced Automatic Generations of Merge Factor for ISODATA- EAGMFI Algorithms overcome the drawbacks of AGMFI in terms of specifying the optimal number of clusters and initialization of good cluster centroids. Experimental results on Gene Expression Data show that the proposed EAGMFI algorithms could identify compact clusters with perform well in terms of the Silhouette Coefficients cluster measure.

  12. 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...

  13. Clustering and Feature Selection using Sparse Principal Component Analysis

    CERN Document Server

    Luss, Ronny

    2007-01-01

    In this paper, we use sparse principal component analysis (PCA) to solve clustering and feature selection problems. Sparse PCA seeks sparse factors, or linear combinations of the data variables, explaining a maximum amount of variance in the data while having only a limited number of nonzero coefficients. PCA is often used as a simple clustering technique and sparse factors allow us here to interpret the clusters in terms of a reduced set of variables. We begin with a brief introduction and motivation on sparse PCA and detail our implementation of the algorithm in d'Aspremont et al. (2005). We finish by describing the application of sparse PCA to clustering and by a brief description of DSPCA, the numerical package used in these experiments.

  14. 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

  15. 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

  16. 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.

  17. Climatology of Mexico: a Description Based on Clustering Analysis

    Science.gov (United States)

    Pineda-Martinez, L. F.; Carbajal, N.

    2007-05-01

    Climate regions of Mexico are delimitated using hierarchical clustering analysis (HCA). We assign the variables, precipitation and temperature, to groups or clusters based on similar statistical characteristics. Since meteorological stations in Mexico expose a heterogonous geographic distribution, we used principal components analysis (PCA) to obtain a standardized reduced matrix to apply conveniently HCA. We consider monthly means of maxima and minima temperature and monthly accumulated precipitation from a meteorological dataset of the National Water Commission of Mexico. It allows defining groups of station delimiting regions of similar climate. It also allows describing the regional effect of events such as the Mexican monsoon and ENSO.

  18. Traffic Accident, System Model and Cluster Analysis in GIS

    Directory of Open Access Journals (Sweden)

    Veronika Vlčková

    2015-07-01

    Full Text Available One of the many often frequented topics as normal journalism, so the professional public, is the problem of traffic accidents. This article illustrates the orientation of considerations to a less known context of accidents, with the help of constructive systems theory and its methods, cluster analysis and geoinformation engineering. Traffic accident is reframing the space-time, and therefore it can be to study with tools of technology of geographic information systems. The application of system approach enabling the formulation of the system model, grabbed by tools of geoinformation engineering and multicriterial and cluster analysis.

  19. Debugging, Advanced Debugging and Runtime Analysis

    OpenAIRE

    Salim Istyaq; Aufaq Zargar

    2010-01-01

    This paper discusses debugging and runtime analysis of software and outlines its enormous benefits to software developers and testers. A debugger is usually quite helpful in tracking down many logic problems. However, even with the most advanced debugger at your disposal, it doesn't guarantee that it will be a straightforward task to rid your program of bugs. Debugging techniques might help you in your task of flushing errors out of your program. Some of these willdirectly involve the debugge...

  20. 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

  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. Advanced Fuel Cycle Economic Sensitivity Analysis

    Energy Technology Data Exchange (ETDEWEB)

    David Shropshire; Kent Williams; J.D. Smith; Brent Boore

    2006-12-01

    A fuel cycle economic analysis was performed on four fuel cycles to provide a baseline for initial cost comparison using the Gen IV Economic Modeling Work Group G4 ECON spreadsheet model, Decision Programming Language software, the 2006 Advanced Fuel Cycle Cost Basis report, industry cost data, international papers, the nuclear power related cost study from MIT, Harvard, and the University of Chicago. The analysis developed and compared the fuel cycle cost component of the total cost of energy for a wide range of fuel cycles including: once through, thermal with fast recycle, continuous fast recycle, and thermal recycle.

  4. Advanced Fuel Cycle Economic Sensitivity Analysis

    International Nuclear Information System (INIS)

    A fuel cycle economic analysis was performed on four fuel cycles to provide a baseline for initial cost comparison using the Gen IV Economic Modeling Work Group G4 ECON spreadsheet model, Decision Programming Language software, the 2006 Advanced Fuel Cycle Cost Basis report, industry cost data, international papers, the nuclear power related cost study from MIT, Harvard, and the University of Chicago. The analysis developed and compared the fuel cycle cost component of the total cost of energy for a wide range of fuel cycles including: once through, thermal with fast recycle, continuous fast recycle, and thermal recycle

  5. 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.

  6. 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.

  7. Cluster analysis of WIBS single-particle bioaerosol data

    OpenAIRE

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

    2013-01-01

    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...

  8. 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...

  9. 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:…

  10. Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches

    OpenAIRE

    Bolin, Jocelyn H.; Edwards, Julianne M.; Finch, W. Holmes; Cassady, Jerrell C.

    2014-01-01

    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 cluster...

  11. Analysis of urban traffic patterns using clustering

    OpenAIRE

    Weijermars, Wilhelmina Adriana Maria

    2007-01-01

    Mobility is still increasing, as are its corresponding negative side effects such as congestion and air pollution. To be able to take adequate measures to minimize these side effects, it is important to obtain insight into the functioning of the traffic system. In common practice, the traffic analysis process deals with average traffic volumes. However, also the variability of traffic volumes is of crucial importance, for example with regard to travel time reliability, the robustness of the r...

  12. Clustering Analysis within Text Classification Techniques

    OpenAIRE

    Madalina ZURINI; Catalin SBORA

    2011-01-01

    The paper represents a personal approach upon the main applications of classification which are presented in the area of knowledge based society by means of methods and techniques widely spread in the literature. Text classification is underlined in chapter two where the main techniques used are described, along with an integrated taxonomy. The transition is made through the concept of spatial representation. Having the elementary elements of geometry and the artificial intelligence analysis,...

  13. 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.

  14. K-means cluster analysis and seismicity partitioning for Pakistan

    Science.gov (United States)

    Rehman, Khaista; Burton, Paul W.; Weatherill, Graeme A.

    2014-07-01

    Pakistan and the western Himalaya is a region of high seismic activity located at the triple junction between the Arabian, Eurasian and Indian plates. Four devastating earthquakes have resulted in significant numbers of fatalities in Pakistan and the surrounding region in the past century (Quetta, 1935; Makran, 1945; Pattan, 1974 and the recent 2005 Kashmir earthquake). It is therefore necessary to develop an understanding of the spatial distribution of seismicity and the potential seismogenic sources across the region. This forms an important basis for the calculation of seismic hazard; a crucial input in seismic design codes needed to begin to effectively mitigate the high earthquake risk in Pakistan. The development of seismogenic source zones for seismic hazard analysis is driven by both geological and seismotectonic inputs. Despite the many developments in seismic hazard in recent decades, the manner in which seismotectonic information feeds the definition of the seismic source can, in many parts of the world including Pakistan and the surrounding regions, remain a subjective process driven primarily by expert judgment. Whilst much research is ongoing to map and characterise active faults in Pakistan, knowledge of the seismogenic properties of the active faults is still incomplete in much of the region. Consequently, seismicity, both historical and instrumental, remains a primary guide to the seismogenic sources of Pakistan. This study utilises a cluster analysis approach for the purposes of identifying spatial differences in seismicity, which can be utilised to form a basis for delineating seismogenic source regions. An effort is made to examine seismicity partitioning for Pakistan with respect to earthquake database, seismic cluster analysis and seismic partitions in a seismic hazard context. A magnitude homogenous earthquake catalogue has been compiled using various available earthquake data. The earthquake catalogue covers a time span from 1930 to 2007 and

  15. Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation

    Directory of Open Access Journals (Sweden)

    Tushar H Jaware

    2013-10-01

    Full Text Available Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clusteringmethods is presented.

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

    DEFF Research Database (Denmark)

    Kent, Peter; Kongsted, Alice

    2012-01-01

    whole group, by including all SMS time points in their original form. It was a 'proof of concept' study to explore the potential, clinical relevance, strengths and weakness of such an approach. METHODS: This was a secondary analysis of longitudinal SMS data collected in two randomised controlled trials...... subgroups in the outcomes of research studies. Two previous studies have investigated detailed clinical course patterns in SMS data obtained from people seeking care for low back pain. One used a visual analysis approach and the other performed a cluster analysis of SMS data that had first been transformed...... conducted simultaneously from a single clinical population (n = 322) . Fortnightly SMS data collected over a year on 'days of problematic low back pain' and on 'days of sick leave' were analysed using Two-Step (probabilistic) Cluster Analysis. RESULTS: Clinical course patterns were identified that were...

  17. Cluster analysis of movement patterns in multiarticular actions: a tutorial.

    Science.gov (United States)

    Rein, Robert; Button, Chris; Davids, Keith; Summers, Jeffery

    2010-04-01

    The present paper proposes a technical analysis method for extracting information about movement patterning in studies of motor control, based on a cluster analysis of movement kinematics. In a tutorial fashion, data from three different experiments are presented to exemplify and validate the technical method. When applied to three different basketball-shooting techniques, the method clearly distinguished between the different patterns. When applied to a cyclical wrist supination-pronation task, the cluster analysis provided the same results as an analysis using the conventional discrete relative phase measure. Finally, when analyzing throwing performance constrained by distance to target, the method grouped movement patterns together according to throwing distance. In conclusion, the proposed technical method provides a valuable tool to improve understanding of coordination and control in different movement models, including multiarticular actions. PMID:20484771

  18. Autonomic features in cluster headache. Exploratory factor analysis

    OpenAIRE

    Gil Gouveia, R.; Parreira, E, et al.; Pavão Martins, I.

    2005-01-01

    The objective is to identify the pathogenesis of each autonomic manifestation in cluster headache (CH). Through a deductive statistics method (factor analysis) we analysed the type of autonomic symptoms reported by 157 CH patients. Three principal components were identified in the analysis: parasympathetic activation (lacrimation, conjunctival injection and rhinorrhoea), sympathetic defect (miosis and ptosis) and parasympathetic mediated effect (nasal congestion, eyelid oedema and forehead sw...

  19. A cluster analysis investigation of workaholism as a syndrome.

    Science.gov (United States)

    Aziz, Shahnaz; Zickar, Michael J

    2006-01-01

    Workaholism has been conceptualized as a syndrome although there have been few tests that explicitly consider its syndrome status. The authors analyzed a three-dimensional scale of workaholism developed by Spence and Robbins (1992) using cluster analysis. The authors identified three clusters of individuals, one of which corresponded to Spence and Robbins's profile of the workaholic (high work involvement, high drive to work, low work enjoyment). Consistent with previously conjectured relations with workaholism, individuals in the workaholic cluster were more likely to label themselves as workaholics, more likely to have acquaintances label them as workaholics, and more likely to have lower life satisfaction and higher work-life imbalance. The importance of considering workaholism as a syndrome and the implications for effective interventions are discussed. PMID:16551174

  20. 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

  1. Cosmological analysis of galaxy clusters surveys in X-rays

    International Nuclear Information System (INIS)

    Clusters of galaxies are the most massive objects in equilibrium in our Universe. Their study allows to test cosmological scenarios of structure formation with precision, bringing constraints complementary to those stemming from the cosmological background radiation, supernovae or galaxies. They are identified through the X-ray emission of their heated gas, thus facilitating their mapping at different epochs of the Universe. This report presents two surveys of galaxy clusters detected in X-rays and puts forward a method for their cosmological interpretation. Thanks to its multi-wavelength coverage extending over 10 sq. deg. and after one decade of expertise, the XMM-LSS allows a systematic census of clusters in a large volume of the Universe. In the framework of this survey, the first part of this report describes the techniques developed to the purpose of characterizing the detected objects. A particular emphasis is placed on the most distant ones (z ≥ 1) through the complementarity of observations in X-ray, optical and infrared bands. Then the X-CLASS survey is fully described. Based on XMM archival data, it provides a new catalogue of 800 clusters detected in X-rays. A cosmological analysis of this survey is performed thanks to 'CR-HR' diagrams. This new method self-consistently includes selection effects and scaling relations and provides a means to bypass the computation of individual cluster masses. Propositions are made for applying this method to future surveys as XMM-XXL and eRosita. (author)

  2. An Optical Analysis of the Merging Cluster Abell 3888

    CERN Document Server

    Shakouri, S; Dehghan, S

    2016-01-01

    In this paper we present new AAOmega spectroscopy of 254 galaxies within a 30' radius around Abell 3888. We combine these data with the existing redshifts measured in a one degree radius around the cluster and performed a substructure analysis. We confirm 71 member galaxies within the core of A3888 and determine a new average redshift and velocity dispersion for the cluster of 0.1535 +\\- 0.0009 and 1181 +\\- 197 km/s, respectively. The cluster is elongated along an East-West axis and we find the core is bimodal along this axis with two sub-groups of 26 and 41 members detected. Our results suggest that A3888 is a merging system putting to rest the previous conjecture about the morphological status of the cluster derived from X-ray observations. In addition to the results on A3888 we also present six newly detected galaxy over-densities in the field, three of which we classify as new galaxy clusters.

  3. An optical analysis of the merging cluster Abell 3888

    Science.gov (United States)

    Shakouri, S.; Johnston-Hollitt, M.; Dehghan, S.

    2016-05-01

    In this paper we present new AAOmega spectroscopy of 254 galaxies within a 30 arcmin radius around Abell 3888. We combine these data with the existing redshifts measured in a one degree radius around the cluster and performed a substructure analysis. We confirm 71 member galaxies within the core of A3888 and determine a new average redshift and velocity dispersion for the cluster of 0.1535 ± 0.0009 and 1181 ± 197 km s-1, respectively. The cluster is elongated along an East-West axis and we find the core is bimodal along this axis with two subgroups of 26 and 41 members detected. Our results suggest that A3888 is a merging system putting to rest the previous conjecture about the morphological status of the cluster derived from X-ray observations. In addition to the results on A3888 we also present six newly detected galaxy overdensities in the field, three of which we classify as new galaxy clusters.

  4. The State-Universal Multi-Reference Coupled-Cluster Theory: An Overview of Some Recent Advances

    Directory of Open Access Journals (Sweden)

    Karol Kowalski

    2002-06-01

    Full Text Available Abstract: Some recent advances in the area of multi-reference coupled-cluster theory of the state-universal type are overviewed. An emphasis is placed on the following new developments: (i the idea of combining the state-universal multi-reference coupled-cluster singles and doubles method (SUMRCCSD with the multi-reference many-body perturbation theory (MRMBPT, in which cluster amplitudes of the SUMRCCSD formalism that carry only core and virtual orbital indices are replaced by their first-order MRMBPT estimates; and (ii the idea of combining the recently proposed method of moments of coupled-cluster equations with the SUMRCC formalism. It is demonstrated that the new SUMRCCSD(1 method, obtained by approximating the SUMRCCSD cluster amplitudes carrying only core and virtual orbital indices by their first-order MRMBPT values, provides the results that are comparable to those obtained with the complete SUMRCCSD approach.

  5. The State-Universal Multi-Reference Coupled-Cluster Theory: An Overview of Some Recent Advances

    OpenAIRE

    Karol Kowalski; Piotr Piecuch

    2002-01-01

    Abstract: Some recent advances in the area of multi-reference coupled-cluster theory of the state-universal type are overviewed. An emphasis is placed on the following new developments: (i) the idea of combining the state-universal multi-reference coupled-cluster singles and doubles method (SUMRCCSD) with the multi-reference many-body perturbation theory (MRMBPT), in which cluster amplitudes of the SUMRCCSD formalism that carry only core and virtual orbital indices are replaced by their first...

  6. 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.

  7. Going beyond clustering in MD trajectory analysis: an application to villin headpiece folding.

    Directory of Open Access Journals (Sweden)

    Aruna Rajan

    Full Text Available Recent advances in computing technology have enabled microsecond long all-atom molecular dynamics (MD simulations of biological systems. Methods that can distill the salient features of such large trajectories are now urgently needed. Conventional clustering methods used to analyze MD trajectories suffer from various setbacks, namely (i they are not data driven, (ii they are unstable to noise and changes in cut-off parameters such as cluster radius and cluster number, and (iii they do not reduce the dimensionality of the trajectories, and hence are unsuitable for finding collective coordinates. We advocate the application of principal component analysis (PCA and a non-metric multidimensional scaling (nMDS method to reduce MD trajectories and overcome the drawbacks of clustering. To illustrate the superiority of nMDS over other methods in reducing data and reproducing salient features, we analyze three complete villin headpiece folding trajectories. Our analysis suggests that the folding process of the villin headpiece is structurally heterogeneous.

  8. How frequently do clusters occur in hierarchical clustering analysis? A graph theoretical approach to studying ties in proximity

    OpenAIRE

    Leal, Wilmer; Llanos, Eugenio J.; RESTREPO Guillermo; Carlos F Suárez; Patarroyo, Manuel Elkin

    2016-01-01

    Background Hierarchical cluster analysis (HCA) is a widely used classificatory technique in many areas of scientific knowledge. Applications usually yield a dendrogram from an HCA run over a given data set, using a grouping algorithm and a similarity measure. However, even when such parameters are fixed, ties in proximity (i.e. two equidistant clusters from a third one) may produce several different dendrograms, having different possible clustering patterns (different classifications). This s...

  9. The Quantitative Analysis of Chennai Automotive Industry Cluster

    Science.gov (United States)

    Bhaskaran, Ethirajan

    2016-05-01

    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

  10. 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

  11. 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.

  12. Advances in quantitative electroencephalogram analysis methods.

    Science.gov (United States)

    Thakor, Nitish V; Tong, Shanbao

    2004-01-01

    Quantitative electroencephalogram (qEEG) plays a significant role in EEG-based clinical diagnosis and studies of brain function. In past decades, various qEEG methods have been extensively studied. This article provides a detailed review of the advances in this field. qEEG methods are generally classified into linear and nonlinear approaches. The traditional qEEG approach is based on spectrum analysis, which hypothesizes that the EEG is a stationary process. EEG signals are nonstationary and nonlinear, especially in some pathological conditions. Various time-frequency representations and time-dependent measures have been proposed to address those transient and irregular events in EEG. With regard to the nonlinearity of EEG, higher order statistics and chaotic measures have been put forward. In characterizing the interactions across the cerebral cortex, an information theory-based measure such as mutual information is applied. To improve the spatial resolution, qEEG analysis has also been combined with medical imaging technology (e.g., CT, MR, and PET). With these advances, qEEG plays a very important role in basic research and clinical studies of brain injury, neurological disorders, epilepsy, sleep studies and consciousness, and brain function. PMID:15255777

  13. Applying cluster analysis to physics education research data

    Science.gov (United States)

    Springuel, R. Padraic

    One major thrust of Physics Education Research (PER) is the identification of student ideas about specific physics concepts, both correct ideas and those that differ from the expert consensus. Typically the research process of eliciting the spectrum of student ideas involves the administration of specially designed questions to students. One major analysis task in PER is the sorting of these student responses into thematically coherent groups. This process is one which has previously been done by eye in PER. This thesis explores the possibility of using cluster analysis to perform the task in a more rigorous and less time-intensive fashion while making fewer assumptions about what the students are doing. Since this technique has not previously been used in PER, a summary of the various kinds of cluster analysis is included as well as a discussion of which might be appropriate for the task of sorting student responses into groups. Two example data sets (one based on the Force and Motion Conceptual Evaluation (DICE) the other looking at acceleration in two-dimensions (A2D) are examined in depth to demonstrate how cluster analysis can be applied to PER data and the various considerations which must be taken into account when doing so. In both cases, the techniques described in this thesis found 5 groups which contained about 90% of the students in the data set. The results of this application are compared to previous research on the topics covered by the two examples to demonstrate that cluster analysis can effectively uncover the same patterns in student responses that have already been identified.

  14. A Comparative Analysis of Density Based Clustering Techniques for Outlier Mining

    Directory of Open Access Journals (Sweden)

    R.Prabahari*,

    2014-11-01

    Full Text Available Density based Clustering Algorithms such as Density Based Spatial Clustering of Applications with Noise (DBSCAN, Ordering Points to Identify the Clustering Structure (OPTICS and DENsity based CLUstering (DENCLUE are designed to discover clusters of arbitrary shape. DBSCAN grows clusters according to a density based connectivity analysis. OPTICS, which is an extension of DBSCAN used to produce clusters ordering obtained by setting range of parameter. DENCLUE clusters object is based on a set of density distribution functions. The comparison of the algorithms in terms of essential parameters such as complexity, clusters shape, input parameters, noise handle, cluster quality and run time are considered. The analysis is useful in finding which density based clustering algorithm is suitable in different criteria.

  15. 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.

  16. Euro area structural convergence? A multi-criterion cluster analysis.

    OpenAIRE

    Irac, D.; Lopez, J.

    2013-01-01

    This paper proposes a classification of the old member countries of the euro area in a structural data rich environment and run a convergence analysis using the same framework. First, we use a clustering approach and identify two structurally distinct groups of countries that are not modified between 1995 and 2007: the South Countries Group (SCG) – composed of Greece, Italy, Portugal and Spain – and the Other Countries Group (OCG). Second, we propose a convergence metrics and reach three key ...

  17. Cluster Analysis of Bioenergetic Potential in Regions of Ukraine

    OpenAIRE

    Bohdan Fedorchenko

    2014-01-01

    The research has its purpose to identify similar groups of regions most suitable for biofuel raw base development. The regions of Ukraine differ in level of economic development as well as natural and climatic conditions that are to define the possibility of growing particular crops. Thus, for evaluation of the regions bioenergetics potential the author has used cluster analysis as one of the object-features classification method. Normally, they are corn and rape that serve the raw materials ...

  18. 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.

  19. 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.

  20. On the analysis of BIS stage epochs via fuzzy clustering.

    Science.gov (United States)

    Nasibov, Efendi; Ozgören, Murat; Ulutagay, Gözde; Oniz, Adile; Kocaaslan, Sibel

    2010-06-01

    Among various types of clustering methods, partition-based methods such as k-means and FCM are widely used in the analysis of such data. However, when duration between stimuli is different, such methods are not able to provide satisfactory results because they find equal size clusters according to the fundamental running principle of these methods. In such cases, neighborhood-based clustering methods can give more satisfactory results because measurement series are separated from one another according to dramatic breaking points. In recent years, bispectral index (BIS) monitoring, which is used for monitoring the level of anesthesia, has been used in sleep studies. Sleep stages are classically scored according to the Rechtschaffen and Kales (R&K) scoring system. BIS has been shown to have a strong correlation with the R&K scoring system. In this study, fuzzy neighborhood/density-based spatial clustering of applications with noise (FN-DBSCAN) that combines speed of the DBSCAN algorithm and robustness of the NRFJP algorithm is applied to BIS measurement series. As a result of experiments, we can conclude that, by using BIS data, the FN-DBSCAN method estimates sleep stages better than the fuzzy c-means method. PMID:20156029

  1. Advancing Behavior Analysis in Zoos and Aquariums.

    Science.gov (United States)

    Maple, Terry L; Segura, Valerie D

    2015-05-01

    Zoos, aquariums, and other captive animal facilities offer promising opportunities to advance the science and practice of behavior analysis. Zoos and aquariums are necessarily concerned with the health and well-being of their charges and are held to a high standard by their supporters (visitors, members, and donors), organized critics, and the media. Zoos and aquariums offer unique venues for teaching and research and a locus for expanding the footprint of behavior analysis. In North America, Europe, and the UK, formal agreements between zoos, aquariums, and university graduate departments have been operating successfully for decades. To expand on this model, it will be necessary to help zoo and aquarium managers throughout the world to recognize the value of behavior analysis in the delivery of essential animal health and welfare services. Academic institutions, administrators, and invested faculty should consider the utility of training students to meet the growing needs of applied behavior analysis in zoos and aquariums and other animal facilities such as primate research centers, sanctuaries, and rescue centers. PMID:27540508

  2. Clustering analysis of ancient celadon based on SOM neural network

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    In the study, chemical compositions of 48 fragments of ancient ceramics excavated in 4 archaeological kiln sites which were located in 3 cities (Hangzhou, Cixi and Longquan in Zhejiang Province, China) have been examined by energy-dispersive X-ray fluorescence (EDXRF) technique. Then the method of SOM was introduced into the clustering analysis based on the major and minor element compositions of the bodies, the results manifested that 48 samples could be perfectly distributed into 3 locations, Hangzhou, Cixi and Longquan. Because the major and minor element compositions of two Royal Kilns were similar to each other, the classification accuracy over them was merely 76.92%. In view of this, the authors have made a SOM clustering analysis again based on the trace element compositions of the bodies, the classification accuracy rose to 84.61%. These results indicated that discrepancies in the trace element compositions of the bodies of the ancient ceramics excavated in two Royal Kiln sites were more distinct than those in the major and minor element compositions, which was in accordance with the fact. We argued that SOM could be employed in the clustering analysis of ancient ceramics.

  3. 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.

  4. Genomic cluster and network analysis for predictive screening for hepatotoxicity.

    Science.gov (United States)

    Fukushima, Tamio; Kikkawa, Rie; Hamada, Yoshimasa; Horii, Ikuo

    2006-12-01

    The present study was undertaken to estimate the usefulness of genomic approaches to predict hepatotoxicity. Male rats were treated with acetaminophen (APAP), carbon tetrachloride (CCL), amiodarone (AD) or tetracycline (TC) at toxic doses. Their livers were extracted 6 or 24 hr after the dosings and were used for subsequent examinations. At 6 hr there were no histological changes noted in any of the groups except for the CCL group, but at 24 hr, such changes were noted in all but the AD group. Regarding genomic analysis, we performed hierarchical cluster analysis using S-plus software. The individual microarray data were clearly classified into 5 treatment-related clusters at 24 hr as well as at 6 hr, even though no morphological changes were noted at 6 hr. In the gene expression analysis using GeneSpring, transcription factor and oxidative stress- and lipid metabolism-related genes were markedly affected in all treatment groups at both time points when compared with the corresponding control values. Finally, we investigated gene networks in the above-affected genes by using Ingenuity Pathway Analysis software. Down-regulation of lipid metabolism-related genes regulated by SREBP1 was observed in all treatment groups at both time points, and up-regulation of oxidative stress-related genes regulated by Nrf2 was observed in the APAP and CCL treatment groups. From the above findings, for the application of genomic approaches to predict hepatotoxicity, we considered that cluster analysis for classification and early prediction of hepatotoxicity and network analysis for investigation of toxicological biomarkers would be useful. PMID:17202758

  5. 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

  6. Advanced development in chemical analysis of Cordyceps.

    Science.gov (United States)

    Zhao, J; Xie, J; Wang, L Y; Li, S P

    2014-01-01

    Cordyceps sinensis, also called DongChongXiaCao (winter worm summer grass) in Chinese, is a well-known and valued traditional Chinese medicine. In 2006, we wrote a review for discussing the markers and analytical methods in quality control of Cordyceps (J. Pharm. Biomed. Anal. 41 (2006) 1571-1584). Since then this review has been cited by others for more than 60 times, which suggested that scientists have great interest in this special herbal material. Actually, the number of publications related to Cordyceps after 2006 is about 2-fold of that in two decades before 2006 according to the data from Web of Science. Therefore, it is necessary to review and discuss the advanced development in chemical analysis of Cordyceps since then. PMID:23688494

  7. 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.

  8. Advanced Technology Lifecycle Analysis System (ATLAS)

    Science.gov (United States)

    O'Neil, Daniel A.; Mankins, John C.

    2004-01-01

    Developing credible mass and cost estimates for space exploration and development architectures require multidisciplinary analysis based on physics calculations, and parametric estimates derived from historical systems. Within the National Aeronautics and Space Administration (NASA), concurrent engineering environment (CEE) activities integrate discipline oriented analysis tools through a computer network and accumulate the results of a multidisciplinary analysis team via a centralized database or spreadsheet Each minute of a design and analysis study within a concurrent engineering environment is expensive due the size of the team and supporting equipment The Advanced Technology Lifecycle Analysis System (ATLAS) reduces the cost of architecture analysis by capturing the knowledge of discipline experts into system oriented spreadsheet models. A framework with a user interface presents a library of system models to an architecture analyst. The analyst selects models of launchers, in-space transportation systems, and excursion vehicles, as well as space and surface infrastructure such as propellant depots, habitats, and solar power satellites. After assembling the architecture from the selected models, the analyst can create a campaign comprised of missions spanning several years. The ATLAS controller passes analyst specified parameters to the models and data among the models. An integrator workbook calls a history based parametric analysis cost model to determine the costs. Also, the integrator estimates the flight rates, launched masses, and architecture benefits over the years of the campaign. An accumulator workbook presents the analytical results in a series of bar graphs. In no way does ATLAS compete with a CEE; instead, ATLAS complements a CEE by ensuring that the time of the experts is well spent Using ATLAS, an architecture analyst can perform technology sensitivity analysis, study many scenarios, and see the impact of design decisions. When the analyst is

  9. Fractal Segmentation and Clustering Analysis for Seismic Time Slices

    Science.gov (United States)

    Ronquillo, G.; Oleschko, K.; Korvin, G.; Arizabalo, R. D.

    2002-05-01

    Fractal analysis has become part of the standard approach for quantifying texture on gray-tone or colored images. In this research we introduce a multi-stage fractal procedure to segment, classify and measure the clustering patterns on seismic time slices from a 3-D seismic survey. Five fractal classifiers (c1)-(c5) were designed to yield standardized, unbiased and precise measures of the clustering of seismic signals. The classifiers were tested on seismic time slices from the AKAL field, Cantarell Oil Complex, Mexico. The generalized lacunarity (c1), fractal signature (c2), heterogeneity (c3), rugosity of boundaries (c4) and continuity resp. tortuosity (c5) of the clusters are shown to be efficient measures of the time-space variability of seismic signals. The Local Fractal Analysis (LFA) of time slices has proved to be a powerful edge detection filter to detect and enhance linear features, like faults or buried meandering rivers. The local fractal dimensions of the time slices were also compared with the self-affinity dimensions of the corresponding parts of porosity-logs. It is speculated that the spectral dimension of the negative-amplitude parts of the time-slice yields a measure of connectivity between the formation's high-porosity zones, and correlates with overall permeability.

  10. A quantitative comparison of functional MRI cluster analysis.

    Science.gov (United States)

    Dimitriadou, Evgenia; Barth, Markus; Windischberger, Christian; Hornik, Kurt; Moser, Ewald

    2004-05-01

    The aim of this work is to compare the efficiency and power of several cluster analysis techniques on fully artificial (mathematical) and synthesized (hybrid) functional magnetic resonance imaging (fMRI) data sets. The clustering algorithms used are hierarchical, crisp (neural gas, self-organizing maps, hard competitive learning, k-means, maximin-distance, CLARA) and fuzzy (c-means, fuzzy competitive learning). To compare these methods we use two performance measures, namely the correlation coefficient and the weighted Jaccard coefficient (wJC). Both performance coefficients (PCs) clearly show that the neural gas and the k-means algorithm perform significantly better than all the other methods using our setup. For the hierarchical methods the ward linkage algorithm performs best under our simulation design. In conclusion, the neural gas method seems to be the best choice for fMRI cluster analysis, given its correct classification of activated pixels (true positives (TPs)) whilst minimizing the misclassification of inactivated pixels (false positives (FPs)), and in the stability of the results achieved. PMID:15182847

  11. 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,...

  12. Network clustering coefficient approach to DNA sequence analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gerhardt, Guenther J.L. [Universidade Federal do Rio Grande do Sul-Hospital de Clinicas de Porto Alegre, Rua Ramiro Barcelos 2350/sala 2040/90035-003 Porto Alegre (Brazil); Departamento de Fisica e Quimica da Universidade de Caxias do Sul, Rua Francisco Getulio Vargas 1130, 95001-970 Caxias do Sul (Brazil); Lemke, Ney [Programa Interdisciplinar em Computacao Aplicada, Unisinos, Av. Unisinos, 950, 93022-000 Sao Leopoldo, RS (Brazil); Corso, Gilberto [Departamento de Biofisica e Farmacologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, Campus Universitario, 59072 970 Natal, RN (Brazil)]. E-mail: corso@dfte.ufrn.br

    2006-05-15

    In this work we propose an alternative DNA sequence analysis tool based on graph theoretical concepts. The methodology investigates the path topology of an organism genome through a triplet network. In this network, triplets in DNA sequence are vertices and two vertices are connected if they occur juxtaposed on the genome. We characterize this network topology by measuring the clustering coefficient. We test our methodology against two main bias: the guanine-cytosine (GC) content and 3-bp (base pairs) periodicity of DNA sequence. We perform the test constructing random networks with variable GC content and imposed 3-bp periodicity. A test group of some organisms is constructed and we investigate the methodology in the light of the constructed random networks. We conclude that the clustering coefficient is a valuable tool since it gives information that is not trivially contained in 3-bp periodicity neither in the variable GC content.

  13. Network clustering coefficient approach to DNA sequence analysis

    International Nuclear Information System (INIS)

    In this work we propose an alternative DNA sequence analysis tool based on graph theoretical concepts. The methodology investigates the path topology of an organism genome through a triplet network. In this network, triplets in DNA sequence are vertices and two vertices are connected if they occur juxtaposed on the genome. We characterize this network topology by measuring the clustering coefficient. We test our methodology against two main bias: the guanine-cytosine (GC) content and 3-bp (base pairs) periodicity of DNA sequence. We perform the test constructing random networks with variable GC content and imposed 3-bp periodicity. A test group of some organisms is constructed and we investigate the methodology in the light of the constructed random networks. We conclude that the clustering coefficient is a valuable tool since it gives information that is not trivially contained in 3-bp periodicity neither in the variable GC content

  14. Steady state subchannel analysis of AHWR fuel cluster

    International Nuclear Information System (INIS)

    Subchannel analysis is a technique used to predict the thermal hydraulic behavior of reactor fuel assemblies. The rod cluster is subdivided into a number of parallel interacting flow subchannels. The conservation equations are solved for each of these subchannels, taking into account subchannel interactions. Subchannel analysis of AHWR D-5 fuel cluster has been carried out to determine the variations in thermal hydraulic conditions of coolant and fuel temperatures along the length of the fuel bundle. The hottest regions within the AHWR fuel bundle have been identified. The effect of creep on the fuel performance has also been studied. MCHFR has been calculated using Jansen-Levy correlation. The calculations have been backed by sensitivity analysis for parameters whose values are not known accurately. The sensitivity analysis showed the calculations to have a very low sensitivity to these parameters. Apart from the analysis, the report also includes a brief introduction of a few subchannel codes. A brief description of the equations and solution methodology used in COBRA-IIIC and COBRA-IV-I is also given. (author)

  15. Modified K-means Algorithm for Clustering Analysis of Hainan Green Tangerine Peel

    OpenAIRE

    Luo, Ying; Fu, Haiyan

    2014-01-01

    Part 1: Digital Services International audience K-means is a classic, the division of the clustering algorithm, apply to the classification of the globular data. According to the initial clustering center, this paper comprehensive consideration the characteristics of various Hierarchical cluster algorithms and choose the appropriate Hierarchical cluster algorithm to improve K-means, and combined with Hainan Green Tangerine Peel cluster analysis of data which is compared experiments. The...

  16. Incorporation of advanced accident analysis methodology into safety analysis reports

    International Nuclear Information System (INIS)

    The IAEA Safety Guide on Safety Assessment and Verification defines that the aim of the safety analysis should be by means of appropriate analytical tools to establish and confirm the design basis for the items important to safety, and to ensure that the overall plant design is capable of meeting the prescribed and acceptable limits for radiation doses and releases for each plant condition category. Practical guidance on how to perform accident analyses of nuclear power plants (NPPs) is provided by the IAEA Safety Report on Accident Analysis for Nuclear Power Plants. The safety analyses are performed both in the form of deterministic and probabilistic analyses for NPPs. It is customary to refer to deterministic safety analyses as accident analyses. This report discusses the aspects of using the advanced accident analysis methods to carry out accident analyses in order to introduce them into the Safety Analysis Reports (SARs). In relation to the SAR, purposes of deterministic safety analysis can be further specified as (1) to demonstrate compliance with specific regulatory acceptance criteria; (2) to complement other analyses and evaluations in defining a complete set of design and operating requirements; (3) to identify and quantify limiting safety system set points and limiting conditions for operation to be used in the NPP limits and conditions; (4) to justify appropriateness of the technical solutions employed in the fulfillment of predetermined safety requirements. The essential parts of accident analyses are performed by applying sophisticated computer code packages, which have been specifically developed for this purpose. These code packages include mainly thermal-hydraulic system codes and reactor dynamics codes meant for the transient and accident analyses. There are also specific codes such as those for the containment thermal-hydraulics, for the radiological consequences and for severe accident analyses. In some cases, codes of a more general nature such

  17. 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

  18. Intelligent Pattern Mining and Data Clustering for Pattern Cluster Analysis using Cancer Data

    OpenAIRE

    G.Raj Kumar; Dr. K. Duraiswamy; M. Thangamani; Dr. P. Thangaraj

    2010-01-01

    Data mining techniques are used for the knowledge discovery process under the large data set environment. Clustering techniques are used to group up the relevant data sets. Hierarchical and partitioned clustering techniques are used for the clustering process. The clustering process is the complex task with high process time. The pattern extraction scheme is applied to find frequent item sets. Association rule mining techniques are applied to carry out the pattern extraction process. The patt...

  19. 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.

  20. Dwarf Galaxies in the Coma Cluster; 2, Photometry and Analysis

    CERN Document Server

    Secker, J; Plummer, J D; Secker, Jeff; Harris, William E.; Plummer, Julia D.

    1997-01-01

    We study the dwarf galaxy population in the central ~700 arcmin^2 of the Coma cluster, the majority of which are early-type dwarf elliptical (dE) galaxies. Analysis of the statistically-decontaminated dE galaxy sequence in the color-magnitude diagram reveals a highly significant trend of color with magnitude (\\Delta (B-R)/\\Delta R = -0.056\\pm0.002 mag), in the sense that fainter dEs are bluer and thus presumably more metal-poor. The mean color of the faintest dEs in our sample is (B-R)~1.15 mag, consistent with a color measurement of the diffuse intracluster light in the Coma core. This intracluster light could then have originated from the tidal disruption of faint dEs in the cluster core. The total galaxy luminosity function (LF) is well modeled as the sum of a log-normal distribution for the giant galaxies, and a Schechter function for the dE galaxies with a faint-end slope \\alpha = -1.41\\pm0.05. This value of \\alpha is consistent with those measured for the Virgo and Fornax clusters. The spatial distribut...

  1. 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.

  2. 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.

  3. 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....

  4. Physicochemical properties of different corn varieties by principal components analysis and cluster analysis

    International Nuclear Information System (INIS)

    Principal components analysis and cluster analysis were used to investigate the properties of different corn varieties. The chemical compositions and some properties of corn flour which processed by drying milling were determined. The results showed that the chemical compositions and physicochemical properties were significantly different among twenty six corn varieties. The quality of corn flour was concerned with five principal components from principal component analysis and the contribution rate of starch pasting properties was important, which could account for 48.90%. Twenty six corn varieties could be classified into four groups by cluster analysis. The consistency between principal components analysis and cluster analysis indicated that multivariate analyses were feasible in the study of corn variety properties. (author)

  5. [Clustering analysis applied to near-infrared spectroscopy analysis of Chinese traditional medicine].

    Science.gov (United States)

    Liu, Mu-qing; Zhou, De-cheng; Xu, Xin-yuan; Sun, Yao-jie; Zhou, Xiao-li; Han, Lei

    2007-10-01

    The present article discusses the clustering analysis used in the near-infrared (NIR) spectroscopy analysis of Chinese traditional medicines, which provides a new method for the classification of Chinese traditional medicines. Samples selected purposely in the authors' research to measure their absorption spectra in seconds by a multi-channel NIR spectrometer developed in the authors' lab were safrole, eucalypt oil, laurel oil, turpentine, clove oil and three samples of costmary oil from different suppliers. The spectra in the range of 0.70-1.7 microm were measured with air as background and the results indicated that they are quite distinct. Qualitative mathematical model was set up and cluster analysis based on the spectra was carried out through different clustering methods for optimization, and came out the cluster correlation coefficient of 0.9742 in the authors' research. This indicated that cluster analysis of the group of samples is practicable. Also it is reasonable to get the result that the calculated classification of 8 samples was quite accorded with their characteristics, especially the three samples of costmary oil were in the closest classification of the clustering analysis. PMID:18306778

  6. Advanced Coal Wind Hybrid: Economic Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Phadke, Amol; Goldman, Charles; Larson, Doug; Carr, Tom; Rath, Larry; Balash, Peter; Yih-Huei, Wan

    2008-11-28

    Growing concern over climate change is prompting new thinking about the technologies used to generate electricity. In the future, it is possible that new government policies on greenhouse gas emissions may favor electric generation technology options that release zero or low levels of carbon emissions. The Western U.S. has abundant wind and coal resources. In a world with carbon constraints, the future of coal for new electrical generation is likely to depend on the development and successful application of new clean coal technologies with near zero carbon emissions. This scoping study explores the economic and technical feasibility of combining wind farms with advanced coal generation facilities and operating them as a single generation complex in the Western US. The key questions examined are whether an advanced coal-wind hybrid (ACWH) facility provides sufficient advantages through improvements to the utilization of transmission lines and the capability to firm up variable wind generation for delivery to load centers to compete effectively with other supply-side alternatives in terms of project economics and emissions footprint. The study was conducted by an Analysis Team that consists of staff from the Lawrence Berkeley National Laboratory (LBNL), National Energy Technology Laboratory (NETL), National Renewable Energy Laboratory (NREL), and Western Interstate Energy Board (WIEB). We conducted a screening level analysis of the economic competitiveness and technical feasibility of ACWH generation options located in Wyoming that would supply electricity to load centers in California, Arizona or Nevada. Figure ES-1 is a simple stylized representation of the configuration of the ACWH options. The ACWH consists of a 3,000 MW coal gasification combined cycle power plant equipped with carbon capture and sequestration (G+CC+CCS plant), a fuel production or syngas storage facility, and a 1,500 MW wind plant. The ACWH project is connected to load centers by a 3,000 MW

  7. Debugging, Advanced Debugging and Runtime Analysis

    Directory of Open Access Journals (Sweden)

    Salim Istyaq

    2010-03-01

    Full Text Available This paper discusses debugging and runtime analysis of software and outlines its enormous benefits to software developers and testers. A debugger is usually quite helpful in tracking down many logic problems. However, even with the most advanced debugger at your disposal, it doesn't guarantee that it will be a straightforward task to rid your program of bugs. Debugging techniques might help you in your task of flushing errors out of your program. Some of these willdirectly involve the debugger but many of them won't. The hope is to add to your debugging repertoire in order to assist your personal debugging quests when things go strangely wrong. Testing is more than just debugging. Testing is not only used to locate defects and correct them.It is also used in validation, verification process, andreliability measurement. Testing is expensive. Automation is a good way to cut down cost and time.When we write software applications, we need to debug them.Software Testing provides an objective, independent view of the software to allow the business to appreciate and understand the risks at implementation of the software. Aprimary purpose for testing is to detect software failures so that defects may be uncovered and corrected. This is a non-trivial pursuit. Testing cannot establish that a product functions properly under all conditions but can only establish that it does not function properly under specific conditions. Program testing and fault detection can beaided significantly by testing tools and debuggers. Testing and debug tools include features such as program monitors, permitting full or partial monitoring of program code including instruction set simulator, permitting complete instruction level monitoring and trace facilities, program animation, permitting step-by-step execution and conditional breakpoint at source level or in machine code, code coverage reports, formatted dump or symbolic debugging tools allowing inspection of program

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

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A; Sidtis, John J; Christiansen, Torben B; Svarer, Claus; Strother, Stephen C.; Rottenberg, David A; Hansen, Lars K; Paulson, Olaf B.; Law, I

    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...

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

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.; Sidtis, John J.; Christiansen, Torben; Svarer, Claus; Strother, Stephen C.; Rottenberg, David A.; Hansen, Lars Kai; Paulson, Olaf B.; Law, Ian

    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...

  10. Cluster Analysis for a Scale-Free Folksodriven Structure Network

    OpenAIRE

    Mas, Massimiliano Dal

    2011-01-01

    Folksonomy is said to provide a democratic tagging system that reflects the opinions of the general public, but it is not a classification system and it is hard to make sense of. It would be necessary to share a representation of contexts by all the users to develop a social and collaborative matching. The solution could be to help the users to choose proper tags thanks to a dynamical driven system of folksonomy that could evolve during the time. This paper uses a cluster analysis to measure ...

  11. Application of Cluster Analysis In Expert System - A Brief Survey

    Directory of Open Access Journals (Sweden)

    Mamta Tiwari

    2011-09-01

    Full Text Available This is era of knowledge and information. One very major task that has been evolved now a day is to mine a knowledge base. On the other hand expert systems are used extensively in many domains. There are many applications of expert systems for predicting and finding a feasible solution for any particular problem. Various tools also have been evolves for upgrading and modifying the existing expert systems and making them more useful in their intended purposes. The current paper explains the expert systems that use cluster analysis as a tool and briefly discusses few such expert systems.

  12. Minimum Information Loss Cluster Analysis for Cathegorical Data

    Czech Academy of Sciences Publication Activity Database

    Grim, Jiří; Hora, Jan

    2007-01-01

    Roč. 2007, Č. 4571 (2007), s. 233-247. ISSN 0302-9743. [International Conference on Machine Learning and Data Mining MLDM 2007 /5./. Leipzig, 18.07.2007-20.07.2007] R&D Projects: GA MŠk 1M0572; GA ČR GA102/07/1594 Grant ostatní: GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Cluster Analysis * Cathegorical Data * EM algorithm Subject RIV: BD - Theory of Information Impact factor: 0.402, year: 2005

  13. Web Log Data Analysis by Enhanced Fuzzy C Means Clustering

    OpenAIRE

    V.Chitraa; Thanamani, Antony Selvadoss

    2014-01-01

    World Wide Web is a huge repository of information and there is a tremendous increase in the volume of information daily. The number of users are also increasing day by day. To reduce users browsing time lot of research is taken place. Web Usage Mining is a type of web mining in which mining techniques are applied in log data to extract the behaviour of users. Clustering plays an important role in a broad range of applications like Web analysis, CRM, marketing, medical diagnostics, computatio...

  14. Robust Regularized Cluster Analysis for High-Dimensional Data

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan; Vlčková, Katarína

    Olomouc: Palacký University, 2014 - (Talašová, J.; Stoklasa, J.; Talášek, T.), s. 378-383 ISBN 978-80-244-4209-9. [MME 2014. International Conference Mathematical Methods in Economics /32./. Olomouc (CZ), 10.09.2014-12.09.2014] R&D Projects: GA ČR GA13-17187S Grant ostatní: GA ČR(CZ) GA13-01930S Institutional support: RVO:67985807 Keywords : cluster analysis * robust data mining * big data * regularization Subject RIV: BB - Applied Statistics, Operational Research

  15. Robust regularized cluster analysis for high-dimensional data

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan; Vlčková, Katarína

    Olomouc: Palacký University, 2014 - (Talašová, J.; Stoklasa, J.; Talášek, T.). s. 86-86 ISBN 978-80-244-4207-5. [MME 2014. International Conference Mathematical Methods in Economics /32./. 10.09.2014-12.09.2014, Olomouc] R&D Projects: GA ČR GA13-17187S Grant ostatní: GA ČR(CZ) GA13-01930S Institutional support: RVO:67985807 Keywords : cluster analysis * robust data mining * big data * regularization Subject RIV: BB - Applied Statistics, Operational Research

  16. Constraining AGN triggering mechanisms through the clustering analysis of active black holes

    Science.gov (United States)

    Gatti, M.; Shankar, F.; Bouillot, V.; Menci, N.; Lamastra, A.; Hirschmann, M.; Fiore, F.

    2016-02-01

    The triggering mechanisms for active galactic nuclei (AGN) are still debated. Some of the most popular ones include galaxy interactions (IT) and disc instabilities (DIs). Using an advanced semi-analytic model (SAM) of galaxy formation, coupled to accurate halo occupation distribution modelling, we investigate the imprint left by each separate triggering process on the clustering strength of AGN at small and large scales. Our main results are as follows: (i) DIs, irrespective of their exact implementation in the SAM, tend to fall short in triggering AGN activity in galaxies at the centre of haloes with Mh > 1013.5 h-1 M⊙. On the contrary, the IT scenario predicts abundance of active central galaxies that generally agrees well with observations at every halo mass. (ii) The relative number of satellite AGN in DIs at intermediate-to-low luminosities is always significantly higher than in IT models, especially in groups and clusters. The low AGN satellite fraction predicted for the IT scenario might suggest that different feeding modes could simultaneously contribute to the triggering of satellite AGN. (iii) Both scenarios are quite degenerate in matching large-scale clustering measurements, suggesting that the sole average bias might not be an effective observational constraint. (iv) Our analysis suggests the presence of both a mild luminosity and a more consistent redshift dependence in the AGN clustering, with AGN inhabiting progressively less massive dark matter haloes as the redshift increases. We also discuss the impact of different observational selection cuts in measuring AGN clustering, including possible discrepancies between optical and X-ray surveys.

  17. Spatial Cluster Analysis by the Adleman-Lipton DNA Computing Model and Flexible Grids

    OpenAIRE

    Xin Wang; Laisheng Xiang; Xiyu Liu

    2012-01-01

    Spatial cluster analysis is an important data-mining task. Typical techniques include CLARANS, density- and gravity-based clustering, and other algorithms based on traditional von Neumann’s computing architecture. The purpose of this paper is to propose a technique for spatial cluster analysis based on DNA computing and a grid technique. We will adopt the Adleman-Lipton model and then design a flexible grid algorithm. Examples are given to show the effect of the algorithm. The new clustering ...

  18. Tokamak advanced pump limiter experiments and analysis

    International Nuclear Information System (INIS)

    Experiments with pump limiter modules on several operating tokamaks establish such limiters as efficient collectors of particles and has demonstrated the importance of ballistic scattering as predicted theoretically. Plasma interaction with recycling neutral gas appears to become important as the plasma density increases and the effective ionization mean free path within the module decreases. In limiters with particle collection but without active internal pumping, the neutral gas pressure is found to vary nonlinearly with the edge plasma density at the highest densities studies. Both experiments and theory indicate that the energy spectrum of gas atoms in the pump ducting is non-thermal, consistent with the results of Monte Carlo neutral atom transport calculations. The distribution of plasma power over the front surface of such modules has been measured and appears to be consistent with the predictions of simple theory. Initial results from the latest experiment on the ISX-B tokamak with an actively pumped limiter module demonstrates that the core plasma density can be controlled with a pump limiter and that the scrape-off layer plasma can partially screen the core plasma from gas injection. The results from module pump limiter experiments and from the theory and design analysis of advanced pump limiters for reactors are used to suggest the major features of a definitive, axisymmetric, toroidal belt pump limiter experiment

  19. Advances in human reliability analysis in Mexico

    International Nuclear Information System (INIS)

    Human Reliability Analysis (HRA) is a very important part of Probabilistic Risk Analysis (PRA), and constant work is dedicated to improving methods, guidance and data in order to approach realism in the results as well as looking for ways to use these to reduce accident frequency at plants. Further, in order to advance in these areas, several HRA studies are being performed globally. Mexico has participated in the International HRA Empirical study with the objective of -benchmarking- HRA methods by comparing HRA predictions to actual crew performance in a simulator, as well as in the empirical study on a US nuclear power plant currently in progress. The focus of the first study was the development of an understanding of how methods are applied by various analysts, and characterize the methods for their capability to guide the analysts to identify potential human failures, and associated causes and performance shaping factors. The HRA benchmarking study has been performed by using the Halden simulator, 14 European crews, and 15 HRA equipment s (NRC, EPRI, and foreign HRA equipment s using different HRA methods). This effort in Mexico is reflected through the work being performed on updating the Laguna Verde PRA to comply with the ASME PRA standard. In order to be considered an HRA with technical adequacy, that is, be considered as a capability category II, for risk-informed applications, the methodology used for the HRA in the original PRA is not considered sufficiently detailed, and the methodology had to upgraded. The HCR/CBDT/THERP method was chosen, since this is used in many nuclear plants with similar design. The HRA update includes identification and evaluation of human errors that can occur during testing and maintenance, as well as human errors that can occur during an accident using the Emergency Operating Procedures. The review of procedures for maintenance, surveillance and operation is a necessary step in HRA and provides insight into the possible

  20. 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…

  1. Assessment of genetic divergence in tomato through agglomerative hierarchical clustering and principal component analysis

    International Nuclear Information System (INIS)

    For the improvement of qualitative and quantitative traits, existence of variability has prime importance in plant breeding. Data on different morphological and reproductive traits of 47 tomato genotypes were analyzed for correlation,agglomerative hierarchical clustering and principal component analysis (PCA) to select genotypes and traits for future breeding program. Correlation analysis revealed significant positive association between yield and yield components like fruit diameter, single fruit weight and number of fruits plant-1. Principal component (PC) analysis depicted first three PCs with Eigen-value higher than 1 contributing 81.72% of total variability for different traits. The PC-I showed positive factor loadings for all the traits except number of fruits plant-1. The contribution of single fruit weight and fruit diameter was highest in PC-1. Cluster analysis grouped all genotypes into five divergent clusters. The genotypes in cluster-II and cluster-V exhibited uniform maturity and higher yield. The D2 statistics confirmed highest distance between cluster- III and cluster-V while maximum similarity was observed in cluster-II and cluster-III. It is therefore suggested that crosses between genotypes of cluster-II and cluster-V with those of cluster-I and cluster-III may exhibit heterosis in F1 for hybrid breeding and for selection of superior genotypes in succeeding generations for cross breeding programme. (author)

  2. 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.

  3. Feasibility Study of Parallel Finite Element Analysis on Cluster-of-Clusters

    Science.gov (United States)

    Muraoka, Masae; Okuda, Hiroshi

    With the rapid growth of WAN infrastructure and development of Grid middleware, it's become a realistic and attractive methodology to connect cluster machines on wide-area network for the execution of computation-demanding applications. Many existing parallel finite element (FE) applications have been, however, designed and developed with a single computing resource in mind, since such applications require frequent synchronization and communication among processes. There have been few FE applications that can exploit the distributed environment so far. In this study, we explore the feasibility of FE applications on the cluster-of-clusters. First, we classify FE applications into two types, tightly coupled applications (TCA) and loosely coupled applications (LCA) based on their communication pattern. A prototype of each application is implemented on the cluster-of-clusters. We perform numerical experiments executing TCA and LCA on both the cluster-of-clusters and a single cluster. Thorough these experiments, by comparing the performances and communication cost in each case, we evaluate the feasibility of FEA on the cluster-of-clusters.

  4. 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.

  5. 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.

  6. 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.

  7. Integrating PROOF Analysis in Cloud and Batch Clusters

    International Nuclear Information System (INIS)

    High Energy Physics (HEP) analysis are becoming more complex and demanding due to the large amount of data collected by the current experiments. The Parallel ROOT Facility (PROOF) provides researchers with an interactive tool to speed up the analysis of huge volumes of data by exploiting parallel processing on both multicore machines and computing clusters. The typical PROOF deployment scenario is a permanent set of cores configured to run the PROOF daemons. However, this approach is incapable of adapting to the dynamic nature of interactive usage. Several initiatives seek to improve the use of computing resources by integrating PROOF with a batch system, such as Proof on Demand (PoD) or PROOF Cluster. These solutions are currently in production at Universidad de Oviedo and IFCA and are positively evaluated by users. Although they are able to adapt to the computing needs of users, they must comply with the specific configuration, OS and software installed at the batch nodes. Furthermore, they share the machines with other workloads, which may cause disruptions in the interactive service for users. These limitations make PROOF a typical use-case for cloud computing. In this work we take profit from Cloud Infrastructure at IFCA in order to provide a dynamic PROOF environment where users can control the software configuration of the machines. The Proof Analysis Framework (PAF) facilitates the development of new analysis and offers a transparent access to PROOF resources. Several performance measurements are presented for the different scenarios (PoD, SGE and Cloud), showing a speed improvement closely correlated with the number of cores used.

  8. The composite sequential clustering technique for analysis of multispectral scanner data

    Science.gov (United States)

    Su, M. Y.

    1972-01-01

    The clustering technique consists of two parts: (1) a sequential statistical clustering which is essentially a sequential variance analysis, and (2) a generalized K-means clustering. In this composite clustering technique, the output of (1) is a set of initial clusters which are input to (2) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum likelihood classification techniques. The mathematical algorithms for the composite sequential clustering program and a detailed computer program description with job setup are given.

  9. 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.

  10. The Use of Cluster Analysis in Typological Research on Community College Students

    Science.gov (United States)

    Bahr, Peter Riley; Bielby, Rob; House, Emily

    2011-01-01

    One useful and increasingly popular method of classifying students is known commonly as cluster analysis. The variety of techniques that comprise the cluster analytic family are intended to sort observations (for example, students) within a data set into subsets (clusters) that share similar characteristics and differ in meaningful ways from other…

  11. 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...

  12. Statistical analysis of plasmaspheric plumes with Cluster/WHISPER observations

    Directory of Open Access Journals (Sweden)

    F. Darrouzet

    2008-08-01

    Full Text Available Plasmaspheric plumes have been routinely observed by the four Cluster spacecraft. This paper presents a statistical analysis of plumes observed during five years (from 1 February 2001 to 1 February 2006 based on four-point measurements of the plasmasphere (outside 4 Earth radii as it is sampled by the spacecraft in a narrow local time sector before and after perigee. Plasmaspheric plumes can be identified from electron density profiles derived from the electron plasma frequency determined by the WHISPER wave sounder onboard Cluster. As the WHISPER instrument has a limited frequency range (2–80 kHz only plumes with densities below 80 cm−3 can be identified in this way. Their occurrence is studied as a function of several geomagnetic indices (Kp, am and Dst. Their transverse equatorial size, magnetic local time distribution, L position and density variation are discussed. Plasmaspheric plumes are observed mostly for moderate Kp and never for small Dst. They are found mainly in the afternoon and pre-midnight MLT sectors. Comparisons are also made between the density profiles of the plumes as they are crossed on the in- and outbound legs of the orbit, before and after perigee crossing, respectively.

  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 model...

  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. A COMPARISON BETWEEN SINGLE LINKAGE AND COMPLETE LINKAGE IN AGGLOMERATIVE HIERARCHICAL CLUSTER ANALYSIS FOR IDENTIFYING TOURISTS SEGMENTS

    OpenAIRE

    Noor Rashidah Rashid

    2012-01-01

    Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis tes...

  16. Analysis and Study of Incremental DBSCAN Clustering Algorithm

    OpenAIRE

    Chakraborty, Sanjay; Nagwani, N. K.

    2014-01-01

    This paper describes the incremental behaviours of Density based clustering. It specially focuses on the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and its incremental approach.DBSCAN relies on a density based notion of clusters.It discovers clusters of arbitrary shapes in spatial databases with noise.In incremental approach, the DBSCAN algorithm is applied to a dynamic database where the data may be frequently updated. After insertions or deletions to the ...

  17. Systemization of Design and Analysis Technology for Advanced Reactor

    International Nuclear Information System (INIS)

    The present study is performed to establish the base for the license application of the original technology by systemization and enhancement of the technology that is indispensable for the design and analysis of the advanced reactors including integral reactors. Technical reports and topical reports are prepared for this purpose on some important design/analysis methodology; design and analysis computer programs, structural integrity evaluation of main components and structures, digital I and C systems and man-machine interface design. PPS design concept is complemented reflecting typical safety analysis results. And test plans and requirements are developed for the verification of the advanced reactor technology. Moreover, studies are performed to draw up plans to apply to current or advanced power reactors the original technologies or base technologies such as patents, computer programs, test results, design concepts of the systems and components of the advanced reactors. Finally, pending issues are studied of the advanced reactors to improve the economics and technology realization

  18. 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.

  19. Ranking and clustering of search results: Analysis of Similarity graph

    OpenAIRE

    Shevchuk, Ksenia Alexander

    2008-01-01

    Evaluate the clustering of the similarity matrix and confirm that it is high. Compare the ranking results of the eigenvector ranking and the Link Popularity ranking and confirm for the high clustered graph the correlation between those is larger than for the low clustered graph.

  20. Analysis of correlation functions in multiperipheral cluster models with simultaneous emission of clusters of different multiplicity

    International Nuclear Information System (INIS)

    Experimental results indicate that the ''Mueller two-body correlation function'' f2/sup ch/) is positive at laboratory momentum above about 50 GeV/c. To obtain positive f2, we examine multiperipheral models modified to include more than one different vertex type, with more than one particle emitted at some vertices. We will show that in such models, f2 = c2 ln(s) + constant at high energy, and c2 can be positive. Furthermore, and all correlation functions f/subj/ also go as c/subj/ ln(s) at high s. As a result, the model features a special form of Koba-Nielsen-Olsen (KNO) scaling. We have made a numerical analysis of the model for clusters of up to eight particles emitted at the vertices, and we calculate the average multiplicity, the Mueller two-body correlation function, and the Mueller three-body correlation function. We obtain a favorable, though crude, fit to the experimental data

  1. Analysis of core calculation schemes for advanced water reactors

    International Nuclear Information System (INIS)

    This research thesis addresses the analysis of the core control of sub-moderated water reactors with plutonium fuel and varying spectrum. Firstly, a calculation scheme is defined, based on transport theory for the three existing assembly configurations. It is based on the efficiency analysis of the control cluster and of the flow sheet shape in the assembly. Secondly, studies of the assembly with control cluster and within a theory of diffusion with homogenization or detailed assembly representation are performed by taking the environment into account in order to assess errors. Thirdly, due to the presence of a very efficient absorbent in control clusters, a deeper physical analysis requires the study of the flow gradient existing at the interface between assemblies. A parameter is defined to assess this gradient, and theoretically calculated by using finite elements. Developed software is validated

  2. Cluster analysis for pattern recognition in solar butterfly diagrams

    Science.gov (United States)

    Illarionov, E.; Sokoloff, D.; Arlt, R.; Khlystova, A.

    2011-07-01

    We investigate to what extent the wings of solar butterfly diagrams can be separated without an explicit usage of Hale's polarity law as well as the location of the solar equator. We apply two algorithms of cluster analysis for this purpose, namely DBSCAN and C-means, and demonstrate their ability to separate the wings of contemporary butterfly diagrams based on the sunspot group density in the diagram only. Then we apply the method to historical data concerning the solar activity in the 18th century (Staudacher data). The method separates the two wings for Cycle 2, but fails to separate them for Cycle 1. In our opinion, this finding supports the interpretation of the Staudacher data as an indication of the unusual nature of the solar cycle in the 18th century.

  3. PIXE cluster analysis of ancient ceramics from North Syria

    Energy Technology Data Exchange (ETDEWEB)

    Kieft, I.E.; Jamieson, D.N. E-mail: dnj@physics.unimelb.edu.au; Rout, B.; Szymanski, R.; Jamieson, A.S

    2002-05-01

    Tell Ahmar is a place situated on the east bank of the Euphrates river, near the Turkish border. The site was well known as a major trade centre in the Iron Age. From the many potsherds excavated from the site, it is necessary to distinguish pottery imported from outside from that made locally. Therefore a sample of the Iron Age potsherds that were excavated from this site was analyzed with particle induced X-ray emission to identify the characteristic composition of the different sherds. Potsherds from four other places near Tell Ahmar were also analyzed. The samples were irradiated with a scanned 3 MeV proton beam in the Melbourne nuclear microprobe. The composition of all sherds measured by this method was similar. However, cluster analysis of the 12 most abundant elements, ranging from Mn to Ba, revealed that the samples known to be from Tell Ahmar could be distinguished from those known to be from elsewhere.

  4. Higgs pair production: choosing benchmarks with cluster analysis

    Science.gov (United States)

    Carvalho, Alexandra; Dall'Osso, Martino; Dorigo, Tommaso; Goertz, Florian; Gottardo, Carlo A.; Tosi, Mia

    2016-04-01

    New physics theories often depend on a large number of free parameters. The phenomenology they predict for fundamental physics processes is in some cases drastically affected by the precise value of those free parameters, while in other cases is left 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 predicted by different models; a clustering algorithm using that metric may allow the division of the space into homogeneous regions, each of which can be successfully represented by a benchmark point. Searches targeting those benchmarks are then guaranteed to be sensitive to a large area of the parameter space.

  5. The Galaxy Cluster RBS380 Xray and Optical Analysis

    CERN Document Server

    Gil-Merino, R

    2002-01-01

    We present X-ray and optical observations of the z=0.52 galaxy cluster RBS380. This is the most distant cluster in the ROSAT Bright Source catalog. The cluster was observed with the CHANDRA satellite in September 2000. The optical observations were carried out with the NTT-SUSI2 camara in filters V and R in August and September 2001. The preliminary conclusions are that we see a very rich optical galaxy cluster but with a relative low X-ray luminosity. We also compare our results to other clusters with similar properties.

  6. Unique Systems Analysis Task 7, Advanced Subsonic Technologies Evaluation Analysis

    Science.gov (United States)

    Eisenberg, Joseph D. (Technical Monitor); Bettner, J. L.; Stratton, S.

    2004-01-01

    To retain a preeminent U.S. position in the aircraft industry, aircraft passenger mile costs must be reduced while at the same time, meeting anticipated more stringent environmental regulations. A significant portion of these improvements will come from the propulsion system. A technology evaluation and system analysis was accomplished under this task, including areas such as aerodynamics and materials and improved methods for obtaining low noise and emissions. Previous subsonic evaluation analyses have identified key technologies in selected components for propulsion systems for year 2015 and beyond. Based on the current economic and competitive environment, it is clear that studies with nearer turn focus that have a direct impact on the propulsion industry s next generation product are required. This study will emphasize the year 2005 entry into service time period. The objective of this study was to determine which technologies and materials offer the greatest opportunities for improving propulsion systems. The goals are twofold. The first goal is to determine an acceptable compromise between the thermodynamic operating conditions for A) best performance, and B) acceptable noise and chemical emissions. The second goal is the evaluation of performance, weight and cost of advanced materials and concepts on the direct operating cost of an advanced regional transport of comparable technology level.

  7. PERFORMANCE ANALYSIS OF ENERGY EFFICIENT CLUSTERING SCHEMES FOR WSN

    Directory of Open Access Journals (Sweden)

    T.A.SHANMUGASUNDARAM

    2014-12-01

    Full Text Available Clustering enables energy constrained wireless sensor networks to consume less battery power for routing the data packets. In order to achieve the key goal of prolonging the lifetime of sensor nodes besides avoiding undesired frequent topological changes, support from clustering mechanism is much appreciable. While proposing clustering mechanisms, attention is needed the way by which the sensor nodes are deployed over the field of interest. The energy consumed by cluster heads of various clusters to collect and aggregate the sensed data from the non-CH members should be balanced over the network to the possible extent. This can not only overcome faster energy depletion of a certain number of cluster heads and improve the performance of wireless sensor networks. Instead of random selection of cluster heads, it is essential to consider the current status of nodes after each round of data transfer to assign the role of cluster heads. The key factor considered for this is the residual energy of sensor nodes. Besides this approach, uniform sensor node deployment is achievable through grid based clustering techniques. This paper is an attempt to analyze the energy efficiency perspectives of a link quality cum residual energy based clustering mechanism and a virtual-grid based clustering mechanism for efficient routing in energy constrained wireless sensor networks.

  8. 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.

  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. 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.

  11. Clustered data analysis under miscategorized ordinal outcomes and missing covariates.

    Science.gov (United States)

    Roy, Surupa; Rana, Subrata; Das, Kalyan

    2016-08-15

    The primary objective in this article is to look into the analysis of clustered ordinal model where complete information on one or more covariates cease to occur. In addition, we also focus on the analysis of miscategorized data that occur in many situations as outcomes are often classified into a category that does not truly reflect its actual state. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. The theoretical motivation actually developed while encountering an orthodontic data to investigate the effects of age, sex and food habit on the extent of plaque deposit. The model we propose is quite flexible and is capable of tackling those additional noises like miscategorization and missingness, which occur in the data most frequently. A new two-step approach has been proposed to estimate the parameters of model framed. A rigorous simulation study has also been carried out to justify the validity of the model taken up for analysis. Copyright © 2015 John Wiley & Sons, Ltd. PMID:26215983

  12. PCA and Cluster Analysis for Criteria Mapping in Landfill Siting

    Directory of Open Access Journals (Sweden)

    Kaoutar BENNIS

    2014-10-01

    Full Text Available Landfill siting is of primordial interest in waste management. As the size of modern cities grows along with the shifts in demographics and composition of solid wastes, it has become important to choose the location of a waste disposal area in a way that insures long-term usability, environmental impact minimization and other considerations; these considerations can vary greatly among cases and from it follows that the criteria involved in siting a new landfill location should be subject to a conscientious and thorough choosing process. This work is a literature review focusing on criteria used in the siting of new landfills, it took advantage of the statistical methods of data mining in order to establish patterns; it can be viewed as a guide. Starting with collection of scoop-compatible articles, an extraction of criteria from each article is done which opens the door for analysis by Principal Component Analysis (PCA, Bivariate Correlation (BC and Cluster Analysis (CA. The corroborate use of this methods and the definition of the conceptual layers of category and supercategory fortifies the statistical significances of the results.

  13. 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.

  14. The relationship between supplier networks and industrial clusters: an analysis based on the cluster mapping method

    Directory of Open Access Journals (Sweden)

    Ichiro IWASAKI

    2010-06-01

    Full Text Available Michael Porter’s concept of competitive advantages emphasizes the importance of regional cooperation of various actors in order to gain competitiveness on globalized markets. Foreign investors may play an important role in forming such cooperation networks. Their local suppliers tend to concentrate regionally. They can form, together with local institutions of education, research, financial and other services, development agencies, the nucleus of cooperative clusters. This paper deals with the relationship between supplier networks and clusters. Two main issues are discussed in more detail: the interest of multinational companies in entering regional clusters and the spillover effects that may stem from their participation. After the discussion on the theoretical background, the paper introduces a relatively new analytical method: “cluster mapping” - a method that can spot regional hot spots of specific economic activities with cluster building potential. Experience with the method was gathered in the US and in the European Union. After the discussion on the existing empirical evidence, the authors introduce their own cluster mapping results, which they obtained by using a refined version of the original methodology.

  15. TreeSOM: Cluster analysis in the self-organizing map.

    Science.gov (United States)

    Samsonova, Elena V; Kok, Joost N; Ijzerman, Ad P

    2006-01-01

    Clustering problems arise in various domains of science and engineering. A large number of methods have been developed to date. The Kohonen self-organizing map (SOM) is a popular tool that maps a high-dimensional space onto a small number of dimensions by placing similar elements close together, forming clusters. Cluster analysis is often left to the user. In this paper we present the method TreeSOM and a set of tools to perform unsupervised SOM cluster analysis, determine cluster confidence and visualize the result as a tree facilitating comparison with existing hierarchical classifiers. We also introduce a distance measure for cluster trees that allows one to select a SOM with the most confident clusters. PMID:16781116

  16. Genome-scale analysis of positional clustering of mouse testis-specific genes

    Directory of Open Access Journals (Sweden)

    Lee Bernett TK

    2005-01-01

    Full Text Available Abstract Background Genes are not randomly distributed on a chromosome as they were thought even after removal of tandem repeats. The positional clustering of co-expressed genes is known in prokaryotes and recently reported in several eukaryotic organisms such as Caenorhabditis elegans, Drosophila melanogaster, and Homo sapiens. In order to further investigate the mode of tissue-specific gene clustering in higher eukaryotes, we have performed a genome-scale analysis of positional clustering of the mouse testis-specific genes. Results Our computational analysis shows that a large proportion of testis-specific genes are clustered in groups of 2 to 5 genes in the mouse genome. The number of clusters is much higher than expected by chance even after removal of tandem repeats. Conclusion Our result suggests that testis-specific genes tend to cluster on the mouse chromosomes. This provides another piece of evidence for the hypothesis that clusters of tissue-specific genes do exist.

  17. An effective fuzzy kernel clustering analysis approach for gene expression data.

    Science.gov (United States)

    Sun, Lin; Xu, Jiucheng; Yin, Jiaojiao

    2015-01-01

    Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms. PMID:26405958

  18. 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.

  19. Cluster analysis of indermediate deep events in the southeastern Aegean

    Science.gov (United States)

    Ruscic, Marija; Becker, Dirk; Brüstle, Andrea; Meier, Thomas

    2015-04-01

    The Hellenic subduction zone (HSZ) is the seismically most active region in Europe where the oceanic African litosphere is subducting beneath the continental Aegean plate. Although there are numerous studies of seismicity in the HSZ, very few focus on the eastern HSZ and the Wadati-Benioff-Zone of the subducting slab in that part of the HSZ. In order to gain a better understanding of the geodynamic processes in the region a dense local seismic network is required. From September 2005 to March 2007, the temporary seismic network EGELADOS has been deployed covering the entire HSZ. It consisted of 56 onshore and 23 offshore broadband stations with addition of 19 stations from GEOFON, NOA and MedNet to complete the network. Here, we focus on a cluster of intermediate deep seismicity recorded by the EGELADOS network within the subducting African slab in the region of the Nysiros volcano. The cluster consists of 159 events at 80 to 190 km depth with magnitudes between 0.2 and 4.1 that were located using nonlinear location tool NonLinLoc. A double-difference earthquake relocation using the HypoDD software is performed with both manual readings of onset times and differential traveltimes obtained by separate cross correlation of P- and S-waveforms. Single event locations are compared to relative relocations. The event hypocenters fall into a thin zone close to the top of the slab defining its geometry with an accuracy of a few kilometers. At intermediate depth the slab is dipping towards the NW at an angle of about 30°. That means it is dipping steeper than in the western part of the HSZ. The edge of the slab is clearly defined by an abrupt disappearance of intermediate depths seismicity towards the NE. It is found approximately beneath the Turkish coastline. Furthermore, results of a cluster analysis based on the cross correlation of three-component waveforms are shown as a function of frequency and the spatio-temporal migration of the seismic activity is analysed.

  20. Cluster-Span Threshold: An unbiased threshold for binarising weighted complete networks in functional connectivity analysis

    OpenAIRE

    Smith, Keith; Azami, Hamed; Parra, Mario A.; Starr, John M.; Escudero, Javier

    2016-01-01

    We propose a new unbiased threshold for network analysis named the Cluster-Span Threshold (CST). This is based on the clustering coefficient, C, following logic that a balance of `clustering' to `spanning' triples results in a useful topology for network analysis and that the product of complementing properties has a unique value only when perfectly balanced. We threshold networks by fixing C at this balanced value, rather than fixing connection density at an arbitrary value, as has been the ...

  1. A generalized analysis of hydrophobic and loop clusters within globular protein sequences

    OpenAIRE

    Mornon Jean-Paul; Delettré Jean; Le Tuan Khanh; Eudes Richard; Callebaut Isabelle

    2007-01-01

    Abstract Background Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These spec...

  2. Spatial Cluster Analysis by the Bin-Packing Problem and DNA Computing Technique

    OpenAIRE

    Xiyu Liu; Jie Xue

    2013-01-01

    Spatial cluster analysis is an important data mining task. Typical techniques include CLARANS, density- and gravity-based clustering, and other algorithms based on traditional von Neumann's computing architecture. The purpose of this paper is to propose a technique for spatial cluster analysis based on sticker systems of DNA computing. We will adopt the Bin-Packing Problem idea and then design algorithms of sticker programming. The proposed technique has a better time complexity. In the case ...

  3. 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.

  4. 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)

  5. CAGE: Combinatorial Analysis of Gene-Cluster Evolution

    OpenAIRE

    Song, Giltae; Zhang, Louxin; Vinar, Tomas; Miller, Webb

    2010-01-01

    Much important evolutionary activity occurs in gene clusters, where a copy of a gene may be free to acquire new functions. Current computational methods to extract evolutionary information from sequence data for such clusters are suboptimal, in part because accurate sequence data are often lacking in these genomic regions, making existing methods difficult to apply. We describe a new method for reconstructing the recent evolutionary history of gene clusters, and evaluate its performance on bo...

  6. A functional clustering algorithm for the analysis of neural relationships

    CERN Document Server

    Feldt, S; Hetrick, V L; Berke, J D; Zochowski, M

    2008-01-01

    We formulate a novel technique for the detection of functional clusters in neural data. In contrast to prior network clustering algorithms, our procedure progressively combines spike trains and derives the optimal clustering cutoff in a simple and intuitive manner. To demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. We observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

  7. A substructure analysis of the A3558 cluster complex

    OpenAIRE

    Bardelli, S.; Pisani, A; Ramella, M.; Zucca, E.; Zamorani, G.

    1998-01-01

    The "algorithm driven by the density estimate for the identification of clusters" (DEDICA, Pisani 1993, 1996) is applied to the A3558 cluster complex in order to find substructures. This complex, located at the center of the Shapley Concentration supercluster, is a chain formed by the ACO clusters A3556, A3558 and A3562 and the two poor clusters SC 1327-312 and SC 1329-313. We find a large number of clumps, indicating that strong dynamical processes are active. In particular, it is necessary ...

  8. Stochastic analysis of the extra clustering model for animal grouping.

    Science.gov (United States)

    Drmota, Michael; Fuchs, Michael; Lee, Yi-Wen

    2016-07-01

    We consider the extra clustering model which was introduced by Durand et al. (J Theor Biol 249(2):262-270, 2007) in order to describe the grouping of social animals and to test whether genetic relatedness is the main driving force behind the group formation process. Durand and François (J Math Biol 60(3):451-468, 2010) provided a first stochastic analysis of this model by deriving (amongst other things) asymptotic expansions for the mean value of the number of groups. In this paper, we will give a much finer analysis of the number of groups. More precisely, we will derive asymptotic expansions for all higher moments and give a complete characterization of the possible limit laws. In the most interesting case (neutral model), we will prove a central limit theorem with a surprising normalization. In the remaining cases, the limit law will be either a mixture of a discrete and continuous law or a discrete law. Our results show that, except of in degenerate cases, strong concentration around the mean value takes place only for the neutral model, whereas in the remaining cases there is also mass concentration away from the mean. PMID:26520857

  9. 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. ...

  10. Advanced Software Methods for Physics Analysis

    International Nuclear Information System (INIS)

    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

  11. 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.

  12. Towards Effective Clustering Techniques for the Analysis of Electric Power Grids

    Energy Technology Data Exchange (ETDEWEB)

    Hogan, Emilie A.; Cotilla Sanchez, Jose E.; Halappanavar, Mahantesh; Wang, Shaobu; Mackey, Patrick S.; Hines, Paul; Huang, Zhenyu

    2013-11-30

    Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques on two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems.

  13. Leukaemia clusters in childhood: geographical analysis in Britain

    Energy Technology Data Exchange (ETDEWEB)

    Knox, E.G.

    1994-08-01

    Study objective - To validate previously demonstrated spatial clustering of childhood leukaemias by showing relative proximities of selected map features to cluster locations, compared with control locations. If clusters are real, then they are likely to be close to a determining hazard. Design -Cluster postcode loci and partially matched control postcodes were compared in terms of distances to railways, main roads, churches, surface water, woodland areas, and railside industrial installations. Further supporting comparisons between non-clustered cases and random postcode controls with those map features representable as single grid points were made. Setting -England, Wales, and Scotland 1966-83. Subjects - Grid referenced registrations of 9406 childhood leukaemias and non-Hodgkin`s lymphomas, including 264 pairs (or more) separated by <150 m, and grid references of random postcodes in equal numbers. Main results - the 264 clusters showed relative proximities (or the inverse) to several map features, of which the most powerful was an association with railways. The non-railway associations seemed to be statistically indirect. Some railside industrial installations, identified from a railway atlas, also showed relative proximities to leukaemia clusters, as well as to non-clustered cases, but did not ``explain`` the railway effect. These installations, with seemingly independent geographical associations, included oil refineries, petrochemical plants, oil storage and oil distribution depots, power stations, and steelworks. Conclusions - The previously shown childhood leukaemia clusters are confirmed to be non-random through their systematic associations with certain map features when compared with the control locations. The common patterns of close association of clustered and non-clustered cases imply a common aetiological component arising from a common environmental hazard - namely the use of fossil fuels, especially petroleum. (UK)

  14. Two worlds collide: Image analysis methods for quantifying structural variation in cluster molecular dynamics

    International Nuclear Information System (INIS)

    Inspired by methods of remote sensing image analysis, we analyze structural variation in cluster molecular dynamics (MD) simulations through a unique application of the principal component analysis (PCA) and Pearson Correlation Coefficient (PCC). The PCA analysis characterizes the geometric shape of the cluster structure at each time step, yielding a detailed and quantitative measure of structural stability and variation at finite temperature. Our PCC analysis captures bond structure variation in MD, which can be used to both supplement the PCA analysis as well as compare bond patterns between different cluster sizes. Relying only on atomic position data, without requirement for a priori structural input, PCA and PCC can be used to analyze both classical and ab initio MD simulations for any cluster composition or electronic configuration. Taken together, these statistical tools represent powerful new techniques for quantitative structural characterization and isomer identification in cluster MD

  15. Application and research of fuzzy clustering analysis algorithm under “micro-lecture” English teaching mode

    Directory of Open Access Journals (Sweden)

    Shi Ying

    2016-01-01

    Full Text Available The fuzzy clustering algorithm is to classify the data or indicators with a greater degree of similarity based on the principle of the same type of individuals possessing a greater similarity, and different types of individuals possessing differences, establish clear category boundaries, form any shape of relationship clusters in the solving process, and input the research indicators at random, in order to accurately analyze the significance of the indicators in the algorithm. The evaluation value of the clustering analysis can be obtained by the establishment of the fuzzy factor set based on the membership analysis, and the evaluation result can be analyzed through reference to the evaluation indicators of the fuzzy clustering analysis. The “micro-lecture” English teaching mode can be estimated and the analysis indicators can be rationally established based on the fuzzy clustering analysis algorithm, with better algorithm applicability.

  16. Shielding analysis of the advanced voloxidation process

    Energy Technology Data Exchange (ETDEWEB)

    Park, Chang Je; Park, J. J.; Lee, J. W.; Shin, J. M.; Park, G. I.; Song, K. C

    2008-09-15

    This report deals describes how much a shielding benefit can be obtained by the Advanced Voloxidation process. The calculation was performed with the MCNPX code and a simple problem was modeled with a spent fuel source which was surrounded by a concrete wall. The source terms were estimated with the ORIGEN-ARP code and the gamma spectrum and the neutron spectrum were also obtained. The thickness of the concrete wall was estimated before and after the voloxidation process. From the results, the gamma spectrum after the voloxidation process was estimated as a 67% reduction compared with that of before the voloxidation process due to the removal of several gamma emission elements such as cesium and rubidium. The MCNPX calculations provided that the thickness of the general concrete wall could be reduced by 12% after the voloxidation process. And the heavy concrete wall provided a 28% reduction in the shielding of the source term after the voloxidation process. This can be explained in that there lots of gamma emission isotopes still exist after the advanced voloxidation process such as Pu-241, Y-90, and Sr-90 which are independent of the voloxidation process.

  17. Gennclus: New Models for General Nonhierarchical Clustering Analysis.

    Science.gov (United States)

    Desarbo, Wayne S.

    1982-01-01

    A general class of nonhierarchical clustering models and associated algorithms for fitting them are presented. These models generalize the Shepard-Arabie Additive clusters model. Two applications are given and extensions to three-way models, nonmetric analyses, and other model specifications are provided. (Author/JKS)

  18. 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.

  19. ANALYSIS OF CO-OPERATION OF PARTICIPANTS FINANCIAL TO CLUSTER

    OpenAIRE

    Yagolnitskiy, A.

    2011-01-01

    In the article certainly groups of organizations, among potential participants financial to the cluster, which must be a kernel. Basic financial institutions which it is expedient to unite in a financial cluster and the degree of co-operation is set between them are selected.

  20. Alternatives to Multilevel Modeling for the Analysis of Clustered Data

    Science.gov (United States)

    Huang, Francis L.

    2016-01-01

    Multilevel modeling has grown in use over the years as a way to deal with the nonindependent nature of observations found in clustered data. However, other alternatives to multilevel modeling are available that can account for observations nested within clusters, including the use of Taylor series linearization for variance estimation, the design…

  1. Detecting Hotspots from Taxi Trajectory Data Using Spatial Cluster Analysis

    Science.gov (United States)

    Zhao, P. X.; Qin, K.; Zhou, Q.; Liu, C. K.; Chen, Y. X.

    2015-07-01

    A method of trajectory clustering based on decision graph and data field is proposed in this paper. The method utilizes data field to describe spatial distribution of trajectory points, and uses decision graph to discover cluster centres. It can automatically determine cluster parameters and is suitable to trajectory clustering. The method is applied to trajectory clustering on taxi trajectory data, which are on the holiday (May 1st, 2014), weekday (Wednesday, May 7th, 2014) and weekend (Saturday, May 10th, 2014) respectively, in Wuhan City, China. The hotspots in four hours (8:00-9:00, 12:00-13:00, 18:00-19:00 and 23:00-24:00) for three days are discovered and visualized in heat maps. In the future, we will further research the spatiotemporal distribution and laws of these hotspots, and use more data to carry out the experiments.

  2. 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

    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 by the...... blood sampling, the Simplified Reference Tissue Model (SRTM) and Logan analysis with cerebellar TAC as an input. There was a good agreement (P < 0.05) between the values of Distribution Volume (DV) obtained from the K-means-clustered input function and those from the arterial blood samples. This work...

  3. A generalized analysis of hydrophobic and loop clusters within globular protein sequences

    Directory of Open Access Journals (Sweden)

    Mornon Jean-Paul

    2007-01-01

    Full Text Available Abstract Background Hydrophobic Cluster Analysis (HCA is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet. Results The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities. Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 % reveals preferences for α-helices or β-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that β-strands and especially α-helices are generally over-predicted within the limits of typical β and α hydrophobic

  4. System Level Analysis of LTE-Advanced

    DEFF Research Database (Denmark)

    Wang, Yuanye

    , 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...... transmission. Next the technique of frequency reuse is investigated. As compared to reuse-1, using different frequency channels in neighboring cells reduces the interference to offer large performance gain. To avoid the frequency planning, several decentralized algorithms are developed for interference...... proposed. It improves the cell edge user throughput by up to 90% over the independent scheduling with full buffer transmission and 40% with finite buffer transmission, depending primarily on the ratio of LTE-Advanced users. Meanwhile, there is no loss in the average cell throughput. The channel aware...

  5. Analysis of break test of 54 cluster fuels

    Energy Technology Data Exchange (ETDEWEB)

    Matsumoto, Mitsuo; Kawamata, Nobuhiro; Kamoshida, Hiroshi

    1998-03-01

    A break test of down pipe and main steam tube of 54 cluster fuels were carried out in the Power Reactor and Nuclear Fuel Development Corporation (PNC) in fiscal 1996. The safety evaluation code for `Fugen` was investigated by analysing the break tests by RELAP 5 code. The tests were carried out by ATR safety experimental facility which was consisted of steam drum, lower header, pressure tube, inlet tube, riser, recirculation pump and non-return valve. Break is modified by breaking a rupture disk in both cases of test. Pressure, pressure difference, temperature, water level and flow rate at channel inlet were measured. The results proved the following: The safety evaluation code for `Fugen` estimated the higher temperature of cladding tube after dry out. A return model of the best evaluation for `Fugen` was confirmed to make reappear dry out and quenching phenomena of temperature behavior of cladding tube under the experimental conditions. RELAP 5 code made a reproduction of heat transfer fluid phenomena of LOCA experiment modifying break of down pipe of Fugen. The result proved that the code is also able to use for LOCA analysis of ATR system. (S.Y.)

  6. 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.

  7. Abundance analysis of the outer halo globular cluster Palomar 14

    CERN Document Server

    Caliskan, S; Grebel, K E

    2011-01-01

    We determine the elemental abundances of nine red giant stars belonging to Palomar 14 (Pal 14). Pal 14 is an outer halo globular cluster (GC) at a distance of \\sim 70 kpc. Our abundance analysis is based on high-resolution spectra and one-dimensional stellar model atmospheres.We derived the abundances for the iron peak elements Sc, V, Cr, Mn, Co, Ni, the {\\alpha}-elements O, Mg, Si, Ca, Ti, the light odd element Na, and the neutron-capture elements Y, Zr, Ba, La, Ce, Nd, Eu, Dy, and Cu. Our data do not permit us to investigate light element (i.e., O to Mg) abundance variations. The neutron-capture elements show an r-process signature. We compare our measurements with the abundance ratios of inner and other outer halo GCs, halo field stars, GCs of recognized extragalactic origin, and stars in dwarf spheroidal galaxies (dSphs). The abundance pattern of Pal 14 is almost identical to those of Pal 3 and Pal 4, the next distant members of the outer halo GC population after Pal 14. The abundance pattern of Pal 14 is...

  8. Topological Analysis of Emerging Bipole Clusters Producing Violent Solar Events

    CERN Document Server

    Mandrini, C H; Démoulin, P; Guo, Y; Cristiani, G D

    2013-01-01

    During the rising phase of Solar Cycle 24 tremendous activity occurred on the Sun with fast and compact emergence of magnetic flux leading to bursts of flares (C to M and even X-class). We investigate the violent events occurring in the cluster of two active regions (ARs), NOAA numbers 11121 and 11123, observed in November 2010 with instruments onboard the {\\it Solar Dynamics Observatory} and from Earth. Within one day the total magnetic flux increased by $70\\%$ with the emergence of new groups of bipoles in AR 11123. From all the events on 11 November, we study, in particular, the ones starting at around 07:16 UT in GOES soft X-ray data and the brightenings preceding them. A magnetic-field topological analysis indicates the presence of null points, associated separatrices and quasi-separatrix layers (QSLs) where magnetic reconnection is prone to occur. The presence of null points is confirmed by a linear and a non-linear force-free magnetic-field model. Their locations and general characteristics are similar...

  9. The Study About the Analysis of Responsiveness Pair Clustering Tosocial Network Bipartite Graph

    OpenAIRE

    Akira Otsuki,; Masayoshi Kawamura

    2013-01-01

    In this study, regional (cities, towns and villages) data and tweet data are obtained from Twitter, andextract information of "purchase information (Whereand what bought)" from the tweet data bymorphological analysis and rule-based dependency analysis. Then, the "The regional information" and the"Theinformation of purchase history (Where and what bought information)" are captured as bipartitegraph, and Responsiveness Pair Clustering analysis(a clustering using correspondence analysis assimila...

  10. The Study about the Analysis of Responsiveness Pair Clustering to Social Network Bipartite Graph

    OpenAIRE

    Otsuki, Akira; Kawamura, Masayoshi

    2013-01-01

    In this study, regional (cities, towns and villages) data and tweet data are obtained from Twitter, and extract information of purchase information (Where and what bought) from the tweet data by morphological analysis and rule-based dependency analysis. Then, the "The regional information" and "The information of purchase history (Where and what bought information)" are captured as bipartite graph, and Responsiveness Pair Clustering analysis (a clustering using correspondence analysis as simi...

  11. Advancing Family Business Research Through Narrative Analysis

    DEFF Research Database (Denmark)

    Dawson, Alexandra; Hjorth, Daniel

    2012-01-01

    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 and...

  12. Comparison of two cluster analysis methods using single particle mass spectra

    Science.gov (United States)

    Zhao, Weixiang; Hopke, Philip K.; Prather, Kimberly A.

    Cluster analysis of aerosol time-of-flight mass spectrometry (ATOFMS) data has been an effective tool for the identification of possible sources of ambient aerosols. In this study, the clustering results of two typical methods, adaptive resonance theory-based neural networks-2a (ART-2a) and density-based clustering of application with noise (DBSCAN), on ATOFMS data were investigated by employing a set of benchmark ATOFMS data. The advantages and disadvantages of these two methods are discussed and some feasible remedies proposed for problems encountered in the clustering process. The results of this study will provide promising directions for future work on ambient aerosol cluster analysis, suggesting a more effective and feasible clustering strategy based on the integration of ART-2a and DBSCAN.

  13. Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis

    DEFF Research Database (Denmark)

    Kaltoft, Mette Kjer; Turner, Robin; Cunich, Michelle;

    2015-01-01

    population in relation to the importance assigned to relevant criteria. It involves combining Cluster Analysis (CA), to generate the subgroup sets of preferences, with Multi-Criteria Decision Analysis (MCDA), to provide the policy framework into which the clustered preferences are entered. We employ three...

  14. 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

  15. Visual cluster analysis in support of clinical decision intelligence.

    Science.gov (United States)

    Gotz, David; Sun, Jimeng; Cao, Nan; Ebadollahi, Shahram

    2011-01-01

    Electronic health records (EHRs) contain a wealth of information about patients. In addition to providing efficient and accurate records for individual patients, large databases of EHRs contain valuable information about overall patient populations. While statistical insights describing an overall population are beneficial, they are often not specific enough to use as the basis for individualized patient-centric decisions. To address this challenge, we describe an approach based on patient similarity which analyzes an EHR database to extract a cohort of patient records most similar to a specific target patient. Clusters of similar patients are then visualized to allow interactive visual refinement by human experts. Statistics are then extracted from the refined patient clusters and displayed to users. The statistical insights taken from these refined clusters provide personalized guidance for complex decisions. This paper focuses on the cluster refinement stage where an expert user must interactively (a) judge the quality and contents of automatically generated similar patient clusters, and (b) refine the clusters based on his/her expertise. We describe the DICON visualization tool which allows users to interactively view and refine multidimensional similar patient clusters. We also present results from a preliminary evaluation where two medical doctors provided feedback on our approach. PMID:22195102

  16. 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.

  17. Cluster analysis of Wisconsin Breast Cancer dataset using self-organizing maps.

    Science.gov (United States)

    Pantazi, Stefan; Kagolovsky, Yuri; Moehr, Jochen R

    2002-01-01

    This work deals with multidimensional data analysis, precisely cluster analysis applied to a very well known dataset, the Wisconsin Breast Cancer dataset. After the introduction of the topics of the paper the cluster analysis concept is shortly explained and different methods of cluster analysis are compared. Further, the Kohonen model of self-organizing maps is briefly described together with an example and with explanations of how the cluster analysis can be performed using the maps. After describing the data set and the methodology used for the analysis we present the findings using textual as well as visual descriptions and conclude that the approach is a useful complement for assessing multidimensional data and that this dataset has been overused for automated decision benchmarking purposes, without a thorough analysis of the data it contains. PMID:15460731

  18. 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...

  19. Classification of open clusters by centroid method of taxonomical analysis

    International Nuclear Information System (INIS)

    Distributions of open clusters of the Galaxy in spaces with coordinates being mass, absolute magnitude, integrated colour index, diameter, metallicity, and age, are considered. Majority of clusters are shown to enter several taxons (classes) with narrow enough limits of these parameters. The classes form a linear sequence by age and two-dimensional sequence on colour - magnitude diagram. They are not isolated but transit into each other continuously. It possibly means an absence of significant gaps in cluster formation process. Bifurcation of age sequence of classes depending on mass and diameter values is found. This allows an evolutionary interpretation

  20. 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.

  1. 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.

  2. 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…

  3. Hypergraph Modelling and Graph Clustering Process Applied to Co-word Analysis

    OpenAIRE

    Polanco, Xavier; San Juan, Eric

    2007-01-01

    We argue that any document set can be modelled as a hypergraph, and we apply a graph clustering process as a way of analysis. A variant of the single link clustering is presented, and we assert that it is better suited to extract interesting clusters formed along easily interpretable paths of associated items than algorithms based on detecting high density regions. We propose a methodology that involves the extraction of similarity graphs from the indexed-dataset represented as a hypergraph. ...

  4. caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data

    OpenAIRE

    Xuan Jianhua; Wang Zuyi; Miller David J; Li Huai; Zhu Yitan; Clarke Robert; Hoffman Eric P; Wang Yue

    2008-01-01

    Abstract Background The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. Results In an ...

  5. On the Timing Analysis of Cluster based Communication Devices for Large Scale Computing Systems

    OpenAIRE

    Mohammed Mahfooz Sheikh; Khan, A.M.; U.N.Sinha

    2012-01-01

    Many parallel computing environments utilize cluster based architecture for large scale computing owing to the ease of their availability. As the cluster based approach may be used extensively,the interconnection mechanism plays a vital role in the performance of the system. The globally coupled class of problem is generally not amenable with the cluster based approach due to its substantial demand for communication across the architecture. In this paper we present a timing analysis of standa...

  6. KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression Analysis

    OpenAIRE

    Griffith, Obi L.; Gao, Byron J.; Bilenky, Mikhail; Prichyna, Yuliya; Ester, Martin; Jones, Steven J.M.

    2009-01-01

    Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a pattern-based subspace cluster, is a subset of rows and columns in a data matrix for which all the rows induce the same linear ordering of columns. Existing OPSM discovery methods do not scale well to increasingly large expression datasets. In particular, twi...

  7. Analysis and Evaluation of Soil Fertility Status Based on Weighted K-means Clustering Algorithm

    OpenAIRE

    Chen, Guifen; Cai, Lixia; Chen, Hang; Cao, Liying; Li, Chunan

    2013-01-01

    Generally K-means clustering algorithm can not distinguish the imbalance between attributes, so it can only be an independent investigation situation of each attribute but can not be comprehensive analysis of the soil fertility status. To solve this problem, this paper proposes a weighted K-means clustering algorithm to evaluate the soil fertility in Nong’an County, Jilin. The algorithm uses AHP to get the weight of soil nutrient attributes. Then combined with K-means clustering algorithm. Fi...

  8. 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.

  9. Some advances in tensor analysis and polynomial optimization

    OpenAIRE

    Li, Zhening; Ling, Chen; Wang, Yiju; Yang, Qingzhi

    2014-01-01

    Tensor analysis (also called as numerical multilinear algebra) mainly includes tensor decomposition, tensor eigenvalue theory and relevant algorithms. Polynomial optimization mainly includes theory and algorithms for solving optimization problems with polynomial objects functions under polynomial constrains. This survey covers the most of recent advances in these two fields. For tensor analysis, we introduce some properties and algorithms concerning the spectral radius of nonnegative tensors'...

  10. 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

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

    International Nuclear Information System (INIS)

    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

  12. Advances in x-ray analysis

    International Nuclear Information System (INIS)

    This book presents papers given at the 1982 Denver Conference on the Applications of X-ray Analysis. Focus is on recent developments in measurement accuracy of two-theta and intensity. Topics include accuracy in x-ray powder diffraction (the theme of the conference); search/match procedures (Powder Diffraction File); quantitative XRD analysis; XRD application and automation; x-ray stress determination (position sensitive detectors, fatigue and fracture characterization); new XRF instrumentation and techniques; XRF computer systems and mathematical corrections; and XRF general applications

  13. 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...

  14. 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.

  15. Assessment of Random Assignment in Training and Test Sets using Generalized Cluster Analysis Technique

    Directory of Open Access Journals (Sweden)

    Sorana D. BOLBOACĂ

    2011-06-01

    Full Text Available Aim: The properness of random assignment of compounds in training and validation sets was assessed using the generalized cluster technique. Material and Method: A quantitative Structure-Activity Relationship model using Molecular Descriptors Family on Vertices was evaluated in terms of assignment of carboquinone derivatives in training and test sets during the leave-many-out analysis. Assignment of compounds was investigated using five variables: observed anticancer activity and four structure descriptors. Generalized cluster analysis with K-means algorithm was applied in order to investigate if the assignment of compounds was or not proper. The Euclidian distance and maximization of the initial distance using a cross-validation with a v-fold of 10 was applied. Results: All five variables included in analysis proved to have statistically significant contribution in identification of clusters. Three clusters were identified, each of them containing both carboquinone derivatives belonging to training as well as to test sets. The observed activity of carboquinone derivatives proved to be normal distributed on every. The presence of training and test sets in all clusters identified using generalized cluster analysis with K-means algorithm and the distribution of observed activity within clusters sustain a proper assignment of compounds in training and test set. Conclusion: Generalized cluster analysis using the K-means algorithm proved to be a valid method in assessment of random assignment of carboquinone derivatives in training and test sets.

  16. 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.

  17. Detecting data fabrication in clinical trials from cluster analysis perspective.

    Science.gov (United States)

    Wu, Xiaoru; Carlsson, Martin

    2011-01-01

    Detecting data fabrication is of great importance in clinical trials. As the role of statisticians in detecting abnormal data patterns has grown, a large number of statistical procedures have been developed, most of which are based on descriptive statistics. Based upon the fact that substantial data fabrication cases have certain clustering structures, this paper discusses the potential for the use of statistical clustering method in fraud detection. Three clustering patterns, angular, neighborhood and repeated measurements clustering, are identified and explored. Correspondingly, simple and efficient test statistics are proposed and randomization tests are carried out. The proposed methods are applied to a 12-week multi-center study for illustration. Extensive simulations are conducted to validate the effectiveness of the procedures. PMID:20936626

  18. 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.

  19. Visual cluster analysis and pattern recognition template and methods

    Energy Technology Data Exchange (ETDEWEB)

    Osbourn, G.C.; Martinez, R.F.

    1993-12-31

    This invention is comprised of a method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.

  20. Advances in Generalized Valence Bond-Coupled Cluster Methods for Electronic Structure Theory

    OpenAIRE

    Lawler, Keith Vanoy

    2009-01-01

    The electron-electron correlation term in the electronic energy of a molecule is the most difficult term to compute, yet it is of both qualitative and quantitative importance for a diverse range of chemical applications of computational quantum chemistry. Generalized Valence Bond-Coupled Cluster (GVB-CC) methods are computationally efficient, size-consistent wavefunction based methods to capture the most important static (valence) contributions to the correlation energy. Despite these advanta...

  1. Advanced Data Analysis for Industrial Applications

    Czech Academy of Sciences Publication Activity Database

    Wagner, Zdeněk; Kovanic, Pavel

    -: -, 2015 - (Antoch, J.), s. 5 ISBN 978-2-910239-82-4. [Modelling Smard Grids 2015. Prague (CZ), 10.09.2015-11.09.2015] Institutional support: RVO:67985858 ; RVO:67985556 Keywords : data analysis * mathematical gnostics * robust methods Subject RIV: BA - General Mathematics

  2. 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.

  3. Identifying the Clusters within Nonmotor Manifestations in Early Parkinson's Disease by Using Unsupervised Cluster Analysis

    OpenAIRE

    Hui-Jun Yang; Young Eun Kim; Ji Young Yun; Han-Joon Kim; Beom Seok Jeon

    2014-01-01

    BACKGROUND: Classical and data-driven classifications of Parkinson's disease (PD) are based primarily on motor symptoms, with little attention being paid to the clustering of nonmotor manifestations. METHODS: Clinical data on demographic, motor and nonmotor features, including the Korean version of the sniffin' stick (KVSS) test results, and responses to the screening questionnaire of the nonmotor features were collected from 56 PD patients with disease onset within 3 years. Nonmotor subgroup...

  4. Functional clustering algorithm for the analysis of dynamic network data

    OpenAIRE

    Feldt, S.; Waddell, J; Hetrick, V. L.; Berke, J. D.; Żochowski, M

    2009-01-01

    We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulat...

  5. Detecting influential observations in a model-based cluster analysis

    OpenAIRE

    Bruckers, L.; Molenberghs, G; Verbeke, G; Geys, H.

    2016-01-01

    Finite mixture models have been used to model population heterogeneity and to relax distributional assumptions. These models are also convenient tools for clustering and classification of complex data such as, for example, repeated-measurements data. The performance of model-based clustering algorithms is sensitive to influential and outlying observations. Methods for identifying outliers in a finite mixture model have been described in the literature. Approaches to identify influential obser...

  6. Fuzzy Meta Node Fuzzy Metagraph and its Cluster Analysis

    OpenAIRE

    Gaur, D.; A. Shastri; Biswas, R.

    2008-01-01

    Problem statement: In this study researchers propose a new fuzzy graph theoretic construct called fuzzy metagraph and a new method of clustering finding the similar fuzzy nodes in a fuzzy metagraph. Approach: We adopted T-norms (Triangular Norms) functions and join two or more T-norms to cluster the fuzzy nodes. Fuzzy metagraph is the fuzzyfication of the crisp Metagraphs using fuzzy Generating sets and the fuzzy edge set. We could efficiently analyze the inexact information and investigate t...

  7. Evidence-Based Clustering of Reads and Taxonomic Analysis of Metagenomic Data

    Science.gov (United States)

    Folino, Gianluigi; Gori, Fabio; Jetten, Mike S. M.; Marchiori, Elena

    The rapidly emerging field of metagenomics seeks to examine the genomic content of communities of organisms to understand their roles and interactions in an ecosystem. In this paper we focus on clustering methods and their application to taxonomic analysis of metagenomic data. Clustering analysis for metagenomics amounts to group similar partial sequences, such as raw sequence reads, into clusters in order to discover information about the internal structure of the considered dataset, or the relative abundance of protein families. Different methods for clustering analysis of metagenomic datasets have been proposed. Here we focus on evidence-based methods for clustering that employ knowledge extracted from proteins identified by a BLASTx search (proxygenes). We consider two clustering algorithms introduced in previous works and a new one. We discuss advantages and drawbacks of the algorithms, and use them to perform taxonomic analysis of metagenomic data. To this aim, three real-life benchmark datasets used in previous work on metagenomic data analysis are used. Comparison of the results indicates satisfactory coherence of the taxonomies output by the three algorithms, with respect to phylogenetic content at the class level and taxonomic distribution at phylum level. In general, the experimental comparative analysis substantiates the effectiveness of evidence-based clustering methods for taxonomic analysis of metagenomic data.

  8. 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

  9. Advances in technology for integrated route analysis

    Energy Technology Data Exchange (ETDEWEB)

    Carey, N.T.; George, P.J.; Khamhawi, K. [SAGE Engineering Ltd., Bath (United Kingdom)

    1998-12-31

    Pipeline and cable routes are becoming ever complex with routes being chosen in increasingly remote and technically demanding areas. Clients now demand greater information and analysis from the surveys and interpretations. By utilising a range of leading edge equipment, greater information can be obtained, visualised and analysed than ever before. Three case studies are presented, indicating how such technology has been employed to provide the client with a greater understanding of complex engineering projects, and what additional technology could have been utilised to further enhance the project. Examples of data, systems and project management used on two major pipe routes and one major cable route are presented. Swath Bathymetry (ISIS), 3D visualisation (Fledermaus), cone penetrometer testing (SAGE Miniature CPT), GIS databases (INfoXProfessional), and the use of pipeline stress analysis, upheaval buckling and rock dump optimisation software (SAFE Profile) are all examined. (author)

  10. Advancing the diagnostic analysis of environmental problems

    OpenAIRE

    Michael Cox

    2011-01-01

    Social-ecological systems exhibit patterns across multiple levels along spatial, temporal, and functional scales. The outcomes that are produced in these systems result from complex, non-additive interactions between different types of social and biophysical components, some of which are common to many systems, and some of which are relatively unique to a particular system. These properties, along with the mostly non-experimental nature of the analysis, make it difficult to construct theories...

  11. Advancing the diagnostic analysis of environmental problems

    Directory of Open Access Journals (Sweden)

    Michael Cox

    2011-09-01

    Full Text Available Social-ecological systems exhibit patterns across multiple levels along spatial, temporal, and functional scales. The outcomes that are produced in these systems result from complex, non-additive interactions between different types of social and biophysical components, some of which are common to many systems, and some of which are relatively unique to a particular system. These properties, along with the mostly non-experimental nature of the analysis, make it difficult to construct theories regarding the sustainability of social-ecological systems. This paper builds on previous work that has initiated a diagnostic approach to the analysis of these systems. The process of diagnosis involves asking a series of questions of a system at increasing levels of specificity based on the answers to previous questions. The answer to each question further unpacks the complexity of a system, allowing an analyst to explore patterns of interactions that produce outcomes. An important feature of this approach is the use of multilevel analysis. This paper explores this concept and introduces another – multilevel causation – to further develop the diagnostic approach. It demonstrates that these concepts can be used to analyze a diversity of environmental problems.

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

    OpenAIRE

    Wołodźko, Tymoteusz; Kokoszka, Andrzej

    2014-01-01

    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 ten...

  13. A clustering analysis of lipoprotein diameters in the metabolic syndrome

    Directory of Open Access Journals (Sweden)

    Frazier-Wood Alexis C

    2011-12-01

    Full Text Available Abstract Background The presence of smaller low-density lipoproteins (LDL has been associated with atherosclerosis risk, and the insulin resistance (IR underlying the metabolic syndrome (MetS. In addition, some research has supported the association of very low-, low- and high-density lipoprotein (VLDL HDL particle diameters with components of the metabolic syndrome (MetS, although this has been the focus of less research. We aimed to explore the relationship of VLDL, LDL and HDL diameters to MetS and its features, and by clustering individuals by their diameters of VLDL, LDL and HDL particles, to capture information across all three fractions of lipoprotein into a unified phenotype. Methods We used nuclear magnetic resonance spectroscopy measurements on fasting plasma samples from a general population sample of 1,036 adults (mean ± SD, 48.8 ± 16.2 y of age. Using latent class analysis, the sample was grouped by the diameter of their fasting lipoproteins, and mixed effects models tested whether the distribution of MetS components varied across the groups. Results Eight discrete groups were identified. Two groups (N = 251 were enriched with individuals meeting criteria for the MetS, and were characterized by the smallest LDL/HDL diameters. One of those two groups, one was additionally distinguished by large VLDL, and had significantly higher blood pressure, fasting glucose, triglycerides, and waist circumference (WC; P Conclusions While small LDL diameters remain associated with IR and the MetS, the occurrence of these in conjunction with a shift to overall larger VLDL diameter may identify those with the highest fasting glucose, TG and WC within the MetS. If replicated, the association of this phenotype with more severe IR-features indicated that it may contribute to identifying of those most at risk for incident type II diabetes and cardiometabolic disease.

  14. Mass spectrometric analysis with cluster projectiles and coincidence counting

    Energy Technology Data Exchange (ETDEWEB)

    Cox, B.D.

    1992-01-01

    Methods for maximizing the amount of secondary ion information, per primary projectile, are described. The method is based on time-of-flight mass spectrometry and event-by-event coincidence counting. The information obtained from coincidence counting time-of-flight mass spectrometry includes: (a) surface composition, (b) relative concentrations, and (c) degree of intermolecular mixing. The technique was applied to the study of an important new class of polymers: polymer blends. Secondary ion mass spectrometry, when applied to the analysis of synthetic polymers, induces backbone fragmentation which is characteristic of the homopolymer. The characteristic fingerprint peaks from polystyrene and poly(vinyl methyl ether) were used to identify the presence of these two polymers in a polymer blend. The percent coincidence between the characteristic secondary ions from each component of the blend were used to determine both the relative concentration and the degree of molecular mixing. Results indicate molecular segregation of the two polymers on the film surface. The largest degree of segregation was determined for the phase separated blends. The performance of this technique depends on the desorption efficiency of the primary projectiles. In practice one seeks primary ions which are surface sensitive, have controllable parameters such as size, velocity, and charge state, and generate high secondary ion yields. Focus was placed on the use of keV organic cluster projectiles to meet these criteria. Of interest to this study were C[sub 18] (chrysene), C[sub 24] (coronene), and C[sub 60] (buckminster-fulleren). Results indicate enhanced secondary ion yields for C[sub 60]. For example, when CsI is bombarded with 30 keV C[sub 60], the yields for I[sup [minus

  15. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  16. 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…

  17. Two-dimensional ordered cluster analysis of component groups in self-organization

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2014-09-01

    Full Text Available An algorithm for two-dimensional cluster analysis of component groups, originally from Zhang et al., (2004, was introduced in this study. The algorithm composes of three procedures, i.e., calculation of distance measures, randomization statistic test, and ordered clustering of components.

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

    NARCIS (Netherlands)

    Q. Zhou; F. Leng; L. Leydesdorff

    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

  19. Competitiveness Analysis of Processing Industry Cluster of Livestock Products in Inner Mongolia Based on "Diamond Model"

    OpenAIRE

    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 ...

  20. Cluster analysis of tropical cyclone tracks in the Southern Hemisphere

    Energy Technology Data Exchange (ETDEWEB)

    Ramsay, Hamish A. [Monash University, Monash Weather and Climate, School of Mathematical Sciences, Clayton, VIC (Australia); Camargo, Suzana J.; Kim, Daehyun [Columbia University, Lamont-Doherty Earth Observatory, Palisades, NY (United States)

    2012-08-15

    A probabilistic clustering method is used to describe various aspects of tropical cyclone (TC) tracks in the Southern Hemisphere, for the period 1969-2008. A total of 7 clusters are examined: three in the South Indian Ocean, three in the Australian Region, and one in the South Pacific Ocean. Large-scale environmental variables related to TC genesis in each cluster are explored, including sea surface temperature, low-level relative vorticity, deep-layer vertical wind shear, outgoing longwave radiation, El Nino-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO). Composite maps, constructed 2 days prior to genesis, show some of these to be significant precursors to TC formation - most prominently, westerly wind anomalies equatorward of the main development regions. Clusters are also evaluated with respect to their genesis location, seasonality, mean peak intensity, track duration, landfall location, and intensity at landfall. ENSO is found to play a significant role in modulating annual frequency and mean genesis location in three of the seven clusters (two in the South Indian Ocean and one in the Pacific). The ENSO-modulating effect on genesis frequency is caused primarily by changes in low-level zonal flow between the equator and 10 S, and associated relative vorticity changes in the main development regions. ENSO also has a significant effect on mean genesis location in three clusters, with TCs forming further equatorward (poleward) during El Nino (La Nina) in addition to large shifts in mean longitude. The MJO has a strong influence on TC genesis in all clusters, though the amount modulation is found to be sensitive to the definition of the MJO. (orig.)

  1. 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

  2. Advances in the homotopy analysis method

    CERN Document Server

    Liao, Shijun

    2013-01-01

    Unlike other analytic techniques, the Homotopy Analysis Method (HAM) is independent of small/large physical parameters. Besides, it provides great freedom to choose equation type and solution expression of related linear high-order approximation equations. The HAM provides a simple way to guarantee the convergence of solution series. Such uniqueness differentiates the HAM from all other analytic approximation methods. In addition, the HAM can be applied to solve some challenging problems with high nonlinearity. This book, edited by the pioneer and founder of the HAM, describes the current ad

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

    OpenAIRE

    George Tsatsaronis; Yongping Yang; Tatiana Morosuk; Ligang Wang

    2012-01-01

    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. Thi...

  4. A scoping review of spatial cluster analysis techniques for point-event data

    Directory of Open Access Journals (Sweden)

    Charles E. Fritz

    2013-05-01

    Full Text Available Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29. The spatial scan statistic was the most popular method for address location data (n = 19. Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

  5. 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.

  6. The Study About the Analysis of Responsiveness Pair Clustering Tosocial Network Bipartite Graph

    Directory of Open Access Journals (Sweden)

    Akira Otsuki

    2013-11-01

    Full Text Available In this study, regional (cities, towns and villages data and tweet data are obtained from Twitter, andextract information of "purchase information (Whereand what bought" from the tweet data bymorphological analysis and rule-based dependency analysis. Then, the "The regional information" and the"Theinformation of purchase history (Where and what bought information" are captured as bipartitegraph, and Responsiveness Pair Clustering analysis(a clustering using correspondence analysis assimilarity measure is conducted. In this study, since it was found to be difficult to analyze a network suchas bipartite graph having limitations in links by using modularity Q, responsiveness is used instead ofmodularity Q as similarity measure. As a result ofthis analysis, "regional information cluster" whichrefersto similar "Theinformation of purchase history" nodes group is generated. Finally, similar regions arevisualized by mapping the regional information cluster on the map. This visualization system is expected tocontribute as an analytical tool for customers’ purchasing behaviour and so on.

  7. 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.

  8. Galaxy cluster mass estimation from stacked spectroscopic analysis

    Science.gov (United States)

    Farahi, Arya; Evrard, August E.; Rozo, Eduardo; Rykoff, Eli S.; Wechsler, Risa H.

    2016-08-01

    We use simulated galaxy surveys to study: i) how galaxy membership in redMaPPer clusters maps to the underlying halo population, and ii) the accuracy of a mean dynamical cluster mass, $M_\\sigma(\\lambda)$, derived from stacked pairwise spectroscopy of clusters with richness $\\lambda$. Using $\\sim\\! 130,000$ galaxy pairs patterned after the SDSS redMaPPer cluster sample study of Rozo et al. (2015 RMIV), we show that the pairwise velocity PDF of central--satellite pairs with $m_i galaxy membership matching. We apply this approach, along with mis-centering and galaxy velocity bias corrections, to estimate the log-mean matched halo mass at $z=0.2$ of SDSS redMaPPer clusters. Employing the velocity bias constraints of Guo et al. (2015), we find $\\langle \\ln(M_{200c})|\\lambda \\rangle = \\ln(M_{30}) + \\alpha_m \\ln(\\lambda/30)$ with $M_{30} = 1.56 \\pm 0.35 \\times 10^{14} M_\\odot$ and $\\alpha_m = 1.31 \\pm 0.06_{stat} \\pm 0.13_{sys}$. Systematic uncertainty in the velocity bias of satellite galaxies overwhelmingly dominates the error budget.

  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. Performance Analysis of Gender Clustering and Classification Algorithms

    Directory of Open Access Journals (Sweden)

    Dr.K.Meena

    2012-03-01

    Full Text Available In speech processing, gender clustering and classification plays a major role. In both gender clustering and classification, selecting the feature is an important process and the often utilized featurefor gender clustering and classification in speech processing is pitch. The pitch value of a male speech differs much from that of a female speech. Normally, there is a considerable frequency value difference between the male and female speech. But, in some cases the frequency of male is almost equal to female or frequency of female is equal to male. In such situation, it is difficult to identify the exact gender. By considering this drawback, here three features namely; energy entropy, zero crossing rate and short time energy are used for identifying the gender. Gender clustering and classification of speech signal are estimated using the aforementioned three features. Here, the gender clustering is computed using Euclidean distance, Mahalanobis distance, Manhattan distance & Bhattacharyya distance method and the gender classification method is computed using combined fuzzy logic and neural network, neuro fuzzy and support vector machine and its performance are analyzed.

  11. 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

  12. Methodological advancements in procedures for common cause failure analysis

    International Nuclear Information System (INIS)

    This paper summarizes the methodological advancements achieved in the process of developing a procedures guide for the analysis of common cause failures (CCF) in safety and reliability studies. The work was sponsored by the Electric Power Research Institute and the U.S. Nuclear Regulatory Commission, and resulted in the publication of a two-volume guidebook. The methodological advancements include the development of a systematic framework for qualitative and quantitative analysis of CCFs, introduction of basic events, improvements in parametric models and their estimators, and development of a series of techniques for the creation of a plant-specific CCF database

  13. Molecular-dynamics analysis of mobile helium cluster reactions near surfaces of plasma-exposed tungsten

    Energy Technology Data Exchange (ETDEWEB)

    Hu, Lin; Maroudas, Dimitrios, E-mail: maroudas@ecs.umass.edu [Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts 01003-9303 (United States); Hammond, Karl D. [Department of Chemical Engineering, University of Missouri, Columbia, Missouri 65211 (United States); Wirth, Brian D. [Department of Nuclear Engineering, University of Tennessee, Knoxville, Tennessee 37996 (United States)

    2015-10-28

    We report the results of a systematic atomic-scale analysis of the reactions of small mobile helium clusters (He{sub n}, 4 ≤ n ≤ 7) near low-Miller-index tungsten (W) surfaces, aiming at a fundamental understanding of the near-surface dynamics of helium-carrying species in plasma-exposed tungsten. These small mobile helium clusters are attracted to the surface and migrate to the surface by Fickian diffusion and drift due to the thermodynamic driving force for surface segregation. As the clusters migrate toward the surface, trap mutation (TM) and cluster dissociation reactions are activated at rates higher than in the bulk. TM produces W adatoms and immobile complexes of helium clusters surrounding W vacancies located within the lattice planes at a short distance from the surface. These reactions are identified and characterized in detail based on the analysis of a large number of molecular-dynamics trajectories for each such mobile cluster near W(100), W(110), and W(111) surfaces. TM is found to be the dominant cluster reaction for all cluster and surface combinations, except for the He{sub 4} and He{sub 5} clusters near W(100) where cluster partial dissociation following TM dominates. We find that there exists a critical cluster size, n = 4 near W(100) and W(111) and n = 5 near W(110), beyond which the formation of multiple W adatoms and vacancies in the TM reactions is observed. The identified cluster reactions are responsible for important structural, morphological, and compositional features in the plasma-exposed tungsten, including surface adatom populations, near-surface immobile helium-vacancy complexes, and retained helium content, which are expected to influence the amount of hydrogen re-cycling and tritium retention in fusion tokamaks.

  14. Molecular-dynamics analysis of mobile helium cluster reactions near surfaces of plasma-exposed tungsten

    International Nuclear Information System (INIS)

    We report the results of a systematic atomic-scale analysis of the reactions of small mobile helium clusters (Hen, 4 ≤ n ≤ 7) near low-Miller-index tungsten (W) surfaces, aiming at a fundamental understanding of the near-surface dynamics of helium-carrying species in plasma-exposed tungsten. These small mobile helium clusters are attracted to the surface and migrate to the surface by Fickian diffusion and drift due to the thermodynamic driving force for surface segregation. As the clusters migrate toward the surface, trap mutation (TM) and cluster dissociation reactions are activated at rates higher than in the bulk. TM produces W adatoms and immobile complexes of helium clusters surrounding W vacancies located within the lattice planes at a short distance from the surface. These reactions are identified and characterized in detail based on the analysis of a large number of molecular-dynamics trajectories for each such mobile cluster near W(100), W(110), and W(111) surfaces. TM is found to be the dominant cluster reaction for all cluster and surface combinations, except for the He4 and He5 clusters near W(100) where cluster partial dissociation following TM dominates. We find that there exists a critical cluster size, n = 4 near W(100) and W(111) and n = 5 near W(110), beyond which the formation of multiple W adatoms and vacancies in the TM reactions is observed. The identified cluster reactions are responsible for important structural, morphological, and compositional features in the plasma-exposed tungsten, including surface adatom populations, near-surface immobile helium-vacancy complexes, and retained helium content, which are expected to influence the amount of hydrogen re-cycling and tritium retention in fusion tokamaks

  15. Molecular-dynamics analysis of mobile helium cluster reactions near surfaces of plasma-exposed tungsten

    Science.gov (United States)

    Hu, Lin; Hammond, Karl D.; Wirth, Brian D.; Maroudas, Dimitrios

    2015-10-01

    We report the results of a systematic atomic-scale analysis of the reactions of small mobile helium clusters (Hen, 4 ≤ n ≤ 7) near low-Miller-index tungsten (W) surfaces, aiming at a fundamental understanding of the near-surface dynamics of helium-carrying species in plasma-exposed tungsten. These small mobile helium clusters are attracted to the surface and migrate to the surface by Fickian diffusion and drift due to the thermodynamic driving force for surface segregation. As the clusters migrate toward the surface, trap mutation (TM) and cluster dissociation reactions are activated at rates higher than in the bulk. TM produces W adatoms and immobile complexes of helium clusters surrounding W vacancies located within the lattice planes at a short distance from the surface. These reactions are identified and characterized in detail based on the analysis of a large number of molecular-dynamics trajectories for each such mobile cluster near W(100), W(110), and W(111) surfaces. TM is found to be the dominant cluster reaction for all cluster and surface combinations, except for the He4 and He5 clusters near W(100) where cluster partial dissociation following TM dominates. We find that there exists a critical cluster size, n = 4 near W(100) and W(111) and n = 5 near W(110), beyond which the formation of multiple W adatoms and vacancies in the TM reactions is observed. The identified cluster reactions are responsible for important structural, morphological, and compositional features in the plasma-exposed tungsten, including surface adatom populations, near-surface immobile helium-vacancy complexes, and retained helium content, which are expected to influence the amount of hydrogen re-cycling and tritium retention in fusion tokamaks.

  16. caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data

    Directory of Open Access Journals (Sweden)

    Xuan Jianhua

    2008-09-01

    Full Text Available Abstract Background The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. Results In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine (divisive hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample

  17. Fuzzy Meta Node Fuzzy Metagraph and its Cluster Analysis

    Directory of Open Access Journals (Sweden)

    D. Gaur

    2008-01-01

    Full Text Available Problem statement: In this study researchers propose a new fuzzy graph theoretic construct called fuzzy metagraph and a new method of clustering finding the similar fuzzy nodes in a fuzzy metagraph. Approach: We adopted T-norms (Triangular Norms functions and join two or more T-norms to cluster the fuzzy nodes. Fuzzy metagraph is the fuzzyfication of the crisp Metagraphs using fuzzy Generating sets and the fuzzy edge set. We could efficiently analyze the inexact information and investigate the fuzzy relation by applying the fuzzy graph theory. Results: In this study researchers suggesting a new method of clustering of a new graph theoretic structure i.e., fuzzy metagraph and investigated fuzzy metanode and fuzzy metagraph structure. Conclusion/Recommendations: Our future research will be to explore all its useful operations on fuzzy metagraph. We will give the more application based implementation of fuzzy metagraph.

  18. 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.

  19. Electronic stress tensor analysis of hydrogenated palladium clusters

    CERN Document Server

    Ichikawa, Kazuhide; Szarek, Pawel; Zhou, Chenggang; Cheng, Hansong; Tachibana, Akitomo

    2011-01-01

    We study the chemical bonds of small palladium clusters Pd_n (n=2-9) saturated by hydrogen atoms using electronic stress tensor. Our calculation includes bond orders which are recently proposed based on the stress tensor. It is shown that our bond orders can classify the different types of chemical bonds in those clusters. In particular, we discuss Pd-H bonds associated with the H atoms with high coordination numbers and the difference of H-H bonds in the different Pd clusters from viewpoint of the electronic stress tensor. The notion of "pseudo-spindle structure" is proposed as the region between two atoms where the largest eigenvalue of the electronic stress tensor is negative and corresponding eigenvectors forming a pattern which connects them.

  20. A Systematic Analysis of Caustic Methods for Galaxy Cluster Masses

    CERN Document Server

    Gifford, Daniel; Kern, Nicholas

    2013-01-01

    We quantify the expected observed statistical and systematic uncertainties of the escape velocity as a measure of the gravitational potential and total mass of galaxy clusters. We focus our attention on low redshift (z 25, the scatter in the escape velocity mass is dominated by projections along the line-of-sight. Algorithmic uncertainties from the determination of the projected escape velocity profile are negligible. We quantify how target selection based on magnitude, color, and projected radial separation can induce small additional biases into the escape velocity masses. Using N_gal = 150 (25), the caustic technique has a per cluster scatter in ln(M|M_200) of 0.3 (0.5) and bias 1+/-3% (16+/-5%) for clusters with masses > 10^14M_solar at z<0.15.

  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. Advanced analysis techniques for uranium assay

    International Nuclear Information System (INIS)

    Uranium has a negligible passive neutron emission rate making its assay practicable only with an active interrogation method. The active interrogation uses external neutron sources to induce fission events in the uranium in order to determine the mass. This technique requires careful calibration with standards that are representative of the items to be assayed. The samples to be measured are not always well represented by the available standards which often leads to large biases. A technique of active multiplicity counting is being developed to reduce some of these assay difficulties. Active multiplicity counting uses the measured doubles and triples count rates to determine the neutron multiplication (f4) and the product of the source-sample coupling ( C ) and the 235U mass (m). Since the 35U mass always appears in the multiplicity equations as the product of Cm, the coupling needs to be determined before the mass can be known. A relationship has been developed that relates the coupling to the neutron multiplication. The relationship is based on both an analytical derivation and also on empirical observations. To determine a scaling constant present in this relationship, known standards must be used. Evaluation of experimental data revealed an improvement over the traditional calibration curve analysis method of fitting the doubles count rate to the 235Um ass. Active multiplicity assay appears to relax the requirement that the calibration standards and unknown items have the same chemical form and geometry.

  3. An Analysis of Spatial Clustering and Implications for Wildlife Management: A Burrowing Owl Example

    Science.gov (United States)

    Fisher, Joshua B.; Trulio, Lynne A.; Biging, Gregory S.; Chromczak, Debra

    2007-03-01

    Analysis tools that combine large spatial and temporal scales are necessary for efficient management of wildlife species, such as the burrowing owl ( Athene cunicularia). We assessed the ability of Ripley’s K-function analysis integrated into a geographic information system (GIS) to determine changes in burrowing owl nest clustering over two years at NASA Ames Research Center. Specifically, we used these tools to detect changes in spatial and temporal nest clustering before, during, and after conducting management by mowing to maintain low vegetation height at nest burrows. We found that the scale and timing of owl nest clustering matched the scale and timing of our conservation management actions over a short time frame. While this study could not determine a causal link between mowing and nest clustering, we did find that Ripley’s K and GIS were effective in detecting owl nest clustering and show promise for future conservation uses.

  4. Segmentation of visitors to shopping centers based on their activities through factor analysis and cluster analysis

    Directory of Open Access Journals (Sweden)

    reza soleymani-damaneh

    2013-08-01

    Full Text Available Knowing customers of shopping centers contributes greatly to increase profits of these centers. Segmentation of the customers is one of the most effective means of knowing the customers. The purpose of this study was to present a segmentation of the customers based on their activities in the shopping centers. The participants were 157 visitors to Milad-e-Noor Shopping Center who were required to answer the questions in the questionnaire. Data were analyzed in three steps. Through the use of factor analysis, in the first step, the number of variables was reduced to the four factors of entertainment activities, planned shopping, shopping information gathering and unplanned shopping. These factors were then inserted into K-mean cluster analysis and, in the second step and the visitors were divided into 4 segments on the basis of their activity as following: traditionalists, shopping center enthusiasts, wandering customers, and entertainment seekers. In the third step, the demographic and behavioral variables were investigated in the identified clusters. Considering the variables of age, academic status and accompanying persons in shopping centers, these clusters were significantly different. In respect to variables of sex, marital status, the length of presence in the shopping centers, occupations and monthly salary they were recognized as homogenous, however.

  5. Using of fuzzy and imitation models and cluster analysis for decision of marketing tasks

    OpenAIRE

    Borisova, Tetyana Myhaylivna; Bryndzya, Zinoviy Fedorovych

    2011-01-01

    Actuality of using of some economic-mathematical methods at the decision of marketing tasks is considered in the article. The examples of using of fuzzy evaluation, cluster analysis and imitation modelling in marketing are presented here.

  6. Representation in GIS of the Results Obtained by Cluster Analysis in Territorial Profile

    Science.gov (United States)

    Dârdalą, Marian; Furtuną, Titus Felix; Reveiu, Adriana

    2010-05-01

    Cluster analysis involves grouping characteristics analyzed by the values of grouping parameters. The statistical cluster analysis uses the method of minimum dispersion of hierarchical tree method, in order to obtain the information necessary to group the administrative units. Territorial profile economic analyses can use the cluster analysis in order to make hierarchical classifications, according to performance, strategies. The hierarchical tree methods consist in identifying certain hierarchies used to take into consideration the units. According to their organization mode, clusters can be: vertically integrated, horizontally integrated, emerging clusters. With GIS, spatial data clustering can be applied to spatial data to represent the territorial analysis performed. In terms of viewing the results of cluster analysis by GIS, a usual way is to generate cartograms. In this case, a cartogram supposes defining a colors ramp, having a number of colors equal with the number of groups that divide the collectivity. The parameters used as the basis of the clustering process may exist as independent data or can be stored in the database of an informatic system. As a case study we implemented an ArcMap extension to analyze the clusters by selecting the grouping parameters and by setting the number of groups that will divide the collectivity. Cartograma can be defined taking into consideration multi-level administrative division of the territory. For example, Romania uses the split on villages, counties, regions and macro-regions. Analysis can be applied on different levels of administrative organization by aggregating the values of parameters. For example, the value of a parameter for a county can be obtained by aggregating all parameter values, for all villages, belonging to the county.

  7. 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.

  8. 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.

  9. Exploratory Analysis of Biological Networks through Visualization, Clustering, and Functional Annotation in Cytoscape.

    Science.gov (United States)

    Baryshnikova, Anastasia

    2016-01-01

    Biological networks define how genes, proteins, and other cellular components interact with one another to carry out specific functions, providing a scaffold for understanding cellular organization. Although in-depth network analysis requires advanced mathematical and computational knowledge, a preliminary visual exploration of biological networks is accessible to anyone with basic computer skills. Visualization of biological networks is used primarily to examine network topology, identify functional modules, and predict gene functions based on gene connectivity within the network. Networks are excellent at providing a bird's-eye view of data sets and have the power of illustrating complex ideas in simple and intuitive terms. In addition, they enable exploratory analysis and generation of new hypotheses, which can then be tested using rigorous statistical and experimental tools. This protocol describes a simple procedure for visualizing a biological network using the genetic interaction similarity network for Saccharomyces cerevisiae as an example. The visualization procedure described here relies on the open-source network visualization software Cytoscape and includes detailed instructions on formatting and loading the data, clustering networks, and overlaying functional annotations. PMID:26988373

  10. DESIGN AND ANALYSIS OF MULTI-MODE CLUSTER SAR

    Institute of Scientific and Technical Information of China (English)

    Fan Luhong; Pi Yiming; Hou Yinming

    2004-01-01

    Cluster Synthetic Aperture Radar (SAR) system is composed of a group of spaceborne SAR systems. With its agility of combination, this system can work in several different modes. In this letter, the basic configuration and the working mode of the system are presented.The special performance of the system compared with the conventional SAR system is indicated.

  11. 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 ...

  12. An Empirical Comparison of Variable Standardization Methods in Cluster Analysis.

    Science.gov (United States)

    Schaffer, Catherine M.; Green, Paul E.

    1996-01-01

    The common marketing research practice of standardizing the columns of a persons-by-variables data matrix prior to clustering the entities corresponding to the rows was evaluated with 10 large-scale data sets. Results indicate that the column standardization practice may be problematic for some kinds of data that marketing researchers used for…

  13. Galaxy Cluster Mass Estimation from Stacked Spectroscopic Analysis

    CERN Document Server

    Farahi, Arya; Rozo, Eduardo; Rykoff, Eli S; Wechsler, Risa H

    2016-01-01

    We use simulated galaxy surveys to study: i) how galaxy membership in redMaPPer clusters maps to the underlying halo population, and ii) the accuracy of a mean dynamical cluster mass, $M_\\sigma(\\lambda)$, derived from stacked pairwise spectroscopy of clusters with richness $\\lambda$. Using $\\sim\\! 130,000$ galaxy pairs patterned after the SDSS redMaPPer cluster sample study of Rozo et al. (2015 RMIV), we show that the pairwise velocity PDF of central--satellite pairs with $m_i < 19$ in the simulation matches the form seen in RMIV. Through joint membership matching, we deconstruct the main Gaussian velocity component into its halo contributions, finding that the top-ranked halo contributes $\\sim 60\\%$ of the stacked signal. The halo mass scale inferred by applying the virial scaling of Evrard et al. (2008) to the velocity normalization matches, to within a few percent, the log-mean halo mass derived through galaxy membership matching. We apply this approach, along with mis-centering and galaxy velocity bias...

  14. 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 ...

  15. 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.

  16. Empirical Power and Sample Size Calculations for Cluster-Randomized and Cluster-Randomized Crossover Studies

    OpenAIRE

    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 c...

  17. Log Analysis as a way to assist Opera mini cluster management decisions

    OpenAIRE

    2008-01-01

    This thesis considers ways that analysis of Opera mini logs can assist decisions related to global and local load balancing of Opera mini clusters. The analy- sis is aimed to determine the distribution of traffic with respect to country of origin and server within the cluster over the period of 2 weeks by creating a system for extraction and analysis of log data. Findings show that a large part of traffic originates in Russia with India and Indonesia being second and third. ...

  18. Functional Cluster Analysis of CT Perfusion Maps: A New Tool for Diagnosis of Acute Stroke?

    OpenAIRE

    Baumgartner, Christian; Gautsch, Kurt; Böhm, Christian; Felber, Stephan

    2005-01-01

    CT perfusion imaging constitutes an important contribution to the early diagnosis of acute stroke. Cerebral blood flow (CBF), cerebral blood volume (CBV) and time-to-peak (TTP) maps are used to estimate the severity of cerebral damage after acute ischemia. We introduce functional cluster analysis as a new tool to evaluate CT perfusion in order to identify normal brain, ischemic tissue and large vessels. CBF, CBV and TTP maps represent the basis for cluster analysis applying a partitioning (k-...

  19. Cluster Analysis of Symptoms Among Patients with Upper Extremity Musculoskeletal Disorders

    OpenAIRE

    Gold, J.E.; Piligian, G.; Glutting, J.J.; Hanlon, A.; Frings-Dresen, M.H.W.; Sluiter, J.K.

    2010-01-01

    Introduction Some musculoskeletal disorders of the upper extremity are not readily classified. The study objective was to determine if there were symptom patterns in self-identified repetitive strain injury (RSI) patients. Methods Members (n = 700) of the Dutch RSI Patients Association filled out a detailed symptom questionnaire. Factor analysis followed by cluster analysis grouped correlated symptoms. Results Eight clusters, based largely on symptom severity and quality were formulated. All ...

  20. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    We present an approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. We have also proposed a buffer size and worst case queuing delay analysis for the gateways......, responsible for routing inter-cluster traffic. Optimization heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of our approaches....

  1. Schedulability Analysis and Optimization for the Synthesis of Multi-Cluster Distributed Embedded Systems

    DEFF Research Database (Denmark)

    Pop, Paul; Eles, Petru; Peng, Zebo

    2003-01-01

    An approach to schedulability analysis for the synthesis of multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways, is presented. A buffer size and worst case queuing delay analysis for the gateways, responsible for routing...... inter-cluster traffic, is also proposed. Optimisation heuristics for the priority assignment and synthesis of bus access parameters aimed at producing a schedulable system with minimal buffer needs have been proposed. Extensive experiments and a real-life example show the efficiency of the approaches....

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

    Science.gov (United States)

    Nascimento, R. S.; Ribeiro, A. L. B.; Trevisan, M.; Carrasco, E. R.; Plana, H.; Dupke, R.

    2016-08-01

    We carried out a dynamical study of the galaxy cluster pair A3407 and A3408 based on a spectroscopic survey obtained with the 4 metre Blanco telescope at the Cerro Tololo Interamerican Observatory, plus 6dF data, and ROSAT All-Sky Survey. The sample consists of 122 member galaxies brighter than mR = 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 ˜847 ± 114 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 per cent cl. This is consistent with the X-ray distribution around this field, which shows no common X-ray halo involving A3407 and A3408. We also estimated virial masses and applied a two-body model to probe the dynamics of the pair. The more likely scenario is that in which the pair is gravitationally bound and probably experiences a collapse phase, with the cluster cores crossing in less than ˜1 h-1 Gyr, a pre-merger scenario. The complex X-ray morphology, the gas temperature, and some signs of galaxy evolution in A3408 suggest a post-merger scenario, with cores having crossed each other ˜1.65 h-1 Gyr ago, as an alternative solution.

  3. METHODS ADVANCEMENT FOR MILK ANALYSIS: THE MAMA STUDY

    Science.gov (United States)

    The Methods Advancement for Milk Analysis (MAMA) study was designed by US EPA and CDC investigators to provide data to support the technological and study design needs of the proposed National Children=s Study (NCS). The NCS is a multi-Agency-sponsored study, authorized under the...

  4. Analysis of luminescence spectra of substrate-free icosahedral and crystalline clusters of argon

    CERN Document Server

    Doronin, Yu S; Kamarchuk, G V; Tkachenko, A A; Samovarov, V N

    2016-01-01

    We propose a new approach to analysis of cathodoluminescence spectra of substrate-free nanoclusters of argon produced in a supersonic jet expanding into a vacuum. It is employed to analyze intensities of the luminescence bands of neutral and charged excimer complexes (Ar2)* and (Ar4+)* measured for clusters with an average size of 500 to 8900 atoms per cluster and diameters ranging from 32 to 87 {\\AA}. Concentration of the jet substance condensed into clusters, which determines the absolute values of the integrated band intensities, is shown to be proportional to the logarithm of the average cluster size. Analysis of reduced intensities of the (Ar2)* and (Ar4+)* bands in the spectra of crystalline clusters with an fcc structure allows us to conclude that emission of the neutral molecules (Ar2)* comes from within the whole volume of the cluster, while the charged complexes (Ar4+)* radiate from its near-surface layers. We find the cluster size range in which the jet is dominated by quasicrystalline clusters wit...

  5. 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

  6. 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...

  7. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

    International Nuclear Information System (INIS)

    Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [18F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JImean= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JIrange: 0.2301–1). Conclusion: Using commonly-used clustering algorithms, we found poor

  8. SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Harmon, S; Wendelberger, B [University of Wisconsin-Madison, Madison, WI (United States); Jeraj, R [University of Wisconsin-Madison, Madison, WI (United States); University of Ljubljana (Slovenia)

    2014-06-01

    Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [{sup 18}F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI{sub mean}= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI{sub range}: 0.2301–1). Conclusion: Using commonly-used clustering algorithms

  9. 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.

  10. 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

  11. Analysis of protein profiles using fuzzy clustering methods

    DEFF Research Database (Denmark)

    Karemore, Gopal Raghunath; Ukendt, Sujatha; Rai, Lavanya; Kartha, V.B; C, Santhosh

    The tissue protein profiles of healthy volunteers and volunteers with cervical cancer were recorded using High Performance Liquid Chromatography combined with Laser Induced Fluorescence  technique  (HPLC-LIF)  developed  in  our  lab.      We analyzed      the protein profile data using different...... 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...

  12. 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.

  13. 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.

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

    DEFF Research Database (Denmark)

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

    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......-transformation of the cost data and apply the normal theory on these data. Again we try to account for the cluster effect. The performance of these two methods is investigated in a simulation study. The advantages and disadvantages of the different approaches are discussed....

  15. LEVEL SET BASED CLUSTERING FOR ANALYSIS OF FUNCTIONAL MRI DATA

    OpenAIRE

    Bathula, D. R.; Papademetris, X.; Duncan, J S

    2007-01-01

    We present a level set based clustering technique to detect activation regions from functional brain images using contextual information. Earlier similar approaches have been primarily concerned with local spatial context. Our approach relies on the idea that voxels within a functional region have similar temporal behavior. Using a level set formulation, a two-dimensional curve is evolved with a speed proportional to a similarity measure between the fMRI signals of voxels lying on the curve a...

  16. Clustering analysis of malware behavior using Self Organizing Map

    DEFF Research Database (Denmark)

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

    2016-01-01

    For the time being, malware behavioral classification is performed by means of Anti-Virus (AV) generated labels. The paper investigates the inconsistencies associated with current practices by evaluating the identified differences between current vendors. In this paper we rely on Self Organizing...... accurate results based on the clusters created by competitive and cooperative algorithms like Self Organizing Map that better describe the behavioral profile of malware....

  17. Insights into quasar UV spectra using unsupervised clustering analysis

    Science.gov (United States)

    Tammour, A.; Gallagher, S. C.; Daley, M.; Richards, G. T.

    2016-06-01

    Machine learning techniques can provide powerful tools to detect patterns in multidimensional parameter space. We use K-means - a simple yet powerful unsupervised clustering algorithm which picks out structure in unlabelled data - to study a sample of quasar UV spectra from the Quasar Catalog of the 10th Data Release of the Sloan Digital Sky Survey (SDSS-DR10) of Paris et al. Detecting patterns in large data sets 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 Å line, the C IV 1549 Å line, and the C III] 1908 Å 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 distribution (SED) probed by the strength of He II and the Si III]/C III] ratio. We find this to be particularly evident when the properties of C III] are used to find the clusters, while those of Mg II proved to be less strongly correlated with the properties of the other lines in the spectra such as the width of C IV or the Si III]/C III] ratio. We conclude that unsupervised clustering methods (such as K-means) are powerful methods for finding `natural' binning boundaries in multidimensional data sets and discuss caveats and future work.

  18. 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

  19. X-ray Analysis of Filaments in Galaxy Clusters

    CERN Document Server

    Walker, S A; Fabian, A C; Sanders, J S

    2015-01-01

    We perform a detailed X-ray study of the filaments surrounding the brightest cluster galaxies in a sample of nearby galaxy clusters using deep Chandra observations, namely the Perseus, Centaurus and Virgo clusters, and Abell 1795. We compare the X-ray properties and spectra of the filaments in all of these systems, and find that their Chandra X-ray spectra are all broadly consistent with an absorbed two temperature thermal model, with temperature components at 0.75 and 1.7 keV. We find that it is also possible to model the Chandra ACIS filament spectra with a charge exchange model provided a thermal component is also present, and the abundance of oxygen is suppressed relative to the abundance of Fe. In this model, charge exchange provides the dominant contribution to the spectrum in the 0.5-1.0 keV band. However, when we study the high spectral resolution RGS spectrum of the filamentary plume seen in X-rays in Centaurus, the opposite appears to be the case. The properties of the filaments in our sample of clu...

  20. 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.

  1. Abundance analysis of an extended sample of open clusters: A search for chemical inhomogeneities

    Science.gov (United States)

    Reddy, Arumalla B. S.; Giridhar, Sunetra; Lambert, David L.

    We have initiated a program to explore the presence of chemical inhomogeneities in the Galactic disk using the open clusters as ideal probes. We have analyzed high-dispersion echelle spectra (R ≥ 55,000) of red giant members for eleven open clusters to derive abundances for many elements. The membership to the cluster has been confirmed through their radial velocities and proper motions. The spread in temperatures and gravities being very small among the red giants, nearly the same stellar lines were employed thereby reducing the random errors. The errors of average abundance for the cluster were generally in 0.02 to 0.07 dex range. Our present sample covers galactocentric distances of 8.3 to 11.3 kpc and an age range of 0.2 to 4.3 Gyrs. Our earlier analysis of four open clusters (Reddy A.B.S. et al., 2012, MNRAS, 419,1350) indicate that abundances relative to Fe for elements from Na to Eu are equal within measurement uncertainties to published abundances for thin disk giants in the field. This supports the view that field stars come from disrupted open clusters. In the enlarged sample of eleven open clusters we find cluster to cluster abundance variations for some s- and r- process elements, with certain elements such as Zr and Ba showing large variation. These differences mark the signatures that these clusters had formed under different environmental conditions (Type II SN, Type Ia SN, AGB stars or a mixture of any of these) unique to the time and site of formation. These eleven clusters support the widely held impression that there is an abundance gradient such that the metallicity [Fe/H] at the solar galactocentric distance decreases outwards at about -0.1 dex per kpc.

  2. The CERN analysis facility-a PROOF cluster for day-one physics analysis

    International Nuclear Information System (INIS)

    ALICE (A Large Ion Collider Experiment) at the LHC plans to use a PROOF cluster at CERN (CAF - CERN Analysis Facility) for analysis. The system is especially aimed at the prototyping phase of analyses that need a high number of development iterations and thus require a short response time. Typical examples are the tuning of cuts during the development of an analysis as well as calibration and alignment. Furthermore, the use of an interactive system with very fast response will allow ALICE to extract physics observables out of first data quickly. An additional use case is fast event simulation and reconstruction. A test setup consisting of 40 machines is used for evaluation since May 2006. The PROOF system enables the parallel processing and xrootd the access to files distributed on the test cluster. An automatic staging system for files either catalogued in the ALICE file catalog or stored in the CASTOR mass storage system has been developed. The current setup and ongoing development towards disk quotas and CPU fairshare are described. Furthermore, the integration of PROOF into ALICE's software framework (AliRoot) is discussed

  3. A general description of AARE [Advanced Analysis for Reactor Engineering]: A modular system for advanced analysis of reactor engineering

    International Nuclear Information System (INIS)

    The AARE [Advanced Analysis for Reactor Engineering] modular code system for physics analysis of a wide range of reactor systems, including fusion blankets and shielding, is being developed jointly between PSI [Paul Scherrer Institute] and the Los Alamos National Laboratory. In the present work, a general description of the system, its philosophy and capabilities, together with a brief summary of its modules and data libraries, is given. The cross-section shielding and reformatting code, TRAMIX, is described in more detail

  4. "ATLAS" Advanced Technology Life-cycle Analysis System

    Science.gov (United States)

    Lollar, Louis F.; Mankins, John C.; ONeil, Daniel A.

    2004-01-01

    Making good decisions concerning research and development portfolios-and concerning the best systems concepts to pursue - as early as possible in the life cycle of advanced technologies is a key goal of R&D management This goal depends upon the effective integration of information from a wide variety of sources as well as focused, high-level analyses intended to inform such decisions Life-cycle Analysis System (ATLAS) methodology and tool kit. ATLAS encompasses a wide range of methods and tools. A key foundation for ATLAS is the NASA-created Technology Readiness. The toolkit is largely spreadsheet based (as of August 2003). This product is being funded by the Human and Robotics The presentation provides a summary of the Advanced Technology Level (TRL) systems Technology Program Office, Office of Exploration Systems, NASA Headquarters, Washington D.C. and is being integrated by Dan O Neil of the Advanced Projects Office, NASA/MSFC, Huntsville, AL

  5. An investigation of cooling flows and general cluster properties from an X-ray image deprojection analysis of 207 clusters of galaxies

    CERN Document Server

    White, D A; Forman, W R

    1997-01-01

    We present an X-ray image deprojection analysis of Einstein Observatory imaging data on 207, clusters of galaxies. The resulting radial profiles for luminosity, temperature, and electron density variations are determined from the cluster surface-brightness profiles according to gravitational potential constraints from average Tx and Vopt observations. This enables us to determine cooling-flow and other cluster properties, such as baryon fractions, S-Z microwave decrements, and Thomson depths. We have compiled a catalogue of the detected cooling flows, and investigated their effects on general cluster properties. Self-consistent correlations between the cluster X-ray luminosity, temperature, and optical velocity-dispersion, are determined accounting for errors in both dimensions of the data. These fits indicate that the temperatures of clusters are isothermal and that they have spectral beta-values consistent with unity. We find that the X-ray Lx, Tx, and optical Vopt relations depend significantly on the cool...

  6. Advanced PWR technology development -Development of advanced PWR system analysis technology-

    Energy Technology Data Exchange (ETDEWEB)

    Jang, Moon Heui; Hwang, Yung Dong; Kim, Sung Oh; Yoon, Joo Hyun; Jung, Bub Dong; Choi, Chul Jin; Lee, Yung Jin; Song, Jin Hoh [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1995-07-01

    The primary scope of this study is to establish the analysis technology for the advanced reactor designed on the basis of the passive and inherent safety concepts. This study is extended to the application of these technology to the safety analysis of the passive reactor. The study was performed for the small and medium sized reactor and the large sized reactor by focusing on the development of the analysis technology for the passive components. Among the identified concepts the once-through steam generator, the natural circulation of the integral reactor, heat pipe for containment cooling, and hydraulic valve were selected as the high priority items to be developed and the related studies are being performed for these items. For the large sized passive reactor, the study plans to extend the applicability of the best estimate computer code RELAP5/MOD3 which is widely used for the safety analyses of the reactor system. The improvement and supplementation study of the analysis modeling and the methodology is planned to be carried out for these purpose. The newly developed technologies are expected to be applied to the domestic advanced reactor design and analysis and these technologies will play a key role in extending the domestic nuclear base technology and consolidating self-reliance in the essential nuclear technology. 72 figs, 15 tabs, 124 refs. (Author).

  7. Advanced PWR technology development -Development of advanced PWR system analysis technology-

    International Nuclear Information System (INIS)

    The primary scope of this study is to establish the analysis technology for the advanced reactor designed on the basis of the passive and inherent safety concepts. This study is extended to the application of these technology to the safety analysis of the passive reactor. The study was performed for the small and medium sized reactor and the large sized reactor by focusing on the development of the analysis technology for the passive components. Among the identified concepts the once-through steam generator, the natural circulation of the integral reactor, heat pipe for containment cooling, and hydraulic valve were selected as the high priority items to be developed and the related studies are being performed for these items. For the large sized passive reactor, the study plans to extend the applicability of the best estimate computer code RELAP5/MOD3 which is widely used for the safety analyses of the reactor system. The improvement and supplementation study of the analysis modeling and the methodology is planned to be carried out for these purpose. The newly developed technologies are expected to be applied to the domestic advanced reactor design and analysis and these technologies will play a key role in extending the domestic nuclear base technology and consolidating self-reliance in the essential nuclear technology. 72 figs, 15 tabs, 124 refs. (Author)

  8. 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.

  9. Functional cluster analysis of CT perfusion maps: a new tool for diagnosis of acute stroke?

    Science.gov (United States)

    Baumgartner, Christian; Gautsch, Kurt; Böhm, Christian; Felber, Stephan

    2005-09-01

    CT perfusion imaging constitutes an important contribution to the early diagnosis of acute stroke. Cerebral blood flow (CBF), cerebral blood volume (CBV) and time-to-peak (TTP) maps are used to estimate the severity of cerebral damage after acute ischemia. We introduce functional cluster analysis as a new tool to evaluate CT perfusion in order to identify normal brain, ischemic tissue and large vessels. CBF, CBV and TTP maps represent the basis for cluster analysis applying a partitioning (k-means) and density-based (density-based spatial clustering of applications with noise, DBSCAN) paradigm. In patients with transient ischemic attack and stroke, cluster analysis identified brain areas with distinct hemodynamic properties (gray and white matter) and segmented territorial ischemia. CBF, CBV and TTP values of each detected cluster were displayed. Our preliminary results indicate that functional cluster analysis of CT perfusion maps may become a helpful tool for the interpretation of perfusion maps and provide a rapid means for the segmentation of ischemic tissue. PMID:15827821

  10. 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.

  11. 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.

  12. Angle-resolved analysis of magnetic hysteresis for micro-magnetic clusters with local deformations

    International Nuclear Information System (INIS)

    Magnetic dynamic process for an octagonal micro-magnetic cluster with one dislocation loop was simulated by pseudo-nonequilibrial Monte Carlo method including pseudo-dipole interaction. The results showed that the magnetic hysteresis curves of micro-magnetic clusters with one dislocation loop and of those without any deformation differ. The difference is more clearly shown for noise pattern of magnetization process, which depends on dynamic behavior of spin ensemble. A series of snapshots for spin ensemble displays that the magnetization process depends on the direction of the applied magnetic field. We propose usefulness of angle-resolved analysis of magnetic dynamic process to evaluate magnetic clusters

  13. Femtosecond laser energy deposition in strongly absorbing cluster gases diagnosed by blast wave trajectory analysis

    International Nuclear Information System (INIS)

    An intense ultrafast laser pulse can be very strongly absorbed in a moderate density gas composed of van der Waals bonded clusters. In this paper, the deposition of the energy of intense 30 fs light pulses in a gas of deuterium clusters has been diagnosed using a technique based on analysis of the trajectories of the resulting cylindrically symmetric blast waves. Using the well-known relation between blast wave velocity and energy deposition in gas, the laser energy deposited per unit length as a function of distance in gas jet plume was measured. These measurements were conducted in jets containing either deuterium clusters or simple deuterium molecules

  14. Chaotic Artificial Bee Colony Used for Cluster Analysis

    Science.gov (United States)

    Zhang, Yudong; Wu, Lenan; Wang, Shuihua; Huo, Yuankai

    A new approach based on artificial bee colony (ABC) with chaotic theory was proposed to solve the partitional clustering problem. We first investigate the optimization model including both the encoding strategy and the variance ratio criterion (VRC). Second, a chaotic ABC algorithm was developed based on the Rossler attractor. Experiments on three types of artificial data of different degrees of overlapping all demonstrate the CABC is superior to both genetic algorithm (GA) and combinatorial particle swarm optimization (CPSO) in terms of robustness and computation time.

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

    DEFF Research Database (Denmark)

    Santoro, Francesco; Pedro, Sergio; Tan, Zheng-Hua; Moeslund, Thomas B.

    2010-01-01

    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, 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...

  16. Segmentation of Shopping Centers Visitors based on their Activities through Factor Analysis and Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Mansoor Momeni

    2013-01-01

    Full Text Available Knowing the customers of shopping centers contributes greatly to increase profits of thesecenters. Customers’ segmentation is one of the most effective means of knowing customers.The purpose of this study was to present a segmentation of customers based on their activitiesin the shopping centers. The participants were 157 visitors of Milad-e-Noor Shopping Centerwho were required to answer the questions in the questionnaire. Data were analyzed in threesteps. Through the use of factor analysis, in the first step, the number of variables wasreduced to the four factors of entertainment activities, planned shopping, shoppinginformation gathering, and unplanned shopping. These factors were then inserted into Kmeancluster analysis and, in the second step the visitors were divided into 4 segments on thebasis of their activities as following: traditionalists, shopping center enthusiasts, wanderingcustomers, and entertainment seekers. In the third step, the demographic and behavioralvariables were investigated in the identified clusters. Considering the variables of age,academic status, and accompanying persons in shopping centers, these clusters weresignificantly different. However, in respect to the variables of sex, marital status, the lengthof presence in the shopping centers, occupations, and monthly salary they were recognized ashomogenous.

  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. Advances in Mid-Infrared Spectroscopy for Chemical Analysis.

    Science.gov (United States)

    Haas, Julian; Mizaikoff, Boris

    2016-06-12

    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. PMID:27070183

  19. 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.

  20. Joint analysis of X-ray and Sunyaev Zel'dovich observations of galaxy clusters using an analytic model of the intra-cluster medium

    CERN Document Server

    Hasler, Nicole; Bonamente, Massimiliano; Carlstrom, John E; Culverhouse, Thomas L; Gralla, Megan; Greer, Christopher; Hawkins, David; Hennessy, Ryan; Joy, Marshall; Kolodziejczak, Jeffery; Lamb, James W; Landry, David; Leitch, Erik M; Mantz, Adam; Marrone, Daniel P; Miller, Amber; Mroczkowski, Tony; Muchovej, Stephen; Plagge, Thomas; Pryke, Clem; Woody, David

    2012-01-01

    We perform a joint analysis of X-ray and Sunyaev Zel'dovich (SZ) effect data using an analytic model that describes the gas properties of galaxy clusters. The joint analysis allows the measurement of the cluster gas mass fraction profile and Hubble constant independent of cosmological parameters. Weak cosmological priors are used to calculate the overdensity radius within which the gas mass fractions are reported. Such an analysis can provide direct constraints on the evolution of the cluster gas mass fraction with redshift. We validate the model and the joint analysis on high signal-to-noise data from the Chandra X-ray Observatory and the Sunyaev-Zel'dovich Array for two clusters, Abell 2631 and Abell 2204.

  1. 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...

  2. Somatosensory nociceptive characteristics differentiate subgroups in people with chronic low back pain: a cluster analysis.

    Science.gov (United States)

    Rabey, Martin; Slater, Helen; OʼSullivan, Peter; Beales, Darren; Smith, Anne

    2015-10-01

    The objectives of this study were to explore the existence of subgroups in a cohort with chronic low back pain (n = 294) based on the results of multimodal sensory testing and profile subgroups on demographic, psychological, lifestyle, and general health factors. Bedside (2-point discrimination, brush, vibration and pinprick perception, temporal summation on repeated monofilament stimulation) and laboratory (mechanical detection threshold, pressure, heat and cold pain thresholds, conditioned pain modulation) sensory testing were examined at wrist and lumbar sites. Data were entered into principal component analysis, and 5 component scores were entered into latent class analysis. Three clusters, with different sensory characteristics, were derived. Cluster 1 (31.9%) was characterised by average to high temperature and pressure pain sensitivity. Cluster 2 (52.0%) was characterised by average to high pressure pain sensitivity. Cluster 3 (16.0%) was characterised by low temperature and pressure pain sensitivity. Temporal summation occurred significantly more frequently in cluster 1. Subgroups were profiled on pain intensity, disability, depression, anxiety, stress, life events, fear avoidance, catastrophizing, perception of the low back region, comorbidities, body mass index, multiple pain sites, sleep, and activity levels. Clusters 1 and 2 had a significantly greater proportion of female participants and higher depression and sleep disturbance scores than cluster 3. The proportion of participants undertaking <300 minutes per week of moderate activity was significantly greater in cluster 1 than in clusters 2 and 3. Low back pain, therefore, does not appear to be homogeneous. Pain mechanisms relating to presentations of each subgroup were postulated. Future research may investigate prognoses and interventions tailored towards these subgroups. PMID:26020225

  3. 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.

  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. Advanced Techniques in Pulmonary Function Test Analysis Interpretation and Diagnosis

    OpenAIRE

    Gildea, T.J.; Bell, C. William

    1980-01-01

    The Pulmonary Functions Analysis and Diagnostic System is an advanced clinical processing system developed for use at the Pulmonary Division, Department of Medicine at the University of Nebraska Medical Center. The system generates comparative results and diagnostic impressions for a variety of routine and specialized pulmonary functions test data. Routine evaluation deals with static lung volumes, breathing mechanics, diffusing capacity, and blood gases while specialized tests include lung c...

  6. ANALYSIS OF RISING TUITION RATES IN THE UNITED STATES BASED ON CLUSTERING ANALYSIS AND REGRESSION MODELS

    Directory of Open Access Journals (Sweden)

    Long Cheng

    2016-05-01

    Full Text Available Since higher education is one of the major driving forces for country development and social prosperity, and tuition plays a significant role in determining whether or not a person can afford to receive higher education, the rising tuition is a topic of big concern today. So it is essentially necessary to understand what factors affect the tuition and how they increase or decrease the tuition. Many existing studies on the rising tuition either lack large amounts of real data and proper quantitative models to support their conclusions, or are limited to focus on only a few factors that might affect the tuition, which fail to make a comprehensive analysis. In this paper, we explore a wide variety of factors that might affect the tuition growth rate by use of large amounts of authentic data and different quantitative methods such as clustering analysis and regression models.

  7. Advanced exergoeconomic analysis of the multistage mixed refrigerant systems

    International Nuclear Information System (INIS)

    Highlights: • Advanced exergoeconomic analysis is performed for mixed refrigerant systems. • Cost of investment is divided into avoidable/unavoidable and endogenous/exogenous. • Results show that interactions between the components is not considerable. - Abstract: Advanced exergoeconomic analysis is applied on three multi stage mixed refrigerant liquefaction processes. They are propane precooled mixed refrigerant, dual mixed refrigerant and mixed fluid cascade. Cost of investment and exergy destruction for the components with high inefficiencies are divided into avoidable/unavoidable and endogenous/exogenous parts. According to the avoidable exergy destruction cost in propane precooled mixed refrigerant process, C-2 compressor with 455.5 ($/h), in dual mixed refrigerant process, C-1 compressor with 510.8 ($/h) and in mixed fluid cascade process, C-2/1 compressor with 338.8 ($/h) should be considered first. A comparison between the conventional and advanced exergoeconomic analysis is done by three important parameters: Exergy efficiency, exergoeconomic factor and total costs. Results show that interactions between the process components are not considerable because cost of investment and exergy destruction in most of them are endogenous. Exergy destruction cost of the compressors is avoidable while heat exchangers and air coolers destruction cost are unavoidable. Investment cost of heat exchangers and air coolers are avoidable while compressor’s are unavoidable

  8. Provenience study of medieval Bulgarian glasses by NAA and cluster analysis

    International Nuclear Information System (INIS)

    The neutron activation analysis results from 30 glass samples were subjected to cluster analysis. The reliable localization of part of the medieval glass finds from Preslav enabled the evaluation of the variety of the production of a medieval glass workshop (ninth-tenth century), allowing conclusions to be made about the technological level of glass-making in Bulgaria during the Middle Ages. The work proved that NAA followed by cluster analysis is a successful approach to finding the local and chronological features of the investigated glasses. (author)

  9. Provenance Study of the Terracotta Army of Qin Shihuang’s Mausoleum by Fuzzy Cluster Analysis

    OpenAIRE

    Rongwu Li; Guoxia Li

    2015-01-01

    20 samples and 44 samples of terracotta warriors and horses from the 1st and 3rd pits of Qin Shihuang’s Mausoleum, 20 samples of clay near Qin’s Mausoleum, and 2 samples of Yaozhou porcelain bodies are obtained to determine the contents of 32 elements in each of them by neutron activation analysis (NAA). The NAA data are further analyzed using fuzzy cluster analysis to obtain the fuzzy cluster trend diagram. The analysis shows that the origins of the raw material of the terracotta warriors an...

  10. A Review on Clustering and Outlier Analysis Techniques in Datamining

    OpenAIRE

    S. Koteeswaran; P. Visu; J. Janet

    2012-01-01

    Problem statement: The modern world is based on using physical, biological and social systems more effectively using advanced computerized techniques. A great amount of data being generated by such systems; it leads to a paradigm shift from classical modeling and analyses based on basic principles to developing models and the corresponding analyses directly from data. The ability to extract useful hidden knowledge in these data and to act on that knowledge is becoming increasingly important i...

  11. 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...

  12. Ecosystem health pattern analysis of urban clusters based on emergy synthesis: Results and implication for management

    International Nuclear Information System (INIS)

    The evaluation of ecosystem health in urban clusters will help establish effective management that promotes sustainable regional development. To standardize the application of emergy synthesis and set pair analysis (EM–SPA) in ecosystem health assessment, a procedure for using EM–SPA models was established in this paper by combining the ability of emergy synthesis to reflect health status from a biophysical perspective with the ability of set pair analysis to describe extensive relationships among different variables. Based on the EM–SPA model, the relative health levels of selected urban clusters and their related ecosystem health patterns were characterized. The health states of three typical Chinese urban clusters – Jing-Jin-Tang, Yangtze River Delta, and Pearl River Delta – were investigated using the model. The results showed that the health status of the Pearl River Delta was relatively good; the health for the Yangtze River Delta was poor. As for the specific health characteristics, the Pearl River Delta and Yangtze River Delta urban clusters were relatively strong in Vigor, Resilience, and Urban ecosystem service function maintenance, while the Jing-Jin-Tang was relatively strong in organizational structure and environmental impact. Guidelines for managing these different urban clusters were put forward based on the analysis of the results of this study. - Highlights: • The use of integrated emergy synthesis and set pair analysis model was standardized. • The integrated model was applied on the scale of an urban cluster. • Health patterns of different urban clusters were compared. • Policy suggestions were provided based on the health pattern analysis

  13. Reflector modeling in advanced nodal analysis of pressurized water reactors

    International Nuclear Information System (INIS)

    Recent progress in the modeling of the reflector regions of pressurized water reactors within the framework of advanced nodal diffusion analysis methods is reviewed. Attention is focused on the modeling of the radial reflector of a PWR which is most problematic because of its irregular and heterogeneous structure. Numerical results are presented to demonstrate the high accuracy of the methods which are now available for generating nodal reflector parameters and it is shown that errors due to reflector modeling in multi-dimensional nodal reactor analysis can be practically eliminated. (author). 23 refs, 1 fig., 2 tabs

  14. 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.

  15. 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.

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

    International Nuclear Information System (INIS)

    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

  17. 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. PMID:24754648

  18. 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. PMID:24749676

  19. 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.

  20. Windows forensic analysis toolkit advanced analysis techniques for Windows 7

    CERN Document Server

    Carvey, Harlan

    2012-01-01

    Now in its third edition, Harlan Carvey has updated "Windows Forensic Analysis Toolkit" to cover Windows 7 systems. The primary focus of this edition is on analyzing Windows 7 systems and on processes using free and open-source tools. The book covers live response, file analysis, malware detection, timeline, and much more. The author presents real-life experiences from the trenches, making the material realistic and showing the why behind the how. New to this edition, the companion and toolkit materials are now hosted online. This material consists of electronic printable checklists, cheat sheets, free custom tools, and walk-through demos. This edition complements "Windows Forensic Analysis Toolkit, 2nd Edition", (ISBN: 9781597494229), which focuses primarily on XP. It includes complete coverage and examples on Windows 7 systems. It contains Lessons from the Field, Case Studies, and War Stories. It features companion online material, including electronic printable checklists, cheat sheets, free custom tools, ...

  1. Event-by-event cluster analysis of final states from heavy ion collisions

    OpenAIRE

    Fialkowski, K.; Wit, R.

    1999-01-01

    We present an event-by-event analysis of the cluster structure of final multihadron states resulting from heavy ion collisions. A comparison of experimental data with the states obtained from Monte Carlo generators is shown. The analysis of the first available experimental events suggests that the method is suitable for selecting some different types of events.

  2. 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…

  3. Online analysis and visualization of EAST experiment data based on Linux cluster

    International Nuclear Information System (INIS)

    To achieve long steady-state performance of the EAST tokamak requires online analysis and visualization for significant experiment data. The designing scenario of the system function structure and hardware structure as well as some concepts of Linux cluster is presented. The approach to realize the online analysis and visualization of experiment data has been described in detail. (authors)

  4. Structural Configuration Systems Analysis for Advanced Aircraft Fuselage Concepts

    Science.gov (United States)

    Mukhopadhyay, Vivek; Welstead, Jason R.; Quinlan, Jesse R.; Guynn, Mark D.

    2016-01-01

    Structural configuration analysis of an advanced aircraft fuselage concept is investigated. This concept is characterized by a double-bubble section fuselage with rear mounted engines. Based on lessons learned from structural systems analysis of unconventional aircraft, high-fidelity finite-element models (FEM) are developed for evaluating structural performance of three double-bubble section configurations. Structural sizing and stress analysis are applied for design improvement and weight reduction. Among the three double-bubble configurations, the double-D cross-section fuselage design was found to have a relatively lower structural weight. The structural FEM weights of these three double-bubble fuselage section concepts are also compared with several cylindrical fuselage models. Since these fuselage concepts are different in size, shape and material, the fuselage structural FEM weights are normalized by the corresponding passenger floor area for a relative comparison. This structural systems analysis indicates that an advanced composite double-D section fuselage may have a relative structural weight ratio advantage over a conventional aluminum fuselage. Ten commercial and conceptual aircraft fuselage structural weight estimates, which are empirically derived from the corresponding maximum takeoff gross weight, are also presented and compared with the FEM- based estimates for possible correlation. A conceptual full vehicle FEM model with a double-D fuselage is also developed for preliminary structural analysis and weight estimation.

  5. 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

  6. A COMPARISON BETWEEN SINGLE LINKAGE AND COMPLETE LINKAGE IN AGGLOMERATIVE HIERARCHICAL CLUSTER ANALYSIS FOR IDENTIFYING TOURISTS SEGMENTS

    Directory of Open Access Journals (Sweden)

    Noor Rashidah Rashid

    2012-02-01

    Full Text Available Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis test. The result of the comparison used for segmenting tourists of Kapas Island. The statistical software SPSS has been applied to analyze data of this research. The result from Kruskal-Wallis test shows Complete Linkage is more useful in identifying tourists segments. Keywords : Agglomerative Hierarchical Cluster Analysis, Single Linkage, Complete Linkage, Kruskal-Wallis test, tourists

  7. Weighted Clustering

    OpenAIRE

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

    2012-01-01

    We investigate a natural generalization of the classical clusteringproblem, considering clustering tasks in which differentinstances may have different weights.We conduct the firstextensive theoretical analysis on the influence of weighteddata on standard clustering algorithms in both the partitionaland hierarchical settings, characterizing the conditions underwhich algorithms react to weights. Extending a recent frameworkfor clustering algorithm selection, we propose intuitiveproperties that...

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Functional Interference Clusters in Cancer Patients With Bone Metastases: A Secondary Analysis of RTOG 9714

    International Nuclear Information System (INIS)

    Purpose: To explore the relationships (clusters) among the functional interference items in the Brief Pain Inventory (BPI) in patients with bone metastases. Methods: Patients enrolled in the Radiation Therapy Oncology Group (RTOG) 9714 bone metastases study were eligible. Patients were assessed at baseline and 4, 8, and 12 weeks after randomization for the palliative radiotherapy with the BPI, which consists of seven functional items: general activity, mood, walking ability, normal work, relations with others, sleep, and enjoyment of life. Principal component analysis with varimax rotation was used to determine the clusters between the functional items at baseline and the follow-up. Cronbach's alpha was used to determine the consistency and reliability of each cluster at baseline and follow-up. Results: There were 448 male and 461 female patients, with a median age of 67 years. There were two functional interference clusters at baseline, which accounted for 71% of the total variance. The first cluster (physical interference) included normal work and walking ability, which accounted for 58% of the total variance. The second cluster (psychosocial interference) included relations with others and sleep, which accounted for 13% of the total variance. The Cronbach's alpha statistics were 0.83 and 0.80, respectively. The functional clusters changed at week 12 in responders but persisted through week 12 in nonresponders. Conclusion: Palliative radiotherapy is effective in reducing bone pain. Functional interference component clusters exist in patients treated for bone metastases. These clusters changed over time in this study, possibly attributable to treatment. Further research is needed to examine these effects.

  13. 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.

  14. 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.

  15. 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 ...

  16. 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.

  17. Advanced hydrogen/oxygen thrust chamber design analysis

    Science.gov (United States)

    Shoji, J. M.

    1973-01-01

    The results are reported of the advanced hydrogen/oxygen thrust chamber design analysis program. The primary objectives of this program were to: (1) provide an in-depth analytical investigation to develop thrust chamber cooling and fatigue life limitations of an advanced, high pressure, high performance H2/O2 engine design of 20,000-pounds (88960.0 N) thrust; and (2) integrate the existing heat transfer analysis, thermal fatigue and stress aspects for advanced chambers into a comprehensive computer program. Thrust chamber designs and analyses were performed to evaluate various combustor materials, coolant passage configurations (tubes and channels), and cooling circuits to define the nominal 1900 psia (1.31 x 10 to the 7th power N/sq m) chamber pressure, 300-cycle life thrust chamber. The cycle life capability of the selected configuration was then determined for three duty cycles. Also the influence of cycle life and chamber pressure on thrust chamber design was investigated by varying in cycle life requirements at the nominal chamber pressure and by varying the chamber pressure at the nominal cycle life requirement.

  18. 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.

  19. Advanced engineering analysis the calculus of variations and functional analysis with applications in mechanics

    CERN Document Server

    Lebedev, Leonid P; Eremeyev, Victor A

    2012-01-01

    Advanced Engineering Analysis is a textbook on modern engineering analysis, covering the calculus of variations, functional analysis, and control theory, as well as applications of these disciplines to mechanics. The book offers a brief and concise, yet complete explanation of essential theory and applications. It contains exercises with hints and solutions, ideal for self-study.

  20. Decaying dark matter: a stacking analysis of galaxy clusters to improve on current limits

    OpenAIRE

    Combet, C.; Maurin, D.; Nezri, E.; Pointecouteau, E.; Hinton, J. A.; R White

    2012-01-01

    We show that a stacking approach to galaxy clusters can improve current limits on decaying dark matter by a factor $\\gtrsim 5-100$, with respect to a single source analysis, for all-sky instruments such as Fermi-LAT. Based on the largest sample of X-ray-selected galaxy clusters available to date (the MCXC meta-catalogue), we provide all the astrophysical information, in particular the astrophysical term for decaying dark matter, required to perform an analysis with current instruments.

  1. The Swift X-ray Telescope Cluster Survey II. X-ray spectral analysis

    OpenAIRE

    P. TozziINAF, Osservatorio Astrofisico di Firenze; A. Moretti(Fermilab, Batavia, IL, USA); Tundo, E.; Liu, T.; Rosati, P.; Borgani, S.; Tagliaferri, G.; S. Campana; Fugazza, D.; Avanzo, P. D.

    2014-01-01

    (Abridged) We present a spectral analysis of a new, flux-limited sample of 72 X-ray selected clusters of galaxies identified with the X-ray Telescope (XRT) on board the Swift satellite down to a flux limit of ~10-14 erg/s/cm2 (SWXCS, Tundo et al. 2012). We carry out a detailed X-ray spectral analysis with the twofold aim of measuring redshifts and characterizing the properties of the Intra-Cluster Medium (ICM). Optical counterparts and spectroscopic or photometric redshifts ...

  2. STATISTICAL ANALYSIS OF DWARF GALAXIES AND THEIR GLOBULAR CLUSTERS IN THE LOCAL VOLUME

    International Nuclear Information System (INIS)

    Although morphological classification of dwarf galaxies into early and late types can account for some of their origin and characteristics, this does not aid the study of their formation mechanism. Thus an objective classification using principal component analysis together with K means cluster analysis of these dwarf galaxies and their globular clusters (GCs) is carried out to overcome this problem. It is found that the classification of dwarf galaxies in the local volume is irrespective of their morphological indices. The more massive (MV0 V0 > - 13.7) are influenced by their environment in the star formation process.

  3. Differentiating Procrastinators from Each Other: A Cluster Analysis.

    Science.gov (United States)

    Rozental, Alexander; Forsell, Erik; Svensson, Andreas; Forsström, David; Andersson, Gerhard; Carlbring, Per

    2015-01-01

    Procrastination refers to the tendency to postpone the initiation and completion of a given course of action. Approximately one-fifth of the adult population and half of the student population perceive themselves as being severe and chronic procrastinators. Albeit not a psychiatric diagnosis, procrastination has been shown to be associated with increased stress and anxiety, exacerbation of illness, and poorer performance in school and work. However, despite being severely debilitating, little is known about the population of procrastinators in terms of possible subgroups, and previous research has mainly investigated procrastination among university students. The current study examined data from a screening process recruiting participants to a randomized controlled trial of Internet-based cognitive behavior therapy for procrastination (Rozental et al., in press). In total, 710 treatment-seeking individuals completed self-report measures of procrastination, depression, anxiety, and quality of life. The results suggest that there might exist five separate subgroups, or clusters, of procrastinators: "Mild procrastinators" (24.93%), "Average procrastinators" (27.89%), "Well-adjusted procrastinators" (13.94%), "Severe procrastinators" (21.69%), and "Primarily depressed" (11.55%). Hence, there seems to be marked differences among procrastinators in terms of levels of severity, as well as a possible subgroup for which procrastinatory problems are primarily related to depression. Tailoring the treatment interventions to the specific procrastination profile of the individual could thus become important, as well as screening for comorbid psychiatric diagnoses in order to target difficulties associated with, for instance, depression. PMID:26178164

  4. An application of GA to normal and malignant tissues cluster analysis

    Science.gov (United States)

    Li, Xiang; Zhang, Guangjun; Yuan, Yan; Li, Qingbo; Wu, Jinguang

    2008-10-01

    In this paper, an application of genetic algorithm (GA) which makes the spectra of malignant tissue and that of normal tissue cluster respectively is investigated. Cluster analysis is a typical optimization problem of permutation and combination. The results of traditional algorithms closely depend on whether the parameters are rightly set. Besides, the physical understanding of sample spectra which has not been clearly known is usually needed to obtain a better result. The high dimension of the spectral data also adds difficulty in the analysis. Thus, it is almost impossible to set every parameter properly. Furthermore, since the variables and object functions are always discrete, there are a mass of local extremums. Conventional methods have no good strategy to deal with these inferior solutions. Therefore, the final cluster result is greatly influenced by the initial cluster centers and the order how the samples are input. Genetic algorithm is established based on the theory of nature selection and evolution. For GA, the understanding of the physical meaning is not necessary. Meanwhile, GA performs in a considerable high efficiency way. In the experiment, the sum of the inter-cluster distances is regarded as the object function. After smoothing, standard normal variate (SNV) processing, and outlier detection on sample spectra, Principal component analysis (PCA) is processed. Then selection, mutation and crossover are carried out on chromosomes whose ith bit value indicates which class sample i belongs to. Once the GA clustering is finished, tissue samples could be easily discriminated based on the characteristic absorbance peaks of protein, fat, nucleic acid and water. In this paper, three kinds of clustering algorithms are processed, and it shows that comparing to the conventional method, GA obtains a better result.

  5. Factor-cluster analysis and enrichment study of Mangrove sediments - An example from Mengkabong, Sabah

    International Nuclear Information System (INIS)

    This paper examines the tidal effects in the sediment of Mengkabong mangrove forest, Sabah. Generally, all the studied parameters showed high value at high tide compared to low tide. Factor-cluster analyses were adopted to allow the identification of controlling factors at high and low tides. Factor analysis extracted six controlling factors at high tide and seven controlling factors at low tide. Cluster analysis extracted two district clusters at high and low tides. The study showed that factor-cluster analysis application is a useful tool to single out the controlling factors at high and low tides. this will provide a basis for describing the tidal effects in the mangrove sediment. The salinity and electrical conductivity clusters as well as component loadings at high and low tide explained the tidal process where there is high contribution of seawater to mangrove sediments that controls the sediment chemistry. The geo accumulation index (Tgeo) values suggest the mangrove sediments are having background concentrations for Al, Cu, Fe and Zn and unpolluted for Pb. (author)

  6. An advanced probabilistic structural analysis method for implicit performance functions

    Science.gov (United States)

    Wu, Y.-T.; Millwater, H. R.; Cruse, T. A.

    1989-01-01

    In probabilistic structural analysis, the performance or response functions usually are implicitly defined and must be solved by numerical analysis methods such as finite element methods. In such cases, the most commonly used probabilistic analysis tool is the mean-based, second-moment method which provides only the first two statistical moments. This paper presents a generalized advanced mean value (AMV) method which is capable of establishing the distributions to provide additional information for reliability design. The method requires slightly more computations than the second-moment method but is highly efficient relative to the other alternative methods. In particular, the examples show that the AMV method can be used to solve problems involving non-monotonic functions that result in truncated distributions.

  7. Analyzing Developing Country Market Integration with Incomplete Price Data Using Cluster Analysis

    OpenAIRE

    Ansah, I.G.; Gardebroek, C.; Ihle, R.; Jaletac, M.

    2014-01-01

    Recent global food price developments have spurred renewed interest in analyzing integration of local markets to global markets. A popular approach to quantify market integration is cointegration analysis. However, local market price data often has missing values, outliers, or short and incomplete series, making cointegration analysis impossible. Instead of imputing missing data, this paper proposes cluster analysis as an alternative methodological approach for analyzing market integration. I...

  8. 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.

  9. Composite Structure Modeling and Analysis of Advanced Aircraft Fuselage Concepts

    Science.gov (United States)

    Mukhopadhyay, Vivek; Sorokach, Michael R.

    2015-01-01

    NASA Environmentally Responsible Aviation (ERA) project and the Boeing Company are collabrating to advance the unitized damage arresting composite airframe technology with application to the Hybrid-Wing-Body (HWB) aircraft. The testing of a HWB fuselage section with Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) construction is presently being conducted at NASA Langley. Based on lessons learned from previous HWB structural design studies, improved finite-element models (FEM) of the HWB multi-bay and bulkhead assembly are developed to evaluate the performance of the PRSEUS construction. In order to assess the comparative weight reduction benefits of the PRSEUS technology, conventional cylindrical skin-stringer-frame models of a cylindrical and a double-bubble section fuselage concepts are developed. Stress analysis with design cabin-pressure load and scenario based case studies are conducted for design improvement in each case. Alternate analysis with stitched composite hat-stringers and C-frames are also presented, in addition to the foam-core sandwich frame and pultruded rod-stringer construction. The FEM structural stress, strain and weights are computed and compared for relative weight/strength benefit assessment. The structural analysis and specific weight comparison of these stitched composite advanced aircraft fuselage concepts demonstrated that the pressurized HWB fuselage section assembly can be structurally as efficient as the conventional cylindrical fuselage section with composite stringer-frame and PRSEUS construction, and significantly better than the conventional aluminum construction and the double-bubble section concept.

  10. 20% inlet header break analysis of Advanced Heavy Water Reactor

    International Nuclear Information System (INIS)

    The proposed Advanced Heavy Water Reactor (AHWR) is a 750 MWt vertical pressure tube type boiling light water cooled and heavy water moderated reactor. A passive design feature of this reactor is that the heat removal is achieved through natural circulation of primary coolant at all power levels, with no primary coolant pumps. Loss of coolant due to failure of inlet header results in depressurization of primary heat transport (PHT) system and containment pressure rise. Depressurization activates various protective and engineered safety systems like reactor trip, isolation condenser and advanced accumulator, limiting the consequences of the event. This paper discusses the thermal hydraulic transient analysis for evaluating the safety of the reactor, following 20% inlet header break using RELAP5/MOD3.2. For the analysis, the system is discretized appropriately to simulate possible flow reversal in one of the core paths during the transient. Various modeling aspects are discussed in this paper and predictions are made for different parameters like pressure, temperature, steam quality and flow in different parts of the Primary Heat Transport (PHT) system. Flow and energy discharges into the containment are also estimated for use in containment analysis. (author)

  11. 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.

  12. Evaluation of sugar beet genotypes for root traits by principal component analysis and cluster analysis

    Directory of Open Access Journals (Sweden)

    Danojević Dario

    2016-01-01

    Full Text Available Sugar beet is the most important crop for sugar production in Europe. Wide genetic variability is essential in sugar beet breeding programs. The aim of this study is to evaluate variability for the main root traits and differences between monogerm and multigerm sugar beet genotypes from the breeding collection at the Institute of Field and Vegetable Crops. The following traits were analyzed: root weight (g, dry matter content (%, root head weight (g, root/head ratio (%, number of cambial rings, root length (cm and root diameter (cm. Mean values for two years per genotype were standardized and used for analysis. Principal Component Analysis (PCA and Cluster Analysis (CA were used to examine the level of diversity for 20 genotypes and to rank the contributions of the variables. According to CA genotypes could be placed into five main groups, where a large number of multigerm genotypes were put in one group. On average multigerm genotypes were characterized by higher mean values for root weight, length, diameter and lower root head ratio. Multigerm genotypes had higher coefficients of variation for nearly all measured root traits. [Projekat Ministarstva nauke Republike Srbije, br. TR31015

  13. Evaluation of Portland cement from X-ray diffraction associated with cluster analysis

    International Nuclear Information System (INIS)

    The Brazilian cement industry produced 64 million tons of cement in 2012, with noteworthy contribution of CP-II (slag), CP-III (blast furnace) and CP-IV (pozzolanic) cements. The industrial pole comprises about 80 factories that utilize raw materials of different origins and chemical compositions that require enhanced analytical technologies to optimize production in order to gain space in the growing consumer market in Brazil. This paper assesses the sensitivity of mineralogical analysis by X-ray diffraction associated with cluster analysis to distinguish different kinds of cements with different additions. This technique can be applied, for example, in the prospection of different types of limestone (calcitic, dolomitic and siliceous) as well as in the qualification of different clinkers. The cluster analysis does not require any specific knowledge of the mineralogical composition of the diffractograms to be clustered; rather, it is based on their similarity. The materials tested for addition have different origins: fly ashes from different power stations from South Brazil and slag from different steel plants in the Southeast. Cement with different additions of limestone and white Portland cement were also used. The Rietveld method of qualitative and quantitative analysis was used for measuring the results generated by the cluster analysis technique. (author)

  14. 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.

  15. Advanced Technology Lifecycle Analysis System (ATLAS) Technology Tool Box (TTB)

    Science.gov (United States)

    Doyle, Monica; ONeil, Daniel A.; Christensen, Carissa B.

    2005-01-01

    The Advanced Technology Lifecycle Analysis System (ATLAS) is a decision support tool designed to aid program managers and strategic planners in determining how to invest technology research and development dollars. It is an Excel-based modeling package that allows a user to build complex space architectures and evaluate the impact of various technology choices. ATLAS contains system models, cost and operations models, a campaign timeline and a centralized technology database. Technology data for all system models is drawn from a common database, the ATLAS Technology Tool Box (TTB). The TTB provides a comprehensive, architecture-independent technology database that is keyed to current and future timeframes.

  16. 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.

  17. Extending the input–output energy balance methodology in agriculture through cluster analysis

    International Nuclear Information System (INIS)

    The input–output balance methodology has been applied to characterize the energy balance of agricultural systems. This study proposes to extend this methodology with the inclusion of multivariate analysis to reveal particular patterns in the energy use of a system. The objective was to demonstrate the usefulness of multivariate exploratory techniques to analyze the variability found in a farming system and, establish efficiency categories that can be used to improve the energy balance of the system. To this purpose an input–output analysis was applied to the major greenhouse tomato production area in Colombia. Individual energy profiles were built and the k-means clustering method was applied to the production factors. On average, the production system in the study zone consumes 141.8 GJ ha−1 to produce 96.4 GJ ha−1, resulting in an energy efficiency of 0.68. With the k-means clustering analysis, three clusters of farmers were identified with energy efficiencies of 0.54, 0.67 and 0.78. The most energy efficient cluster grouped 56.3% of the farmers. It is possible to optimize the production system by improving the management practices of those with the lowest energy use efficiencies. Multivariate analysis techniques demonstrated to be a complementary pathway to improve the energy efficiency of a system. -- Highlights: ► An input–output energy balance was estimated for greenhouse tomatoes in Colombia. ► We used the k-means clustering method to classify growers based on their energy use. ► Three clusters of growers were found with energy efficiencies of 0.54, 0.67 and 0.78. ► Overall system optimization is possible by improving the energy use of the less efficient.

  18. FEATURE-MODEL-BASED COMMONALITY AND VARIABILITY ANALYSIS FOR VIRTUAL CLUSTER DISK PROVISIONING

    Directory of Open Access Journals (Sweden)

    Nayun Cho

    2016-01-01

    Full Text Available The rapid growth of networking and storage capacity allows collecting and analyzing massive amount of data by relying increasingly on scalable, flexible, and on-demand provisioned largescale computing resources. Virtualization is one of the feasible solution to provide large amounts of computational power with dynamic provisioning of underlying computing resources. Typically, distributed scientific applications for analyzing data run on cluster nodes to perform the same task in parallel. However, on-demand virtual disk provisioning for a set of virtual machines, called virtual cluster, is not a trivial task. This paper presents a feature model-based commonality and variability analysis system for virtual cluster disk provisioning to categorize types of virtual disks that should be provisioned. Also, we present an applicable case study to analyze common and variant software features between two different subgroups of the big data processing virtual cluster. Consequently, by using the analysis system, it is possible to provide an ability to accelerate the virtual disk creation process by reducing duplicate software installation activities on a set of virtual disks that need to be provisioned in the same virtual cluster.

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

    Directory of Open Access Journals (Sweden)

    Kartsakli Elli

    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.

  20. Profitability and efficiency of Italian utilities: cluster analysis of financial statement ratios

    International Nuclear Information System (INIS)

    The last ten years have witnessed conspicuous changes in European and Italian regulation of public utility services and in the strategies of the major players in these fields. In response to these changes Italian utilities have made a variety of choices regarding size, presence in more or less capital-intensive stages of different value chains, and diversification. These choices have been implemented both through internal growth and by means of mergers and acquisitions. In this context it is interesting to try to establish whether there is a nexus between these choices and the performance of Italian utilities in terms of profitability and efficiency. Therefore statistical multivariate analysis techniques (cluster analysis and factor analysis) have been applied to several ratios obtained from the 2005 financial statement of 34 utilities. First, a hierarchical cluster analysis method has been applied to financial statement data in order to identify homogeneous groups based on several indicators of the incidence of costs (external costs, personnel costs, depreciation and amortization), profitability (return on sales, return on assets, return on equity) and efficiency (in the utilization of personnel, of total assets, of property, plant and equipment). Five clusters have been found. Then the clusters have been characterized in terms of the aforementioned indicators, the presence in different stages of the energy value chains (electricity and gas) and other descriptive variables (such as turnover, number of employees, assets, percentage of property, plant and equipment on total assets, sales revenues from electricity, gas, water supply and sanitation, waste collection and treatment and other services). In a second round cluster analysis has been preceded by factor analysis, in order to find a smaller set of variables. This procedure has revealed three not directly observable factors that can be interpreted as follows: i) efficiency in ordinary and financial management

  1. 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

  2. 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

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

    Science.gov (United States)

    Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.

    2015-11-01

    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 underestimation of

  4. Analysis of brood sex ratios: implications of offspring clustering

    Czech Academy of Sciences Publication Activity Database

    Krackow, S.; Tkadlec, Emil

    Roc. 50, č. 4 (2001), s. 293-301. ISSN 0340-5443 R&D Projects: GA ČR GA524/01/1316 Institutional research plan: CEZ:AV0Z6093917 Keywords : generalized linear mixed models * random coefficients * multilevel analysis Subject RIV: EG - Zoology Impact factor: 2.353, year: 2001

  5. Spectral analysis of A and F dwarf members of the open cluster M6: preliminary results

    Science.gov (United States)

    Kılıçoǧlu, T.; Monier, R.; Fossati, L.

    2010-12-01

    We present the first abundance analysis of CD-32 13109 (NGC 6405 47), member of the M6 open cluster. The photospheric abundances of 14 chemical elements were determined by comparing synthetic spectra and observed spectra of the star. Findings show that this star should be an Am star.

  6. Subtypes of autism by cluster analysis based on structural MRI Data

    Czech Academy of Sciences Publication Activity Database

    Hrdlička, M.; Neuwirth, J.; Komárek, V.; Havlovicova, M.; Sedláček, Z.; Blatný, Marek; Urbánek, Tomáš

    Steinkopff Verlag Darmstadt, 2004, s. 295. [World Congress of the IACAPAP /16th./. Berlín (DE), 22.08.2004-26.08.2004] Institutional research plan: CEZ:AV0Z7025918 Keywords : autism * cluster analysis * MRI Data Subject RIV: AN - Psychology

  7. A NOVEL METHOD FOR MULTISTAGE SCENARIO GENERATION BASED ON CLUSTER ANALYSIS

    OpenAIRE

    XIAODONG JI; XIUJUAN ZHAO; XIULI CHAO

    2006-01-01

    Based on cluster analysis, a novel method is introduced in this paper to generate multistage scenarios. A linear programming model is proposed to exclude the arbitrage opportunity by appending a scenario to the generated scenario set. By means of a cited stochastic linear goal programming portfolio model, a case is given to exhibit the virtues of this scenario generation approach.

  8. 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.

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

    DEFF Research Database (Denmark)

    Wildgaard, Lorna Elizabeth

    2015-01-01

    . 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...

  10. Exploring the Relationship between Autism Spectrum Disorder and Epilepsy Using Latent Class Cluster Analysis

    Science.gov (United States)

    Cuccaro, Michael L.; Tuchman, Roberto F.; Hamilton, Kara L.; Wright, Harry H.; Abramson, Ruth K.; Haines, Jonathan L.; Gilbert, John R.; Pericak-Vance, Margaret

    2012-01-01

    Epilepsy co-occurs frequently in autism spectrum disorders (ASD). Understanding this co-occurrence requires a better understanding of the ASD-epilepsy phenotype (or phenotypes). To address this, we conducted latent class cluster analysis (LCCA) on an ASD dataset (N = 577) which included 64 individuals with epilepsy. We identified a 5-cluster…

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

    Directory of Open Access Journals (Sweden)

    Nicholas G Reich

    Full Text Available 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.

  12. 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.

  13. Imaging spectroscopic analysis at the Advanced Light Source

    International Nuclear Information System (INIS)

    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

  14. Advances In Burnup Credit Criticality Safety Analysis Methods And Applications

    International Nuclear Information System (INIS)

    An International Workshop on “Advances in Applications of Burnup Credit for Spent Fuel Storage, Transport, Reprocessing, and Disposition” organized by the Nuclear Safety Council of Spain (CSN) in cooperation with the International Atomic Energy Agency (IAEA) was held at Córdoba, Spain, on October 27– 30, 2009. The objectives of this workshop were to identify the benefits that accrue from recent improvements of the burnup credit (BUC) analysis methodologies, to analyze the implications of applying improved BUC methodologies, focusing on both the safety-related and operational aspects, and to foster the exchange of international experience in licensing and implementation of BUC applications. In the paper on hand the attention is focused on the improvements of BUC analysis methodologies. (author)

  15. 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.

  16. Evaluation of Categorical Data Clustering

    Czech Academy of Sciences Publication Activity Database

    Řezanková, H.; Löster, T.; Húsek, Dušan

    Berlin: Springer, 2011 - (Mugellini, E.; Szczepaniak, P.; Pettenati, M.; Sokhn, M.), s. 173-182. (Advances in Intelligent and Soft Computing. 86). ISBN 978-3-642-18028-6. ISSN 1867-5662. [AWIC 2011. Atlantic Web Intelligence Conference /7./. Fribourg (CH), 26.01.2011-28.01.2011] R&D Projects: GA ČR GAP202/10/0262; GA ČR GA205/09/1079 Institutional research plan: CEZ:AV0Z10300504 Keywords : cluster analysis * nominal variable * determination of cluster numbers * evaluation of clustering Subject RIV: IN - Informatics, Computer Science

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

    International Nuclear Information System (INIS)

    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 (SO2), respirable suspended particulates (RSP) and nitrogen dioxide (NO2), 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)

  18. Cluster analysis of residential heat load profiles and the role of technical and household characteristics

    DEFF Research Database (Denmark)

    Carmo, Carolina; Christensen, Toke Haunstrup

    2016-01-01

    temporality of the energy demand is needed. This paper contributes to this by focusing on the daily load profiles of energy demand for heating of Danish dwellings with heat pumps. Based on hourly recordings from 139 dwellings and employing cluster and regression analysis, the paper explores patterns...... (typologies) in daily heating load profiles and how these relate to socio-economic and technical characteristics of the included households. The study shows that the load profiles vary according to the external load conditions. Two main clusters were identified for both weekdays and weekends and across load...

  19. Cluster analysis of breeding values for milk yield and lactation persistency in Guzerá cattle

    OpenAIRE

    Diego Augusto Campos da Cruz; Rodrigo Pelicioni Savegnago; Annaíza Braga Bignardi Santana; Maria Gabriela Campolina Diniz Peixoto; Frank Angelo Tomita Bruneli; Lenira El Faro

    2016-01-01

    ABSTRACT: The aim of this study was to explore the pattern of genetic lactation curves of Guzerá cattle using cluster analysis. Test-day milk yields of 5,274 first-lactation Guzerá cows were recorded in a progeny test. A total of 34,193 monthly records were analyzed with a random regression animal model using Legendre polynomials to fit additive genetic and permanent environmental random effects and mean trends. Hierarchical and non-hierarchical cluster analyses were performed based on the EB...

  20. 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.