The Facebook Influence Model: A Concept Mapping Approach

PubMed Central

Kota, Rajitha; Schoohs, Shari; Whitehill, Jennifer M.

2013-01-01

Abstract Facebook is a popular social media Web site that has been hypothesized to exert potential influence over users' attitudes, intentions, or behaviors. The purpose of this study was to develop a conceptual framework to explain influential aspects of Facebook. This mixed methods study applied concept mapping methodology, a validated five-step method to visually represent complex topics. The five steps comprise preparation, brainstorming, sort and rank, analysis, and interpretation. College student participants were identified using purposeful sampling. The 80 participants had a mean age of 20.5 years, and included 36% males. A total of 169 statements were generated during brainstorming, and sorted into between 6 and 22 groups. The final concept map included 13 clusters. Interpretation data led to grouping of clusters into four final domains, including connection, comparison, identification, and Facebook as an experience. The Facebook Influence Concept Map illustrates key constructs that contribute to influence, incorporating perspectives of older adolescent Facebook users. While Facebook provides a novel lens through which to consider behavioral influence, it can best be considered in the context of existing behavioral theory. The concept map may be used toward development of potential future intervention efforts. PMID:23621717

The Facebook influence model: a concept mapping approach.

PubMed

Moreno, Megan A; Kota, Rajitha; Schoohs, Shari; Whitehill, Jennifer M

2013-07-01

Facebook is a popular social media Web site that has been hypothesized to exert potential influence over users' attitudes, intentions, or behaviors. The purpose of this study was to develop a conceptual framework to explain influential aspects of Facebook. This mixed methods study applied concept mapping methodology, a validated five-step method to visually represent complex topics. The five steps comprise preparation, brainstorming, sort and rank, analysis, and interpretation. College student participants were identified using purposeful sampling. The 80 participants had a mean age of 20.5 years, and included 36% males. A total of 169 statements were generated during brainstorming, and sorted into between 6 and 22 groups. The final concept map included 13 clusters. Interpretation data led to grouping of clusters into four final domains, including connection, comparison, identification, and Facebook as an experience. The Facebook Influence Concept Map illustrates key constructs that contribute to influence, incorporating perspectives of older adolescent Facebook users. While Facebook provides a novel lens through which to consider behavioral influence, it can best be considered in the context of existing behavioral theory. The concept map may be used toward development of potential future intervention efforts.

Assessing and grouping chemicals applying partial ordering Alkyl anilines as an illustrative example.

PubMed

Carlsen, Lars; Bruggemann, Rainer

2018-06-03

In chemistry there is a long tradition in classification. Usually methods are adopted from the wide field of cluster analysis. Here, based on the example of 21 alkyl anilines we show that also concepts taken out from the mathematical discipline of partially ordered sets may also be applied. The chemical compounds are described by a multi-indicator system. For the present study four indicators, mainly taken from the field of environmental chemistry were applied and a Hasse diagram was constructed. A Hasse diagram is an acyclic, transitively reduced, triangle free graph that may have several components. The crucial question is, whether or not the Hasse diagram can be interpreted from a structural chemical point of view. This is indeed the case, but it must be clearly stated that a guarantee for meaningful results in general cannot be given. For that further theoretical work is needed. Two cluster analysis methods are applied (K-means and a hierarchical cluster method). In both cases the partitioning of the set of 21 compounds by the component structure of the Hasse diagram appears to be better interpretable. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Using concept mapping to mobilize a Black faith community to address HIV

PubMed Central

Szaflarski, Magdalena; Vaughn, Lisa M; McLinden, Daniel; Wess, Yolanda; Ruffner, Andrew

2017-01-01

Research that partners with community stakeholders increases contextual relevance and community buy-in and maximizes the chance for intervention success. Within a framework of an academic-community partnership, this project assessed a Black faith-community’s needs and opportunities to address HIV. We used concept mapping to identify/prioritize specific HIV-related strategies that would be acceptable to congregations. Ninety stakeholders brainstormed strategies to address HIV; 21 sorted strategies into groups and rated their importance and feasibility. Multidimensional scaling and cluster analysis were applied to the sorting to produce maps that illustrated the stakeholders’ conceptual thinking about HIV interventions. Of 278 responses, 93 were used in the sorting task. The visual maps represented eight clusters: church acceptance of people living with HIV; education (most feasible); mobilization and communication; church/leaders’ empowerment; church involvement/collaboration; safety/HIV prevention; media outreach; and, stigma (most important). Concept mapping clarified multifaceted issues of HIV in the Black faith community. The results will guide HIV programming in congregations. PMID:28239439

Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

PubMed

Chen, Ying-Chih; Wen, Chih-Yu

2012-11-08

This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.

Using concept mapping to design an indicator framework for addiction treatment centres.

PubMed

Nabitz, Udo; van Den Brink, Wim; Jansen, Paul

2005-06-01

The objective of this study is to determine an indicator framework for addiction treatment centres based on the demands of stakeholders and in alignment with the European Foundation for Quality Management (EFQM) Excellence Model. The setting is the Jellinek Centre based in Amsterdam, the Netherlands, which serves as a prototype for an addiction treatment centre. Concept mapping was used in the construction of the indicator framework. During the 1-day workshop, 16 stakeholders generated, prioritized and sorted 73 items concerning quality and performance. Multidimensional scaling and cluster analysis was applied in constructing a framework consisting of two dimensions and eight clusters. The horizontal axis of the indicator framework is named 'Organization' and has two poles, namely, 'Processes' and 'Results'. The vertical axis is named ' Task' and the poles are named 'Efficient treatment' and 'Prevention programs'. The eight clusters in the two-dimensional framework are arranged in the following, prioritized sequence: 'Efficient treatment network', 'Effective service', ' Target group', 'Quality of life', 'Efficient service', 'Knowledge transfer', 'Reducing addiction related problems', and 'Prevention programs'. The most important items in the framework are: 'patients are satisfied with their treatment', 'early interventions', and 'efficient treatment chain'. The indicator framework aligns with three clusters of the results criteria of the EFQM Excellence Model. It is based on the stakeholders' perspectives and is believed to be specific for addiction treatment centres. The study demonstrates that concept mapping is a suitable strategy for generating indicator frameworks.

A Multidimensional Approach to Explore the Understanding of the Notion of Absolute Value

ERIC Educational Resources Information Center

Gagatsis, Athanasios; Panaoura, Areti

2014-01-01

The study aimed to investigate students' conceptions on the notion of absolute value and their abilities in applying the specific notion in routine and non-routine situations. A questionnaire was constructed and administered to 17-year-old students. Data were analysed using the hierarchical clustering of variables and the implicative method, while…

Applying circular economy innovation theory in business process modeling and analysis

NASA Astrophysics Data System (ADS)

Popa, V.; Popa, L.

2017-08-01

The overall aim of this paper is to develop a new conceptual framework for business process modeling and analysis using circular economy innovative theory as a source for business knowledge management. The last part of the paper presents an author’s proposed basic structure for a new business models applying circular economy innovation theories. For people working on new innovative business models in the field of the circular economy this paper provides new ideas for clustering their concepts.

The Communication AssessmenT Checklist in Health (CATCH): a tool for assessing the quality of printed educational materials for clinicians.

PubMed

Genova, Juliana; Nahon-Serfaty, Isaac; Dansokho, Selma Chipenda; Gagnon, Marie-Pierre; Renaud, Jean-Sébastien; Giguère, Anik M C

2014-01-01

There is little guidance available on strategies to improve the communication quality of printed educational materials (PEMs) for clinicians. The purposes of this study were to conceptualize PEM communication quality, develop a checklist based on this conceptualization, and validate the checklist with a selection of PEMs. From a literature review of the strategies influencing communication quality, we generated a conceptual map and developed the Communication AssessmenT Checklist in Health (CATCH) consisting of 55 items nested in 12 concepts. Two raters independently applied CATCH to 45 PEMs evaluated in the studies included in a Cochrane systematic review. From these results, we conducted an item analysis and assessed content validity of CATCH using a hierarchical cluster analysis to explore the extent to which our CATCH operationalization truly represented the communication quality concepts. Some concepts were better covered in the studied PEMs, whereas others were not covered consistently. We observed 3 contrasting PEM clusters. A first cluster (n = 22) was characterized by longer PEMs and comprised mostly high-impact peer-reviewed scientific articles or clinical practice guidelines. A second cluster (n = 22) consisted of PEMs shorter than 4 pages that used special fonts, color, pictures, and graphics. A third cluster consisted of a single brief PEM. With CATCH it is possible to categorize and understand the mechanisms that can trigger a change in behavior in health care providers. Additional research is needed to validate CATCH before it can be recommended for use. © 2014 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on Continuing Medical Education, Association for Hospital Medical Education.

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

PubMed Central

Craig, Hugh; Berretta, Regina; Moscato, Pablo

2016-01-01

In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays. PMID:27571416

Degree-based statistic and center persistency for brain connectivity analysis.

PubMed

Yoo, Kwangsun; Lee, Peter; Chung, Moo K; Sohn, William S; Chung, Sun Ju; Na, Duk L; Ju, Daheen; Jeong, Yong

2017-01-01

Brain connectivity analyses have been widely performed to investigate the organization and functioning of the brain, or to observe changes in neurological or psychiatric conditions. However, connectivity analysis inevitably introduces the problem of mass-univariate hypothesis testing. Although, several cluster-wise correction methods have been suggested to address this problem and shown to provide high sensitivity, these approaches fundamentally have two drawbacks: the lack of spatial specificity (localization power) and the arbitrariness of an initial cluster-forming threshold. In this study, we propose a novel method, degree-based statistic (DBS), performing cluster-wise inference. DBS is designed to overcome the above-mentioned two shortcomings. From a network perspective, a few brain regions are of critical importance and considered to play pivotal roles in network integration. Regarding this notion, DBS defines a cluster as a set of edges of which one ending node is shared. This definition enables the efficient detection of clusters and their center nodes. Furthermore, a new measure of a cluster, center persistency (CP) was introduced. The efficiency of DBS with a known "ground truth" simulation was demonstrated. Then they applied DBS to two experimental datasets and showed that DBS successfully detects the persistent clusters. In conclusion, by adopting a graph theoretical concept of degrees and borrowing the concept of persistence from algebraic topology, DBS could sensitively identify clusters with centric nodes that would play pivotal roles in an effect of interest. DBS is potentially widely applicable to variable cognitive or clinical situations and allows us to obtain statistically reliable and easily interpretable results. Hum Brain Mapp 38:165-181, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Intercreativity: Mapping Online Activism

NASA Astrophysics Data System (ADS)

Meikle, Graham

How do activists use the Internet? This article maps a wide range of activist practice and research by applying and developing Tim Berners-Lee's concept of ‘intercreativity' (1999). It identifies four dimensions of Net activism: intercreative texts, tactics, strategies and networks. It develops these through examples of manifestations of Net activism around one cluster of issues: support campaigns for refugees and asylum seekers.

A recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure.

PubMed

Liao, Fuyuan; Jan, Yih-Kuen

2012-06-01

This paper presents a recurrence network approach for the analysis of skin blood flow dynamics in response to loading pressure. Recurrence is a fundamental property of many dynamical systems, which can be explored in phase spaces constructed from observational time series. A visualization tool of recurrence analysis called recurrence plot (RP) has been proved to be highly effective to detect transitions in the dynamics of the system. However, it was found that delay embedding can produce spurious structures in RPs. Network-based concepts have been applied for the analysis of nonlinear time series recently. We demonstrate that time series with different types of dynamics exhibit distinct global clustering coefficients and distributions of local clustering coefficients and that the global clustering coefficient is robust to the embedding parameters. We applied the approach to study skin blood flow oscillations (BFO) response to loading pressure. The results showed that global clustering coefficients of BFO significantly decreased in response to loading pressure (p<0.01). Moreover, surrogate tests indicated that such a decrease was associated with a loss of nonlinearity of BFO. Our results suggest that the recurrence network approach can practically quantify the nonlinear dynamics of BFO.

Clustering and pasta phases in nuclear density functional theory

DOE PAGES

Schuetrumpf, Bastian; Zhang, Chunli; Nazarewicz, Witold

2017-05-23

Nuclear density functional theory is the tool of choice in describing properties of complex nuclei and intricate phases of bulk nucleonic matter. It is a microscopic approach based on an energy density functional representing the nuclear interaction. An attractive feature of nuclear DFT is that it can be applied to both finite nuclei and pasta phases appearing in the inner crust of neutron stars. While nuclear pasta clusters in a neutron star can be easily characterized through their density distributions, the level of clustering of nucleons in a nucleus can often be difficult to assess. To this end, we usemore » the concept of nucleon localization. We demonstrate that the localization measure provides us with fingerprints of clusters in light and heavy nuclei, including fissioning systems. Furthermore we investigate the rod-like pasta phase using twist-averaged boundary conditions, which enable calculations in finite volumes accessible by state of the art DFT solvers.« less

Analyzing polysemous concepts from a clinical perspective: Application to auditing concept categorization in the UMLS

PubMed Central

Mougin, Fleur; Bodenreider, Olivier; Burgun, Anita

2015-01-01

Objectives Polysemy is a frequent issue in biomedical terminologies. In the Unified Medical Language System (UMLS), polysemous terms are either represented as several independent concepts, or clustered into a single, multiply-categorized concept. The objective of this study is to analyze polysemous concepts in the UMLS through their categorization and hierarchical relations for auditing purposes. Methods We used the association of a concept with multiple Semantic Groups (SGs) as a surrogate for polysemy. We first extracted multi-SG (MSG) concepts from the UMLS Metathesaurus and characterized them in terms of the combinations of SGs with which they are associated. We then clustered MSG concepts in order to identify major types of polysemy. We also analyzed the inheritance of SGs in MSG concepts. Finally, we manually reviewed the categorization of the MSG concepts for auditing purposes. Results The 1208 MSG concepts in the Metathesaurus are associated with 30 distinct pairs of SGs. We created 75 semantically homogeneous clusters of MSG concepts, and 276 MSG concepts could not be clustered for lack of hierarchical relations. The clusters were characterized by the most frequent pairs of semantic types of their constituent MSG concepts. MSG concepts exhibit limited semantic compatibility with their parent and child concepts. A large majority of MSG concepts (92%) are adequately categorized. Examples of miscategorized concepts are presented. Conclusion This work is a systematic analysis and manual review of all concepts categorized by multiple SGs in the UMLS. The correctly-categorized MSG concepts do reflect polysemy in the UMLS Metathesaurus. The analysis of inheritance of SGs proved useful for auditing concept categorization in the UMLS. PMID:19303057

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.

PubMed

Zhang, Zhaoyang; Fang, Hua; Wang, Honggang

2016-06-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering are more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services.

Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth

PubMed Central

Zhang, Zhaoyang; Wang, Honggang

2016-01-01

Web-delivered trials are an important component in eHealth services. These trials, mostly behavior-based, generate big heterogeneous data that are longitudinal, high dimensional with missing values. Unsupervised learning methods have been widely applied in this area, however, validating the optimal number of clusters has been challenging. Built upon our multiple imputation (MI) based fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation (MIV) framework and corresponding MIV algorithms for clustering big longitudinal eHealth data with missing values, more generally for fuzzy-logic based clustering methods. Specifically, we detect the optimal number of clusters by auto-searching and -synthesizing a suite of MI-based validation methods and indices, including conventional (bootstrap or cross-validation based) and emerging (modularity-based) validation indices for general clustering methods as well as the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was demonstrated on a big longitudinal dataset from a real web-delivered trial and using simulation. The results indicate MI-based Xie and Beni index for fuzzy-clustering is more appropriate for detecting the optimal number of clusters for such complex data. The MIV concept and algorithms could be easily adapted to different types of clustering that could process big incomplete longitudinal trial data in eHealth services. PMID:27126063

Molecular Networking and Pattern-Based Genome Mining Improves Discovery of Biosynthetic Gene Clusters and their Products from Salinispora Species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna

Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. In this paper, we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains, including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated themore » identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. Finally, these efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches.« less

Molecular Networking and Pattern-Based Genome Mining Improves Discovery of Biosynthetic Gene Clusters and their Products from Salinispora Species

DOE PAGES

Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna; ...

2015-04-09

Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. In this paper, we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains, including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated themore » identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. Finally, these efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches.« less

Molecular Networking and Pattern-Based Genome Mining Improves discovery of biosynthetic gene clusters and their products from Salinispora species

PubMed Central

Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna; Sarkar, Anindita; Li, Jie; Ziemert, Nadine; Wang, Mingxun; Bandeira, Nuno; Moore, Bradley S.; Dorrestein, Pieter C.; Jensen, Paul R.

2015-01-01

Summary Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. Here we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated the identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. These efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches. PMID:25865308

Hetero-phase fluctuations in the pre-melting region in ionic crystals

NASA Astrophysics Data System (ADS)

Matsunaga, S.; Tamaki, S.

2008-06-01

The theory of the pre-melting phenomena in ionic crystals on the basis of the concept of the hetero phase fluctuation has been applied to KCl and AgCl crystal. The large scale molecular dynamics simulations (MD) in KCl and AgCl crystals are also performed to examine the ionic configuration in premelting region in the vicinity of their melting points. The size of the liquid like clusters are estimated by the theory and MD. The structural features of liquid like clusters are discussed by MD results using the Lindemann instability condition. The ionic conductivities in the pre-melting region are also discussed on the same theoretical basis.

Machine Learning for Biological Trajectory Classification Applications

NASA Technical Reports Server (NTRS)

Sbalzarini, Ivo F.; Theriot, Julie; Koumoutsakos, Petros

2002-01-01

Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

Population Genomics and the Statistical Values of Race: An Interdisciplinary Perspective on the Biological Classification of Human Populations and Implications for Clinical Genetic Epidemiological Research

PubMed Central

Maglo, Koffi N.; Mersha, Tesfaye B.; Martin, Lisa J.

2016-01-01

The biological status and biomedical significance of the concept of race as applied to humans continue to be contentious issues despite the use of advanced statistical and clustering methods to determine continental ancestry. It is thus imperative for researchers to understand the limitations as well as potential uses of the concept of race in biology and biomedicine. This paper deals with the theoretical assumptions behind cluster analysis in human population genomics. Adopting an interdisciplinary approach, it demonstrates that the hypothesis that attributes the clustering of human populations to “frictional” effects of landform barriers at continental boundaries is empirically incoherent. It then contrasts the scientific status of the “cluster” and “cline” constructs in human population genomics, and shows how cluster may be instrumentally produced. It also shows how statistical values of race vindicate Darwin's argument that race is evolutionarily meaningless. Finally, the paper explains why, due to spatiotemporal parameters, evolutionary forces, and socio-cultural factors influencing population structure, continental ancestry may be pragmatically relevant to global and public health genomics. Overall, this work demonstrates that, from a biological systematic and evolutionary taxonomical perspective, human races/continental groups or clusters have no natural meaning or objective biological reality. In fact, the utility of racial categorizations in research and in clinics can be explained by spatiotemporal parameters, socio-cultural factors, and evolutionary forces affecting disease causation and treatment response. PMID:26925096

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

PubMed

Orsi, Rebecca

2017-02-01

Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research. Copyright © 2016 Elsevier Ltd. All rights reserved.

AN INVESTIGATION AND DEVELOPMENT OF THE CLUSTER CONCEPT AS A PROGRAM IN VOCATIONAL EDUCATION AT THE SECONDARY SCHOOL LEVEL--FINAL REPORT, PHASE 1.

ERIC Educational Resources Information Center

MALEY, DONALD

THE INVESTIGATION AND DEVELOPMENT OF THE CLUSTER CONCEPT AS A PROGRAM IN VOCATIONAL EDUCATION AT THE SECONDARY SCHOOL LEVEL WERE REPORTED. THE "CLUSTER CONCEPT" PROGRAM IS AIMED AT THE DEVELOPMENT OF SKILLS AND UNDERSTANDINGS RELATED TO A NUMBER OF ALLIED FIELDS, AND WOULD PREPARE THE PERSON TO ENTER INTO A FAMILY OF OCCUPATIONS RATHER…

Phenotypes Determined by Cluster Analysis in Moderate to Severe Bronchial Asthma.

PubMed

Youroukova, Vania M; Dimitrova, Denitsa G; Valerieva, Anna D; Lesichkova, Spaska S; Velikova, Tsvetelina V; Ivanova-Todorova, Ekaterina I; Tumangelova-Yuzeir, Kalina D

2017-06-01

Bronchial asthma is a heterogeneous disease that includes various subtypes. They may share similar clinical characteristics, but probably have different pathological mechanisms. To identify phenotypes using cluster analysis in moderate to severe bronchial asthma and to compare differences in clinical, physiological, immunological and inflammatory data between the clusters. Forty adult patients with moderate to severe bronchial asthma out of exacerbation were included. All underwent clinical assessment, anthropometric measurements, skin prick testing, standard spirometry and measurement fraction of exhaled nitric oxide. Blood eosinophilic count, serum total IgE and periostin levels were determined. Two-step cluster approach, hierarchical clustering method and k-mean analysis were used for identification of the clusters. We have identified four clusters. Cluster 1 (n=14) - late-onset, non-atopic asthma with impaired lung function, Cluster 2 (n=13) - late-onset, atopic asthma, Cluster 3 (n=6) - late-onset, aspirin sensitivity, eosinophilic asthma, and Cluster 4 (n=7) - early-onset, atopic asthma. Our study is the first in Bulgaria in which cluster analysis is applied to asthmatic patients. We identified four clusters. The variables with greatest force for differentiation in our study were: age of asthma onset, duration of diseases, atopy, smoking, blood eosinophils, nonsteroidal anti-inflammatory drugs hypersensitivity, baseline FEV1/FVC and symptoms severity. Our results support the concept of heterogeneity of bronchial asthma and demonstrate that cluster analysis can be an useful tool for phenotyping of disease and personalized approach to the treatment of patients.

Using Self-Organizing Neural Network Map Combined with Ward's Clustering Algorithm for Visualization of Students' Cognitive Structural Models about Aliveness Concept

PubMed Central

Ugulu, Ilker; Aydin, Halil

2016-01-01

We propose an approach to clustering and visualization of students' cognitive structural models. We use the self-organizing map (SOM) combined with Ward's clustering to conduct cluster analysis. In the study carried out on 100 subjects, a conceptual understanding test consisting of open-ended questions was used as a data collection tool. The results of analyses indicated that students constructed the aliveness concept by associating it predominantly with human. Motion appeared as the most frequently associated term with the aliveness concept. The results suggest that the aliveness concept has been constructed using anthropocentric and animistic cognitive structures. In the next step, we used the data obtained from the conceptual understanding test for training the SOM. Consequently, we propose a visualization method about cognitive structure of the aliveness concept. PMID:26819579

Premelting phenomena in pseudo-binary ionic crystals

NASA Astrophysics Data System (ADS)

Matsunaga, Shigeki

2010-04-01

The theory of the premelting phenomena in ionic crystals on the basis of the concept of the heterophase fluctuation has been applied to the pseudo-binary ionic crystals, KCl-NaCl, AgBr-AgCl and AgBr-CuBr systems. Molecular dynamics simulations (MD) have been performed to examine the ionic configurations in their premelting region in the vicinity of their melting points. Liquid-like clusters have been observed in the results of MD utilizing the Lindemann instability condition. The sizes of liquid-like clusters have been estimated by theory and MD. The characteristics of the dynamical behavior of ions in the premelting region have been examined by the mean square displacement and the velocity correlation functions.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

PubMed

Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

2015-01-01

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering

PubMed Central

Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

2015-01-01

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed. PMID:25972896

Teaching High School Students Machine Learning Algorithms to Analyze Flood Risk Factors in River Deltas

NASA Astrophysics Data System (ADS)

Rose, R.; Aizenman, H.; Mei, E.; Choudhury, N.

2013-12-01

High School students interested in the STEM fields benefit most when actively participating, so I created a series of learning modules on how to analyze complex systems using machine-learning that give automated feedback to students. The automated feedbacks give timely responses that will encourage the students to continue testing and enhancing their programs. I have designed my modules to take the tactical learning approach in conveying the concepts behind correlation, linear regression, and vector distance based classification and clustering. On successful completion of these modules, students will learn how to calculate linear regression, Pearson's correlation, and apply classification and clustering techniques to a dataset. Working on these modules will allow the students to take back to the classroom what they've learned and then apply it to the Earth Science curriculum. During my research this summer, we applied these lessons to analyzing river deltas; we looked at trends in the different variables over time, looked for similarities in NDVI, precipitation, inundation, runoff and discharge, and attempted to predict floods based on the precipitation, waves mean, area of discharge, NDVI, and inundation.

Concept mapping applied to the intersection between older adults' outdoor walking and the built and social environments.

PubMed

Hanson, Heather M; Schiller, Claire; Winters, Meghan; Sims-Gould, Joanie; Clarke, Philippa; Curran, Eileen; Donaldson, Meghan G; Pitman, Beverley; Scott, Vicky; McKay, Heather A; Ashe, Maureen C

2013-12-01

For older adults, the ability to navigate walking routes in the outdoor environment allows them to remain active and socially engaged, facilitating community participation and independence. In order to enhance outdoor walking, it is important to understand the interaction of older adults within their local environments and the influence of broader stakeholder priorities that impact these environments. Thus, we aimed to synthesize perspectives from stakeholders to identify elements of the built and social environments that influence older adults' ability to walk outdoors. We applied a concept mapping approach with the input of diverse stakeholders (N=75) from British Columbia, Canada in 2012. A seven-cluster map best represented areas that influence older adults' outdoor walking. Priority areas identified included sidewalks, crosswalks, and neighborhood features. Individual perceptions and elements of the built and social environments intersect to influence walking behaviors, although targeted studies that address this area are needed. © 2013.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Deen

A profound connection has been found between the structures of thiolated gold clusters and the combinatorial problem of pairing up dots on a surface. The bridge is the concept of staple fitness: the fittest combination corresponds to the experimental structure. This connection has been demonstrated for both Au{sub 25}(SR){sub 18} and Au{sub 38}(SR){sub 24} (-SR being a thiolate group) and applied to predict a promising structure for the recently synthesized Au{sub 19}(SR){sub 13}.

Implementation of megaprojects for the creation of tourist clusters in Russia based on the concept of energy efficiency and sustainable construction

NASA Astrophysics Data System (ADS)

Orlov, Alexandr K.

2017-10-01

The article deals with the application of sustainable construction concept within implementation of megaprojects of tourist clusters development using energy saving technologies. The concept of sustainable construction includes the elements of green construction, energy management as well as aspects of the economic efficiency of construction projects implementation. The methodical approach to the implementation of megaprojects for the creation of tourist clusters in Russia based on the concept of energy efficiency and sustainable construction is proved. The conceptual approach to the evaluation of the ecological, social and economic components of the integral indicator of the effectiveness of the megaproject for the development of the tourist cluster is provided. The algorithm for estimation of the efficiency of innovative solutions in green construction is considered.

A knowledge-driven approach to biomedical document conceptualization.

PubMed

Zheng, Hai-Tao; Borchert, Charles; Jiang, Yong

2010-06-01

Biomedical document conceptualization is the process of clustering biomedical documents based on ontology-represented domain knowledge. The result of this process is the representation of the biomedical documents by a set of key concepts and their relationships. Most of clustering methods cluster documents based on invariant domain knowledge. The objective of this work is to develop an effective method to cluster biomedical documents based on various user-specified ontologies, so that users can exploit the concept structures of documents more effectively. We develop a flexible framework to allow users to specify the knowledge bases, in the form of ontologies. Based on the user-specified ontologies, we develop a key concept induction algorithm, which uses latent semantic analysis to identify key concepts and cluster documents. A corpus-related ontology generation algorithm is developed to generate the concept structures of documents. Based on two biomedical datasets, we evaluate the proposed method and five other clustering algorithms. The clustering results of the proposed method outperform the five other algorithms, in terms of key concept identification. With respect to the first biomedical dataset, our method has the F-measure values 0.7294 and 0.5294 based on the MeSH ontology and gene ontology (GO), respectively. With respect to the second biomedical dataset, our method has the F-measure values 0.6751 and 0.6746 based on the MeSH ontology and GO, respectively. Both results outperforms the five other algorithms in terms of F-measure. Based on the MeSH ontology and GO, the generated corpus-related ontologies show informative conceptual structures. The proposed method enables users to specify the domain knowledge to exploit the conceptual structures of biomedical document collections. In addition, the proposed method is able to extract the key concepts and cluster the documents with a relatively high precision. Copyright 2010 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schuetrumpf, Bastian; Zhang, Chunli; Nazarewicz, Witold

Nuclear density functional theory is the tool of choice in describing properties of complex nuclei and intricate phases of bulk nucleonic matter. It is a microscopic approach based on an energy density functional representing the nuclear interaction. An attractive feature of nuclear DFT is that it can be applied to both finite nuclei and pasta phases appearing in the inner crust of neutron stars. While nuclear pasta clusters in a neutron star can be easily characterized through their density distributions, the level of clustering of nucleons in a nucleus can often be difficult to assess. To this end, we usemore » the concept of nucleon localization. We demonstrate that the localization measure provides us with fingerprints of clusters in light and heavy nuclei, including fissioning systems. Furthermore we investigate the rod-like pasta phase using twist-averaged boundary conditions, which enable calculations in finite volumes accessible by state of the art DFT solvers.« less

The Cluster Concept Program Developed by the University of Maryland, Industrial Education Department.

ERIC Educational Resources Information Center

Kratochvil, Daniel W.; Thompson, Lorna J.

This report, one of 21 case studies, describes the history of a recent educational product. The Cluster Concept Program, developed at the University of Maryland, is directed toward the preparation of individuals for entrance into a spectrum of occupations. Three clusters of occupations are included: (1) Construction, (2) Electro-Mechanical…

Conceptions of Memorizing and Understanding in Learning, and Self-Efficacy Held by University Biology Majors

NASA Astrophysics Data System (ADS)

Lin, Tzu-Chiang; Liang, Jyh-Chong; Tsai, Chin-Chung

2015-02-01

This study aims to explore Taiwanese university students' conceptions of learning biology as memorizing or as understanding, and their self-efficacy. To this end, two questionnaires were utilized to survey 293 Taiwanese university students with biology-related majors. A questionnaire for measuring students' conceptions of memorizing and understanding was validated through an exploratory factor analysis of participants' responses. As for the questionnaire regarding the students' biology learning self-efficacy (BLSE), an exploratory factor analysis revealed a total of four factors including higher-order cognitive skills (BLSE-HC), everyday application (BLSE-EA), science communication (BLSE-SC), and practical works (BLSE-PW). The results of the cluster analysis according to the participants' conceptions of learning biology indicated that students in the two major clusters either viewed learning biology as understanding or possessed mixed-conceptions of memorizing and understanding. The students in the third cluster mainly focused on memorizing in their learning while the students in the fourth cluster showed less agreement with both conceptions of memorizing and understanding. This study further revealed that the conception of learning as understanding was positively associated with the BLSE of university students with biology-related majors. However, the conception of learning as memorizing may foster students' BLSE only when such a notion co-exists with the conception of learning with understanding.

Improving performance through concept formation and conceptual clustering

NASA Technical Reports Server (NTRS)

Fisher, Douglas H.

1992-01-01

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes.

Artist's concept of Skylab space station cluster in Earth's orbit

NASA Image and Video Library

1971-10-01

S71-52192 (1971) --- An artist's concept of the Skylab space station cluster in Earth's orbit. The cutaway view shows astronaut activity in the Orbital Workshop (OWS). The Skylab cluster is composed of the OWS, Airlock Module (AM), Multiple Docking Adapter (MDA), Apollo Telescope Mount (ATM), and the Command and Service Module (CSM). Photo credit: NASA

Bonding reactivity descriptor from conceptual density functional theory and its applications to elucidate bonding formation

NASA Astrophysics Data System (ADS)

Zhou, Pan-Pan; Liu, Shubin; Ayers, Paul W.; Zhang, Rui-Qin

2017-10-01

Condensed-to-atom Fukui functions which reflect the atomic reactivity like the tendency susceptible to either nucleophilic or electrophilic attack demonstrate the bonding trend of an atom in a molecule. Accordingly, Fukui functions based concepts, that is, bonding reactivity descriptors which reveal the bonding properties of molecules in the reaction were put forward and then applied to pericyclic and cluster reactions to confirm their effectiveness and reliability. In terms of the results from the bonding descriptors, a covalent bond can readily be predicted between two atoms with large Fukui functions (i.e., one governs nucleophilic attack while the other one governs electrophilic attack, or both of them govern radical attacks) for pericyclic reactions. For SinOm clusters' reactions, the clusters with a low O atom ratio readily form a bond between two Si atoms with big values of their Fukui functions in which they respectively govern nucleophilic and electrophilic attacks or both govern radical attacks. Also, our results from bonding descriptors show that Si—Si bonds can be formed via the radical mechanism between two Si atoms, and formations of Si—O and O—O bonds are possible when the O content is high. These results conform with experimental findings and can help experimentalists design appropriate clusters to synthesize Si nanowires with high yields. The approach established in this work could be generalized and applied to study reactivity properties for other systems.

Special and general superatoms.

PubMed

Luo, Zhixun; Castleman, A Welford

2014-10-21

Bridging the gap between atoms and macroscopic matter, clusters continue to be a subject of increasing research interest. Among the realm of cluster investigations, an exciting development is the realization that chosen stable clusters can mimic the chemical behavior of an atom or a group of the periodic table of elements. This major finding known as a superatom concept was originated experimentally from the study of aluminum cluster reactivity conducted in 1989 by noting a dramatic size dependence of the reactivity where cluster anions containing a certain number of Al atoms were unreactive toward oxygen while the other species were etched away. This observation was well interpreted by shell closings on the basis of the jellium model, and the related concept (originally termed "unified atom") spawned a wide range of pioneering studies in the 1990s pertaining to the understanding of factors governing the properties of clusters. Under the inspiration of a superatom concept, advances in cluster science in finding stable species not only shed light on magic clusters (i.e., superatomic noble gas) but also enlightened the exploration of stable clusters to mimic the chemical behavior of atoms leading to the discovery of superhalogens, alkaline-earth metals, superalkalis, etc. Among them, certain clusters could enable isovalent isomorphism of precious metals, indicating application potential for inexpensive superatoms for industrial catalysis, while a few superalkalis were found to validate the interesting "harpoon mechanism" involved in the superatomic cluster reactivity; recently also found were the magnetic superatoms of which the cluster-assembled materials could be used in spin electronics. Up to now, extensive studies in cluster science have allowed the stability of superatomic clusters to be understood within a few models, including the jellium model, also aromaticity and Wade-Mingos rules depending on the geometry and metallicity of the cluster. However, the scope of application of the jellium model and modification of the theory to account for nonspherical symmetry and nonmetal-doped metal clusters are still illusive to be further developed. It is still worth mentioning that a superatom concept has also been introduced in ligand-stabilized metal clusters which could also follow the major shell-closing electron count for a spherical, square-well potential. By proposing a new concept named as special and general superatoms, herein we try to summarize all these investigations in series, expecting to provide an overview of this field with a primary focus on the joint undertakings which have given rise to the superatom concept. To be specific, for special superatoms, we limit to clusters under a strict jellium model and simply classify them into groups based on their valence electron counts. While for general superatoms we emphasize on nonmetal-doped metal clusters and ligand-stabilized metal clusters, as well as a few isovalent cluster systems. Hopefully this summary of special and general superatoms benefits the further development of cluster-related theory, and lights up the prospect of using them as building blocks of new materials with tailored properties, such as inexpensive isovalent systems for industrial catalysis, semiconductive superatoms for transistors, and magnetic superatoms for spin electronics.

Molecular taxonomy of phytopathogenic fungi: a case study in Peronospora.

PubMed

Göker, Markus; García-Blázquez, Gema; Voglmayr, Hermann; Tellería, M Teresa; Martín, María P

2009-07-29

Inappropriate taxon definitions may have severe consequences in many areas. For instance, biologically sensible species delimitation of plant pathogens is crucial for measures such as plant protection or biological control and for comparative studies involving model organisms. However, delimiting species is challenging in the case of organisms for which often only molecular data are available, such as prokaryotes, fungi, and many unicellular eukaryotes. Even in the case of organisms with well-established morphological characteristics, molecular taxonomy is often necessary to emend current taxonomic concepts and to analyze DNA sequences directly sampled from the environment. Typically, for this purpose clustering approaches to delineate molecular operational taxonomic units have been applied using arbitrary choices regarding the distance threshold values, and the clustering algorithms. Here, we report on a clustering optimization method to establish a molecular taxonomy of Peronospora based on ITS nrDNA sequences. Peronospora is the largest genus within the downy mildews, which are obligate parasites of higher plants, and includes various economically important pathogens. The method determines the distance function and clustering setting that result in an optimal agreement with selected reference data. Optimization was based on both taxonomy-based and host-based reference information, yielding the same outcome. Resampling and permutation methods indicate that the method is robust regarding taxon sampling and errors in the reference data. Tests with newly obtained ITS sequences demonstrate the use of the re-classified dataset in molecular identification of downy mildews. A corrected taxonomy is provided for all Peronospora ITS sequences contained in public databases. Clustering optimization appears to be broadly applicable in automated, sequence-based taxonomy. The method connects traditional and modern taxonomic disciplines by specifically addressing the issue of how to optimally account for both traditional species concepts and genetic divergence.

Molecular Taxonomy of Phytopathogenic Fungi: A Case Study in Peronospora

PubMed Central

Göker, Markus; García-Blázquez, Gema; Voglmayr, Hermann; Tellería, M. Teresa; Martín, María P.

2009-01-01

Background Inappropriate taxon definitions may have severe consequences in many areas. For instance, biologically sensible species delimitation of plant pathogens is crucial for measures such as plant protection or biological control and for comparative studies involving model organisms. However, delimiting species is challenging in the case of organisms for which often only molecular data are available, such as prokaryotes, fungi, and many unicellular eukaryotes. Even in the case of organisms with well-established morphological characteristics, molecular taxonomy is often necessary to emend current taxonomic concepts and to analyze DNA sequences directly sampled from the environment. Typically, for this purpose clustering approaches to delineate molecular operational taxonomic units have been applied using arbitrary choices regarding the distance threshold values, and the clustering algorithms. Methodology Here, we report on a clustering optimization method to establish a molecular taxonomy of Peronospora based on ITS nrDNA sequences. Peronospora is the largest genus within the downy mildews, which are obligate parasites of higher plants, and includes various economically important pathogens. The method determines the distance function and clustering setting that result in an optimal agreement with selected reference data. Optimization was based on both taxonomy-based and host-based reference information, yielding the same outcome. Resampling and permutation methods indicate that the method is robust regarding taxon sampling and errors in the reference data. Tests with newly obtained ITS sequences demonstrate the use of the re-classified dataset in molecular identification of downy mildews. Conclusions A corrected taxonomy is provided for all Peronospora ITS sequences contained in public databases. Clustering optimization appears to be broadly applicable in automated, sequence-based taxonomy. The method connects traditional and modern taxonomic disciplines by specifically addressing the issue of how to optimally account for both traditional species concepts and genetic divergence. PMID:19641601

ELECTROMECHANICAL INSTALLATION AND REPAIR CLUSTER--AN INVESTIGATION AND DEVELOPMENT OF THE CLUSTER CONCEPT AS A PROGRAM IN VOCATIONAL EDUCATION AT THE SECONDARY SCHOOL LEVEL.

ERIC Educational Resources Information Center

MALEY, DONALD

THIS COURSE OUTLINE ON ELECTROMECHANICAL INSTALLATION AND REPAIR IS PART OF THE FINAL REPORT ON "CLUSTER CONCEPT" COURSES IN VOCATIONAL EDUCATION FOR SECONDARY EDUCATION (ED 010 301). EACH JOB ENTRY TASK WAS ANALYZED FOR HUMAN REQUIREMENTS (COMMUNICATION, MEASUREMENT, MATHEMATICS, SCIENCE, SKILLS, AND INFORMATION) NECESSARY TO…

Graph partitions and cluster synchronization in networks of oscillators

PubMed Central

Schaub, Michael T.; O’Clery, Neave; Billeh, Yazan N.; Delvenne, Jean-Charles; Lambiotte, Renaud; Barahona, Mauricio

2017-01-01

Synchronization over networks depends strongly on the structure of the coupling between the oscillators. When the coupling presents certain regularities, the dynamics can be coarse-grained into clusters by means of External Equitable Partitions of the network graph and their associated quotient graphs. We exploit this graph-theoretical concept to study the phenomenon of cluster synchronization, in which different groups of nodes converge to distinct behaviors. We derive conditions and properties of networks in which such clustered behavior emerges, and show that the ensuing dynamics is the result of the localization of the eigenvectors of the associated graph Laplacians linked to the existence of invariant subspaces. The framework is applied to both linear and non-linear models, first for the standard case of networks with positive edges, before being generalized to the case of signed networks with both positive and negative interactions. We illustrate our results with examples of both signed and unsigned graphs for consensus dynamics and for partial synchronization of oscillator networks under the master stability function as well as Kuramoto oscillators. PMID:27781454

Suicide Contagion: A Systematic Review of Definitions and Research Utility

PubMed Central

Cheng, Qijin; Li, Hong; Silenzio, Vincent; Caine, Eric D.

2014-01-01

Objectives Despite the common use of contagion to analogize the spread of suicide, there is a lack of rigorous assessment of the underlying concept or theory supporting the use of this term. The present study aims to examine the varied definitions and potential utility of the term contagion in suicide-related research. Methods 100 initial records and 240 reference records in English were identified as relevant with our research objectives, through systematic literature screening. We then conducted narrative syntheses of various definitions and assessed their potential value for generating new research. Results 20.3% of the 340 records used contagion as equivalent to clustering (contagion-as-cluster); 68.5% used it to refer to various, often related mechanisms underlying the clustering phenomenon (contagion-as-mechanism); and 11.2% without clear definition. Under the category of contagion-as-mechanism, four mechanisms have been proposed to explain how suicide clusters occurred: transmission (contagion-as-transmission), imitation (contagion-as-imitation), contextual influence (contagion-as-context), and affiliation (contagion-as-affiliation). Contagion-as-cluster both confounds and constrains inquiry into suicide clustering by blending proposed mechanism with the phenomenon to be studied. Contagion-as-transmission is, in essence, a double or internally redundant metaphor. Contagion-as-affiliation and contagion-as-context involve mechanisms that are common mechanisms that often occur independently of apparent contagion, or may serve as a facilitating background. When used indiscriminately, these terms may create research blind spots. Contagion-as-imitation combines perspectives from psychology, sociology, and public health research and provides the greatest heuristic utility for examining whether and how suicide and suicidal behaviors may spread among persons at both individual and population levels. Conclusion Clarifying the concept of “suicide contagion” is an essential step for more thoroughly investigating its mechanisms. Developing a clearer understanding of the apparent spread of suicide-promoting influences can, in turn, offer insights necessary to build the scientific foundation for prevention and intervention strategies that can be applied at both individual and community levels. PMID:25259604

Extraction of a group-pair relation: problem-solving relation from web-board documents.

PubMed

Pechsiri, Chaveevan; Piriyakul, Rapepun

2016-01-01

This paper aims to extract a group-pair relation as a Problem-Solving relation, for example a DiseaseSymptom-Treatment relation and a CarProblem-Repair relation, between two event-explanation groups, a problem-concept group as a symptom/CarProblem-concept group and a solving-concept group as a treatment-concept/repair concept group from hospital-web-board and car-repair-guru-web-board documents. The Problem-Solving relation (particularly Symptom-Treatment relation) including the graphical representation benefits non-professional persons by supporting knowledge of primarily solving problems. The research contains three problems: how to identify an EDU (an Elementary Discourse Unit, which is a simple sentence) with the event concept of either a problem or a solution; how to determine a problem-concept EDU boundary and a solving-concept EDU boundary as two event-explanation groups, and how to determine the Problem-Solving relation between these two event-explanation groups. Therefore, we apply word co-occurrence to identify a problem-concept EDU and a solving-concept EDU, and machine-learning techniques to solve a problem-concept EDU boundary and a solving-concept EDU boundary. We propose using k-mean and Naïve Bayes to determine the Problem-Solving relation between the two event-explanation groups involved with clustering features. In contrast to previous works, the proposed approach enables group-pair relation extraction with high accuracy.

Performance map of a cluster detection test using extended power

PubMed Central

2013-01-01

Background Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region. Methods To explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff’s spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region. Results Consistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors. Conclusions The area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region. PMID:24156765

Cluster compression algorithm: A joint clustering/data compression concept

NASA Technical Reports Server (NTRS)

Hilbert, E. E.

1977-01-01

The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.

Machine learning-based coreference resolution of concepts in clinical documents

PubMed Central

Ware, Henry; Mullett, Charles J; El-Rawas, Oussama

2012-01-01

Objective Coreference resolution of concepts, although a very active area in the natural language processing community, has not yet been widely applied to clinical documents. Accordingly, the 2011 i2b2 competition focusing on this area is a timely and useful challenge. The objective of this research was to collate coreferent chains of concepts from a corpus of clinical documents. These concepts are in the categories of person, problems, treatments, and tests. Design A machine learning approach based on graphical models was employed to cluster coreferent concepts. Features selected were divided into domain independent and domain specific sets. Training was done with the i2b2 provided training set of 489 documents with 6949 chains. Testing was done on 322 documents. Results The learning engine, using the un-weighted average of three different measurement schemes, resulted in an F measure of 0.8423 where no domain specific features were included and 0.8483 where the feature set included both domain independent and domain specific features. Conclusion Our machine learning approach is a promising solution for recognizing coreferent concepts, which in turn is useful for practical applications such as the assembly of problem and medication lists from clinical documents. PMID:22582205

Concept design and cluster control of advanced space connectable intelligent microsatellite

NASA Astrophysics Data System (ADS)

Wang, Xiaohui; Li, Shuang; She, Yuchen

2017-12-01

In this note, a new type of advanced space connectable intelligent microsatellite is presented to extend the range of potential application of microsatellite and improve the efficiency of cooperation. First, the overall concept of the micro satellite cluster is described, which is characterized by autonomously connecting with each other and being able to realize relative rotation through the external interfaces. Second, the multi-satellite autonomous assembly algorithm and control algorithm of the cluster motion are developed to make the cluster system combine into a variety of configurations in order to achieve different types of functionality. Finally, the design of the satellite cluster system is proposed, and the possible applications are discussed.

Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals

NASA Technical Reports Server (NTRS)

Thompson, David R.; Mandrake, Lukas; Green, Robert O.

2013-01-01

Mapping localized spectral features in large images demands sensitive and robust detection algorithms. Two aspects of large images that can harm matched-filter detection performance are addressed simultaneously. First, multimodal backgrounds may thwart the typical Gaussian model. Second, outlier features can trigger false detections from large projections onto the target vector. Two state-of-the-art approaches are combined that independently address outlier false positives and multimodal backgrounds. The background clustering models multimodal backgrounds, and the mixture tuned matched filter (MT-MF) addresses outliers. Combining the two methods captures significant additional performance benefits. The resulting mixture tuned clutter matched filter (MT-CMF) shows effective performance on simulated and airborne datasets. The classical MNF transform was applied, followed by k-means clustering. Then, each cluster s mean, covariance, and the corresponding eigenvalues were estimated. This yields a cluster-specific matched filter estimate as well as a cluster- specific feasibility score to flag outlier false positives. The technology described is a proof of concept that may be employed in future target detection and mapping applications for remote imaging spectrometers. It is of most direct relevance to JPL proposals for airborne and orbital hyperspectral instruments. Applications include subpixel target detection in hyperspectral scenes for military surveillance. Earth science applications include mineralogical mapping, species discrimination for ecosystem health monitoring, and land use classification.

Interpretation of sedimentological processes of coarse-grained deposits applying a novel combined cluster and discriminant analysis

NASA Astrophysics Data System (ADS)

Farics, Éva; Farics, Dávid; Kovács, József; Haas, János

2017-10-01

The main aim of this paper is to determine the depositional environments of an Upper-Eocene coarse-grained clastic succession in the Buda Hills, Hungary. First of all, we measured some commonly used parameters of samples (size, amount, roundness and sphericity) in a much more objective overall and faster way than with traditional measurement approaches, using the newly developed Rock Analyst application. For the multivariate data obtained, we applied Combined Cluster and Discriminant Analysis (CCDA) in order to determine homogeneous groups of the sampling locations based on the quantitative composition of the conglomerate as well as the shape parameters (roundness and sphericity). The result is the spatial pattern of these groups, which assists with the interpretation of the depositional processes. According to our concept, those sampling sites which belong to the same homogeneous groups were likely formed under similar geological circumstances and by similar geological processes. In the Buda Hills, we were able to distinguish various sedimentological environments within the area based on the results: fan, intermittent stream or marine.

Does the use of the Informed Healthcare Choices (IHC) primary school resources improve the ability of grade-5 children in Uganda to assess the trustworthiness of claims about the effects of treatments: protocol for a cluster-randomised trial.

PubMed

Nsangi, Allen; Semakula, Daniel; Oxman, Andrew D; Oxman, Matthew; Rosenbaum, Sarah; Austvoll-Dahlgren, Astrid; Nyirazinyoye, Laetitia; Kaseje, Margaret; Chalmers, Iain; Fretheim, Atle; Sewankambo, Nelson K

2017-05-18

The ability to appraise claims about the benefits and harms of treatments is crucial for informed health care decision-making. This research aims to enable children in East African primary schools (the clusters) to acquire and retain skills that can help them make informed health care choices by improving their ability to obtain, process and understand health information. The trial will evaluate (at the individual participant level) whether specially designed learning resources can teach children some of the key concepts relevant to appraising claims about the benefits and harms of health care interventions (treatments). This is a two-arm, cluster-randomised trial with stratified random allocation. We will recruit 120 primary schools (the clusters) between April and May 2016 in the central region of Uganda. We will stratify participating schools by geographical setting (rural, semi-urban, or urban) and ownership (public or private). The Informed Healthcare Choices (IHC) primary school resources consist of a textbook and a teachers' guide. Each of the students in the intervention arm will receive a textbook and attend nine lessons delivered by their teachers during a school term, with each lesson lasting 80 min. The lessons cover 12 key concepts that are relevant to assessing claims about treatments and making informed health care choices. The second arm will carry on with the current primary school curriculum. We have designed the Claim Evaluation Tools to measure people's ability to apply key concepts related to assessing claims about the effects of treatments and making informed health care choices. The Claim Evaluation Tools use multiple choice questions addressing each of the 12 concepts covered by the IHC school resources. Using the Claim Evaluation Tools we will measure two primary outcomes: (1) the proportion of children who 'pass', based on an absolute standard and (2) their average scores. As far as we are aware this is the first randomised trial to assess whether key concepts needed to judge claims about the effects of treatment can be taught to primary school children. Whatever the results, they will be relevant to learning how to promote critical thinking about treatment claims. Trial status: the recruitment of study participants was ongoing at the time of manuscript submission. Pan African Clinical Trial Registry, trial identifier: PACTR201606001679337 . Registered on 13 June 2016.

Integration of low level and ontology derived features for automatic weapon recognition and identification

NASA Astrophysics Data System (ADS)

Sirakov, Nikolay M.; Suh, Sang; Attardo, Salvatore

2011-06-01

This paper presents a further step of a research toward the development of a quick and accurate weapons identification methodology and system. A basic stage of this methodology is the automatic acquisition and updating of weapons ontology as a source of deriving high level weapons information. The present paper outlines the main ideas used to approach the goal. In the next stage, a clustering approach is suggested on the base of hierarchy of concepts. An inherent slot of every node of the proposed ontology is a low level features vector (LLFV), which facilitates the search through the ontology. Part of the LLFV is the information about the object's parts. To partition an object a new approach is presented capable of defining the objects concavities used to mark the end points of weapon parts, considered as convexities. Further an existing matching approach is optimized to determine whether an ontological object matches the objects from an input image. Objects from derived ontological clusters will be considered for the matching process. Image resizing is studied and applied to decrease the runtime of the matching approach and investigate its rotational and scaling invariance. Set of experiments are preformed to validate the theoretical concepts.

Novel layered clustering-based approach for generating ensemble of classifiers.

PubMed

Rahman, Ashfaqur; Verma, Brijesh

2011-05-01

This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult-to-classify patterns through clustering as well as achievement of diversity through layering leads to better classification results as evidenced from the experimental results.

Aromaticity and Antiaromaticity in Zintl Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Zhong -Ming; Liu, Chao; Popov, Ivan Aleksandrovich

Originally, the concepts of aromaticity and antiaromaticity were introduced to explain the stability and reactivity of unsaturated organic compounds. Since then, they have been extended to other species with delocalized electrons including various saturated systems, organometallic compounds, and even inorganic clusters and molecules. In this study, we focus on the most recent progress of using these concepts to guide experimental synthesis and rationalize geometrical and electronic structures of a particular family of polyanions composed of Group 14 and 15 elements, namely Zintl clusters.

Aromaticity and Antiaromaticity in Zintl Clusters

DOE PAGES

Sun, Zhong -Ming; Liu, Chao; Popov, Ivan Aleksandrovich; ...

2018-05-18

Originally, the concepts of aromaticity and antiaromaticity were introduced to explain the stability and reactivity of unsaturated organic compounds. Since then, they have been extended to other species with delocalized electrons including various saturated systems, organometallic compounds, and even inorganic clusters and molecules. In this study, we focus on the most recent progress of using these concepts to guide experimental synthesis and rationalize geometrical and electronic structures of a particular family of polyanions composed of Group 14 and 15 elements, namely Zintl clusters.

A new concept to study the effect of climate change on different flood types

NASA Astrophysics Data System (ADS)

Nissen, Katrin; Nied, Manuela; Pardowitz, Tobias; Ulbrich, Uwe; Merz, Bruno

2014-05-01

Flooding is triggered by the interaction of various processes. Especially important are the hydrological conditions prior to the event (e.g. soil saturation, snow cover) and the meteorological conditions during flood development (e.g. rainfall, temperature). Depending on these (pre-) conditions different flood types may develop such as long-rain floods, short-rain floods, flash floods, snowmelt floods and rain-on-snow floods. A new concept taking these factors into account is introduced and applied to flooding in the Elbe River basin. During the period September 1957 to August 2002, 82 flood events are identified and classified according to their flood type. The hydrological and meteorological conditions at each day during the analysis period are detemined. In case of the hydrological conditions, a soil moisture pattern classification is carried out. Soil moisture is simulated with a rainfall-runoff model driven by atmospheric observations. Days of similar soil moisture patterns are identified by a principle component analysis and a subsequent cluster analysis on the leading principal components. The meteorological conditions are identified by applying a cluster analysis to the geopotential height, temperature and humidity fields of the ERA40 reanalysis data set using the SANDRA cluster algorithm. We are able to identify specific pattern combinations of hydrological pre-conditions and meteorological conditions which favour different flood types. Based on these results it is possible to analyse the effect of climate change on different flood types. As an example we show first results obtained using an ensemble of climate scenario simulations of ECHAM5 MPIOM model, taking only the changes in the meteorological conditions into account. According to the simulations, the frequency of the meteorological patterns favouring long-rain, short-rain and flash floods will not change significantly under future climate conditions. A significant increase is, however, predicted for the amount of precipitation associated with many of the relevant meteorological patterns. The increase varies between 12 and 67% depending on the weather pattern.

Noble Metal Immersion Spectroscopy of Silica Alcogels and Aerogels

NASA Technical Reports Server (NTRS)

Smith, David D.; Sibille, Laurent; Cronise, Raymond J.; Noever, David A.

1998-01-01

We have fabricated aerogels containing gold and silver nanoparticles for gas catalysis applications. By applying the concept of an average or effective dielectric constant to the heterogeneous interlayer surrounding each particle, we extend the technique of immersion spectroscopy to porous or heterogeneous media. Specifically, we apply the predominant effective medium theories for the determination of the average fractional composition of each component in this inhomogeneous layer. Hence, the surface area of metal available for catalytic gas reaction is determined. The technique is satisfactory for statistically random metal particle distributions but needs further modification for aggregated or surfactant modified systems. Additionally, the kinetics suggest that collective particle interactions in coagulated clusters are perturbed during silica gelation resulting in a change in the aggregate geometry.

Surface Plasmon Resonance Evaluation of Colloidal Metal Aerogel Filters

NASA Technical Reports Server (NTRS)

Smith, David D.; Sibille, Laurent; Cronise, Raymond J.; Noever, David A.

1997-01-01

We have fabricated aerogels containing gold, silver, and platinum nanoparticles for gas catalysis applications. By applying the concept of an average or effective dielectric constant to the heterogeneous interlayer surrounding each particle, we extend the technique of immersion spectroscopy to porous or heterogeneous media. Specifically, we apply the predominant effective medium theories for the determination of the average fractional composition of each component in this inhomogeneous layer. Hence, the surface area of metal available for catalytic gas reaction is determined. The technique is satisfactory for statistically random metal particle distributions but needs further modification for aggregated or surfactant modified systems. Additionally, the kinetics suggest that collective particle interactions in coagulated clusters are perturbed during silica gelation resulting in a change in the aggregate geometry.

Specific modification of polysulfone with cluster bombardment with assistance of Ar ion irradiation

NASA Astrophysics Data System (ADS)

Xu, Guochun; Hibino, Y.; Awazu, K.; Tanihara, M.; Imanishi, Y.

2000-02-01

Objective: To develop a rapid method for the modification of polysulfone with ammonium sulfamate with the assistance of Ar ion irradiation with a multi-source cluster deposition apparatus. These surfaces mimicking the structure of heparin, a bioactive molecule, have a high anti-thrombosis property. Experimental Design: Polysulfone film, setting on a turning holder, was irradiated by Ar ions during bombardment with ammonium sulfamate clusters. The Ar ion source serves for the activation of a polymer surface and a cluster ion source supplies ammonium sulfamate molecules to react with the activated surface. After thorough washing with de-ionized sterile water, the modified surfaces were evaluated in terms of the contact angle of water, elemental composition, and binding state on electron spectroscopy for chemical analysis and platelet adhesion with platelet rich plasma. Results: The modification of polysulfone decreased the contact angle of water on surfaces from 82.6 ° down to 34.5 °. Ammonium, amine, sulfate, and thiophene combinations were formed on the modified surfaces. The adhesion numbers of the platelet were decreased to one tenth compared to the original surface. The same process was also applied to other polymers such as polyethylene, polypropylene, and polystyrene and similar outcomes were also observed. Conclusion: The primary studies showed successful modification of polysulfone with ammonium sulfamate with the assistance of Ar ion irradiation. Since the same concept can also be applied to other materials with various substrates, combined with the features of no solvent and no topographic changes, this method might be developed into a promising way for modification of polymeric materials.

Complexity of major UK companies between 2006 and 2010: Hierarchical structure method approach

NASA Astrophysics Data System (ADS)

Ulusoy, Tolga; Keskin, Mustafa; Shirvani, Ayoub; Deviren, Bayram; Kantar, Ersin; Çaǧrı Dönmez, Cem

2012-11-01

This study reports on topology of the top 40 UK companies that have been analysed for predictive verification of markets for the period 2006-2010, applying the concept of minimal spanning tree and hierarchical tree (HT) analysis. Construction of the minimal spanning tree (MST) and the hierarchical tree (HT) is confined to a brief description of the methodology and a definition of the correlation function between a pair of companies based on the London Stock Exchange (LSE) index in order to quantify synchronization between the companies. A derivation of hierarchical organization and the construction of minimal-spanning and hierarchical trees for the 2006-2008 and 2008-2010 periods have been used and the results validate the predictive verification of applied semantics. The trees are known as useful tools to perceive and detect the global structure, taxonomy and hierarchy in financial data. From these trees, two different clusters of companies in 2006 were detected. They also show three clusters in 2008 and two between 2008 and 2010, according to their proximity. The clusters match each other as regards their common production activities or their strong interrelationship. The key companies are generally given by major economic activities as expected. This work gives a comparative approach between MST and HT methods from statistical physics and information theory with analysis of financial markets that may give new valuable and useful information of the financial market dynamics.

Functional cliques in the amygdala and related brain networks driven by fear assessment acquired during movie viewing.

PubMed

Kinreich, Sivan; Intrator, Nathan; Hendler, Talma

2011-01-01

One of the greatest challenges involved in studying the brain mechanisms of fear is capturing the individual's unique instantaneous experience. Brain imaging studies to date commonly sacrifice valuable information regarding the individual real-time conscious experience, especially when focusing on elucidating the amygdala's activity. Here, we assumed that by using a minimally intrusive cue along with applying a robust clustering approach to probe the amygdala, it would be possible to rate fear in real time and to derive the related network of activation. During functional magnetic resonance imaging scanning, healthy volunteers viewed two excerpts from horror movies and were periodically auditory cued to rate their instantaneous experience of "I'm scared." Using graph theory and community mathematical concepts, data-driven clustering of the fear-related functional cliques in the amygdala was performed guided by the individually marked periods of heightened fear. Individually tailored functions derived from these amygdala activation cliques were subsequently applied as general linear model predictors to a whole-brain analysis to reveal the correlated networks. Our results suggest that by using a localized robust clustering approach, it is possible to probe activation in the right dorsal amygdala that is directly related to individual real-time emotional experience. Moreover, this fear-evoked amygdala revealed two opposing networks of co-activation and co-deactivation, which correspond to vigilance and rest-related circuits, respectively.

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

PubMed

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

GOClonto: an ontological clustering approach for conceptualizing PubMed abstracts.

PubMed

Zheng, Hai-Tao; Borchert, Charles; Kim, Hong-Gee

2010-02-01

Concurrent with progress in biomedical sciences, an overwhelming of textual knowledge is accumulating in the biomedical literature. PubMed is the most comprehensive database collecting and managing biomedical literature. To help researchers easily understand collections of PubMed abstracts, numerous clustering methods have been proposed to group similar abstracts based on their shared features. However, most of these methods do not explore the semantic relationships among groupings of documents, which could help better illuminate the groupings of PubMed abstracts. To address this issue, we proposed an ontological clustering method called GOClonto for conceptualizing PubMed abstracts. GOClonto uses latent semantic analysis (LSA) and gene ontology (GO) to identify key gene-related concepts and their relationships as well as allocate PubMed abstracts based on these key gene-related concepts. Based on two PubMed abstract collections, the experimental results show that GOClonto is able to identify key gene-related concepts and outperforms the STC (suffix tree clustering) algorithm, the Lingo algorithm, the Fuzzy Ants algorithm, and the clustering based TRS (tolerance rough set) algorithm. Moreover, the two ontologies generated by GOClonto show significant informative conceptual structures.

A concept for routine emergency-care data-based syndromic surveillance in Europe.

PubMed

Ziemann, A; Rosenkötter, N; Garcia-Castrillo Riesgo, L; Schrell, S; Kauhl, B; Vergeiner, G; Fischer, M; Lippert, F K; Krämer, A; Brand, H; Krafft, T

2014-11-01

We developed a syndromic surveillance (SyS) concept using emergency dispatch, ambulance and emergency-department data from different European countries. Based on an inventory of sub-national emergency data availability in 12 countries, we propose framework definitions for specific syndromes and a SyS system design. We tested the concept by retrospectively applying cumulative sum and spatio-temporal cluster analyses for the detection of local gastrointestinal outbreaks in four countries and comparing the results with notifiable disease reporting. Routine emergency data was available daily and electronically in 11 regions, following a common structure. We identified two gastrointestinal outbreaks in two countries; one was confirmed as a norovirus outbreak. We detected 1/147 notified outbreaks. Emergency-care data-based SyS can supplement local surveillance with near real-time information on gastrointestinal patients, especially in special circumstances, e.g. foreign tourists. It most likely cannot detect the majority of local gastrointestinal outbreaks with few, mild or dispersed cases.

Using hierarchical cluster models to systematically identify groups of jobs with similar occupational questionnaire response patterns to assist rule-based expert exposure assessment in population-based studies.

PubMed

Friesen, Melissa C; Shortreed, Susan M; Wheeler, David C; Burstyn, Igor; Vermeulen, Roel; Pronk, Anjoeka; Colt, Joanne S; Baris, Dalsu; Karagas, Margaret R; Schwenn, Molly; Johnson, Alison; Armenti, Karla R; Silverman, Debra T; Yu, Kai

2015-05-01

Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure. Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (µg m(-3) respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters' homogeneity (defined as >75% with the same estimate) was examined compared to a dichotomized probability estimate (<5 versus ≥5%; <50 versus ≥50%). Second, for the ordinal probability metric and continuous intensity and frequency metrics, we calculated the intraclass correlation coefficients (ICCs) between each job's estimate and the mean estimate for all jobs within the cluster. Within-cluster homogeneity increased when more clusters were used. For example, ≥80% of the clusters were homogeneous when 500 clusters were used. Similarly, ICCs were generally above 0.7 when ≥200 clusters were used, indicating minimal within-cluster variability. The most within-cluster variability was observed for the frequency metric (ICCs from 0.4 to 0.8). We estimated that using an expert to assign exposure at the cluster-level assignment and then to review each job in non-homogeneous clusters would require ~2000 decisions per expert, in contrast to evaluating 4255 unique questionnaire patterns or 14983 individual jobs. This proof-of-concept shows that using cluster models as a data reduction step to identify jobs with similar response patterns prior to obtaining expert ratings has the potential to aid rule-based assessment by systematically reducing the number of exposure decisions needed. While promising, additional research is needed to quantify the actual reduction in exposure decisions and the resulting homogeneity of exposure estimates within clusters for an exposure assessment effort that obtains cluster-level expert assessments as part of the assessment process. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2014.

Exploring undergraduates' understanding of photosynthesis using diagnostic question clusters.

PubMed

Parker, Joyce M; Anderson, Charles W; Heidemann, Merle; Merrill, John; Merritt, Brett; Richmond, Gail; Urban-Lurain, Mark

2012-01-01

We present a diagnostic question cluster (DQC) that assesses undergraduates' thinking about photosynthesis. This assessment tool is not designed to identify individual misconceptions. Rather, it is focused on students' abilities to apply basic concepts about photosynthesis by reasoning with a coordinated set of practices based on a few scientific principles: conservation of matter, conservation of energy, and the hierarchical nature of biological systems. Data on students' responses to the cluster items and uses of some of the questions in multiple-choice, multiple-true/false, and essay formats are compared. A cross-over study indicates that the multiple-true/false format shows promise as a machine-gradable format that identifies students who have a mixture of accurate and inaccurate ideas. In addition, interviews with students about their choices on three multiple-choice questions reveal the fragility of students' understanding. Collectively, the data show that many undergraduates lack both a basic understanding of the role of photosynthesis in plant metabolism and the ability to reason with scientific principles when learning new content. Implications for instruction are discussed.

Exploring Undergraduates' Understanding of Photosynthesis Using Diagnostic Question Clusters

PubMed Central

Parker, Joyce M.; Anderson, Charles W.; Heidemann, Merle; Merrill, John; Merritt, Brett; Richmond, Gail; Urban-Lurain, Mark

2012-01-01

We present a diagnostic question cluster (DQC) that assesses undergraduates' thinking about photosynthesis. This assessment tool is not designed to identify individual misconceptions. Rather, it is focused on students' abilities to apply basic concepts about photosynthesis by reasoning with a coordinated set of practices based on a few scientific principles: conservation of matter, conservation of energy, and the hierarchical nature of biological systems. Data on students' responses to the cluster items and uses of some of the questions in multiple-choice, multiple-true/false, and essay formats are compared. A cross-over study indicates that the multiple-true/false format shows promise as a machine-gradable format that identifies students who have a mixture of accurate and inaccurate ideas. In addition, interviews with students about their choices on three multiple-choice questions reveal the fragility of students' understanding. Collectively, the data show that many undergraduates lack both a basic understanding of the role of photosynthesis in plant metabolism and the ability to reason with scientific principles when learning new content. Implications for instruction are discussed. PMID:22383617

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

PubMed Central

2010-01-01

Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters. PMID:21034451

Clustering Coefficients for Correlation Networks.

PubMed

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties.

Clustering Coefficients for Correlation Networks

PubMed Central

Masuda, Naoki; Sakaki, Michiko; Ezaki, Takahiro; Watanabe, Takamitsu

2018-01-01

Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an application in the assessment of small-worldness of brain networks, which is affected by attentional and cognitive conditions, age, psychiatric disorders and so forth. However, it remains unclear how the clustering coefficient should be measured in a correlation-based network, which is among major representations of brain networks. In the present article, we propose clustering coefficients tailored to correlation matrices. The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes. Our method avoids the difficulties of previous applications of clustering coefficient (and other) measures in defining correlational networks, i.e., thresholding on the correlation value, discarding of negative correlation values, the pseudo-correlation problem and full partial correlation matrices whose estimation is computationally difficult. For proof of concept, we apply the proposed clustering coefficient measures to functional magnetic resonance imaging data obtained from healthy participants of various ages and compare them with conventional clustering coefficients. We show that the clustering coefficients decline with the age. The proposed clustering coefficients are more strongly correlated with age than the conventional ones are. We also show that the local variants of the proposed clustering coefficients (i.e., abundance of triangles around a focal node) are useful in characterizing individual nodes. In contrast, the conventional local clustering coefficients were strongly correlated with and therefore may be confounded by the node's connectivity. The proposed methods are expected to help us to understand clustering and lack thereof in correlational brain networks, such as those derived from functional time series and across-participant correlation in neuroanatomical properties. PMID:29599714

BioTextQuest: a web-based biomedical text mining suite for concept discovery.

PubMed

Papanikolaou, Nikolas; Pafilis, Evangelos; Nikolaou, Stavros; Ouzounis, Christos A; Iliopoulos, Ioannis; Promponas, Vasilis J

2011-12-01

BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed. http://biotextquest.biol.ucy.ac.cy vprobon@ucy.ac.cy; iliopj@med.uoc.gr Supplementary data are available at Bioinformatics online.

Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Takayama, Kozo

2015-01-01

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and design space. In this article, we integrate thin-plate spline (TPS) interpolation, Kohonen's self-organizing map (SOM) and a Bayesian network (BN) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared using a standard formulation. We measured the tensile strength and disintegration time as response variables and the compressibility, cohesion and dispersibility of the pretableting blend as latent variables. We predicted these variables quantitatively using nonlinear TPS, generated a large amount of data on pretableting blends and tablets and clustered these data into several clusters using a SOM. Our results show that we are able to predict the experimental values of the latent and response variables with a high degree of accuracy and are able to classify the tablet data into several distinct clusters. In addition, to visualize the latent structure between the causal and latent factors and the response variables, we applied a BN method to the SOM clustering results. We found that despite having inserted latent variables between the causal factors and response variables, their relation is equivalent to the results for the SOM clustering, and thus we are able to explain the underlying latent structure. Consequently, this technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulation.

Using Hierarchical Cluster Models to Systematically Identify Groups of Jobs With Similar Occupational Questionnaire Response Patterns to Assist Rule-Based Expert Exposure Assessment in Population-Based Studies

PubMed Central

Friesen, Melissa C.; Shortreed, Susan M.; Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Pronk, Anjoeka; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Armenti, Karla R.; Silverman, Debra T.; Yu, Kai

2015-01-01

Objectives: Rule-based expert exposure assessment based on questionnaire response patterns in population-based studies improves the transparency of the decisions. The number of unique response patterns, however, can be nearly equal to the number of jobs. An expert may reduce the number of patterns that need assessment using expert opinion, but each expert may identify different patterns of responses that identify an exposure scenario. Here, hierarchical clustering methods are proposed as a systematic data reduction step to reproducibly identify similar questionnaire response patterns prior to obtaining expert estimates. As a proof-of-concept, we used hierarchical clustering methods to identify groups of jobs (clusters) with similar responses to diesel exhaust-related questions and then evaluated whether the jobs within a cluster had similar (previously assessed) estimates of occupational diesel exhaust exposure. Methods: Using the New England Bladder Cancer Study as a case study, we applied hierarchical cluster models to the diesel-related variables extracted from the occupational history and job- and industry-specific questionnaires (modules). Cluster models were separately developed for two subsets: (i) 5395 jobs with ≥1 variable extracted from the occupational history indicating a potential diesel exposure scenario, but without a module with diesel-related questions; and (ii) 5929 jobs with both occupational history and module responses to diesel-relevant questions. For each subset, we varied the numbers of clusters extracted from the cluster tree developed for each model from 100 to 1000 groups of jobs. Using previously made estimates of the probability (ordinal), intensity (µg m−3 respirable elemental carbon), and frequency (hours per week) of occupational exposure to diesel exhaust, we examined the similarity of the exposure estimates for jobs within the same cluster in two ways. First, the clusters’ homogeneity (defined as >75% with the same estimate) was examined compared to a dichotomized probability estimate (<5 versus ≥5%; <50 versus ≥50%). Second, for the ordinal probability metric and continuous intensity and frequency metrics, we calculated the intraclass correlation coefficients (ICCs) between each job’s estimate and the mean estimate for all jobs within the cluster. Results: Within-cluster homogeneity increased when more clusters were used. For example, ≥80% of the clusters were homogeneous when 500 clusters were used. Similarly, ICCs were generally above 0.7 when ≥200 clusters were used, indicating minimal within-cluster variability. The most within-cluster variability was observed for the frequency metric (ICCs from 0.4 to 0.8). We estimated that using an expert to assign exposure at the cluster-level assignment and then to review each job in non-homogeneous clusters would require ~2000 decisions per expert, in contrast to evaluating 4255 unique questionnaire patterns or 14983 individual jobs. Conclusions: This proof-of-concept shows that using cluster models as a data reduction step to identify jobs with similar response patterns prior to obtaining expert ratings has the potential to aid rule-based assessment by systematically reducing the number of exposure decisions needed. While promising, additional research is needed to quantify the actual reduction in exposure decisions and the resulting homogeneity of exposure estimates within clusters for an exposure assessment effort that obtains cluster-level expert assessments as part of the assessment process. PMID:25477475

Large Data at Small Universities: Astronomical processing using a computer classroom

NASA Astrophysics Data System (ADS)

Fuller, Nathaniel James; Clarkson, William I.; Fluharty, Bill; Belanger, Zach; Dage, Kristen

2016-06-01

The use of large computing clusters for astronomy research is becoming more commonplace as datasets expand, but access to these required resources is sometimes difficult for research groups working at smaller Universities. As an alternative to purchasing processing time on an off-site computing cluster, or purchasing dedicated hardware, we show how one can easily build a crude on-site cluster by utilizing idle cycles on instructional computers in computer-lab classrooms. Since these computers are maintained as part of the educational mission of the University, the resource impact on the investigator is generally low.By using open source Python routines, it is possible to have a large number of desktop computers working together via a local network to sort through large data sets. By running traditional analysis routines in an “embarrassingly parallel” manner, gains in speed are accomplished without requiring the investigator to learn how to write routines using highly specialized methodology. We demonstrate this concept here applied to 1. photometry of large-format images and 2. Statistical significance-tests for X-ray lightcurve analysis. In these scenarios, we see a speed-up factor which scales almost linearly with the number of cores in the cluster. Additionally, we show that the usage of the cluster does not severely limit performance for a local user, and indeed the processing can be performed while the computers are in use for classroom purposes.

LoFEx - A local framework for calculating excitation energies: Illustrations using RI-CC2 linear response theory.

PubMed

Baudin, Pablo; Kristensen, Kasper

2016-06-14

We present a local framework for the calculation of coupled cluster excitation energies of large molecules (LoFEx). The method utilizes time-dependent Hartree-Fock information about the transitions of interest through the concept of natural transition orbitals (NTOs). The NTOs are used in combination with localized occupied and virtual Hartree-Fock orbitals to generate a reduced excitation orbital space (XOS) specific to each transition where a standard coupled cluster calculation is carried out. Each XOS is optimized to ensure that the excitation energies are determined to a predefined precision. We apply LoFEx in combination with the RI-CC2 model to calculate the lowest excitation energies of a set of medium-sized organic molecules. The results demonstrate the black-box nature of the LoFEx approach and show that significant computational savings can be gained without affecting the accuracy of CC2 excitation energies.

Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space.

PubMed

Loewenstein, Yaniv; Portugaly, Elon; Fromer, Menachem; Linial, Michal

2008-07-01

UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets. We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any practical memory size constraint, this framework guarantees the correct clustering solution without explicitly requiring all dissimilarities in memory. The algorithms are general and are applicable to any dataset. We present a data-dependent characterization of hardness and clustering efficiency. The presented concepts are applicable to any agglomerative clustering formulation. We apply our algorithm to the entire collection of protein sequences, to automatically build a comprehensive evolutionary-driven hierarchy of proteins from sequence alone. The newly created tree captures protein families better than state-of-the-art large-scale methods such as CluSTr, ProtoNet4 or single-linkage clustering. We demonstrate that leveraging the entire mass embodied in all sequence similarities allows to significantly improve on current protein family clusterings which are unable to directly tackle the sheer mass of this data. Furthermore, we argue that non-metric constraints are an inherent complexity of the sequence space and should not be overlooked. The robustness of UPGMA allows significant improvement, especially for multidomain proteins, and for large or divergent families. A comprehensive tree built from all UniProt sequence similarities, together with navigation and classification tools will be made available as part of the ProtoNet service. A C++ implementation of the algorithm is available on request.

Advanced Solar Observatory (ASO) accommodations requirements study

NASA Technical Reports Server (NTRS)

1989-01-01

Results of an accommodations analysis for the Advanced Solar Observatory on Space Station Freedom are reported. Concepts for the High Resolution Telescope Cluster, Pinhole/Occulter Facility, and High Energy Cluster were developed which can be accommodated on Space Station Freedom. It is shown that workable accommodations concepts are possible. Areas of emphasis for the next stage of engineering development are identified.

Self-concept differentiation and self-concept clarity across adulthood: associations with age and psychological well-being.

PubMed

Diehl, Manfred; Hay, Elizabeth L

2011-01-01

This study focused on the identification of conceptually meaningful groups of individuals based on their joint self-concept differentiation (SCD) and self-concept clarity (SCC) scores. Notably, we examined whether membership in different SCD-SCC groups differed by age and also was associated with differences in psychological well-being (PWB). Cluster analysis revealed five distinct SCD-SCC groups: a self-assured, unencumbered, fragmented-only, confused-only, and fragmented and confused group. Individuals in the self-assured group had the highest mean scores for positive PWB and the lowest mean scores for negative PWB, whereas individuals in the fragmented and confused group showed the inverse pattern. Findings showed that it was psychologically advantageous to belong to the self-assured group at all ages. As hypothesized, older adults were more likely than young adults to be in the self-assured cluster, whereas young adults were more likely to be in the fragmented and confused cluster. Thus, consistent with extant theorizing, age was positively associated with psychologically adaptive self-concept profiles.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

PubMed

Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

2018-04-15

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

PubMed Central

Sotiropoulos, Konstantinos

2018-01-01

In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing. PMID:29662043

Understanding network concepts in modules

PubMed Central

2007-01-01

Background Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory. Results Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks. Conclusion Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks PMID:17547772

Diversity in migratory patterns among Neotropical fishes in a highly regulated river basin.

PubMed

Makrakis, M C; Miranda, L E; Makrakis, S; Fontes Júnior, H M; Morlis, W G; Dias, J H P; Garcia, J O

2012-07-01

Migratory behaviour of selected fish species is described in the Paraná River, Brazil-Argentina-Paraguay, to search for patterns relevant to tropical regulated river systems. In a 10 year mark-recapture study, spanning a 1425 km section of the river, 32 867 fishes composed of 18 species were released and 1083 fishes were recaptured. The fishes recaptured were at liberty an average 166 days (maximum 1548 days) and travelled an average 35 km (range 0-625 km). Cluster analysis applied to variables descriptive of movement behaviour identified four general movement patterns. Cluster 1 included species that moved long distances (mean 164 km) upstream (54%) and downstream (40%) the mainstem river and showed high incidence (27%) of passage through dams; cluster 2 also exhibited high rate of movement along the mainstem (49% upstream, 13% downstream), but moved small distances (mean 10 km); cluster 3 included the most fishes moving laterally into tributaries (45%) or not moving at all (25%), but little downstream movement (8%); fishes in cluster 4 exhibited little upstream movement (13%) and farthest downstream movements (mean 41 km). Whereas species could be numerically clustered with statistical models, a species ordination showed ample spread, suggesting that species exhibit diverse movement patterns that cannot be easily classified into just a few classes. The cluster and ordination procedures also showed that adults and juveniles of the same species exhibit similar movement patterns. Conventional concepts about Neotropical migratory fishes portray them as travelling long distances upstream. The present results broaden these concepts suggesting that migratory movements are more diverse, could be long, short or at times absent, upriver, downriver or lateral, and the diversity of movements can vary within and among species. The intense lateral migrations exhibited by a diversity of species, especially to and from large tributaries (above reservoirs) and reservoir tributaries, illustrate the importance of these habitats for the fish species life cycle. Considering that the Paraná River is highly impounded, special attention should be given to the few remaining low-impact habitats as they continue to be targets of hydropower development that will probably intensify the effects on migratory fish stocks. © 2012 The Authors. Journal of Fish Biology © 2012 The Fisheries Society of the British Isles.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

An Extension and Test of Sutherland's Concept of Differential Social Organization: The Geographic Clustering of Japanese Suicide and Homicide Rates

ERIC Educational Resources Information Center

Baller, Robert D.; Shin, Dong-Joon; Richardson, Kelly K.

2005-01-01

In an effort to explain the spatial patterning of violence, we expanded Sutherland's (1947) concept of differential social organization to include the level of deviance exhibited by neighboring areas. To test the value of this extension, the geographic clustering of Japanese suicide and homicide rates is assessed using 1985 and 1995 data for…

Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program.

PubMed

Chen, Robert; Sun, Jimeng; Dittus, Robert S; Fabbri, Daniel; Kirby, Jacqueline; Laffer, Cheryl L; McNaughton, Candace D; Malin, Bradley

2016-01-04

The goal of this study is to devise a machine learning framework to assist care coordination programs in prognostic stratification to design and deliver personalized care plans and to allocate financial and medical resources effectively. This study is based on a de-identified cohort of 2,521 hypertension patients from a chronic care coordination program at the Vanderbilt University Medical Center. Patients were modeled as vectors of features derived from electronic health records (EHRs) over a six-year period. We applied a stepwise regression to identify risk factors associated with a decrease in mean arterial pressure of at least 2 mmHg after program enrollment. The resulting features were subsequently validated via a logistic regression classifier. Finally, risk factors were applied to group the patients through model-based clustering. We identified a set of predictive features that consisted of a mix of demographic, medication, and diagnostic concepts. Logistic regression over these features yielded an area under the ROC curve (AUC) of 0.71 (95% CI: [0.67, 0.76]). Based on these features, four clinically meaningful groups are identified through clustering - two of which represented patients with more severe disease profiles, while the remaining represented patients with mild disease profiles. Patients with hypertension can exhibit significant variation in their blood pressure control status and responsiveness to therapy. Yet this work shows that a clustering analysis can generate more homogeneous patient groups, which may aid clinicians in designing and implementing customized care programs. The study shows that predictive modeling and clustering using EHR data can be beneficial for providing a systematic, generalized approach for care providers to tailor their management approach based upon patient-level factors.

Some Applications of Graph Theory to Clustering

ERIC Educational Resources Information Center

Hubert, Lawrence J.

1974-01-01

The connection between graph theory and clustering is reviewed and extended. Major emphasis is on restating, in a graph-theoretic context, selected past work in clustering, and conversely, developing alternative strategies from several standard concepts used in graph theory per se. (Author/RC)

[Cluster analysis in biomedical researches].

PubMed

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.

Inflation data clustering of some cities in Indonesia

NASA Astrophysics Data System (ADS)

Setiawan, Adi; Susanto, Bambang; Mahatma, Tundjung

2017-06-01

In this paper, it is presented how to cluster inflation data of cities in Indonesia by using k-means cluster method and fuzzy c-means method. The data that are used is limited to the monthly inflation data from 15 cities across Indonesia which have highest weight of donations and is supplemented with 5 cities used in the calculation of inflation in Indonesia. When they are applied into two clusters with k = 2 for k-means cluster method and c = 2, w = 1.25 for fuzzy c-means cluster method, Ambon, Manado and Jayapura tend to become one cluster (high inflation) meanwhile other cities tend to become members of other cluster (low inflation). However, if they are applied into two clusters with c=2, w=1.5, Surabaya, Medan, Makasar, Samarinda, Makasar, Manado, Ambon dan Jayapura tend to become one cluster (high inflation) meanwhile other cities tend to become members of other cluster (low inflation). Furthermore, when we use two clusters with k=3 for k-means cluster method and c=3, w = 1.25 for fuzzy c-means cluster method, Ambon tends to become member of first cluster (high inflation), Manado and Jayapura tend to become member of second cluster (moderate inflation), other cities tend to become members of third cluster (low inflation). If it is applied c=3, w = 1.5, Ambon, Manado and Jayapura tend to become member of first cluster (high inflation), Surabaya, Bandung, Medan, Makasar, Banyuwangi, Denpasar, Samarinda dan Mataram tend to become members of second cluster (moderate inflation), meanwhile other cities tend to become members of third cluster (low inflation). Similarly, interpretation can be made to the results of applying 5 clusters.

Load Balancing in Distributed Web Caching: A Novel Clustering Approach

NASA Astrophysics Data System (ADS)

Tiwari, R.; Kumar, K.; Khan, G.

2010-11-01

The World Wide Web suffers from scaling and reliability problems due to overloaded and congested proxy servers. Caching at local proxy servers helps, but cannot satisfy more than a third to half of requests; more requests are still sent to original remote origin servers. In this paper we have developed an algorithm for Distributed Web Cache, which incorporates cooperation among proxy servers of one cluster. This algorithm uses Distributed Web Cache concepts along with static hierarchies with geographical based clusters of level one proxy server with dynamic mechanism of proxy server during the congestion of one cluster. Congestion and scalability problems are being dealt by clustering concept used in our approach. This results in higher hit ratio of caches, with lesser latency delay for requested pages. This algorithm also guarantees data consistency between the original server objects and the proxy cache objects.

Nonlocalized clustering: a new concept in nuclear cluster structure physics.

PubMed

Zhou, Bo; Funaki, Y; Horiuchi, H; Ren, Zhongzhou; Röpke, G; Schuck, P; Tohsaki, A; Xu, Chang; Yamada, T

2013-06-28

We investigate the α+^{16}O cluster structure in the inversion-doublet band (Kπ=0(1)±}) states of 20Ne with an angular-momentum-projected version of the Tohsaki-Horiuchi-Schuck-Röpke (THSR) wave function, which was successful "in its original form" for the description of, e.g., the famous Hoyle state. In contrast with the traditional view on clusters as localized objects, especially in inversion doublets, we find that these single THSR wave functions, which are based on the concept of nonlocalized clustering, can well describe the Kπ=0(1)- band and the Kπ=0(1)+ band. For instance, they have 99.98% and 99.87% squared overlaps for 1- and 3- states (99.29%, 98.79%, and 97.75% for 0+, 2+, and 4+ states), respectively, with the corresponding exact solution of the α+16O resonating group method. These astounding results shed a completely new light on the physics of low energy nuclear cluster states in nuclei: The clusters are nonlocalized and move around in the whole nuclear volume, only avoiding mutual overlap due to the Pauli blocking effect.

Neurolinguistic approach to natural language processing with applications to medical text analysis.

PubMed

Duch, Włodzisław; Matykiewicz, Paweł; Pestian, John

2008-12-01

Understanding written or spoken language presumably involves spreading neural activation in the brain. This process may be approximated by spreading activation in semantic networks, providing enhanced representations that involve concepts not found directly in the text. The approximation of this process is of great practical and theoretical interest. Although activations of neural circuits involved in representation of words rapidly change in time snapshots of these activations spreading through associative networks may be captured in a vector model. Concepts of similar type activate larger clusters of neurons, priming areas in the left and right hemisphere. Analysis of recent brain imaging experiments shows the importance of the right hemisphere non-verbal clusterization. Medical ontologies enable development of a large-scale practical algorithm to re-create pathways of spreading neural activations. First concepts of specific semantic type are identified in the text, and then all related concepts of the same type are added to the text, providing expanded representations. To avoid rapid growth of the extended feature space after each step only the most useful features that increase document clusterization are retained. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Expanded texts show significantly improved clustering and may be classified with much higher accuracy. Although better approximations to the spreading of neural activations may be devised a practical approach presented in this paper helps to discover pathways used by the brain to process specific concepts, and may be used in large-scale applications.

Social cohesion matters in health.

PubMed

Chuang, Ying-Chih; Chuang, Kun-Yang; Yang, Tzu-Hsuan

2013-10-28

The concept of social cohesion has invoked debate due to the vagueness of its definition and the limitations of current measurements. This paper attempts to examine the concept of social cohesion, develop measurements, and investigate the relationship between social cohesion and individual health. This study used a multilevel study design. The individual-level samples from 29 high-income countries were obtained from the 2000 World Value Survey (WVS) and the 2002 European Value Survey. National-level social cohesion statistics were obtained from Organization of Economic Cooperation and Development datasets, World Development Indicators, and Asian Development Bank key indicators for the year 2000, and from aggregating responses from the WVS. In total 47,923 individuals were included in this study. The factor analysis was applied to identify dimensions of social cohesion, which were used as entities in the cluster analysis to generate a regime typology of social cohesion. Then, multilevel regression models were applied to assess the influences of social cohesion on an individual's self-rated health. Factor analysis identified five dimensions of social cohesion: social equality, social inclusion, social development, social capital, and social diversity. Then, the cluster analysis revealed five regimes of social cohesion. A multi-level analysis showed that respondents in countries with higher social inclusion, social capital, and social diversity were more likely to report good health above and beyond individual-level characteristics. This study is an innovative effort to incorporate different aspects of social cohesion. This study suggests that social cohesion was associated with individual self-rated after controlling individual characteristics. To achieve further advancement in population health, developed countries should consider policies that would foster a society with a high level of social inclusion, social capital, and social diversity. Future research could focus on identifying possible pathways by which social cohesion influences various health outcomes.

Automated clustering-based workload characterization

NASA Technical Reports Server (NTRS)

Pentakalos, Odysseas I.; Menasce, Daniel A.; Yesha, Yelena

1996-01-01

The demands placed on the mass storage systems at various federal agencies and national laboratories are continuously increasing in intensity. This forces system managers to constantly monitor the system, evaluate the demand placed on it, and tune it appropriately using either heuristics based on experience or analytic models. Performance models require an accurate workload characterization. This can be a laborious and time consuming process. It became evident from our experience that a tool is necessary to automate the workload characterization process. This paper presents the design and discusses the implementation of a tool for workload characterization of mass storage systems. The main features of the tool discussed here are: (1)Automatic support for peak-period determination. Histograms of system activity are generated and presented to the user for peak-period determination; (2) Automatic clustering analysis. The data collected from the mass storage system logs is clustered using clustering algorithms and tightness measures to limit the number of generated clusters; (3) Reporting of varied file statistics. The tool computes several statistics on file sizes such as average, standard deviation, minimum, maximum, frequency, as well as average transfer time. These statistics are given on a per cluster basis; (4) Portability. The tool can easily be used to characterize the workload in mass storage systems of different vendors. The user needs to specify through a simple log description language how the a specific log should be interpreted. The rest of this paper is organized as follows. Section two presents basic concepts in workload characterization as they apply to mass storage systems. Section three describes clustering algorithms and tightness measures. The following section presents the architecture of the tool. Section five presents some results of workload characterization using the tool.Finally, section six presents some concluding remarks.

Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space

PubMed Central

Loewenstein, Yaniv; Portugaly, Elon; Fromer, Menachem; Linial, Michal

2008-01-01

Motivation: UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets. Application: We present a novel class of memory-constrained UPGMA (MC-UPGMA) algorithms. Given any practical memory size constraint, this framework guarantees the correct clustering solution without explicitly requiring all dissimilarities in memory. The algorithms are general and are applicable to any dataset. We present a data-dependent characterization of hardness and clustering efficiency. The presented concepts are applicable to any agglomerative clustering formulation. Results: We apply our algorithm to the entire collection of protein sequences, to automatically build a comprehensive evolutionary-driven hierarchy of proteins from sequence alone. The newly created tree captures protein families better than state-of-the-art large-scale methods such as CluSTr, ProtoNet4 or single-linkage clustering. We demonstrate that leveraging the entire mass embodied in all sequence similarities allows to significantly improve on current protein family clusterings which are unable to directly tackle the sheer mass of this data. Furthermore, we argue that non-metric constraints are an inherent complexity of the sequence space and should not be overlooked. The robustness of UPGMA allows significant improvement, especially for multidomain proteins, and for large or divergent families. Availability: A comprehensive tree built from all UniProt sequence similarities, together with navigation and classification tools will be made available as part of the ProtoNet service. A C++ implementation of the algorithm is available on request. Contact: lonshy@cs.huji.ac.il PMID:18586742

Constructing Concept Schemes From Astronomical Telegrams Via Natural Language Clustering

NASA Astrophysics Data System (ADS)

Graham, Matthew; Zhang, M.; Djorgovski, S. G.; Donalek, C.; Drake, A. J.; Mahabal, A.

2012-01-01

The rapidly emerging field of time domain astronomy is one of the most exciting and vibrant new research frontiers, ranging in scientific scope from studies of the Solar System to extreme relativistic astrophysics and cosmology. It is being enabled by a new generation of large synoptic digital sky surveys - LSST, PanStarrs, CRTS - that cover large areas of sky repeatedly, looking for transient objects and phenomena. One of the biggest challenges facing these is the automated classification of transient events, a process that needs machine-processible astronomical knowledge. Semantic technologies enable the formal representation of concepts and relations within a particular domain. ATELs (http://www.astronomerstelegram.org) are a commonly-used means for reporting and commenting upon new astronomical observations of transient sources (supernovae, stellar outbursts, blazar flares, etc). However, they are loose and unstructured and employ scientific natural language for description: this makes automated processing of them - a necessity within the next decade with petascale data rates - a challenge. Nevertheless they represent a potentially rich corpus of information that could lead to new and valuable insights into transient phenomena. This project lies in the cutting-edge field of astrosemantics, a branch of astroinformatics, which applies semantic technologies to astronomy. The ATELs have been used to develop an appropriate concept scheme - a representation of the information they contain - for transient astronomy using hierarchical clustering of processed natural language. This allows us to automatically organize ATELs based on the vocabulary used. We conclude that we can use simple algorithms to process and extract meaning from astronomical textual data.

[Cultural conceptions on dengue in urban contexts in Mexico].

PubMed

Caballero Hoyos, Ramiro; Torres López, Teresa; Chong Villarreal, Francisco; Pineda Lucatero, Alicia; Altuzar González, Marlene; López Coutiño, Berenice

2006-02-01

To explore the conceptual dimensions of dengue in the urban context, aiming at creating hypotheses about community attitudes toward prevention campaigns. An exploratory cross-sectional study was carried out between March and April 2003 comprising 130 people selected by proposition sampling in three municipalities with different dengue prevalences in Mexico. Semi-structured interviews were applied using free lists, pile sorts and triads. Dengue-related terms and groups of conceptual dimensions were investigated. A consensual analysis was performed by factorizing the major components as well as a dimensional analysis with hierarchical clustering and multidimensional scales. The consensual model showed high homogeneity in dengue conceptions (values of 14.5 and 13.5 in the most prevalent contexts, and 5.4 in the least prevalent one). The common dimensions of conceptions were: preventive measures, symptoms, causes and reservoirs of Aedes aegypti (goodness of fit test values: <0.28). In the three contexts studied, a conception of basic prevention based on public actions by health officials predominated while individual and community actions were almost never mentioned. A moral dimension also appeared in the conception based on a notion of hygiene as a differentiating mechanism between the nearby community (clean) and outside people and communities (dirty and sick). The cultural conceptions of dengue do not favor self-managed community involvement in vertical prevention campaigns, and create obstacles to modifying community and individual prevention and control practices.

On the universal structure of human lexical semantics

PubMed Central

Sutton, Logan; Smith, Eric; Moore, Cristopher; Wilkins, Jon F.; Maddieson, Ian; Croft, William

2016-01-01

How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides indirect access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Here, we provide an empirical measure of semantic proximity between concepts using cross-linguistic dictionaries to translate words to and from languages carefully selected to be representative of worldwide diversity. These translations reveal cases where a particular language uses a single “polysemous” word to express multiple concepts that another language represents using distinct words. We use the frequency of such polysemies linking two concepts as a measure of their semantic proximity and represent the pattern of these linkages by a weighted network. This network is highly structured: Certain concepts are far more prone to polysemy than others, and naturally interpretable clusters of closely related concepts emerge. Statistical analysis of the polysemies observed in a subset of the basic vocabulary shows that these structural properties are consistent across different language groups, and largely independent of geography, environment, and the presence or absence of a literary tradition. The methods developed here can be applied to any semantic domain to reveal the extent to which its conceptual structure is, similarly, a universal attribute of human cognition and language use. PMID:26831113

The geometry of chaotic dynamics — a complex network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Heitzig, J.; Donges, J. F.; Zou, Y.; Marwan, N.; Kurths, J.

2011-12-01

Recently, several complex network approaches to time series analysis have been developed and applied to study a wide range of model systems as well as real-world data, e.g., geophysical or financial time series. Among these techniques, recurrence-based concepts and prominently ɛ-recurrence networks, most faithfully represent the geometrical fine structure of the attractors underlying chaotic (and less interestingly non-chaotic) time series. In this paper we demonstrate that the well known graph theoretical properties local clustering coefficient and global (network) transitivity can meaningfully be exploited to define two new local and two new global measures of dimension in phase space: local upper and lower clustering dimension as well as global upper and lower transitivity dimension. Rigorous analytical as well as numerical results for self-similar sets and simple chaotic model systems suggest that these measures are well-behaved in most non-pathological situations and that they can be estimated reasonably well using ɛ-recurrence networks constructed from relatively short time series. Moreover, we study the relationship between clustering and transitivity dimensions on the one hand, and traditional measures like pointwise dimension or local Lyapunov dimension on the other hand. We also provide further evidence that the local clustering coefficients, or equivalently the local clustering dimensions, are useful for identifying unstable periodic orbits and other dynamically invariant objects from time series. Our results demonstrate that ɛ-recurrence networks exhibit an important link between dynamical systems and graph theory.

A Constrained-Clustering Approach to the Analysis of Remote Sensing Data.

DTIC Science & Technology

1983-01-01

One old and two new clustering methods were applied to the constrained-clustering problem of separating different agricultural fields based on multispectral remote sensing satellite data. (Constrained-clustering involves double clustering in multispectral measurement similarity and geographical location.) The results of applying the three methods are provided along with a discussion of their relative strengths and weaknesses and a detailed description of their algorithms.

Endoventricular Deep Brain Stimulation of the Third Ventricle: Proof of Concept and Application to Cluster Headache.

PubMed

Chabardès, Stéphan; Carron, Romain; Seigneuret, Eric; Torres, Napoleon; Goetz, Laurent; Krainik, Alexandre; Piallat, Brigitte; Pham, Pascale; David, Olivier; Giraud, Pierrick; Benabid, Alim Louis

2016-12-01

The third ventricle (3rd V) is surrounded by centers related to satiety, homeostasis, hormones, sleep, memory, and pain. Stimulation of the wall of the 3rd V could be useful to treat disorders related to dysfunction of the hypothalamus. To assess safety and efficacy of endoventricular electrical stimulation of the hypothalamus using a floating deep brain stimulation (DBS) lead laid on the floor of the 3rd V to treat refractory cluster headaches (CH). Seven patients, aged 24 to 60 years, experiencing chronic CH (mean chronic duration 5.8 ± 2.5 years) were enrolled in this pilot, prospective, open study assessing the safety and potential efficacy of chronic DBS of the 3rd V. Number of attacks was collected during baseline and was compared with those occurring at 3, 6, and 12 months postoperation. Any side effects that occurred during or after surgery were reported. Effect on mood was assessed using the Hospital Anxiety and Depression scale during baseline and at 6 and 12 months postoperation. Insertion of the lead into the posterior 3rd V and chronic stimulation was feasible and safe in all patients. The voltage ranged from 0.9 to 2.3 volts. The most common side effect was transient trembling vision during stimulation. At 12 months, 3 of 7 patients were pain free, 2 had 90% improvement, 1 of 7 had 75% improvement, and 1 of 7 was not significantly improved. This proof of concept demonstrates the feasibility, safety, and potential efficacy of 3rd V DBS using an endoventricular road that could be applied to treat various diseases involving hypothalamic areas. CCH, chronic cluster headacheCH, cluster headacheDBS, deep brain stimulationHAD, hospital anxiety depressionONS, occipital nerve stimulationPAG, periaqueductal gray matterPH, posterior hypothalamusPVG, periventricular gray matter3rd V, third ventricle.

Mimicking the magnetic properties of rare earth elements using superatoms.

PubMed

Cheng, Shi-Bo; Berkdemir, Cuneyt; Castleman, A W

2015-04-21

Rare earth elements (REs) consist of a very important group in the periodic table that is vital to many modern technologies. The mining process, however, is extremely damaging to the environment, making them low yield and very expensive. Therefore, mimicking the properties of REs in a superatom framework is especially valuable but at the same time, technically challenging and requiring advanced concepts about manipulating properties of atom/molecular complexes. Herein, by using photoelectron imaging spectroscopy, we provide original idea and direct experimental evidence that chosen boron-doped clusters could mimic the magnetic characteristics of REs. Specifically, the neutral LaB and NdB clusters are found to have similar unpaired electrons and magnetic moments as their isovalent REs (namely Nd and Eu, respectively), opening up the great possibility in accomplishing rare earth mimicry. Extension of the superatom concept into the rare earth group not only further shows the power and advance of this concept but also, will stimulate more efforts to explore new superatomic clusters to mimic the chemistry of these heavy atoms, which will be of great importance in designing novel building blocks in the application of cluster-assembled nanomaterials. Additionally, based on these experimental findings, a novel "magic boron" counting rule is proposed to estimate the numbers of unpaired electrons in diatomic LnB clusters.

The Africa Yoga Project: A Participant-Driven Concept Map of Kenyan Teachers' Reported Experiences.

PubMed

Klein, Jessalyn E; Cook-Cottone, Catherine; Giambrone, Carla

2015-01-01

The Africa Yoga Project (AYP) trains and funds Kenyans to teach community yoga classes. Preliminary research with a small sample of AYP teachers suggested the program had a positive impact. This study used concept mapping to explore the experiences of a larger sample. Participants brainstormed statements about how practicing and/or teaching yoga changed them. They sorted statements into self-defined piles and rated them in terms of perceived importance. Multidimensional scaling (MDS) of sort data calculated statement coordinates wherein each statement is placed in proximity to other statements as a function of how frequently statements are sorted together by participants. These results are then and mapped in a two-dimensional space. Hierarchical cluster analysis (HCA) of these data identified clusters (i.e., concepts) among statements. Cluster average importance ratings gave the concept map depth and indicated concept importance. Bridging analysis and researchers' conceptual understanding of yoga literature facilitated HCA interpretive decisions. Of 72 AYP teachers, 52 and 48 teachers participated in brainstorming and sorting/rating activities, respectively. Teachers brainstormed 93 statements about how they had changed. The resultant MDS statement map had adequate validity (stress value = .29). HCA created a 12-cluster solution with the following concepts of perceived change: Identity as a Yoga Teacher; Prosocial Development; Existential Possibility; Genuine Positive Regard; Value and Respect for Others (highest importance); Presence, Acceptance, and Competence; Service and Trust; Non-judgment and Emotion Regulation (lowest importance); Engagement and Connection; Interpersonal Effectiveness; Psychosocial Functioning; and Physical Competence and Security. Teachers perceived the AYP as facilitating change across physical, mental, and spiritual domains. Additional research is needed to quantify and compare this change to other health promotion program outcomes.

Seizure clustering.

PubMed

Haut, Sheryl R

2006-02-01

Seizure clusters, also known as repetitive or serial seizures, occur commonly in epilepsy. Clustering implies that the occurrence of one seizure may influence the probability of a subsequent seizure; thus, the investigation of the clustering phenomenon yields insights into both specific mechanisms of seizure clustering and more general concepts of seizure occurrence. Seizure clustering has been defined clinically as a number of seizures per unit time and, statistically, as a deviation from a random distribution, or interseizure interval dependence. This review explores the pathophysiology, epidemiology, and clinical implications of clustering, as well as other periodic patterns of seizure occurrence. Risk factors for experiencing clusters and potential precipitants of clustering are also addressed.

Module Cluster: UG - 001.00 (GSC) Urban Geography.

ERIC Educational Resources Information Center

Currier, Wade R.

This is one of several module clusters developed for the Camden Teacher Corps project. This module cluster is designed to introduce students to urban studies through the application of a geographic approach. Although geography shares with other social sciences many concepts and methods, it has contributed a distinctive set of viewpoints and a…

A Technique of Two-Stage Clustering Applied to Environmental and Civil Engineering and Related Methods of Citation Analysis.

ERIC Educational Resources Information Center

Miyamoto, S.; Nakayama, K.

1983-01-01

A method of two-stage clustering of literature based on citation frequency is applied to 5,065 articles from 57 journals in environmental and civil engineering. Results of related methods of citation analysis (hierarchical graph, clustering of journals, multidimensional scaling) applied to same set of articles are compared. Ten references are…

A mixed-methods study identifying key intervention targets to improve participation in daily living activities in primary Sjögren's syndrome patients.

PubMed

Hackett, Katie L; Deane, Katherine H O; Newton, Julia L; Deary, Vincent; Bowman, Simon; Rapley, Tim; Ng, Wan-Fai

2018-02-06

Functional ability and participation in life situations are compromised in many primary Sjögren's syndrome (PSS) patients. This study aims to identify the key barriers and priorities to participation in daily living activities, in order to develop potential future interventions. Group concept mapping (GCM), a semi-quantitative, mixed-methods, approach was used to identify and structure ideas from UK PSS patients, adults living with a PSS patient (AHMs) and health care professionals (HCPs). Brainstorming generated ideas, which were summarised into a final set of statements. Participants individually arranged these statements into themes and rated each statement for importance. Multidimensional scaling and hierarchical cluster analysis were applied to sorted and rated data to produce visual representations of the ideas (concept maps), enabling identification of agreed priority areas for interventions. 121 patients, 43 AHMs and 67 HCPs took part. 463 ideas were distilled down to 94 statements. These statements were grouped into seven clusters; 'Patient empowerment', 'Symptoms', 'Wellbeing', 'Access and coordination of healthcare', 'Knowledge and support', 'Public awareness and support' and 'Family and friends'. Patient empowerment and Symptoms were rated as priority conceptual themes. Important statements within priority clusters indicate patients should be taken seriously and supported to self-manage symptoms of oral and ocular dryness, fatigue, pain and poor sleep. Our data highlighted that in addition to managing PSS symptoms; interventions aiming to improve patient empowerment, general wellbeing, access to healthcare, patient education and social support are important to facilitate improved participation in daily living activities. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Neurolinguistic Approach to Natural Language Processing with Applications to Medical Text Analysis

PubMed Central

Matykiewicz, Paweł; Pestian, John

2008-01-01

Understanding written or spoken language presumably involves spreading neural activation in the brain. This process may be approximated by spreading activation in semantic networks, providing enhanced representations that involve concepts that are not found directly in the text. Approximation of this process is of great practical and theoretical interest. Although activations of neural circuits involved in representation of words rapidly change in time snapshots of these activations spreading through associative networks may be captured in a vector model. Concepts of similar type activate larger clusters of neurons, priming areas in the left and right hemisphere. Analysis of recent brain imaging experiments shows the importance of the right hemisphere non-verbal clusterization. Medical ontologies enable development of a large-scale practical algorithm to re-create pathways of spreading neural activations. First concepts of specific semantic type are identified in the text, and then all related concepts of the same type are added to the text, providing expanded representations. To avoid rapid growth of the extended feature space after each step only the most useful features that increase document clusterization are retained. Short hospital discharge summaries are used to illustrate how this process works on a real, very noisy data. Expanded texts show significantly improved clustering and may be classified with much higher accuracy. Although better approximations to the spreading of neural activations may be devised a practical approach presented in this paper helps to discover pathways used by the brain to process specific concepts, and may be used in large-scale applications. PMID:18614334

Concept mapping and network analysis: an analytic approach to measure ties among constructs.

PubMed

Goldman, Alyssa W; Kane, Mary

2014-12-01

Group concept mapping is a mixed-methods approach that helps a group visually represent its ideas on a topic of interest through a series of related maps. The maps and additional graphics are useful for planning, evaluation and theory development. Group concept maps are typically described, interpreted and utilized through points, clusters and distances, and the implications of these features in understanding how constructs relate to one another. This paper focuses on the application of network analysis to group concept mapping to quantify the strength and directionality of relationships among clusters. The authors outline the steps of this analysis, and illustrate its practical use through an organizational strategic planning example. Additional benefits of this analysis to evaluation projects are also discussed, supporting the overall utility of this supplemental technique to the standard concept mapping methodology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Measuring Community Resilience to Coastal Hazards along the Northern Gulf of Mexico

PubMed Central

Lam, Nina S. N.; Reams, Margaret; Li, Kenan; Li, Chi; Mata, Lillian P.

2016-01-01

The abundant research examining aspects of social-ecological resilience, vulnerability, and hazards and risk assessment has yielded insights into these concepts and suggested the importance of quantifying them. Quantifying resilience is complicated by several factors including the varying definitions of the term applied in the research, difficulties involved in selecting and aggregating indicators of resilience, and the lack of empirical validation for the indices derived. This paper applies a new model, called the resilience inference measurement (RIM) model, to quantify resilience to climate-related hazards for 52 U.S. counties along the northern Gulf of Mexico. The RIM model uses three elements (exposure, damage, and recovery indicators) to denote two relationships (vulnerability and adaptability), and employs both K-means clustering and discriminant analysis to derive the resilience rankings, thus enabling validation and inference. The results yielded a classification accuracy of 94.2% with 28 predictor variables. The approach is theoretically sound and can be applied to derive resilience indices for other study areas at different spatial and temporal scales. PMID:27499707

Measuring Community Resilience to Coastal Hazards along the Northern Gulf of Mexico.

PubMed

Lam, Nina S N; Reams, Margaret; Li, Kenan; Li, Chi; Mata, Lillian P

2016-02-01

The abundant research examining aspects of social-ecological resilience, vulnerability, and hazards and risk assessment has yielded insights into these concepts and suggested the importance of quantifying them. Quantifying resilience is complicated by several factors including the varying definitions of the term applied in the research, difficulties involved in selecting and aggregating indicators of resilience, and the lack of empirical validation for the indices derived. This paper applies a new model, called the resilience inference measurement (RIM) model, to quantify resilience to climate-related hazards for 52 U.S. counties along the northern Gulf of Mexico. The RIM model uses three elements (exposure, damage, and recovery indicators) to denote two relationships (vulnerability and adaptability), and employs both K-means clustering and discriminant analysis to derive the resilience rankings, thus enabling validation and inference. The results yielded a classification accuracy of 94.2% with 28 predictor variables. The approach is theoretically sound and can be applied to derive resilience indices for other study areas at different spatial and temporal scales.

A transcriptomics resource reveals a transcriptional transition during ordered sarcomere morphogenesis in flight muscle.

PubMed

Spletter, Maria L; Barz, Christiane; Yeroslaviz, Assa; Zhang, Xu; Lemke, Sandra B; Bonnard, Adrien; Brunner, Erich; Cardone, Giovanni; Basler, Konrad; Habermann, Bianca H; Schnorrer, Frank

2018-05-30

Muscles organise pseudo-crystalline arrays of actin, myosin and titin filaments to build force-producing sarcomeres. To study sarcomerogenesis, we have generated a transcriptomics resource of developing Drosophila flight muscles and identified 40 distinct expression profile clusters. Strikingly, most sarcomeric components group in two clusters, which are strongly induced after all myofibrils have been assembled, indicating a transcriptional transition during myofibrillogenesis. Following myofibril assembly, many short sarcomeres are added to each myofibril. Subsequently, all sarcomeres mature, reaching 1.5 µm diameter and 3.2 µm length and acquiring stretch-sensitivity. The efficient induction of the transcriptional transition during myofibrillogenesis, including the transcriptional boost of sarcomeric components, requires in part the transcriptional regulator Spalt major. As a consequence of Spalt knock-down, sarcomere maturation is defective and fibers fail to gain stretch-sensitivity. Together, this defines an ordered sarcomere morphogenesis process under precise transcriptional control - a concept that may also apply to vertebrate muscle or heart development. © 2018, Spletter et al.

Phylogeny-guided (meta)genome mining approach for the targeted discovery of new microbial natural products.

PubMed

Kang, Hahk-Soo

2017-02-01

Genomics-based methods are now commonplace in natural products research. A phylogeny-guided mining approach provides a means to quickly screen a large number of microbial genomes or metagenomes in search of new biosynthetic gene clusters of interest. In this approach, biosynthetic genes serve as molecular markers, and phylogenetic trees built with known and unknown marker gene sequences are used to quickly prioritize biosynthetic gene clusters for their metabolites characterization. An increase in the use of this approach has been observed for the last couple of years along with the emergence of low cost sequencing technologies. The aim of this review is to discuss the basic concept of a phylogeny-guided mining approach, and also to provide examples in which this approach was successfully applied to discover new natural products from microbial genomes and metagenomes. I believe that the phylogeny-guided mining approach will continue to play an important role in genomics-based natural products research.

Why Do Children Engage in Sedentary Behavior? Child- and Parent-Perceived Determinants.

PubMed

Hidding, Lisan M; Altenburg, Teatske M; van Ekris, Evi; Chinapaw, Mai J M

2017-06-22

Todays children spend a large amount of their time sedentary. There is limited evidence on the determinants of sedentary behavior in children, and qualitative studies are especially lacking. Therefore, this study aimed to explore determinants of children's sedentary behavior from the child- and parent perspective. Qualitative data were collected during concept mapping sessions with four groups of 11-13 years old children ( n = 38) and two online sessions with parents ( n = 21). Children and parents generated sedentary behavior motives, sorted related motives, and rated their importance in influencing children's sedentary time. Next, multidimensional scaling and hierarchical cluster analysis was performed to create clusters of motives resulting in a concept map. Finally, the researchers named the clusters in the concept map. Concept maps of children yielded eight to ten perceived determinants, and concept maps of parents six to seven. Children and parents identified six similar potential determinants, and both rated as important: Sitting because… "it is the norm (I have to)", and "I can work/play better that way". In addition, children rated "there is nobody to play with" as an important potential determinant for engaging in sedentary behavior. The most important child- and parent perceived determinants were related to the social/cultural and physical environment, indicating that these are promising targets for future interventions.

The X-Ray Surveyor Mission: A Concept Study

NASA Technical Reports Server (NTRS)

Gaskin, Jessica A.; Weisskopf, Martin C.; Vikhlinin, Alexey; Tananbaum, Harvey D.; Bandler, Simon R.; Bautz, Marshall W.; Burrows, David N.; Falcone, Abraham D.; Harrison, Fiona A.; Heilmann, Ralf K.;

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

PubMed

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

*K-means and cluster models for cancer signatures.

PubMed

Kakushadze, Zura; Yu, Willie

2017-09-01

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means' computational cost is a fraction of NMF's. Using 1389 published samples for 14 cancer types, we find that 3 cancers (liver cancer, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high within-cluster correlations with 11 other cancers indicating common underlying structures. Our approach opens a novel avenue for studying such structures. *K-means is universal and can be applied in other fields. We discuss some potential applications in quantitative finance.

The DAFT/FADA survey. I.Photometric redshifts along lines of sight to clusters in the z=[0.4,0.9] interval

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guennou, L.; /Northwestern U. /Marseille, Lab. Astrophys.; Adami, C.

2010-08-01

As a contribution to the understanding of the dark energy concept, the Dark energy American French Team (DAFT, in French FADA) has started a large project to characterize statistically high redshift galaxy clusters, infer cosmological constraints from Weak Lensing Tomography, and understand biases relevant for constraining dark energy and cluster physics in future cluster and cosmological experiments. Aims. The purpose of this paper is to establish the basis of reference for the photo-z determination used in all our subsequent papers, including weak lensing tomography studies. This project is based on a sample of 91 high redshift (z {ge} 0.4), massivemore » ({approx}> 3 x 10{sup 14} M{sub {circle_dot}}) clusters with existing HST imaging, for which we are presently performing complementary multi-wavelength imaging. This allows us in particular to estimate spectral types and determine accurate photometric redshifts for galaxies along the lines of sight to the first ten clusters for which all the required data are available down to a limit of I{sub AB} = 24./24.5 with the LePhare software. The accuracy in redshift is of the order of 0.05 for the range 0.2 {le} z {le} 1.5. We verified that the technique applied to obtain photometric redshifts works well by comparing our results to with previous works. In clusters, photo-z accuracy is degraded for bright absolute magnitudes and for the latest and earliest type galaxies. The photo-z accuracy also only slightly varies as a function of the spectral type for field galaxies. As a consequence, we find evidence for an environmental dependence of the photo-z accuracy, interpreted as the standard used Spectral Energy Distributions being not very well suited to cluster galaxies. Finally, we modeled the LCDCS 0504 mass with the strong arcs detected along this line of sight.« less

Cleaning by clustering: methodology for addressing data quality issues in biomedical metadata.

PubMed

Hu, Wei; Zaveri, Amrapali; Qiu, Honglei; Dumontier, Michel

2017-09-18

The ability to efficiently search and filter datasets depends on access to high quality metadata. While most biomedical repositories require data submitters to provide a minimal set of metadata, some such as the Gene Expression Omnibus (GEO) allows users to specify additional metadata in the form of textual key-value pairs (e.g. sex: female). However, since there is no structured vocabulary to guide the submitter regarding the metadata terms to use, consequently, the 44,000,000+ key-value pairs in GEO suffer from numerous quality issues including redundancy, heterogeneity, inconsistency, and incompleteness. Such issues hinder the ability of scientists to hone in on datasets that meet their requirements and point to a need for accurate, structured and complete description of the data. In this study, we propose a clustering-based approach to address data quality issues in biomedical, specifically gene expression, metadata. First, we present three different kinds of similarity measures to compare metadata keys. Second, we design a scalable agglomerative clustering algorithm to cluster similar keys together. Our agglomerative cluster algorithm identified metadata keys that were similar, based on (i) name, (ii) core concept and (iii) value similarities, to each other and grouped them together. We evaluated our method using a manually created gold standard in which 359 keys were grouped into 27 clusters based on six types of characteristics: (i) age, (ii) cell line, (iii) disease, (iv) strain, (v) tissue and (vi) treatment. As a result, the algorithm generated 18 clusters containing 355 keys (four clusters with only one key were excluded). In the 18 clusters, there were keys that were identified correctly to be related to that cluster, but there were 13 keys which were not related to that cluster. We compared our approach with four other published methods. Our approach significantly outperformed them for most metadata keys and achieved the best average F-Score (0.63). Our algorithm identified keys that were similar to each other and grouped them together. Our intuition that underpins cleaning by clustering is that, dividing keys into different clusters resolves the scalability issues for data observation and cleaning, and keys in the same cluster with duplicates and errors can easily be found. Our algorithm can also be applied to other biomedical data types.

GalWeight: A New and Effective Weighting Technique for Determining Galaxy Cluster and Group Membership

NASA Astrophysics Data System (ADS)

Abdullah, Mohamed H.; Wilson, Gillian; Klypin, Anatoly

2018-07-01

We introduce GalWeight, a new technique for assigning galaxy cluster membership. This technique is specifically designed to simultaneously maximize the number of bona fide cluster members while minimizing the number of contaminating interlopers. The GalWeight technique can be applied to both massive galaxy clusters and poor galaxy groups. Moreover, it is effective in identifying members in both the virial and infall regions with high efficiency. We apply the GalWeight technique to MDPL2 and Bolshoi N-body simulations, and find that it is >98% accurate in correctly assigning cluster membership. We show that GalWeight compares very favorably against four well-known existing cluster membership techniques (shifting gapper, den Hartog, caustic, SIM). We also apply the GalWeight technique to a sample of 12 Abell clusters (including the Coma cluster) using observations from the Sloan Digital Sky Survey. We conclude by discussing GalWeight’s potential for other astrophysical applications.

Clustering: An Interactive Technique to Enhance Learning in Biology.

ERIC Educational Resources Information Center

Ambron, Joanna

1988-01-01

Explains an interdisciplinary approach to biology and writing which increases students' mastery of vocabulary, scientific concepts, creativity, and expression. Describes modifications of the clustering technique used to summarize lectures, integrate reading and understand textbook material. (RT)

On the universal structure of human lexical semantics

DOE PAGES

Youn, Hyejin; Sutton, Logan; Smith, Eric; ...

2016-02-01

How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides indirect access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Here, we provide an empirical measure of semantic proximity between concepts using cross-linguistic dictionaries to translate words to and from languages carefully selected to be representative of worldwide diversity. These translations reveal cases where a particular language uses a single “polysemous” word tomore » express multiple concepts that another language represents using distinct words. We use the frequency of such polysemies linking two concepts as a measure of their semantic proximity and represent the pattern of these linkages by a weighted network. This network is highly structured: Certain concepts are far more prone to polysemy than others, and naturally interpretable clusters of closely related concepts emerge. Statistical analysis of the polysemies observed in a subset of the basic vocabulary shows that these structural properties are consistent across different language groups, and largely independent of geography, environment, and the presence or absence of a literary tradition. As a result, the methods developed here can be applied to any semantic domain to reveal the extent to which its conceptual structure is, similarly, a universal attribute of human cognition and language use.« less

On the universal structure of human lexical semantics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Youn, Hyejin; Sutton, Logan; Smith, Eric

How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides indirect access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Here, we provide an empirical measure of semantic proximity between concepts using cross-linguistic dictionaries to translate words to and from languages carefully selected to be representative of worldwide diversity. These translations reveal cases where a particular language uses a single “polysemous” word tomore » express multiple concepts that another language represents using distinct words. We use the frequency of such polysemies linking two concepts as a measure of their semantic proximity and represent the pattern of these linkages by a weighted network. This network is highly structured: Certain concepts are far more prone to polysemy than others, and naturally interpretable clusters of closely related concepts emerge. Statistical analysis of the polysemies observed in a subset of the basic vocabulary shows that these structural properties are consistent across different language groups, and largely independent of geography, environment, and the presence or absence of a literary tradition. As a result, the methods developed here can be applied to any semantic domain to reveal the extent to which its conceptual structure is, similarly, a universal attribute of human cognition and language use.« less

Science-based occupations and the science curriculum: Concepts of evidence

NASA Astrophysics Data System (ADS)

Aikenhead, Glen S.

2005-03-01

What science-related knowledge is actually used by nurses in their day-to-day clinical reasoning when attending patients? The study investigated the knowledge-in-use of six acute-care nurses in a hospital surgical unit. It was found that the nurses mainly drew upon their professional knowledge of nursing and upon their procedural understanding that included a common core of concepts of evidence (concepts implicitly applied to the evaluation of data and the evaluation of evidence - the focus of this research). This core included validity triangulation, normalcy range, accuracy, and a general predilection for direct sensual access to a phenomenon over indirect machine-managed access. A cluster of emotion-related concepts of evidence (e.g. cultural sensitivity) was also discovered. These results add to a compendium of concepts of evidence published in the literature. Only a small proportion of nurses (one of the six nurses in the study) used canonical science content in their clinical reasoning, a result consistent with other research. This study also confirms earlier research on employees in science-rich workplaces in general, and on professional development programs for nurses specifically: canonical science content found in a typical science curriculum (e.g. high school physics) does not appear relevant to many nurses' knowledge-in-use. These findings support a curriculum policy that gives emphasis to students learning how to learn science content as required by an authentic everyday or workplace context, and to students learning concepts of evidence.

ClusterControl: a web interface for distributing and monitoring bioinformatics applications on a Linux cluster.

PubMed

Stocker, Gernot; Rieder, Dietmar; Trajanoski, Zlatko

2004-03-22

ClusterControl is a web interface to simplify distributing and monitoring bioinformatics applications on Linux cluster systems. We have developed a modular concept that enables integration of command line oriented program into the application framework of ClusterControl. The systems facilitate integration of different applications accessed through one interface and executed on a distributed cluster system. The package is based on freely available technologies like Apache as web server, PHP as server-side scripting language and OpenPBS as queuing system and is available free of charge for academic and non-profit institutions. http://genome.tugraz.at/Software/ClusterControl

SELF-CONCEPT DIFFERENTIATION AND SELF-CONCEPT CLARITY ACROSS ADULTHOOD: ASSOCIATIONS WITH AGE AND PSYCHOLOGICAL WELL-BEING*

PubMed Central

DIEHL, MANFRED; HAY, ELIZABETH L.

2011-01-01

This study focused on the identification of conceptually meaningful groups of individuals based on their joint self-concept differentiation (SCD) and self-concept clarity (SCC) scores. Notably, we examined whether membership in different SCD-SCC groups differed by age and also was associated with differences in psychological well-being (PWB). Cluster analysis revealed five distinct SCD-SCC groups: a self-assured, unencumbered, fragmented-only, confused-only, and fragmented and confused group. Individuals in the self-assured group had the highest mean scores for positive PWB and the lowest mean scores for negative PWB, whereas individuals in the fragmented and confused group showed the inverse pattern. Findings showed that it was psychologically advantageous to belong to the self-assured group at all ages. As hypothesized, older adults were more likely than young adults to be in the self-assured cluster, whereas young adults were more likely to be in the fragmented and confused cluster. Thus, consistent with extant theorizing, age was positively associated with psychologically adaptive self-concept profiles. This study examined whether conceptually meaningful subgroups of individuals can be identified based on their joint scores on self-concept differentiation (SCD) and self-concept clarity (SCC). Specifically, we considered whether individuals within such subgroups differed systematically from one another on measures of positive and negative psychological well-being (PWB). Of interest to us was also whether there were age differences in the distribution of adults across the SCD-SCC groups and whether age moderated the association between PWB and SCD-SCC grouping. PMID:22010361

`Inter-Arrival Time' Inspired Algorithm and its Application in Clustering and Molecular Phylogeny

NASA Astrophysics Data System (ADS)

Kolekar, Pandurang S.; Kale, Mohan M.; Kulkarni-Kale, Urmila

2010-10-01

Bioinformatics, being multidisciplinary field, involves applications of various methods from allied areas of Science for data mining using computational approaches. Clustering and molecular phylogeny is one of the key areas in Bioinformatics, which help in study of classification and evolution of organisms. Molecular phylogeny algorithms can be divided into distance based and character based methods. But most of these methods are dependent on pre-alignment of sequences and become computationally intensive with increase in size of data and hence demand alternative efficient approaches. `Inter arrival time distribution' (IATD) is a popular concept in the theory of stochastic system modeling but its potential in molecular data analysis has not been fully explored. The present study reports application of IATD in Bioinformatics for clustering and molecular phylogeny. The proposed method provides IATDs of nucleotides in genomic sequences. The distance function based on statistical parameters of IATDs is proposed and distance matrix thus obtained is used for the purpose of clustering and molecular phylogeny. The method is applied on a dataset of 3' non-coding region sequences (NCR) of Dengue virus type 3 (DENV-3), subtype III, reported in 2008. The phylogram thus obtained revealed the geographical distribution of DENV-3 isolates. Sri Lankan DENV-3 isolates were further observed to be clustered in two sub-clades corresponding to pre and post Dengue hemorrhagic fever emergence groups. These results are consistent with those reported earlier, which are obtained using pre-aligned sequence data as an input. These findings encourage applications of the IATD based method in molecular phylogenetic analysis in particular and data mining in general.

Visualising crystal packing interactions in solid-state NMR: Concepts and applications

NASA Astrophysics Data System (ADS)

Zilka, Miri; Sturniolo, Simone; Brown, Steven P.; Yates, Jonathan R.

2017-10-01

In this article, we introduce and apply a methodology, based on density functional theory and the gauge-including projector augmented wave approach, to explore the effects of packing interactions on solid-state nuclear magnetic resonance (NMR) parameters. A visual map derived from a so-termed "magnetic shielding contribution field" can be made of the contributions to the magnetic shielding of a specific site—partitioning the chemical shift to specific interactions. The relation to the established approaches of examining the molecule to crystal change in the chemical shift and the nuclear independent chemical shift is established. The results are applied to a large sample of 71 molecular crystals and three further specific examples from supermolecular chemistry and pharmaceuticals. This approach extends the NMR crystallography toolkit and provides insight into the development of both cluster based approaches to the predictions of chemical shifts and for empirical predictions of chemical shifts in solids.

Virtual data

NASA Astrophysics Data System (ADS)

Bjorklund, E.

1994-12-01

In the 1970s, when computers were memory limited, operating system designers created the concept of "virtual memory", which gave users the ability to address more memory than physically existed. In the 1990s, many large control systems have the potential of becoming data limited. We propose that many of the principles behind virtual memory systems (working sets, locality, caching and clustering) can also be applied to data-limited systems, creating, in effect, "virtual data systems". At the Los Alamos National Laboratory's Clinton P. Anderson Meson Physics Facility (LAMPF), we have applied these principles to a moderately sized (10 000 data points) data acquisition and control system. To test the principles, we measured the system's performance during tune-up, production, and maintenance periods. In this paper, we present a general discussion of the principles of a virtual data system along with some discussion of our own implementation and the results of our performance measurements.

Computer-Assisted Concept Analysis of "HCR"'s First 25 Years.

ERIC Educational Resources Information Center

Stephen, Timothy

1999-01-01

Normalizes titles of 634 "Human Communication Research" articles using linguistic reduction, elimination of common words, and terms with indiscriminate meaning, and tokenization of phrases and compound concepts. Finds that concepts were grouped into five large clusters: media, family, conflict, and learning; culture, social organization, and self;…

Clustering of disulfide-rich peptides provides scaffolds for hit discovery by phage display: application to interleukin-23.

PubMed

Barkan, David T; Cheng, Xiao-Li; Celino, Herodion; Tran, Tran T; Bhandari, Ashok; Craik, Charles S; Sali, Andrej; Smythe, Mark L

2016-11-23

Disulfide-rich peptides (DRPs) are found throughout nature. They are suitable scaffolds for drug development due to their small cores, whose disulfide bonds impart extraordinary chemical and biological stability. A challenge in developing a DRP therapeutic is to engineer binding to a specific target. This challenge can be overcome by (i) sampling the large sequence space of a given scaffold through a phage display library and by (ii) panning multiple libraries encoding structurally distinct scaffolds. Here, we implement a protocol for defining these diverse scaffolds, based on clustering structurally defined DRPs according to their conformational similarity. We developed and applied a hierarchical clustering protocol based on DRP structural similarity, followed by two post-processing steps, to classify 806 unique DRP structures into 81 clusters. The 20 most populated clusters comprised 85% of all DRPs. Representative scaffolds were selected from each of these clusters; the representatives were structurally distinct from one another, but similar to other DRPs in their respective clusters. To demonstrate the utility of the clusters, phage libraries were constructed for three of the representative scaffolds and panned against interleukin-23. One library produced a peptide that bound to this target with an IC 50 of 3.3 μM. Most DRP clusters contained members that were diverse in sequence, host organism, and interacting proteins, indicating that cluster members were functionally diverse despite having similar structure. Only 20 peptide scaffolds accounted for most of the natural DRP structural diversity, providing suitable starting points for seeding phage display experiments. Through selection of the scaffold surface to vary in phage display, libraries can be designed that present sequence diversity in architecturally distinct, biologically relevant combinations of secondary structures. We supported this hypothesis with a proof-of-concept experiment in which three phage libraries were constructed and panned against the IL-23 target, resulting in a single-digit μM hit and suggesting that a collection of libraries based on the full set of 20 scaffolds increases the potential to identify efficiently peptide binders to a protein target in a drug discovery program.

Exploring the effects of climatic variables on monthly precipitation variation using a continuous wavelet-based multiscale entropy approach.

PubMed

Roushangar, Kiyoumars; Alizadeh, Farhad; Adamowski, Jan

2018-08-01

Understanding precipitation on a regional basis is an important component of water resources planning and management. The present study outlines a methodology based on continuous wavelet transform (CWT) and multiscale entropy (CWME), combined with self-organizing map (SOM) and k-means clustering techniques, to measure and analyze the complexity of precipitation. Historical monthly precipitation data from 1960 to 2010 at 31 rain gauges across Iran were preprocessed by CWT. The multi-resolution CWT approach segregated the major features of the original precipitation series by unfolding the structure of the time series which was often ambiguous. The entropy concept was then applied to components obtained from CWT to measure dispersion, uncertainty, disorder, and diversification of subcomponents. Based on different validity indices, k-means clustering captured homogenous areas more accurately, and additional analysis was performed based on the outcome of this approach. The 31 rain gauges in this study were clustered into 6 groups, each one having a unique CWME pattern across different time scales. The results of clustering showed that hydrologic similarity (multiscale variation of precipitation) was not based on geographic contiguity. According to the pattern of entropy across the scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation data in each cluster. Based on the pattern of mean CWME for each cluster, a characteristic signature was assigned, which provided an estimation of the CWME of a cluster across scales of 1-2, 3-8, and 9-13 months relative to other stations. The validity of the homogeneous clusters demonstrated the usefulness of the proposed approach to regionalize precipitation. Further analysis based on wavelet coherence (WTC) was performed by selecting central rain gauges in each cluster and analyzing against temperature, wind, Multivariate ENSO index (MEI), and East Atlantic (EA) and North Atlantic Oscillation (NAO), indeces. The results revealed that all climatic features except NAO influenced precipitation in Iran during the 1960-2010 period. Copyright © 2018 Elsevier Inc. All rights reserved.

Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts

DOE PAGES

Durret, F.; Adami, C.; Bertin, E.; ...

2015-06-10

Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe ~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photoz differing by less thanmore » 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour-magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy with absolute magnitude brighter than -19.0 in the r band, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. We also detected 3663 clusters in the redshift range 0.1513 and a few 10 14 solar masses. Furthermore, by stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g'-r') versus r' colour-magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in high mass clusters) and a decrease with detection level, i.e. cluster mass. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.« less

Galaxy clusters in the SDSS Stripe 82 based on photometric redshifts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Durret, F.; Adami, C.; Bertin, E.

Based on a recent photometric redshift galaxy catalogue, we have searched for galaxy clusters in the Stripe ~82 region of the Sloan Digital Sky Survey by applying the Adami & MAzure Cluster FInder (AMACFI). Extensive tests were made to fine-tune the AMACFI parameters and make the cluster detection as reliable as possible. The same method was applied to the Millennium simulation to estimate our detection efficiency and the approximate masses of the detected clusters. Considering all the cluster galaxies (i.e. within a 1 Mpc radius of the cluster to which they belong and with a photoz differing by less thanmore » 0.05 from that of the cluster), we stacked clusters in various redshift bins to derive colour-magnitude diagrams and galaxy luminosity functions (GLFs). For each galaxy with absolute magnitude brighter than -19.0 in the r band, we computed the disk and spheroid components by applying SExtractor, and by stacking clusters we determined how the disk-to-spheroid flux ratio varies with cluster redshift and mass. We also detected 3663 clusters in the redshift range 0.1513 and a few 10 14 solar masses. Furthermore, by stacking the cluster galaxies in various redshift bins, we find a clear red sequence in the (g'-r') versus r' colour-magnitude diagrams, and the GLFs are typical of clusters, though with a possible contamination from field galaxies. The morphological analysis of the cluster galaxies shows that the fraction of late-type to early-type galaxies shows an increase with redshift (particularly in high mass clusters) and a decrease with detection level, i.e. cluster mass. From the properties of the cluster galaxies, the majority of the candidate clusters detected here seem to be real clusters with typical cluster properties.« less

The Musical Self-Concept of Chinese Music Students.

PubMed

Petersen, Suse; Camp, Marc-Antoine

2016-01-01

The relationship between self-concept and societal settings has been widely investigated in several Western and Asian countries, with respect to the academic self-concept in an educational environment. Although the musical self-concept is highly relevant to musical development and performance, there is a lack of research exploring how the musical self-concept evolves in different cultural settings and societies. In particular, there have been no enquiries yet in the Chinese music education environment. This study's goal was the characterization of musical self-concept types among music students at a University in Beijing, China. The Musical Self-Concept Inquiry-including ability, emotional, physical, cognitive, and social facets-was used to assess the students' musical self-concepts (N = 97). The data analysis led to three significantly distinct clusters and corresponding musical self-concept types. The types were especially distinct, in the students' perception of their musical ambitions and abilities; their movement, rhythm and dancing affinity; and the spiritual and social aspects of music. The professional aims and perspectives, and the aspects of the students' sociodemographic background also differed between the clusters. This study is one of the first research endeavors addressing musical self-concepts in China. The empirical identification of the self-concept types offers a basis for future research on the connections between education, the development of musical achievement, and the musical self-concept in societal settings with differing understandings of the self.

The Musical Self-Concept of Chinese Music Students

PubMed Central

Petersen, Suse; Camp, Marc-Antoine

2016-01-01

The relationship between self-concept and societal settings has been widely investigated in several Western and Asian countries, with respect to the academic self-concept in an educational environment. Although the musical self-concept is highly relevant to musical development and performance, there is a lack of research exploring how the musical self-concept evolves in different cultural settings and societies. In particular, there have been no enquiries yet in the Chinese music education environment. This study’s goal was the characterization of musical self-concept types among music students at a University in Beijing, China. The Musical Self-Concept Inquiry—including ability, emotional, physical, cognitive, and social facets—was used to assess the students’ musical self-concepts (N = 97). The data analysis led to three significantly distinct clusters and corresponding musical self-concept types. The types were especially distinct, in the students’ perception of their musical ambitions and abilities; their movement, rhythm and dancing affinity; and the spiritual and social aspects of music. The professional aims and perspectives, and the aspects of the students’ sociodemographic background also differed between the clusters. This study is one of the first research endeavors addressing musical self-concepts in China. The empirical identification of the self-concept types offers a basis for future research on the connections between education, the development of musical achievement, and the musical self-concept in societal settings with differing understandings of the self. PMID:27303337

Mapping New Mobility business, innovation, and employment opportunities in Michigan : developing a data-driven graphical platform for assessing and advancing industry cluster development and entrepreneurship opportunities in urban regions : USDOT Region V

DOT National Transportation Integrated Search

2016-12-15

Across regional economic development leaders and policy makers, the concept of clustering has grown in importance as a framework for structuring economic growth and resurgence (Muro & Katz, 2010). Cluster identification is most often treated as a com...

Blue Stragglers in Clusters and Integrated Spectral Properties of Stellar Populations

NASA Astrophysics Data System (ADS)

Xin, Yu; Deng, Licai

Blue straggler stars are the most prominent bright objects in the colour-magnitude diagram of a star cluster that challenges the theory of stellar evolution. Star clusters are the closest counterparts of the theoretical concept of simple stellar populations (SSPs) in the Universe. SSPs are widely used as the basic building blocks to interpret stellar contents in galaxies. The concept of an SSP is a group of coeval stars which follows a given distribution in mass, and has the same chemical property and age. In practice, SSPs are more conveniently made by the latest stellar evolutionary models of single stars. In reality, however, stars can be more complicated than just single either at birth time or during the course of evolution in a typical environment. Observations of star clusters show that there are always exotic objects which do not follow the predictions of standard theory of stellar evolution. Blue straggler stars (BSSs), as discussed intensively in this book both observationally and theoretically, are very important in our context when considering the integrated spectral properties of a cluster, or a simple stellar population. In this chapter, we are going to describe how important the contribution of BSSs is to the total light of a cluster.

SOTXTSTREAM: Density-based self-organizing clustering of text streams.

PubMed

Bryant, Avory C; Cios, Krzysztof J

2017-01-01

A streaming data clustering algorithm is presented building upon the density-based self-organizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clustering algorithms use a two-phase clustering approach. In the first phase, a micro-clustering solution is maintained online, while in the second phase, the micro-clustering solution is clustered offline to produce a macro solution. By performing self-organization techniques on micro-clusters in the online phase, SOSTREAM is able to maintain a macro clustering solution in a single phase. Leveraging concepts from SOSTREAM, a new density-based self-organizing text stream clustering algorithm, SOTXTSTREAM, is presented that addresses several shortcomings of SOSTREAM. Gains in clustering performance of this new algorithm are demonstrated on several real-world text stream datasets.

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

PubMed

Braschel, Melissa C; Svec, Ivana; Darlington, Gerarda A; Donner, Allan

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith's method provide nominal or close to nominal coverage when the intraclass correlation coefficient is small (<0.05), as is the case in most community intervention trials. This study concludes that when a binary outcome variable is measured in a small number of large clusters, confidence intervals for the intraclass correlation coefficient may be constructed by dividing existing clusters into sub-clusters (e.g. groups of 5) and using Smith's method. The resulting confidence intervals provide nominal or close to nominal coverage across a wide range of parameters when the intraclass correlation coefficient is small (<0.05). Application of this method should provide investigators with a better understanding of the uncertainty associated with a point estimator of the intraclass correlation coefficient used for determining the sample size needed for a newly designed community-based trial. © The Author(s) 2015.

Understanding boron through size-selected clusters: structure, chemical bonding, and fluxionality.

PubMed

Sergeeva, Alina P; Popov, Ivan A; Piazza, Zachary A; Li, Wei-Li; Romanescu, Constantin; Wang, Lai-Sheng; Boldyrev, Alexander I

2014-04-15

Boron is an interesting element with unusual polymorphism. While three-dimensional (3D) structural motifs are prevalent in bulk boron, atomic boron clusters are found to have planar or quasi-planar structures, stabilized by localized two-center-two-electron (2c-2e) σ bonds on the periphery and delocalized multicenter-two-electron (nc-2e) bonds in both σ and π frameworks. Electron delocalization is a result of boron's electron deficiency and leads to fluxional behavior, which has been observed in B13(+) and B19(-). A unique capability of the in-plane rotation of the inner atoms against the periphery of the cluster in a chosen direction by employing circularly polarized infrared radiation has been suggested. Such fluxional behaviors in boron clusters are interesting and have been proposed as molecular Wankel motors. The concepts of aromaticity and antiaromaticity have been extended beyond organic chemistry to planar boron clusters. The validity of these concepts in understanding the electronic structures of boron clusters is evident in the striking similarities of the π-systems of planar boron clusters to those of polycyclic aromatic hydrocarbons, such as benzene, naphthalene, coronene, anthracene, or phenanthrene. Chemical bonding models developed for boron clusters not only allowed the rationalization of the stability of boron clusters but also lead to the design of novel metal-centered boron wheels with a record-setting planar coordination number of 10. The unprecedented highly coordinated borometallic molecular wheels provide insights into the interactions between transition metals and boron and expand the frontier of boron chemistry. Another interesting feature discovered through cluster studies is boron transmutation. Even though it is well-known that B(-), formed by adding one electron to boron, is isoelectronic to carbon, cluster studies have considerably expanded the possibilities of new structures and new materials using the B(-)/C analogy. It is believed that the electronic transmutation concept will be effective and valuable in aiding the design of new boride materials with predictable properties. The study of boron clusters with intermediate properties between those of individual atoms and bulk solids has given rise to a unique opportunity to broaden the frontier of boron chemistry. Understanding boron clusters has spurred experimentalists and theoreticians to find new boron-based nanomaterials, such as boron fullerenes, nanotubes, two-dimensional boron, and new compounds containing boron clusters as building blocks. Here, a brief and timely overview is presented addressing the recent progress made on boron clusters and the approaches used in the authors' laboratories to determine the structure, stability, and chemical bonding of size-selected boron clusters by joint photoelectron spectroscopy and theoretical studies. Specifically, key findings on all-boron hydrocarbon analogues, metal-centered boron wheels, and electronic transmutation in boron clusters are summarized.

Understanding Boron through Size-Selected Clusters: Structure, Chemical Bonding, and Fluxionality

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sergeeva, Alina P.; Popov, Ivan A.; Piazza, Zachary A.

Conspectus Boron is an interesting element with unusual polymorphism. While three-dimensional (3D) structural motifs are prevalent in bulk boron, atomic boron clusters are found to have planar or quasi-planar structures, stabilized by localized two-center–two-electron (2c–2e) σ bonds on the periphery and delocalized multicenter–two-electron (nc–2e) bonds in both σ and π frameworks. Electron delocalization is a result of boron’s electron deficiency and leads to fluxional behavior, which has been observed in B13+ and B19–. A unique capability of the in-plane rotation of the inner atoms against the periphery of the cluster in a chosen direction by employing circularly polarized infrared radiationmore » has been suggested. Such fluxional behaviors in boron clusters are interesting and have been proposed as molecular Wankel motors. The concepts of aromaticity and antiaromaticity have been extended beyond organic chemistry to planar boron clusters. The validity of these concepts in understanding the electronic structures of boron clusters is evident in the striking similarities of the π-systems of planar boron clusters to those of polycyclic aromatic hydrocarbons, such as benzene, naphthalene, coronene, anthracene, or phenanthrene. Chemical bonding models developed for boron clusters not only allowed the rationalization of the stability of boron clusters but also lead to the design of novel metal-centered boron wheels with a record-setting planar coordination number of 10. The unprecedented highly coordinated borometallic molecular wheels provide insights into the interactions between transition metals and boron and expand the frontier of boron chemistry. Another interesting feature discovered through cluster studies is boron transmutation. Even though it is well-known that B–, formed by adding one electron to boron, is isoelectronic to carbon, cluster studies have considerably expanded the possibilities of new structures and new materials using the B–/C analogy. It is believed that the electronic transmutation concept will be effective and valuable in aiding the design of new boride materials with predictable properties. The study of boron clusters with intermediate properties between those of individual atoms and bulk solids has given rise to a unique opportunity to broaden the frontier of boron chemistry. Understanding boron clusters has spurred experimentalists and theoreticians to find new boron-based nanomaterials, such as boron fullerenes, nanotubes, two-dimensional boron, and new compounds containing boron clusters as building blocks. Here, a brief and timely overview is presented addressing the recent progress made on boron clusters and the approaches used in the authors’ laboratories to determine the structure, stability, and chemical bonding of size-selected boron clusters by joint photoelectron spectroscopy and theoretical studies. Specifically, key findings on all-boron hydrocarbon analogues, metal-centered boron wheels, and electronic transmutation in boron clusters are summarized.« less

Why Do Children Engage in Sedentary Behavior? Child- and Parent-Perceived Determinants

PubMed Central

Hidding, Lisan M.; Altenburg, Teatske M.; van Ekris, Evi; Chinapaw, Mai J. M.

2017-01-01

Todays children spend a large amount of their time sedentary. There is limited evidence on the determinants of sedentary behavior in children, and qualitative studies are especially lacking. Therefore, this study aimed to explore determinants of children’s sedentary behavior from the child- and parent perspective. Qualitative data were collected during concept mapping sessions with four groups of 11–13 years old children (n = 38) and two online sessions with parents (n = 21). Children and parents generated sedentary behavior motives, sorted related motives, and rated their importance in influencing children’s sedentary time. Next, multidimensional scaling and hierarchical cluster analysis was performed to create clusters of motives resulting in a concept map. Finally, the researchers named the clusters in the concept map. Concept maps of children yielded eight to ten perceived determinants, and concept maps of parents six to seven. Children and parents identified six similar potential determinants, and both rated as important: Sitting because… “it is the norm (I have to)”, and “I can work/play better that way”. In addition, children rated “there is nobody to play with” as an important potential determinant for engaging in sedentary behavior. The most important child- and parent perceived determinants were related to the social/cultural and physical environment, indicating that these are promising targets for future interventions. PMID:28640232

Minimal disease detection of B-cell lymphoproliferative disorders by flow cytometry: multidimensional cluster analysis.

PubMed

Duque, Ricardo E

2012-04-01

Flow cytometric analysis of cell suspensions involves the sequential 'registration' of intrinsic and extrinsic parameters of thousands of cells in list mode files. Thus, it is almost irresistible to describe phenomena in numerical terms or by 'ratios' that have the appearance of 'accuracy' due to the presence of numbers obtained from thousands of cells. The concepts involved in the detection and characterization of B cell lymphoproliferative processes are revisited in this paper by identifying parameters that, when analyzed appropriately, are both necessary and sufficient. The neoplastic process (cluster) can be visualized easily because the parameters that distinguish it form a cluster in multidimensional space that is unique and distinguishable from neighboring clusters that are not of diagnostic interest but serve to provide a background. For B cell neoplasia it is operationally necessary to identify the multidimensional space occupied by a cluster whose kappa:lambda ratio is 100:0 or 0:100. Thus, the concept of kappa:lambda ratio is without meaning and would not detect B cell neoplasia in an unacceptably high number of cases.

Analyzing Patients' Values by Applying Cluster Analysis and LRFM Model in a Pediatric Dental Clinic in Taiwan

PubMed Central

Lin, Shih-Yen; Liu, Chih-Wei

2014-01-01

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients' values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs. PMID:25045741

Analyzing patients' values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan.

PubMed

Wu, Hsin-Hung; Lin, Shih-Yen; Liu, Chih-Wei

2014-01-01

This study combines cluster analysis and LRFM (length, recency, frequency, and monetary) model in a pediatric dental clinic in Taiwan to analyze patients' values. A two-stage approach by self-organizing maps and K-means method is applied to segment 1,462 patients into twelve clusters. The average values of L, R, and F excluding monetary covered by national health insurance program are computed for each cluster. In addition, customer value matrix is used to analyze customer values of twelve clusters in terms of frequency and monetary. Customer relationship matrix considering length and recency is also applied to classify different types of customers from these twelve clusters. The results show that three clusters can be classified into loyal patients with L, R, and F values greater than the respective average L, R, and F values, while three clusters can be viewed as lost patients without any variable above the average values of L, R, and F. When different types of patients are identified, marketing strategies can be designed to meet different patients' needs.

Chaos theory perspective for industry clusters development

NASA Astrophysics Data System (ADS)

Yu, Haiying; Jiang, Minghui; Li, Chengzhang

2016-03-01

Industry clusters have outperformed in economic development in most developing countries. The contributions of industrial clusters have been recognized as promotion of regional business and the alleviation of economic and social costs. It is no doubt globalization is rendering clusters in accelerating the competitiveness of economic activities. In accordance, many ideas and concepts involve in illustrating evolution tendency, stimulating the clusters development, meanwhile, avoiding industrial clusters recession. The term chaos theory is introduced to explain inherent relationship of features within industry clusters. A preferred life cycle approach is proposed for industrial cluster recessive theory analysis. Lyapunov exponents and Wolf model are presented for chaotic identification and examination. A case study of Tianjin, China has verified the model effectiveness. The investigations indicate that the approaches outperform in explaining chaos properties in industrial clusters, which demonstrates industrial clusters evolution, solves empirical issues and generates corresponding strategies.

Exploring the presentation of HPV information online: A semantic network analysis of websites.

PubMed

Ruiz, Jeanette B; Barnett, George A

2015-06-26

Negative vaccination-related information online leads some to opt out of recommended vaccinations. To determine how HPV vaccine information is presented online and what concepts co-occur. A semantic network analysis of the words in first-page Google search results was conducted using three negative, three neutral, and three positive search terms for 10 base concepts such as HPV vaccine, and HPV immunizations. In total, 223 of the 300 websites retrieved met inclusion requirements. Website information was analyzed using network statistics to determine what words most frequently appear, which words co-occur, and the sentiment of the words. High levels of word interconnectivity were found suggesting a rich set of semantic links and a very integrated set of concepts. Limited number of words held centrality indicating limited concept prominence. This dense network signifies concepts that are well connected. Negative words were most prevalent and were associated with describing the HPV vaccine's side-effects as well as the negative effects of HPV and cervical cancer. A smaller cluster focuses on reporting negative vaccine side-effects. Clustering shows the words women and girls closely located to the words sexually, virus, and infection. Information about the HPV vaccine online centered on a limited number of concepts. HPV vaccine benefits as well as the risks of HPV, including severity and susceptibility, were centrally presented. Word cluster results imply that HPV vaccine information for women and girls is discussed in more sexual terms than for men and boys. Copyright © 2015 Elsevier Ltd. All rights reserved.

Characterising large-scale structure with the REFLEX II cluster survey

NASA Astrophysics Data System (ADS)

Chon, Gayoung

2016-10-01

We study the large-scale structure with superclusters from the REFLEX X-ray cluster survey together with cosmological N-body simulations. It is important to construct superclusters with criteria such that they are homogeneous in their properties. We lay out our theoretical concept considering future evolution of superclusters in their definition, and show that the X-ray luminosity and halo mass functions of clusters in superclusters are found to be top-heavy, different from those of clusters in the field. We also show a promising aspect of using superclusters to study the local cluster bias and mass scaling relation with simulations.

Initiating Training Stations As Clusters of Learning in Fashion Merchandising

ERIC Educational Resources Information Center

Garber, Jayne

1974-01-01

A Chicago business school offers fashion merchandising as one of several business curriculums that combines on-the-job training and classroom instruction. Instruction is organized around the occupational cluster concept which requires training stations that provide a wide variety of learning experiences. (EA)

Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Support Distribution Machines

NASA Astrophysics Data System (ADS)

Ntampaka, Michelle; Trac, Hy; Sutherland, Dougal; Fromenteau, Sebastien; Poczos, Barnabas; Schneider, Jeff

2018-01-01

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark’s publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership infor- mation and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width E=0.87. Interlopers introduce additional scatter, significantly widening the error distribution further (E=2.13). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (E=0.67) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncon- taminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

What Is High School Economics? Posttest Knowledge, Attitudes, and Course Content.

ERIC Educational Resources Information Center

Soper, John C.; Walstad, William B.

1988-01-01

Reports a study designed to measure the effectiveness of teaching and learning major economic concepts and concept clusters in high school. Includes findings on student attitudes toward the subject of economics and toward economic issues. (BSR)

Eliciting end-user expectations to guide the implementation process of a new electronic health record: A case study using concept mapping.

PubMed

Joukes, Erik; Cornet, Ronald; de Bruijne, Martine C; de Keizer, Nicolette F

2016-03-01

To evaluate the usability of concept mapping to elicit the expectations of healthcare professionals regarding the implementation of a new electronic health record (EHR). These expectations need to be taken into account during the implementation process to maximize the chance of success of the EHR. Two university hospitals in Amsterdam, The Netherlands, in the preparation phase of jointly implementing a new EHR. During this study the hospitals had different methods of documenting patient information (legacy EHR vs. paper-based records). Concept mapping was used to determine and classify the expectations of healthcare professionals regarding the implementation of a new EHR. A multidisciplinary group of 46 healthcare professionals from both university hospitals participated in this study. Expectations were elicited in focus groups, their relevance and feasibility were assessed through a web-questionnaire. Nonmetric multidimensional scaling and clustering methods were used to identify clusters of expectations. We found nine clusters of expectations, each covering an important topic to enable the healthcare professionals to work properly with the new EHR once implemented: usability, data use and reuse, facility conditions, data registration, support, training, internal communication, patients, and collaboration. Average importance and feasibility of each of the clusters was high. Concept mapping is an effective method to find topics that, according to healthcare professionals, are important to consider during the implementation of a new EHR. The method helps to combine the input of a large group of stakeholders at limited efforts. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Change the Collective Behaviors of Colloidal Motors by Tuning Electrohydrodynamic Flow at the Subparticle Level.

PubMed

Yang, Xingfu; Wu, Ning

2018-01-23

As demonstrated in biological systems, breaking the symmetry of surrounding hydrodynamic flow is the key to achieve autonomous locomotion of microscopic objects. In recent years, a variety of synthetic motors have been developed based on different propulsion mechanisms. Most work, however, focuses on the propulsion of individual motors. Here, we study the collective behaviors of colloidal dimers actuated by a perpendicularly applied AC electric field, which controls the electrohydrodynamic flow at subparticle levels. Although these motors experience strong dipolar repulsion from each other and are highly active, surprisingly, they assemble into a family of stable planar clusters with handedness. We show that this type of unusual structure arises from the contractile hydrodynamic flow around small lobes but extensile flow around the large lobes. We further reveal that the collective behavior, assembled structure, and assembly dynamics of these motors all depend on the specific directions of electrohydrodynamic flow surrounding each lobe of the dimers. By fine-tuning the surface charge asymmetry on particles and salt concentration in solution, we demonstrate the ability to control their collective behaviors on demand. This novel type of active assembly via hydrodynamic interactions has the potential to grow monodisperse clusters in a self-limiting fashion. The underlying concept revealed in this work should also apply to other types of active and asymmetric particles.

Social cohesion matters in health

PubMed Central

2013-01-01

Introduction The concept of social cohesion has invoked debate due to the vagueness of its definition and the limitations of current measurements. This paper attempts to examine the concept of social cohesion, develop measurements, and investigate the relationship between social cohesion and individual health. Methods This study used a multilevel study design. The individual-level samples from 29 high-income countries were obtained from the 2000 World Value Survey (WVS) and the 2002 European Value Survey. National-level social cohesion statistics were obtained from Organization of Economic Cooperation and Development datasets, World Development Indicators, and Asian Development Bank key indicators for the year 2000, and from aggregating responses from the WVS. In total 47,923 individuals were included in this study. The factor analysis was applied to identify dimensions of social cohesion, which were used as entities in the cluster analysis to generate a regime typology of social cohesion. Then, multilevel regression models were applied to assess the influences of social cohesion on an individual’s self-rated health. Results and discussion Factor analysis identified five dimensions of social cohesion: social equality, social inclusion, social development, social capital, and social diversity. Then, the cluster analysis revealed five regimes of social cohesion. A multi-level analysis showed that respondents in countries with higher social inclusion, social capital, and social diversity were more likely to report good health above and beyond individual-level characteristics. Conclusions This study is an innovative effort to incorporate different aspects of social cohesion. This study suggests that social cohesion was associated with individual self-rated after controlling individual characteristics. To achieve further advancement in population health, developed countries should consider policies that would foster a society with a high level of social inclusion, social capital, and social diversity. Future research could focus on identifying possible pathways by which social cohesion influences various health outcomes. PMID:24165541

Chemodynamical Clustering Applied to APOGEE Data: Rediscovering Globular Clusters

NASA Astrophysics Data System (ADS)

Chen, Boquan; D’Onghia, Elena; Pardy, Stephen A.; Pasquali, Anna; Bertelli Motta, Clio; Hanlon, Bret; Grebel, Eva K.

2018-06-01

We have developed a novel technique based on a clustering algorithm that searches for kinematically and chemically clustered stars in the APOGEE DR12 Cannon data. As compared to classical chemical tagging, the kinematic information included in our methodology allows us to identify stars that are members of known globular clusters with greater confidence. We apply our algorithm to the entire APOGEE catalog of 150,615 stars whose chemical abundances are derived by the Cannon. Our methodology found anticorrelations between the elements Al and Mg, Na and O, and C and N previously identified in the optical spectra in globular clusters, even though we omit these elements in our algorithm. Our algorithm identifies globular clusters without a priori knowledge of their locations in the sky. Thus, not only does this technique promise to discover new globular clusters, but it also allows us to identify candidate streams of kinematically and chemically clustered stars in the Milky Way.

Helpful self-management strategies to cope with enduring depression from the patients' point of view: a concept map study.

PubMed

van Grieken, Rosa A; Kirkenier, Anneloes C E; Koeter, Maarten W J; Schene, Aart H

2014-12-13

Despite the development of various self-management programmes that attempt to ameliorate symptoms of patients with chronic major depressive disorder (MDD), little is known about what these patients perceive as helpful in their struggle during daily live. The present study aims to explore what patients believe they can do themselves to cope with enduring MDD besides professional treatment, and which self-management strategies patients perceive as being most helpful to cope with their MDD. We used concept mapping, a method specifically designed for the conceptualisation of a specific subject, in this case patients' point of view (n = 25) on helpful self-management strategies in their coping with enduring MDD. A purposive sample of participants was invited at the Academic Medical Center and through requests on several MDD-patient websites in the Netherlands. Participants generated strategies in focus group discussions which were successively clustered on a two-dimensional concept map by hierarchical cluster analysis. Fifty strategies were perceived as helpful. They were combined into three meta-clusters each comprising two clusters: A focus on the depression (sub clusters: Being aware that my depression needs active coping and Active coping with professional treatment); An active lifestyle (sub clusters: Active self-care, structure and planning and Free time activities) and Participation in everyday social life (sub clusters: Social engagement and Work-related activities). MDD patients believe they can use various strategies to cope with enduring MDD in daily life. Although current developments in e-health occur, patients emphasise on face-to-face treatments and long-term relations, being engaged in social and working life, and involving their family, friends, colleagues and clinicians in their disease management. Our findings may help clinicians to improve their knowledge about what patients consider beneficial to cope with enduring MDD and to incorporate these suggested self-management strategies in their treatments.

Using Concept Mapping in the Development of a School of Public Health.

PubMed

Hsu, Laura J; Pacheco, Misty Y; Crabtree, Christopher; Maddock, Jay E

2015-07-01

Schools of Public Health have a wide variety of essential stakeholders. Broad input in program planning should assist in ensuring well-developed plans and strong community buy-in. The planning of a school can better address the needs of multiple stakeholders from systematic broad-based input from these constituents using concept mapping. In this study, we used concept mapping to prioritize a set of recommendations from diverse stakeholders to assist in the process of planning a school. A set of statements was generated on essential elements for the proposed school from a broad group of stakeholders. The statements were then distilled into unique themes, which were then rated on importance and feasibility. Cluster maps and pattern matches were used to analyze the ratings. Unique themes (N = 147) were identified and grouped into 12 clusters. Cluster themes included leadership, faculty, culture, school, and curriculum. Pattern matches revealed a significant, modest correlation between importance and feasibility (r = 0.27). A broad range of perspectives was used to identify relevant areas to address in the development of a school.

Task Analysis for Health Occupations. Cluster: Rehabilitation Services. Occupation: Physical Therapist Assistant. Education for Employment Task Lists.

ERIC Educational Resources Information Center

Lathrop, Janice

Task analyses are provided for two duty areas for the occupation of physical therapist assistant in the rehabilitation services cluster. Ten tasks are listed for the duty area "providing therapeutic measures": apply cold compress, administer hot soak, apply heat lamp, apply warm compress, apply ice bag, assist with dressing change, apply…

The Views of Turkish Pre-Service Teachers about Effectiveness of Cluster Method as a Teaching Writing Method

ERIC Educational Resources Information Center

Kitis, Emine; Türkel, Ali

2017-01-01

The aim of this study is to find out Turkish pre-service teachers' views on effectiveness of cluster method as a writing teaching method. The Cluster Method can be defined as a connotative creative writing method. The way the method works is that the person who brainstorms on connotations of a word or a concept in abscence of any kind of…

Presentation on systems cluster research

NASA Technical Reports Server (NTRS)

Morgenthaler, George W.

1989-01-01

This viewgraph presentation presents an overview of systems cluster research performed by the Center for Space Construction. The goals of the research are to develop concepts, insights, and models for space construction and to develop systems engineering/analysis curricula for training future aerospace engineers. The following topics are covered: CSC systems analysis/systems engineering (SIMCON) model, CSC systems cluster schedule, system life-cycle, model optimization techniques, publications, cooperative efforts, and sponsored research.

Sensory analysis of characterising flavours: evaluating tobacco product odours using an expert panel.

PubMed

Krüsemann, Erna J Z; Lasschuijt, Marlou P; de Graaf, C; de Wijk, René A; Punter, Pieter H; van Tiel, Loes; Cremers, Johannes W J M; van de Nobelen, Suzanne; Boesveldt, Sanne; Talhout, Reinskje

2018-05-23

Tobacco flavours are an important regulatory concept in several jurisdictions, for example in the USA, Canada and Europe. The European Tobacco Products Directive 2014/40/EU prohibits cigarettes and roll-your-own tobacco having a characterising flavour. This directive defines characterising flavour as 'a clearly noticeable smell or taste other than one of tobacco […]'. To distinguish between products with and without a characterising flavour, we trained an expert panel to identify characterising flavours by smelling. An expert panel (n=18) evaluated the smell of 20 tobacco products using self-defined odour attributes, following Quantitative Descriptive Analysis. The panel was trained during 14 attribute training, consensus training and performance monitoring sessions. Products were assessed during six test sessions. Principal component analysis, hierarchical clustering (four and six clusters) and Hotelling's T-tests (95% and 99% CIs) were used to determine differences and similarities between tobacco products based on odour attributes. The final attribute list contained 13 odour descriptors. Panel performance was sufficient after 14 training sessions. Products marketed as unflavoured that formed a cluster were considered reference products. A four-cluster method distinguished cherry-flavoured, vanilla-flavoured and menthol-flavoured products from reference products. Six clusters subdivided reference products into tobacco leaves, roll-your-own and commercial products. An expert panel was successfully trained to assess characterising odours in cigarettes and roll-your-own tobacco. This method could be applied to other product types such as e-cigarettes. Regulatory decisions on the choice of reference products and significance level are needed which directly influences the products being assessed as having a characterising odour. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Anharmonic effects in the quantum cluster equilibrium method

NASA Astrophysics Data System (ADS)

von Domaros, Michael; Perlt, Eva

2017-03-01

The well-established quantum cluster equilibrium (QCE) model provides a statistical thermodynamic framework to apply high-level ab initio calculations of finite cluster structures to macroscopic liquid phases using the partition function. So far, the harmonic approximation has been applied throughout the calculations. In this article, we apply an important correction in the evaluation of the one-particle partition function and account for anharmonicity. Therefore, we implemented an analytical approximation to the Morse partition function and the derivatives of its logarithm with respect to temperature, which are required for the evaluation of thermodynamic quantities. This anharmonic QCE approach has been applied to liquid hydrogen chloride and cluster distributions, and the molar volume, the volumetric thermal expansion coefficient, and the isobaric heat capacity have been calculated. An improved description for all properties is observed if anharmonic effects are considered.

On the problem of boundaries and scaling for urban street networks

PubMed Central

Masucci, A. Paolo; Arcaute, Elsa; Hatna, Erez; Stanilov, Kiril; Batty, Michael

2015-01-01

Urban morphology has presented significant intellectual challenges to mathematicians and physicists ever since the eighteenth century, when Euler first explored the famous Königsberg bridges problem. Many important regularities and scaling laws have been observed in urban studies, including Zipf's law and Gibrat's law, rendering cities attractive systems for analysis within statistical physics. Nevertheless, a broad consensus on how cities and their boundaries are defined is still lacking. Applying an elementary clustering technique to the street intersection space, we show that growth curves for the maximum cluster size of the largest cities in the UK and in California collapse to a single curve, namely the logistic. Subsequently, by introducing the concept of the condensation threshold, we show that natural boundaries of cities can be well defined in a universal way. This allows us to study and discuss systematically some of the regularities that are present in cities. We show that some scaling laws present consistent behaviour in space and time, thus suggesting the presence of common principles at the basis of the evolution of urban systems. PMID:26468071

On the problem of boundaries and scaling for urban street networks.

PubMed

Masucci, A Paolo; Arcaute, Elsa; Hatna, Erez; Stanilov, Kiril; Batty, Michael

2015-10-06

Urban morphology has presented significant intellectual challenges to mathematicians and physicists ever since the eighteenth century, when Euler first explored the famous Königsberg bridges problem. Many important regularities and scaling laws have been observed in urban studies, including Zipf's law and Gibrat's law, rendering cities attractive systems for analysis within statistical physics. Nevertheless, a broad consensus on how cities and their boundaries are defined is still lacking. Applying an elementary clustering technique to the street intersection space, we show that growth curves for the maximum cluster size of the largest cities in the UK and in California collapse to a single curve, namely the logistic. Subsequently, by introducing the concept of the condensation threshold, we show that natural boundaries of cities can be well defined in a universal way. This allows us to study and discuss systematically some of the regularities that are present in cities. We show that some scaling laws present consistent behaviour in space and time, thus suggesting the presence of common principles at the basis of the evolution of urban systems. © 2015 The Authors.

Automated UMLS-Based Comparison of Medical Forms

PubMed Central

Dugas, Martin; Fritz, Fleur; Krumm, Rainer; Breil, Bernhard

2013-01-01

Medical forms are very heterogeneous: on a European scale there are thousands of data items in several hundred different systems. To enable data exchange for clinical care and research purposes there is a need to develop interoperable documentation systems with harmonized forms for data capture. A prerequisite in this harmonization process is comparison of forms. So far – to our knowledge – an automated method for comparison of medical forms is not available. A form contains a list of data items with corresponding medical concepts. An automatic comparison needs data types, item names and especially item with these unique concept codes from medical terminologies. The scope of the proposed method is a comparison of these items by comparing their concept codes (coded in UMLS). Each data item is represented by item name, concept code and value domain. Two items are called identical, if item name, concept code and value domain are the same. Two items are called matching, if only concept code and value domain are the same. Two items are called similar, if their concept codes are the same, but the value domains are different. Based on these definitions an open-source implementation for automated comparison of medical forms in ODM format with UMLS-based semantic annotations was developed. It is available as package compareODM from http://cran.r-project.org. To evaluate this method, it was applied to a set of 7 real medical forms with 285 data items from a large public ODM repository with forms for different medical purposes (research, quality management, routine care). Comparison results were visualized with grid images and dendrograms. Automated comparison of semantically annotated medical forms is feasible. Dendrograms allow a view on clustered similar forms. The approach is scalable for a large set of real medical forms. PMID:23861827

Transparent Flexible Electronics By Directed Integration of Inorganic Micro and Nanomaterials

NASA Astrophysics Data System (ADS)

Cole, Jesse J.

This thesis focuses on nanomanufacturing processes for the heterogeneous integration of nanomaterials. Our approaches involved local adjustment of electrostatics at the surfaces to control material flux. Templating of surface electrostatics was implemented differently for three broad concepts resulting in control over nanomaterial synthesis, deposition, and printing. These three general concepts are: (A) Tailored ZnO nanowire synthesis and integration out of the liquid phase; (B) Arc discharge synthesis and continuous nanocluster deposition from the gas phase; (C) Contact electrification and xerographic printing of nanoparticles from the gas phase. Concept (A): We report a method to fabricate and transfer crystalline ZnO with control over location, orientation, size, and shape. The process uses an oxygen plasma treatment in combination with a photoresist pattern on Magnesium-doped GaN substrates to define narrow nucleation regions and attachment points with 100 nanometer scale dimensions. Lateral epitaxial overgrowth follows nucleation to produce single crystalline ZnO which were fabricated into LEDs and photovoltaic cells. Concept (B): We report a gas phase nanoparticle deposition system which shares characteristics with liquid phase electrodeposition. Clusters of charged nanoparticles selectively deposit onto electrically grounded surfaces. Similar to electroplating, the continued deposition of Au nanoparticles onto underlying resistive traces increased overall line conductivity. Alternatively, semiconducting ZnO and Ge nanomaterial sequentially deposited between interdigitated electrodes and served as addressable sensor active areas. Concept (C): We report patterned transfer of charge between conformal material interfaces through a concept referred to as nanocontact electrification. Nanocontacts of different size and shape are formed between surface functionalized polydimethylsiloxane (PDMS) stamps and other dielectric materials (PMMA, SiO 2). Forced delamination and cleavage of the interface yields a well defined charge pattern with a minimal feature size of 100 nm. The process produces charged surfaces and associated fields that exceed the breakdown strength of air leading to strong long range adhesive forces and force distance curves which are recorded over macroscopic distances. The process is applied to fabricate charge patterned surfaces for nanoxerography demonstrating 200 nm resolution nanoparticle prints and applied to thin film electronics where the patterned charges are used to shift the threshold voltages of underlying transistors by over 500 mV.

On the Right Track: Southern Maryland Schools Revamp Their Curriculum around Tech Prep.

ERIC Educational Resources Information Center

Leftwich, Kathy

1992-01-01

In St. Mary's County (Maryland) schools' revamped curriculum, tech prep encompasses four clusters: applied business/management, applied engineering/mechanics, applied health/human services, and college prep. Career counselors help eighth graders choose a cluster and monitor their satisfaction with their choice, allowing them to change until junior…

Business Education: The Creative Approaches of Three Small High Schools.

ERIC Educational Resources Information Center

Rawers, Lois J.

1983-01-01

To encourage the schools to train students in broad and flexible occupational competencies rather than specific job skills, the Oregon Department of Education has promoted the cluster concept of vocational education. Of the 14 occupational clusters identified as being economically significant in Oregon, business competencies in the accounting,…

Using Clustering Strategies for Creating Authority Files.

ERIC Educational Resources Information Center

French, James C.; Powell, Allison L.; Schulman, Eric

2000-01-01

Discussion of quality control of data in online bibliographic databases focuses on authority files. Describes approximate string matching, introduces the concept of approximate word matching and clustering, and presents a case study using the Astrophysics Data System (ADS) that shows how to reduce human effort involved in authority work. (LRW)

Jobs in Manufacturing. Job Family Series.

ERIC Educational Resources Information Center

Science Research Associates, Inc., Chicago, IL.

The instructional booklet explores various occupations in the occupational cluster of manufacturing. In the first chapter, the student is briefly introduced to the occupational cluster concept and to the general area of manufacturing. Chapter 2 describes jobs involved in the production of materials and processing of goods. Chapter 3 discusses…

Saturn Apollo Program

NASA Image and Video Library

1960-01-01

Pictured is one of the earliest testing of the Saturn I S-I (first) stage, with a cluster of eight H-1 engines, at the Marshall Space Flight Center (MSFC). It was a part of the test program to prove out the clustered-booster concept. MSFC was responsible for designing and development the Saturn launch vehicles.

Personal Learning Network Clusters: A Comparison between Mathematics and Computer Science Students

ERIC Educational Resources Information Center

Harding, Ansie; Engelbrecht, Johann

2015-01-01

"Personal learning environments" (PLEs) and "personal learning networks" (PLNs) are well-known concepts. A personal learning network "cluster" is a small group of people who regularly interact academically and whose PLNs have a non-empty intersection that includes all the other members. At university level PLN…

Bioinformatics and Astrophysics Cluster (BinAc)

NASA Astrophysics Data System (ADS)

Krüger, Jens; Lutz, Volker; Bartusch, Felix; Dilling, Werner; Gorska, Anna; Schäfer, Christoph; Walter, Thomas

2017-09-01

BinAC provides central high performance computing capacities for bioinformaticians and astrophysicists from the state of Baden-Württemberg. The bwForCluster BinAC is part of the implementation concept for scientific computing for the universities in Baden-Württemberg. Community specific support is offered through the bwHPC-C5 project.

Clustered Integrin Ligands as a Novel Approach for the Targeting of Non-Viral Vectors

NASA Astrophysics Data System (ADS)

Ng, Quinn Kwan Tai

Gene transfer or gene delivery is described as the process in which foreign DNA is introduced into cells. Over the years, gene delivery has gained the attention of many researchers and has been developed as powerful tools for use in biotechnology and medicine. With the completion of the Human Genome Project, such advances in technology allowed for the identification of diseases ranging from hereditary disorders to acquired ones (cancer) which were thought to be incurable. Gene therapy provides the means necessary to treat or eliminate genetic diseases from its origin, unlike traditional medicine which only treat symptoms. With ongoing clinical trials for gene therapy increasing, the greatest difficulty still lies in developing safe systems which can target cells of interest to provide efficient delivery. Nature, over millions of years of evolution, has provided an example of one of the most efficient delivery systems: viruses. Although the use of viruses for gene delivery has been well studied, the safety issues involving immunogenicity, insertional mutagenesis, high cost, and poor reproducibility has provided problems for their clinical application. From understanding viruses, we gain insight to designing new systems for non-viral gene delivery. One of these techniques utilized by adenoviruses is the clustering of ligands on its surface through the use of a protein called a penton base. Through the use of nanotechnology we can mimic this basic concept in non-viral gene delivery systems. This dissertation research is focused on developing and applying a novel system for displaying the integrin binding ligand (RGD) in a constrained manner to form a clustered integrin ligand binding platform to be used to enhance the targeting and efficiency of non-viral gene delivery vectors. Peptide mixed monolayer protected gold nanoparticles provides a suitable surface for ligand clustering. A relationship between the peptide ratios in the reaction solution used to form these ligand clusters compared to the reacted amounts on the surface of the particle was studied. This provided us the ability to control the size of the clusters formed and the spacing between the integrins for gold nanoparticles of various sizes. We then applied the clustered ligand binding system for targeting of DNA/PEI polyplexes and demonstrated that the use of RGD nanoclusters enhances gene transfer up to 35-fold which was dependent on the density of alphavbeta3 integrins on the cell surface. Cell integrin sensitivity was shown in which cells with higher alpha vbeta3 densities resulting in higher luciferase transgene expression. The targeting of RGD nanoclusters for DNA/PEI polyplexes was further shown in vivo using PET/CT technology which displayed improved targeting towards high level alphavbeta3 integrin expression (U87MG) tumors over medium level alphavbeta 3 integrin expression (HeLa). In addition to studying the clustered integrin binding system, the current non-viral vectors used suffer from stability and toxicity issues in vitro and in vivo. We have applied a new chemistry for synthesizing nanogels utilizing a Traut's reagent initiated Michael addition reaction for modification of diamine containing crosslikers which will allow for the development of stable and cell demanded release of oligonucleotides. We have shown bulk gels made were capable of encapsulating and holding DNA within the gel and were able to synthesize them into nanogels. The combined research shown here using clustered integrin ligands and a new type of nanogel synthesis provides an ideal system for gene delivery in the future.

Clusters of Concepts in Molecular Genetics: A Study of Swedish Upper Secondary Science Students' Understanding

ERIC Educational Resources Information Center

Gericke, Niklas; Wahlberg, Sara

2013-01-01

To understand genetics, students need to be able to explain and draw connections between a large number of concepts. The purpose of the study reported herein was to explore the way upper secondary science students reason about concepts in molecular genetics in order to understand protein synthesis. Data were collected by group interviews. Concept…

Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning

NASA Astrophysics Data System (ADS)

Ntampaka, M.; Trac, H.; Sutherland, D. J.; Fromenteau, S.; Póczos, B.; Schneider, J.

2016-11-01

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark’s publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership information and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width of {{Δ }}ε ≈ 0.87. Interlopers introduce additional scatter, significantly widening the error distribution further ({{Δ }}ε ≈ 2.13). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement ({{Δ }}ε ≈ 0.67) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncontaminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

Preliminary Study of Arcjet Neutralization of Hall Thruster Clusters (Postprint)

DTIC Science & Technology

2007-01-18

Clustered Hall thrusters have emerged as a favored choice for extending Hall thruster options to very high powers (50 kW - 150 kW). This paper...examines the possible use of an arcjet to neutralize clustered Hall thrusters, as the hybrid arcjet- Hall thruster concept can fill a performance niche...and helium, and then demonstrate the first successful operation of a low power Hall thruster -arcjet neutralizer package. In the surrogate anode studies

R-CMap-An open-source software for concept mapping.

PubMed

Bar, Haim; Mentch, Lucas

2017-02-01

Planning and evaluating projects often involves input from many stakeholders. Fusing and organizing many different ideas, opinions, and interpretations into a coherent and acceptable plan or project evaluation is challenging. This is especially true when seeking contributions from a large number of participants, especially when not all can participate in group discussions, or when some prefer to contribute their perspectives anonymously. One of the major breakthroughs in the area of evaluation and program planning has been the use of graphical tools to represent the brainstorming process. This provides a quantitative framework for organizing ideas and general concepts into simple-to-interpret graphs. We developed a new, open-source concept mapping software called R-CMap, which is implemented in R. This software provides a graphical user interface to guide users through the analytical process of concept mapping. The R-CMap software allows users to generate a variety of plots, including cluster maps, point rating and cluster rating maps, as well as pattern matching and go-zone plots. Additionally, R-CMap is capable of generating detailed reports that contain useful statistical summaries of the data. The plots and reports can be embedded in Microsoft Office tools such as Word and PowerPoint, where users may manually adjust various plot and table features to achieve the best visual results in their presentations and official reports. The graphical user interface of R-CMap allows users to define cluster names, change the number of clusters, select rating variables for relevant plots, and importantly, select subsets of respondents by demographic criteria. The latter is particularly useful to project managers in order to identify different patterns of preferences by subpopulations. R-CMap is user-friendly, and does not require any programming experience. However, proficient R users can add to its functionality by directly accessing built-in functions in R and sharing new features with the concept mapping community. Copyright © 2016 Elsevier Ltd. All rights reserved.

Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

PubMed

Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

2013-01-02

Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

A cluster-randomized controlled study to evaluate a team coaching concept for improving teamwork and patient-centeredness in rehabilitation teams.

PubMed

Körner, Mirjam; Luzay, Leonie; Plewnia, Anne; Becker, Sonja; Rundel, Manfred; Zimmermann, Linda; Müller, Christian

2017-01-01

Although the relevance of interprofessional teamwork in the delivery of patient-centered care is well known, there is a lack of interventions for improving team interaction in the context of rehabilitation in Germany. The aim of the present study is to evaluate whether a specially developed team coaching concept (TCC) could improve both teamwork and patient-centeredness. A multicenter, cluster-randomized controlled intervention study was conducted with both staff and patient questionnaires. Data was collected at ten German rehabilitation clinics (five clusters) of different indication fields before (t1) and after (t2) the intervention. Intervention clinics received the TCC, while control clinics did not receive any treatment. Staff questionnaires were used to measure internal participation and other aspects of teamwork, such as team organization, while patient questionnaires assessed patient-centeredness. A multivariate analysis of variance was applied for data analysis. In order to analyze the effect of TCC on internal participation and teamwork, 305 questionnaires were included for t1 and 213 for t2 in the staff survey. In the patient survey, 523 questionnaires were included for t1 and 545 for t2. The TCC improved team organization, willingness to accept responsibility and knowledge integration according to staff, with small effect sizes (univariate: η2=.010-.017), whereas other parameters including internal participation, team leadership and cohesion did not improve due to the intervention. The patient survey did not show any improvements on the assessed dimensions. The TCC improved dimensions that were addressed directly by the approach and were linked to the clinics' needs, such as restructured team meetings and better exchange of information. The TCC can be used to improve team organization, willingness to accept responsibility, and knowledge integration in rehabilitation practice, but some further evaluation is needed to understand contextual factors and processes regarding the implementation of the intervention.

A cluster-randomized controlled study to evaluate a team coaching concept for improving teamwork and patient-centeredness in rehabilitation teams

PubMed Central

Körner, Mirjam; Luzay, Leonie; Plewnia, Anne; Becker, Sonja; Rundel, Manfred; Zimmermann, Linda; Müller, Christian

2017-01-01

Purpose Although the relevance of interprofessional teamwork in the delivery of patient-centered care is well known, there is a lack of interventions for improving team interaction in the context of rehabilitation in Germany. The aim of the present study is to evaluate whether a specially developed team coaching concept (TCC) could improve both teamwork and patient-centeredness. Method A multicenter, cluster-randomized controlled intervention study was conducted with both staff and patient questionnaires. Data was collected at ten German rehabilitation clinics (five clusters) of different indication fields before (t1) and after (t2) the intervention. Intervention clinics received the TCC, while control clinics did not receive any treatment. Staff questionnaires were used to measure internal participation and other aspects of teamwork, such as team organization, while patient questionnaires assessed patient-centeredness. A multivariate analysis of variance was applied for data analysis. Results In order to analyze the effect of TCC on internal participation and teamwork, 305 questionnaires were included for t1 and 213 for t2 in the staff survey. In the patient survey, 523 questionnaires were included for t1 and 545 for t2. The TCC improved team organization, willingness to accept responsibility and knowledge integration according to staff, with small effect sizes (univariate: η2=.010–.017), whereas other parameters including internal participation, team leadership and cohesion did not improve due to the intervention. The patient survey did not show any improvements on the assessed dimensions. Conclusion The TCC improved dimensions that were addressed directly by the approach and were linked to the clinics’ needs, such as restructured team meetings and better exchange of information. The TCC can be used to improve team organization, willingness to accept responsibility, and knowledge integration in rehabilitation practice, but some further evaluation is needed to understand contextual factors and processes regarding the implementation of the intervention. PMID:28704377

Voronoi distance based prospective space-time scans for point data sets: a dengue fever cluster analysis in a southeast Brazilian town

PubMed Central

2011-01-01

Background The Prospective Space-Time scan statistic (PST) is widely used for the evaluation of space-time clusters of point event data. Usually a window of cylindrical shape is employed, with a circular or elliptical base in the space domain. Recently, the concept of Minimum Spanning Tree (MST) was applied to specify the set of potential clusters, through the Density-Equalizing Euclidean MST (DEEMST) method, for the detection of arbitrarily shaped clusters. The original map is cartogram transformed, such that the control points are spread uniformly. That method is quite effective, but the cartogram construction is computationally expensive and complicated. Results A fast method for the detection and inference of point data set space-time disease clusters is presented, the Voronoi Based Scan (VBScan). A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance MST linking the cases. The successive removal of edges from the Voronoi distance MST generates sub-trees which are the potential space-time clusters. Finally, those clusters are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate the significance of the clusters. An application for dengue fever in a small Brazilian city is presented. Conclusions The ability to promptly detect space-time clusters of disease outbreaks, when the number of individuals is large, was shown to be feasible, due to the reduced computational load of VBScan. Instead of changing the map, VBScan modifies the metric used to define the distance between cases, without requiring the cartogram construction. Numerical simulations showed that VBScan has higher power of detection, sensitivity and positive predicted value than the Elliptic PST. Furthermore, as VBScan also incorporates topological information from the point neighborhood structure, in addition to the usual geometric information, it is more robust than purely geometric methods such as the elliptic scan. Those advantages were illustrated in a real setting for dengue fever space-time clusters. PMID:21513556

Impact of gas backing pressure and geometry of conical nozzle on the formation of methane clusters in supersonic jets.

PubMed

Lu, Haiyang; Chen, Guanglong; Ni, Guoquan; Li, Ruxin; Xu, Zhizhan

2010-01-14

We present an experimental investigation of the dependence of the production of large methane clusters on the cluster source conditions. The clusters were produced at room temperature through supersonic expansion of methane gas at the backing pressures P(0) ranging from 10 to 84 bar using five conical nozzles of different geometries. The cluster size was characterized by Rayleigh scattering measurements and calibrated with Coulomb explosion of the clusters at P(0) = 44 bar subjected to an ultraintense laser pulse. A quantitative evaluation of the performance of the conical nozzles against the nozzle geometry and the backing pressure was made by introducing a parameter delta. Differ from the idealized case where the performance of the conical nozzle can be described by the equivalent sonic nozzle of diameter d(eq), in the present work, the "effective equivalent sonic-nozzle diameter" of the conical nozzle defined by d(eq)* = deltad(eq) is introduced. delta represents the deviation of the performance in cluster formation of the conical nozzles from that predicted on the basis of the concept of the equivalent diameter d(eq) = d/tan alpha, with d being the throat diameter, and alpha the half-opening angle of the conical nozzle. Experimental results show that the cluster growth process will be restricted when the gas backing pressure P(0) is higher and/or d/tan alpha of the conical nozzle becomes larger, resulting in smaller delta. From the experimental data, delta can be expressed by an empirical relation delta = A/[P(0)(B)(d/tan alpha)(1.36)], where A = 8.4 and B = 0.26 for 24 bar

Task Analysis for Health Occupations. Cluster: Medical Assisting. Occupation: Medical Assistant. Education for Employment Task Lists.

ERIC Educational Resources Information Center

Lathrop, Janice

Task analyses are provided for two duty areas for the occupation of medical assistant in the medical assisting cluster. Five tasks for the duty area "providing therapeutic measures" are as follows: assist with dressing change, apply clean dressing, apply elastic bandage, assist physician in therapeutic procedure, and apply topical…

Complementary and conventional medicine: a concept map

PubMed Central

Baldwin, Carol M; Kroesen, Kendall; Trochim, William M; Bell, Iris R

2004-01-01

Background Despite the substantive literature from survey research that has accumulated on complementary and alternative medicine (CAM) in the United States and elsewhere, very little research has been done to assess conceptual domains that CAM and conventional providers would emphasize in CAM survey studies. The objective of this study is to describe and interpret the results of concept mapping with conventional and CAM practitioners from a variety of backgrounds on the topic of CAM. Methods Concept mapping, including free sorts, ratings, and multidimensional scaling was used to organize conceptual domains relevant to CAM into a visual "cluster map." The panel consisted of CAM providers, conventional providers, and university faculty, and was convened to help formulate conceptual domains to guide the development of a CAM survey for use with United States military veterans. Results Eight conceptual clusters were identified: 1) Self-assessment, Self-care, and Quality of Life; 2) Health Status, Health Behaviors; 3) Self-assessment of Health; 4) Practical/Economic/ Environmental Concerns; 5) Needs Assessment; 6) CAM vs. Conventional Medicine; 7) Knowledge of CAM; and 8) Experience with CAM. The clusters suggest panelists saw interactions between CAM and conventional medicine as a critical component of the current medical landscape. Conclusions Concept mapping provided insight into how CAM and conventional providers view the domain of health care, and was shown to be a useful tool in the formulation of CAM-related conceptual domains. PMID:15018623

What is needed to implement a computer-assisted health risk assessment tool? An exploratory concept mapping study.

PubMed

Ahmad, Farah; Norman, Cameron; O'Campo, Patricia

2012-12-19

Emerging eHealth tools could facilitate the delivery of comprehensive care in time-constrained clinical settings. One such tool is interactive computer-assisted health-risk assessments (HRA), which may improve provider-patient communication at the point of care, particularly for psychosocial health concerns, which remain under-detected in clinical encounters. The research team explored the perspectives of healthcare providers representing a variety of disciplines (physicians, nurses, social workers, allied staff) regarding the factors required for implementation of an interactive HRA on psychosocial health. The research team employed a semi-qualitative participatory method known as Concept Mapping, which involved three distinct phases. First, in face-to-face and online brainstorming sessions, participants responded to an open-ended central question: "What factors should be in place within your clinical setting to support an effective computer-assisted screening tool for psychosocial risks?" The brainstormed items were consolidated by the research team. Then, in face-to-face and online sorting sessions, participants grouped the items thematically as 'it made sense to them'. Participants also rated each item on a 5-point scale for its 'importance' and 'action feasibility' over the ensuing six month period. The sorted and rated data was analyzed using multidimensional scaling and hierarchical cluster analyses which produced visual maps. In the third and final phase, the face-to-face Interpretation sessions, the concept maps were discussed and illuminated by participants collectively. Overall, 54 providers participated (emergency care 48%; primary care 52%). Participants brainstormed 196 items thought to be necessary for the implementation of an interactive HRA emphasizing psychosocial health. These were consolidated by the research team into 85 items. After sorting and rating, cluster analysis revealed a concept map with a seven-cluster solution: 1) the HRA's equitable availability; 2) the HRA's ease of use and appropriateness; 3) the content of the HRA survey; 4) patient confidentiality and choice; 5) patient comfort through humanistic touch; 6) professional development, care and workload; and 7) clinical management protocol. Drawing insight from the theoretical lens of Sociotechnical theory, the seven clusters of factors required for HRA implementation could be read as belonging to three overarching aspects : Technical (cluster 1, 2 and 3), Social-Patient (cluster 4 and 5), and Social-Provider (cluster 6 and 7). Participants rated every one of the clusters as important, with mean scores from 4.0 to 4.5. Their scores for feasibility were somewhat lower, ranging from 3.4 to. 4.3. Comparing the scores for importance and feasibility, a significant difference was found for one cluster from each region (cluster 2, 5, 6). The cluster on professional development, care and workload was perceived as especially challenging in emergency department settings, and possible reasons were discussed in the interpretation sessions. A number of intertwined multilevel factors emerged as important for the implementation of a computer-assisted, interactive HRA with a focus on psychosocial health. Future developments in this area could benefit from systems thinking and insights from theoretical perspectives, such as sociotechnical system theory for joint optimization and responsible autonomy, with emphasis on both the technical and social aspects of HRA implementation.

THE PREPARATION OF CURRICULUM MATERIALS AND THE DEVELOPMENT OF TEACHERS FOR AN EXPERIMENTAL APPLICATION OF THE CLUSTER CONCEPT OF VOCATIONAL EDUCATION AT THE SECONDARY SCHOOL LEVEL. VOLUME II, INSTRUCTIONAL PLANS FOR THE CONSTRUCTION CLUSTER.

ERIC Educational Resources Information Center

MALEY, DONALD

DESIGNED FOR USE WITH 11TH AND 12TH GRADE STUDENTS, THIS CURRICULUM GUIDE FOR THE OCCUPATIONAL CLUSTER IN CONSTRUCTION WAS DEVELOPED BY PARTICIPATING TEACHERS FROM RESULTS OF THE RESEARCH PROCEDURES DESCRIBED IN VOLUME I (VT 004 162). THE COURSE DESCRIPTION, NEED FOR THE COURSE, COURSE OBJECTIVES, PROCEDURE, AND INSTRUCTIONAL PLAN ARE DISCUSSED…

Combining symmetry collective states with coupled-cluster theory: Lessons from the Agassi model Hamiltonian

NASA Astrophysics Data System (ADS)

Hermes, Matthew R.; Dukelsky, Jorge; Scuseria, Gustavo E.

2017-06-01

The failures of single-reference coupled-cluster theory for strongly correlated many-body systems is flagged at the mean-field level by the spontaneous breaking of one or more physical symmetries of the Hamiltonian. Restoring the symmetry of the mean-field determinant by projection reveals that coupled-cluster theory fails because it factorizes high-order excitation amplitudes incorrectly. However, symmetry-projected mean-field wave functions do not account sufficiently for dynamic (or weak) correlation. Here we pursue a merger of symmetry projection and coupled-cluster theory, following previous work along these lines that utilized the simple Lipkin model system as a test bed [J. Chem. Phys. 146, 054110 (2017), 10.1063/1.4974989]. We generalize the concept of a symmetry-projected mean-field wave function to the concept of a symmetry projected state, in which the factorization of high-order excitation amplitudes in terms of low-order ones is guided by symmetry projection and is not exponential, and combine them with coupled-cluster theory in order to model the ground state of the Agassi Hamiltonian. This model has two separate channels of correlation and two separate physical symmetries which are broken under strong correlation. We show how the combination of symmetry collective states and coupled-cluster theory is effective in obtaining correlation energies and order parameters of the Agassi model throughout its phase diagram.

Plug cluster engine concept for in-space missions

NASA Technical Reports Server (NTRS)

Obrien, C. J.; Aukerman, C. A.

1979-01-01

The development of a suitable orbital transfer vehicle (OTV) engine is discussed. The OTV's dimensions are limited by those of the Space Shuttle payload bay on which it will be carried. An approach to utilize the available diameter to achieve high area ratio and thus high engine performance, is presented. Unconventional nozzles, such as clusters of small thrusters around a large diameter contoured plug, are investigated to arrive at engine designs which feature lower chamber pressures, with attendant lower heat flux, lower wall temperature, longer fatigue life, and less critical turbomachinery. Attention is also given to plug nozzle technology, high area ratio module- and scarfed bell- Plug Cluster Engine (PCE) concepts, as well as PCE performance, weight, and assessment. A conceptual design of a PCE formed from a cluster of high area ratio, scarfed, bell nozzles proved to be competitive with bell and spike nozzle engines. PCE advantages cited include increased payload length due to shorter engine length, ability to increase or decrease the number of modules and thereby the thrust, and low cost due to utilization of off-the-shelf technology.

Uptake of an innovation in surgery: observations from the cluster-randomized Quality Initiative in Rectal Cancer trial.

PubMed

Simunovic, Marko; Coates, Angela; Smith, Andrew; Thabane, Lehana; Goldsmith, Charles H; Levine, Mark N

2013-12-01

Theory suggests the uptake of a medical innovation is influenced by how potential adopters perceive innovation characteristics and by characteristics of potential adopters. Innovation adoption is slow among the first 20% of individuals in a target group and then accelerates. The Quality Initiative in Rectal Cancer (QIRC) trial assessed if rectal cancer surgery outcomes could be improved through surgeon participation in the QIRC strategy. We tested if traditional uptake of innovation concepts applied to surgeons in the experimental arm of the trial. The QIRC strategy included workshops, access to opinion leaders, intraoperative demonstrations, postoperative questionnaires, and audit and feedback. For intraoperative demonstrations, a participating surgeon invited an outside surgeon to demonstrate optimal rectal surgery techniques. We used surgeon timing in a demonstration to differentiate early and late adopters of the QIRC strategy. Surgeons completed surveys on perceptions of the strategy and personal characteristics. Nineteen of 56 surgeons (34%) requested an operative demonstration on their first case of rectal surgery. Early and late adopters had similar perceptions of the QIRC strategy and similar characteristics. Late adopters were less likely than early adopters to perceive an advantage for the surgical techniques promoted by the trial (p = 0.023). Most traditional diffusion of innovation concepts did not apply to surgeons in the QIRC trial, with the exception of the importance of perceptions of comparative advantage.

A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis.

PubMed

Zhao, Xing; Zhou, Xiao-Hua; Feng, Zijian; Guo, Pengfei; He, Hongyan; Zhang, Tao; Duan, Lei; Li, Xiaosong

2013-01-01

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

Teachers' Misunderstanding: The Concept of Inclusive Education

ERIC Educational Resources Information Center

Sanagi, Tomomi

2016-01-01

Teachers' misunderstanding the concept of inclusive education will not lead to good practices, rather make an exclusive environment for pupils with special educational needs in mainstream schools. This study clarified teachers' attitudes towards the image of inclusive education with conjoint analysis and cluster analysis. The participants for this…

The use of concept mapping to identify community-driven intervention strategies for physical and mental health.

PubMed

Vaughn, Lisa M; Jacquez, Farrah; McLinden, Daniel

2013-09-01

Research that partners with youth and community stakeholders increases contextual relevance and community buy-in and therefore maximizes the chance for intervention success. Concept mapping is a mixed-method participatory research process that accesses the input of the community in a collaborative manner. After a school-wide health needs assessment at a low-income, minority/immigrant K-8 school identified bullying and obesity as the most important health issues, concept mapping was used to identify and prioritize specific strategies to address these two areas. Stakeholders including 160 K-8 students, 33 college students working in the school, 35 parents, 20 academic partners, and 22 teachers/staff brainstormed strategies to reduce and prevent obesity and bullying. A smaller group of stakeholders worked individually to complete an unstructured sorting of these strategies into groups of similar ideas, once for obesity and again for bullying. Multidimensional scaling and cluster analysis was applied to the sorting data to produce a series of maps that illustrated the stakeholders' conceptual thinking about obesity and bullying prevention strategies. The maps for both obesity and bullying organized specific strategies into themes that included education, parental role, teacher/school supervision, youth role, expert/professional role, and school structure/support.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

PubMed Central

Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

Concept analysis of culture applied to nursing.

PubMed

Marzilli, Colleen

2014-01-01

Culture is an important concept, especially when applied to nursing. A concept analysis of culture is essential to understanding the meaning of the word. This article applies Rodgers' (2000) concept analysis template and provides a definition of the word culture as it applies to nursing practice. This article supplies examples of the concept of culture to aid the reader in understanding its application to nursing and includes a case study demonstrating components of culture that must be respected and included when providing health care.

Clustering analysis strategies for electron energy loss spectroscopy (EELS).

PubMed

Torruella, Pau; Estrader, Marta; López-Ortega, Alberto; Baró, Maria Dolors; Varela, Maria; Peiró, Francesca; Estradé, Sònia

2018-02-01

In this work, the use of cluster analysis algorithms, widely applied in the field of big data, is proposed to explore and analyze electron energy loss spectroscopy (EELS) data sets. Three different data clustering approaches have been tested both with simulated and experimental data from Fe 3 O 4 /Mn 3 O 4 core/shell nanoparticles. The first method consists on applying data clustering directly to the acquired spectra. A second approach is to analyze spectral variance with principal component analysis (PCA) within a given data cluster. Lastly, data clustering on PCA score maps is discussed. The advantages and requirements of each approach are studied. Results demonstrate how clustering is able to recover compositional and oxidation state information from EELS data with minimal user input, giving great prospects for its usage in EEL spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method.

PubMed

Yoo, Illhoi; Hu, Xiaohua; Song, Il-Yeol

2007-11-27

A huge amount of biomedical textual information has been produced and collected in MEDLINE for decades. In order to easily utilize biomedical information in the free text, document clustering and text summarization together are used as a solution for text information overload problem. In this paper, we introduce a coherent graph-based semantic clustering and summarization approach for biomedical literature. Our extensive experimental results show the approach shows 45% cluster quality improvement and 72% clustering reliability improvement, in terms of misclassification index, over Bisecting K-means as a leading document clustering approach. In addition, our approach provides concise but rich text summary in key concepts and sentences. Our coherent biomedical literature clustering and summarization approach that takes advantage of ontology-enriched graphical representations significantly improves the quality of document clusters and understandability of documents through summaries.

A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method

PubMed Central

Yoo, Illhoi; Hu, Xiaohua; Song, Il-Yeol

2007-01-01

Background A huge amount of biomedical textual information has been produced and collected in MEDLINE for decades. In order to easily utilize biomedical information in the free text, document clustering and text summarization together are used as a solution for text information overload problem. In this paper, we introduce a coherent graph-based semantic clustering and summarization approach for biomedical literature. Results Our extensive experimental results show the approach shows 45% cluster quality improvement and 72% clustering reliability improvement, in terms of misclassification index, over Bisecting K-means as a leading document clustering approach. In addition, our approach provides concise but rich text summary in key concepts and sentences. Conclusion Our coherent biomedical literature clustering and summarization approach that takes advantage of ontology-enriched graphical representations significantly improves the quality of document clusters and understandability of documents through summaries. PMID:18047705

Monitoring of changes in cluster structures in water under AC magnetic field

NASA Astrophysics Data System (ADS)

Usanov, A. D.; Ulyanov, S. S.; Ilyukhina, N. S.; Usanov, D. A.

2016-01-01

A fundamental possibility of visualizing cluster structures formed in distilled water by an optical method based on the analysis of dynamic speckle structures is demonstrated. It is shown for the first time that, in contrast to the existing concepts, water clusters can be rather large (up to 200 -m in size), and their lifetime is several tens of seconds. These clusters are found to have an internal spatially inhomogeneous structure, constantly changing in time. The properties of magnetized and non-magnetized water are found to differ significantly. In particular, the number of clusters formed in magnetized water is several times larger than that formed in the same volume of non-magnetized water.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marquez, Andres; Manzano Franco, Joseph B.; Song, Shuaiwen

With Exascale performance and its challenges in mind, one ubiquitous concern among architects is energy efficiency. Petascale systems projected to Exascale systems are unsustainable at current power consumption rates. One major contributor to system-wide power consumption is the number of memory operations leading to data movement and management techniques applied by the runtime system. To address this problem, we present the concept of the Architected Composite Data Types (ACDT) framework. The framework is made aware of data composites, assigning them a specific layout, transformations and operators. Data manipulation overhead is amortized over a larger number of elements and program performancemore » and power efficiency can be significantly improved. We developed the fundamentals of an ACDT framework on a massively multithreaded adaptive runtime system geared towards Exascale clusters. Showcasing the capability of ACDT, we exercised the framework with two representative processing kernels - Matrix Vector Multiply and the Cholesky Decomposition – applied to sparse matrices. As transformation modules, we applied optimized compress/decompress engines and configured invariant operators for maximum energy/performance efficiency. Additionally, we explored two different approaches based on transformation opaqueness in relation to the application. Under the first approach, the application is agnostic to compression and decompression activity. Such approach entails minimal changes to the original application code, but leaves out potential applicationspecific optimizations. The second approach exposes the decompression process to the application, hereby exposing optimization opportunities that can only be exploited with application knowledge. The experimental results show that the two approaches have their strengths in HW and SW respectively, where the SW approach can yield performance and power improvements that are an order of magnitude better than ACDT-oblivious, hand-optimized implementations.We consider the ACDT runtime framework an important component of compute nodes that will lead towards power efficient Exascale clusters.« less

A cluster analytic study of the Wechsler Intelligence Test for Children-IV in children referred for psychoeducational assessment due to persistent academic difficulties.

PubMed

Hale, Corinne R; Casey, Joseph E; Ricciardi, Philip W R

2014-02-01

Wechsler Intelligence Test for Children-IV core subtest scores of 472 children were cluster analyzed to determine if reliable and valid subgroups would emerge. Three subgroups were identified. Clusters were reliable across different stages of the analysis as well as across algorithms and samples. With respect to external validity, the Globally Low cluster differed from the other two clusters on Wechsler Individual Achievement Test-II Word Reading, Numerical Operations, and Spelling subtests, whereas the latter two clusters did not differ from one another. The clusters derived have been identified in studies using previous WISC editions. Clusters characterized by poor performance on subtests historically associated with the VIQ (i.e., VCI + WMI) and PIQ (i.e., POI + PSI) did not emerge, nor did a cluster characterized by low scores on PRI subtests. Picture Concepts represented the highest subtest score in every cluster, failing to vary in a predictable manner with the other PRI subtests.

Chemical Abundances of Giants in Globular Clusters

NASA Astrophysics Data System (ADS)

Gratton, Raffaele G.; Bragaglia, Angela; Carretta, Eugenio; D'Orazi, Valentina; Lucatello, Sara

A large fraction of stars form in clusters. According to a widespread paradigma, stellar clusters are prototypes of single stellar populations. According to this concept, they formed on a very short time scale, and all their stars share the same chemical composition. Recently it has been understood that massive stellar clusters (the globular clusters) rather host various stellar populations, characterized by different chemical composition: these stellar populations have also slightly different ages, stars of the second generations being formed from the ejecta of part of those of an earlier one. Furthermore, it is becoming clear that the efficiency of the process is quite low: many more stars formed within this process than currently present in the clusters. This implies that a significant, perhaps even dominant fraction of the ancient population of galaxies formed within the episodes that lead to formation the globular clusters.

Operational Dynamic Configuration Analysis

NASA Technical Reports Server (NTRS)

Lai, Chok Fung; Zelinski, Shannon

2010-01-01

Sectors may combine or split within areas of specialization in response to changing traffic patterns. This method of managing capacity and controller workload could be made more flexible by dynamically modifying sector boundaries. Much work has been done on methods for dynamically creating new sector boundaries [1-5]. Many assessments of dynamic configuration methods assume the current day baseline configuration remains fixed [6-7]. A challenging question is how to select a dynamic configuration baseline to assess potential benefits of proposed dynamic configuration concepts. Bloem used operational sector reconfigurations as a baseline [8]. The main difficulty is that operational reconfiguration data is noisy. Reconfigurations often occur frequently to accommodate staff training or breaks, or to complete a more complicated reconfiguration through a rapid sequence of simpler reconfigurations. Gupta quantified a few aspects of airspace boundary changes from this data [9]. Most of these metrics are unique to sector combining operations and not applicable to more flexible dynamic configuration concepts. To better understand what sort of reconfigurations are acceptable or beneficial, more configuration change metrics should be developed and their distribution in current practice should be computed. This paper proposes a method to select a simple sequence of configurations among operational configurations to serve as a dynamic configuration baseline for future dynamic configuration concept assessments. New configuration change metrics are applied to the operational data to establish current day thresholds for these metrics. These thresholds are then corroborated, refined, or dismissed based on airspace practitioner feedback. The dynamic configuration baseline selection method uses a k-means clustering algorithm to select the sequence of configurations and trigger times from a given day of operational sector combination data. The clustering algorithm selects a simplified schedule containing k configurations based on stability score of the sector combinations among the raw operational configurations. In addition, the number of the selected configurations is determined based on balance between accuracy and assessment complexity.

Automated Interpretation of Blood Culture Gram Stains by Use of a Deep Convolutional Neural Network.

PubMed

Smith, Kenneth P; Kang, Anthony D; Kirby, James E

2018-03-01

Microscopic interpretation of stained smears is one of the most operator-dependent and time-intensive activities in the clinical microbiology laboratory. Here, we investigated application of an automated image acquisition and convolutional neural network (CNN)-based approach for automated Gram stain classification. Using an automated microscopy platform, uncoverslipped slides were scanned with a 40× dry objective, generating images of sufficient resolution for interpretation. We collected 25,488 images from positive blood culture Gram stains prepared during routine clinical workup. These images were used to generate 100,213 crops containing Gram-positive cocci in clusters, Gram-positive cocci in chains/pairs, Gram-negative rods, or background (no cells). These categories were targeted for proof-of-concept development as they are associated with the majority of bloodstream infections. Our CNN model achieved a classification accuracy of 94.9% on a test set of image crops. Receiver operating characteristic (ROC) curve analysis indicated a robust ability to differentiate between categories with an area under the curve of >0.98 for each. After training and validation, we applied the classification algorithm to new images collected from 189 whole slides without human intervention. Sensitivity and specificity were 98.4% and 75.0% for Gram-positive cocci in chains and pairs, 93.2% and 97.2% for Gram-positive cocci in clusters, and 96.3% and 98.1% for Gram-negative rods. Taken together, our data support a proof of concept for a fully automated classification methodology for blood-culture Gram stains. Importantly, the algorithm was highly adept at identifying image crops with organisms and could be used to present prescreened, classified crops to technologists to accelerate smear review. This concept could potentially be extended to all Gram stain interpretive activities in the clinical laboratory. Copyright © 2018 American Society for Microbiology.

[Applying the clustering technique for characterising maintenance outsourcing].

PubMed

Cruz, Antonio M; Usaquén-Perilla, Sandra P; Vanegas-Pabón, Nidia N; Lopera, Carolina

2010-06-01

Using clustering techniques for characterising companies providing health institutions with maintenance services. The study analysed seven pilot areas' equipment inventory (264 medical devices). Clustering techniques were applied using 26 variables. Response time (RT), operation duration (OD), availability and turnaround time (TAT) were amongst the most significant ones. Average biomedical equipment obsolescence value was 0.78. Four service provider clusters were identified: clusters 1 and 3 had better performance, lower TAT, RT and DR values (56 % of the providers coded O, L, C, B, I, S, H, F and G, had 1 to 4 day TAT values:

Direct Numerical Simulation of Fluid Flow and Mass Transfer in Particle Clusters

PubMed Central

2018-01-01

In this paper, an efficient ghost-cell based immersed boundary method is applied to perform direct numerical simulation (DNS) of mass transfer problems in particle clusters. To be specific, a nine-sphere cuboid cluster and a random-generated spherical cluster consisting of 100 spheres are studied. In both cases, the cluster is composed of active catalysts and inert particles, and the mutual influence of particles on their mass transfer performance is studied. To simulate active catalysts the Dirichlet boundary condition is imposed at the external surface of spheres, while the zero-flux Neumann boundary condition is applied for inert particles. Through our studies, clustering is found to have negative influence on the mass transfer performance, which can be then improved by dilution with inert particles and higher Reynolds numbers. The distribution of active/inert particles may lead to large variations of the cluster mass transfer performance, and individual particle deep inside the cluster may possess a high Sherwood number. PMID:29657359

An analysis of the optimal multiobjective inventory clustering decision with small quantity and great variety inventory by applying a DPSO.

PubMed

Wang, Shen-Tsu; Li, Meng-Hua

2014-01-01

When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions.

Spatial event cluster detection using an approximate normal distribution.

PubMed

Torabi, Mahmoud; Rosychuk, Rhonda J

2008-12-12

In geographic surveillance of disease, areas with large numbers of disease cases are to be identified so that investigations of the causes of high disease rates can be pursued. Areas with high rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. Typically cluster detection tests are applied to incident or prevalent cases of disease, but surveillance of disease-related events, where an individual may have multiple events, may also be of interest. Previously, a compound Poisson approach that detects clusters of events by testing individual areas that may be combined with their neighbours has been proposed. However, the relevant probabilities from the compound Poisson distribution are obtained from a recursion relation that can be cumbersome if the number of events are large or analyses by strata are performed. We propose a simpler approach that uses an approximate normal distribution. This method is very easy to implement and is applicable to situations where the population sizes are large and the population distribution by important strata may differ by area. We demonstrate the approach on pediatric self-inflicted injury presentations to emergency departments and compare the results for probabilities based on the recursion and the normal approach. We also implement a Monte Carlo simulation to study the performance of the proposed approach. In a self-inflicted injury data example, the normal approach identifies twelve out of thirteen of the same clusters as the compound Poisson approach, noting that the compound Poisson method detects twelve significant clusters in total. Through simulation studies, the normal approach well approximates the compound Poisson approach for a variety of different population sizes and case and event thresholds. A drawback of the compound Poisson approach is that the relevant probabilities must be determined through a recursion relation and such calculations can be computationally intensive if the cluster size is relatively large or if analyses are conducted with strata variables. On the other hand, the normal approach is very flexible, easily implemented, and hence, more appealing for users. Moreover, the concepts may be more easily conveyed to non-statisticians interested in understanding the methodology associated with cluster detection test results.

Expert Concept Mapping Study on Mobile Learning

ERIC Educational Resources Information Center

Borner, Dirk; Glahn, Christian; Stoyanov, Slavi; Kalz, Marco; Specht, Marcus

2010-01-01

Purpose: The present paper introduces concept mapping as a structured participative conceptualization approach to identify clusters of ideas and opinions generated by experts within the domain of mobile learning. Utilizing this approach, the paper aims to contribute to a definition of key domain characteristics by identifying the main educational…

Using concept mapping in the development of the EU-PAD framework (EUropean-Physical Activity Determinants across the life course): a DEDIPAC-study.

PubMed

Condello, Giancarlo; Ling, Fiona Chun Man; Bianco, Antonino; Chastin, Sebastien; Cardon, Greet; Ciarapica, Donatella; Conte, Daniele; Cortis, Cristina; De Craemer, Marieke; Di Blasio, Andrea; Gjaka, Masar; Hansen, Sylvia; Holdsworth, Michelle; Iacoviello, Licia; Izzicupo, Pascal; Jaeschke, Lina; Leone, Liliana; Manoni, Livia; Menescardi, Cristina; Migliaccio, Silvia; Nazare, Julie-Anne; Perchoux, Camille; Pesce, Caterina; Pierik, Frank; Pischon, Tobias; Polito, Angela; Puggina, Anna; Sannella, Alessandra; Schlicht, Wolfgang; Schulz, Holger; Simon, Chantal; Steinbrecher, Astrid; MacDonncha, Ciaran; Capranica, Laura

2016-11-09

A large proportion of European children, adults and older adults do not engage in sufficient physical activity (PA). Understanding individual and contextual factors associated with PA behaviours is essential for the identification and implementation of effective preventative environments, policies, and programmes that can promote an active lifestyle across life course and can potentially improve health. The current paper intends to provide 1) a multi-disciplinary, Pan-European and life course view of key determinants of PA behaviours and 2) a proposal of how these factors may cluster. After gathering a list of 183 potential PA behaviours-associated factors and a consensus meeting to unify/consolidate terminology, a concept mapping software was used to collate European experts' views of 106 identified factors for youth (<19 years), adults (19-64 years), and older adults (≥65 years). The analysis evaluated common trends in the clustering of factors and the ratings of the distinct factors' expected modifiability and population-level impact on PA behaviours across the life course. Priority for research was also assessed for each cluster. The concept mapping resulted in six distinct clusters, broadly merged in two themes: 1) the 'Person', which included clusters 'Intra-Personal Context and Wellbeing' and 'Family and Social Economic Status' (42 % of all factors) and 2) the 'Society', which included the remaining four clusters 'Policy and Provision', 'Cultural Context and Media', 'Social Support and Modelling', and 'Supportive Environment' (58 % of all factors). Overall, 25 factors were rated as the most impactful on PA behaviours across the life course and being the most modifiable. They were mostly situated in the 'Intra-Personal Context and Wellbeing' cluster. Furthermore, 16 of them were rated as top priority for research. The current framework provides a preliminary overview of factors which may account for PA behaviour across the life course and are most relevant to the European community. These insights could potentially be a foundation for future Pan-European research on how these factors might interact with each other, and assist policy makers to identify appropriate interventions to maximize PA behaviours and thus the health of European citizens.

THE PREPARATION OF CURRICULUM MATERIALS AND THE DEVELOPMENT OF TEACHERS FOR AN EXPERIMENTAL APPLICATION OF THE CLUSTER CONCEPT OF VOCATIONAL EDUCATION AT THE SECONDARY SCHOOL LEVEL. VOLUME III, INSTRUCTIONAL PLANS FOR THE METAL FORMING AND FABRICATION CLUSTER.

ERIC Educational Resources Information Center

MALEY, DONALD

DESIGNED FOR USE WITH 11TH AND 12TH GRADE STUDENTS, THIS CURRICULUM GUIDE FOR THE OCCUPATIONAL CLUSTER IN METAL FORMING AND FABRICATION WAS DEVELOPED BY PARTICIPATING TEACHERS FROM RESULTS OF THE RESEARCH PROCEDURES DESCRIBED IN VOLUME I (VT 004 162). THE COURSE DESCRIPTION, NEED FOR THE COURSE, COURSE OBJECTIVES, PROCEDURES AND INSTRUCTIONAL PLAN…

THE PREPARATION OF CURRICULUM MATERIALS AND THE DEVELOPMENT OF TEACHERS FOR AN EXPERIMENTAL APPLICATION OF THE CLUSTER CONCEPT OF VOCATIONAL EDUCATION AT THE SECONDARY SCHOOL LEVEL. VOLUME IV, INSTRUCTIONAL PLANS FOR THE ELECTRO-MECHANICAL CLUSTER.

ERIC Educational Resources Information Center

MALEY, DONALD

DESIGNED FOR USE WITH 11TH AND 12TH GRADE STUDENTS, THIS CURRICULUM GUIDE FOR THE OCCUPATIONAL CLUSTER IN ELECTRO-MECHANICAL INSTALLATION AND REPAIR WAS DEVELOPED BY PARTICIPATING TEACHERS FROM RESULTS OF THE RESEARCH PROCEDURES DESCRIBED IN VOLUME I (VT 004 162). THE COURSE DESCRIPTIONS, NEED FOR THE COURSE, COURSE OBJECTIVES, PROCEDURES, AND…

Multilevel joint competing risk models

NASA Astrophysics Data System (ADS)

Karunarathna, G. H. S.; Sooriyarachchi, M. R.

2017-09-01

Joint modeling approaches are often encountered for different outcomes of competing risk time to event and count in many biomedical and epidemiology studies in the presence of cluster effect. Hospital length of stay (LOS) has been the widely used outcome measure in hospital utilization due to the benchmark measurement for measuring multiple terminations such as discharge, transferred, dead and patients who have not completed the event of interest at the follow up period (censored) during hospitalizations. Competing risk models provide a method of addressing such multiple destinations since classical time to event models yield biased results when there are multiple events. In this study, the concept of joint modeling has been applied to the dengue epidemiology in Sri Lanka, 2006-2008 to assess the relationship between different outcomes of LOS and platelet count of dengue patients with the district cluster effect. Two key approaches have been applied to build up the joint scenario. In the first approach, modeling each competing risk separately using the binary logistic model, treating all other events as censored under the multilevel discrete time to event model, while the platelet counts are assumed to follow a lognormal regression model. The second approach is based on the endogeneity effect in the multilevel competing risks and count model. Model parameters were estimated using maximum likelihood based on the Laplace approximation. Moreover, the study reveals that joint modeling approach yield more precise results compared to fitting two separate univariate models, in terms of AIC (Akaike Information Criterion).

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

PubMed

Fritz, Charles E; Schuurman, Nadine; Robertson, Colin; Lear, Scott

2013-05-01

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.

Application of Geostatistical Methods and Machine Learning for spatio-temporal Earthquake Cluster Analysis

NASA Astrophysics Data System (ADS)

Schaefer, A. M.; Daniell, J. E.; Wenzel, F.

2014-12-01

Earthquake clustering tends to be an increasingly important part of general earthquake research especially in terms of seismic hazard assessment and earthquake forecasting and prediction approaches. The distinct identification and definition of foreshocks, aftershocks, mainshocks and secondary mainshocks is taken into account using a point based spatio-temporal clustering algorithm originating from the field of classic machine learning. This can be further applied for declustering purposes to separate background seismicity from triggered seismicity. The results are interpreted and processed to assemble 3D-(x,y,t) earthquake clustering maps which are based on smoothed seismicity records in space and time. In addition, multi-dimensional Gaussian functions are used to capture clustering parameters for spatial distribution and dominant orientations. Clusters are further processed using methodologies originating from geostatistics, which have been mostly applied and developed in mining projects during the last decades. A 2.5D variogram analysis is applied to identify spatio-temporal homogeneity in terms of earthquake density and energy output. The results are mitigated using Kriging to provide an accurate mapping solution for clustering features. As a case study, seismic data of New Zealand and the United States is used, covering events since the 1950s, from which an earthquake cluster catalogue is assembled for most of the major events, including a detailed analysis of the Landers and Christchurch sequences.

Progeny Clustering: A Method to Identify Biological Phenotypes

PubMed Central

Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.

2015-01-01

Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476

Participation of adults with visual and severe or profound intellectual disabilities: Definition and operationalization.

PubMed

Hanzen, Gineke; van Nispen, Ruth M A; van der Putten, Annette A J; Waninge, Aly

2017-02-01

The available opinions regarding participation do not appear to be applicable to adults with visual and severe or profound intellectual disabilities (VSPID). Because a clear definition and operationalization are lacking, it is difficult for support professionals to give meaning to participation for adults with VSPID. The purpose of the present study was to develop a definition and operationalization of the concept of participation of adults with VSPID. Parents or family members, professionals, and experts participated in an online concept mapping procedure. This procedure includes generating statements, clustering them, and rating their importance. The data were analyzed quantitatively using multidimensional scaling and qualitatively with triangulation. A total of 53 participants generated 319 statements of which 125 were clustered and rated. The final cluster map of the statements contained seven clusters: (1) Experience and discover; (2) Inclusion; (3) Involvement; (4) Leisure and recreation; (5) Communication and being understood; (6) Social relations; and (7) Self-management and autonomy. The average importance rating of the statements varied from 6.49 to 8.95. A definition of participation of this population was developed which included these seven clusters. The combination of the developed definition, the clusters, and the statements in these clusters, derived from the perceptions of parents or family members, professionals, and experts, can be employed to operationalize the construct of participation of adults with VSPID. This operationalization supports professionals in their ability to give meaning to participation in these adults. Future research will focus on using the operationalization as a checklist of participation for adults with VSPID. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

Speaker Linking and Applications using Non-Parametric Hashing Methods

DTIC Science & Technology

2016-09-08

clustering method based on hashing—canopy- clustering . We apply this method to a large corpus of speaker recordings, demonstrate performance tradeoffs...and compare to other hash- ing methods. Index Terms: speaker recognition, clustering , hashing, locality sensitive hashing. 1. Introduction We assume...speaker in our corpus. Second, given a QBE method, how can we perform speaker clustering —each clustering should be a single speaker, and a cluster should

An energy-efficient and compact clustering scheme with temporary support nodes for cognitive radio sensor networks.

PubMed

Salim, Shelly; Moh, Sangman; Choi, Dongmin; Chung, Ilyong

2014-08-11

A cognitive radio sensor network (CRSN) is a wireless sensor network whose sensor nodes are equipped with cognitive radio capability. Clustering is one of the most challenging issues in CRSNs, as all sensor nodes, including the cluster head, have to use the same frequency band in order to form a cluster. However, due to the nature of heterogeneous channels in cognitive radio, it is difficult for sensor nodes to find a cluster head. This paper proposes a novel energy-efficient and compact clustering scheme named clustering with temporary support nodes (CENTRE). CENTRE efficiently achieves a compact cluster formation by adopting two-phase cluster formation with fixed duration. By introducing a novel concept of temporary support nodes to improve the cluster formation, the proposed scheme enables sensor nodes in a network to find a cluster head efficiently. The performance study shows that not only is the clustering process efficient and compact but it also results in remarkable energy savings that prolong the overall network lifetime. In addition, the proposed scheme decreases both the clustering overhead and the average distance between cluster heads and their members.

An Energy-Efficient and Compact Clustering Scheme with Temporary Support Nodes for Cognitive Radio Sensor Networks

PubMed Central

Salim, Shelly; Moh, Sangman; Choi, Dongmin; Chung, Ilyong

2014-01-01

A cognitive radio sensor network (CRSN) is a wireless sensor network whose sensor nodes are equipped with cognitive radio capability. Clustering is one of the most challenging issues in CRSNs, as all sensor nodes, including the cluster head, have to use the same frequency band in order to form a cluster. However, due to the nature of heterogeneous channels in cognitive radio, it is difficult for sensor nodes to find a cluster head. This paper proposes a novel energy-efficient and compact clustering scheme named clustering with temporary support nodes (CENTRE). CENTRE efficiently achieves a compact cluster formation by adopting two-phase cluster formation with fixed duration. By introducing a novel concept of temporary support nodes to improve the cluster formation, the proposed scheme enables sensor nodes in a network to find a cluster head efficiently. The performance study shows that not only is the clustering process efficient and compact but it also results in remarkable energy savings that prolong the overall network lifetime. In addition, the proposed scheme decreases both the clustering overhead and the average distance between cluster heads and their members. PMID:25116905

An Automated DAKOTA and VULCAN-CFD Framework with Application to Supersonic Facility Nozzle Flowpath Optimization

NASA Technical Reports Server (NTRS)

Axdahl, Erik L.

2015-01-01

Removing human interaction from design processes by using automation may lead to gains in both productivity and design precision. This memorandum describes efforts to incorporate high fidelity numerical analysis tools into an automated framework and applying that framework to applications of practical interest. The purpose of this effort was to integrate VULCAN-CFD into an automated, DAKOTA-enabled framework with a proof-of-concept application being the optimization of supersonic test facility nozzles. It was shown that the optimization framework could be deployed on a high performance computing cluster with the flow of information handled effectively to guide the optimization process. Furthermore, the application of the framework to supersonic test facility nozzle flowpath design and optimization was demonstrated using multiple optimization algorithms.

Software tool for data mining and its applications

NASA Astrophysics Data System (ADS)

Yang, Jie; Ye, Chenzhou; Chen, Nianyi

2002-03-01

A software tool for data mining is introduced, which integrates pattern recognition (PCA, Fisher, clustering, hyperenvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, Hyper Envelop, support vector machine, visualization. The principle and knowledge representation of some function models of data mining are described. The software tool of data mining is realized by Visual C++ under Windows 2000. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining has satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.

Microoptical artificial compound eyes: from design to experimental verification of two different concepts

NASA Astrophysics Data System (ADS)

Duparré, Jacques; Wippermann, Frank; Dannberg, Peter; Schreiber, Peter; Bräuer, Andreas; Völkel, Reinhard; Scharf, Toralf

2005-09-01

Two novel objective types on the basis of artificial compound eyes are examined. Both imaging systems are well suited for fabrication using microoptics technology due to the small required lens sags. In the apposition optics a microlens array (MLA) and a photo detector array of different pitch in its focal plane are applied. The image reconstruction is based on moire magnification. Several generations of demonstrators of this objective type are manufactured by photo lithographic processes. This includes a system with opaque walls between adjacent channels and an objective which is directly applied onto a CMOS detector array. The cluster eye approach, which is based on a mixture of superposition compound eyes and the vision system of jumping spiders, produces a regular image. Here, three microlens arrays of different pitch form arrays of Keplerian microtelescopes with tilted optical axes, including a field lens. The microlens arrays of this demonstrator are also fabricated using microoptics technology, aperture arrays are applied. Subsequently the lens arrays are stacked to the overall microoptical system on wafer scale. Both fabricated types of artificial compound eye imaging systems are experimentally characterized with respect to resolution, sensitivity and cross talk between adjacent channels. Captured images are presented.

What is needed to implement a computer-assisted health risk assessment tool? An exploratory concept mapping study

PubMed Central

2012-01-01

Background Emerging eHealth tools could facilitate the delivery of comprehensive care in time-constrained clinical settings. One such tool is interactive computer-assisted health-risk assessments (HRA), which may improve provider-patient communication at the point of care, particularly for psychosocial health concerns, which remain under-detected in clinical encounters. The research team explored the perspectives of healthcare providers representing a variety of disciplines (physicians, nurses, social workers, allied staff) regarding the factors required for implementation of an interactive HRA on psychosocial health. Methods The research team employed a semi-qualitative participatory method known as Concept Mapping, which involved three distinct phases. First, in face-to-face and online brainstorming sessions, participants responded to an open-ended central question: “What factors should be in place within your clinical setting to support an effective computer-assisted screening tool for psychosocial risks?” The brainstormed items were consolidated by the research team. Then, in face-to-face and online sorting sessions, participants grouped the items thematically as ‘it made sense to them’. Participants also rated each item on a 5-point scale for its ‘importance’ and ‘action feasibility’ over the ensuing six month period. The sorted and rated data was analyzed using multidimensional scaling and hierarchical cluster analyses which produced visual maps. In the third and final phase, the face-to-face Interpretation sessions, the concept maps were discussed and illuminated by participants collectively. Results Overall, 54 providers participated (emergency care 48%; primary care 52%). Participants brainstormed 196 items thought to be necessary for the implementation of an interactive HRA emphasizing psychosocial health. These were consolidated by the research team into 85 items. After sorting and rating, cluster analysis revealed a concept map with a seven-cluster solution: 1) the HRA’s equitable availability; 2) the HRA’s ease of use and appropriateness; 3) the content of the HRA survey; 4) patient confidentiality and choice; 5) patient comfort through humanistic touch; 6) professional development, care and workload; and 7) clinical management protocol. Drawing insight from the theoretical lens of Sociotechnical theory, the seven clusters of factors required for HRA implementation could be read as belonging to three overarching aspects : Technical (cluster 1, 2 and 3), Social-Patient (cluster 4 and 5), and Social-Provider (cluster 6 and 7). Participants rated every one of the clusters as important, with mean scores from 4.0 to 4.5. Their scores for feasibility were somewhat lower, ranging from 3.4 to. 4.3. Comparing the scores for importance and feasibility, a significant difference was found for one cluster from each region (cluster 2, 5, 6). The cluster on professional development, care and workload was perceived as especially challenging in emergency department settings, and possible reasons were discussed in the interpretation sessions. Conclusion A number of intertwined multilevel factors emerged as important for the implementation of a computer-assisted, interactive HRA with a focus on psychosocial health. Future developments in this area could benefit from systems thinking and insights from theoretical perspectives, such as sociotechnical system theory for joint optimization and responsible autonomy, with emphasis on both the technical and social aspects of HRA implementation. PMID:23253913

Effect of rotating electric field on 3D complex (dusty) plasma

NASA Astrophysics Data System (ADS)

Wörner, L.; Nosenko, V.; Ivlev, A. V.; Zhdanov, S. K.; Thomas, H. M.; Morfill, G. E.; Kroll, M.; Schablinski, J.; Block, D.

2011-06-01

The effect of rotating electric field on 3D particle clusters suspended in rf plasma was studied experimentally. Spheroidal clusters were suspended inside a glass box mounted on the lower horizontal rf electrode, with gravity partially balanced by thermophoretic force. Clusters rotated in the horizontal plane, in response to rotating electric field that was created inside the box using conducting coating on its inner surfaces ("rotating wall" technique). Cluster rotation was always in the direction of applied field and had a shear in the vertical direction. The angular speed of rotation was 104-107 times lower than applied frequency. The experiment is compared to a recent theory.

Ab initio calculation of one-nucleon halo states

NASA Astrophysics Data System (ADS)

Rodkin, D. M.; Tchuvil'sky, Yu M.

2018-02-01

We develop an approach to microscopic and ab initio description of clustered systems, states with halo nucleon and one-nucleon resonances. For these purposes a basis combining ordinary shell-model components and cluster-channel terms is built up. The transformation of clustered wave functions to the uniform Slater-determinant type is performed using the concept of cluster coefficients. The resulting basis of orthonormalized wave functions is used for calculating the eigenvalues and the eigenvectors of Hamiltonians built in the framework of ab initio approaches. Calculations of resonance and halo states of 5He, 9Be and 9B nuclei demonstrate that the approach is workable and labor-saving.

Cluster structures influenced by interaction with a surface.

PubMed

Witt, Christopher; Dieterich, Johannes M; Hartke, Bernd

2018-05-30

Clusters on surfaces are vitally important for nanotechnological applications. Clearly, cluster-surface interactions heavily influence the preferred cluster structures, compared to clusters in vacuum. Nevertheless, systematic explorations and an in-depth understanding of these interactions and how they determine the cluster structures are still lacking. Here we present an extension of our well-established non-deterministic global optimization package OGOLEM from isolated clusters to clusters on surfaces. Applying this approach to intentionally simple Lennard-Jones test systems, we produce a first systematic exploration that relates changes in cluster-surface interactions to resulting changes in adsorbed cluster structures.

Four-dimensional reconstruction of cultural heritage sites based on photogrammetry and clustering

NASA Astrophysics Data System (ADS)

Voulodimos, Athanasios; Doulamis, Nikolaos; Fritsch, Dieter; Makantasis, Konstantinos; Doulamis, Anastasios; Klein, Michael

2017-01-01

A system designed and developed for the three-dimensional (3-D) reconstruction of cultural heritage (CH) assets is presented. Two basic approaches are presented. The first one, resulting in an "approximate" 3-D model, uses images retrieved in online multimedia collections; it employs a clustering-based technique to perform content-based filtering and eliminate outliers that significantly reduce the performance of 3-D reconstruction frameworks. The second one is based on input image data acquired through terrestrial laser scanning, as well as close range and airborne photogrammetry; it follows a sophisticated multistep strategy, which leads to a "precise" 3-D model. Furthermore, the concept of change history maps is proposed to address the computational limitations involved in four-dimensional (4-D) modeling, i.e., capturing 3-D models of a CH landmark or site at different time instances. The system also comprises a presentation viewer, which manages the display of the multifaceted CH content collected and created. The described methods have been successfully applied and evaluated in challenging real-world scenarios, including the 4-D reconstruction of the historic Market Square of the German city of Calw in the context of the 4-D-CH-World EU project.

Beyond Exemplars and Prototypes as Memory Representations of Natural Concepts: A Clustering Approach

ERIC Educational Resources Information Center

Verbeemen, Timothy; Vanpaemel, Wolf; Pattyn, Sven; Storms, Gert; Verguts, Tom

2007-01-01

Categorization in well-known natural concepts is studied using a special version of the Varying Abstraction Framework (Vanpaemel, W., & Storms, G. (2006). A varying abstraction framework for categorization. Manuscript submitted for publication; Vanpaemel, W., Storms, G., & Ons, B. (2005). A varying abstraction model for categorization. In B. Bara,…

Metaphor Clusters: Characterizing Instructor Metaphorical Reasoning on Limit Concepts in College Calculus

ERIC Educational Resources Information Center

Patel, Rita Manubhai; McCombs, Paul; Zollman, Alan

2014-01-01

Novice students have difficulty with the topic of limits in calculus. We believe this is in part because of the multiple perspectives and shifting metaphors available to solve items correctly. We investigated college calculus instructors' personal concepts of limits. Based upon previous research investigating introductory calculus student…

Elementary School Students' Understandings of Technology Concepts.

ERIC Educational Resources Information Center

Davis, Robert S.; Ginns, Ian S.; McRobbie, Campbell J.

2002-01-01

Students in grades 2 (n=27), 4 (n=37), and 6 (n=28) were asked questions about artifacts or pictures. Their explanations, which revealed their understanding of such technological concepts as material properties and stability, were classified as naive, artifact related, or not artifact related. Explanations tended to cluster in a classification at…

ARTIST CONCEPT - CUTAWAY VIEW SKYLAB 1 ORBITAL WORKSHOP (OWS)

NASA Image and Video Library

1973-05-23

S73-23919 (May 1973) --- An artist's concept illustrating a cutaway view of the Skylab 1 Orbital Workshop (OWS). The OWS is one of the five major components of the Skylab 1 space station cluster which was launched by a Saturn V on May 14, 1973 into Earth orbit. Photo credit: NASA

Artist Concept - Illustration Cutaway View - Skylab (SL)-1 Orbital Workshop (OWS)

NASA Image and Video Library

1973-05-23

S73-23918 (May 1973) --- An artist's concept illustrating a cutaway view of the Skylab 1 Orbital Workshop (OWS). The OWS is one of the five major components of the Skylab 1 space station cluster which was launched by a Saturn V on May 14, 1973 into Earth orbit. Photo credit: NASA

Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies

NASA Astrophysics Data System (ADS)

Zhou, Shuguang; Zhou, Kefa; Wang, Jinlin; Yang, Genfang; Wang, Shanshan

2017-12-01

Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms (the hierarchical, k-means, and fuzzy c-means algorithms) and six data transformation methods (the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT) are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of column- or variable-based (R-type) cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based (Q-type) cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy c-means clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.

Using concept mapping in the knowledge-to-action process to compare stakeholder opinions on barriers to use of cancer screening among South Asians.

PubMed

Lobb, Rebecca; Pinto, Andrew D; Lofters, Aisha

2013-03-23

Using the knowledge-to-action (KTA) process, this study examined barriers to use of evidence-based interventions to improve early detection of cancer among South Asians from the perspective of multiple stakeholders. In 2011, we used concept mapping with South Asian residents, and representatives from health service and community service organizations in the region of Peel Ontario. As part of concept mapping procedures, brainstorming sessions were conducted with stakeholders (n = 53) to identify barriers to cancer screening among South Asians. Participants (n = 46) sorted barriers into groups, and rated barriers from lowest (1) to highest (6) in terms of importance for use of mammograms, Pap tests and fecal occult blood tests, and how feasible it would be to address them. Multi-dimensional scaling, cluster analysis, and descriptive statistics were used to analyze the data. A total of 45 unique barriers to use of mammograms, Pap tests, and fecal occult blood tests among South Asians were classified into seven clusters using concept mapping procedures: patient's beliefs, fears, lack of social support; health system; limited knowledge among residents; limited knowledge among physicians; health education programs; ethno-cultural discordance with the health system; and cost. Overall, the top three ranked clusters of barriers were 'limited knowledge among residents,' 'ethno-cultural discordance,' and 'health education programs' across surveys. Only residents ranked 'cost' second in importance for fecal occult blood testing, and stakeholders from health service organizations ranked 'limited knowledge among physicians' third for the feasibility survey. Stakeholders from health services organizations ranked 'limited knowledge among physicians' fourth for all other surveys, but this cluster consistently ranked lowest among residents. The limited reach of cancer control programs to racial and ethnic minority groups is a critical implementation issue that requires attention. Opinions of community service and health service organizations on why this deficit in implementation occurs are fundamental to understanding the solutions because these are the settings in which evidence-based interventions are implemented. Using concept mapping within a KTA process can facilitate the engagement of multiple stakeholders in the utilization of study results and in identifying next steps for action.

Using cluster ensemble and validation to identify subtypes of pervasive developmental disorders.

PubMed

Shen, Jess J; Lee, Phil-Hyoun; Holden, Jeanette J A; Shatkay, Hagit

2007-10-11

Pervasive Developmental Disorders (PDD) are neurodevelopmental disorders characterized by impairments in social interaction, communication and behavior. Given the diversity and varying severity of PDD, diagnostic tools attempt to identify homogeneous subtypes within PDD. Identifying subtypes can lead to targeted etiology studies and to effective type-specific intervention. Cluster analysis can suggest coherent subsets in data; however, different methods and assumptions lead to different results. Several previous studies applied clustering to PDD data, varying in number and characteristics of the produced subtypes. Most studies used a relatively small dataset (fewer than 150 subjects), and all applied only a single clustering method. Here we study a relatively large dataset (358 PDD patients), using an ensemble of three clustering methods. The results are evaluated using several validation methods, and consolidated through an integration step. Four clusters are identified, analyzed and compared to subtypes previously defined by the widely used diagnostic tool DSM-IV.

Using Cluster Ensemble and Validation to Identify Subtypes of Pervasive Developmental Disorders

PubMed Central

Shen, Jess J.; Lee, Phil Hyoun; Holden, Jeanette J.A.; Shatkay, Hagit

2007-01-01

Pervasive Developmental Disorders (PDD) are neurodevelopmental disorders characterized by impairments in social interaction, communication and behavior.1 Given the diversity and varying severity of PDD, diagnostic tools attempt to identify homogeneous subtypes within PDD. Identifying subtypes can lead to targeted etiology studies and to effective type-specific intervention. Cluster analysis can suggest coherent subsets in data; however, different methods and assumptions lead to different results. Several previous studies applied clustering to PDD data, varying in number and characteristics of the produced subtypes19. Most studies used a relatively small dataset (fewer than 150 subjects), and all applied only a single clustering method. Here we study a relatively large dataset (358 PDD patients), using an ensemble of three clustering methods. The results are evaluated using several validation methods, and consolidated through an integration step. Four clusters are identified, analyzed and compared to subtypes previously defined by the widely used diagnostic tool DSM-IV.2 PMID:18693920

Interactive visual exploration and analysis of origin-destination data

NASA Astrophysics Data System (ADS)

Ding, Linfang; Meng, Liqiu; Yang, Jian; Krisp, Jukka M.

2018-05-01

In this paper, we propose a visual analytics approach for the exploration of spatiotemporal interaction patterns of massive origin-destination data. Firstly, we visually query the movement database for data at certain time windows. Secondly, we conduct interactive clustering to allow the users to select input variables/features (e.g., origins, destinations, distance, and duration) and to adjust clustering parameters (e.g. distance threshold). The agglomerative hierarchical clustering method is applied for the multivariate clustering of the origin-destination data. Thirdly, we design a parallel coordinates plot for visualizing the precomputed clusters and for further exploration of interesting clusters. Finally, we propose a gradient line rendering technique to show the spatial and directional distribution of origin-destination clusters on a map view. We implement the visual analytics approach in a web-based interactive environment and apply it to real-world floating car data from Shanghai. The experiment results show the origin/destination hotspots and their spatial interaction patterns. They also demonstrate the effectiveness of our proposed approach.

Mining biological information from 3D short time-series gene expression data: the OPTricluster algorithm.

PubMed

Tchagang, Alain B; Phan, Sieu; Famili, Fazel; Shearer, Heather; Fobert, Pierre; Huang, Yi; Zou, Jitao; Huang, Daiqing; Cutler, Adrian; Liu, Ziying; Pan, Youlian

2012-04-04

Nowadays, it is possible to collect expression levels of a set of genes from a set of biological samples during a series of time points. Such data have three dimensions: gene-sample-time (GST). Thus they are called 3D microarray gene expression data. To take advantage of the 3D data collected, and to fully understand the biological knowledge hidden in the GST data, novel subspace clustering algorithms have to be developed to effectively address the biological problem in the corresponding space. We developed a subspace clustering algorithm called Order Preserving Triclustering (OPTricluster), for 3D short time-series data mining. OPTricluster is able to identify 3D clusters with coherent evolution from a given 3D dataset using a combinatorial approach on the sample dimension, and the order preserving (OP) concept on the time dimension. The fusion of the two methodologies allows one to study similarities and differences between samples in terms of their temporal expression profile. OPTricluster has been successfully applied to four case studies: immune response in mice infected by malaria (Plasmodium chabaudi), systemic acquired resistance in Arabidopsis thaliana, similarities and differences between inner and outer cotyledon in Brassica napus during seed development, and to Brassica napus whole seed development. These studies showed that OPTricluster is robust to noise and is able to detect the similarities and differences between biological samples. Our analysis showed that OPTricluster generally outperforms other well known clustering algorithms such as the TRICLUSTER, gTRICLUSTER and K-means; it is robust to noise and can effectively mine the biological knowledge hidden in the 3D short time-series gene expression data.

A Putative Gene Cluster from a Lyngbya wollei Bloom that Encodes Paralytic Shellfish Toxin Biosynthesis

PubMed Central

Mihali, Troco K.; Carmichael, Wayne W.; Neilan, Brett A.

2011-01-01

Saxitoxin and its analogs cause the paralytic shellfish-poisoning syndrome, adversely affecting human health and coastal shellfish industries worldwide. Here we report the isolation, sequencing, annotation, and predicted pathway of the saxitoxin biosynthetic gene cluster in the cyanobacterium Lyngbya wollei. The gene cluster spans 36 kb and encodes enzymes for the biosynthesis and export of the toxins. The Lyngbya wollei saxitoxin gene cluster differs from previously identified saxitoxin clusters as it contains genes that are unique to this cluster, whereby the carbamoyltransferase is truncated and replaced by an acyltransferase, explaining the unique toxin profile presented by Lyngbya wollei. These findings will enable the creation of toxin probes, for water monitoring purposes, as well as proof-of-concept for the combinatorial biosynthesis of these natural occurring alkaloids for the production of novel, biologically active compounds. PMID:21347365

Estimating the concrete compressive strength using hard clustering and fuzzy clustering based regression techniques.

PubMed

Nagwani, Naresh Kumar; Deo, Shirish V

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm.

Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

PubMed Central

Nagwani, Naresh Kumar; Deo, Shirish V.

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm. PMID:25374939

Digital Signal Processing Based on a Clustering Algorithm for Ir/Au TES Microcalorimeter

NASA Astrophysics Data System (ADS)

Zen, N.; Kunieda, Y.; Takahashi, H.; Hiramoto, K.; Nakazawa, M.; Fukuda, D.; Ukibe, M.; Ohkubo, M.

2006-02-01

In recent years, cryogenic microcalorimeters using their superconducting transition edge have been under development for possible application to the research for astronomical X-ray observations. To improve the energy resolution of superconducting transition edge sensors (TES), several correction methods have been developed. Among them, a clustering method based on digital signal processing has recently been proposed. In this paper, we applied the clustering method to Ir/Au bilayer TES. This method resulted in almost a 10% improvement in the energy resolution. Conversely, from the point of view of imaging X-ray spectroscopy, we applied the clustering method to pixellated Ir/Au-TES devices. We will thus show how a clustering method which sorts signals by their shapes is also useful for position identification

Clusters of Occupations Based on Systematically Derived Work Dimensions: An Exploratory Study.

ERIC Educational Resources Information Center

Cunningham, J. W.; And Others

The study explored the feasibility of deriving an educationally relevant occupational cluster structure based on Occupational Analysis Inventory (OAI) work dimensions. A hierarchical cluster analysis was applied to the factor score profiles of 814 occupations on 22 higher-order OAI work dimensions. From that analysis, 73 occupational clusters were…

An Analysis of the Optimal Multiobjective Inventory Clustering Decision with Small Quantity and Great Variety Inventory by Applying a DPSO

PubMed Central

Li, Meng-Hua

2014-01-01

When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions. PMID:25197713

Superhydrophilic nanostructure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mao, Samuel S; Zormpa, Vasileia; Chen, Xiaobo

2015-05-12

An embodiment of a superhydrophilic nanostructure includes nanoparticles. The nanoparticles are formed into porous clusters. The porous clusters are formed into aggregate clusters. An embodiment of an article of manufacture includes the superhydrophilic nanostructure on a substrate. An embodiment of a method of fabricating a superhydrophilic nanostructure includes applying a solution that includes nanoparticles to a substrate. The substrate is heated to form aggregate clusters of porous clusters of the nanoparticles.

Minimal spanning tree algorithm for γ-ray source detection in sparse photon images: cluster parameters and selection strategies

DOE PAGES

Campana, R.; Bernieri, E.; Massaro, E.; ...

2013-05-22

We present that the minimal spanning tree (MST) algorithm is a graph-theoretical cluster-finding method. We previously applied it to γ-ray bidimensional images, showing that it is quite sensitive in finding faint sources. Possible sources are associated with the regions where the photon arrival directions clusterize. MST selects clusters starting from a particular “tree” connecting all the point of the image and performing a cut based on the angular distance between photons, with a number of events higher than a given threshold. In this paper, we show how a further filtering, based on some parameters linked to the cluster properties, canmore » be applied to reduce spurious detections. We find that the most efficient parameter for this secondary selection is the magnitudeM of a cluster, defined as the product of its number of events by its clustering degree. We test the sensitivity of the method by means of simulated and real Fermi-Large Area Telescope (LAT) fields. Our results show that √M is strongly correlated with other statistical significance parameters, derived from a wavelet based algorithm and maximum likelihood (ML) analysis, and that it can be used as a good estimator of statistical significance of MST detections. Finally, we apply the method to a 2-year LAT image at energies higher than 3 GeV, and we show the presence of new clusters, likely associated with BL Lac objects.« less

From isosuperatoms to isosupermolecules: new concepts in cluster science

NASA Astrophysics Data System (ADS)

Liu, Liren; Li, Pai; Yuan, Lan-Feng; Cheng, Longjiu; Yang, Jinlong

2016-06-01

As an extension of the superatom concept, a new concept ``isosuperatom'' is proposed, reflecting the physical phenomenon that a superatom cluster can take multiple geometrical structures with their electronic structures topologically invariant. The icosahedral and cuboctahedral Au135+ units in the Au25(SCH2CH2Ph)18-, Au23(SC6H11)16- and Au24(SAdm)16 nanoclusters are found to be examples of this concept. Furthermore, two isosuperatoms can combine to form a supermolecule. For example, the structure of the {Ag32(DPPE)5(SC6H4CF3)24}2- nanocluster can be understood well in terms of a Ag2212+ supermolecule formed by two Ag138+ isosuperatoms. On the next level of complexity, various combinations of isosuperatoms can lead to supermolecules with different geometrical structures but similar electronic structures, i.e., ``isosupermolecules''. We take two synthesized nanoclusters Au20(PPhpy2)10Cl42+ and Au30S(StBu)18 to illustrate two Au206+ isosupermolecules. The proposed concepts of isosuperatom and isosupermolecule significantly enrich the superatom concept, give a new framework for understanding a wide range of nanoclusters, and open a new door for designing assembled materials.As an extension of the superatom concept, a new concept ``isosuperatom'' is proposed, reflecting the physical phenomenon that a superatom cluster can take multiple geometrical structures with their electronic structures topologically invariant. The icosahedral and cuboctahedral Au135+ units in the Au25(SCH2CH2Ph)18-, Au23(SC6H11)16- and Au24(SAdm)16 nanoclusters are found to be examples of this concept. Furthermore, two isosuperatoms can combine to form a supermolecule. For example, the structure of the {Ag32(DPPE)5(SC6H4CF3)24}2- nanocluster can be understood well in terms of a Ag2212+ supermolecule formed by two Ag138+ isosuperatoms. On the next level of complexity, various combinations of isosuperatoms can lead to supermolecules with different geometrical structures but similar electronic structures, i.e., ``isosupermolecules''. We take two synthesized nanoclusters Au20(PPhpy2)10Cl42+ and Au30S(StBu)18 to illustrate two Au206+ isosupermolecules. The proposed concepts of isosuperatom and isosupermolecule significantly enrich the superatom concept, give a new framework for understanding a wide range of nanoclusters, and open a new door for designing assembled materials. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr01998f

Mining Co-Location Patterns with Clustering Items from Spatial Data Sets

NASA Astrophysics Data System (ADS)

Zhou, G.; Li, Q.; Deng, G.; Yue, T.; Zhou, X.

2018-05-01

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.

"To be or not to be Retained … That's the Question!" Retention, Self-esteem, Self-concept, Achievement Goals, and Grades.

PubMed

Peixoto, Francisco; Monteiro, Vera; Mata, Lourdes; Sanches, Cristina; Pipa, Joana; Almeida, Leandro S

2016-01-01

Keeping students back in the same grade - retention - has always been a controversial issue in Education, with some defending it as a beneficial remedial practice and others arguing against its detrimental effects. This paper undertakes an analysis of this issue, focusing on the differences in student motivation and self-related variables according to their retention related status, and the interrelationship between retention and these variables. The participants were 695 students selected from two cohorts (5th and 7th graders) of a larger group of students followed over a 3-year project. The students were assigned to four groups according to their retention-related status over time: (1) students with past and recent retention; (2) students with past but no recent retention; (3) students with no past but recent retention; (4) students with no past or recent retention. Measures of achievement goal orientations, self-concept, self-esteem, importance given to school subjects and Grade Point Average (GPA) were collected for all students. Repeated measures MANCOVA analyses were carried out showing group differences in self-esteem, academic self-concept, importance attributed to academic competencies, task and avoidance orientation and academic achievement. To attain a deeper understanding of these results and to identify profiles across variables, a cluster analysis based on achievement goals was conducted and four clusters were identified. Students who were retained at the end of the school year are mainly represented in clusters with less adaptive motivational profiles and almost absent from clusters exhibiting more adaptive ones. Findings highlight that retention leaves a significant mark that remains even when students recover academic achievement and retention is in the distant past. This is reflected in the low academic self-concept as well as in the devaluation of academic competencies and in the avoidance orientation which, taken together, can undermine students' academic adjustment and turn retention into a risk factor.

“To be or not to be Retained … That’s the Question!” Retention, Self-esteem, Self-concept, Achievement Goals, and Grades

PubMed Central

Peixoto, Francisco; Monteiro, Vera; Mata, Lourdes; Sanches, Cristina; Pipa, Joana; Almeida, Leandro S.

2016-01-01

Keeping students back in the same grade – retention – has always been a controversial issue in Education, with some defending it as a beneficial remedial practice and others arguing against its detrimental effects. This paper undertakes an analysis of this issue, focusing on the differences in student motivation and self-related variables according to their retention related status, and the interrelationship between retention and these variables. The participants were 695 students selected from two cohorts (5th and 7th graders) of a larger group of students followed over a 3-year project. The students were assigned to four groups according to their retention-related status over time: (1) students with past and recent retention; (2) students with past but no recent retention; (3) students with no past but recent retention; (4) students with no past or recent retention. Measures of achievement goal orientations, self-concept, self-esteem, importance given to school subjects and Grade Point Average (GPA) were collected for all students. Repeated measures MANCOVA analyses were carried out showing group differences in self-esteem, academic self-concept, importance attributed to academic competencies, task and avoidance orientation and academic achievement. To attain a deeper understanding of these results and to identify profiles across variables, a cluster analysis based on achievement goals was conducted and four clusters were identified. Students who were retained at the end of the school year are mainly represented in clusters with less adaptive motivational profiles and almost absent from clusters exhibiting more adaptive ones. Findings highlight that retention leaves a significant mark that remains even when students recover academic achievement and retention is in the distant past. This is reflected in the low academic self-concept as well as in the devaluation of academic competencies and in the avoidance orientation which, taken together, can undermine students’ academic adjustment and turn retention into a risk factor. PMID:27790167

Application of Scan Statistics to Detect Suicide Clusters in Australia

PubMed Central

Cheung, Yee Tak Derek; Spittal, Matthew J.; Williamson, Michelle Kate; Tung, Sui Jay; Pirkis, Jane

2013-01-01

Background Suicide clustering occurs when multiple suicide incidents take place in a small area or/and within a short period of time. In spite of the multi-national research attention and particular efforts in preparing guidelines for tackling suicide clusters, the broader picture of epidemiology of suicide clustering remains unclear. This study aimed to develop techniques in using scan statistics to detect clusters, with the detection of suicide clusters in Australia as example. Methods and Findings Scan statistics was applied to detect clusters among suicides occurring between 2004 and 2008. Manipulation of parameter settings and change of area for scan statistics were performed to remedy shortcomings in existing methods. In total, 243 suicides out of 10,176 (2.4%) were identified as belonging to 15 suicide clusters. These clusters were mainly located in the Northern Territory, the northern part of Western Australia, and the northern part of Queensland. Among the 15 clusters, 4 (26.7%) were detected by both national and state cluster detections, 8 (53.3%) were only detected by the state cluster detection, and 3 (20%) were only detected by the national cluster detection. Conclusions These findings illustrate that the majority of spatial-temporal clusters of suicide were located in the inland northern areas, with socio-economic deprivation and higher proportions of indigenous people. Discrepancies between national and state/territory cluster detection by scan statistics were due to the contrast of the underlying suicide rates across states/territories. Performing both small-area and large-area analyses, and applying multiple parameter settings may yield the maximum benefits for exploring clusters. PMID:23342098

A Pareto frontier intersection-based approach for efficient multiobjective optimization of competing concept alternatives

NASA Astrophysics Data System (ADS)

Rousis, Damon A.

The expected growth of civil aviation over the next twenty years places significant emphasis on revolutionary technology development aimed at mitigating the environmental impact of commercial aircraft. As the number of technology alternatives grows along with model complexity, current methods for Pareto finding and multiobjective optimization quickly become computationally infeasible. Coupled with the large uncertainty in the early stages of design, optimal designs are sought while avoiding the computational burden of excessive function calls when a single design change or technology assumption could alter the results. This motivates the need for a robust and efficient evaluation methodology for quantitative assessment of competing concepts. This research presents a novel approach that combines Bayesian adaptive sampling with surrogate-based optimization to efficiently place designs near Pareto frontier intersections of competing concepts. Efficiency is increased over sequential multiobjective optimization by focusing computational resources specifically on the location in the design space where optimality shifts between concepts. At the intersection of Pareto frontiers, the selection decisions are most sensitive to preferences place on the objectives, and small perturbations can lead to vastly different final designs. These concepts are incorporated into an evaluation methodology that ultimately reduces the number of failed cases, infeasible designs, and Pareto dominated solutions across all concepts. A set of algebraic samples along with a truss design problem are presented as canonical examples for the proposed approach. The methodology is applied to the design of ultra-high bypass ratio turbofans to guide NASA's technology development efforts for future aircraft. Geared-drive and variable geometry bypass nozzle concepts are explored as enablers for increased bypass ratio and potential alternatives over traditional configurations. The method is shown to improve sampling efficiency and provide clusters of feasible designs that motivate a shift towards revolutionary technologies that reduce fuel burn, emissions, and noise on future aircraft.

Implementation of hybrid clustering based on partitioning around medoids algorithm and divisive analysis on human Papillomavirus DNA

NASA Astrophysics Data System (ADS)

Arimbi, Mentari Dian; Bustamam, Alhadi; Lestari, Dian

2017-03-01

Data clustering can be executed through partition or hierarchical method for many types of data including DNA sequences. Both clustering methods can be combined by processing partition algorithm in the first level and hierarchical in the second level, called hybrid clustering. In the partition phase some popular methods such as PAM, K-means, or Fuzzy c-means methods could be applied. In this study we selected partitioning around medoids (PAM) in our partition stage. Furthermore, following the partition algorithm, in hierarchical stage we applied divisive analysis algorithm (DIANA) in order to have more specific clusters and sub clusters structures. The number of main clusters is determined using Davies Bouldin Index (DBI) value. We choose the optimal number of clusters if the results minimize the DBI value. In this work, we conduct the clustering on 1252 HPV DNA sequences data from GenBank. The characteristic extraction is initially performed, followed by normalizing and genetic distance calculation using Euclidean distance. In our implementation, we used the hybrid PAM and DIANA using the R open source programming tool. In our results, we obtained 3 main clusters with average DBI value is 0.979, using PAM in the first stage. After executing DIANA in the second stage, we obtained 4 sub clusters for Cluster-1, 9 sub clusters for Cluster-2 and 2 sub clusters in Cluster-3, with the BDI value 0.972, 0.771, and 0.768 for each main cluster respectively. Since the second stage produce lower DBI value compare to the DBI value in the first stage, we conclude that this hybrid approach can improve the accuracy of our clustering results.

The burden of neck pain: its meaning for persons with neck pain and healthcare providers, explored by concept mapping.

PubMed

van Randeraad-van der Zee, Carlijn H; Beurskens, Anna J H M; Swinkels, Raymond A H M; Pool, Jan J M; Batterham, Roy W; Osborne, Richard H; de Vet, Henrica C W

2016-05-01

To empirically define the concept of burden of neck pain. The lack of a clear understanding of this construct from the perspective of persons with neck pain and care providers hampers adequate measurement of this burden. An additional aim was to compare the conceptual model obtained with the frequently used Neck Disability Index (NDI). Concept mapping, combining qualitative (nominal group technique and group consensus) and quantitative research methods (cluster analysis and multidimensional scaling), was applied to groups of persons with neck pain (n = 3) and professionals treating persons with neck pain (n = 2). Group members generated statements, which were organized into concept maps. Group members achieved consensus about the number and description of domains and the researchers then generated an overall mind map covering the full breadth of the burden of neck pain. Concept mapping revealed 12 domains of burden of neck pain: impaired mobility neck, neck pain, fatigue/concentration, physical complaints, psychological aspects/consequences, activities of daily living, social participation, financial consequences, difficult to treat/difficult to diagnose, difference of opinion with care providers, incomprehension by social environment, and how person with neck pain deal with complaints. All ten items of the NDI could be linked to the mind map, but the NDI measures only part of the burden of neck pain. This study revealed the relevant domains for the burden of neck pain from the viewpoints of persons with neck pain and their care providers. These results can guide the identification of existing measurements instruments for each domain or the development of new ones to measure the burden of neck pain.

Relationship of Academic, Physical and Social Self-Concepts of Students with Their Academic Achievement

ERIC Educational Resources Information Center

Zahra, Asma-Tuz; Arif, Manzoor H.; Yousuf, Muhammad Imran

2010-01-01

This study investigated relationship between self-concept and academic achievement of bachelor degree students. Female students at bachelor were considered the target population. A sample of 1500 students was selected by using two stage cluster sampling technique. An amended form of Self-Descriptive Questionnaire developed by Marsh (1985) was used…

Cobweb/3: A portable implementation

NASA Technical Reports Server (NTRS)

Mckusick, Kathleen; Thompson, Kevin

1990-01-01

An algorithm is examined for data clustering and incremental concept formation. An overview is given of the Cobweb/3 system and the algorithm on which it is based, as well as the practical details of obtaining and running the system code. The implementation features a flexible user interface which includes a graphical display of the concept hierarchies that the system constructs.

An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs.

PubMed

Gharaei, Niayesh; Abu Bakar, Kamalrulnizam; Mohd Hashim, Siti Zaiton; Hosseingholi Pourasl, Ali; Siraj, Mohammad; Darwish, Tasneem

2017-08-11

Network lifetime and energy efficiency are crucial performance metrics used to evaluate wireless sensor networks (WSNs). Decreasing and balancing the energy consumption of nodes can be employed to increase network lifetime. In cluster-based WSNs, one objective of applying clustering is to decrease the energy consumption of the network. In fact, the clustering technique will be considered effective if the energy consumed by sensor nodes decreases after applying clustering, however, this aim will not be achieved if the cluster size is not properly chosen. Therefore, in this paper, the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage Genetic Algorithm (GA) is employed to determine the optimal interval of cluster size and derive the exact value from the interval. Furthermore, the energy hole is an inherent problem which leads to a remarkable decrease in the network's lifespan. This problem stems from the asynchronous energy depletion of nodes located in different layers of the network. For this reason, we propose Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2SV2) to balance the energy consumption ratio of cluster heads (CH). According to the results, these strategies could largely increase the network's lifetime by decreasing the energy consumption of sensors and balancing the energy consumption among CHs.

Differences in Coping Styles among Persons with Spinal Cord Injury: A Cluster-Analytic Approach.

ERIC Educational Resources Information Center

Frank, Robert G.; And Others

1987-01-01

Identified and validated two subgroups in group of 53 persons with spinal cord injury by applying cluster-analytic procedures to subjects' self-reported coping and health locus of control belief scores. Cluster 1 coped less effectively and tended to be psychologically distressed; Cluster 2 subjects emphasized internal health attributions and…

Optimal Partitioning of a Data Set Based on the "p"-Median Model

ERIC Educational Resources Information Center

Brusco, Michael J.; Kohn, Hans-Friedrich

2008-01-01

Although the "K"-means algorithm for minimizing the within-cluster sums of squared deviations from cluster centroids is perhaps the most common method for applied cluster analyses, a variety of other criteria are available. The "p"-median model is an especially well-studied clustering problem that requires the selection of "p" objects to serve as…

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

ERIC Educational Resources Information Center

Raker, Jeffrey R.; Holme, Thomas A.

2014-01-01

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

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

DTIC Science & Technology

2005-09-30

and Pre- Clustering MVN Test.....................126 4.2.3 Pre- Clustering Detection Results.................................................130...4.2.4 Pre- Clustering Target Influence..................................................134 4.2.5 Statistical Distance Exclusion and Low Contrast...al, 2001] Figure 2.7 ROC Curve Comparison of RX, K-Means, and Bayesian Pre- Clustering Applied to Anomaly Detection [Ashton, 1998] Figure 2.8 ROC

A proximity-based graph clustering method for the identification and application of transcription factor clusters.

PubMed

Spadafore, Maxwell; Najarian, Kayvan; Boyle, Alan P

2017-11-29

Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions. Here, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF. We show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions. The interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.

Fuzzy Subspace Clustering

NASA Astrophysics Data System (ADS)

Borgelt, Christian

In clustering we often face the situation that only a subset of the available attributes is relevant for forming clusters, even though this may not be known beforehand. In such cases it is desirable to have a clustering algorithm that automatically weights attributes or even selects a proper subset. In this paper I study such an approach for fuzzy clustering, which is based on the idea to transfer an alternative to the fuzzifier (Klawonn and Höppner, What is fuzzy about fuzzy clustering? Understanding and improving the concept of the fuzzifier, In: Proc. 5th Int. Symp. on Intelligent Data Analysis, 254-264, Springer, Berlin, 2003) to attribute weighting fuzzy clustering (Keller and Klawonn, Int J Uncertain Fuzziness Knowl Based Syst 8:735-746, 2000). In addition, by reformulating Gustafson-Kessel fuzzy clustering, a scheme for weighting and selecting principal axes can be obtained. While in Borgelt (Feature weighting and feature selection in fuzzy clustering, In: Proc. 17th IEEE Int. Conf. on Fuzzy Systems, IEEE Press, Piscataway, NJ, 2008) I already presented such an approach for a global selection of attributes and principal axes, this paper extends it to a cluster-specific selection, thus arriving at a fuzzy subspace clustering algorithm (Parsons, Haque, and Liu, 2004).

Designing flexible engineering systems utilizing embedded architecture options

NASA Astrophysics Data System (ADS)

Pierce, Jeff G.

This dissertation develops and applies an integrated framework for embedding flexibility in an engineered system architecture. Systems are constantly faced with unpredictability in the operational environment, threats from competing systems, obsolescence of technology, and general uncertainty in future system demands. Current systems engineering and risk management practices have focused almost exclusively on mitigating or preventing the negative consequences of uncertainty. This research recognizes that high uncertainty also presents an opportunity to design systems that can flexibly respond to changing requirements and capture additional value throughout the design life. There does not exist however a formalized approach to designing appropriately flexible systems. This research develops a three stage integrated flexibility framework based on the concept of architecture options embedded in the system design. Stage One defines an eight step systems engineering process to identify candidate architecture options. This process encapsulates the operational uncertainty though scenario development, traces new functional requirements to the affected design variables, and clusters the variables most sensitive to change. The resulting clusters can generate insight into the most promising regions in the architecture to embed flexibility in the form of architecture options. Stage Two develops a quantitative option valuation technique, grounded in real options theory, which is able to value embedded architecture options that exhibit variable expiration behavior. Stage Three proposes a portfolio optimization algorithm, for both discrete and continuous options, to select the optimal subset of architecture options, subject to budget and risk constraints. Finally, the feasibility, extensibility and limitations of the framework are assessed by its application to a reconnaissance satellite system development problem. Detailed technical data, performance models, and cost estimates were compiled for the Tactical Imaging Constellation Architecture Study and leveraged to complete a realistic proof-of-concept.

Determining requirements for patient-centred care: a participatory concept mapping study.

PubMed

Ogden, Kathryn; Barr, Jennifer; Greenfield, David

2017-11-28

Recognition of a need for patient-centred care is not new, however making patient-centred care a reality remains a challenge to organisations. We need empirical studies to extend current understandings, create new representations of the complexity of patient-centred care, and guide collective action toward patient-centred health care. To achieve these ends, the research aim was to empirically determine what organisational actions are required for patient-centred care to be achieved. We used an established participatory concept mapping methodology. Cross-sector stakeholders contributed to the development of statements for patient-centred care requirements, sorting statements into groupings according to similarity, and rating each statement according to importance, feasibility, and achievement. The resultant data were analysed to produce a visual concept map representing participants' conceptualisation of patient-centred care requirements. Analysis included the development of a similarity matrix, multidimensional scaling, hierarchical cluster analysis, selection of the number of clusters and their labels, identifying overarching domains and quantitative representation of rating data. The outcome was the development of a conceptual map for the Requirements of Patient-Centred Care Systems (ROPCCS). ROPCCS incorporates 123 statements sorted into 13 clusters. Cluster labels were: shared responsibility for personalised health literacy; patient provider dynamic for care partnership; collaboration; shared power and responsibility; resources for coordination of care; recognition of humanity - skills and attributes; knowing and valuing the patient; relationship building; system review evaluation and new models; commitment to supportive structures and processes; elements to facilitate change; professional identity and capability development; and explicit education and learning. The clusters were grouped into three overarching domains, representing a cross-sectoral approach: humanity and partnership; career spanning education and training; and health systems, policy and management. Rating of statements allowed the generation of go-zone maps for further interrogation of the relative importance, feasibility, and achievement of each patient-centred care requirement and cluster. The study has empirically determined requirements for patient-centred care through the development of ROPCCS. The unique map emphasises collaborative responsibility of stakeholders to ensure that patient-centred care is comprehensively progressed. ROPCCS allows the complex requirements for patient-centred care to be understood, implemented, evaluated, measured, and shown to be occurring.

Iterative Stable Alignment and Clustering of 2D Transmission Electron Microscope Images

PubMed Central

Yang, Zhengfan; Fang, Jia; Chittuluru, Johnathan; Asturias, Francisco J.; Penczek, Pawel A.

2012-01-01

SUMMARY Identification of homogeneous subsets of images in a macromolecular electron microscopy (EM) image data set is a critical step in single-particle analysis. The task is handled by iterative algorithms, whose performance is compromised by the compounded limitations of image alignment and K-means clustering. Here we describe an approach, iterative stable alignment and clustering (ISAC) that, relying on a new clustering method and on the concepts of stability and reproducibility, can extract validated, homogeneous subsets of images. ISAC requires only a small number of simple parameters and, with minimal human intervention, can eliminate bias from two-dimensional image clustering and maximize the quality of group averages that can be used for ab initio three-dimensional structural determination and analysis of macromolecular conformational variability. Repeated testing of the stability and reproducibility of a solution within ISAC eliminates heterogeneous or incorrect classes and introduces critical validation to the process of EM image clustering. PMID:22325773

Hierarchical clustering using correlation metric and spatial continuity constraint

DOEpatents

Stork, Christopher L.; Brewer, Luke N.

2012-10-02

Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.

Entrapment of metal clusters in metal-organic framework channels by extended hooks anchored at open metal sites.

PubMed

Zheng, Shou-Tian; Zhao, Xiang; Lau, Samuel; Fuhr, Addis; Feng, Pingyun; Bu, Xianhui

2013-07-17

Reported here are the new concept of utilizing open metal sites (OMSs) for architectural pore design and its practical implementation. Specifically, it is shown here that OMSs can be used to run extended hooks (isonicotinates in this work) from the framework walls to the channel centers to effect the capture of single metal ions or clusters, with the concurrent partitioning of the large channel spaces into multiple domains, alteration of the host-guest charge relationship and associated guest-exchange properties, and transfer of OMSs from the walls to the channel centers. The concept of the extended hook, demonstrated here in the multicomponent dual-metal and dual-ligand system, should be generally applicable to a range of framework types.

Metabotyping for the development of tailored dietary advice solutions in a European population: the Food4Me study.

PubMed

O'Donovan, Clare B; Walsh, Marianne C; Woolhead, Clara; Forster, Hannah; Celis-Morales, Carlos; Fallaize, Rosalind; Macready, Anna L; Marsaux, Cyril F M; Navas-Carretero, Santiago; Rodrigo San-Cristobal, S; Kolossa, Silvia; Tsirigoti, Lydia; Mvrogianni, Christina; Lambrinou, Christina P; Moschonis, George; Godlewska, Magdalena; Surwillo, Agnieszka; Traczyk, Iwona; Drevon, Christian A; Daniel, Hannelore; Manios, Yannis; Martinez, J Alfredo; Saris, Wim H M; Lovegrove, Julie A; Mathers, John C; Gibney, Michael J; Gibney, Eileen R; Brennan, Lorraine

2017-10-01

Traditionally, personalised nutrition was delivered at an individual level. However, the concept of delivering tailored dietary advice at a group level through the identification of metabotypes or groups of metabolically similar individuals has emerged. Although this approach to personalised nutrition looks promising, further work is needed to examine this concept across a wider population group. Therefore, the objectives of this study are to: (1) identify metabotypes in a European population and (2) develop targeted dietary advice solutions for these metabotypes. Using data from the Food4Me study (n 1607), k-means cluster analysis revealed the presence of three metabolically distinct clusters based on twenty-seven metabolic markers including cholesterol, individual fatty acids and carotenoids. Cluster 2 was identified as a metabolically healthy metabotype as these individuals had the highest Omega-3 Index (6·56 (sd 1·29) %), carotenoids (2·15 (sd 0·71) µm) and lowest total saturated fat levels. On the basis of its fatty acid profile, cluster 1 was characterised as a metabolically unhealthy cluster. Targeted dietary advice solutions were developed per cluster using a decision tree approach. Testing of the approach was performed by comparison with the personalised dietary advice, delivered by nutritionists to Food4Me study participants (n 180). Excellent agreement was observed between the targeted and individualised approaches with an average match of 82 % at the level of delivery of the same dietary message. Future work should ascertain whether this proposed method could be utilised in a healthcare setting, for the rapid and efficient delivery of tailored dietary advice solutions.

Clustering P-Wave Receiver Functions To Constrain Subsurface Seismic Structure

NASA Astrophysics Data System (ADS)

Chai, C.; Larmat, C. S.; Maceira, M.; Ammon, C. J.; He, R.; Zhang, H.

2017-12-01

The acquisition of high-quality data from permanent and temporary dense seismic networks provides the opportunity to apply statistical and machine learning techniques to a broad range of geophysical observations. Lekic and Romanowicz (2011) used clustering analysis on tomographic velocity models of the western United States to perform tectonic regionalization and the velocity-profile clusters agree well with known geomorphic provinces. A complementary and somewhat less restrictive approach is to apply cluster analysis directly to geophysical observations. In this presentation, we apply clustering analysis to teleseismic P-wave receiver functions (RFs) continuing efforts of Larmat et al. (2015) and Maceira et al. (2015). These earlier studies validated the approach with surface waves and stacked EARS RFs from the USArray stations. In this study, we experiment with both the K-means and hierarchical clustering algorithms. We also test different distance metrics defined in the vector space of RFs following Lekic and Romanowicz (2011). We cluster data from two distinct data sets. The first, corresponding to the western US, was by smoothing/interpolation of receiver-function wavefield (Chai et al. 2015). Spatial coherence and agreement with geologic region increase with this simpler, spatially smoothed set of observations. The second data set is composed of RFs for more than 800 stations of the China Digital Seismic Network (CSN). Preliminary results show a first order agreement between clusters and tectonic region and each region cluster includes a distinct Ps arrival, which probably reflects differences in crustal thickness. Regionalization remains an important step to characterize a model prior to application of full waveform and/or stochastic imaging techniques because of the computational expense of these types of studies. Machine learning techniques can provide valuable information that can be used to design and characterize formal geophysical inversion, providing information on spatial variability in the subsurface geology.

Dielectric elastomer actuator for the measurement of cell traction forces with sub-cellular resolution

NASA Astrophysics Data System (ADS)

Rosset, Samuel; Poulin, Alexandre; Zollinger, Alicia; Smith, Michael; Shea, Herbert

2017-04-01

We report on the use of dielectric elastomer actuators (DEAs) to measure the traction force field of cells with subcellular resolution. The study of cellular electrochemical and mechanical response to deformation is an important area of research, as mechanotransduction has been shown to be linked with fundamental cell functions, or the progression of diseases such as cancer or atherosclerosis. Experimental cell mechanics is based on two fundamental concepts: the ability to measure cell stiffness, and to apply controlled strains to small clusters of cells. However, there is a lack of tools capable of applying precise deformation to a small cell population while being compatible with an inverted microscope (stable focal plane, transparency, compactness, etc.). Here, we use an anisotropically prestretched silicone-based DEA to deform a soft (7.6kPa) polyacrylamide gel on which the cells are cultured. An array of micro-dots of fluorescent fibronectin is transferred on the gel by micro-contact printing and serves as attachment points for the cells. In addition, the fluorescent dots (which have a diameter of 2 μm with a spacing of 6 μm) are used during the experiment to monitor the traction forces of a single cell (or small cluster of cells). The cell locally exerts traction on the gel, thus deforming the matrix of dots. The position of dots versus time is monitored live when the cells are submitted to a uniaxial strain step. Our deformable bioreactor enables the measurement of the local stiffness of cells submitted to mechanical strain, and is fully compatible with an inverted microscope set-up.

Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

PubMed

Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

2017-07-01

Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

A modified procedure for mixture-model clustering of regional geochemical data

USGS Publications Warehouse

Ellefsen, Karl J.; Smith, David B.; Horton, John D.

2014-01-01

A modified procedure is proposed for mixture-model clustering of regional-scale geochemical data. The key modification is the robust principal component transformation of the isometric log-ratio transforms of the element concentrations. This principal component transformation and the associated dimension reduction are applied before the data are clustered. The principal advantage of this modification is that it significantly improves the stability of the clustering. The principal disadvantage is that it requires subjective selection of the number of clusters and the number of principal components. To evaluate the efficacy of this modified procedure, it is applied to soil geochemical data that comprise 959 samples from the state of Colorado (USA) for which the concentrations of 44 elements are measured. The distributions of element concentrations that are derived from the mixture model and from the field samples are similar, indicating that the mixture model is a suitable representation of the transformed geochemical data. Each cluster and the associated distributions of the element concentrations are related to specific geologic and anthropogenic features. In this way, mixture model clustering facilitates interpretation of the regional geochemical data.

Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Ling; Lee, Doris; Sim, Alex

Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checkingmore » of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.« less

Using cluster analysis for medical resource decision making.

PubMed

Dilts, D; Khamalah, J; Plotkin, A

1995-01-01

Escalating costs of health care delivery have in the recent past often made the health care industry investigate, adapt, and apply those management techniques relating to budgeting, resource control, and forecasting that have long been used in the manufacturing sector. A strategy that has contributed much in this direction is the definition and classification of a hospital's output into "products" or groups of patients that impose similar resource or cost demands on the hospital. Existing classification schemes have frequently employed cluster analysis in generating these groupings. Unfortunately, the myriad articles and books on clustering and classification contain few formalized selection methodologies for choosing a technique for solving a particular problem, hence they often leave the novice investigator at a loss. This paper reviews the literature on clustering, particularly as it has been applied in the medical resource-utilization domain, addresses the critical choices facing an investigator in the medical field using cluster analysis, and offers suggestions (using the example of clustering low-vision patients) for how such choices can be made.

Cross-language information retrieval using PARAFAC2.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bader, Brett William; Chew, Peter; Abdelali, Ahmed

A standard approach to cross-language information retrieval (CLIR) uses Latent Semantic Analysis (LSA) in conjunction with a multilingual parallel aligned corpus. This approach has been shown to be successful in identifying similar documents across languages - or more precisely, retrieving the most similar document in one language to a query in another language. However, the approach has severe drawbacks when applied to a related task, that of clustering documents 'language-independently', so that documents about similar topics end up closest to one another in the semantic space regardless of their language. The problem is that documents are generally more similar tomore » other documents in the same language than they are to documents in a different language, but on the same topic. As a result, when using multilingual LSA, documents will in practice cluster by language, not by topic. We propose a novel application of PARAFAC2 (which is a variant of PARAFAC, a multi-way generalization of the singular value decomposition [SVD]) to overcome this problem. Instead of forming a single multilingual term-by-document matrix which, under LSA, is subjected to SVD, we form an irregular three-way array, each slice of which is a separate term-by-document matrix for a single language in the parallel corpus. The goal is to compute an SVD for each language such that V (the matrix of right singular vectors) is the same across all languages. Effectively, PARAFAC2 imposes the constraint, not present in standard LSA, that the 'concepts' in all documents in the parallel corpus are the same regardless of language. Intuitively, this constraint makes sense, since the whole purpose of using a parallel corpus is that exactly the same concepts are expressed in the translations. We tested this approach by comparing the performance of PARAFAC2 with standard LSA in solving a particular CLIR problem. From our results, we conclude that PARAFAC2 offers a very promising alternative to LSA not only for multilingual document clustering, but also for solving other problems in cross-language information retrieval.« less

Legal assessment tool (LAT): an interactive tool to address privacy and data protection issues for data sharing.

PubMed

Kuchinke, Wolfgang; Krauth, Christian; Bergmann, René; Karakoyun, Töresin; Woollard, Astrid; Schluender, Irene; Braasch, Benjamin; Eckert, Martin; Ohmann, Christian

2016-07-07

In an unprecedented rate data in the life sciences is generated and stored in many different databases. An ever increasing part of this data is human health data and therefore falls under data protected by legal regulations. As part of the BioMedBridges project, which created infrastructures that connect more than 10 ESFRI research infrastructures (RI), the legal and ethical prerequisites of data sharing were examined employing a novel and pragmatic approach. We employed concepts from computer science to create legal requirement clusters that enable legal interoperability between databases for the areas of data protection, data security, Intellectual Property (IP) and security of biosample data. We analysed and extracted access rules and constraints from all data providers (databases) involved in the building of data bridges covering many of Europe's most important databases. These requirement clusters were applied to five usage scenarios representing the data flow in different data bridges: Image bridge, Phenotype data bridge, Personalised medicine data bridge, Structural data bridge, and Biosample data bridge. A matrix was built to relate the important concepts from data protection regulations (e.g. pseudonymisation, identifyability, access control, consent management) with the results of the requirement clusters. An interactive user interface for querying the matrix for requirements necessary for compliant data sharing was created. To guide researchers without the need for legal expert knowledge through legal requirements, an interactive tool, the Legal Assessment Tool (LAT), was developed. LAT provides researchers interactively with a selection process to characterise the involved types of data and databases and provides suitable requirements and recommendations for concrete data access and sharing situations. The results provided by LAT are based on an analysis of the data access and sharing conditions for different kinds of data of major databases in Europe. Data sharing for research purposes must be opened for human health data and LAT is one of the means to achieve this aim. In summary, LAT provides requirements in an interactive way for compliant data access and sharing with appropriate safeguards, restrictions and responsibilities by introducing a culture of responsibility and data governance when dealing with human data.

Adiabatic invariants in stellar dynamics. 1: Basic concepts

NASA Technical Reports Server (NTRS)

Weinberg, Martin D.

1994-01-01

The adiabatic criterion, widely used in astronomical dynamics, is based on the harmonic oscillator. It asserts that the change in action under a slowly varying perturbation is exponentially small. Recent mathematical results that precisely define the conditions for invariance show that this model does not apply in general. In particular, a slowly varying perturbation may cause significant evolution stellar dynamical systems even if its time scale is longer than any internal orbital time scale. This additional 'heating' may have serious implications for the evolution of star clusters and dwarf galaxies which are subject to long-term environmental forces. The mathematical developments leading to these results are reviewed, and the conditions for applicability to and further implications for stellar systems are discussed. Companion papers present a computational method for a general time-dependent disturbance and detailed example.

Cluster Analysis in Sociometric Research: A Pattern-Oriented Approach to Identifying Temporally Stable Peer Status Groups of Girls

ERIC Educational Resources Information Center

Zettergren, Peter

2007-01-01

A modern clustering technique was applied to age-10 and age-13 sociometric data with the purpose of identifying longitudinally stable peer status clusters. The study included 445 girls from a Swedish longitudinal study. The identified temporally stable clusters of rejected, popular, and average girls were essentially larger than corresponding…

Key-Node-Separated Graph Clustering and Layouts for Human Relationship Graph Visualization.

PubMed

Itoh, Takayuki; Klein, Karsten

2015-01-01

Many graph-drawing methods apply node-clustering techniques based on the density of edges to find tightly connected subgraphs and then hierarchically visualize the clustered graphs. However, users may want to focus on important nodes and their connections to groups of other nodes for some applications. For this purpose, it is effective to separately visualize the key nodes detected based on adjacency and attributes of the nodes. This article presents a graph visualization technique for attribute-embedded graphs that applies a graph-clustering algorithm that accounts for the combination of connections and attributes. The graph clustering step divides the nodes according to the commonality of connected nodes and similarity of feature value vectors. It then calculates the distances between arbitrary pairs of clusters according to the number of connecting edges and the similarity of feature value vectors and finally places the clusters based on the distances. Consequently, the technique separates important nodes that have connections to multiple large clusters and improves the visibility of such nodes' connections. To test this technique, this article presents examples with human relationship graph datasets, including a coauthorship and Twitter communication network dataset.

Substructures in Clusters of Galaxies

NASA Astrophysics Data System (ADS)

Lehodey, Brigitte Tome

2000-01-01

This dissertation presents two methods for the detection of substructures in clusters of galaxies and the results of their application to a group of four clusters. In chapters 2 and 3, we remember the main properties of clusters of galaxies and give the definition of substructures. We also try to show why the study of substructures in clusters of galaxies is so important for Cosmology. Chapters 4 and 5 describe these two methods, the first one, the adaptive Kernel, is applied to the study of the spatial and kinematical distribution of the cluster galaxies. The second one, the MVM (Multiscale Vision Model), is applied to analyse the cluster diffuse X-ray emission, i.e., the intracluster gas distribution. At the end of these two chapters, we also present the results of the application of these methods to our sample of clusters. In chapter 6, we draw the conclusions from the comparison of the results we obtain with each method. In the last chapter, we present the main conclusions of this work trying to point out possible developments. We close with two appendices in which we detail some questions raised in this work not directly linked to the problem of substructures detection.

[Permanent education in health: a review].

PubMed

Miccas, Fernanda Luppino; Batista, Sylvia Helena Souza da Silva

2014-02-01

To undertake a meta-synthesis of the literature on the main concepts and practices related to permanent education in health. A bibliographical search was conducted for original articles in the PubMed, Web of Science, LILACS, IBECS and SciELO databases, using the following search terms: "public health professional education", "permanent education", "continuing education", "permanent education health". Of the 590 articles identified, after applying inclusion and exclusion criteria, 48 were selected for further analysis, grouped according to the criteria of key elements, and then underwent meta-synthesis. The 48 original publications were classified according to four thematic units of key elements: 1) concepts, 2) strategies and difficulties, 3) public policies and 4) educational institutions. Three main conceptions of permanent education in health were found: problem-focused and team work, directly related to continuing education and education that takes place throughout life. The main strategies for executing permanent education in health are discussion, maintaining an open space for permanent education , and permanent education clusters. The most limiting factor is mainly related to directly or indirect management. Another highlight is the requirement for implementation and maintenance of public policies, and the availability of financial and human resources. The educational institutions need to combine education and service aiming to form critical-reflexive graduates. The coordination between health and education is based as much on the actions of health services as on management and educational institutions. Thus, it becomes a challenge to implement the teaching-learning processes that are supported by critical-reflexive actions. It is necessary to carry out proposals for permanent education in health involving the participation of health professionals, teachers and educational institutions. To undertake a meta-synthesis of the literature on the main concepts and practices related to permanent education in health. A bibliographical search was conducted for original articles in the PubMed, Web of Science, LILACS, IBECS and SciELO databases, using the following search terms: "public health professional education", "permanent education", "continuing education", "permanent education health". Of the 590 articles identified, after applying inclusion and exclusion criteria, 48 were selected for further analysis, grouped according to the criteria of key elements, and then underwent meta-synthesis. The 48 original publications were classified according to four thematic units of key elements: 1) concepts, 2) strategies and difficulties, 3) public policies and 4) educational institutions. Three main conceptions of permanent education in health were found: problem-focused and team work, directly related to continuing education and education that takes place throughout life. The main strategies for executing permanent education in health are discussion, maintaining an open space for permanent education, and permanent education clusters. The most limiting factor is mainly related to directly or indirect management. Another highlight is the requirement for implementation and maintenance of public policies, and the availability of financial and human resources. The educational institutions need to combine education and service aiming to form critical-reflexive graduates. The coordination between health and education is based as much on the actions of health services as on management and educational institutions. Thus, it becomes a challenge to implement the teaching-learning processes that are supported by critical-reflexive actions. It is necessary to carry out proposals for permanent education in health involving the participation of health professionals, teachers and educational institutions.

Cluster management.

PubMed

Katz, R

1992-11-01

Cluster management is a management model that fosters decentralization of management, develops leadership potential of staff, and creates ownership of unit-based goals. Unlike shared governance models, there is no formal structure created by committees and it is less threatening for managers. There are two parts to the cluster management model. One is the formation of cluster groups, consisting of all staff and facilitated by a cluster leader. The cluster groups function for communication and problem-solving. The second part of the cluster management model is the creation of task forces. These task forces are designed to work on short-term goals, usually in response to solving one of the unit's goals. Sometimes the task forces are used for quality improvement or system problems. Clusters are groups of not more than five or six staff members, facilitated by a cluster leader. A cluster is made up of individuals who work the same shift. For example, people with job titles who work days would be in a cluster. There would be registered nurses, licensed practical nurses, nursing assistants, and unit clerks in the cluster. The cluster leader is chosen by the manager based on certain criteria and is trained for this specialized role. The concept of cluster management, criteria for choosing leaders, training for leaders, using cluster groups to solve quality improvement issues, and the learning process necessary for manager support are described.

From spoken narratives to domain knowledge: mining linguistic data for medical image understanding.

PubMed

Guo, Xuan; Yu, Qi; Alm, Cecilia Ovesdotter; Calvelli, Cara; Pelz, Jeff B; Shi, Pengcheng; Haake, Anne R

2014-10-01

Extracting useful visual clues from medical images allowing accurate diagnoses requires physicians' domain knowledge acquired through years of systematic study and clinical training. This is especially true in the dermatology domain, a medical specialty that requires physicians to have image inspection experience. Automating or at least aiding such efforts requires understanding physicians' reasoning processes and their use of domain knowledge. Mining physicians' references to medical concepts in narratives during image-based diagnosis of a disease is an interesting research topic that can help reveal experts' reasoning processes. It can also be a useful resource to assist with design of information technologies for image use and for image case-based medical education systems. We collected data for analyzing physicians' diagnostic reasoning processes by conducting an experiment that recorded their spoken descriptions during inspection of dermatology images. In this paper we focus on the benefit of physicians' spoken descriptions and provide a general workflow for mining medical domain knowledge based on linguistic data from these narratives. The challenge of a medical image case can influence the accuracy of the diagnosis as well as how physicians pursue the diagnostic process. Accordingly, we define two lexical metrics for physicians' narratives--lexical consensus score and top N relatedness score--and evaluate their usefulness by assessing the diagnostic challenge levels of corresponding medical images. We also report on clustering medical images based on anchor concepts obtained from physicians' medical term usage. These analyses are based on physicians' spoken narratives that have been preprocessed by incorporating the Unified Medical Language System for detecting medical concepts. The image rankings based on lexical consensus score and on top 1 relatedness score are well correlated with those based on challenge levels (Spearman correlation>0.5 and Kendall correlation>0.4). Clustering results are largely improved based on our anchor concept method (accuracy>70% and mutual information>80%). Physicians' spoken narratives are valuable for the purpose of mining the domain knowledge that physicians use in medical image inspections. We also show that the semantic metrics introduced in the paper can be successfully applied to medical image understanding and allow discussion of additional uses of these metrics. Copyright © 2014 Elsevier B.V. All rights reserved.

Quasi-elastic light scattering of carnauba wax in the liquid phase: dynamics 2.

PubMed

de Almeida, F J; Barbosa, G A

1983-12-01

Quasi-elastic light scattering of carnauba wax in the liquid phase is obtained in a heterodyne setup, and dynamic processes are analyzed through electrophoresis. Nonspherical polar clusters are found, containing a net electrical charge. An applied square-wave electric field induces drift and rotation of these clusters.These effects are dependent on strength and frequency of the applied electric field. At 373 K and in the low frequency limit the local electric field strength is approximately 70 times the strength of the applied one. This enhancement is believed to be caused by collective orientation of the clusters. The electrophoretic mobility is 1.1 X 10(-12) m2/V sec in the high frequency limit and 7.4 X 10(-11) m2/V sec in the low frequency limit. The electric dipole moment is 6.3 X 10(-16) N(-1/2) m(-1/2) where N is the cluster density/cubic meter and the net charge is about one or two elementary charges.

Elementary Career Education Units for Integration in Subject Areas at Grades Kindergarten through Six. Project SPAN.

ERIC Educational Resources Information Center

Bell, Lorraine P., Comp.

Units in the curriculum guide are presented in two sections, K-3 and 4-6, and emphasize hands-on activities, role-playing, resource persons, field trips, and classroom career corners. Organized on the career cluster concept, the K-3 units cover self-concept, home and family, familiar community occupations, zoo animals, travel, school, and the…

Self-Organizing Neural Network Map for the Purpose of Visualizing the Concept Images of Students on Angles

ERIC Educational Resources Information Center

Kaya, Deniz

2017-01-01

The purpose of the study is to perform a less-dimensional thorough visualization process for the purpose of determining the images of the students on the concept of angle. The Ward clustering analysis combined with Self-Organizing Neural Network Map (SOM) has been used for the dimension process. The Conceptual Understanding Tool, which consisted…

Beyond Academic Tracking: Using Cluster Analysis and Self-Organizing Maps to Investigate Secondary Students' Chemistry Self-Concept

ERIC Educational Resources Information Center

Nielsen, Sara E.; Yezierski, Ellen J.

2016-01-01

Academic tracking, placing students in different classes based on past performance, is a common feature of the American secondary school system. A longitudinal study of secondary students' chemistry self-concept scores was conducted, and one feature of the study was the presence of academic tracking. Though academic tracking is one way to group…

Conceptions of Biology and Approaches to Learning of First Year Biology Students: Introducing a Technique for Tracking Changes in Learner Profiles over Time

ERIC Educational Resources Information Center

Quinnell, Rosanne; May, Elizabeth; Peat, Mary

2012-01-01

We surveyed first year students at the start and at the end of their first semester of university biology (n = 285) as to their approaches to study ("surface", "deep") and their conceptions of biology ("fragmented", "cohesive"). Hierarchical cluster analysis was used to group students who responded similarly…

Cluster analysis and its application to healthcare claims data: a study of end-stage renal disease patients who initiated hemodialysis.

PubMed

Liao, Minlei; Li, Yunfeng; Kianifard, Farid; Obi, Engels; Arcona, Stephen

2016-03-02

Cluster analysis (CA) is a frequently used applied statistical technique that helps to reveal hidden structures and "clusters" found in large data sets. However, this method has not been widely used in large healthcare claims databases where the distribution of expenditure data is commonly severely skewed. The purpose of this study was to identify cost change patterns of patients with end-stage renal disease (ESRD) who initiated hemodialysis (HD) by applying different clustering methods. A retrospective, cross-sectional, observational study was conducted using the Truven Health MarketScan® Research Databases. Patients aged ≥18 years with ≥2 ESRD diagnoses who initiated HD between 2008 and 2010 were included. The K-means CA method and hierarchical CA with various linkage methods were applied to all-cause costs within baseline (12-months pre-HD) and follow-up periods (12-months post-HD) to identify clusters. Demographic, clinical, and cost information was extracted from both periods, and then examined by cluster. A total of 18,380 patients were identified. Meaningful all-cause cost clusters were generated using K-means CA and hierarchical CA with either flexible beta or Ward's methods. Based on cluster sample sizes and change of cost patterns, the K-means CA method and 4 clusters were selected: Cluster 1: Average to High (n = 113); Cluster 2: Very High to High (n = 89); Cluster 3: Average to Average (n = 16,624); or Cluster 4: Increasing Costs, High at Both Points (n = 1554). Median cost changes in the 12-month pre-HD and post-HD periods increased from $185,070 to $884,605 for Cluster 1 (Average to High), decreased from $910,930 to $157,997 for Cluster 2 (Very High to High), were relatively stable and remained low from $15,168 to $13,026 for Cluster 3 (Average to Average), and increased from $57,909 to $193,140 for Cluster 4 (Increasing Costs, High at Both Points). Relatively stable costs after starting HD were associated with more stable scores on comorbidity index scores from the pre-and post-HD periods, while increasing costs were associated with more sharply increasing comorbidity scores. The K-means CA method appeared to be the most appropriate in healthcare claims data with highly skewed cost information when taking into account both change of cost patterns and sample size in the smallest cluster.

Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering

PubMed Central

Almeida, Fernando R.; Brayner, Angelo; Rodrigues, Joel J. P. C.; Maia, Jose E. Bessa

2017-01-01

An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering. To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE). PMID:28590450

Improving Multidimensional Wireless Sensor Network Lifetime Using Pearson Correlation and Fractal Clustering.

PubMed

Almeida, Fernando R; Brayner, Angelo; Rodrigues, Joel J P C; Maia, Jose E Bessa

2017-06-07

An efficient strategy for reducing message transmission in a wireless sensor network (WSN) is to group sensors by means of an abstraction denoted cluster. The key idea behind the cluster formation process is to identify a set of sensors whose sensed values present some data correlation. Nowadays, sensors are able to simultaneously sense multiple different physical phenomena, yielding in this way multidimensional data. This paper presents three methods for clustering sensors in WSNs whose sensors collect multidimensional data. The proposed approaches implement the concept of multidimensional behavioral clustering . To show the benefits introduced by the proposed methods, a prototype has been implemented and experiments have been carried out on real data. The results prove that the proposed methods decrease the amount of data flowing in the network and present low root-mean-square error (RMSE).

Cosmology, Clusters and Calorimeters

NASA Technical Reports Server (NTRS)

Figueroa-Feliciano, Enectali

2005-01-01

I will review the current state of Cosmology with Clusters and discuss the application of microcalorimeter arrays to this field. With the launch of Astro-E2 this summer and a slew of new missions being developed, microcalorimeters are the next big thing in x-ray astronomy. I will cover the basics and not-so-basic concepts of microcalorimeter designs and look at the future to see where this technology will go.

Transformation to equivalent dimensions—a new methodology to study earthquake clustering

NASA Astrophysics Data System (ADS)

Lasocki, Stanislaw

2014-05-01

A seismic event is represented by a point in a parameter space, quantified by the vector of parameter values. Studies of earthquake clustering involve considering distances between such points in multidimensional spaces. However, the metrics of earthquake parameters are different, hence the metric in a multidimensional parameter space cannot be readily defined. The present paper proposes a solution of this metric problem based on a concept of probabilistic equivalence of earthquake parameters. Under this concept the lengths of parameter intervals are equivalent if the probability for earthquakes to take values from either interval is the same. Earthquake clustering is studied in an equivalent rather than the original dimensions space, where the equivalent dimension (ED) of a parameter is its cumulative distribution function. All transformed parameters are of linear scale in [0, 1] interval and the distance between earthquakes represented by vectors in any ED space is Euclidean. The unknown, in general, cumulative distributions of earthquake parameters are estimated from earthquake catalogues by means of the model-free non-parametric kernel estimation method. Potential of the transformation to EDs is illustrated by two examples of use: to find hierarchically closest neighbours in time-space and to assess temporal variations of earthquake clustering in a specific 4-D phase space.

Using concept mapping to explore why patients become lost to follow up from an antiretroviral therapy program in the Zomba District of Malawi

PubMed Central

2013-01-01

Background Retention in antiretroviral therapy (ART) programmes remains a challenge in many settings including Malawi, in part due to high numbers of losses to follow-up. Concept Mapping (CM), a mix-method participatory approach, was used to explore why patients on ART are lost to follow-up (LTFU) by identifying: 1) factors that influence patient losses to follow-up and 2) barriers to effective and efficient tracing in Zomba, Malawi. Methods CM sessions (brainstorming, sorting and rating, interpretation) were conducted in urban and rural settings in Zomba, Malawi. Participants included ART patients, ART providers, Health Surveillance Assistants, and health managers from the Zomba District Health Office. In brainstorming, participants generated statements in response to “A specific reason why an individual on ART becomes lost to follow-up is…” Participants then sorted and rated the consolidated list of brainstormed items. Analysis included inductive qualitative methods for grouping of data and quantitative cluster identification to produce visual maps which were then interpreted by participants. Results In total, 90 individuals brainstormed 371 statements, 64 consolidated statements were sorted (participant n = 46), and rated on importance and feasibility (participant n = 69). A nine-cluster concept map was generated and included both patient- and healthcare-related clusters such as: Stigma and Fears, Beliefs, Acceptance and Knowledge of ART, Access to ART, Poor Documentation, Social and Financial Support Issues, Health Worker Attitudes, Resources Needed for Effective Tracing, and Health Worker Issues Related to Tracing. Strategies to respond to the clusters were generated in Interpretation. Conclusions Multiple patient- and healthcare focused factors influence why patients become LTFU. Findings have implications particularly for programs with limited resources struggling with the retention of ART patients. PMID:23758879

Using concept mapping to explore why patients become lost to follow up from an antiretroviral therapy program in the Zomba District of Malawi.

PubMed

Rachlis, Beth; Ahmad, Farah; van Lettow, Monique; Muula, Adamson S; Semba, Medson; Cole, Donald C

2013-06-11

Retention in antiretroviral therapy (ART) programmes remains a challenge in many settings including Malawi, in part due to high numbers of losses to follow-up. Concept Mapping (CM), a mix-method participatory approach, was used to explore why patients on ART are lost to follow-up (LTFU) by identifying: 1) factors that influence patient losses to follow-up and 2) barriers to effective and efficient tracing in Zomba, Malawi. CM sessions (brainstorming, sorting and rating, interpretation) were conducted in urban and rural settings in Zomba, Malawi. Participants included ART patients, ART providers, Health Surveillance Assistants, and health managers from the Zomba District Health Office. In brainstorming, participants generated statements in response to "A specific reason why an individual on ART becomes lost to follow-up is…" Participants then sorted and rated the consolidated list of brainstormed items. Analysis included inductive qualitative methods for grouping of data and quantitative cluster identification to produce visual maps which were then interpreted by participants. In total, 90 individuals brainstormed 371 statements, 64 consolidated statements were sorted (participant n = 46), and rated on importance and feasibility (participant n = 69). A nine-cluster concept map was generated and included both patient- and healthcare-related clusters such as: Stigma and Fears, Beliefs, Acceptance and Knowledge of ART, Access to ART, Poor Documentation, Social and Financial Support Issues, Health Worker Attitudes, Resources Needed for Effective Tracing, and Health Worker Issues Related to Tracing. Strategies to respond to the clusters were generated in Interpretation. Multiple patient- and healthcare focused factors influence why patients become LTFU. Findings have implications particularly for programs with limited resources struggling with the retention of ART patients.

A partial list of southern clusters of galaxies

NASA Technical Reports Server (NTRS)

Quintana, H.; White, R. A.

1990-01-01

An inspection of 34 SRC/ESO J southern sky fields is the basis of the present list of clusters of galaxies and their approximate classifications in terms of cluster concentration, defined independently of richness and shape-symmetry. Where possible, an estimate of the cluster morphological population is provided. The Bautz-Morgan classification was applied using a strict comparison with clusters on the Palomar Sky Survey. Magnitudes were estimated on the basis of galaxies with photoelectric or photographic magnitudes.

Emergy-based comparative analysis on industrial clusters: economic and technological development zone of Shenyang area, China.

PubMed

Liu, Zhe; Geng, Yong; Zhang, Pan; Dong, Huijuan; Liu, Zuoxi

2014-09-01

In China, local governments of many areas prefer to give priority to the development of heavy industrial clusters in pursuit of high value of gross domestic production (GDP) growth to get political achievements, which usually results in higher costs from ecological degradation and environmental pollution. Therefore, effective methods and reasonable evaluation system are urgently needed to evaluate the overall efficiency of industrial clusters. Emergy methods links economic and ecological systems together, which can evaluate the contribution of ecological products and services as well as the load placed on environmental systems. This method has been successfully applied in many case studies of ecosystem but seldom in industrial clusters. This study applied the methodology of emergy analysis to perform the efficiency of industrial clusters through a series of emergy-based indices as well as the proposed indicators. A case study of Shenyang Economic Technological Development Area (SETDA) was investigated to show the emergy method's practical potential to evaluate industrial clusters to inform environmental policy making. The results of our study showed that the industrial cluster of electric equipment and electronic manufacturing produced the most economic value and had the highest efficiency of energy utilization among the four industrial clusters. However, the sustainability index of the industrial cluster of food and beverage processing was better than the other industrial clusters.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

NASA Astrophysics Data System (ADS)

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-04-01

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra.

PubMed

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-03-13

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models' performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Cluster preformation law for heavy and superheavy nuclei

NASA Astrophysics Data System (ADS)

Wei, K.; Zhang, H. F.

2017-08-01

The concept of cluster radioactivity has been extended to allow emitted particles with ZC>28 for superheavy nuclei by nuclear theory [Poenaru et al., Phys. Rev. Lett. 107, 062503 (2011), 10.1103/PhysRevLett.107.062503]. The preformation and emission mechanics of heavy-ion particles must be examined again before the fascinating radioactivity is observed for superheavy nuclei in laboratory. We extract the cluster preformation factor for heavy and superheavy nuclei within a preformed cluster model, in which the decay constant is the product of the preformation factor, assault frequency, and penetration probability. The calculated results show that the cluster penetration probability for superheavy nuclei is larger than that for actinide elements. The preformation factor depends on the nuclear structures of the emitted cluster and mother nucleus, and the well-known cluster preformation law S (AC) =S (α) (AC-1 )/3 [Blendowske and Walliser, Phys. Rev. Lett. 61, 1930 (1988), 10.1103/PhysRevLett.61.1930] will break down when the mass number of the emitted cluster Ac>28 , and new preformation formulas are proposed to estimate the preformation factor for heavy and superheavy nuclei.

Automatic document classification of biological literature

PubMed Central

Chen, David; Müller, Hans-Michael; Sternberg, Paul W

2006-01-01

Background Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a text-mining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis elegans literature. Results We present a two-step text categorization algorithm to classify a corpus of C. elegans papers. Our classification method first uses a support vector machine-trained classifier, followed by a novel, phrase-based clustering algorithm. This clustering step autonomously creates cluster labels that are descriptive and understandable by humans. This clustering engine performed better on a standard test-set (Reuters 21578) compared to previously published results (F-value of 0.55 vs. 0.49), while producing cluster descriptions that appear more useful. A web interface allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept. Conclusion We have demonstrated a simple method to classify biological documents that embodies an improvement over current methods. While the classification results are currently optimized for Caenorhabditis elegans papers by human-created rules, the classification engine can be adapted to different types of documents. We have demonstrated this by presenting a web interface that allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept. PMID:16893465

Data Mining in the Context of Monitoring Mt Etna, Italy

NASA Astrophysics Data System (ADS)

Aliotta, Marco; Cassisi, Carmelo; D'Agostino, Marcello; Falsaperla, Susanna; Ferrari, Ferruccio; Langer, Horst; Messina, Alfio; Montalto, Placido; Reitano, Danilo; Spampinato, Salvatore

2015-04-01

The persistent volcanic activity of Mt Etna makes the continuous monitoring of multidisciplinary data a first-class issue. Indeed, the monitoring systems rapidly accumulate huge quantity of data, arising specific problems of andling and interpretation. In order to respond to these problems, the INGV staff has developed a number of software tools for data mining. These tools have the scope of identifying structures in the data that can be related to volcanic activity, furnishing criteria for the identification of precursory scenarios. In particular, we use methods of clustering and classification in which data are divided into groups according to a-priori-defined measures of similarity or distance. Data groups may assume various shapes, such as convex clouds or complex concave bodies.The "KKAnalysis" software package is a basket of clustering methods. Currently, it is one of the key techniques of the tremor-based automatic alarm systems of INGV Osservatorio Etneo. It exploits both Self-Organizing Maps and Fuzzy Clustering. Beside seismic data, the software has been applied to the geochemical composition of eruptive products as well as a combined analysis of gas-emission (radon) and seismic data. The "DBSCAN" package exploits a concept based on density-based clustering. This method allows discovering clusters with arbitrary shape. Clusters are defined as dense regions of objects in the data space separated by regions of low density. In DBSCAN a cluster grows as long as the density within a group of objects exceeds some threshold. In the context of volcano monitoring, the method is particularly promising in the recognition of ash particles as they have a rather irregular shape. The "MOTIF" software allows us to identify typical waveforms in time series, outperforming methods like cross-correlation that entail a high computational effort. MOTIF can recognize the non-imilarity of two patterns on a small number of data points without going through the whole length of data vectors. All the developments aforementioned come along with modules for feature extraction and post-processing. Specific attention is devoted to the obustness of the feature extraction to avoid misinterpretations due to the presence of disturbances from environmental noise or other undesired signals originating from the source, which are not relevant for the purpose of volcano surveillance.

Dynamic Segmentation Of Behavior Patterns Based On Quantity Value Movement Using Fuzzy Subtractive Clustering Method

NASA Astrophysics Data System (ADS)

Sangadji, Iriansyah; Arvio, Yozika; Indrianto

2018-03-01

to understand by analyzing the pattern of changes in value movements that can dynamically vary over a given period with relative accuracy, an equipment is required based on the utilization of technical working principles or specific analytical method. This will affect the level of validity of the output that will occur from this system. Subtractive clustering is based on the density (potential) size of data points in a space (variable). The basic concept of subtractive clustering is to determine the regions in a variable that has high potential for the surrounding points. In this paper result is segmentation of behavior pattern based on quantity value movement. It shows the number of clusters is formed and that has many members.

Locality-Aware CTA Clustering For Modern GPUs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Ang; Song, Shuaiwen; Liu, Weifeng

2017-04-08

In this paper, we proposed a novel clustering technique for tapping into the performance potential of a largely ignored type of locality: inter-CTA locality. We first demonstrated the capability of the existing GPU hardware to exploit such locality, both spatially and temporally, on L1 or L1/Tex unified cache. To verify the potential of this locality, we quantified its existence in a broad spectrum of applications and discussed its sources of origin. Based on these insights, we proposed the concept of CTA-Clustering and its associated software techniques. Finally, We evaluated these techniques on all modern generations of NVIDIA GPU architectures. Themore » experimental results showed that our proposed clustering techniques could significantly improve on-chip cache performance.« less

Associations between dairy cow inter-service interval and probability of conception.

PubMed

Remnant, J G; Green, M J; Huxley, J N; Hudson, C D

2018-07-01

Recent research has indicated that the interval between inseminations in modern dairy cattle is often longer than the commonly accepted cycle length of 18-24 days. This study analysed 257,396 inseminations in 75,745 cows from 312 herds in England and Wales. The interval between subsequent inseminations in the same cow in the same lactation (inter-service interval, ISI) were calculated and inseminations categorised as successful or unsuccessful depending on whether there was a corresponding calving event. Conception risk was calculated for each individual ISI between 16 and 28 days. A random effects logistic regression model was fitted to the data with pregnancy as the outcome variable and ISI (in days) included in the model as a categorical variable. The modal ISI was 22 days and the peak conception risk was 44% for ISIs of 21 days rising from 27% at 16 days. The logistic regression model revealed significant associations of conception risk with ISI as well as 305 day milk yield, insemination number, parity and days in milk. Predicted conception risk was lower for ISIs of 16, 17 and 18 days and higher for ISIs of 20, 21 and 22 days compared to 25 day ISIs. A mixture model was specified to identify clusters in insemination frequency and conception risk for ISIs between 3 and 50 days. A "high conception risk, high insemination frequency" cluster was identified between 19 and 26 days which indicated that this time period was the true latent distribution for ISI with optimal reproductive outcome. These findings suggest that the period of increased numbers of inseminations around 22 days identified in existing work coincides with the period of increased probability of conception and therefore likely represents true return estrus events. Copyright © 2018 Elsevier Inc. All rights reserved.

Health, vital goals, and central human capabilities.

PubMed

Venkatapuram, Sridhar

2013-06-01

I argue for a conception of health as a person's ability to achieve or exercise a cluster of basic human activities. These basic activities are in turn specified through free-standing ethical reasoning about what constitutes a minimal conception of a human life with equal human dignity in the modern world. I arrive at this conception of health by closely following and modifying Lennart Nordenfelt's theory of health which presents health as the ability to achieve vital goals. Despite its strengths I transform Nordenfelt's argument in order to overcome three significant drawbacks. Nordenfelt makes vital goals relative to each community or context and significantly reflective of personal preferences. By doing so, Nordenfelt's conception of health faces problems with both socially relative concepts of health and subjectively defined wellbeing. Moreover, Nordenfelt does not ever explicitly specify a set of vital goals. The theory of health advanced here replaces Nordenfelt's (seemingly) empty set of preferences and society-relative vital goals with a human species-wide conception of basic vital goals, or 'central human capabilities and functionings'. These central human capabilities come out of the capabilities approach (CA) now familiar in political philosophy and economics, and particularly reflect the work of Martha Nussbaum. As a result, the health of an individual should be understood as the ability to achieve a basic cluster of beings and doings-or having the overarching capability, a meta-capability, to achieve a set of central or vital inter-related capabilities and functionings. © 2012 John Wiley & Sons Ltd.

HEALTH, VITAL GOALS, AND CENTRAL HUMAN CAPABILITIES

PubMed Central

Venkatapuram, Sridhar

2013-01-01

I argue for a conception of health as a person's ability to achieve or exercise a cluster of basic human activities. These basic activities are in turn specified through free-standing ethical reasoning about what constitutes a minimal conception of a human life with equal human dignity in the modern world. I arrive at this conception of health by closely following and modifying Lennart Nordenfelt's theory of health which presents health as the ability to achieve vital goals. Despite its strengths I transform Nordenfelt's argument in order to overcome three significant drawbacks. Nordenfelt makes vital goals relative to each community or context and significantly reflective of personal preferences. By doing so, Nordenfelt's conception of health faces problems with both socially relative concepts of health and subjectively defined wellbeing. Moreover, Nordenfelt does not ever explicitly specify a set of vital goals. The theory of health advanced here replaces Nordenfelt's (seemingly) empty set of preferences and society-relative vital goals with a human species-wide conception of basic vital goals, or ‘central human capabilities and functionings’. These central human capabilities come out of the capabilities approach (CA) now familiar in political philosophy and economics, and particularly reflect the work of Martha Nussbaum. As a result, the health of an individual should be understood as the ability to achieve a basic cluster of beings and doings—or having the overarching capability, a meta-capability, to achieve a set of central or vital inter-related capabilities and functionings. PMID:22420910

WIYN open cluster study: photometric determination of binary mass ratios

NASA Astrophysics Data System (ADS)

Cai, Kai; Durisen, Richard H.; Deliyannis, Constantine P.

Taking advantage of WIYN Open Cluster Survey (WOCS) precision photometry, we have developed a method using appropriate Yonsei-Yale Isochrones to determine primary masses M2 and q (=M2/M1) for cluster binary stars and applied it to proper motion members of M35.

A roadmap of clustering algorithms: finding a match for a biomedical application.

PubMed

Andreopoulos, Bill; An, Aijun; Wang, Xiaogang; Schroeder, Michael

2009-05-01

Clustering is ubiquitously applied in bioinformatics with hierarchical clustering and k-means partitioning being the most popular methods. Numerous improvements of these two clustering methods have been introduced, as well as completely different approaches such as grid-based, density-based and model-based clustering. For improved bioinformatics analysis of data, it is important to match clusterings to the requirements of a biomedical application. In this article, we present a set of desirable clustering features that are used as evaluation criteria for clustering algorithms. We review 40 different clustering algorithms of all approaches and datatypes. We compare algorithms on the basis of desirable clustering features, and outline algorithms' benefits and drawbacks as a basis for matching them to biomedical applications.

The Cluster Concept and Community Services

ERIC Educational Resources Information Center

Collins, Charles; Tillery, Dale

1972-01-01

An innovative design for the organization of community services and continuing education in a community college district calls for the centralization of administration and the decentralization of program offerings. (RN)

Space Adaptation of Active Mirror Segment Concepts

NASA Technical Reports Server (NTRS)

Ames, Gregory H.

1999-01-01

This report summarizes the results of a three year effort by Blue Line Engineering Co. to advance the state of segmented mirror systems in several separate but related areas. The initial set of tasks were designed to address the issues of system level architecture, digital processing system, cluster level support structures, and advanced mirror fabrication concepts. Later in the project new tasks were added to provide support to the existing segmented mirror testbed at Marshall Space Flight Center (MSFC) in the form of upgrades to the 36 subaperture wavefront sensor. Still later, tasks were added to build and install a new system processor based on the results of the new system architecture. The project was successful in achieving a number of important results. These include the following most notable accomplishments: 1) The creation of a new modular digital processing system that is extremely capable and may be applied to a wide range of segmented mirror systems as well as many classes of Multiple Input Multiple Output (MIMO) control systems such as active structures or industrial automation. 2) A new graphical user interface was created for operation of segmented mirror systems. 3) The development of a high bit rate serial data loop that permits bi-directional flow of data to and from as many as 39 segments daisy-chained to form a single cluster of segments. 4) Upgrade of the 36 subaperture Hartmann type Wave Front Sensor (WFS) of the Phased Array Mirror, Extendible Large Aperture (PAMELA) testbed at MSFC resulting in a 40 to 5OX improvement in SNR which in turn enabled NASA personnel to achieve many significant strides in improved closed-loop system operation in 1998. 5) A new system level processor was built and delivered to MSFC for use with the PAMELA testbed. This new system featured a new graphical user interface to replace the obsolete and non-supported menu system originally delivered with the PAMELA system. The hardware featured Blue Line's new stackable processing system which included fiber optic data links, a WFS digital interface, and a very compact and reliable electronics package. The project also resulted in substantial advances in the evolution of concepts for integrated structures to be used to support clusters of segments while also serving as the means to distribute power, timing, and data communications resources. A prototype cluster base was built and delivered that would support a small array of 7 cm mirror segments. Another conceptual design effort led to substantial progress in the area of laminated silicon mirror segments. While finished mirrors were never successfully produced in this exploratory effort, the basic feasibility of the concept was established through a significant amount of experimental development in microelectronics processing laboratories at the University of Colorado in Colorado Springs. Ultimately lightweighted aluminum mirrors with replicated front surfaces were produced and delivered as part of a separate contract to develop integrated segmented mirror assemblies. Overall the project was very successful in advancing segmented mirror system architectures on several fronts. In fact, the results of this work have already served as the basic foundation for the system architectures of several projects proposed by Blue Line for different missions and customers. These include the NMSD and AMSD procurements for NASA's Next Generation Space Telescope, the HET figure maintenance system, and the 1 meter FAST telescope project.

Concepts of formal concept analysis

NASA Astrophysics Data System (ADS)

Žáček, Martin; Homola, Dan; Miarka, Rostislav

2017-07-01

The aim of this article is apply of Formal Concept Analysis on concept of world. Formal concept analysis (FCA) as a methodology of data analysis, information management and knowledge representation has potential to be applied to a verity of linguistic problems. FCA is mathematical theory for concepts and concept hierarchies that reflects an understanding of concept. Formal concept analysis explicitly formalizes extension and intension of a concept, their mutual relationships. A distinguishing feature of FCA is an inherent integration of three components of conceptual processing of data and knowledge, namely, the discovery and reasoning with concepts in data, discovery and reasoning with dependencies in data, and visualization of data, concepts, and dependencies with folding/unfolding capabilities.

The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images

NASA Astrophysics Data System (ADS)

Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Halim, Nurul Hazwani Abd; Mohamed, Zeehaida

2015-05-01

Malaria is a life-threatening parasitic infectious disease that corresponds for nearly one million deaths each year. Due to the requirement of prompt and accurate diagnosis of malaria, the current study has proposed an unsupervised pixel segmentation based on clustering algorithm in order to obtain the fully segmented red blood cells (RBCs) infected with malaria parasites based on the thin blood smear images of P. vivax species. In order to obtain the segmented infected cell, the malaria images are first enhanced by using modified global contrast stretching technique. Then, an unsupervised segmentation technique based on clustering algorithm has been applied on the intensity component of malaria image in order to segment the infected cell from its blood cells background. In this study, cascaded moving k-means (MKM) and fuzzy c-means (FCM) clustering algorithms has been proposed for malaria slide image segmentation. After that, median filter algorithm has been applied to smooth the image as well as to remove any unwanted regions such as small background pixels from the image. Finally, seeded region growing area extraction algorithm has been applied in order to remove large unwanted regions that are still appeared on the image due to their size in which cannot be cleaned by using median filter. The effectiveness of the proposed cascaded MKM and FCM clustering algorithms has been analyzed qualitatively and quantitatively by comparing the proposed cascaded clustering algorithm with MKM and FCM clustering algorithms. Overall, the results indicate that segmentation using the proposed cascaded clustering algorithm has produced the best segmentation performances by achieving acceptable sensitivity as well as high specificity and accuracy values compared to the segmentation results provided by MKM and FCM algorithms.

Prospective associations between socio-economic status and dietary patterns in European children: the Identification and Prevention of Dietary- and Lifestyle-induced Health Effects in Children and Infants (IDEFICS) Study.

PubMed

Fernández-Alvira, Juan Miguel; Börnhorst, Claudia; Bammann, Karin; Gwozdz, Wencke; Krogh, Vittorio; Hebestreit, Antje; Barba, Gianvincenzo; Reisch, Lucia; Eiben, Gabriele; Iglesia, Iris; Veidebaum, Tomas; Kourides, Yannis A; Kovacs, Eva; Huybrechts, Inge; Pigeot, Iris; Moreno, Luis A

2015-02-14

Exploring changes in children's diet over time and the relationship between these changes and socio-economic status (SES) may help to understand the impact of social inequalities on dietary patterns. The aim of the present study was to describe dietary patterns by applying a cluster analysis to 9301 children participating in the baseline (2-9 years old) and follow-up (4-11 years old) surveys of the Identification and Prevention of Dietary- and Lifestyle-induced Health Effects in Children and Infants Study, and to describe the cluster memberships of these children over time and their association with SES. We applied the K-means clustering algorithm based on the similarities between the relative frequencies of consumption of forty-two food items. The following three consistent clusters were obtained at baseline and follow-up: processed (higher frequency of consumption of snacks and fast food); sweet (higher frequency of consumption of sweet foods and sweetened drinks); healthy (higher frequency of consumption of fruits, vegetables and wholemeal products). Children with higher-educated mothers and fathers and the highest household income were more likely to be allocated to the healthy cluster at baseline and follow-up and less likely to be allocated to the sweet cluster. Migrants were more likely to be allocated to the processed cluster at baseline and follow-up. Applying the cluster analysis to derive dietary patterns at the two time points allowed us to identify groups of children from a lower socio-economic background presenting persistently unhealthier dietary profiles. This finding reflects the need for healthy eating interventions specifically targeting children from lower socio-economic backgrounds.

The angler specialization concept applied: New York’s Salmon River anglers

Treesearch

Chad P. Dawson; Tommy L. Brown; Nancy Connelly

1992-01-01

The concept of angler specialization was applied to a study of Salmon River anglers to test this concept when using a variety of angling techniques and two species groups within the same environmental setting. A revision of the concept is suggested to account for angler expectancy and cognitive processes.

Electrophobic interaction induced impurity clustering in metals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Hong-Bo; Wang, Jin-Long; Jiang, W.

2016-10-01

We introduce the concept of electrophobic interaction, analogous to hydrophobic interaction, for describing the behavior of impurity atoms in a metal, a 'solvent of electrons'. We demonstrate that there exists a form of electrophobic interaction between impurities with closed electron shell structure, which governs their dissolution behavior in a metal. Using He, Be and Ar as examples, we predict by first-principles calculations that the electrophobic interaction drives He, Be or Ar to form a close-packed cluster with a clustering energy that follows a universal power-law scaling with the number of atoms (N) dissolved in a free electron gas, as wellmore » as W or Al lattice, as Ec is proportional to (N2/3-N). This new concept unifies the explanation for a series of experimental observations of close-packed inert-gas bubble formation in metals, and significantly advances our fundamental understanding and capacity to predict the solute behavior of impurities in metals, a useful contribution to be considered in future material design of metals for nuclear, metallurgical, and energy applications.« less

Effects of applied strain on nanoscale self-interstitial cluster formation in BCC iron

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Ning; Setyawan, Wahyu; Kurtz, Richard J.

2017-09-01

The effect of applied strains on the configurational evolution of self-interstitial clusters in BCC iron (Fe) is explored with atomistic simulations. A novel cluster configuration is discovered at low temperatures (<600 K), which consists of <110> dumbbells and <111> crowdions in a specific configuration, resulting in an immobile defect. The stability and diffusion of this cluster at higher temperatures is explored. In addition, an anisotropy distribution factor of a particular [hkl] interstitial loop within the family of loops is calculated as a function of strain. The results show that loop anisotropy is governed by the angle between the stress directionmore » and the orientation of the <111> crowdions in the loop, and directly linked to the stress induced preferred nucleation of self-interstitial atoms.« less

Is globalisation outpacing ethics and social responsibility in occupational health?

PubMed

Voyi, Kuku

2006-01-01

The definition of globalisation is varied. However, one certainty is that in a globalised world the borders are porous in many aspects; people movement, goods exchange, knowledge sharing and redistribution of labour. The concept of globalisation, its impact on society, and its direction leads to a two-sided argument. Could this be the effect of globalisation on ethics and social responsibility, as it is perceived? This paper endeavours to further our understanding of the dynamic relationship of globalisation, ethics and social responsibility in occupational health. The multidisciplinary activity approach to occupational health was used. The globalisation, ethical and social responsibility relationship of the activities in occupational health was analysed using a schematic map of the direct and indirect influences. The analysis revealed areas that can be clustered to address the interaction between driving forces in occupational health ethics and social responsibility for a healthy workforce. Each cluster is discussed highlighting areas of concern. In the discussion proposals are made on how we can modify the way we think in order to avoid repeating mistakes. Suggestion is made of using an innovative method borrowed from other disciplines and adopted for use in occupational health. A partnership approach is proposed and explored on how it will be applied in situations of unequal balance of power.

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks.

PubMed

Puente Fernández, Jesús Antonio; García Villalba, Luis Javier; Kim, Tai-Hoon

2018-04-03

Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Due to the existence of the southbound interface, it is possible to deploy monitoring tools to obtain the network status and retrieve a statistics collection. Therefore, achieving the most accurate statistics depends on a strategy of monitoring and information requests of network devices. In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. Such grouping occurs by avoiding monitoring queries in network switches with common characteristics and then, by omitting redundant information. In this way, the present proposal decreases the number of monitoring queries to switches, improving the network traffic and preventing the switching overload. We have tested our optimization in a video streaming simulation using different types of videos. The experiments and comparison with traditional monitoring techniques demonstrate the feasibility of our proposal maintaining similar values decreasing the number of queries to the switches.

The Nature of Conceptual Understanding in Biomedicine: The Deep Structure of Complex Ideas and the Development of Misconceptions. Technical Report No. 440.

ERIC Educational Resources Information Center

Feltovich, Paul J.; And Others

This report presents a general framework for studying the acquisition and cognitive representation of biomedical concepts and analyzing the nature and development of misconceptions. The central approach of the report is a selective and highly concentrated analysis of the true nature of clusters of complex concepts and the manner in which they are…

GRC RBCC Concept Multidisciplinary Analysis

NASA Technical Reports Server (NTRS)

Suresh, Ambady

2001-01-01

This report outlines the GRC RBCC Concept for Multidisciplinary Analysis. The multidisciplinary coupling procedure is presented, along with technique validations and axisymmetric multidisciplinary inlet and structural results. The NPSS (Numerical Propulsion System Simulation) test bed developments and code parallelization are also presented. These include milestones and accomplishments, a discussion of running R4 fan application on the PII cluster as compared to other platforms, and the National Combustor Code speedup.

A clustering algorithm for determining community structure in complex networks

NASA Astrophysics Data System (ADS)

Jin, Hong; Yu, Wei; Li, ShiJun

2018-02-01

Clustering algorithms are attractive for the task of community detection in complex networks. DENCLUE is a representative density based clustering algorithm which has a firm mathematical basis and good clustering properties allowing for arbitrarily shaped clusters in high dimensional datasets. However, this method cannot be directly applied to community discovering due to its inability to deal with network data. Moreover, it requires a careful selection of the density parameter and the noise threshold. To solve these issues, a new community detection method is proposed in this paper. First, we use a spectral analysis technique to map the network data into a low dimensional Euclidean Space which can preserve node structural characteristics. Then, DENCLUE is applied to detect the communities in the network. A mathematical method named Sheather-Jones plug-in is chosen to select the density parameter which can describe the intrinsic clustering structure accurately. Moreover, every node on the network is meaningful so there were no noise nodes as a result the noise threshold can be ignored. We test our algorithm on both benchmark and real-life networks, and the results demonstrate the effectiveness of our algorithm over other popularity density based clustering algorithms adopted to community detection.

Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

NASA Astrophysics Data System (ADS)

Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.

2017-09-01

Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

Self concept of the cleft lip and or palate child.

PubMed

Kapp, K

1979-04-01

This investigation examined the relationship of the self-concept of children with cleft lip and/or palate to the self-concept of noncleft children. Thirty-four cleft lip and/or palate children between the ages of 11 and 13 were individually matched with thirty-four noncleft school children. Each child was given the Piers-Harris Children's Self Concept Scale. Children with clefts, regardless of sex, reported a significantly greater dissatisfaction with physical appearance. A significant interaction effect between sex and presence or absence of cleft was found on three cluster scores with cleft girls reporting greater unhappiness and dissatisfaction, less success in school, and more anxiety.

Cluster stability in the analysis of mass cytometry data.

PubMed

Melchiotti, Rossella; Gracio, Filipe; Kordasti, Shahram; Todd, Alan K; de Rinaldis, Emanuele

2017-01-01

Manual gating has been traditionally applied to cytometry data sets to identify cells based on protein expression. The advent of mass cytometry allows for a higher number of proteins to be simultaneously measured on cells, therefore providing a means to define cell clusters in a high dimensional expression space. This enhancement, whilst opening unprecedented opportunities for single cell-level analyses, makes the incremental replacement of manual gating with automated clustering a compelling need. To this aim many methods have been implemented and their successful applications demonstrated in different settings. However, the reproducibility of automatically generated clusters is proving challenging and an analytical framework to distinguish spurious clusters from more stable entities, and presumably more biologically relevant ones, is still missing. One way to estimate cell clusters' stability is the evaluation of their consistent re-occurrence within- and between-algorithms, a metric that is commonly used to evaluate results from gene expression. Herein we report the usage and importance of cluster stability evaluations, when applied to results generated from three popular clustering algorithms - SPADE, FLOCK and PhenoGraph - run on four different data sets. These algorithms were shown to generate clusters with various degrees of statistical stability, many of them being unstable. By comparing the results of automated clustering with manually gated populations, we illustrate how information on cluster stability can assist towards a more rigorous and informed interpretation of clustering results. We also explore the relationships between statistical stability and other properties such as clusters' compactness and isolation, demonstrating that whilst cluster stability is linked to other properties it cannot be reliably predicted by any of them. Our study proposes the introduction of cluster stability as a necessary checkpoint for cluster interpretation and contributes to the construction of a more systematic and standardized analytical framework for the assessment of cytometry clustering results. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

A multimembership catalogue for 1876 open clusters using UCAC4 data

NASA Astrophysics Data System (ADS)

Sampedro, L.; Dias, W. S.; Alfaro, E. J.; Monteiro, H.; Molino, A.

2017-10-01

The main objective of this work is to determine the cluster members of 1876 open clusters, using positions and proper motions of the astrometric fourth United States Naval Observatory (USNO) CCD Astrograph Catalog (UCAC4). For this purpose, we apply three different methods, all based on a Bayesian approach, but with different formulations: a purely parametric method, another completely non-parametric algorithm and a third, recently developed by Sampedro & Alfaro, using both formulations at different steps of the whole process. The first and second statistical moments of the members' phase-space subspace, obtained after applying the three methods, are compared for every cluster. Although, on average, the three methods yield similar results, there are also specific differences between them, as well as for some particular clusters. The comparison with other published catalogues shows good agreement. We have also estimated, for the first time, the mean proper motion for a sample of 18 clusters. The results are organized in a single catalogue formed by two main files, one with the most relevant information for each cluster, partially including that in UCAC4, and the other showing the individual membership probabilities for each star in the cluster area. The final catalogue, with an interface design that enables an easy interaction with the user, is available in electronic format at the Stellar Systems Group (SSG-IAA) web site (http://ssg.iaa.es/en/content/sampedro-cluster-catalog).

Bayesian hierarchical models for cost-effectiveness analyses that use data from cluster randomized trials.

PubMed

Grieve, Richard; Nixon, Richard; Thompson, Simon G

2010-01-01

Cost-effectiveness analyses (CEA) may be undertaken alongside cluster randomized trials (CRTs) where randomization is at the level of the cluster (for example, the hospital or primary care provider) rather than the individual. Costs (and outcomes) within clusters may be correlated so that the assumption made by standard bivariate regression models, that observations are independent, is incorrect. This study develops a flexible modeling framework to acknowledge the clustering in CEA that use CRTs. The authors extend previous Bayesian bivariate models for CEA of multicenter trials to recognize the specific form of clustering in CRTs. They develop new Bayesian hierarchical models (BHMs) that allow mean costs and outcomes, and also variances, to differ across clusters. They illustrate how each model can be applied using data from a large (1732 cases, 70 primary care providers) CRT evaluating alternative interventions for reducing postnatal depression. The analyses compare cost-effectiveness estimates from BHMs with standard bivariate regression models that ignore the data hierarchy. The BHMs show high levels of cost heterogeneity across clusters (intracluster correlation coefficient, 0.17). Compared with standard regression models, the BHMs yield substantially increased uncertainty surrounding the cost-effectiveness estimates, and altered point estimates. The authors conclude that ignoring clustering can lead to incorrect inferences. The BHMs that they present offer a flexible modeling framework that can be applied more generally to CEA that use CRTs.

Hund’s rule in superatoms with transition metal impurities

PubMed Central

Medel, Victor M.; Reveles, Jose Ulises; Khanna, Shiv N.; Chauhan, Vikas; Sen, Prasenjit; Castleman, A. Welford

2011-01-01

The quantum states in metal clusters bunch into supershells with associated orbitals having shapes resembling those in atoms, giving rise to the concept that selected clusters could mimic the characteristics of atoms and be classified as superatoms. Unlike atoms, the superatom orbitals span over multiple atoms and the filling of orbitals does not usually exhibit Hund’s rule seen in atoms. Here, we demonstrate the possibility of enhancing exchange splitting in superatom shells via a composite cluster of a central transition metal and surrounding nearly free electron metal atoms. The transition metal d states hybridize with superatom D states and result in enhanced splitting between the majority and minority sets where the moment and the splitting can be controlled by the nature of the central atom. We demonstrate these findings through studies on TMMgn clusters where TM is a 3d atom. The clusters exhibit Hund’s filling, opening the pathway to superatoms with magnetic shells. PMID:21646542

Hund's rule in superatoms with transition metal impurities.

PubMed

Medel, Victor M; Reveles, Jose Ulises; Khanna, Shiv N; Chauhan, Vikas; Sen, Prasenjit; Castleman, A Welford

2011-06-21

The quantum states in metal clusters bunch into supershells with associated orbitals having shapes resembling those in atoms, giving rise to the concept that selected clusters could mimic the characteristics of atoms and be classified as superatoms. Unlike atoms, the superatom orbitals span over multiple atoms and the filling of orbitals does not usually exhibit Hund's rule seen in atoms. Here, we demonstrate the possibility of enhancing exchange splitting in superatom shells via a composite cluster of a central transition metal and surrounding nearly free electron metal atoms. The transition metal d states hybridize with superatom D states and result in enhanced splitting between the majority and minority sets where the moment and the splitting can be controlled by the nature of the central atom. We demonstrate these findings through studies on TMMg(n) clusters where TM is a 3d atom. The clusters exhibit Hund's filling, opening the pathway to superatoms with magnetic shells.

A Survey of Popular R Packages for Cluster Analysis

ERIC Educational Resources Information Center

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…

Dynamic evolution of nearby galaxy clusters

NASA Astrophysics Data System (ADS)

Biernacka, M.; Flin, P.

2011-06-01

A study of the evolution of 377 rich ACO clusters with redshift z<0.2 is presented. The data concerning galaxies in the investigated clusters were obtained using FOCAS packages applied to Digital Sky Survey I. The 377 galaxy clusters constitute a statistically uniform sample to which visual galaxy/star reclassifications were applied. Cluster shape within 2.0 h-1 Mpc from the adopted cluster centre (the mean and the median of all galaxy coordinates, the position of the brightest and of the third brightest galaxy in the cluster) was determined through its ellipticity calculated using two methods: the covariance ellipse method (hereafter CEM) and the method based on Minkowski functionals (hereafter MFM). We investigated ellipticity dependence on the radius of circular annuli, in which ellipticity was calculated. This was realized by varying the radius from 0.5 to 2 Mpc in steps of 0.25 Mpc. By performing Monte Carlo simulations, we generated clusters to which the two ellipticity methods were applied. We found that the covariance ellipse method works better than the method based on Minkowski functionals. We also found that ellipticity distributions are different for different methods used. Using the ellipticity-redshift relation, we investigated the possibility of cluster evolution in the low-redshift Universe. The correlation of cluster ellipticities with redshifts is undoubtly an indicator of structural evolution. Using the t-Student statistics, we found a statistically significant correlation between ellipticity and redshift at the significance level of α = 0.95. In one of the two shape determination methods we found that ellipticity grew with redshift, while the other method gave opposite results. Monte Carlo simulations showed that only ellipticities calculated at the distance of 1.5 Mpc from cluster centre in the Minkowski functional method are robust enough to be taken into account, but for that radius we did not find any relation between e and z. Since CEM pointed towards the existence of the e(z) relation, we conclude that such an effect is real though rather weak. A detailed study of the e(z) relation showed that the observed relation is nonlinear, and the number of elongated structures grows rapidly for z>0.14.

The DAFT/FADA survey. I. Photometric redshifts along lines of sight to clusters in the z = [0.4, 0.9] interval

NASA Astrophysics Data System (ADS)

Guennou, L.; Adami, C.; Ulmer, M. P.; Lebrun, V.; Durret, F.; Johnston, D.; Ilbert, O.; Clowe, D.; Gavazzi, R.; Murphy, K.; Schrabback, T.; Allam, S.; Annis, J.; Basa, S.; Benoist, C.; Biviano, A.; Cappi, A.; Kubo, J. M.; Marshall, P.; Mazure, A.; Rostagni, F.; Russeil, D.; Slezak, E.

2010-11-01

Context. As a contribution to the understanding of the dark energy concept, the Dark energy American French Team (DAFT, in French FADA) has started a large project to characterize statistically high redshift galaxy clusters, infer cosmological constraints from weak lensing tomography, and understand biases relevant for constraining dark energy and cluster physics in future cluster and cosmological experiments. Aims: The purpose of this paper is to establish the basis of reference for the photo-z determination used in all our subsequent papers, including weak lensing tomography studies. Methods: This project is based on a sample of 91 high redshift (z ≥ 0.4), massive (⪆3 × 1014 M_⊙) clusters with existing HST imaging, for which we are presently performing complementary multi-wavelength imaging. This allows us in particular to estimate spectral types and determine accurate photometric redshifts for galaxies along the lines of sight to the first ten clusters for which all the required data are available down to a limit of IAB = 24./24.5 with the LePhare software. The accuracy in redshift is of the order of 0.05 for the range 0.2 ≤ z ≤ 1.5. Results: We verified that the technique applied to obtain photometric redshifts works well by comparing our results to with previous works. In clusters, photo-z accuracy is degraded for bright absolute magnitudes and for the latest and earliest type galaxies. The photo-z accuracy also only slightly varies as a function of the spectral type for field galaxies. As a consequence, we find evidence for an environmental dependence of the photo-z accuracy, interpreted as the standard used spectral energy distributions being not very well suited to cluster galaxies. Finally, we modeled the LCDCS 0504 mass with the strong arcs detected along this line of sight. Based on observations made with the NASA/ESA Hubble Space Telescope, obtained from the data archive at the Space Telescope Institute and the Space Telescope European Coordinating Facility. STScI is operated by the association of Universities for Research in Astronomy, Inc. under the NASA contract NAS 5-26555. Also based on observations made with ESO Telescopes at Paranal and La Silla Observatories under programme ESO LP 166.A-0162. Also based on visiting astronomer observations, at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy, under contract with the National Science Foundation.

Defining objective clusters for rabies virus sequences using affinity propagation clustering

PubMed Central

Fischer, Susanne; Freuling, Conrad M.; Pfaff, Florian; Bodenhofer, Ulrich; Höper, Dirk; Fischer, Mareike; Marston, Denise A.; Fooks, Anthony R.; Mettenleiter, Thomas C.; Conraths, Franz J.; Homeier-Bachmann, Timo

2018-01-01

Rabies is caused by lyssaviruses, and is one of the oldest known zoonoses. In recent years, more than 21,000 nucleotide sequences of rabies viruses (RABV), from the prototype species rabies lyssavirus, have been deposited in public databases. Subsequent phylogenetic analyses in combination with metadata suggest geographic distributions of RABV. However, these analyses somewhat experience technical difficulties in defining verifiable criteria for cluster allocations in phylogenetic trees inviting for a more rational approach. Therefore, we applied a relatively new mathematical clustering algorythm named ‘affinity propagation clustering’ (AP) to propose a standardized sub-species classification utilizing full-genome RABV sequences. Because AP has the advantage that it is computationally fast and works for any meaningful measure of similarity between data samples, it has previously been applied successfully in bioinformatics, for analysis of microarray and gene expression data, however, cluster analysis of sequences is still in its infancy. Existing (516) and original (46) full genome RABV sequences were used to demonstrate the application of AP for RABV clustering. On a global scale, AP proposed four clusters, i.e. New World cluster, Arctic/Arctic-like, Cosmopolitan, and Asian as previously assigned by phylogenetic studies. By combining AP with established phylogenetic analyses, it is possible to resolve phylogenetic relationships between verifiably determined clusters and sequences. This workflow will be useful in confirming cluster distributions in a uniform transparent manner, not only for RABV, but also for other comparative sequence analyses. PMID:29357361

Clustering approach for unsupervised segmentation of malarial Plasmodium vivax parasite

NASA Astrophysics Data System (ADS)

Abdul-Nasir, Aimi Salihah; Mashor, Mohd Yusoff; Mohamed, Zeehaida

2017-10-01

Malaria is a global health problem, particularly in Africa and south Asia where it causes countless deaths and morbidity cases. Efficient control and prompt of this disease require early detection and accurate diagnosis due to the large number of cases reported yearly. To achieve this aim, this paper proposes an image segmentation approach via unsupervised pixel segmentation of malaria parasite to automate the diagnosis of malaria. In this study, a modified clustering algorithm namely enhanced k-means (EKM) clustering, is proposed for malaria image segmentation. In the proposed EKM clustering, the concept of variance and a new version of transferring process for clustered members are used to assist the assignation of data to the proper centre during the process of clustering, so that good segmented malaria image can be generated. The effectiveness of the proposed EKM clustering has been analyzed qualitatively and quantitatively by comparing this algorithm with two popular image segmentation techniques namely Otsu's thresholding and k-means clustering. The experimental results show that the proposed EKM clustering has successfully segmented 100 malaria images of P. vivax species with segmentation accuracy, sensitivity and specificity of 99.20%, 87.53% and 99.58%, respectively. Hence, the proposed EKM clustering can be considered as an image segmentation tool for segmenting the malaria images.

Cancer Cluster Investigations: Review of the Past and Proposals for the Future

PubMed Central

Goodman, Michael; LaKind, Judy S.; Fagliano, Jerald A.; Lash, Timothy L.; Wiemels, Joseph L.; Winn, Deborah M.; Patel, Chirag; Van Eenwyk, Juliet; Kohler, Betsy A.; Schisterman, Enrique F.; Albert, Paul; Mattison, Donald R.

2014-01-01

Residential clusters of non-communicable diseases are a source of enduring public concern, and at times, controversy. Many clusters reported to public health agencies by concerned citizens are accompanied by expectations that investigations will uncover a cause of disease. While goals, methods and conclusions of cluster studies are debated in the scientific literature and popular press, investigations of reported residential clusters rarely provide definitive answers about disease etiology. Further, it is inherently difficult to study a cluster for diseases with complex etiology and long latency (e.g., most cancers). Regardless, cluster investigations remain an important function of local, state and federal public health agencies. Challenges limiting the ability of cluster investigations to uncover causes for disease include the need to consider long latency, low statistical power of most analyses, uncertain definitions of cluster boundaries and population of interest, and in- and out-migration. A multi-disciplinary Workshop was held to discuss innovative and/or under-explored approaches to investigate cancer clusters. Several potentially fruitful paths forward are described, including modern methods of reconstructing residential history, improved approaches to analyzing spatial data, improved utilization of electronic data sources, advances using biomarkers of carcinogenesis, novel concepts for grouping cases, investigations of infectious etiology of cancer, and “omics” approaches. PMID:24477211

Clustering in complex directed networks

NASA Astrophysics Data System (ADS)

Fagiolo, Giorgio

2007-08-01

Many empirical networks display an inherent tendency to cluster, i.e., to form circles of connected nodes. This feature is typically measured by the clustering coefficient (CC). The CC, originally introduced for binary, undirected graphs, has been recently generalized to weighted, undirected networks. Here we extend the CC to the case of (binary and weighted) directed networks and we compute its expected value for random graphs. We distinguish between CCs that count all directed triangles in the graph (independently of the direction of their edges) and CCs that only consider particular types of directed triangles (e.g., cycles). The main concepts are illustrated by employing empirical data on world-trade flows.

A medical ontology for intelligent web-based skin lesions image retrieval.

PubMed

Maragoudakis, Manolis; Maglogiannis, Ilias

2011-06-01

Researchers have applied increasing efforts towards providing formal computational frameworks to consolidate the plethora of concepts and relations used in the medical domain. In the domain of skin related diseases, the variability of semantic features contained within digital skin images is a major barrier to the medical understanding of the symptoms and development of early skin cancers. The desideratum of making these standards machine-readable has led to their formalization in ontologies. In this work, in an attempt to enhance an existing Core Ontology for skin lesion images, hand-coded from image features, high quality images were analyzed by an autonomous ontology creation engine. We show that by exploiting agglomerative clustering methods with distance criteria upon the existing ontological structure, the original domain model could be enhanced with new instances, attributes and even relations, thus allowing for better classification and retrieval of skin lesion categories from the web.

Green-noise halftoning with dot diffusion

NASA Astrophysics Data System (ADS)

Lippens, Stefaan; Philips, Wilfried

2007-02-01

Dot diffusion is a halftoning technique that is based on the traditional error diffusion concept, but offers a high degree of parallel processing by its block based approach. Traditional dot diffusion however suffers from periodicity artifacts. To limit the visibility of these artifacts, we propose grid diffusion, which applies different class matrices for different blocks. Furthermore, in this paper we will discuss two approaches in the dot diffusion framework to generate green-noise halftone patterns. The first approach is based on output dependent feedback (hysteresis), analogous to the standard green-noise error diffusion techniques. We observe that the resulting halftones are rather coarse and highly dependent on the used dot diffusion class matrices. In the second approach we don't limit the diffusion to the nearest neighbors. This leads to less coarse halftones, compared to the first approach. The drawback is that it can only cope with rather limited cluster sizes. We can reduce these drawbacks by combining the two approaches.

Energy spectra of X-ray clusters of galaxies

NASA Technical Reports Server (NTRS)

Avni, Y.

1976-01-01

A procedure for estimating the ranges of parameters that describe the spectra of X-rays from clusters of galaxies is presented. The applicability of the method is proved by statistical simulations of cluster spectra; such a proof is necessary because of the nonlinearity of the spectral functions. Implications for the spectra of the Perseus, Coma, and Virgo clusters are discussed. The procedure can be applied in more general problems of parameter estimation.

Method and apparatus for detecting and/or imaging clusters of small scattering centers in the body

DOEpatents

Perez-Mendez, V.; Sommer, F.G.

1982-07-13

An ultrasonic method and apparatus are provided for detecting and imaging clusters of small scattering centers in the breast wherein periodic pulses are applied to an ultrasound emitting transducer and projected into the body, thereafter being received by at least one receiving transducer positioned to receive scattering from the scattering center clusters. The signals are processed to provide an image showing cluster extent and location. 6 figs.

Method and apparatus for detecting and/or imaging clusters of small scattering centers in the body

DOEpatents

Perez-Mendez, Victor; Sommer, Frank G.

1982-01-01

An ultrasonic method and apparatus are provided for detecting and imaging clusters of small scattering centers in the breast wherein periodic pulses are applied to an ultrasound emitting transducer and projected into the body, thereafter being received by at least one receiving transducer positioned to receive scattering from the scattering center clusters. The signals are processed to provide an image showing cluster extent and location.

A concept mapping study on organic food consumers in Shanghai, China.

PubMed

Hasimu, Huliyeti; Marchesini, Sergio; Canavari, Maurizio

2017-01-01

Despite some similarities with developed countries, the growth of organic market in China seems to follow a different path. Thus, important questions are how Chinese urban consumers perceive organic food, and what are the main concepts associated to the organic attribute. We aimed at representing in graphic form the network of mental associations with the organic concept. We used an adapted version of the "Brand concept mapping" method to acquire, process, and draw individual concept networks perceived by 50 organic food consumers in Shanghai. We then analyzed the data using network and cluster analysis to create aggregated maps for two distinct groups of consumers. Similarly to their peers in developed countries, Chinese consumers perceive organic food as healthy, safe and expensive. However, organic is not necessarily synonymous with natural produce in China, also due to a translation of the term that conveys the idea of a "technology advanced" product. Organic overlaps with the green food label in terms of image and positioning in the market, since they are easily associated and often confused. The two groups we identified show clear differences in the way the organic concept is associated to other concepts and features. The study provides useful information for practitioners: marketers of organic products in China should invest in communication to emphasize the differences with Green Food products and they should consider the possibility of segmenting organic consumers; Chinese policy makers should consider implementing information campaigns aimed at achieving a better understanding of the features of these quality labels among consumers. For researchers, the study confirms that the BCM method is effective and its integration with network and cluster analysis improves the interpretation of individual and aggregated maps. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification of Chinese herbs based on the cluster analysis of delayed luminescence.

PubMed

Pang, Jingxiang; Yang, Meina; Fu, Jialei; Zhao, Xiaolei; van Wijk, Eduard; Wang, Mei; Liu, Yanli; Zhou, Xiaoyan; Fan, Hua; Han, Jinxiang

2016-03-01

Traditional Chinese material medica are an important component of the Chinese pharmacopeia. According to the traditional Chinese medicinal concept, Chinese herbal medicines are classified into different categories based on their therapeutic effects, however, the bioactive principles cannot be solely explained by chemical analysis. The aim of this study is to classify different Chinese herbs based on their therapeutic effects by using delayed luminescence (DL). The DL of 56 Chinese herbs was measured using an ultra-sensitive luminescence detection system. The different DL parameters were used to classify Chinese herbs according to a hierarchical cluster analysis. The samples were divided into two groups based on their DL kinetic parameters. Interestingly, the DL classification results were quite consistent with classification according to the Chinese medicinal concepts of 'cold' and 'heat' properties. In this paper, we show for the first time that by using DL technology, it is possible to classify Chinese herbs according to the Chinese medicinal concept and it may even be possible to predict their therapeutic properties. Copyright © 2015 John Wiley & Sons, Ltd.

Self-concept and ego development in deaf adolescents: a comparative study.

PubMed

van Gent, Tiejo; Goedhart, Arnold W; Knoors, Harry E T; Westenberg, P Michiel; Treffers, Philip D A

2012-01-01

Self-concept and ego development, two intertwined aspects of self-indicating well-being and social-cognitive maturation, respectively, were examined in a representative sample of deaf adolescents of normal intelligence (N = 68), using translated and adapted versions of Harter's (1988, Manual for the self-perception profile for adolescents. Denver, CO: University of Denver) multidimensional measure of self-concept and Loevinger's (1998, Technical foundations for measuring ego development. Mahwah, NJ: Lawrence Erlbaum) measure of ego development. Compared to hearing norm groups, deaf adolescents showed lower levels of self-perceived social acceptance, close friendships and ego development and higher physical appearance. Hierarchical multiple regression analyses controlling for sociodemographic variables showed positive associations of global self-worth with support for signing during childhood and quality of parent-child communication and of ego development with attending a regular school. Cluster analysis identified three social competence profiles: uniformly low competence, uniformly high competence, and low social acceptance with high physical appearance. Cluster membership was associated with school type, ego development, and (past) neurological disorder. The results are discussed in reference to interventions aimed at the well-being of deaf youth.

Do residents of food deserts express different food buying preferences compared to residents of food oases? A mixed-methods analysis.

PubMed

Walker, Renee E; Block, Jason; Kawachi, Ichiro

2012-04-10

Many people lack access to food stores that provide healthful food. Neighborhoods with poor supermarket access have been characterized as "food deserts" (as contrast with "food oases"). This study explored factors influencing food buying practices among residents of food deserts versus food oases in the city of Boston, USA. We used the mixed-methods approach of concept mapping, which allows participants to identify, list, and organize their perceptions according to importance. Resulting maps visually illustrate priority areas. Sixty-seven low-income adults completed the concept mapping process that identified 163 unique statements (e.g. relating to affordability, taste, and convenience) that influence food buying practices. Multivariate statistical techniques grouped the 163 statements into 8 clusters or concepts. Results showed that average cluster ratings and rankings were similar between residents of food deserts and food oases. The implication of this study pertains to the importance of community resources and emergency food assistance programs that have served to minimize the burden associated with hunger and poor food access among low-income, urban populations.

Do residents of food deserts express different food buying preferences compared to residents of food oases? A mixed-methods analysis

PubMed Central

2012-01-01

Background Many people lack access to food stores that provide healthful food. Neighborhoods with poor supermarket access have been characterized as “food deserts” (as contrast with “food oases”). This study explored factors influencing food buying practices among residents of food deserts versus food oases in the city of Boston, USA. Methods We used the mixed-methods approach of concept mapping, which allows participants to identify, list, and organize their perceptions according to importance. Resulting maps visually illustrate priority areas. Results Sixty-seven low-income adults completed the concept mapping process that identified 163 unique statements (e.g. relating to affordability, taste, and convenience) that influence food buying practices. Multivariate statistical techniques grouped the 163 statements into 8 clusters or concepts. Results showed that average cluster ratings and rankings were similar between residents of food deserts and food oases. Conclusions The implication of this study pertains to the importance of community resources and emergency food assistance programs that have served to minimize the burden associated with hunger and poor food access among low-income, urban populations. PMID:22490237

Effects of applied strain on nanoscale self-interstitial cluster formation in BCC iron

NASA Astrophysics Data System (ADS)

Gao, Ning; Setyawan, Wahyu; Kurtz, Richard J.; Wang, Zhiguang

2017-09-01

The effect of applied strains on the configurational evolution of self-interstitial clusters in BCC iron (Fe) is explored with atomistic simulations. A novel cluster configuration is discovered at low temperatures (<600 K), which consists of 〈 110 〉 dumbbells and 〈 111 〉 crowdions in a specific configuration, resulting in an immobile defect. The stability and diffusion of this cluster at higher temperatures is explored. In addition, an anisotropy distribution factor of a particular [ hkl ] interstitial loop within the family of 〈 hkl 〉 loops is calculated as a function of strain. The results show that loop anisotropy is governed by the angle between the stress direction and the orientation of the 〈 111 〉 crowdions in the loop, and directly linked to the stress induced preferred nucleation of self-interstitial atoms.

Utilizing Hierarchical Clustering to improve Efficiency of Self-Organizing Feature Map to Identify Hydrological Homogeneous Regions

NASA Astrophysics Data System (ADS)

Farsadnia, Farhad; Ghahreman, Bijan

2016-04-01

Hydrologic homogeneous group identification is considered both fundamental and applied research in hydrology. Clustering methods are among conventional methods to assess the hydrological homogeneous regions. Recently, Self-Organizing feature Map (SOM) method has been applied in some studies. However, the main problem of this method is the interpretation on the output map of this approach. Therefore, SOM is used as input to other clustering algorithms. The aim of this study is to apply a two-level Self-Organizing feature map and Ward hierarchical clustering method to determine the hydrologic homogenous regions in North and Razavi Khorasan provinces. At first by principal component analysis, we reduced SOM input matrix dimension, then the SOM was used to form a two-dimensional features map. To determine homogeneous regions for flood frequency analysis, SOM output nodes were used as input into the Ward method. Generally, the regions identified by the clustering algorithms are not statistically homogeneous. Consequently, they have to be adjusted to improve their homogeneity. After adjustment of the homogeneity regions by L-moment tests, five hydrologic homogeneous regions were identified. Finally, adjusted regions were created by a two-level SOM and then the best regional distribution function and associated parameters were selected by the L-moment approach. The results showed that the combination of self-organizing maps and Ward hierarchical clustering by principal components as input is more effective than the hierarchical method, by principal components or standardized inputs to achieve hydrologic homogeneous regions.

User Identified Positive Outcome Expectancies of Electronic Cigarette Use: a Concept Mapping Study

PubMed Central

Soule, Eric K.; Maloney, Sarah F.; Guy, Mignonne C.; Eissenberg, Thomas; Fagan, Pebbles

2017-01-01

Electronic cigarette (ECIG) use is growing in popularity, however, little is known about the perceived positive outcomes of ECIG use. This study used concept mapping (CM) to examine positive ECIG outcome expectancies. Sixty-three past 30-day ECIG users (38.1% female) between the ages of 18 and 64 (M = 37.8, SD = 13.3) completed a CM module. In an online program, participants provided statements that completed a prompt: “A specific positive, enjoyable, or exciting effect (i.e., physical or psychological) that I have experienced WHILE USING or IMMEDIATELY AFTER USING an electronic cigarette/electronic vaping device is…”. Participants (n = 35) sorted 123 statements into “piles” of similar content and rated (n = 43) each statement on a 7-point scale (1-Definitely NOT a positive effect to 7-Definitely a positive effect). A cluster map was created using data from the sorting task and analysis indicated a seven cluster model of positive ECIG use outcome expectancies: Therapeutic/Affect Regulation, High/Euphoria, Sensation Enjoyment, Perceived Health Effects, Benefits of Decreased Cigarette Use, Convenience, and Social Impacts. The Perceived Health Effects cluster was rated highest, though all mean ratings were greater than 4.69. Mean cluster ratings were compared and females, younger adults, past 30-day cigarette smokers, users of more “advanced” ECIG devices, and non-lifetime (less than 100 lifetime cigarettes) participants rated certain clusters higher than comparison groups (ps < 0.05). ECIG users associate positive outcomes with ECIG use. ECIG outcome expectancies may affect product appeal and tobacco use behaviors and should be examined further to inform regulatory policies. PMID:28277706

A measurement concept for hot-spot BRDFs from space

NASA Technical Reports Server (NTRS)

Gerstl, S.A.W.

1996-01-01

Several concepts for canopy hot-spot measurements from space have been investigated. The most promising involves active illumination and bistatic detection that would allow hot-spot angular distribution (BRDF) measurements from space in a search-light mode. The concept includes a pointable illumination source, such as a laser operating at an atmospheric window wavelength, coupled with a number of high spatial-resolution detectors that are clustered around the illumination source in space, receiving photons nearly coaxial with the reto-reflection direction. Microwave control and command among the satellite cluster would allow orienting the direction of the laser beam as well as the focusing detectors simultaneously so that the coupled system can function like a search light with almost unlimited pointing capabilities. The concept is called the Hot-Spot Search-Light (HSSL) satellite. A nominal satellite altitude of 600 km will allow hot-spot BRDF measurements out to about 18 degrees phase angle. The distributed are taking radiometric measurements of the intensity wings of the hot-spot angular distribution without the need for complex imaging detectors. The system can be operated at night for increased signal-to-noise ratio. This way the hot-spot angular signatures can be quantified and parameterized in sufficient detail to extract the biophysical information content of plant architectures.

On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms.

PubMed

Chen, Chunlei; He, Li; Zhang, Huixiang; Zheng, Hao; Wang, Lei

2017-01-01

Incremental clustering algorithms play a vital role in various applications such as massive data analysis and real-time data processing. Typical application scenarios of incremental clustering raise high demand on computing power of the hardware platform. Parallel computing is a common solution to meet this demand. Moreover, General Purpose Graphic Processing Unit (GPGPU) is a promising parallel computing device. Nevertheless, the incremental clustering algorithm is facing a dilemma between clustering accuracy and parallelism when they are powered by GPGPU. We formally analyzed the cause of this dilemma. First, we formalized concepts relevant to incremental clustering like evolving granularity. Second, we formally proved two theorems. The first theorem proves the relation between clustering accuracy and evolving granularity. Additionally, this theorem analyzes the upper and lower bounds of different-to-same mis-affiliation. Fewer occurrences of such mis-affiliation mean higher accuracy. The second theorem reveals the relation between parallelism and evolving granularity. Smaller work-depth means superior parallelism. Through the proofs, we conclude that accuracy of an incremental clustering algorithm is negatively related to evolving granularity while parallelism is positively related to the granularity. Thus the contradictory relations cause the dilemma. Finally, we validated the relations through a demo algorithm. Experiment results verified theoretical conclusions.

Validating clustering of molecular dynamics simulations using polymer models.

PubMed

Phillips, Joshua L; Colvin, Michael E; Newsam, Shawn

2011-11-14

Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers.

Validating clustering of molecular dynamics simulations using polymer models

PubMed Central

2011-01-01

Background Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. Results We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. Conclusions We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers. PMID:22082218

Cluster analysis of the hot subdwarfs in the PG survey

NASA Technical Reports Server (NTRS)

Thejll, Peter; Charache, Darryl; Shipman, Harry L.

1989-01-01

Application of cluster analysis to the hot subdwarfs in the Palomar Green (PG) survey of faint blue high-Galactic-latitude objects is assessed, with emphasis on data noise and the number of clusters to subdivide the data into. The data used in the study are presented, and cluster analysis, using the CLUSTAN program, is applied to it. Distances are calculated using the Euclidean formula, and clustering is done by Ward's method. The results are discussed, and five groups representing natural divisions of the subdwarfs in the PG survey are presented.

The ToxBank Data Warehouse: Supporting the Replacement of In Vivo Repeated Dose Systemic Toxicity Testing.

PubMed

Kohonen, Pekka; Benfenati, Emilio; Bower, David; Ceder, Rebecca; Crump, Michael; Cross, Kevin; Grafström, Roland C; Healy, Lyn; Helma, Christoph; Jeliazkova, Nina; Jeliazkov, Vedrin; Maggioni, Silvia; Miller, Scott; Myatt, Glenn; Rautenberg, Michael; Stacey, Glyn; Willighagen, Egon; Wiseman, Jeff; Hardy, Barry

2013-01-01

The aim of the SEURAT-1 (Safety Evaluation Ultimately Replacing Animal Testing-1) research cluster, comprised of seven EU FP7 Health projects co-financed by Cosmetics Europe, is to generate a proof-of-concept to show how the latest technologies, systems toxicology and toxicogenomics can be combined to deliver a test replacement for repeated dose systemic toxicity testing on animals. The SEURAT-1 strategy is to adopt a mode-of-action framework to describe repeated dose toxicity, combining in vitro and in silico methods to derive predictions of in vivo toxicity responses. ToxBank is the cross-cluster infrastructure project whose activities include the development of a data warehouse to provide a web-accessible shared repository of research data and protocols, a physical compounds repository, reference or "gold compounds" for use across the cluster (available via wiki.toxbank.net), and a reference resource for biomaterials. Core technologies used in the data warehouse include the ISA-Tab universal data exchange format, REpresentational State Transfer (REST) web services, the W3C Resource Description Framework (RDF) and the OpenTox standards. We describe the design of the data warehouse based on cluster requirements, the implementation based on open standards, and finally the underlying concepts and initial results of a data analysis utilizing public data related to the gold compounds. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Development of a 12-Thrust Chamber Kerosene /Oxygen Primary Rocket Sub-System for an Early (1964) Air-Augmented Rocket Ground-Test System

NASA Technical Reports Server (NTRS)

Pryor, D.; Hyde, E. H.; Escher, W. J. D.

1999-01-01

Airbreathing/Rocket combined-cycle, and specifically rocket-based combined- cycle (RBCC), propulsion systems, typically employ an internal engine flow-path installed primary rocket subsystem. To achieve acceptably short mixing lengths in effecting the "air augmentation" process, a large rocket-exhaust/air interfacial mixing surface is needed. This leads, in some engine design concepts, to a "cluster" of small rocket units, suitably arrayed in the flowpath. To support an early (1964) subscale ground-test of a specific RBCC concept, such a 12-rocket cluster was developed by NASA's Marshall Space Flight Center (MSFC). The small primary rockets used in the cluster assembly were modified versions of an existing small kerosene/oxygen water-cooled rocket engine unit routinely tested at MSFC. Following individual thrust-chamber tests and overall subsystem qualification testing, the cluster assembly was installed at the U. S. Air Force's Arnold Engineering Development Center (AEDC) for RBCC systems testing. (The results of the special air-augmented rocket testing are not covered here.) While this project was eventually successfully completed, a number of hardware integration problems were met, leading to catastrophic thrust chamber failures. The principal "lessons learned" in conducting this early primary rocket subsystem experimental effort are documented here as a basic knowledge-base contribution for the benefit of today's RBCC research and development community.

Spatial clustering of pixels of a multispectral image

DOEpatents

Conger, James Lynn

2014-08-19

A method and system for clustering the pixels of a multispectral image is provided. A clustering system computes a maximum spectral similarity score for each pixel that indicates the similarity between that pixel and the most similar neighboring. To determine the maximum similarity score for a pixel, the clustering system generates a similarity score between that pixel and each of its neighboring pixels and then selects the similarity score that represents the highest similarity as the maximum similarity score. The clustering system may apply a filtering criterion based on the maximum similarity score so that pixels with similarity scores below a minimum threshold are not clustered. The clustering system changes the current pixel values of the pixels in a cluster based on an averaging of the original pixel values of the pixels in the cluster.

Under What Circumstances Does External Knowledge about the Correlation Structure Improve Power in Cluster Randomized Designs?

ERIC Educational Resources Information Center

Rhoads, Christopher

2014-01-01

Recent publications have drawn attention to the idea of utilizing prior information about the correlation structure to improve statistical power in cluster randomized experiments. Because power in cluster randomized designs is a function of many different parameters, it has been difficult for applied researchers to discern a simple rule explaining…

Clustering Binary Data in the Presence of Masking Variables

ERIC Educational Resources Information Center

Brusco, Michael J.

2004-01-01

A number of important applications require the clustering of binary data sets. Traditional nonhierarchical cluster analysis techniques, such as the popular K-means algorithm, can often be successfully applied to these data sets. However, the presence of masking variables in a data set can impede the ability of the K-means algorithm to recover the…

Slicing cluster mass functions with a Bayesian razor

NASA Astrophysics Data System (ADS)

Sealfon, C. D.

2010-08-01

We apply a Bayesian ``razor" to forecast Bayes factors between different parameterizations of the galaxy cluster mass function. To demonstrate this approach, we calculate the minimum size N-body simulation needed for strong evidence favoring a two-parameter mass function over one-parameter mass functions and visa versa, as a function of the minimum cluster mass.

What Should We Include in a Cultural Competence Curriculum? An Emerging Formative Evaluation Process to Foster Curriculum Development

PubMed Central

Crenshaw, Katie; Shewchuk, Richard M.; Qu, Haiyan; Staton, Lisa J.; Bigby, Judy Ann; Houston, Thomas K.; Allison, Jeroan; Estrada, Carlos A.

2011-01-01

Purpose To identify, prioritize, and organize components of a cultural competence curriculum to address disparities in cardiovascular disease. Method In 2006, four separate nominal group technique sessions were conducted with medical students, residents, community physicians, and academic physicians to generate and prioritize a list of concepts (i.e., ideas) to include in a curriculum. Afterward, 45 educators and researchers organized and prioritized the concepts using a card-sorting exercise. Multidimensional scaling (MDS) and hierarchical cluster analysis produced homogeneous groupings of related concepts and generated a cognitive map. The main outcome measures were the number of cultural competence concepts, their relative ranks, and the cognitive map. Results Thirty participants generated 61 concepts, 29 were identified by at least 2 participants. The cognitive map organized concepts into four clusters, interpreted as: (1) patient’s cultural background (e.g.,, information on cultures, habits, values); (2) provider and health care (e.g., clinical skills, awareness of one’s bias, patient-centeredness, and professionalism), communication skills (e.g., history, stereotype avoidance, and health disparities epidemiology); (3) cross-culture (e.g., idiomatic expressions, examples of effective communication); and (4) resources to manage cultural diversity (e.g., translator guides, instructions and community resources). The MDS two-dimensional solution demonstrated a good fit (stress=0.07; R2=0.97). Conclusions A novel, combined approach allowed stakeholders’ inputs to identify and cognitively organize critical domains used to guide development of a cultural competence curriculum. Educators may use this approach to develop and organize educational content for their target audiences, especially in ill-defined areas like cultural competence. PMID:21248602

Towards the development of a comprehensive framework: Qualitative systematic survey of definitions of clinical research quality

PubMed Central

von Niederhäusern, Belinda; Schandelmaier, Stefan; Mi Bonde, Marie; Brunner, Nicole; Hemkens, Lars G.; Rutquist, Marielle; Bhatnagar, Neera; Guyatt, Gordon H.; Pauli-Magnus, Christiane; Briel, Matthias

2017-01-01

Objective To systematically survey existing definitions, concepts, and criteria of clinical research quality, both developed by stakeholder groups as well as in the medical literature. This study serves as a first step in the development of a comprehensive framework for the quality of clinical research. Study design and setting We systematically and in duplicate searched definitions, concepts and criteria of clinical research quality on websites of stakeholders in clinical research until no further insights emerged and in MEDLINE up to February 2015. Stakeholders included governmental bodies, regulatory agencies, the pharmaceutical industry, academic and commercial contract research organizations, initiatives, research ethics committees, patient organizations and funding agencies from 13 countries. Data synthesis involved descriptive and qualitative analyses following the Framework Method on definitions, concepts, and criteria of clinical research quality. Descriptive codes were applied and grouped into clusters to identify common and stakeholder-specific quality themes. Results Stakeholder concepts on how to assure quality throughout study conduct or articles on quality assessment tools were common, generally with no a priori definition of the term quality itself. We identified a total of 20 explicit definitions of clinical research quality including varying quality dimensions and focusing on different stages in the clinical research process. Encountered quality dimensions include ethical conduct, patient safety/rights/priorities, internal validity, precision of results, generalizability or external validity, scientific and societal relevance, transparency and accessibility of information, research infrastructure and sustainability. None of the definitions appeared to be comprehensive either in terms of quality dimensions, research stages, or stakeholder perspectives. Conclusion Clinical research quality is often discussed but rarely defined. A framework defining clinical research quality across stakeholders’ individual perspectives is desirable to facilitate discussion, assessment, and improvement of quality at all stages of clinical research. PMID:28715491

Towards the development of a comprehensive framework: Qualitative systematic survey of definitions of clinical research quality.

PubMed

von Niederhäusern, Belinda; Schandelmaier, Stefan; Mi Bonde, Marie; Brunner, Nicole; Hemkens, Lars G; Rutquist, Marielle; Bhatnagar, Neera; Guyatt, Gordon H; Pauli-Magnus, Christiane; Briel, Matthias

2017-01-01

To systematically survey existing definitions, concepts, and criteria of clinical research quality, both developed by stakeholder groups as well as in the medical literature. This study serves as a first step in the development of a comprehensive framework for the quality of clinical research. We systematically and in duplicate searched definitions, concepts and criteria of clinical research quality on websites of stakeholders in clinical research until no further insights emerged and in MEDLINE up to February 2015. Stakeholders included governmental bodies, regulatory agencies, the pharmaceutical industry, academic and commercial contract research organizations, initiatives, research ethics committees, patient organizations and funding agencies from 13 countries. Data synthesis involved descriptive and qualitative analyses following the Framework Method on definitions, concepts, and criteria of clinical research quality. Descriptive codes were applied and grouped into clusters to identify common and stakeholder-specific quality themes. Stakeholder concepts on how to assure quality throughout study conduct or articles on quality assessment tools were common, generally with no a priori definition of the term quality itself. We identified a total of 20 explicit definitions of clinical research quality including varying quality dimensions and focusing on different stages in the clinical research process. Encountered quality dimensions include ethical conduct, patient safety/rights/priorities, internal validity, precision of results, generalizability or external validity, scientific and societal relevance, transparency and accessibility of information, research infrastructure and sustainability. None of the definitions appeared to be comprehensive either in terms of quality dimensions, research stages, or stakeholder perspectives. Clinical research quality is often discussed but rarely defined. A framework defining clinical research quality across stakeholders' individual perspectives is desirable to facilitate discussion, assessment, and improvement of quality at all stages of clinical research.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

NASA Astrophysics Data System (ADS)

Salimi, F.; Ristovski, Z.; Mazaheri, M.; Laiman, R.; Crilley, L. R.; He, C.; Clifford, S.; Morawska, L.

2014-06-01

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

NASA Astrophysics Data System (ADS)

Salimi, F.; Ristovski, Z.; Mazaheri, M.; Laiman, R.; Crilley, L. R.; He, C.; Clifford, S.; Morawska, L.

2014-11-01

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods that have been recently employed to analyse PNSD data; however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectrum to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Modifiers of Neighbors' Bystander Intervention in Intimate Partner Violence: A Concept Mapping Study.

PubMed

Wee, Sara; Todd, Mary-Justine; Oshiro, Michael; Greene, Emily; Frye, Victoria

2016-03-01

Encouraging bystander intervention in intimate partner violence (IPV) against women is potentially an important method of reducing the prevalence of such violence in urban communities. Most existing research has been conducted on campuses and in relation to sexual violence among teens or young adults. Our understanding of which bystander behaviors are feasible is nascent, and our knowledge of which situational factors influence neighbors' self-reported willingness to intervene is underdeveloped. We conducted a concept mapping study to identify potential bystander intervention behaviors in IPV among neighbors in urban settings; we also assessed whether perceived feasibility and effectiveness of those behaviors varied by situational characteristics. Using data collected from 41 residents of a low-income New York City neighborhood in late 2011, concept mapping was used to create a conceptual map of the 74 behaviors identified by participants. We examined participant differences in mean feasibility (i.e., that the participants "could" or "would" enact a behavior), feasibility given two situational characteristics (if the couple was perceived to have a history of IPV, and if children were believed to be involved or present), and perceived effectiveness of bystander behaviors. Differences across select sociodemographic factors of participants were also analyzed. A 13-cluster solution emerged, with clusters of bystander behaviors grouped into four larger cluster areas: victim focused, parenting/education focused, perpetrator focused, and community involvement focused. Bivariate analyses revealed that participants rated the four cluster areas as more feasible when a child was believed to be involved. Male participants rated intervention as less feasible when the couple was believed to have a history of IPV. Participants who reported a history of IPV victimization rated all four cluster areas as less effective on average, as compared with participants without a history of IPV. This study explored bystander intervention into IPV outside of a college context and among urban adults living in high-poverty areas. Results suggest that the presence of children and perceived history of IPV may affect bystander intervention. Specific recommendations to build the research base on bystander intervention in adult IPV as well as what situational, sociodemographic, and other factors mitigate against intervention among potential responders are offered.

EXPLORING FUNCTIONAL CONNECTIVITY IN FMRI VIA CLUSTERING.

PubMed

Venkataraman, Archana; Van Dijk, Koene R A; Buckner, Randy L; Golland, Polina

2009-04-01

In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering algorithms as alternatives to the commonly used Seed-Based Analysis. To enable clustering of the entire brain volume, we use the Nyström Method to approximate the necessary spectral decompositions. We apply K-Means, Spectral Clustering and Seed-Based Analysis to resting-state fMRI data collected from 45 healthy young adults. Without placing any a priori constraints, both clustering methods yield partitions that are associated with brain systems previously identified via Seed-Based Analysis. Our empirical results suggest that clustering provides a valuable tool for functional connectivity analysis.

Individual differences in wisdom conceptions: relationships to gratitude and wisdom.

PubMed

König, Susanne; Glück, Judith

2013-01-01

Previous research has shown that most laypeople hold one of two typical conceptions of wisdom--a cognitive or an integrative conception. The current study extends previous research by including a qualitative assessment of people's views of what wisdom is and how it develops, and by relating wisdom conceptions are related to levels of wisdom and gratitude. A sample of 443 young adults rated the relevance of cognitive, reflective, and affective aspects for wisdom. Cluster analyses confirmed the two typical wisdom conceptions: a primarily cognitive view of wisdom and a view emphasizing the integration of cognition, reflection, and affect. The two groups also differed in freely-generated characteristics of wisdom and its development. Additionally, the integrative conception was more frequent in individuals with higher levels of gratitude and wisdom. In sum, laypeople's conceptions of wisdom vary along similar lines as those of wisdom psychologists.

Modelling students' knowledge organisation: Genealogical conceptual networks

NASA Astrophysics Data System (ADS)

Koponen, Ismo T.; Nousiainen, Maija

2018-04-01

Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.

A local search for a graph clustering problem

NASA Astrophysics Data System (ADS)

Navrotskaya, Anna; Il'ev, Victor

2016-10-01

In the clustering problems one has to partition a given set of objects (a data set) into some subsets (called clusters) taking into consideration only similarity of the objects. One of most visual formalizations of clustering is graph clustering, that is grouping the vertices of a graph into clusters taking into consideration the edge structure of the graph whose vertices are objects and edges represent similarities between the objects. In the graph k-clustering problem the number of clusters does not exceed k and the goal is to minimize the number of edges between clusters and the number of missing edges within clusters. This problem is NP-hard for any k ≥ 2. We propose a polynomial time (2k-1)-approximation algorithm for graph k-clustering. Then we apply a local search procedure to the feasible solution found by this algorithm and hold experimental research of obtained heuristics.

Students' Changing Attitudes and Aspirations Towards Physics During Secondary School

NASA Astrophysics Data System (ADS)

Sheldrake, Richard; Mujtaba, Tamjid; Reiss, Michael J.

2017-11-01

Many countries desire more students to study science subjects, although relatively few students decide to study non-compulsory physics at upper-secondary school and at university. To gain insight into students' intentions to study non-compulsory physics, a longitudinal sample (covering 2258 students across 88 secondary schools in England) was surveyed in year 8 (age 12/13) and again in year 10 (age 14/15). Predictive modelling highlighted that perceived advice, perceived utility of physics, interest in physics, self-concept beliefs (students' subjective beliefs of their current abilities and performance) and home support specifically orientated to physics were key predictors of students' intentions. Latent-transition analysis via Markov models revealed clusters of students, given these factors at years 8 and 10. Students' intentions varied across the clusters, and at year 10 even varied when accounting for the students' underlying attitudes and beliefs, highlighting that considering clusters offered additional explanatory power and insight. Regardless of whether three-cluster, four-cluster, or five-cluster models were considered, the majority of students remained in the same cluster over time; for those who transitioned clusters, more students changed clusters reflecting an increase in attitudes than changed clusters reflecting a decrease. Students in the cluster with the most positive attitudes were most likely to remain within that cluster, while students in clusters with less positive attitudes were more likely to change clusters. Overall, the cluster profiles highlighted that students' attitudes and beliefs may be more closely related than previously assumed, but that changes in their attitudes and beliefs were indeed possible.

A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa

PubMed Central

Petegrosso, Raphael; Tolar, Jakub

2018-01-01

Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC. PMID:29630593

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Gulgundi, Mohammad Shahid; Shetty, Amba

2018-03-01

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

Self-concept of children and adolescents with cleft lip and/or palate.

PubMed

Leonard, B J; Brust, J D; Abrahams, G; Sielaff, B

1991-10-01

The self-concept of 105 children (8 to 11 years) and adolescents (12 to 18 years) with cleft lip and/or palate (CLP) was studied using the Piers-Harris Children's Self-Concept Scale and selected demographic and medical variables. Results indicated that most (98%) of children had average or above average self-concept scores. Further analysis, however, demonstrated an interaction between age and gender: adolescent girls experienced a more negative self-concept in comparison to younger girls and adolescent boys experienced a more positive self-concept in comparison to younger boys. In addition, popularity cluster scores for all children were below the mean for the normed population. Because children with CLP have additional difficulties (i.e., facial disfigurement, speech and language deficits, multiple surgeries), professionals should intercede to prevent or interrupt negative psychosocial outcomes, particularly for adolescent girls.

Wettability behavior of water droplet on organic-polluted fused quartz surfaces of pillar-type nanostructures applying molecular dynamics simulation

NASA Astrophysics Data System (ADS)

Chen, Jiaxuan; Chen, Wenyang; Xie, Yajing; Wang, Zhiguo; Qin, Jianbo

2017-02-01

Molecular dynamics (MD) is applied to research the wettability behaviors of different scale of water clusters absorbed on organic-polluted fused quartz (FQ) surface and different surface structures. The wettability of water clusters is studied under the effect of organic pollutant. With the combined influence of pillar height and interval, the stair-step Wenzel-Cassie transition critical line is obtained by analyzing stable state of water clusters on different surface structures. The results also show that when interval of pillars and the height of pillars keep constant respectively, the changing rules are exactly the opposite and these are termed as the "waterfall" rules. The substrate models of water clusters at Cassie-Baxter state which are at the vicinity of critical line are chosen to analyze the relationship of HI (refers to the pillar height/interval) ratio and scale of water cluster. The study has found that there is a critical changing threshold in the wettability changing process. When the HI ratio keeps constant, the wettability decreases first and then increase as the size of cluster increases; on the contrary, when the size of cluster keeps constant, the wettability decreases and then increase with the decrease of HI ratio, but when the size of water cluster is close to the threshold the HI ratio has little effect on the wettability.

Does clinical equipoise apply to cluster randomized trials in health research?

PubMed Central

2011-01-01

This article is part of a series of papers examining ethical issues in cluster randomized trials (CRTs) in health research. In the introductory paper in this series, Weijer and colleagues set out six areas of inquiry that must be addressed if the cluster trial is to be set on a firm ethical foundation. This paper addresses the third of the questions posed, namely, does clinical equipoise apply to CRTs in health research? The ethical principle of beneficence is the moral obligation not to harm needlessly and, when possible, to promote the welfare of research subjects. Two related ethical problems have been discussed in the CRT literature. First, are control groups that receive only usual care unduly disadvantaged? Second, when accumulating data suggests the superiority of one intervention in a trial, is there an ethical obligation to act? In individually randomized trials involving patients, similar questions are addressed by the concept of clinical equipoise, that is, the ethical requirement that, at the start of a trial, there be a state of honest, professional disagreement in the community of expert practitioners as to the preferred treatment. Since CRTs may not involve physician-researchers and patient-subjects, the applicability of clinical equipoise to CRTs is uncertain. Here we argue that clinical equipoise may be usefully grounded in a trust relationship between the state and research subjects, and, as a result, clinical equipoise is applicable to CRTs. Clinical equipoise is used to argue that control groups receiving only usual care are not disadvantaged so long as the evidence supporting the experimental and control interventions is such that experts would disagree as to which is preferred. Further, while data accumulating during the course of a CRT may favor one intervention over another, clinical equipoise supports continuing the trial until the results are likely to be broadly convincing, often coinciding with the planned completion of the trial. Finally, clinical equipoise provides research ethics committees with formal and procedural guidelines that form an important part of the assessment of the benefits and harms of CRTs in health research. PMID:21569349

Reconstruction of the mass distribution of galaxy clusters from the inversion of the thermal Sunyaev-Zel'dovich effect

NASA Astrophysics Data System (ADS)

Majer, C. L.; Meyer, S.; Konrad, S.; Sarli, E.; Bartelmann, M.

2016-07-01

This paper continues a series in which we intend to show how all observables of galaxy clusters can be combined to recover the two-dimensional, projected gravitational potential of individual clusters. Our goal is to develop a non-parametric algorithm for joint cluster reconstruction taking all cluster observables into account. For this reason we focus on the line-of-sight projected gravitational potential, proportional to the lensing potential, in order to extend existing reconstruction algorithms. In this paper, we begin with the relation between the Compton-y parameter and the Newtonian gravitational potential, assuming hydrostatic equilibrium and a polytropic stratification of the intracluster gas. Extending our first publication we now consider a spheroidal rather than a spherical cluster symmetry. We show how a Richardson-Lucy deconvolution can be used to convert the intensity change of the CMB due to the thermal Sunyaev-Zel'dovich effect into an estimate for the two-dimensional gravitational potential. We apply our reconstruction method to a cluster based on an N-body/hydrodynamical simulation processed with the characteristics (resolution and noise) of the ALMA interferometer for which we achieve a relative error of ≲20 per cent for a large fraction of the virial radius. We further apply our method to an observation of the galaxy cluster RXJ1347 for which we can reconstruct the potential with a relative error of ≲20 per cent for the observable cluster range.

Grid Computing Environment using a Beowulf Cluster

NASA Astrophysics Data System (ADS)

Alanis, Fransisco; Mahmood, Akhtar

2003-10-01

Custom-made Beowulf clusters using PCs are currently replacing expensive supercomputers to carry out complex scientific computations. At the University of Texas - Pan American, we built a 8 Gflops Beowulf Cluster for doing HEP research using RedHat Linux 7.3 and the LAM-MPI middleware. We will describe how we built and configured our Cluster, which we have named the Sphinx Beowulf Cluster. We will describe the results of our cluster benchmark studies and the run-time plots of several parallel application codes that were compiled in C on the cluster using the LAM-XMPI graphics user environment. We will demonstrate a "simple" prototype grid environment, where we will submit and run parallel jobs remotely across multiple cluster nodes over the internet from the presentation room at Texas Tech. University. The Sphinx Beowulf Cluster will be used for monte-carlo grid test-bed studies for the LHC-ATLAS high energy physics experiment. Grid is a new IT concept for the next generation of the "Super Internet" for high-performance computing. The Grid will allow scientist worldwide to view and analyze huge amounts of data flowing from the large-scale experiments in High Energy Physics. The Grid is expected to bring together geographically and organizationally dispersed computational resources, such as CPUs, storage systems, communication systems, and data sources.

Analytical Energy Gradients for Excited-State Coupled-Cluster Methods

NASA Astrophysics Data System (ADS)

Wladyslawski, Mark; Nooijen, Marcel

The equation-of-motion coupled-cluster (EOM-CC) and similarity transformed equation-of-motion coupled-cluster (STEOM-CC) methods have been firmly established as accurate and routinely applicable extensions of single-reference coupled-cluster theory to describe electronically excited states. An overview of these methods is provided, with emphasis on the many-body similarity transform concept that is the key to a rationalization of their accuracy. The main topic of the paper is the derivation of analytical energy gradients for such non-variational electronic structure approaches, with an ultimate focus on obtaining their detailed algebraic working equations. A general theoretical framework using Lagrange's method of undetermined multipliers is presented, and the method is applied to formulate the EOM-CC and STEOM-CC gradients in abstract operator terms, following the previous work in [P.G. Szalay, Int. J. Quantum Chem. 55 (1995) 151] and [S.R. Gwaltney, R.J. Bartlett, M. Nooijen, J. Chem. Phys. 111 (1999) 58]. Moreover, the systematics of the Lagrange multiplier approach is suitable for automation by computer, enabling the derivation of the detailed derivative equations through a standardized and direct procedure. To this end, we have developed the SMART (Symbolic Manipulation and Regrouping of Tensors) package of automated symbolic algebra routines, written in the Mathematica programming language. The SMART toolkit provides the means to expand, differentiate, and simplify equations by manipulation of the detailed algebraic tensor expressions directly. The Lagrangian multiplier formulation establishes a uniform strategy to perform the automated derivation in a standardized manner: A Lagrange multiplier functional is constructed from the explicit algebraic equations that define the energy in the electronic method; the energy functional is then made fully variational with respect to all of its parameters, and the symbolic differentiations directly yield the explicit equations for the wavefunction amplitudes, the Lagrange multipliers, and the analytical gradient via the perturbation-independent generalized Hellmann-Feynman effective density matrix. This systematic automated derivation procedure is applied to obtain the detailed gradient equations for the excitation energy (EE-), double ionization potential (DIP-), and double electron affinity (DEA-) similarity transformed equation-of-motion coupled-cluster singles-and-doubles (STEOM-CCSD) methods. In addition, the derivatives of the closed-shell-reference excitation energy (EE-), ionization potential (IP-), and electron affinity (EA-) equation-of-motion coupled-cluster singles-and-doubles (EOM-CCSD) methods are derived. Furthermore, the perturbative EOM-PT and STEOM-PT gradients are obtained. The algebraic derivative expressions for these dozen methods are all derived here uniformly through the automated Lagrange multiplier process and are expressed compactly in a chain-rule/intermediate-density formulation, which facilitates a unified modular implementation of analytic energy gradients for CCSD/PT-based electronic methods. The working equations for these analytical gradients are presented in full detail, and their factorization and implementation into an efficient computer code are discussed.

Wide Field X-Ray Telescope Mission Concept Study Results

NASA Technical Reports Server (NTRS)

Hopkins, R. C.; Thomas, H. D.; Fabisinski, L. L.; Baysinger, M.; Hornsby, L. S.; Maples, C. D.; Purlee, T. E.; Capizzo, P. D.; Percy, T. K.

2014-01-01

The Wide Field X-Ray Telescope (WFXT) is an astrophysics mission concept for detecting and studying extra-galactic x-ray sources, including active galactic nuclei and clusters of galaxies, in an effort to further understand cosmic evolution and structure. This Technical Memorandum details the results of a mission concept study completed by the Advanced Concepts Office at NASA Marshall Space Flight Center in 2012. The design team analyzed the mission and instrument requirements, and designed a spacecraft that enables the WFXT mission while using high heritage components. Design work included selecting components and sizing subsystems for power, avionics, guidance, navigation and control, propulsion, structures, command and data handling, communications, and thermal control.

Unified Technical Concepts. Math for Technicians.

ERIC Educational Resources Information Center

Center for Occupational Research and Development, Inc., Waco, TX.

Unified Technical Concepts (UTC) is a modular system for teaching applied physics in two-year postsecondary technician programs. This UTC classroom textbook, consisting of 10 chapters, deals with mathematical concepts as they apply to the study of physics. Addressed in the individual chapters of the text are the following topics: angles and…

A Computer Vision Approach to Identify Einstein Rings and Arcs

NASA Astrophysics Data System (ADS)

Lee, Chien-Hsiu

2017-03-01

Einstein rings are rare gems of strong lensing phenomena; the ring images can be used to probe the underlying lens gravitational potential at every position angles, tightly constraining the lens mass profile. In addition, the magnified images also enable us to probe high-z galaxies with enhanced resolution and signal-to-noise ratios. However, only a handful of Einstein rings have been reported, either from serendipitous discoveries or or visual inspections of hundred thousands of massive galaxies or galaxy clusters. In the era of large sky surveys, an automated approach to identify ring pattern in the big data to come is in high demand. Here, we present an Einstein ring recognition approach based on computer vision techniques. The workhorse is the circle Hough transform that recognise circular patterns or arcs in the images. We propose a two-tier approach by first pre-selecting massive galaxies associated with multiple blue objects as possible lens, than use Hough transform to identify circular pattern. As a proof-of-concept, we apply our approach to SDSS, with a high completeness, albeit with low purity. We also apply our approach to other lenses in DES, HSC-SSP, and UltraVISTA survey, illustrating the versatility of our approach.

A multicomponent matched filter cluster confirmation tool for eROSITA: initial application to the RASS and DES-SV data sets

DOE PAGES

Klein, M.; Mohr, J. J.; Desai, S.; ...

2017-11-14

We describe a multi-component matched filter cluster confirmation tool (MCMF) designed for the study of large X-ray source catalogs produced by the upcoming X-ray all-sky survey mission eROSITA. We apply the method to confirm a sample of 88 clusters with redshifts $0.05

A multicomponent matched filter cluster confirmation tool for eROSITA: initial application to the RASS and DES-SV data sets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klein, M.; Mohr, J. J.; Desai, S.

We describe a multi-component matched filter cluster confirmation tool (MCMF) designed for the study of large X-ray source catalogs produced by the upcoming X-ray all-sky survey mission eROSITA. We apply the method to confirm a sample of 88 clusters with redshifts $0.05

Hausdorff clustering

NASA Astrophysics Data System (ADS)

Basalto, Nicolas; Bellotti, Roberto; de Carlo, Francesco; Facchi, Paolo; Pantaleo, Ester; Pascazio, Saverio

2008-10-01

A clustering algorithm based on the Hausdorff distance is analyzed and compared to the single, complete, and average linkage algorithms. The four clustering procedures are applied to a toy example and to the time series of financial data. The dendrograms are scrutinized and their features compared. The Hausdorff linkage relies on firm mathematical grounds and turns out to be very effective when one has to discriminate among complex structures.

Multivariate Analysis of the Visual Information Processing of Numbers

ERIC Educational Resources Information Center

Levine, David M.

1977-01-01

Nonmetric multidimensional scaling and hierarchical clustering procedures are applied to a confusion matrix of numerals. Two dimensions were interpreted: straight versus curved, and locus of curvature. Four major clusters of numerals were developed. (Author/JKS)

Unsupervised analysis of small animal dynamic Cerenkov luminescence imaging

NASA Astrophysics Data System (ADS)

Spinelli, Antonello E.; Boschi, Federico

2011-12-01

Clustering analysis (CA) and principal component analysis (PCA) were applied to dynamic Cerenkov luminescence images (dCLI). In order to investigate the performances of the proposed approaches, two distinct dynamic data sets obtained by injecting mice with 32P-ATP and 18F-FDG were acquired using the IVIS 200 optical imager. The k-means clustering algorithm has been applied to dCLI and was implemented using interactive data language 8.1. We show that cluster analysis allows us to obtain good agreement between the clustered and the corresponding emission regions like the bladder, the liver, and the tumor. We also show a good correspondence between the time activity curves of the different regions obtained by using CA and manual region of interest analysis on dCLIT and PCA images. We conclude that CA provides an automatic unsupervised method for the analysis of preclinical dynamic Cerenkov luminescence image data.

New triethoxysilylated 10-vertex closo-decaborate clusters. Synthesis and controlled immobilization into mesoporous silica.

PubMed

Abi-Ghaida, Fatima; Laila, Zahra; Ibrahim, Ghassan; Naoufal, Daoud; Mehdi, Ahmad

2014-09-14

Novel silylated hydroborate clusters comprising the closo-decaborate cage were prepared and characterized by (1)H, (13)C, (11)B, (29)Si NMR and mass spectroscopy ESI. The synthesis of such silylated clusters was achieved using reactive derivatives of [B10H10](2-), [1-B10H9N2](-) and [2-B10H9CO](-). These silylated decaborate clusters constitute a new class of precursors that can be covalently anchored onto various silica supports without any prior surface modification. As a proof of concept, the synthesized precursors were successfully anchored on mesoporous silica, SBA-15 type, in different percentages, where the mesoporous material retained its structure. All materials modified with closo-decaborate were characterized by (11)B and (29)Si solid state NMR, XRD, TEM and nitrogen sorption.

Clustering and optimal arrangement of enzymes in reaction-diffusion systems.

PubMed

Buchner, Alexander; Tostevin, Filipe; Gerland, Ulrich

2013-05-17

Enzymes within biochemical pathways are often colocalized, yet the consequences of specific spatial enzyme arrangements remain poorly understood. We study the impact of enzyme arrangement on reaction efficiency within a reaction-diffusion model. The optimal arrangement transitions from a cluster to a distributed profile as a single parameter, which controls the probability of reaction versus diffusive loss of pathway intermediates, is varied. We introduce the concept of enzyme exposure to explain how this transition arises from the stochastic nature of molecular reactions and diffusion.

Explaining ecological clusters of maternal depression in South Western Sydney.

PubMed

Eastwood ED, John; Kemp, Lynn; Jalaludin, Bin

2014-01-24

The aim of the qualitative study reported here was to: 1) explain the observed clustering of postnatal depressive symptoms in South Western Sydney; and 2) identify group-level mechanisms that would add to our understanding of the social determinants of maternal depression. Critical realism provided the methodological underpinning for the study. The setting was four local government areas in South Western Sydney, Australia. Child and Family practitioners and mothers in naturally occurring mothers groups were interviewed. Using an open coding approach to maximise emergence of patterns and relationships we have identified seven theoretical concepts that might explain the observed spatial clustering of maternal depression. The theoretical concepts identified were: Community-level social networks; Social Capital and Social Cohesion; "Depressed community"; Access to services at the group level; Ethnic segregation and diversity; Supportive social policy; and Big business. We postulate that these regional structural, economic, social and cultural mechanisms partially explain the pattern of maternal depression observed in families and communities within South Western Sydney. We further observe that powerful global economic and political forces are having an impact on the local situation. The challenge for policy and practice is to support mothers and their families within this adverse regional and global-economic context.

SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies

PubMed Central

Bouaziz, Matthieu; Paccard, Caroline; Guedj, Mickael; Ambroise, Christophe

2012-01-01

Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns. PMID:23077494

Children's patterns of reasoning about reading and addition concepts.

PubMed

Farrington-Flint, Lee; Canobi, Katherine H; Wood, Clare; Faulkner, Dorothy

2010-06-01

Children's reasoning was examined within two educational contexts (word reading and addition) so as to understand the factors that contribute to relational reasoning in the two domains. Sixty-seven 5- to 7-year-olds were given a series of related words to read or single-digit addition items to solve (interspersed with unrelated items). The frequency, accuracy, and response times of children's self-reports on the conceptually related items provided a measure of relational reasoning, while performance on the unrelated addition and reading items provided a measure of procedural skill. The results indicated that the children's ability to use conceptual relations to solve both reading and addition problems enhanced speed and accuracy levels, increased with age, and was related to procedural skill. However, regression analyses revealed that domain-specific competencies can best explain the use of conceptual relations in both reading and addition. Moreover, a cluster analysis revealed that children differ according to the academic domain in which they first apply conceptual relations and these differences are related to individual variation in their procedural skills within these particular domains. These results highlight the developmental significance of relational reasoning in the context of reading and addition and underscore the importance of concept-procedure links in explaining children's literacy and arithmetical development.

Application of Percolation Theory to Complex Interconnected Networks in Advanced Functional Composites

NASA Astrophysics Data System (ADS)

Hing, P.

2011-11-01

Percolation theory deals with the behaviour of connected clusters in a system. Originally developed for studying the flow of liquid in a porous body, the percolation theory has been extended to quantum computation and communication, entanglement percolation in quantum networks, cosmology, chaotic situations, properties of disordered solids, pandemics, petroleum industry, finance, control of traffic and so on. In this paper, the application of various models of the percolation theory to predict and explain the properties of a specially developed family of dense sintered and highly refractory Al2O3-W composites for potential application in high intensity discharge light sources such as high pressure sodium lamps and ceramic metal halide lamps are presented and discussed. The low cost, core-shell concept can be extended to develop functional composite materials with unusual dielectric, electrical, magnetic, superconducting, and piezoelectric properties starting from a classical insulator. The core shell concept can also be applied to develop catalysts with high specific surface areas with minimal amount of expensive platinium, palladium or rare earth nano structured materials for light harvesting, replicating natural photosynthesis, in synthetic zeolite composites for the cracking and separation of crude oil. There is also possibility of developing micron and nanosize Faraday cages for quantum devices, nano electronics and spintronics. The possibilities are limitless.

Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

PubMed Central

Zheng, Ming; Sun, Ying; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

2012-01-01

Background Gravitation field algorithm (GFA) is a new optimization algorithm which is based on an imitation of natural phenomena. GFA can do well both for searching global minimum and multi-minima in computational biology. But GFA needs to be improved for increasing efficiency, and modified for applying to some discrete data problems in system biology. Method An improved GFA called IGFA was proposed in this paper. Two parts were improved in IGFA. The first one is the rule of random division, which is a reasonable strategy and makes running time shorter. The other one is rotation factor, which can improve the accuracy of IGFA. And to apply IGFA to the hierarchical clustering, the initial part and the movement operator were modified. Results Two kinds of experiments were used to test IGFA. And IGFA was applied to hierarchical clustering. The global minimum experiment was used with IGFA, GFA, GA (genetic algorithm) and SA (simulated annealing). Multi-minima experiment was used with IGFA and GFA. The two experiments results were compared with each other and proved the efficiency of IGFA. IGFA is better than GFA both in accuracy and running time. For the hierarchical clustering, IGFA is used to optimize the smallest distance of genes pairs, and the results were compared with GA and SA, singular-linkage clustering, UPGMA. The efficiency of IGFA is proved. PMID:23173043

Tissue Probability Map Constrained 4-D Clustering Algorithm for Increased Accuracy and Robustness in Serial MR Brain Image Segmentation

PubMed Central

Xue, Zhong; Shen, Dinggang; Li, Hai; Wong, Stephen

2010-01-01

The traditional fuzzy clustering algorithm and its extensions have been successfully applied in medical image segmentation. However, because of the variability of tissues and anatomical structures, the clustering results might be biased by the tissue population and intensity differences. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue probability map constrained clustering algorithm and apply it to serial MR brain image segmentation, i.e., a series of 3-D MR brain images of the same subject at different time points. Using the new serial image segmentation algorithm in the framework of the CLASSIC framework, which iteratively segments the images and estimates the longitudinal deformations, we improved both accuracy and robustness for serial image computing, and at the mean time produced longitudinally consistent segmentation and stable measures. In the algorithm, the tissue probability maps consist of both the population-based and subject-specific segmentation priors. Experimental study using both simulated longitudinal MR brain data and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data confirmed that using both priors more accurate and robust segmentation results can be obtained. The proposed algorithm can be applied in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders. PMID:26566399

A coupled-cluster study of photodetachment cross sections of closed-shell anions

NASA Astrophysics Data System (ADS)

Cukras, Janusz; Decleva, Piero; Coriani, Sonia

2014-11-01

We investigate the performance of Stieltjes Imaging applied to Lanczos pseudo-spectra generated at the coupled cluster singles and doubles, coupled cluster singles and approximate iterative doubles and coupled cluster singles levels of theory in modeling the photodetachment cross sections of the closed shell anions H-, Li-, Na-, F-, Cl-, and OH-. The accurate description of double excitations is found to play a much more important role than in the case of photoionization of neutral species.

A coupled-cluster study of photodetachment cross sections of closed-shell anions.

PubMed

Cukras, Janusz; Decleva, Piero; Coriani, Sonia

2014-11-07

We investigate the performance of Stieltjes Imaging applied to Lanczos pseudo-spectra generated at the coupled cluster singles and doubles, coupled cluster singles and approximate iterative doubles and coupled cluster singles levels of theory in modeling the photodetachment cross sections of the closed shell anions H(-), Li(-), Na(-), F(-), Cl(-), and OH(-). The accurate description of double excitations is found to play a much more important role than in the case of photoionization of neutral species.

Reconstruction of the two-dimensional gravitational potential of galaxy clusters from X-ray and Sunyaev-Zel'dovich measurements

NASA Astrophysics Data System (ADS)

Tchernin, C.; Bartelmann, M.; Huber, K.; Dekel, A.; Hurier, G.; Majer, C. L.; Meyer, S.; Zinger, E.; Eckert, D.; Meneghetti, M.; Merten, J.

2018-06-01

Context. The mass of galaxy clusters is not a direct observable, nonetheless it is commonly used to probe cosmological models. Based on the combination of all main cluster observables, that is, the X-ray emission, the thermal Sunyaev-Zel'dovich (SZ) signal, the velocity dispersion of the cluster galaxies, and gravitational lensing, the gravitational potential of galaxy clusters can be jointly reconstructed. Aims: We derive the two main ingredients required for this joint reconstruction: the potentials individually reconstructed from the observables and their covariance matrices, which act as a weight in the joint reconstruction. We show here the method to derive these quantities. The result of the joint reconstruction applied to a real cluster will be discussed in a forthcoming paper. Methods: We apply the Richardson-Lucy deprojection algorithm to data on a two-dimensional (2D) grid. We first test the 2D deprojection algorithm on a β-profile. Assuming hydrostatic equilibrium, we further reconstruct the gravitational potential of a simulated galaxy cluster based on synthetic SZ and X-ray data. We then reconstruct the projected gravitational potential of the massive and dynamically active cluster Abell 2142, based on the X-ray observations collected with XMM-Newton and the SZ observations from the Planck satellite. Finally, we compute the covariance matrix of the projected reconstructed potential of the cluster Abell 2142 based on the X-ray measurements collected with XMM-Newton. Results: The gravitational potentials of the simulated cluster recovered from synthetic X-ray and SZ data are consistent, even though the potential reconstructed from X-rays shows larger deviations from the true potential. Regarding Abell 2142, the projected gravitational cluster potentials recovered from SZ and X-ray data reproduce well the projected potential inferred from gravitational-lensing observations. We also observe that the covariance matrix of the potential for Abell 2142 reconstructed from XMM-Newton data sensitively depends on the resolution of the deprojected grid and on the smoothing scale used in the deprojection. Conclusions: We show that the Richardson-Lucy deprojection method can be effectively applied on a grid and that the projected potential is well recovered from real and simulated data based on X-ray and SZ signal. The comparison between the reconstructed potentials from the different observables provides additional information on the validity of the assumptions as function of the projected radius.

Process for metallization of a substrate by irradiative curing of a catalyst applied thereto

DOEpatents

Chen, Ken S.; Morgan, William P.; Zich, John L.

1999-01-01

An improved additive process for metallization of substrates is described whereby a catalyst solution is applied to a surface of a substrate. Metallic catalytic clusters can be formed in the catalyst solution on the substrate surface by irradiating the substrate. Electroless plating can then deposit metal onto the portion of the substrate surface having metallic clusters. Additional metallization thickness can be obtained by electrolytically plating the substrate surface after the electroless plating step.

A search for mini-clusters in Japan-USSR Joint Experiment at Pamir

NASA Technical Reports Server (NTRS)

1985-01-01

A search for mini-clusters, very collimated shower clusters of hadrons and electromagnetic particles, is made for the hadron and gamma families observed by Japan-USSR joint carbon chamber at Pamir. The existence of anomalous correlation between hadrons and electromagnetic particles is found. The decascading method is applied to the families and it is found that 11 clusters which include hadrons as members have smaller spread, Er 3.5 GeV.m and larger lateral spread, E'R' 100 GeV.m, from the family center. In the simulated events, such clusters were found to be very rare.

Semantic Clustering of Search Engine Results

PubMed Central

Soliman, Sara Saad; El-Sayed, Maged F.; Hassan, Yasser F.

2015-01-01

This paper presents a novel approach for search engine results clustering that relies on the semantics of the retrieved documents rather than the terms in those documents. The proposed approach takes into consideration both lexical and semantics similarities among documents and applies activation spreading technique in order to generate semantically meaningful clusters. This approach allows documents that are semantically similar to be clustered together rather than clustering documents based on similar terms. A prototype is implemented and several experiments are conducted to test the prospered solution. The result of the experiment confirmed that the proposed solution achieves remarkable results in terms of precision. PMID:26933673

Efficient clustering aggregation based on data fragments.

PubMed

Wu, Ou; Hu, Weiming; Maybank, Stephen J; Zhu, Mingliang; Li, Bing

2012-06-01

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.

Population clustering based on copy number variations detected from next generation sequencing data.

PubMed

Duan, Junbo; Zhang, Ji-Gang; Wan, Mingxi; Deng, Hong-Wen; Wang, Yu-Ping

2014-08-01

Copy number variations (CNVs) can be used as significant bio-markers and next generation sequencing (NGS) provides a high resolution detection of these CNVs. But how to extract features from CNVs and further apply them to genomic studies such as population clustering have become a big challenge. In this paper, we propose a novel method for population clustering based on CNVs from NGS. First, CNVs are extracted from each sample to form a feature matrix. Then, this feature matrix is decomposed into the source matrix and weight matrix with non-negative matrix factorization (NMF). The source matrix consists of common CNVs that are shared by all the samples from the same group, and the weight matrix indicates the corresponding level of CNVs from each sample. Therefore, using NMF of CNVs one can differentiate samples from different ethnic groups, i.e. population clustering. To validate the approach, we applied it to the analysis of both simulation data and two real data set from the 1000 Genomes Project. The results on simulation data demonstrate that the proposed method can recover the true common CNVs with high quality. The results on the first real data analysis show that the proposed method can cluster two family trio with different ancestries into two ethnic groups and the results on the second real data analysis show that the proposed method can be applied to the whole-genome with large sample size consisting of multiple groups. Both results demonstrate the potential of the proposed method for population clustering.

Metal Cluster Models for Heterogeneous Catalysis: A Matrix-Isolation Perspective.

PubMed

Hübner, Olaf; Himmel, Hans-Jörg

2018-02-19

Metal cluster models are of high relevance for establishing new mechanistic concepts for heterogeneous catalysis. The high reactivity and particular selectivity of metal clusters is caused by the wealth of low-lying electronically excited states that are often thermally populated. Thereby the metal clusters are flexible with regard to their electronic structure and can adjust their states to be appropriate for the reaction with a particular substrate. The matrix isolation technique is ideally suited for studying excited state reactivity. The low matrix temperatures (generally 4-40 K) of the noble gas matrix host guarantee that all clusters are in their electronic ground-state (with only a very few exceptions). Electronically excited states can then be selectively populated and their reactivity probed. Unfortunately, a systematic research in this direction has not been made up to date. The purpose of this review is to provide the grounds for a directed approach to understand cluster reactivity through matrix-isolation studies combined with quantum chemical calculations. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Automated method to differentiate between native and mirror protein models obtained from contact maps.

PubMed

Kurczynska, Monika; Kotulska, Malgorzata

2018-01-01

Mirror protein structures are often considered as artifacts in modeling protein structures. However, they may soon become a new branch of biochemistry. Moreover, methods of protein structure reconstruction, based on their residue-residue contact maps, need methodology to differentiate between models of native and mirror orientation, especially regarding the reconstructed backbones. We analyzed 130 500 structural protein models obtained from contact maps of 1 305 SCOP domains belonging to all 7 structural classes. On average, the same numbers of native and mirror models were obtained among 100 models generated for each domain. Since their structural features are often not sufficient for differentiating between the two types of model orientations, we proposed to apply various energy terms (ETs) from PyRosetta to separate native and mirror models. To automate the procedure for differentiating these models, the k-means clustering algorithm was applied. Using total energy did not allow to obtain appropriate clusters-the accuracy of the clustering for class A (all helices) was no more than 0.52. Therefore, we tested a series of different k-means clusterings based on various combinations of ETs. Finally, applying two most differentiating ETs for each class allowed to obtain satisfying results. To unify the method for differentiating between native and mirror models, independent of their structural class, the two best ETs for each class were considered. Finally, the k-means clustering algorithm used three common ETs: probability of amino acid assuming certain values of dihedral angles Φ and Ψ, Ramachandran preferences and Coulomb interactions. The accuracies of clustering with these ETs were in the range between 0.68 and 0.76, with sensitivity and selectivity in the range between 0.68 and 0.87, depending on the structural class. The method can be applied to all fully-automated tools for protein structure reconstruction based on contact maps, especially those analyzing big sets of models.

Methanol clusters (CH3OH)n, n = 3-6 in external electric fields: density functional theory approach.

PubMed

Rai, Dhurba; Kulkarni, Anant D; Gejji, Shridhar P; Pathak, Rajeev K

2011-07-14

Structural evolution of cyclic and branched-cyclic methanol clusters containing three to six molecules, under the influence of externally applied uniform static electric field is studied within the density functional theory. Akin to the situation for water clusters, the electric field is seen to stretch the intermolecular hydrogen bonds, and eventually break the H-bonded network at certain characteristic threshold field values of field strength in the range 0.009-0.016 a.u., yielding linear or branched structures with a lower energy. These structural transitions are characterized by an abrupt increase in the electric dipole moment riding over its otherwise steady nonlinear increase with the applied field. The field tends to rupture the H-bonded structure; consequently, the number of hydrogen bonds decreases with increasing field strength. Vibrational spectra analyzed for fields applied perpendicular to the cyclic ring structures bring out the shifts in the OH ring vibrations (blueshift) and the CO stretch vibrations (redshift). For a given field strength, the blueshifts increase with the number of molecules in the ring and are found to be generally larger than those in the corresponding water cluster counterparts.

Conceptual model and map of financial exploitation of older adults.

PubMed

Conrad, Kendon J; Iris, Madelyn; Ridings, John W; Fairman, Kimberly P; Rosen, Abby; Wilber, Kathleen H

2011-10-01

This article describes the processes and outcomes of three-dimensional concept mapping to conceptualize financial exploitation of older adults. Statements were generated from a literature review and by local and national panels consisting of 16 experts in the field of financial exploitation. These statements were sorted and rated using Concept Systems software, which grouped the statements into clusters and depicted them as a map. Statements were grouped into six clusters, and ranked by the experts as follows in descending severity: (a) theft and scams, (b) financial victimization, (c) financial entitlement, (d) coercion, (e) signs of possible financial exploitation, and (f) money management difficulties. The hierarchical model can be used to identify elder financial exploitation and differentiate it from related but distinct areas of victimization. The severity hierarchy may be used to develop measures that will enable more precise screening for triage of clients into appropriate interventions.

Predicted performance of an integrated modular engine system

NASA Technical Reports Server (NTRS)

Binder, Michael; Felder, James L.

1993-01-01

Space vehicle propulsion systems are traditionally comprised of a cluster of discrete engines, each with its own set of turbopumps, valves, and a thrust chamber. The Integrated Modular Engine (IME) concept proposes a vehicle propulsion system comprised of multiple turbopumps, valves, and thrust chambers which are all interconnected. The IME concept has potential advantages in fault-tolerance, weight, and operational efficiency compared with the traditional clustered engine configuration. The purpose of this study is to examine the steady-state performance of an IME system with various components removed to simulate fault conditions. An IME configuration for a hydrogen/oxygen expander cycle propulsion system with four sets of turbopumps and eight thrust chambers has been modeled using the Rocket Engine Transient Simulator (ROCETS) program. The nominal steady-state performance is simulated, as well as turbopump thrust chamber and duct failures. The impact of component failures on system performance is discussed in the context of the system's fault tolerant capabilities.

Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

EPA Science Inventory

Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

Statistical sampling of the distribution of uranium deposits using geologic/geographic clusters

USGS Publications Warehouse

Finch, W.I.; Grundy, W.D.; Pierson, C.T.

1992-01-01

The concept of geologic/geographic clusters was developed particularly to study grade and tonnage models for sandstone-type uranium deposits. A cluster is a grouping of mined as well as unmined uranium occurrences within an arbitrary area about 8 km across. A cluster is a statistical sample that will reflect accurately the distribution of uranium in large regions relative to various geologic and geographic features. The example of the Colorado Plateau Uranium Province reveals that only 3 percent of the total number of clusters is in the largest tonnage-size category, greater than 10,000 short tons U3O8, and that 80 percent of the clusters are hosted by Triassic and Jurassic rocks. The distributions of grade and tonnage for clusters in the Powder River Basin show a wide variation; the grade distribution is highly variable, reflecting a difference between roll-front deposits and concretionary deposits, and the Basin contains about half the number in the greater-than-10,000 tonnage-size class as does the Colorado Plateau, even though it is much smaller. The grade and tonnage models should prove useful in finding the richest and largest uranium deposits. ?? 1992 Oxford University Press.

Applied Epistemology and Understanding in Information Studies

ERIC Educational Resources Information Center

Gorichanaz, Tim

2017-01-01

Introduction: Applied epistemology allows information studies to benefit from developments in philosophy. In information studies, epistemic concepts are rarely considered in detail. This paper offers a review of several epistemic concepts, focusing on understanding, as a call for further work in applied epistemology in information studies. Method:…

Buckets, Clusters and Dienst

NASA Technical Reports Server (NTRS)

Nelson, Michael L.; Maly, Kurt; Shen, Stewart N. T.

1997-01-01

In this paper we describe NCSTRL+, a unified, canonical digital library for scientific and technical information (STI). NCSTRL+ is based on the Networked Computer Science Technical Report Library (NCSTRL), a World Wide Web (WWW) accessible digital library (DL) that provides access to over 80 university departments and laboratories. NCSTRL+ implements two new technologies: cluster functionality and publishing "buckets." We have extended the Dienst protocol, the protocol underlying NCSTRL, to provide the ability to "cluster" independent collections into a logically centralized digital library based upon subject category classification, type of organization, and genres of material. The concept of "buckets" provides a mechanism for publishing and managing logically linked entities with multiple data formats. The NCSTRL+ prototype DL contains the holdings of NCSTRL and the NASA Technical Report Server (NTRS). The prototype demonstrates the feasibility of publishing into a multi-cluster DL, searching across clusters, and storing and presenting buckets of information. We show that the overhead for these additional capabilities is minimal to both the author and the user when compared to the equivalent process within NCSTRL.

Extending cluster Lot Quality Assurance Sampling designs for surveillance programs

PubMed Central

Hund, Lauren; Pagano, Marcello

2014-01-01

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance based on the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible non-parametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate. PMID:24633656

Generating clustered scale-free networks using Poisson based localization of edges

NASA Astrophysics Data System (ADS)

Türker, İlker

2018-05-01

We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.

Spatial clustering of metal and metalloid mixtures in unregulated water sources on the Navajo Nation - Arizona, New Mexico, and Utah, USA.

PubMed

Hoover, Joseph H; Coker, Eric; Barney, Yolanda; Shuey, Chris; Lewis, Johnnye

2018-08-15

Contaminant mixtures are identified regularly in public and private drinking water supplies throughout the United States; however, the complex and often correlated nature of mixtures makes identification of relevant combinations challenging. This study employed a Bayesian clustering method to identify subgroups of water sources with similar metal and metalloid profiles. Additionally, a spatial scan statistic assessed spatial clustering of these subgroups and a human health metric was applied to investigate potential for human toxicity. These methods were applied to a dataset comprised of metal and metalloid measurements from unregulated water sources located on the Navajo Nation, in the southwest United States. Results indicated distinct subgroups of water sources with similar contaminant profiles and that some of these subgroups were spatially clustered. Several profiles had metal and metalloid concentrations that may have potential for human toxicity including arsenic, uranium, lead, manganese, and selenium. This approach may be useful for identifying mixtures in water sources, spatially evaluating the clusters, and help inform toxicological research investigating mixtures. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Extending cluster lot quality assurance sampling designs for surveillance programs.

PubMed

Hund, Lauren; Pagano, Marcello

2014-07-20

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate. Copyright © 2014 John Wiley & Sons, Ltd.

Applying Pose Clustering and MD Simulations To Eliminate False Positives in Molecular Docking.

PubMed

Makeneni, Spandana; Thieker, David F; Woods, Robert J

2018-03-26

In this work, we developed a computational protocol that employs multiple molecular docking experiments, followed by pose clustering, molecular dynamic simulations (10 ns), and energy rescoring to produce reliable 3D models of antibody-carbohydrate complexes. The protocol was applied to 10 antibody-carbohydrate co-complexes and three unliganded (apo) antibodies. Pose clustering significantly reduced the number of potential poses. For each system, 15 or fewer clusters out of 100 initial poses were generated and chosen for further analysis. Molecular dynamics (MD) simulations allowed the docked poses to either converge or disperse, and rescoring increased the likelihood that the best-ranked pose was an acceptable pose. This approach is amenable to automation and can be a valuable aid in determining the structure of antibody-carbohydrate complexes provided there is no major side chain rearrangement or backbone conformational change in the H3 loop of the CDR regions. Further, the basic protocol of docking a small ligand to a known binding site, clustering the results, and performing MD with a suitable force field is applicable to any protein ligand system.

A concept mapping approach to identifying the barriers to implementing an evidence-based sports injury prevention programme.

PubMed

Donaldson, Alex; Callaghan, Aisling; Bizzini, Mario; Jowett, Andrew; Keyzer, Patrick; Nicholson, Matthew

2018-01-20

Understanding the barriers to programme use is important to facilitate implementation of injury prevention programmes in real-word settings. This study investigated the barriers to coaches of adolescent female soccer teams, in Victoria, Australia, implementing the evidence-based FIFA 11+ injury prevention programme. Concept mapping with data collected from 19 soccer coaches and administrators. Brainstorming generated 65 statements as barriers to 11+ implementation. After the statements were synthesised and edited, participants sorted 59 statements into groups (mean, 6.2 groups; range, 3-10 groups). Multidimensional scaling and hierarchical cluster analysis identified a six-cluster solution: Lack of 11+ knowledge among coaches (15 statements), Lack of player enjoyment and engagement (14), Lack of link to football-related goals (11), Lack of facilities and resources (8), Lack of leadership (6) and Lack of time at training (5). Statements in the 'Lack of 11+ knowledge among coaches' cluster received the highest mean importance (3.67 out of 5) and feasibility for the Football Federation to address (3.20) rating. Statements in the 'Lack of facilities and resources' cluster received the lowest mean importance rating (2.23), while statements in the 'Lack of time at training' cluster received the lowest mean feasibility rating (2.19). A multistrategy, ecological approach to implementing the 11+-with specific attention paid to improving coach knowledge about the 11+ and how to implement it, linking the 11+ to the primary goal of soccer training, and organisational leadership-is required to improve the uptake of the 11+ among the targeted coaches. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Reasons for Using Flavored Liquids among Electronic Cigarette Users: a Concept Mapping Study

PubMed Central

Soule, Eric K.; Lopez, Alexa A.; Guy, Mignonne C.; Cobb, Caroline O.

2016-01-01

Background Electronic cigarettes (ECIGs) aerosolize liquids often containing flavorants for inhalation. Few studies have examined the role of flavors in ECIG use. This study’s purpose was to examine reasons for flavored ECIG use using a mixed-method approach, concept mapping (CM). Methods Forty-six past 30-day adult ECIG users recruited from vape forums/conferences completed three online CM tasks. Participants brainstormed responses to a prompt: “A specific reason I use flavored e-liquid in my electronic cigarette product is…”. The final 107 brainstormed statements were sorted by participants into groups of similar content. Participants rated each statement on a 7-point scale (1-Definitely NOT a reason to 7-Definitely a reason) based on a prompt: “This is a specific reason why I used flavored e-liquid in my electronic cigarette product in the past month.” A cluster map was generated from participants’ sorting and ratings using CM statistical software. Cluster mean ratings were compared. Results Analysis revealed five clusters of reasons for flavored ECIG use including Increased Satisfaction/Enjoyment, Better Feel/Taste than Cigarettes, Variety/Customization, Food Craving Suppression, and Social Impacts. Statements in the Increased Satisfaction/Enjoyment and Better Feel/Taste than Cigarettes clusters were rated significantly higher than statements from other clusters (ps<.05). Some statements indicated flavors were perceived as masking agents for nicotine or other bad tastes associated with cigarette smoking making ECIG use more palatable. Conclusions Flavored ECIGs are used for many reasons. Some statements suggested flavors may increase the risk of ECIG addiction. These results support continued examination of the role of flavors and ECIG use behaviors. PMID:27460860

User identified positive outcome expectancies of electronic cigarette use: A concept mapping study.

PubMed

Soule, Eric K; Maloney, Sarah F; Guy, Mignonne C; Eissenberg, Thomas; Fagan, Pebbles

2017-05-01

Electronic cigarette (ECIG) use is growing in popularity, but little is known about the perceived positive outcomes of ECIG use. This study used concept mapping (CM) to examine positive ECIG outcome expectancies. Sixty-three past 30-day ECIG users (38.1% female) between the ages of 18 and 64 (M = 37.8, SD = 13.3) completed a CM module. In an online program, participants provided statements that completed a prompt: "A specific positive, enjoyable, or exciting effect (i.e., physical or psychological) that I have experienced WHILE USING or IMMEDIATELY AFTER USING an electronic cigarette/electronic vaping device is. . . ." Participants (n = 35) sorted 123 statements into "piles" of similar content and rated (n = 43) each statement on a 7-point scale (1 = Definitely NOT a positive effect to 7 = Definitely a positive effect). A cluster map was created using data from the sorting task, and analysis indicated a 7 cluster model of positive ECIG use outcome expectancies: Therapeutic/Affect Regulation, High/Euphoria, Sensation Enjoyment, Perceived Health Effects, Benefits of Decreased Cigarette Use, Convenience, and Social Impacts. The Perceived Health Effects cluster was rated highest, although all mean ratings were greater than 4.69. Mean cluster ratings were compared, and females, younger adults, past 30-day cigarette smokers, users of more "advanced" ECIG devices, and nonlifetime (less than 100 lifetime cigarettes) participants rated certain clusters higher than comparison groups (ps < 0.05). ECIG users associate positive outcomes with ECIG use. ECIG outcome expectancies may affect product appeal and tobacco use behaviors and should be examined further to inform regulatory policies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Hierarchical structure and importance of patients' reasons for treatment choices in knee and hip osteoarthritis: a concept mapping study.

PubMed

Selten, Ellen M H; Geenen, Rinie; van der Laan, Willemijn H; van der Meulen-Dilling, Roelien G; Schers, Henk J; Nijhof, Marc W; van den Ende, Cornelia H M; Vriezekolk, Johanna E

2017-02-01

To improve patients' use of conservative treatment options of hip and knee OA, in-depth understanding of reasons underlying patients' treatment choices is required. The current study adopted a concept mapping method to thematically structure and prioritize reasons for treatment choice in knee and hip OA from a patients' perspective. Multiple reasons for treatment choices were previously identified using in-depth interviews. In consensus meetings, experts derived 51 representative reasons from the interviews. Thirty-six patients individually sorted the 51 reasons in two card-sorting tasks: one based on content similarity, and one based on importance of reasons. The individual sortings of the first card-sorting task provided input for a hierarchical cluster analysis (squared Euclidian distances, Ward's method). The importance of the reasons and clusters were examined using descriptive statistics. The hierarchical structure of reasons for treatment choices showed a core distinction between two categories of clusters: barriers [subdivided into context (e.g. the healthcare system) and disadvantages] and outcome (subdivided into treatment and personal life). At the lowest level, 15 clusters were identified of which the clusters Physical functioning, Risks and Prosthesis were considered most important when making a treatment decision for hip or knee OA. Patients' treatment choices in knee and hip OA are guided by contextual barriers, disadvantages of the treatment, outcomes of the treatment and consequences for personal life. The structured overview of reasons can be used to support shared decision-making. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Reasons for using flavored liquids among electronic cigarette users: A concept mapping study.

PubMed

Soule, Eric K; Lopez, Alexa A; Guy, Mignonne C; Cobb, Caroline O

2016-09-01

Electronic cigarettes (ECIGs) aerosolize liquids often containing flavorants for inhalation. Few studies have examined the role of flavors in ECIG use. This study's purpose was to examine reasons for flavored ECIG use using a mixed-method approach, concept mapping (CM). Forty-six past 30-day adult ECIG users recruited from vape forums/conferences completed three online CM tasks. Participants brainstormed responses to a prompt: "A specific reason I use flavored e-liquid in my electronic cigarette product is…". The final 107 brainstormed statements were sorted by participants into groups of similar content. Participants rated each statement on a 7-point scale (1-Definitely NOT a reason to 7-Definitely a reason) based on a prompt: "This is a specific reason why I used flavored e-liquid in my electronic cigarette product in the past month." A cluster map was generated from participants' sorting and ratings using CM statistical software. Cluster mean ratings were compared. Analysis revealed five clusters of reasons for flavored ECIG use including Increased Satisfaction/Enjoyment, Better Feel/Taste than Cigarettes, Variety/Customization, Food Craving Suppression, and Social Impacts. Statements in the Increased Satisfaction/Enjoyment and Better Feel/Taste than Cigarettes clusters were rated significantly higher than statements from other clusters (ps<0.05). Some statements indicated flavors were perceived as masking agents for nicotine or other bad tastes associated with cigarette smoking making ECIG use more palatable. Flavored ECIGs are used for many reasons. Some statements suggested flavors may increase the rewarding and possible addictive effects of ECIGs. These results support continued examination of the role of flavors and ECIG use behaviors. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

NASA Astrophysics Data System (ADS)

Honda, Katsuhiro; Notsu, Akira; Ichihashi, Hidetomo

Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

X-ray aspects of the DAFT/FADA clusters

NASA Astrophysics Data System (ADS)

Guennou, L.; Durret, F.; Lima Neto, G. B.; Adami, C.

2012-12-01

We have undertaken the DAFT/FADA survey with the aim of applying constraints on dark energy based on weak lensing tomography as well as obtaining homogeneous and high quality data for a sample of 91 massive clusters in the redshift range [0.4,0.9] for which there are HST archive data. We have analysed the XMM-Newton data available for 42 of these clusters to derive their X-ray temperatures and luminosities and search for substructures. This study was coupled with a dynamical analysis for the 26 clusters having at least 30 spectroscopic galaxy redshifts in the cluster range. We present preliminary results on the coupled X-ray and dynamical analyses of these clusters.

Internal Cluster Validation on Earthquake Data in the Province of Bengkulu

NASA Astrophysics Data System (ADS)

Rini, D. S.; Novianti, P.; Fransiska, H.

2018-04-01

K-means method is an algorithm for cluster n object based on attribute to k partition, where k < n. There is a deficiency of algorithms that is before the algorithm is executed, k points are initialized randomly so that the resulting data clustering can be different. If the random value for initialization is not good, the clustering becomes less optimum. Cluster validation is a technique to determine the optimum cluster without knowing prior information from data. There are two types of cluster validation, which are internal cluster validation and external cluster validation. This study aims to examine and apply some internal cluster validation, including the Calinski-Harabasz (CH) Index, Sillhouette (S) Index, Davies-Bouldin (DB) Index, Dunn Index (D), and S-Dbw Index on earthquake data in the Bengkulu Province. The calculation result of optimum cluster based on internal cluster validation is CH index, S index, and S-Dbw index yield k = 2, DB Index with k = 6 and Index D with k = 15. Optimum cluster (k = 6) based on DB Index gives good results for clustering earthquake in the Bengkulu Province.

A method of using cluster analysis to study statistical dependence in multivariate data

NASA Technical Reports Server (NTRS)

Borucki, W. J.; Card, D. H.; Lyle, G. C.

1975-01-01

A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.

Clustering and Installing Satellite Nodes

NASA Astrophysics Data System (ADS)

Lotts, A. P.

This note describes basic clustering and the installation of a MicroVax or VaxStation as a Satellite node of an LAVC (Local Area VaxCluster). It will NOT describe Dual Porting of a MicroVax. It assumes that VMS 4.6 is running on the Boot node, that the LAVC key has been applied and that BOOT_CONFIG has been run as described in the LAVC manual.

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

NASA Astrophysics Data System (ADS)

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

2015-07-01

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 due to poor centroid definition and failure to assign particles to a cluster as a result of the subsampling and comparative attribution method employed by WASP. The methods used here allow for the entire fluorescent population of particles to be analysed yielding an explict cluster attribution for each particle, improving cluster centroid definition and our capacity to discriminate and quantify PBAP meta-classes compared to previous approaches.

Correlation Functions Quantify Super-Resolution Images and Estimate Apparent Clustering Due to Over-Counting

PubMed Central

Veatch, Sarah L.; Machta, Benjamin B.; Shelby, Sarah A.; Chiang, Ethan N.; Holowka, David A.; Baird, Barbara A.

2012-01-01

We present an analytical method using correlation functions to quantify clustering in super-resolution fluorescence localization images and electron microscopy images of static surfaces in two dimensions. We use this method to quantify how over-counting of labeled molecules contributes to apparent self-clustering and to calculate the effective lateral resolution of an image. This treatment applies to distributions of proteins and lipids in cell membranes, where there is significant interest in using electron microscopy and super-resolution fluorescence localization techniques to probe membrane heterogeneity. When images are quantified using pair auto-correlation functions, the magnitude of apparent clustering arising from over-counting varies inversely with the surface density of labeled molecules and does not depend on the number of times an average molecule is counted. In contrast, we demonstrate that over-counting does not give rise to apparent co-clustering in double label experiments when pair cross-correlation functions are measured. We apply our analytical method to quantify the distribution of the IgE receptor (FcεRI) on the plasma membranes of chemically fixed RBL-2H3 mast cells from images acquired using stochastic optical reconstruction microscopy (STORM/dSTORM) and scanning electron microscopy (SEM). We find that apparent clustering of FcεRI-bound IgE is dominated by over-counting labels on individual complexes when IgE is directly conjugated to organic fluorophores. We verify this observation by measuring pair cross-correlation functions between two distinguishably labeled pools of IgE-FcεRI on the cell surface using both imaging methods. After correcting for over-counting, we observe weak but significant self-clustering of IgE-FcεRI in fluorescence localization measurements, and no residual self-clustering as detected with SEM. We also apply this method to quantify IgE-FcεRI redistribution after deliberate clustering by crosslinking with two distinct trivalent ligands of defined architectures, and we evaluate contributions from both over-counting of labels and redistribution of proteins. PMID:22384026

On the Accuracy and Parallelism of GPGPU-Powered Incremental Clustering Algorithms

PubMed Central

He, Li; Zheng, Hao; Wang, Lei

2017-01-01

Incremental clustering algorithms play a vital role in various applications such as massive data analysis and real-time data processing. Typical application scenarios of incremental clustering raise high demand on computing power of the hardware platform. Parallel computing is a common solution to meet this demand. Moreover, General Purpose Graphic Processing Unit (GPGPU) is a promising parallel computing device. Nevertheless, the incremental clustering algorithm is facing a dilemma between clustering accuracy and parallelism when they are powered by GPGPU. We formally analyzed the cause of this dilemma. First, we formalized concepts relevant to incremental clustering like evolving granularity. Second, we formally proved two theorems. The first theorem proves the relation between clustering accuracy and evolving granularity. Additionally, this theorem analyzes the upper and lower bounds of different-to-same mis-affiliation. Fewer occurrences of such mis-affiliation mean higher accuracy. The second theorem reveals the relation between parallelism and evolving granularity. Smaller work-depth means superior parallelism. Through the proofs, we conclude that accuracy of an incremental clustering algorithm is negatively related to evolving granularity while parallelism is positively related to the granularity. Thus the contradictory relations cause the dilemma. Finally, we validated the relations through a demo algorithm. Experiment results verified theoretical conclusions. PMID:29123546

Indicators to facilitate the early identification of patients with major depressive disorder in need of highly specialized care: A concept mapping study.

PubMed

van Krugten, F C W; Goorden, M; van Balkom, A J L M; Spijker, J; Brouwer, W B F; Hakkaart-van Roijen, L

2018-04-01

Early identification of the subgroup of patients with major depressive disorder (MDD) in need of highly specialized care could enhance personalized intervention. This, in turn, may reduce the number of treatment steps needed to achieve and sustain an adequate treatment response. The aim of this study was to identify patient-related indicators that could facilitate the early identification of the subgroup of patients with MDD in need of highly specialized care. Initial patient indicators were derived from a systematic review. Subsequently, a structured conceptualization methodology known as concept mapping was employed to complement the initial list of indicators by clinical expertise and develop a consensus-based conceptual framework. Subject-matter experts were invited to participate in the subsequent steps (brainstorming, sorting, and rating) of the concept mapping process. A final concept map solution was generated using nonmetric multidimensional scaling and agglomerative hierarchical cluster analyses. In total, 67 subject-matter experts participated in the concept mapping process. The final concept map revealed the following 10 major clusters of indicators: 1-depression severity, 2-onset and (treatment) course, 3-comorbid personality disorder, 4-comorbid substance use disorder, 5-other psychiatric comorbidity, 6-somatic comorbidity, 7-maladaptive coping, 8-childhood trauma, 9-social factors, and 10-psychosocial dysfunction. The study findings highlight the need for a comprehensive assessment of patient indicators in determining the need for highly specialized care, and suggest that the treatment allocation of patients with MDD to highly specialized mental healthcare settings should be guided by the assessment of clinical and nonclinical patient factors. © 2018 Wiley Periodicals, Inc.

Off-stoichiometric defect clustering in irradiated oxides

NASA Astrophysics Data System (ADS)

Khalil, Sarah; Allen, Todd; EL-Azab, Anter

2017-04-01

A cluster dynamics model describing the formation of vacancy and interstitial clusters in irradiated oxides has been developed. The model, which tracks the composition of the oxide matrix and the defect clusters, was applied to the early stage formation of voids and dislocation loops in UO2, and the effects of irradiation temperature and dose rate on the evolution of their densities and composition was investigated. The results show that Frenkel defects dominate the nucleation process in irradiated UO2. The results also show that oxygen vacancies drive vacancy clustering while the migration energy of uranium vacancies is a rate-limiting factor for the nucleation and growth of voids. In a stoichiometric UO2 under irradiation, off-stoichiometric vacancy clusters exist with a higher concentration of hyper-stoichiometric clusters. Similarly, off-stoichiometric interstitial clusters form with a higher concentration of hyper-stoichiometric clusters. The UO2 matrix was found to be hyper-stoichiometric due to the accumulation of uranium vacancies.

Information Theory and Voting Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures.

PubMed

Saeed, Faisal; Salim, Naomie; Abdo, Ammar

2013-07-01

Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster, and the results were compared with Ward's method. The chemical dataset MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) dataset were used. Experiments suggest that the adaptive cumulative voting-based consensus method can improve the effectiveness of combining multiple clusterings of chemical structures. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Method of Randomization for Cluster-Randomized Trials: Challenges of Including Patients with Multiple Chronic Conditions

PubMed Central

Esserman, Denise; Allore, Heather G.; Travison, Thomas G.

2016-01-01

Cluster-randomized clinical trials (CRT) are trials in which the unit of randomization is not a participant but a group (e.g. healthcare systems or community centers). They are suitable when the intervention applies naturally to the cluster (e.g. healthcare policy); when lack of independence among participants may occur (e.g. nursing home hygiene); or when it is most ethical to apply an intervention to all within a group (e.g. school-level immunization). Because participants in the same cluster receive the same intervention, CRT may approximate clinical practice, and may produce generalizable findings. However, when not properly designed or interpreted, CRT may induce biased results. CRT designs have features that add complexity to statistical estimation and inference. Chief among these is the cluster-level correlation in response measurements induced by the randomization. A critical consideration is the experimental unit of inference; often it is desirable to consider intervention effects at the level of the individual rather than the cluster. Finally, given that the number of clusters available may be limited, simple forms of randomization may not achieve balance between intervention and control arms at either the cluster- or participant-level. In non-clustered clinical trials, balance of key factors may be easier to achieve because the sample can be homogenous by exclusion of participants with multiple chronic conditions (MCC). CRTs, which are often pragmatic, may eschew such restrictions. Failure to account for imbalance may induce bias and reducing validity. This article focuses on the complexities of randomization in the design of CRTs, such as the inclusion of patients with MCC, and imbalances in covariate factors across clusters. PMID:27478520

The structure of clusters of galaxies

NASA Astrophysics Data System (ADS)

Fox, David Charles

When infalling gas is accreted onto a cluster of galaxies, its kinetic energy is converted to thermal energy in a shock, heating the ions. Using a self-similar spherical model, we calculate the collisional heating of the electrons by the ions, and predict the electron and ion temperature profiles. While there are significant differences between the two, they occur at radii larger than currently observable, and too large to explain observed X-ray temperature declines in clusters. Numerical simulations by Navarro, Frenk, & White (1996) predict a universal dark matter density profile. We calculate the expected number of multiply-imaged background galaxies in the Hubble Deep Field due to foreground groups and clusters with this profile. Such groups are up to 1000 times less efficient at lensing than the standard singular isothermal spheres. However, with either profile, the expected number of galaxies lensed by groups in the Hubble Deep Field is at most one, consistent with the lack of clearly identified group lenses. X-ray and Sunyaev-Zel'dovich (SZ) effect observations can be combined to determine the distance to clusters of galaxies, provided the clusters are spherical. When applied to an aspherical cluster, this method gives an incorrect distance. We demonstrate a method for inferring the three-dimensional shape of a cluster and its correct distance from X-ray, SZ effect, and weak gravitational lensing observations, under the assumption of hydrostatic equilibrium. We apply this method to simple, analytic models of clusters, and to a numerically simulated cluster. Using artificial observations based on current X-ray and SZ effect instruments, we recover the true distance without detectable bias and with uncertainties of 4 percent.

Connecting optical and X-ray tracers of galaxy cluster relaxation

NASA Astrophysics Data System (ADS)

Roberts, Ian D.; Parker, Laura C.; Hlavacek-Larrondo, Julie

2018-04-01

Substantial effort has been devoted in determining the ideal proxy for quantifying the morphology of the hot intracluster medium in clusters of galaxies. These proxies, based on X-ray emission, typically require expensive, high-quality X-ray observations making them difficult to apply to large surveys of groups and clusters. Here, we compare optical relaxation proxies with X-ray asymmetries and centroid shifts for a sample of Sloan Digital Sky Survey clusters with high-quality, archival X-ray data from Chandra and XMM-Newton. The three optical relaxation measures considered are the shape of the member-galaxy projected velocity distribution - measured by the Anderson-Darling (AD) statistic, the stellar mass gap between the most-massive and second-most-massive cluster galaxy, and the offset between the most-massive galaxy (MMG) position and the luminosity-weighted cluster centre. The AD statistic and stellar mass gap correlate significantly with X-ray relaxation proxies, with the AD statistic being the stronger correlator. Conversely, we find no evidence for a correlation between X-ray asymmetry or centroid shift and the MMG offset. High-mass clusters (Mhalo > 1014.5 M⊙) in this sample have X-ray asymmetries, centroid shifts, and Anderson-Darling statistics which are systematically larger than for low-mass systems. Finally, considering the dichotomy of Gaussian and non-Gaussian clusters (measured by the AD test), we show that the probability of being a non-Gaussian cluster correlates significantly with X-ray asymmetry but only shows a marginal correlation with centroid shift. These results confirm the shape of the radial velocity distribution as a useful proxy for cluster relaxation, which can then be applied to large redshift surveys lacking extensive X-ray coverage.

The HectoMAP Cluster Survey. II. X-Ray Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sohn, Jubee; Chon, Gayoung; Bohringer, Hans

Here, we apply a friends-of-friends algorithm to the HectoMAP redshift survey and cross-identify associated X-ray emission in the ROSAT All-Sky Survey data (RASS). The resulting flux-limited catalog of X-ray cluster surveys is complete to a limiting flux of ~3 × 10 –13 erg s –1 cm –2 and includes 15 clusters (7 newly discovered) with redshifts z ≤ 0.4. HectoMAP is a dense survey (~1200 galaxies deg –2) that provides ~50 members (median) in each X-ray cluster. We provide redshifts for the 1036 cluster members. Subaru/Hyper Suprime-Cam imaging covers three of the X-ray systems and confirms that they are impressivemore » clusters. The HectoMAP X-ray clusters have an L X–σ cl scaling relation similar to that of known massive X-ray clusters. The HectoMAP X-ray cluster sample predicts ~12,000 ± 3000 detectable X-ray clusters in RASS to the limiting flux, comparable with previous estimates.« less

The HectoMAP Cluster Survey. II. X-Ray Clusters

DOE PAGES

Sohn, Jubee; Chon, Gayoung; Bohringer, Hans; ...

2018-03-10

Here, we apply a friends-of-friends algorithm to the HectoMAP redshift survey and cross-identify associated X-ray emission in the ROSAT All-Sky Survey data (RASS). The resulting flux-limited catalog of X-ray cluster surveys is complete to a limiting flux of ~3 × 10 –13 erg s –1 cm –2 and includes 15 clusters (7 newly discovered) with redshifts z ≤ 0.4. HectoMAP is a dense survey (~1200 galaxies deg –2) that provides ~50 members (median) in each X-ray cluster. We provide redshifts for the 1036 cluster members. Subaru/Hyper Suprime-Cam imaging covers three of the X-ray systems and confirms that they are impressivemore » clusters. The HectoMAP X-ray clusters have an L X–σ cl scaling relation similar to that of known massive X-ray clusters. The HectoMAP X-ray cluster sample predicts ~12,000 ± 3000 detectable X-ray clusters in RASS to the limiting flux, comparable with previous estimates.« less

Semi-supervised clustering methods.

PubMed

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

PubMed

Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

2015-05-01

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

A new type of simplified fuzzy rule-based system

NASA Astrophysics Data System (ADS)

Angelov, Plamen; Yager, Ronald

2012-02-01

Over the last quarter of a century, two types of fuzzy rule-based (FRB) systems dominated, namely Mamdani and Takagi-Sugeno type. They use the same type of scalar fuzzy sets defined per input variable in their antecedent part which are aggregated at the inference stage by t-norms or co-norms representing logical AND/OR operations. In this paper, we propose a significantly simplified alternative to define the antecedent part of FRB systems by data Clouds and density distribution. This new type of FRB systems goes further in the conceptual and computational simplification while preserving the best features (flexibility, modularity, and human intelligibility) of its predecessors. The proposed concept offers alternative non-parametric form of the rules antecedents, which fully reflects the real data distribution and does not require any explicit aggregation operations and scalar membership functions to be imposed. Instead, it derives the fuzzy membership of a particular data sample to a Cloud by the data density distribution of the data associated with that Cloud. Contrast this to the clustering which is parametric data space decomposition/partitioning where the fuzzy membership to a cluster is measured by the distance to the cluster centre/prototype ignoring all the data that form that cluster or approximating their distribution. The proposed new approach takes into account fully and exactly the spatial distribution and similarity of all the real data by proposing an innovative and much simplified form of the antecedent part. In this paper, we provide several numerical examples aiming to illustrate the concept.

Hierarchical Dirichlet process model for gene expression clustering

PubMed Central

2013-01-01

Clustering is an important data processing tool for interpreting microarray data and genomic network inference. In this article, we propose a clustering algorithm based on the hierarchical Dirichlet processes (HDP). The HDP clustering introduces a hierarchical structure in the statistical model which captures the hierarchical features prevalent in biological data such as the gene express data. We develop a Gibbs sampling algorithm based on the Chinese restaurant metaphor for the HDP clustering. We apply the proposed HDP algorithm to both regulatory network segmentation and gene expression clustering. The HDP algorithm is shown to outperform several popular clustering algorithms by revealing the underlying hierarchical structure of the data. For the yeast cell cycle data, we compare the HDP result to the standard result and show that the HDP algorithm provides more information and reduces the unnecessary clustering fragments. PMID:23587447

Art Concepts- Skylab (Sun Shade)

NASA Image and Video Library

1973-05-18

S73-26127 (1973) --- An artist's concept of the Skylab space station cluster in Earth orbit illustrating the deployment of the twin pole thermal shield to shade the Orbital Workshop (OWS) from the sun. This is one of the sunshade possibilities considered to solve the problem of the overheated OWS. Here the two Skylab 2 astronauts have completely deployed the sunshade. Note the evidence of another Skylab problem - the solar panels on the OWS are not deployed as required. Photo credit: NASA

Evidence for formation of DNA repair centers and dose-response nonlinearity in human cells

PubMed Central

Neumaier, Teresa; Swenson, Joel; Pham, Christopher; Polyzos, Aris; Lo, Alvin T.; Yang, PoAn; Dyball, Jane; Asaithamby, Aroumougame; Chen, David J.; Bissell, Mina J.; Thalhammer, Stefan; Costes, Sylvain V.

2012-01-01

The concept of DNA “repair centers” and the meaning of radiation-induced foci (RIF) in human cells have remained controversial. RIFs are characterized by the local recruitment of DNA damage sensing proteins such as p53 binding protein (53BP1). Here, we provide strong evidence for the existence of repair centers. We used live imaging and mathematical fitting of RIF kinetics to show that RIF induction rate increases with increasing radiation dose, whereas the rate at which RIFs disappear decreases. We show that multiple DNA double-strand breaks (DSBs) 1 to 2 μm apart can rapidly cluster into repair centers. Correcting mathematically for the dose dependence of induction/resolution rates, we observe an absolute RIF yield that is surprisingly much smaller at higher doses: 15 RIF/Gy after 2 Gy exposure compared to approximately 64 RIF/Gy after 0.1 Gy. Cumulative RIF counts from time lapse of 53BP1-GFP in human breast cells confirmed these results. The standard model currently in use applies a linear scale, extrapolating cancer risk from high doses to low doses of ionizing radiation. However, our discovery of DSB clustering over such large distances casts considerable doubts on the general assumption that risk to ionizing radiation is proportional to dose, and instead provides a mechanism that could more accurately address risk dose dependency of ionizing radiation. PMID:22184222

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks

PubMed Central

Puente Fernández, Jesús Antonio

2018-01-01

Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Due to the existence of the southbound interface, it is possible to deploy monitoring tools to obtain the network status and retrieve a statistics collection. Therefore, achieving the most accurate statistics depends on a strategy of monitoring and information requests of network devices. In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. Such grouping occurs by avoiding monitoring queries in network switches with common characteristics and then, by omitting redundant information. In this way, the present proposal decreases the number of monitoring queries to switches, improving the network traffic and preventing the switching overload. We have tested our optimization in a video streaming simulation using different types of videos. The experiments and comparison with traditional monitoring techniques demonstrate the feasibility of our proposal maintaining similar values decreasing the number of queries to the switches. PMID:29614049

VisBricks: multiform visualization of large, inhomogeneous data.

PubMed

Lex, Alexander; Schulz, Hans-Jörg; Streit, Marc; Partl, Christian; Schmalstieg, Dieter

2011-12-01

Large volumes of real-world data often exhibit inhomogeneities: vertically in the form of correlated or independent dimensions and horizontally in the form of clustered or scattered data items. In essence, these inhomogeneities form the patterns in the data that researchers are trying to find and understand. Sophisticated statistical methods are available to reveal these patterns, however, the visualization of their outcomes is mostly still performed in a one-view-fits-all manner. In contrast, our novel visualization approach, VisBricks, acknowledges the inhomogeneity of the data and the need for different visualizations that suit the individual characteristics of the different data subsets. The overall visualization of the entire data set is patched together from smaller visualizations, there is one VisBrick for each cluster in each group of interdependent dimensions. Whereas the total impression of all VisBricks together gives a comprehensive high-level overview of the different groups of data, each VisBrick independently shows the details of the group of data it represents. State-of-the-art brushing and visual linking between all VisBricks furthermore allows the comparison of the groupings and the distribution of data items among them. In this paper, we introduce the VisBricks visualization concept, discuss its design rationale and implementation, and demonstrate its usefulness by applying it to a use case from the field of biomedicine. © 2011 IEEE

Mutation Clusters from Cancer Exome.

PubMed

Kakushadze, Zura; Yu, Willie

2017-08-15

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method. Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage cancer diagnostics, such as novel blood-test methods currently in development.

Post-decomposition optimizations using pattern matching and rule-based clustering for multi-patterning technology

NASA Astrophysics Data System (ADS)

Wang, Lynn T.-N.; Madhavan, Sriram

2018-03-01

A pattern matching and rule-based polygon clustering methodology with DFM scoring is proposed to detect decomposition-induced manufacturability detractors and fix the layout designs prior to manufacturing. A pattern matcher scans the layout for pre-characterized patterns from a library. If a pattern were detected, rule-based clustering identifies the neighboring polygons that interact with those captured by the pattern. Then, DFM scores are computed for the possible layout fixes: the fix with the best score is applied. The proposed methodology was applied to two 20nm products with a chip area of 11 mm2 on the metal 2 layer. All the hotspots were resolved. The number of DFM spacing violations decreased by 7-15%.

Mutation Clusters from Cancer Exome

PubMed Central

Kakushadze, Zura; Yu, Willie

2017-01-01

We apply our statistically deterministic machine learning/clustering algorithm *K-means (recently developed in https://ssrn.com/abstract=2908286) to 10,656 published exome samples for 32 cancer types. A majority of cancer types exhibit a mutation clustering structure. Our results are in-sample stable. They are also out-of-sample stable when applied to 1389 published genome samples across 14 cancer types. In contrast, we find in- and out-of-sample instabilities in cancer signatures extracted from exome samples via nonnegative matrix factorization (NMF), a computationally-costly and non-deterministic method. Extracting stable mutation structures from exome data could have important implications for speed and cost, which are critical for early-stage cancer diagnostics, such as novel blood-test methods currently in development. PMID:28809811

Weighted community detection and data clustering using message passing

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Yanchen; Zhang, Pan

2018-03-01

Grouping objects into clusters based on the similarities or weights between them is one of the most important problems in science and engineering. In this work, by extending message-passing algorithms and spectral algorithms proposed for an unweighted community detection problem, we develop a non-parametric method based on statistical physics, by mapping the problem to the Potts model at the critical temperature of spin-glass transition and applying belief propagation to solve the marginals corresponding to the Boltzmann distribution. Our algorithm is robust to over-fitting and gives a principled way to determine whether there are significant clusters in the data and how many clusters there are. We apply our method to different clustering tasks. In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms. In the clustering problem, where the data were generated by mixture models in the sparse regime, we show that our method works all the way down to the theoretical limit of detectability and gives accuracy very close to that of the optimal Bayesian inference. In the semi-supervised clustering problem, our method only needs several labels to work perfectly in classic datasets. Finally, we further develop Thouless-Anderson-Palmer equations which heavily reduce the computation complexity in dense networks but give almost the same performance as belief propagation.

Applying Machine Learning to Star Cluster Classification

NASA Astrophysics Data System (ADS)

Fedorenko, Kristina; Grasha, Kathryn; Calzetti, Daniela; Mahadevan, Sridhar

2016-01-01

Catalogs describing populations of star clusters are essential in investigating a range of important issues, from star formation to galaxy evolution. Star cluster catalogs are typically created in a two-step process: in the first step, a catalog of sources is automatically produced; in the second step, each of the extracted sources is visually inspected by 3-to-5 human classifiers and assigned a category. Classification by humans is labor-intensive and time consuming, thus it creates a bottleneck, and substantially slows down progress in star cluster research.We seek to automate the process of labeling star clusters (the second step) through applying supervised machine learning techniques. This will provide a fast, objective, and reproducible classification. Our data is HST (WFC3 and ACS) images of galaxies in the distance range of 3.5-12 Mpc, with a few thousand star clusters already classified by humans as a part of the LEGUS (Legacy ExtraGalactic UV Survey) project. The classification is based on 4 labels (Class 1 - symmetric, compact cluster; Class 2 - concentrated object with some degree of asymmetry; Class 3 - multiple peak system, diffuse; and Class 4 - spurious detection). We start by looking at basic machine learning methods such as decision trees. We then proceed to evaluate performance of more advanced techniques, focusing on convolutional neural networks and other Deep Learning methods. We analyze the results, and suggest several directions for further improvement.

Cosmological Constraints from Galaxy Cluster Velocity Statistics

NASA Astrophysics Data System (ADS)

Bhattacharya, Suman; Kosowsky, Arthur

2007-04-01

Future microwave sky surveys will have the sensitivity to detect the kinematic Sunyaev-Zeldovich signal from moving galaxy clusters, thus providing a direct measurement of their line-of-sight peculiar velocity. We show that cluster peculiar velocity statistics applied to foreseeable surveys will put significant constraints on fundamental cosmological parameters. We consider three statistical quantities that can be constructed from a cluster peculiar velocity catalog: the probability density function, the mean pairwise streaming velocity, and the pairwise velocity dispersion. These quantities are applied to an envisioned data set that measures line-of-sight cluster velocities with normal errors of 100 km s-1 for all clusters with masses larger than 1014 Msolar over a sky area of up to 5000 deg2. A simple Fisher matrix analysis of this survey shows that the normalization of the matter power spectrum and the dark energy equation of state can be constrained to better than 10%, and that the Hubble constant and the primordial power spectrum index can be constrained to a few percent, independent of any other cosmological observations. We also find that the current constraint on the power spectrum normalization can be improved by more than a factor of 2 using data from a 400 deg2 survey and WMAP third-year priors. We also show how the constraints on cosmological parameters change if cluster velocities are measured with normal errors of 300 km s-1.

Towards Tunable Consensus Clustering for Studying Functional Brain Connectivity During Affective Processing.

PubMed

Liu, Chao; Abu-Jamous, Basel; Brattico, Elvira; Nandi, Asoke K

2017-03-01

In the past decades, neuroimaging of humans has gained a position of status within neuroscience, and data-driven approaches and functional connectivity analyses of functional magnetic resonance imaging (fMRI) data are increasingly favored to depict the complex architecture of human brains. However, the reliability of these findings is jeopardized by too many analysis methods and sometimes too few samples used, which leads to discord among researchers. We propose a tunable consensus clustering paradigm that aims at overcoming the clustering methods selection problem as well as reliability issues in neuroimaging by means of first applying several analysis methods (three in this study) on multiple datasets and then integrating the clustering results. To validate the method, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity during affective processing. The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data involving a variety of stimuli and affective evaluations of them. The consensus clustering method is implemented in the R package "UNCLES" available on http://cran.r-project.org/web/packages/UNCLES/index.html .

Cluster optical coding: from biochips to counterfeit security

NASA Astrophysics Data System (ADS)

Haglmueller, Jakob; Alguel, Yilmaz; Mayer, Christian; Matyushin, Viacheslav; Bauer, Georg; Pittner, Fritz; Leitner, Alfred; Aussenegg, Franz R.; Schalkhammer, Thomas G.

2004-07-01

Spatially tuned resonant nano-clusters allow high local field enhancement when exited by electromagnetic radiation. A number of phenomena had been described and subsequently applied to novel nano- and bionano-devices. Decisive for these types of devices and sensors is the precise nanometric assembly, coupling the local field surrounding a cluster to allow resonance with other elements interacting with this field. In particular, the distance cluster-mirror or cluster-fluorophore gives rise to a variety of enhancement phenomena. High throughput transducers using metal cluster resonance technology are based on surface-enhancement of metal cluster light absorption (SEA). The optical property for the analytical application of metal cluster films is the so-called anomalous absorption. At a well defined nanometric distance of a cluster to a mirror the reflected electromagnetic field has the same phase at the position of the absorbing cluster as the incident fields. This feedback mechanism strongly enhances the effective cluster absorption coefficient. The system is characterised by a narrow reflection minimum. Based on this SEA-phenomenon (licensed to and further developed and optimized by NovemberAG, Germany Erlangen) a number of commercial products have been constructed. Brandsealing(R) uses the patented SEA cluster technology to produce optical codings. Cluster SEA thin film systems show a characteristic color-flip effect and are extremely mechanically and thermally robust. This is the basis for its application as an unique security feature. The specific spectroscopic properties as e.g. narrow band multi-resonance of the cluster layers allow the authentication of the optical code which can be easily achieved with a mobile hand-held reader developed by november AG and Siemens AG. Thus, these features are machine-readable which makes them superior to comparable technologies. Cluster labels are available in two formats: as a label for tamper-proof product packaging, and as a direct label, where label and logo are permanently applied directly and unremovable to the product surface. Together with Infineon Technologies and HUECK FOLIEN, the SEA technology is currently developed as a direct label for e.g. SmartCards.

The Amerasians.

ERIC Educational Resources Information Center

Ranard, Donald A.; Gilzow, Douglas F.

1989-01-01

Articles in this newsletter issue examine the experiences, strengths, and problems that Amerasian refugees from Vietnam have had while living in the United States. Topics of discussion include discrimination, educational difficulties, resettlement experiences, and cultural difficulties. The concept of cluster site resettlement, a possible solution…

Interpretative Communities in Conflict: A Master Syllabus for Political Communication.

ERIC Educational Resources Information Center

Smith, Craig Allen

1992-01-01

Advocates the interpretive communities approach to teaching political communication. Discusses philosophical issues in the teaching of political communication courses, and pedagogical techniques (including concepts versus cases, clustering examples, C-SPAN video examples, and simulations and games). (SR)

Resequencing Skills and Concepts in Applied Calculus Using the Computer as a Tool.

ERIC Educational Resources Information Center

Heid, M. Kathleen

1988-01-01

During the first 12 weeks of an applied calculus course, two classes of college students studied calculus concepts using graphical and symbol-manipulation computer programs to perform routine manipulations. Three weeks were spent on skill development. Students showed better understanding of concepts and performed almost as well on routine skills.…

Implementation of spectral clustering on microarray data of carcinoma using k-means algorithm

NASA Astrophysics Data System (ADS)

Frisca, Bustamam, Alhadi; Siswantining, Titin

2017-03-01

Clustering is one of data analysis methods that aims to classify data which have similar characteristics in the same group. Spectral clustering is one of the most popular modern clustering algorithms. As an effective clustering technique, spectral clustering method emerged from the concepts of spectral graph theory. Spectral clustering method needs partitioning algorithm. There are some partitioning methods including PAM, SOM, Fuzzy c-means, and k-means. Based on the research that has been done by Capital and Choudhury in 2013, when using Euclidian distance k-means algorithm provide better accuracy than PAM algorithm. So in this paper we use k-means as our partition algorithm. The major advantage of spectral clustering is in reducing data dimension, especially in this case to reduce the dimension of large microarray dataset. Microarray data is a small-sized chip made of a glass plate containing thousands and even tens of thousands kinds of genes in the DNA fragments derived from doubling cDNA. Application of microarray data is widely used to detect cancer, for the example is carcinoma, in which cancer cells express the abnormalities in his genes. The purpose of this research is to classify the data that have high similarity in the same group and the data that have low similarity in the others. In this research, Carcinoma microarray data using 7457 genes. The result of partitioning using k-means algorithm is two clusters.

Zeitgeists and development trends in long-term care facility design.

PubMed

Wang, Chia-Hui; Kuo, Nai-Wen

2006-06-01

Through literature analysis, in-depth interviews, and the application of the Delphi survey, this study explored long-term care resident priorities with regard to long-term care facility design in terms of both physical and psychological needs. This study further clarified changing trends in long-term care concepts; illustrated the impact that such changes are having on long-term care facility design; and summarized zeitgeists related to the architectural design of long-term care facilities. Results of our Delphi survey indicated the following top five priorities in long-term care facility design: (1) creating a home-like feeling; (2) adhering to Universal Design concepts; (3) providing well-defined private sleeping areas; (4) providing adequate social space; and (5) decentralizing residents' rooms into clusters. The three major zeitgeists related to long-term care facility design include: (1) modern long-term care facilities should abandon their traditional "hospital" image and gradually reposition facilities into homelike settings; (2) institution-based care for the elderly should be de-institutionalized under the concept of aging-in-place; and (3) living clusters, rather than traditional hospital-like wards, should be designed into long-term care facilities.

Clustering of Variables for Mixed Data

NASA Astrophysics Data System (ADS)

Saracco, J.; Chavent, M.

2016-05-01

This chapter presents clustering of variables which aim is to lump together strongly related variables. The proposed approach works on a mixed data set, i.e. on a data set which contains numerical variables and categorical variables. Two algorithms of clustering of variables are described: a hierarchical clustering and a k-means type clustering. A brief description of PCAmix method (that is a principal component analysis for mixed data) is provided, since the calculus of the synthetic variables summarizing the obtained clusters of variables is based on this multivariate method. Finally, the R packages ClustOfVar and PCAmixdata are illustrated on real mixed data. The PCAmix and ClustOfVar approaches are first used for dimension reduction (step 1) before applying in step 2 a standard clustering method to obtain groups of individuals.

Autonomous distributed self-organization for mobile wireless sensor networks.

PubMed

Wen, Chih-Yu; Tang, Hung-Kai

2009-01-01

This paper presents an adaptive combined-metrics-based clustering scheme for mobile wireless sensor networks, which manages the mobile sensors by utilizing the hierarchical network structure and allocates network resources efficiently A local criteria is used to help mobile sensors form a new cluster or join a current cluster. The messages transmitted during hierarchical clustering are applied to choose distributed gateways such that communication for adjacent clusters and distributed topology control can be achieved. In order to balance the load among clusters and govern the topology change, a cluster reformation scheme using localized criterions is implemented. The proposed scheme is simulated and analyzed to abstract the network behaviors in a number of settings. The experimental results show that the proposed algorithm provides efficient network topology management and achieves high scalability in mobile sensor networks.

Stokes space modulation format classification based on non-iterative clustering algorithm for coherent optical receivers.

PubMed

Mai, Xiaofeng; Liu, Jie; Wu, Xiong; Zhang, Qun; Guo, Changjian; Yang, Yanfu; Li, Zhaohui

2017-02-06

A Stokes-space modulation format classification (MFC) technique is proposed for coherent optical receivers by using a non-iterative clustering algorithm. In the clustering algorithm, two simple parameters are calculated to help find the density peaks of the data points in Stokes space and no iteration is required. Correct MFC can be realized in numerical simulations among PM-QPSK, PM-8QAM, PM-16QAM, PM-32QAM and PM-64QAM signals within practical optical signal-to-noise ratio (OSNR) ranges. The performance of the proposed MFC algorithm is also compared with those of other schemes based on clustering algorithms. The simulation results show that good classification performance can be achieved using the proposed MFC scheme with moderate time complexity. Proof-of-concept experiments are finally implemented to demonstrate MFC among PM-QPSK/16QAM/64QAM signals, which confirm the feasibility of our proposed MFC scheme.

Design considerations in clustering nuclear rocket engines

NASA Technical Reports Server (NTRS)

Sager, Paul H.

1992-01-01

An initial investigation of the design considerations in clustering nuclear rocket engines for space transfer vehicles has been made. The clustering of both propulsion modules (which include start tanks) and nuclear rocket engines installed directly to a vehicle core tank appears to be feasible. Special provisions to shield opposite run tanks and the opposite side of a core tank - in the case of the boost pump concept - are required; the installation of a circumferential reactor side shield sector appears to provide an effective solution to this problem. While the time response to an engine-out event does not appear to be critical, the gimbal displacement required appears to be important. Since an installation of three engines offers a substantial reduction in gimbal requirements for engine-out and it may be possible to further enhance mission reliability with the greater number of engines, it is recommended that a cluster of four engines be considered.

Clustering Tree-structured Data on Manifold

PubMed Central

Lu, Na; Miao, Hongyu

2016-01-01

Tree-structured data usually contain both topological and geometrical information, and are necessarily considered on manifold instead of Euclidean space for appropriate data parameterization and analysis. In this study, we propose a novel tree-structured data parameterization, called Topology-Attribute matrix (T-A matrix), so the data clustering task can be conducted on matrix manifold. We incorporate the structure constraints embedded in data into the non-negative matrix factorization method to determine meta-trees from the T-A matrix, and the signature vector of each single tree can then be extracted by meta-tree decomposition. The meta-tree space turns out to be a cone space, in which we explore the distance metric and implement the clustering algorithm based on the concepts like Fréchet mean. Finally, the T-A matrix based clustering (TAMBAC) framework is evaluated and compared using both simulated data and real retinal images to illus trate its efficiency and accuracy. PMID:26660696

A Multi-Discipline, Multi-Genre Digital Library for Research and Education

NASA Technical Reports Server (NTRS)

Nelson, Michael L.; Maly, Kurt; Shen, Stewart N. T.

2004-01-01

We describe NCSTRL+, a unified, canonical digital library for educational and scientific and technical information (STI). NCSTRL+ is based on the Networked Computer Science Technical Report Library (NCSTRL), a World Wide Web (WWW) accessible digital library (DL) that provides access to over 100 university departments and laboratories. NCSTRL+ implements two new technologies: cluster functionality and publishing "buckets". We have extended the Dienst protocol, the protocol underlying NCSTRL, to provide the ability to "cluster" independent collections into a logically centralized digital library based upon subject category classification, type of organization, and genres of material. The concept of "buckets" provides a mechanism for publishing and managing logically linked entities with multiple data formats. The NCSTRL+ prototype DL contains the holdings of NCSTRL and the NASA Technical Report Server (NTRS). The prototype demonstrates the feasibility of publishing into a multi-cluster DL, searching across clusters, and storing and presenting buckets of information.

Ontology-based topic clustering for online discussion data

NASA Astrophysics Data System (ADS)

Wang, Yongheng; Cao, Kening; Zhang, Xiaoming

2013-03-01

With the rapid development of online communities, mining and extracting quality knowledge from online discussions becomes very important for the industrial and marketing sector, as well as for e-commerce applications and government. Most of the existing techniques model a discussion as a social network of users represented by a user-based graph without considering the content of the discussion. In this paper we propose a new multilayered mode to analysis online discussions. The user-based and message-based representation is combined in this model. A novel frequent concept sets based clustering method is used to cluster the original online discussion network into topic space. Domain ontology is used to improve the clustering accuracy. Parallel methods are also used to make the algorithms scalable to very large data sets. Our experimental study shows that the model and algorithms are effective when analyzing large scale online discussion data.

Design considerations in clustering nuclear rocket engines

NASA Astrophysics Data System (ADS)

Sager, Paul H.

1992-07-01

An initial investigation of the design considerations in clustering nuclear rocket engines for space transfer vehicles has been made. The clustering of both propulsion modules (which include start tanks) and nuclear rocket engines installed directly to a vehicle core tank appears to be feasible. Special provisions to shield opposite run tanks and the opposite side of a core tank - in the case of the boost pump concept - are required; the installation of a circumferential reactor side shield sector appears to provide an effective solution to this problem. While the time response to an engine-out event does not appear to be critical, the gimbal displacement required appears to be important. Since an installation of three engines offers a substantial reduction in gimbal requirements for engine-out and it may be possible to further enhance mission reliability with the greater number of engines, it is recommended that a cluster of four engines be considered.

On the nature of bonding in binary Be2O2 and Si2O2 clusters: rhombic four-center four-electron π and σ bonds.

PubMed

Wang, Kang; Wang, Ying-Jin; Li, Da-Zhi; Ou, Ting; Zhao, Xiao-Yun; Zhai, Hua-Jin

2016-04-14

The structural and electronic properties and chemical bonding of binary Be2O2 and Si2O2 clusters have been studied using quantum chemical calculations at the B3LYP level. For the Be2O2 cluster, the potential energy surface is probed by unbiased structural searches and the global-minimum structure was established using the B3LYP calculations, complemented by PBE0 and single-point CCSD(T) calculations for top isomers. The perfectly planar D2h Be2O2 ((1)Ag) global minimum is well defined, being at least 3.64 eV lower in energy than alternative structures at the CCSD(T)//B3LYP/aug-cc-pVTZ level. Chemical bonding analyses show that D2h Be2O2 and Si2O2 clusters possess the rhombic four-center four-electron (4c-4e) π bond, that is, the o-bond, a conception derived from electron-deficient boron oxide clusters lately. Furthermore, the Be2O2 and Si2O2 clusters also exhibit rhombic 4c-4e σ bonds, both for the radial and tangential σ frameworks (σr and σt). The σt framework is classified as an o-bond only formally, due to the secondary contribution from the Be/Si s component. The three-fold (π, σr, and σt) o-bonds in Be2O2 and Si2O2 are considered to resemble the three-fold aromaticity in all-metal Al4(2-) dianions. A 4c-4e o-bond makes use of four O 2p electrons, which would otherwise be two lone-pairs, for a delocalized and completely bonding orbital, as well as a residual nonbonding orbital. Three-fold o-bonds thus greatly stabilize the binary Be2O2 and Si2O2 clusters. We anticipate that the bonding concept should be applicable to additional molecular systems, including those with larger heterocyclic rings.

Photometric redshifts as a tool for studying the Coma cluster galaxy populations

NASA Astrophysics Data System (ADS)

Adami, C.; Ilbert, O.; Pelló, R.; Cuillandre, J. C.; Durret, F.; Mazure, A.; Picat, J. P.; Ulmer, M. P.

2008-12-01

Aims: We apply photometric redshift techniques to an investigation of the Coma cluster galaxy luminosity function (GLF) at faint magnitudes, in particular in the u* band where basically no studies are presently available at these magnitudes. Methods: Cluster members were selected based on probability distribution function from photometric redshift calculations applied to deep u^*, B, V, R, I images covering a region of almost 1 deg2 (completeness limit R ~ 24). In the area covered only by the u* image, the GLF was also derived after a statistical background subtraction. Results: Global and local GLFs in the B, V, R, and I bands obtained with photometric redshift selection are consistent with our previous results based on a statistical background subtraction. The GLF in the u* band shows an increase in the faint end slope towards the outer regions of the cluster. The analysis of the multicolor type spatial distribution reveals that late type galaxies are distributed in clumps in the cluster outskirts, where X-ray substructures are also detected and where the GLF in the u* band is steeper. Conclusions: We can reproduce the GLFs computed with classical statistical subtraction methods by applying a photometric redshift technique. The u* GLF slope is steeper in the cluster outskirts, varying from α ~ -1 in the cluster center to α ~ -2 in the cluster periphery. The concentrations of faint late type galaxies in the cluster outskirts could explain these very steep slopes, assuming a short burst of star formation in these galaxies when entering the cluster. Based on observations obtained with MegaPrime/MegaCam, a joint project of CFHT and CEA/DAPNIA, at the Canada-France-Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. This work is also partly based on data products produced at TERAPIX and the Canadian Astronomy Data Centre as part of the Canada-France-Hawaii Telescope Legacy Survey, a collaborative project of NRC and CNRS. Also based on data from W. M. Keck Observatory which is operated as a scientific partnership between the California Institute of Technology, the University of California, and NASA. It was made possible by the generous financial support of the W. M. Keck Foundation.

An algorithm for spatial heirarchy clustering

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Velasco, F. R. D.

1981-01-01

A method for utilizing both spectral and spatial redundancy in compacting and preclassifying images is presented. In multispectral satellite images, a high correlation exists between neighboring image points which tend to occupy dense and restricted regions of the feature space. The image is divided into windows of the same size where the clustering is made. The classes obtained in several neighboring windows are clustered, and then again successively clustered until only one region corresponding to the whole image is obtained. By employing this algorithm only a few points are considered in each clustering, thus reducing computational effort. The method is illustrated as applied to LANDSAT images.

[Concept analysis "Competency-based education"].

PubMed

Loosli, Clarence

2016-03-01

Competency-based education (CBE) stands out at global level as the best educational practice. Indeed, CBE is supposed to improve the quality of care provided by newly graduated nurses. Yet, there is a dearth of knowledge in nursing literature regarding CBE concept's definition. CBE is implemented differently in each entity even inside the same discipline in a single country. What accounts for CBE in nursing education ? to clarify CBE concept meaning according to literature review in order to propose a definition. Wilson concept analysis method framed our literature review through two databases: CINHAL and ERIC. following the 11 Wilson techniques analysis, we identified CBE concept as a multidimensional concept clustering three dimensions : learning, teaching and assessment. nurses educators are accountable for providing performants newly graduated professional to the society. Schools should struggle for the visibility and the transparency of means they are using to accomplish their educational activities. This first attempt to understand CBE concept opens a matter of debate concerning further development and clarification of the concept. This first description of CBE concept is a step toward its identification and assessment.

A Taxonomic Approach to the Gestalt Theory of Perls

ERIC Educational Resources Information Center

Raming, Henry E.; Frey, David H.

1974-01-01

This study applied content analysis and cluster analysis to the ideas of Fritz Perls to develop a taxonomy of Gestalt processes and goals. Summaries of the typal groups or clusters were written and the implications of taxonomic research in counseling discussed. (Author)

Estimation of Carcinogenicity using Hierarchical Clustering and Nearest Neighbor Methodologies

EPA Science Inventory

Previously a hierarchical clustering (HC) approach and a nearest neighbor (NN) approach were developed to model acute aquatic toxicity end points. These approaches were developed to correlate the toxicity for large, noncongeneric data sets. In this study these approaches applie...