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Sample records for network analysis approach

  1. Tourism Destinations Network Analysis, Social Network Analysis Approach

    Directory of Open Access Journals (Sweden)

    2015-09-01

    Full Text Available The tourism industry is becoming one of the world's largest economical sources, and is expected to become the world's first industry by 2020. Previous studies have focused on several aspects of this industry including sociology, geography, tourism management and development, but have paid less attention to analytical and quantitative approaches. This study introduces some network analysis techniques and measures aiming at studying the structural characteristics of tourism networks. More specifically, it presents a methodology to analyze tourism destinations network. We apply the methodology to analyze mazandaran’s Tourism destination network, one of the most famous tourism areas of Iran.

  2. Ensemble approach to the analysis of weighted networks

    Science.gov (United States)

    Ahnert, S. E.; Garlaschelli, D.; Fink, T. M. A.; Caldarelli, G.

    2007-07-01

    We present an approach to the analysis of weighted networks, by providing a straightforward generalization of any network measure defined on unweighted networks, such as the average degree of the nearest neighbors, the clustering coefficient, the “betweenness,” the distance between two nodes, and the diameter of a network. All these measures are well established for unweighted networks but have hitherto proven difficult to define for weighted networks. Our approach is based on the translation of a weighted network into an ensemble of edges. Further introducing this approach we demonstrate its advantages by applying the clustering coefficient constructed in this way to two real-world weighted networks.

  3. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  4. The network analysis of urban streets: A dual approach

    Science.gov (United States)

    Porta, Sergio; Crucitti, Paolo; Latora, Vito

    2006-09-01

    The application of the network approach to the urban case poses several questions in terms of how to deal with metric distances, what kind of graph representation to use, what kind of measures to investigate, how to deepen the correlation between measures of the structure of the network and measures of the dynamics on the network, what are the possible contributions from the GIS community. In this paper, the author considers six cases of urban street networks characterized by different patterns and historical roots. The authors propose a representation of the street networks based firstly on a primal graph, where intersections are turned into nodes and streets into edges. In a second step, a dual graph, where streets are nodes and intersections are edges, is constructed by means of a generalization model named Intersection Continuity Negotiation, which allows to acknowledge the continuity of streets over a plurality of edges. Finally, the authors address a comparative study of some structural properties of the dual graphs, seeking significant similarities among clusters of cases. A wide set of network analysis techniques are implemented over the dual graph: in particular the authors show that the absence of any clue of assortativity differentiates urban street networks from other non-geographic systems and that most of the considered networks have a broad degree distribution typical of scale-free networks and exhibit small-world properties as well.

  5. A New Approach for the Stability Analysis of Wave Networks

    Directory of Open Access Journals (Sweden)

    Ya Xuan Zhang

    2014-01-01

    Full Text Available We introduce a new approach to investigate the stability of controlled tree-shaped wave networks and subtrees of complex wave networks. It is motivated by regarding the network as branching out from a single edge. We present the recursive relations of the Laplacian transforms of adjacent edges of the system in its branching order, which form the characteristic equation. In the stability analysis, we estimate the infimums of the recursive expressions in the inverse order based on the spectral analysis. It is a feasible way to check whether the system is exponentially stable under any control strategy or parameter choice. As an application we design the control law and study the stability of a 12-edge tree-shaped wave network.

  6. Stochastic approach to observability analysis in water networks

    Directory of Open Access Journals (Sweden)

    S. Díaz

    2016-07-01

    Full Text Available This work presents an alternative technique to the existing methods for observability analysis (OA in water networks, which is a prior essential step for the implementation of state estimation (SE techniques within such systems. The methodology presented here starts from a known hydraulic state and assumes random gaussian distributions for the uncertainty of some hydraulic variables, which is then propagated to the rest of the system. This process is repeated again to analyze the change in the network uncertainty when metering devices considered as error-free are included, based on which the network observability can be evaluated. The method’s potential is presented in an illustrative example, which shows the additional information that this methodology provides with respect to traditional OA approaches. This proposal allows a better understanding of the network and constitutes a practical tool to prioritize the location of additional meters, thus enhancing the transformation of large urban areas into actual smart cities.

  7. Network analysis literacy a practical approach to the analysis of networks

    CERN Document Server

    Zweig, Katharina A

    2014-01-01

    Network Analysis Literacy focuses on design principles for network analytics projects. The text enables readers to: pose a defined network analytic question; build a network to answer the question; choose or design the right network analytic methods for a particular purpose, and more.

  8. NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration.

    Science.gov (United States)

    Xia, Jianguo; Benner, Maia J; Hancock, Robert E W

    2014-07-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  9. Network Analysis: A Novel Approach to Understand Suicidal Behaviour

    Directory of Open Access Journals (Sweden)

    Derek de Beurs

    2017-02-01

    Full Text Available Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behaviour. Suicidal behaviour is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behaviour, but we do not yet understand how their interaction increases the risk for suicidal behaviour. A new approach called network analysis can help us better understand this process as it allows us to visualize and quantify the complex association between many different symptoms or risk factors. A network analysis of data containing information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.

  10. Network analysis and synthesis a modern systems theory approach

    CERN Document Server

    Anderson, Brian D O

    2006-01-01

    Geared toward upper-level undergraduates and graduate students, this book offers a comprehensive look at linear network analysis and synthesis. It explores state-space synthesis as well as analysis, employing modern systems theory to unite the classical concepts of network theory. The authors stress passive networks but include material on active networks. They avoid topology in dealing with analysis problems and discuss computational techniques. The concepts of controllability, observability, and degree are emphasized in reviewing the state-variable description of linear systems. Explorations

  11. A novel quantitative approach to concept analysis: the internomological network.

    Science.gov (United States)

    Cook, Paul F; Larsen, Kai R; Sakraida, Teresa J; Pedro, Leli

    2012-01-01

    When a construct such as patients' "transition to self-management" of chronic illness is studied by researchers across multiple disciplines, the meaning of key terms can become confused. This results from inherent problems in language where a term can have multiple meanings (polysemy) and different words can mean the same thing (synonymy). The aim of this study was to test a novel quantitative method for clarifying the meaning of constructs by examining the similarity of published contexts in which they are used. Published terms related to the concept transition to self-management of chronic illness were analyzed using the internomological network (INN), a type of latent semantic analysis performed to calculate the mathematical relationships between constructs based on the contexts in which researchers use each term. This novel approach was tested by comparing results with those from concept analysis, a best-practice qualitative approach to clarifying meanings of terms. By comparing results of the 2 methods, the best synonyms of transition to self-management, as well as key antecedent, attribute, and consequence terms, were identified. Results from INN analysis were consistent with those from concept analysis. The potential synonyms self-management, transition, and adaptation had the greatest utility. Adaptation was the clearest overall synonym but had lower cross-disciplinary use. The terms coping and readiness had more circumscribed meanings. The INN analysis confirmed key features of transition to self-management and suggested related concepts not found by the previous review. The INN analysis is a promising novel methodology that allows researchers to quantify the semantic relationships between constructs. The method works across disciplinary boundaries and may help to integrate the diverse literature on self-management of chronic illness.

  12. A Graph Oriented Approach for Network Forensic Analysis

    Science.gov (United States)

    Wang, Wei

    2010-01-01

    Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex…

  13. An Approach to Data Analysis in 5G Networks

    Directory of Open Access Journals (Sweden)

    Lorena Isabel Barona López

    2017-02-01

    Full Text Available 5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.

  14. Network analysis: an integrative approach to the structure of psychopathology

    NARCIS (Netherlands)

    Borsboom, D.; Cramer, A.O.J.

    2013-01-01

    In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry → insomnia → fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review

  15. A social network analysis of alcohol-impaired drivers in Maryland : an egocentric approach.

    Science.gov (United States)

    2011-04-01

    This study examined the personal, household, and social structural attributes of alcoholimpaired : drivers in Maryland. The study used an egocentric approach of social network : analysis. This approach concentrated on specific actors (alcohol-impaire...

  16. Diffusion of Latent Semantic Analysis as a Research Tool: A Social Network Analysis Approach

    OpenAIRE

    Tonta, Yaşar; DARVISH, HAMID

    2010-01-01

    Latent semantic analysis (LSA) is a relatively new research tool with a wide range of applications in different fields ranging from discourse analysis to cognitive science, from information retrieval to machine learning and so on. In this paper, we chart the develop- ment and diffusion of LSA as a research tool using social network analysis (SNA) approach that reveals the social structure of a discipline in terms of collaboration among scientists. Using Thomson Reuters’ Web of Science (WoS), ...

  17. Development of Computer Science Disciplines - A Social Network Analysis Approach

    CERN Document Server

    Pham, Manh Cuong; Jarke, Matthias

    2011-01-01

    In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published JCR (Journal Citation Report). Although this data covers most of important journals, it lacks computer science conference and workshop proceedings. That results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investiga...

  18. Sport, how people choose it: A network analysis approach.

    Science.gov (United States)

    Ferreri, Luca; Ivaldi, Marco; Daolio, Fabio; Giacobini, Mario; Rainoldi, Alberto; Tomassini, Marco

    2015-01-01

    In order to investigate the behaviour of athletes in choosing sports, we analyse data from part of the We-Sport database, a vertical social network that links athletes through sports. In particular, we explore connections between people sharing common sports and the role of age and gender by applying "network science" approaches and methods. The results show a disassortative tendency of athletes in choosing sports, a negative correlation between age and number of chosen sports and a positive correlation between age of connected athletes. Some interesting patterns of connection between age classes are depicted. In addition, we propose a method to classify sports, based on the analyses of the behaviour of people practising them. Thanks to this brand new classifications, we highlight the links of class of sports and their unexpected features. We emphasise some gender dependency affinity in choosing sport classes.

  19. Trauma-Exposed Latina Immigrants' Networks: A Social Network Analysis Approach.

    Science.gov (United States)

    Hurtado-de-Mendoza, Alejandra; Serrano, Adriana; Gonzales, Felisa A; Fernandez, Nicole C; Cabling, Mark; Kaltman, Stacey

    2016-11-01

    Trauma exposure among Latina immigrants is common. Social support networks can buffer the impact of trauma on mental health. This study characterizes the social networks of trauma-exposed Latina immigrants using a social network analysis perspective. In 2011-2012 a convenience sample (n=28) of Latina immigrants with trauma exposure and presumptive depression or posttraumatic stress disorder was recruited from a community clinic in Washington DC. Participants completed a social network assessment and listed up to ten persons in their network (alters). E-Net was used to describe the aggregate structural, interactional, and functional characteristics of networks and Node-XL was used in a case study to diagram one network. Most participants listed children (93%), siblings (82%), and friends (71%) as alters, and most alters lived in the US (69%). Perceived emotional support and positive social interaction were higher compared to tangible, language, information, and financial support. A case study illustrates the use of network visualizations to assess the strengths and weaknesses of social networks. Targeted social network interventions to enhance supportive networks among trauma-exposed Latina immigrants are warranted.

  20. Network topology analysis approach on China's QFII stock investment behavior

    Science.gov (United States)

    Zhang, Yongjie; Cao, Xing; He, Feng; Zhang, Wei

    2017-05-01

    In this paper, the investment behavior of QFII in China stock market from 2004 to 2015 is studied with the network topology method. Based on the nodes topological characteristics, stock holding fluctuations correlation is studied from the micro network level. We conclude that the QFII mutual stock holding network have both scale free and small world properties, which presented mainly small world characteristics from 2005 to 2011, and scale free characteristics from 2012 to 2015. Moreover, fluctuations correlation is different with different nodes topological characteristics. In different economic periods, QFII represented different connection patterns and they reacted to the market crash spontaneously. Thus, this paper provides the first evidence of complex network research on QFII' investment behavior in China as an emerging market.

  1. A network approach in analysis of the matching hypothesis

    Science.gov (United States)

    Jia, Tao; Spivey, Robert; Korniss, Gyorgy; Szymanski, Boleslaw

    2014-03-01

    The matching hypothesis in social psychology claimed that people are more likely to form a committed relationship with someone who is equally attractive. This phenomenon can be well interpreted by the principle of homophily that people are apt to get in touch with others similar to them. Yet, social experiments indicate that people in general tend to prefer more attractive individuals regardless of their own attractiveness. Here study the stochastic matching process for different underlying networks and different attractiveness distributions. We showed that the correlation of attractiveness within couples could purely due to the limited number of acquaintance each person has and such correlation decreases as the network becomes more sparse. We also analyzed the effect of the degree distribution and the attractiveness on the number of individuals that can not find their partners. This work is supported by ARL NS-CTA, ARO, and ONR.

  2. Multiscale complex network analysis: An approach to study spatiotemporal rainfall pattern in south Germany

    Science.gov (United States)

    Agarwal, Ankit; Marwan, Norbert; Rathinasamy, Maheswaran; Oeztuerk, Ugur; Merz, Bruno; Kurths, Jürgen

    2017-04-01

    Understanding of the climate sytems has been of tremendous importance to different branches such as agriculture, flood, drought and water resources management etc. In this regard, complex networks analysis and time series analysis attracted considerable attention, owing to their potential role in understanding the climate system through characteristic properties. One of the basic requirements in studying climate network dynamics is to identify connections in space or time or space-time, depending upon the purpose. Although a wide variety of approaches have been developed and applied to identify and analyse spatio-temporal relationships by climate networks, there is still further need for improvements in particular when considering precipitation time series or interactions on different scales. In this regard, recent developments in the area of network theory, especially complex networks, offer new avenues, both for their generality about systems and for their holistic perspective about spatio-temporal relationships. The present study has made an attempt to apply the ideas developed in the field of complex networks to examine connections in regional climate networks with particular focus on multiscale spatiotemporal connections. This paper proposes a novel multiscale understanding of regional climate networks using wavelets. The proposed approach is applied to daily precipitation records observed at 543 selected stations from south Germany for a period of 110 years (1901-2010). Further, multiscale community mining is performed on the same study region to shed more light on the underlying processes at different time scales. Various network measure and tools so far employed provide micro-level (individual station) and macro-level (community structure) information of the network. It is interesting to investigate how the result of this study can be useful for future climate predictions and for evaluating climate models on their implementation regarding heavy

  3. A Holistic Approach to Networked Information Systems Design and Analysis

    Science.gov (United States)

    2016-04-15

    approximate dynamic program- ming algorithm that we call one-step rollout algorithm (ORA) We show that ORA results in an optimal solution extremely close to...cient Data Centers,” IIE Transactions (to appear), 2015 J13 Jonathan Ponniah, Yih-Chun Hu and P. R. Kumar, “A System-Theoretic Clean Slate Approach to...Machinery (Kumar) • Research highlighted in Industrial Engineer magazine (for potentially impactful journal articles among those that appear in IIE

  4. Applying a social network analysis (SNA) approach to understanding radiologists' performance in reading mammograms

    Science.gov (United States)

    Tavakoli Taba, Seyedamir; Hossain, Liaquat; Heard, Robert; Brennan, Patrick; Lee, Warwick; Lewis, Sarah

    2017-03-01

    Rationale and objectives: Observer performance has been widely studied through examining the characteristics of individuals. Applying a systems perspective, while understanding of the system's output, requires a study of the interactions between observers. This research explains a mixed methods approach to applying a social network analysis (SNA), together with a more traditional approach of examining personal/ individual characteristics in understanding observer performance in mammography. Materials and Methods: Using social networks theories and measures in order to understand observer performance, we designed a social networks survey instrument for collecting personal and network data about observers involved in mammography performance studies. We present the results of a study by our group where 31 Australian breast radiologists originally reviewed 60 mammographic cases (comprising of 20 abnormal and 40 normal cases) and then completed an online questionnaire about their social networks and personal characteristics. A jackknife free response operating characteristic (JAFROC) method was used to measure performance of radiologists. JAFROC was tested against various personal and network measures to verify the theoretical model. Results: The results from this study suggest a strong association between social networks and observer performance for Australian radiologists. Network factors accounted for 48% of variance in observer performance, in comparison to 15.5% for the personal characteristics for this study group. Conclusion: This study suggest a strong new direction for research into improving observer performance. Future studies in observer performance should consider social networks' influence as part of their research paradigm, with equal or greater vigour than traditional constructs of personal characteristics.

  5. A Network Analysis Approach to fMRI Condition-Specific Functional Connectivity

    CERN Document Server

    Shinkareva, Svetlana V; Wang, Jing

    2010-01-01

    In this work we focus on examination and comparison of whole-brain functional connectivity patterns measured with fMRI across experimental conditions. Direct examination and comparison of condition-specific matrices is challenging due to the large number of elements in a connectivity matrix. We present a framework that uses network analysis to describe condition-specific functional connectivity. Treating the brain as a complex system in terms of a network, we extract the most relevant connectivity information by partitioning each network into clusters representing functionally connected brain regions. Extracted clusters are used as features for predicting experimental condition in a new data set. The approach is illustrated on fMRI data examining functional connectivity patterns during processing of abstract and concrete concepts. Topological (brain regions) and functional (level of connectivity and information flow) systematic differences in the ROI-based functional networks were identified across participan...

  6. A root cause analysis approach to risk assessment of a pipeline network for Kuwait Oil Company

    Energy Technology Data Exchange (ETDEWEB)

    Davies, Ray J.; Alfano, Tony D. [Det Norske Veritas (DNV), Rio de Janeiro, RJ (Brazil); Waheed, Farrukh [Kuwait Oil Company, Ahmadi (Kuwait); Komulainen, Tiina [Kongsberg Oil and Gas Technologies, Sandvika (Norway)

    2009-07-01

    A large scale risk assessment was performed by Det Norske Veritas (DNV) for the entire Kuwait Oil Company (KOC) pipeline network. This risk assessment was unique in that it incorporated the assessment of all major sources of process related risk faced by KOC and included root cause management system related risks in addition to technical risks related to more immediate causes. The assessment was conducted across the entire pipeline network with the scope divided into three major categories:1. Integrity Management 2. Operations 3. Management Systems Aspects of integrity management were ranked and prioritized using a custom algorithm based on critical data sets. A detailed quantitative risk assessment was then used to further evaluate those issues deemed unacceptable, and finally a cost benefit analysis approach was used to compare and select improvement options. The operations assessment involved computer modeling of the entire pipeline network to assess for bottlenecks, surge and erosion analysis, and to identify opportunities within the network that could potentially lead to increased production. The management system assessment was performed by conducting a gap analysis on the existing system and by prioritizing those improvement actions that best aligned with KOC's strategic goals for pipelines. Using a broad and three-pronged approach to their overall risk assessment, KOC achieved a thorough, root cause analysis-based understanding of risks to their system as well as a detailed list of recommended remediation measures that were merged into a 5-year improvement plan. (author)

  7. Protein network analysis - A new approach for quantifying wheat dough microstructure.

    Science.gov (United States)

    Bernklau, Isabelle; Lucas, Lars; Jekle, Mario; Becker, Thomas

    2016-11-01

    Clarification of wheat dough functionalities by visualizing the protein microstructure demands a precise image analysis, which is still challenging. Thus, a novel method for quantifying dough microstructure called protein network analysis (PNA) was established in this study. Hereby, absolute morphological attributes such as junctions' density, branching rate, end-point rate, and lacunarity quantify and characterize the strength of a network. The method was validated in a large range of varying microstructural shapes by increasing the bulk water concentration. In addition, the effect of two different magnifications (objectives with various numerical apparatus) was studied. Resulting values of the branching rate showed a significant linear decrease (R 2 =0.97) by ~40% for both magnifications indicating a decrease in connectivity and cohesion within the network. Rheological measurements, used as reference methods confirmed the loss of a network structure with increasing water addition (e.g. G* decreased by 89%). Additionally, significant correlations between both methods validated the innovative image analysis PNA. With this new approach of image analysis, effects of additives, varying dough ingredients or changing process conditions on gluten network - the most structure-relevant component in wheat dough - can be quantitatively identified, and targeted functionalities can be controlled. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Error performance analysis in K-tier uplink cellular networks using a stochastic geometric approach

    KAUST Repository

    Afify, Laila H.

    2015-09-14

    In this work, we develop an analytical paradigm to analyze the average symbol error probability (ASEP) performance of uplink traffic in a multi-tier cellular network. The analysis is based on the recently developed Equivalent-in-Distribution approach that utilizes stochastic geometric tools to account for the network geometry in the performance characterization. Different from the other stochastic geometry models adopted in the literature, the developed analysis accounts for important communication system parameters and goes beyond signal-to-interference-plus-noise ratio characterization. That is, the presented model accounts for the modulation scheme, constellation type, and signal recovery techniques to model the ASEP. To this end, we derive single integral expressions for the ASEP for different modulation schemes due to aggregate network interference. Finally, all theoretical findings of the paper are verified via Monte Carlo simulations.

  9. Meta-Analysis Approach identifies Candidate Genes and associated Molecular Networks for Type-2 Diabetes Mellitus

    Directory of Open Access Journals (Sweden)

    Herwig Ralf

    2008-06-01

    Full Text Available Abstract Background Multiple functional genomics data for complex human diseases have been published and made available by researchers worldwide. The main goal of these studies is the detailed analysis of a particular aspect of the disease. Complementary, meta-analysis approaches try to extract supersets of disease genes and interaction networks by integrating and combining these individual studies using statistical approaches. Results Here we report on a meta-analysis approach that integrates data of heterogeneous origin in the domain of type-2 diabetes mellitus (T2DM. Different data sources such as DNA microarrays and, complementing, qualitative data covering several human and mouse tissues are integrated and analyzed with a Bootstrap scoring approach in order to extract disease relevance of the genes. The purpose of the meta-analysis is two-fold: on the one hand it identifies a group of genes with overall disease relevance indicating common, tissue-independent processes related to the disease; on the other hand it identifies genes showing specific alterations with respect to a single study. Using a random sampling approach we computed a core set of 213 T2DM genes across multiple tissues in human and mouse, including well-known genes such as Pdk4, Adipoq, Scd, Pik3r1, Socs2 that monitor important hallmarks of T2DM, for example the strong relationship between obesity and insulin resistance, as well as a large fraction (128 of yet barely characterized novel candidate genes. Furthermore, we explored functional information and identified cellular networks associated with this core set of genes such as pathway information, protein-protein interactions and gene regulatory networks. Additionally, we set up a web interface in order to allow users to screen T2DM relevance for any – yet non-associated – gene. Conclusion In our paper we have identified a core set of 213 T2DM candidate genes by a meta-analysis of existing data sources. We have

  10. Systematic Analysis of the Multiple Bioactivities of Green Tea through a Network Pharmacology Approach

    Directory of Open Access Journals (Sweden)

    Shoude Zhang

    2014-01-01

    Full Text Available During the past decades, a number of studies have demonstrated multiple beneficial health effects of green tea. Polyphenolics are the most biologically active components of green tea. Many targets can be targeted or affected by polyphenolics. In this study, we excavated all of the targets of green tea polyphenolics (GTPs though literature mining and target calculation and analyzed the multiple pharmacology actions of green tea comprehensively through a network pharmacology approach. In the end, a total of 200 Homo sapiens targets were identified for fifteen GTPs. These targets were classified into six groups according to their related disease, which included cancer, diabetes, neurodegenerative disease, cardiovascular disease, muscular disease, and inflammation. Moreover, these targets mapped into 143 KEGG pathways, 26 of which were more enriched, as determined though pathway enrichment analysis and target-pathway network analysis. Among the identified pathways, 20 pathways were selected for analyzing the mechanisms of green tea in these diseases. Overall, this study systematically illustrated the mechanisms of the pleiotropic activity of green tea by analyzing the corresponding “drug-target-pathway-disease” interaction network.

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

    Science.gov (United States)

    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.

  12. An integrated data envelopment analysis-artificial neural network approach for benchmarking of bank branches

    Science.gov (United States)

    Shokrollahpour, Elsa; Hosseinzadeh Lotfi, Farhad; Zandieh, Mostafa

    2016-02-01

    Efficiency and quality of services are crucial to today's banking industries. The competition in this section has become increasingly intense, as a result of fast improvements in Technology. Therefore, performance analysis of the banking sectors attracts more attention these days. Even though data envelopment analysis (DEA) is a pioneer approach in the literature as of an efficiency measurement tool and finding benchmarks, it is on the other hand unable to demonstrate the possible future benchmarks. The drawback to it could be that the benchmarks it provides us with, may still be less efficient compared to the more advanced future benchmarks. To cover for this weakness, artificial neural network is integrated with DEA in this paper to calculate the relative efficiency and more reliable benchmarks of one of the Iranian commercial bank branches. Therefore, each branch could have a strategy to improve the efficiency and eliminate the cause of inefficiencies based on a 5-year time forecast.

  13. Employee Engagement for Sustainable Organizations: Keyword Analysis Using Social Network Analysis and Burst Detection Approach

    Directory of Open Access Journals (Sweden)

    Woocheol Kim

    2016-07-01

    Full Text Available The issue of sustainability is a vital long-term goal for organizations and as such has formed the basis of much academic research over the last two decades. Organizational sustainability is defined as the ability for an organization to accomplish a range of economic, environmental, and human performance objectives. As one of the most studied topics in organizational science, employee engagement at work is seen as a critical component to achieving sustainable organizational success. In order to better understand the employee engagement discourse, this study examined the keywords that appear in the titles and abstract of the employee engagement research domain using the burst detection and social network analysis techniques. A total of 1406 employee engagement relevant articles that were published from 1990 to 2015 were included and investigated in the study. The results revealed the fading, emerging, and central themes within the employee engagement domain.

  14. A methodological approach to the analysis of egocentric social networks in public health research: a practical example.

    Science.gov (United States)

    Djomba, Janet Klara; Zaletel-Kragelj, Lijana

    2016-12-01

    Research on social networks in public health focuses on how social structures and relationships influence health and health-related behaviour. While the sociocentric approach is used to study complete social networks, the egocentric approach is gaining popularity because of its focus on individuals, groups and communities. One of the participants of the healthy lifestyle health education workshop 'I'm moving', included in the study of social support for exercise was randomly selected. The participant was denoted as the ego and members of her/his social network as the alteri. Data were collected by personal interviews using a self-made questionnaire. Numerical methods and computer programmes for the analysis of social networks were used for the demonstration of analysis. The size, composition and structure of the egocentric social network were obtained by a numerical analysis. The analysis of composition included homophily and homogeneity. Moreover, the analysis of the structure included the degree of the egocentric network, the strength of the ego-alter ties and the average strength of ties. Visualisation of the network was performed by three freely available computer programmes, namely: Egonet.QF, E-net and Pajek. The computer programmes were described and compared by their usefulness. Both numerical analysis and visualisation have their benefits. The decision what approach to use is depending on the purpose of the social network analysis. While the numerical analysis can be used in large-scale population-based studies, visualisation of personal networks can help health professionals at creating, performing and evaluation of preventive programmes, especially if focused on behaviour change.

  15. A user exposure based approach for non-structural road network vulnerability analysis.

    Directory of Open Access Journals (Sweden)

    Lei Jin

    Full Text Available Aiming at the dense urban road network vulnerability without structural negative consequences, this paper proposes a novel non-structural road network vulnerability analysis framework. Three aspects of the framework are mainly described: (i the rationality of non-structural road network vulnerability, (ii the metrics for negative consequences accounting for variant road conditions, and (iii the introduction of a new vulnerability index based on user exposure. Based on the proposed methodology, a case study in the Sioux Falls network which was usually threatened by regular heavy snow during wintertime is detailedly discussed. The vulnerability ranking of links of Sioux Falls network with respect to heavy snow scenario is identified. As a result of non-structural consequences accompanied by conceivable degeneration of network, there are significant increases in generalized travel time costs which are measurements for "emotionally hurt" of topological road network.

  16. Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty.

    Science.gov (United States)

    Fathollah Bayati, Mohsen; Sadjadi, Seyed Jafar

    2017-01-01

    In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore, in this paper, the efficiency of the entire networks of electricity power, involving generation, transmission and distribution stages is measured. While DEA has been widely used to evaluate the efficiency of the components of electricity power networks during the past two decades, there is no study to evaluate the efficiency of the electricity power networks as a whole. The proposed models are applied to evaluate the efficiency of 16 regional electricity power networks in Iran and the effect of data uncertainty is also investigated. The results are compared with the traditional network DEA and parametric SFA methods. Validity and verification of the proposed models are also investigated. The preliminary results indicate that the proposed models were more reliable than the traditional Network DEA model.

  17. Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty

    Science.gov (United States)

    Sadjadi, Seyed Jafar

    2017-01-01

    In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore, in this paper, the efficiency of the entire networks of electricity power, involving generation, transmission and distribution stages is measured. While DEA has been widely used to evaluate the efficiency of the components of electricity power networks during the past two decades, there is no study to evaluate the efficiency of the electricity power networks as a whole. The proposed models are applied to evaluate the efficiency of 16 regional electricity power networks in Iran and the effect of data uncertainty is also investigated. The results are compared with the traditional network DEA and parametric SFA methods. Validity and verification of the proposed models are also investigated. The preliminary results indicate that the proposed models were more reliable than the traditional Network DEA model. PMID:28953900

  18. Preeclampsia: a bioinformatics approach through protein-protein interaction networks analysis

    Directory of Open Access Journals (Sweden)

    Tejera Eduardo

    2012-08-01

    Full Text Available Abstract Background In this study we explored preeclampsia through a bioinformatics approach. We create a comprehensive genes/proteins dataset by the analysis of both public proteomic data and text mining of public scientific literature. From this dataset the associated protein-protein interaction network has been obtained. Several indexes of centrality have been explored for hubs detection as well as the enrichment statistical analysis of metabolic pathway and disease. Results We confirmed the well known relationship between preeclampsia and cardiovascular diseases but also identified statistically significant relationships with respect to cancer and aging. Moreover, significant metabolic pathways such as apoptosis, cancer and cytokine-cytokine receptor interaction have also been identified by enrichment analysis. We obtained FLT1, VEGFA, FN1, F2 and PGF genes with the highest scores by hubs analysis; however, we also found other genes as PDIA3, LYN, SH2B2 and NDRG1 with high scores. Conclusions The applied methodology not only led to the identification of well known genes related to preeclampsia but also to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which eventually need to be validated experimentally. Moreover, new possible connections were detected between preeclampsia and other diseases that could open new areas of research. More must be done in this area to resolve the identification of unknown interactions of proteins/genes and also for a better integration of metabolic pathways and diseases.

  19. Connecting the Disconnected: Social Work and Social Network Analysis. A Methodological Approach to Identifying Network Peer Leaders

    Directory of Open Access Journals (Sweden)

    del Fresno García, Miguel

    2015-02-01

    Full Text Available Social network theory and analysis (SNA offers a useful conceptual framework and a robust set of methods for understanding, analysing, and representing the pattern of social interactions that surround individuals forming an overall network of ties. SNA provides both insights and applications regarding relational structures that may be consequential for individual and collective agency. Despite the fact that both SNA and social work focus on relationships and behaviour, and that each discipline could substantively inform the other, there remains a significant lack of intersection between the two disciplines. In response to this gap, SNA applied to social work can provide additional ways to both diagnose and intervene behaviourally through the following approaches: a by identifying key players in promoting the dissemination of behavioral changes in networks; b by segmenting and identifying groups, cliques and communities; c by supporting behavioural change through social ties surrounding the individual; and d by aligning and applying specific interventions that draw on mutually interactive processes in terms of individual influences on networks, as well as network influences on individuals. SNA provides social work with an additional lens and set of tools based on the constellation of interactions surrounding individuals, families, groups or communities that supports understanding, diagnosis, and intervention.La Teoría y Análisis de Redes Sociales (SNA ofrece un conjunto de métodos de análisis de las interacciones sociales de los seres humanos, que permiten de forma específica investigar las estructuras relacionales y la representación de éstas como redes. SNA proporciona tanto acceso a nuevo conocimiento como la representación de las estructuras relacionales y como éstas pueden ser consecuencia de la acción individual y colectiva. A pesar de que tanto el SNA como el Trabajo Social tienen su foco en las relaciones y el comportamiento, de

  20. Evolutionary Game Analysis of Competitive Information Dissemination on Social Networks: An Agent-Based Computational Approach

    Directory of Open Access Journals (Sweden)

    Qing Sun

    2015-01-01

    Full Text Available Social networks are formed by individuals, in which personalities, utility functions, and interaction rules are made as close to reality as possible. Taking the competitive product-related information as a case, we proposed a game-theoretic model for competitive information dissemination in social networks. The model is presented to explain how human factors impact competitive information dissemination which is described as the dynamic of a coordination game and players’ payoff is defined by a utility function. Then we design a computational system that integrates the agent, the evolutionary game, and the social network. The approach can help to visualize the evolution of % of competitive information adoption and diffusion, grasp the dynamic evolution features in information adoption game over time, and explore microlevel interactions among users in different network structure under various scenarios. We discuss several scenarios to analyze the influence of several factors on the dissemination of competitive information, ranging from personality of individuals to structure of networks.

  1. Systemic Approach to Virulence Gene Network Analysis for Gaining New Insight into Cryptococcal Virulence

    Directory of Open Access Journals (Sweden)

    Antoni N Malachowski

    2016-10-01

    Full Text Available Cryptococcus neoformans is pathogenic yeast, responsible for highly lethal infections in compromised patients around the globe. C. neoformans typically initiates infections in mammalian lung tissue and subsequently disseminates to the central nervous system where it causes significant pathologies. Virulence genes of C. neoformans are being characterized at an increasing rate, however, we are far from a comprehensive understanding of their roles and genetic interactions. Some of these reported virulence genes are scattered throughout different databases, while others are not yet included. This study gathered and analyzed 150 reported virulence associated factors (VAFs of C. neoformans. Using the web resource STRING database, our study identified different interactions between the total VAFs and those involved specifically in lung and brain infections and identified a new strain specific virulence gene, sho1, involved in the mitogen-activated protein kinase signaling pathway. As predicted by our analysis, sho1 expression enhanced C. neoformans virulence in a mouse model of pulmonary infection, contributing to enhanced non-protective immune Th2 bias and progressively enhancing fungal growth in the infected lungs. Sequence analysis indicated 77.4% (116 of total studied VAFs are soluble proteins, and 22.7% (34 are transmembrane proteins. Motifs involved in regulation and signaling such as protein kinases and transcription factors are highly enriched in Cryptococcus VAFs. Altogether, this study represents a pioneering effort in analysis of the virulence composite network of C. neoformans using a systems biology approach.

  2. Systemic Approach to Virulence Gene Network Analysis for Gaining New Insight into Cryptococcal Virulence.

    Science.gov (United States)

    Malachowski, Antoni N; Yosri, Mohamed; Park, Goun; Bahn, Yong-Sun; He, Yongqun; Olszewski, Michal A

    2016-01-01

    Cryptococcus neoformans is pathogenic yeast, responsible for highly lethal infections in compromised patients around the globe. C. neoformans typically initiates infections in mammalian lung tissue and subsequently disseminates to the central nervous system where it causes significant pathologies. Virulence genes of C. neoformans are being characterized at an increasing rate, however, we are far from a comprehensive understanding of their roles and genetic interactions. Some of these reported virulence genes are scattered throughout different databases, while others are not yet included. This study gathered and analyzed 150 reported virulence associated factors (VAFs) of C. neoformans. Using the web resource STRING database, our study identified different interactions between the total VAFs and those involved specifically in lung and brain infections and identified a new strain specific virulence gene, SHO1, involved in the mitogen-activated protein kinase signaling pathway. As predicted by our analysis, SHO1 expression enhanced C. neoformans virulence in a mouse model of pulmonary infection, contributing to enhanced non-protective immune Th2 bias and progressively enhancing fungal growth in the infected lungs. Sequence analysis indicated 77.4% (116) of total studied VAFs are soluble proteins, and 22.7% (34) are transmembrane proteins. Motifs involved in regulation and signaling such as protein kinases and transcription factors are highly enriched in Cryptococcus VAFs. Altogether, this study represents a pioneering effort in analysis of the virulence composite network of C. neoformans using a systems biology approach.

  3. Analysis and models of bilateral investment treaties using a social networks approach

    Science.gov (United States)

    Saban, Daniela; Bonomo, Flavia; Stier-Moses, Nicolás E.

    2010-09-01

    Bilateral investment treaties (BITs) are agreements between two countries for the reciprocal encouragement, promotion and protection of investments in each other’s territories by companies based in either country. Germany and Pakistan signed the first BIT in 1959 and since then, BITs are one of the most popular and widespread form of international agreement. In this work we study the proliferation of BITs using a social networks approach. We propose a network growth model that dynamically replicates the empirical topological characteristics of the BIT network.

  4. How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.

    Science.gov (United States)

    Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall

    2016-01-01

    Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

  5. Solid waste facilities location using of analytical network process and data envelopment analysis approaches.

    Science.gov (United States)

    Khadivi, M R; Fatemi Ghomi, S M T

    2012-06-01

    Selection of the appropriate site for solid waste facilities is a complex problem and requires an extensive evaluation process, because it is very difficult to develop a selection criterion that can precisely describe the preference of one location over another. Therefore selection of these sites can be viewed as a multiple criteria decision-making or multiple attributes decision-making problem. For this purpose, we propose a technique that can effectively take managerial preferences and subjective data into consideration, along with quantitative factors. The tool proposed here relies on the use of the analytical network process (ANP) and to help integrate managerial evaluations into a more quantitatively based decision tool, data envelopment analysis (DEA) is applied. In this paper, a location selection procedure is presented to construct an undesirable facility applying ANP and DEA approaches in two stages. In the first stage ANP approach is used, results of this stage are inputs for the second stage. In this stage, DEA is applied to select the best location. Finally, to illustrate the proposed framework, at "Results and discussion" section, a total of four undesirable facility locations are evaluated. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. The Application of Cognitive Diagnostic Approaches via Neural Network Analysis of Serious Educational Games

    Science.gov (United States)

    Lamb, Richard L.

    Serious Educational Games (SEGs) have been a topic of increased popularity within the educational realm since the early millennia. SEGs are generalized form of Serious Games to mean games for purposes other than entertainment but, that also specifically include training, educational purpose and pedagogy within their design. This rise in popularity (for SEGs) has occurred at a time when school systems have increased the type, number, and presentations of student achievement tests for decision-making purposes. These tests often task the form of end of course (year) tests and periodic benchmark testing. As the use of these tests, has increased policymakers have suggested their use as a measure for teacher accountability. The change in testing resulted from a push by school districts and policy makers at various component levels for a data-driven decision-making (D3M) approach. With the data-driven decision making approaches by school districts, there has been an increased focus on the measurement and assessment of student content knowledge with little focus on the contributing factors and cognitive attributes within learning that cross multiple-content areas. One-way to increase the focus on these aspects of learning (factors and attributes) that are additional to content learning is through assessments based in cognitive diagnostics. Cognitive diagnostics are a family of methodological approaches in which tasks tie to specific cognitive attributes for analytical purposes. This study explores data derived from computer data logging (n=158,000) in an observational design, using traditional statistical techniques such as clustering (exploratory and confirmatory), item response theory and through data mining techniques such as artificial neural network analysis. From these analyses, a model of student learning emerges illustrating student thinking and learning while engaged in SEG Design. This study seeks to use cognitive diagnostic type approaches to measure student

  7. Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks.

    Science.gov (United States)

    Samaga, Regina; Klamt, Steffen

    2013-06-26

    A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models.Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input-output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous.We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to logical

  8. Parallel Approach for Time Series Analysis with General Regression Neural Networks

    Directory of Open Access Journals (Sweden)

    J.C. Cuevas-Tello

    2012-04-01

    Full Text Available The accuracy on time delay estimation given pairs of irregularly sampled time series is of great relevance in astrophysics. However the computational time is also important because the study of large data sets is needed. Besides introducing a new approach for time delay estimation, this paper presents a parallel approach to obtain a fast algorithm for time delay estimation. The neural network architecture that we use is general Regression Neural Network (GRNN. For the parallel approach, we use Message Passing Interface (MPI on a beowulf-type cluster and on a Cray supercomputer and we also use the Compute Unified Device Architecture (CUDA™ language on Graphics Processing Units (GPUs. We demonstrate that, with our approach, fast algorithms can be obtained for time delay estimation on large data sets with the same accuracy as state-of-the-art methods.

  9. A Complex Network Approach for Nanoparticle Agglomeration Analysis in Nanoscale Images

    CERN Document Server

    Brandoli, Bruno; Orue, Jonathan; Arruda, Mauro; Goncalves, Diogo; Goncalves, Wesley; Moreira, Raphaell; Rodrigues-Jr, Jose

    2016-01-01

    Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the synthesis of new nanoparticles is a hot topic. However, the interpretation of experimental results in the search of new nanoparticles poses several challenges. This is due to the characteristics of nanoparticles images and due to their multiple intricate properties; one property of recurrent interest is the agglomeration of particles. Addressing this issue, this paper introduces an approach that uses complex networks to detect and describe nanoparticle agglomerates so to foster easier and more insightful analyses. In this approach, each detected particle in an image corresponds to a vertice and the distances between the particles define a criterion for creating edges. Edges are created if the distance is smaller than a radius of interest. Once this network is set, we calculate several discrete measures able to reveal the most outsta...

  10. Analysis and control of Boolean networks a semi-tensor product approach

    CERN Document Server

    Cheng, Daizhan; Li, Zhiqiang

    2010-01-01

    This book presents a new approach to the investigation of Boolean control networks, using the semi-tensor product (STP), which can express a logical function as a conventional discrete-time linear system. This makes it possible to analyze basic control problems.

  11. [Social network analysis of animal behavioral ecology: a cross-discipline approach].

    Science.gov (United States)

    Zhang, Peng

    2013-12-01

    Social network analysis (SNA) is a framework used to study the structure of societies. As an umbrella term that encompasses various tools of graph theory and mathematical models to visualize networks, SNA allows researchers to detect and quantify patterns in social networks. Within SNA, individuals are not independent, but are symbiotic or linked with one another in a network. Given its powerful analytical tools, SNA is capable of addressing a range of animal behaviors, and has accordingly become increasingly popular in behavioral ecology studies examining such notions as mate choice/sexual selection, cooperation, information flow and disease transition, behavioral strategies of individuals, fitness consequences of sociality and network stability. Nevertheless, SNA it relatively underutilized among Chinese behavioral ecologists. This study aims at highlighting the benefits of SNA in studying animal behaviors in order to promote greater utilization of SNA within Chinese studies. By first introducing social network theory and demonstrating how social networks can influence individual and collective behaviors, this paper provide a prospective overview of SNA's general utilization for the study of animal behavioral ecology as well as promising directions in the overall use of SNA.

  12. A Lyapunov-Razumikhin approach for stability analysis of logistics networks with time-delays

    Science.gov (United States)

    Dashkovskiy, Sergey; Karimi, Hamid Reza; Kosmykov, Michael

    2012-05-01

    Logistics network represents a complex system where different elements that are logistic locations interact with each other. This interaction contains delays caused by time needed for delivery of the material. Complexity of the system, time-delays and perturbations in a customer demand may cause unstable behaviour of the network. This leads to the loss of the customers and high inventory costs. Thus the investigation of the network on stability is desired during its design. In this article we consider local input-to-state stability of such logistics networks. Their behaviour is described by a functional differential equation with a constant time-delay. We are looking for verifiable conditions that guarantee stability of the network under consideration. Lyapunov-Razumikhin functions and the local small gain condition are utilised to obtain such conditions. Our stability conditions for the logistics network are based on the information about the interconnection properties between logistic locations and their production rates. Finally, numerical results are provided to demonstrate the proposed approach.

  13. WLPVG approach to the analysis of EEG-based functional brain network under manual acupuncture.

    Science.gov (United States)

    Pei, Xin; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao

    2014-10-01

    Functional brain network, one of the main methods for brain functional studies, can provide the connectivity information among brain regions. In this research, EEG-based functional brain network is built and analyzed through a new wavelet limited penetrable visibility graph (WLPVG) approach. This approach first decompose EEG into δ, θ, α, β sub-bands, then extracting nonlinear features from single channel signal, in addition forming a functional brain network for each sub-band. Manual acupuncture (MA) as a stimulation to the human nerve system, may evoke varied modulating effects in brain activities. To investigating whether and how this happens, WLPVG approach is used to analyze the EEGs of 15 healthy subjects with MA at acupoint ST36 on the right leg. It is found that MA can influence the complexity of EEG sub-bands in different ways and lead the functional brain networks to obtain higher efficiency and stronger small-world property compared with pre-acupuncture control state.

  14. Stability analysis of switched cellular neural networks: A mode-dependent average dwell time approach.

    Science.gov (United States)

    Huang, Chuangxia; Cao, Jie; Cao, Jinde

    2016-10-01

    This paper addresses the exponential stability of switched cellular neural networks by using the mode-dependent average dwell time (MDADT) approach. This method is quite different from the traditional average dwell time (ADT) method in permitting each subsystem to have its own average dwell time. Detailed investigations have been carried out for two cases. One is that all subsystems are stable and the other is that stable subsystems coexist with unstable subsystems. By employing Lyapunov functionals, linear matrix inequalities (LMIs), Jessen-type inequality, Wirtinger-based inequality, reciprocally convex approach, we derived some novel and less conservative conditions on exponential stability of the networks. Comparing to ADT, the proposed MDADT show that the minimal dwell time of each subsystem is smaller and the switched system stabilizes faster. The obtained results extend and improve some existing ones. Moreover, the validness and effectiveness of these results are demonstrated through numerical simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Analyzing psychotherapy process as intersubjective sensemaking: an approach based on discourse analysis and neural networks.

    Science.gov (United States)

    Nitti, Mariangela; Ciavolino, Enrico; Salvatore, Sergio; Gennaro, Alessandro

    2010-09-01

    The authors propose a method for analyzing the psychotherapy process: discourse flow analysis (DFA). DFA is a technique representing the verbal interaction between therapist and patient as a discourse network, aimed at measuring the therapist-patient discourse ability to generate new meanings through time. DFA assumes that the main function of psychotherapy is to produce semiotic novelty. DFA is applied to the verbatim transcript of the psychotherapy. It defines the main meanings active within the therapeutic discourse by means of the combined use of text analysis and statistical techniques. Subsequently, it represents the dynamic interconnections among these meanings in terms of a "discursive network." The dynamic and structural indexes of the discursive network have been shown to provide a valid representation of the patient-therapist communicative flow as well as an estimation of its clinical quality. Finally, a neural network is designed specifically to identify patterns of functioning of the discursive network and to verify the clinical validity of these patterns in terms of their association with specific phases of the psychotherapy process. An application of the DFA to a case of psychotherapy is provided to illustrate the method and the kinds of results it produces.

  16. A social network analysis approach to alcohol use and co-occurring addictive behavior in young adults.

    Science.gov (United States)

    Meisel, Matthew K; Clifton, Allan D; MacKillop, James; Goodie, Adam S

    2015-12-01

    The current study applied egocentric social network analysis (SNA) to investigate the prevalence of addictive behavior and co-occurring substance use in college students' networks. Specifically, we examined individuals' perceptions of the frequency of network members' co-occurring addictive behavior and investigated whether co-occurring addictive behavior is spread evenly throughout networks or is more localized in clusters. We also examined differences in network composition between individuals with varying levels of alcohol use. The study utilized an egocentric SNA approach in which respondents ("egos") enumerated 30 of their closest friends, family members, co-workers, and significant others ("alters") and the relations among alters listed. Participants were 281 undergraduates at a large university in the Southeastern United States. Robust associations were observed among the frequencies of gambling, smoking, drinking, and using marijuana by network members. We also found that alters tended to cluster together into two distinct groups: one cluster moderate-to-high on co-occurring addictive behavior and the other low on co-occurring addictive behavior. Lastly, significant differences were present when examining egos' perceptions of alters' substance use between the networks of at-risk, light, and nondrinkers. These findings provide empirical evidence of distinct clustering of addictive behavior among young adults and suggest the promise of social network-based interventions for this cohort. Copyright © 2015. Published by Elsevier Ltd.

  17. A complex network approach for nanoparticle agglomeration analysis in nanoscale images

    Science.gov (United States)

    Machado, Bruno Brandoli; Scabini, Leonardo Felipe; Margarido Orue, Jonatan Patrick; de Arruda, Mauro Santos; Goncalves, Diogo Nunes; Goncalves, Wesley Nunes; Moreira, Raphaell; Rodrigues-Jr, Jose F.

    2017-02-01

    Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the synthesis of new nanoparticles is a hot topic. However, the interpretation of experimental results in the search of new nanoparticles poses several challenges. This is due to the characteristics of nanoparticle images and due to their multiple intricate properties; one property of recurrent interest is the agglomeration of particles. Addressing this issue, this paper introduces an approach that uses complex networks to detect and describe nanoparticle agglomerates so to foster easier and more insightful analyses. In this approach, each detected particle in an image corresponds to a vertice and the distances between the particles define a criterion for creating edges. Edges are created if the distance is smaller than a radius of interest. Once this network is set, we calculate several discrete measures able to reveal the most outstanding agglomerates in a nanoparticle image. Experimental results using images of scanning tunneling microscopy (STM) of gold nanoparticles demonstrated the effectiveness of the proposed approach over several samples, as reflected by the separability between particles in three usual settings. The results also demonstrated efficacy for both convex and non-convex agglomerates.

  18. A complex network approach for nanoparticle agglomeration analysis in nanoscale images

    Energy Technology Data Exchange (ETDEWEB)

    Machado, Bruno Brandoli, E-mail: bruno.brandoli@ufms.br; Scabini, Leonardo Felipe, E-mail: leo.scabini@ufms.br; Margarido Orue, Jonatan Patrick, E-mail: jonatan.orue@ufms.br; Arruda, Mauro Santos de, E-mail: m.arruda@ufms.br; Goncalves, Diogo Nunes, E-mail: diogo.goncalves@ufms.br; Goncalves, Wesley Nunes, E-mail: wesley.goncalves@ufms.br [Federal University of Mato Grosso do Sul, CS Department (Brazil); Moreira, Raphaell, E-mail: moreira.raphaell@fu-berlin.de [Freie Universitat BerlinTakustr 3 (Germany); Rodrigues-Jr, Jose F, E-mail: junio@usp.br [University of Sao Paulo, CS Department (Brazil)

    2017-02-15

    Complex networks have been widely used in science and technology because of their ability to represent several systems. One of these systems is found in Biochemistry, in which the synthesis of new nanoparticles is a hot topic. However, the interpretation of experimental results in the search of new nanoparticles poses several challenges. This is due to the characteristics of nanoparticle images and due to their multiple intricate properties; one property of recurrent interest is the agglomeration of particles. Addressing this issue, this paper introduces an approach that uses complex networks to detect and describe nanoparticle agglomerates so to foster easier and more insightful analyses. In this approach, each detected particle in an image corresponds to a vertice and the distances between the particles define a criterion for creating edges. Edges are created if the distance is smaller than a radius of interest. Once this network is set, we calculate several discrete measures able to reveal the most outstanding agglomerates in a nanoparticle image. Experimental results using images of scanning tunneling microscopy (STM) of gold nanoparticles demonstrated the effectiveness of the proposed approach over several samples, as reflected by the separability between particles in three usual settings. The results also demonstrated efficacy for both convex and non-convex agglomerates.

  19. Residue Geometry Networks: A Rigidity-Based Approach to the Amino Acid Network and Evolutionary Rate Analysis

    Science.gov (United States)

    Fokas, Alexander S.; Cole, Daniel J.; Ahnert, Sebastian E.; Chin, Alex W.

    2016-09-01

    Amino acid networks (AANs) abstract the protein structure by recording the amino acid contacts and can provide insight into protein function. Herein, we describe a novel AAN construction technique that employs the rigidity analysis tool, FIRST, to build the AAN, which we refer to as the residue geometry network (RGN). We show that this new construction can be combined with network theory methods to include the effects of allowed conformal motions and local chemical environments. Importantly, this is done without costly molecular dynamics simulations required by other AAN-related methods, which allows us to analyse large proteins and/or data sets. We have calculated the centrality of the residues belonging to 795 proteins. The results display a strong, negative correlation between residue centrality and the evolutionary rate. Furthermore, among residues with high closeness, those with low degree were particularly strongly conserved. Random walk simulations using the RGN were also successful in identifying allosteric residues in proteins involved in GPCR signalling. The dynamic function of these residues largely remain hidden in the traditional distance-cutoff construction technique. Despite being constructed from only the crystal structure, the results in this paper suggests that the RGN can identify residues that fulfil a dynamical function.

  20. Neural networks and principle component analysis approaches to predict pile capacity in sand

    Directory of Open Access Journals (Sweden)

    Benali A

    2018-01-01

    Full Text Available Determination of pile bearing capacity from the in-situ tests has developed considerably due to the significant development of their technology. The project presented in this paper is a combination of two approaches, artificial neural networks and main component analyses that allow the development of a neural network model that provides a more accurate prediction of axial load bearing capacity based on the SPT test data. The retropropagation multi-layer perceptron with Bayesian regularization (RB was used in this model. This was established by the incorporation of about 260 data, obtained from the published literature, of experimental programs for large displacement driven piles. The PCA method is proposed for compression and suppression of the correlation between these data. This will improve the performance of generalization of the model.

  1. Mal-Netminer: Malware Classification Approach Based on Social Network Analysis of System Call Graph

    OpenAIRE

    Jae-wook Jang; Jiyoung Woo; Aziz Mohaisen; Jaesung Yun; Huy Kang Kim

    2015-01-01

    As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a ...

  2. Analysis and optimal design of air quality monitoring networks using a variational approach

    Directory of Open Access Journals (Sweden)

    Adolfo Henriquez

    2015-10-01

    Full Text Available Air quality networks need revision and optimisation as instruments and network requirements, both scientific and societal, evolve over time. Assessing and optimising the information content of a monitoring network is a non-trivial problem. Here, we introduce a methodology formulated in a variational framework using an air quality model to simulate the dispersion of carbon monoxide (CO as a passive tracer at the city scale. We address the specific case of adding or removing stations, and the more general situation of optimally distributing a given number of stations in a domain taking into account transport patterns and spatial factors such as population density and emission patterns. We consider three quality indicators: precision gain, information gain and degrees of freedom for a signal. These metrics are all functions of the singular values of the sensitivity matrix that links emissions and observations in the variational framework. We illustrate the application of the methodology in the case of Santiago (33.5°S, 70.5°W, 500 m a.s.l., a city of ca. 7 million inhabitants with significant pollution levels. We deem information gain as the best of the above indicators for this case. We then quantify the actual evolution of Santiago's network and compare it with the optimal configuration suggested by our methodology and with results previously obtained using a statistical approach. The application is restricted to diurnal and summer conditions, for which the dispersion model shows a good agreement with observations. The current method offers advantages in that it allows extending a network to include new sites, and it explicitly considers the effects of dispersion patterns, and desired weighting functions such as emission fluxes and population density. We find that Santiago's air quality has improved two-fold since 1988, regarding CO under diurnal summer conditions. Still, according to our results, the current configuration could be improved by

  3. Clock Synchronization in Wireless Sensor Networks: A New Model and Analysis Approach Based on Networked Control Perspective

    Directory of Open Access Journals (Sweden)

    Wang Ting

    2014-01-01

    Full Text Available Motivated by the importance of the clock synchronization in wireless sensor networks (WSNs, this paper proposes a new research approach and model approach, which quantitatively analyzes clock synchronization from the perspective of modern control theory. Two kinds of control strategies are used as examples to analyze the effect of the control strategy on clock synchronization from different perspectives, namely, the single-step optimal control and the LQG global optimal control. The proposed method establishes a state space model for clock relationship, thus making dimension extension and parameter identification easier, and is robust to changes under the condition of node failures and new nodes. And through the design of different control strategies and performance index functions, the method can satisfy various requirements of the synchronization precision, convergence speed, energy consumption and the computational complexity, and so on. Finally, the simulations show that the synchronization accuracy of the proposed method is higher than that of the existing protocol, and the former convergence speed of the synchronization error is faster.

  4. Regulatory component analysis: a semi-blind extraction approach to infer gene regulatory networks with imperfect biological knowledge.

    Science.gov (United States)

    Wang, Chen; Xuan, Jianhua; Shih, Ie-Ming; Clarke, Robert; Wang, Yue

    2012-08-01

    With the advent of high-throughput biotechnology capable of monitoring genomic signals, it becomes increasingly promising to understand molecular cellular mechanisms through systems biology approaches. One of the active research topics in systems biology is to infer gene transcriptional regulatory networks using various genomic data; this inference problem can be formulated as a linear model with latent signals associated with some regulatory proteins called transcription factors (TFs). As common statistical assumptions may not hold for genomic signals, typical latent variable algorithms such as independent component analysis (ICA) are incapable to reveal underlying true regulatory signals. Liao et al. [1] proposed to perform inference using an approach named network component analysis (NCA), the optimization of which is achieved by a least-squares fitting approach with biological knowledge constraints. However, the incompleteness of biological knowledge and its inconsistency with gene expression data are not considered in the original NCA solution, which could greatly affect the inference accuracy. To overcome these limitations, we propose a linear extraction scheme, namely regulatory component analysis (RCA), to infer underlying regulatory signals even with partial biological knowledge. Numerical simulations show a significant improvement of our proposed RCA over NCA, not only when signal-to-noise-ratio (SNR) is low, but also when the given biological knowledge is incomplete and inconsistent to gene expression data. Furthermore, real biological experiments on E. coli are performed for regulatory network inference in comparison with several typical linear latent variable methods, which again demonstrates the effectiveness and improved performance of the proposed algorithm.

  5. Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach

    Directory of Open Access Journals (Sweden)

    Pourya Pourhejazy

    2017-02-01

    Full Text Available Supply chains can be vulnerable to sudden disruptions, especially when it emphasizes efficient operation. In this regard, supply chain resilience (SCR has received attention recently to cope with disruptions and improve competitiveness. This paper presents a novel methodology to measure resilience between different configurations of a supply chain network (SCN, based on a number of influential factors. For this reason, data envelopment analysis (DEA is employed to identify the best-practice and less-performing SCN configurations among a group of alternatives. On this basis, the extent to which a current configuration can improve its resiliency is also measured. The methodology is applied to the case of E1, a liquefied petroleum gas (LPG company in Korea. Topological and operational measures were used as variables to assess resilience. The results suggest that the LPG supply chain in the case study requires an addition in the number and capacity of supply nodes in its network.

  6. Power structure among the actors of financial support to the poor to access health services: Social network analysis approach.

    Science.gov (United States)

    Etemadi, Manal; Gorji, Hasan Abolghasem; Kangarani, Hannaneh Mohammadi; Ashtarian, Kioomars

    2017-12-01

    The extent of universal health coverage in terms of financial protection is worrisome in Iran. There are challenges in health policies to guarantee financial accessibility to health services, especially for poor people. Various institutions offer support to ensure that the poor have financial access to health services. The aim of this study is to investigate the relationship network among the institutions active in this field. This study is a policy document analysis. It evaluates the country's legal documents in the field of financial support to the poor for healthcare after the Islamic Revolution in Iran. The researchers looked for the documents on the related websites and referred to the related organizations. The social network analysis approach was chosen for the analysis of the documents. Block-modelling and multi-dimensional scaling (MDS) was used to determine the network structures. The UCINET software was employed to analyse the data. Most the main actors of this network are chosen from the government budget. There is no legal communication and cooperation among some of the actors because of their improper position in the network. Seven blocks have been clustered by CONCOR in terms of the actor's degree of similarity. The social distance among the actors of the seven blocks is very short. Power distribution in the field of financial support to the poor has a fragmented structure; however, it is mainly run by a dominant block consisting of The Supreme Council of Welfare and Social Security, Health Insurance Organization, and the Ministry of Health and Medical Education. The financial support for the poor network involves multiple actors. This variety has created a series of confusions in terms of the type, level, and scope of responsibilities among the actors. The weak presence legislative and regulatory institutions and also non-governmental institutions are the main weak points of this network. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Fighting Dark Networks: Using Social Network Analysis to Implement the Special Operations Targeting Process for Direct and Indirect Approaches

    Science.gov (United States)

    2013-03-01

    environmental impacts -- must be considered. H. A FINAL WORD ON SNA CONCEPTS AND DARK NETWORKS. To construct a useful database of network relational...http://www.jstor.org/stable/2780199?origin= JSTOR -pdf. 145 30 Martin Kilduff and Wenpin Tsai, Social Networks and Organizations (London: SAGE, 2003

  8. Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches

    Energy Technology Data Exchange (ETDEWEB)

    Khakzad, Nima [Process Engineering, Faculty of Engineering and Applied Science, Memorial University, St. John' s, NL, A1B 3X5 (Canada); Khan, Faisal, E-mail: fikhan@mun.c [Process Engineering, Faculty of Engineering and Applied Science, Memorial University, St. John' s, NL, A1B 3X5 (Canada); Amyotte, Paul [Department of Process Engineering and Applied Science, Dalhousie University, Halifax, NS, B3J 2X4 (Canada)

    2011-08-15

    Safety analysis in gas process facilities is necessary to prevent unwanted events that may cause catastrophic accidents. Accident scenario analysis with probability updating is the key to dynamic safety analysis. Although conventional failure assessment techniques such as fault tree (FT) have been used effectively for this purpose, they suffer severe limitations of static structure and uncertainty handling, which are of great significance in process safety analysis. Bayesian network (BN) is an alternative technique with ample potential for application in safety analysis. BNs have a strong similarity to FTs in many respects; however, the distinct advantages making them more suitable than FTs are their ability in explicitly representing the dependencies of events, updating probabilities, and coping with uncertainties. The objective of this paper is to demonstrate the application of BNs in safety analysis of process systems. The first part of the paper shows those modeling aspects that are common between FT and BN, giving preference to BN due to its ability to update probabilities. The second part is devoted to various modeling features of BN, helping to incorporate multi-state variables, dependent failures, functional uncertainty, and expert opinion which are frequently encountered in safety analysis, but cannot be considered by FT. The paper concludes that BN is a superior technique in safety analysis because of its flexible structure, allowing it to fit a wide variety of accident scenarios.

  9. Mal-Netminer: Malware Classification Approach Based on Social Network Analysis of System Call Graph

    Directory of Open Access Journals (Sweden)

    Jae-wook Jang

    2015-01-01

    Full Text Available As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that “influence-based” graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.

  10. Computer Networks A Systems Approach

    CERN Document Server

    Peterson, Larry L

    2011-01-01

    This best-selling and classic book teaches you the key principles of computer networks with examples drawn from the real world of network and protocol design. Using the Internet as the primary example, the authors explain various protocols and networking technologies. Their systems-oriented approach encourages you to think about how individual network components fit into a larger, complex system of interactions. Whatever your perspective, whether it be that of an application developer, network administrator, or a designer of network equipment or protocols, you will come away with a "big pictur

  11. An objective approach for feature extraction: distribution analysis and statistical descriptors for scale choice and channel network identification

    Directory of Open Access Journals (Sweden)

    G. Sofia

    2011-05-01

    Full Text Available A statistical approach to LiDAR derived topographic attributes for the automatic extraction of channel network and for the choice of the scale to apply for parameter evaluation is presented in this paper. The basis of this approach is to use distribution analysis and statistical descriptors to identify channels where terrain geometry denotes significant convergences. Two case study areas with different morphology and degree of organization are used with their 1 m LiDAR Digital Terrain Models (DTMs. Topographic attribute maps (curvature and openness for various window sizes are derived from the DTMs in order to detect surface convergences. A statistical analysis on value distributions considering each window size is carried out for the choice of the optimum kernel. We propose a three-step method to extract the network based (a on the normalization and overlapping of openness and minimum curvature to highlight the more likely surface convergences, (b a weighting of the upslope area according to these normalized maps to identify drainage flow paths and flow accumulation consistent with terrain geometry, (c the standard score normalization of the weighted upslope area and the use of standard score values as non subjective threshold for channel network identification. As a final step for optimal definition and representation of the whole network, a noise-filtering and connection procedure is applied. The advantage of the proposed methodology, and the efficiency and accurate localization of extracted features are demonstrated using LiDAR data of two different areas and comparing both extractions with field surveyed networks.

  12. Multidimensional Analysis of Linguistic Networks

    Science.gov (United States)

    Araújo, Tanya; Banisch, Sven

    Network-based approaches play an increasingly important role in the analysis of data even in systems in which a network representation is not immediately apparent. This is particularly true for linguistic networks, which use to be induced from a linguistic data set for which a network perspective is only one out of several options for representation. Here we introduce a multidimensional framework for network construction and analysis with special focus on linguistic networks. Such a framework is used to show that the higher is the abstraction level of network induction, the harder is the interpretation of the topological indicators used in network analysis. Several examples are provided allowing for the comparison of different linguistic networks as well as to networks in other fields of application of network theory. The computation and the intelligibility of some statistical indicators frequently used in linguistic networks are discussed. It suggests that the field of linguistic networks, by applying statistical tools inspired by network studies in other domains, may, in its current state, have only a limited contribution to the development of linguistic theory.

  13. A novel approach to parameter uncertainty analysis of hydrological models using neural networks

    Directory of Open Access Journals (Sweden)

    D. P. Solomatine

    2009-07-01

    Full Text Available In this study, a methodology has been developed to emulate a time consuming Monte Carlo (MC simulation by using an Artificial Neural Network (ANN for the assessment of model parametric uncertainty. First, MC simulation of a given process model is run. Then an ANN is trained to approximate the functional relationships between the input variables of the process model and the synthetic uncertainty descriptors estimated from the MC realizations. The trained ANN model encapsulates the underlying characteristics of the parameter uncertainty and can be used to predict uncertainty descriptors for the new data vectors. This approach was validated by comparing the uncertainty descriptors in the verification data set with those obtained by the MC simulation. The method is applied to estimate the parameter uncertainty of a lumped conceptual hydrological model, HBV, for the Brue catchment in the United Kingdom. The results are quite promising as the prediction intervals estimated by the ANN are reasonably accurate. The proposed techniques could be useful in real time applications when it is not practicable to run a large number of simulations for complex hydrological models and when the forecast lead time is very short.

  14. A methodological approach to the analysis of egocentric social networks in public health research: a practical example

    Directory of Open Access Journals (Sweden)

    Djomba Janet Klara

    2016-12-01

    Full Text Available Research on social networks in public health focuses on how social structures and relationships influence health and health-related behaviour. While the sociocentric approach is used to study complete social networks, the egocentric approach is gaining popularity because of its focus on individuals, groups and communities.

  15. Stability and Time Delay Tolerance Analysis Approach for Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Ashraf F. Khalil

    2015-01-01

    Full Text Available Networked control system is a research area where the theory is behind practice. Closing the feedback loop through shared network induces time delay and some of the data could be lost. So the network induced time delay and data loss are inevitable in networked control Systems. The time delay may degrade the performance of control systems or even worse lead to system instability. Once the structure of a networked control system is confirmed, it is essential to identify the maximum time delay allowed for maintaining the system stability which, in turn, is also associated with the process of controller design. Some studies reported methods for estimating the maximum time delay allowed for maintaining system stability; however, most of the reported methods are normally overcomplicated for practical applications. A method based on the finite difference approximation is proposed in this paper for estimating the maximum time delay tolerance, which has a simple structure and is easy to apply.

  16. Illegal Trade of Tortoises (Testudinata in Colombia: A Network Analysis Approach

    Directory of Open Access Journals (Sweden)

    Felber Jair Arroyave

    2014-03-01

    Full Text Available The use of wildlife is important for supporting the economic and demographic growth in emerging countries. Nevertheless, the products of wildlife usually come from illegal trade to supply fur, wild meat and pet markets. Illegal trade puts great pressure over wild populations and threats some endangered species. In Colombia, the trade of wildlife is important because of thevolumes traded and the cultural and economic connotation of some products. We describe the spatial structure of illegal trade of wildlife at departmental level for the five most traded genera of Colombian tortoises (Trachemys, Chelonoidis, Kinosternon, Podocnemis and Rhinoclemmys. This study is based on thereports of seizures between 2005 and 2009 compiled by the Ministerio de Medio Ambiente y Desarrollo of Colombia. Weapply Network Analysis to study and evidence that the illegal trade network of tortoises includes international markets and supplies the Andean region. The Caribbean, Pacific and Orinoquia regions are the principal suppliers. Quindio, Santander, Antioquia and Putumayo are the biggest jobbers and consumers of wild tortoises. We propose sociocultural and cohercitive actions to fragment the trade network andtheir illegal market as well as promoting the conservation and sustainable use of tortoises.TRÁFICO ILEGAL DE TORTUGAS CONTINENTALES (TESTUDINATA EN COLOMBIA: UNA APROXIMACIÓN DESDEEL ANÁLISIS DE REDESEl uso de productos extraídos o provenientes de la fauna silvestre es relevante para el desarrollo económico y el bienestar social en muchos lugares del mundo. Sin embargo, frecuentemente la fauna silvestre entra en los circuitos de tráfico ilegal para abastecer los mercados de mascotas y productos como pieles, plumas, “carne de monte”, entre otros. El tráfico ilegal genera enormes presiones sobre las especies sujetas a extracción y es una de las principales amenazas para estas. En Colombia, el tráfico de tortugas es de importancia debido a los vol

  17. Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis.

    Science.gov (United States)

    Brignardello-Petersen, Romina; Bonner, Ashley; Alexander, Paul E; Siemieniuk, Reed A; Furukawa, Toshi A; Rochwerg, Bram; Hazlewood, Glen S; Alhazzani, Waleed; Mustafa, Reem A; Murad, M Hassan; Puhan, Milo A; Schünemann, Holger J; Guyatt, Gordon H

    2018-01-01

    This article describes conceptual advances of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group guidance to evaluate the certainty of evidence (confidence in evidence, quality of evidence) from network meta-analysis (NMA). Application of the original GRADE guidance, published in 2014, in a number of NMAs has resulted in advances that strengthen its conceptual basis and make the process more efficient. This guidance will be useful for systematic review authors who aim to assess the certainty of all pairwise comparisons from an NMA and who are familiar with the basic concepts of NMA and the traditional GRADE approach for pairwise meta-analysis. Two principles of the original GRADE NMA guidance are that we need to rate the certainty of the evidence for each pairwise comparison within a network separately and that in doing so we need to consider both the direct and indirect evidence. We present, discuss, and illustrate four conceptual advances: (1) consideration of imprecision is not necessary when rating the direct and indirect estimates to inform the rating of NMA estimates, (2) there is no need to rate the indirect evidence when the certainty of the direct evidence is high and the contribution of the direct evidence to the network estimate is at least as great as that of the indirect evidence, (3) we should not trust a statistical test of global incoherence of the network to assess incoherence at the pairwise comparison level, and (4) in the presence of incoherence between direct and indirect evidence, the certainty of the evidence of each estimate can help decide which estimate to believe. Copyright © 2017 Elsevier Inc. All rights reserved.

  18. Information Flow Through Stages of Complex Engineering Design Projects: A Dynamic Network Analysis Approach

    DEFF Research Database (Denmark)

    Parraguez, Pedro; Eppinger, Steven D.; Maier, Anja

    2015-01-01

    as those activities are implemented through the network of people executing the project. To address this gap, we develop a dynamic modeling method that integrates both the network of people and the network of activities in the project. We then employ a large dataset collected from an industrial setting...... design process and thus support theory-building toward the evolution of information flows through systems engineering stages. Implications include guidance on how to analyze and predict information flows as well as better planning of information flows in engineering design projects according......, consisting of project-related e-mails and activity records from the design and development of a renewable energy plant over the course of more than three years. Using network metrics for centrality and clustering, we make three important contributions: 1)We demonstrate a novel method for analyzing...

  19. Network Routing Using the Network Tasking Order, a Chron Approach

    Science.gov (United States)

    2015-03-26

    iv BATMAN Better Approach to Mobile Ad-hoc Networks...routing in wireless mesh networks. MicroTik has created MME based on the Better Approach to Mobile Ad-hoc Network ( BATMAN ) routing protocol. MME

  20. Approaching human language with complex networks.

    Science.gov (United States)

    Cong, Jin; Liu, Haitao

    2014-12-01

    The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics). Copyright © 2014 Elsevier B.V. All rights reserved.

  1. Modeling and Optimization of M/G/1-Type Queueing Networks: An Efficient Sensitivity Analysis Approach

    Directory of Open Access Journals (Sweden)

    Liang Tang

    2010-01-01

    Full Text Available A mathematical model for M/G/1-type queueing networks with multiple user applications and limited resources is established. The goal is to develop a dynamic distributed algorithm for this model, which supports all data traffic as efficiently as possible and makes optimally fair decisions about how to minimize the network performance cost. An online policy gradient optimization algorithm based on a single sample path is provided to avoid suffering from a “curse of dimensionality”. The asymptotic convergence properties of this algorithm are proved. Numerical examples provide valuable insights for bridging mathematical theory with engineering practice.

  2. Climate dynamics: a network-based approach for the analysis of global precipitation.

    Directory of Open Access Journals (Sweden)

    Stefania Scarsoglio

    Full Text Available Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010. The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that

  3. Network Medicine: A Network-based Approach to Human Diseases

    Science.gov (United States)

    Ghiassian, Susan Dina

    With the availability of large-scale data, it is now possible to systematically study the underlying interaction maps of many complex systems in multiple disciplines. Statistical physics has a long and successful history in modeling and characterizing systems with a large number of interacting individuals. Indeed, numerous approaches that were first developed in the context of statistical physics, such as the notion of random walks and diffusion processes, have been applied successfully to study and characterize complex systems in the context of network science. Based on these tools, network science has made important contributions to our understanding of many real-world, self-organizing systems, for example in computer science, sociology and economics. Biological systems are no exception. Indeed, recent studies reflect the necessity of applying statistical and network-based approaches in order to understand complex biological systems, such as cells. In these approaches, a cell is viewed as a complex network consisting of interactions among cellular components, such as genes and proteins. Given the cellular network as a platform, machinery, functionality and failure of a cell can be studied with network-based approaches, a field known as systems biology. Here, we apply network-based approaches to explore human diseases and their associated genes within the cellular network. This dissertation is divided in three parts: (i) A systematic analysis of the connectivity patterns among disease proteins within the cellular network. The quantification of these patterns inspires the design of an algorithm which predicts a disease-specific subnetwork containing yet unknown disease associated proteins. (ii) We apply the introduced algorithm to explore the common underlying mechanism of many complex diseases. We detect a subnetwork from which inflammatory processes initiate and result in many autoimmune diseases. (iii) The last chapter of this dissertation describes the

  4. Analysis and assessment of injury risk in female gymnastics:Bayesian Network approach

    Directory of Open Access Journals (Sweden)

    Lyudmila Dimitrova

    2015-02-01

    Full Text Available This paper presents a Bayesian network (BN model for estimating injury risk in female artistic gymnastics. The model illustrates the connections betweenunderlying injury risk factorsthrough a series ofcausal dependencies. The quantitativepart of the model – the conditional probability tables, are determined using ТNormal distribution with parameters, derived by experts. The injury rates calculated by the network are in an agreement with injury statistic data and correctly reports the impact of various risk factors on injury rates. The model is designed to assist coaches and supporting teams in planning the training activity so that injuries are minimized. This study provides important background for further data collection and research necessary to improve the precision of the quantitative predictions of the model.

  5. Performance Analysis for Wireless Networks: An Analytical Approach by Multifarious Sym Teredo

    Directory of Open Access Journals (Sweden)

    D. Shalini Punithavathani

    2014-01-01

    Full Text Available IPv4-IPv6 transition rolls out numerous challenges to the world of Internet as the Internet is drifting from IPv4 to IPv6. IETF recommends few transition techniques which includes dual stack and translation and tunneling. By means of tunneling the IPv6 packets over IPv4 UDP, Teredo maintains IPv4/IPv6 dual stack node in isolated IPv4 networks behindhand network address translation (NAT. However, the proposed tunneling protocol works with the symmetric and asymmetric NATs. In order to make a Teredo support several symmetric NATs along with several asymmetric NATs, we propose multifarious Sym Teredo (MTS, which is an extension of Teredo with a capability of navigating through several symmetric NATs. The work preserves the Teredo architecture and also offers a backward compatibility with the original Teredo protocol.

  6. Quantum Phase Transitions: A Network Approach

    Science.gov (United States)

    Vargas, David L.; Larue, David M.; Carr, Lincoln D.

    2014-03-01

    Understanding the network structure of complex systems has opened up new avenues of research in sociology, biology, technology, and physics. In this talk we present evidence that complex network measures are able to identify the phases in two well known models. We distinguish the ferromagnetic and paramagnetic phases of the transverse Ising Hamiltonian. We also identify the Mott-insulator to superfluid transition of the Bose-Hubbard Hamiltonian. The network approach to the analysis of quantum phase transitions provides us with a new set of tools to explore the many body physics of quantum phase transitions. Supported by NSF and AFOSR.

  7. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

    DEFF Research Database (Denmark)

    Schlechtingen, Meik; Santos, Ilmar

    2011-01-01

    approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies.The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time...

  8. Exploration of unstructured narrative crime reports : An unsupervised neural network and point pattern analysis approach

    NARCIS (Netherlands)

    Helbich, M.|info:eu-repo/dai/nl/370530349; Hagenauer, J.; Leitner, M.; Edwards, R.

    2013-01-01

    Crime intelligence analysis and criminal investigations are increasingly making use of geospatial methodologies to improve tactical and strategic decision-making. However, the full potential of geospatial technologies is yet to be exploited. In particular, geospatial technology currently applied by

  9. Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach.

    Science.gov (United States)

    Zeng, Nianyin; Wang, Zidong; Zineddin, Bachar; Li, Yurong; Du, Min; Xiao, Liang; Liu, Xiaohui; Young, Terry

    2014-05-01

    Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.

  10. Qualitative networks: a symbolic approach to analyze biological signaling networks

    Directory of Open Access Journals (Sweden)

    Henzinger Thomas A

    2007-01-01

    Full Text Available Abstract Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology.

  11. ANALYSIS OF DOMESTIC AND INTERNATIONAL APPROACHES TO THE ADVANCED EDUCATIONAL PRACTICES IN THE ELECTRONIC NETWORK ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    Tatiana N. Noskova

    2016-12-01

    Full Text Available Introduction: human activities related to the use of information are being transformed under the influence of computer technology. Variable solutions to information problems are emerging; demands and require¬ments for the competence are changing on the labour market. Educational practices are destined to form a new learning behaviour for the 21st century, adopting lifelong learning strategy. The main purpose of the article is to answer the question as to how to transform existing pedagogical theory and practice under current conditions of electronic environment. Publishing of this article is coherent with concept of the journal Integration of Education, analyzing Russian and world experience in the development of education systems. This approach is important for dissemination and implementation in practice. This article explores the challenges of information technology and technical support of the educational process in universities and schools. The study of these issues is in the field of view of the journa l. Materials and Methods: the paper elaborates on the results of domestic and international educational theory and practice, comparison methods, drawing on student’s survey in the framework of international research in the field of e-learning in higher education institutions. Results: the main approaches, applied to the formulation of educational practices in the electronic environ-ment, were analyzed. The most topical national approaches include system, activity, polysubject (dialogical, context, and dialogical ones. Among international approaches self-directed learning, educational communication strategies, experiential learning, training in partnership, collaborative learning, learning in online communities, situational training were analyzed. Specifics of electronic educational interactions with distributed in time and space activities of teachers and students, create the preconditions for the implementation of new educational

  12. Statistical network analysis for analyzing policy networks

    DEFF Research Database (Denmark)

    Robins, Garry; Lewis, Jenny; Wang, Peng

    2012-01-01

    To analyze social network data using standard statistical approaches is to risk incorrect inference. The dependencies among observations implied in a network conceptualization undermine standard assumptions of the usual general linear models. One of the most quickly expanding areas of social...... and policy network methodology is the development of statistical modeling approaches that can accommodate such dependent data. In this article, we review three network statistical methods commonly used in the current literature: quadratic assignment procedures, exponential random graph models (ERGMs...

  13. Network analysis applications in hydrology

    Science.gov (United States)

    Price, Katie

    2017-04-01

    Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

  14. Deciphering ascorbic acid regulatory pathways in ripening tomato fruit using a weighted gene correlation network analysis approach.

    Science.gov (United States)

    Gao, Chao; Ju, Zheng; Li, Shan; Zuo, Jinhua; Fu, Daqi; Tian, Huiqin; Luo, Yunbo; Zhu, Benzhong

    2013-11-01

    Genotype is generally determined by the co-expression of diverse genes and multiple regulatory pathways in plants. Gene co-expression analysis combining with physiological trait data provides very important information about the gene function and regulatory mechanism. L-Ascorbic acid (AsA), which is an essential nutrient component for human health and plant metabolism, plays key roles in diverse biological processes such as cell cycle, cell expansion, stress resistance, hormone synthesis, and signaling. Here, we applied a weighted gene correlation network analysis approach based on gene expression values and AsA content data in ripening tomato (Solanum lycopersicum L.) fruit with different AsA content levels, which leads to identification of AsA relevant modules and vital genes in AsA regulatory pathways. Twenty-four modules were compartmentalized according to gene expression profiling. Among these modules, one negatively related module containing genes involved in redox processes and one positively related module enriched with genes involved in AsA biosynthetic and recycling pathways were further analyzed. The present work herein indicates that redox pathways as well as hormone-signal pathways are closely correlated with AsA accumulation in ripening tomato fruit, and allowed us to prioritize candidate genes for follow-up studies to dissect this interplay at the biochemical and molecular level. © 2013 Institute of Botany, Chinese Academy of Sciences.

  15. Analysis of Semantic Networks using Complex Networks Concepts

    DEFF Research Database (Denmark)

    Ortiz-Arroyo, Daniel

    2013-01-01

    In this paper we perform a preliminary analysis of semantic networks to determine the most important terms that could be used to optimize a summarization task. In our experiments, we measure how the properties of a semantic network change, when the terms in the network are removed. Our preliminar...... results indicate that this approach provides good results on the semantic network analyzed in this paper....

  16. Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

    Directory of Open Access Journals (Sweden)

    Lun Zhang

    2015-01-01

    Full Text Available This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN. Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers’ route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity.

  17. Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis

    Science.gov (United States)

    Hasmi, Laila; Drukker, Marjan; Guloksuz, Sinan; Menne-Lothmann, Claudia; Decoster, Jeroen; van Winkel, Ruud; Collip, Dina; Delespaul, Philippe; De Hert, Marc; Derom, Catherine; Thiery, Evert; Jacobs, Nele; Rutten, Bart P. F.; Wichers, Marieke; van Os, Jim

    2017-01-01

    Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions “cheerful,” “insecure,” “relaxed,” “anxious,” “irritated,” and “down”—collected using the ESM method. Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network. Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p emotions. The present finding partially replicates an earlier analysis, suggesting it may be instructive to model negative emotional dynamics as a function of genetic influence. PMID:29163289

  18. Queueing networks a fundamental approach

    CERN Document Server

    Dijk, Nico

    2011-01-01

    This handbook aims to highlight fundamental, methodological and computational aspects of networks of queues to provide insights and to unify results that can be applied in a more general manner.  The handbook is organized into five parts: Part 1 considers exact analytical results such as of product form type. Topics include characterization of product forms by physical balance concepts and simple traffic flow equations, classes of service and queue disciplines that allow a product form, a unified description of product forms for discrete time queueing networks, insights for insensitivity, and aggregation and decomposition results that allow subnetworks to be aggregated into single nodes to reduce computational burden. Part 2 looks at monotonicity and comparison results such as for computational simplification by either of two approaches: stochastic monotonicity and ordering results based on the ordering of the proces generators, and comparison results and explicit error bounds based on an underlying Markov r...

  19. Social Network Analysis and Critical Realism

    DEFF Research Database (Denmark)

    Buch-Hansen, Hubert

    2014-01-01

    Social network analysis ( SNA) is an increasingly popular approach that provides researchers with highly developed tools to map and analyze complexes of social relations. Although a number of network scholars have explicated the assumptions that underpin SNA, the approach has yet to be discussed ...

  20. A network approach toward literature review

    NARCIS (Netherlands)

    van de Wijngaert, Lidwien; Bouwman, Harry; Contractor, Noshir

    2012-01-01

    This study introduces a method that uses a network approach towards literature review. To employ this approach, we use hypotheses proposed in scientific publications as building blocks. In network terms, a hypothesis is a directed tie between two concepts or nodes. The network emerges by aggregating

  1. Neural network approaches for noisy language modeling.

    Science.gov (United States)

    Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid

    2013-11-01

    Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.

  2. Social network analysis

    NARCIS (Netherlands)

    de Nooy, W.; Crothers, C.

    2009-01-01

    Social network analysis (SNA) focuses on the structure of ties within a set of social actors, e.g., persons, groups, organizations, and nations, or the products of human activity or cognition such as web sites, semantic concepts, and so on. It is linked to structuralism in sociology stressing the

  3. Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis.

    Science.gov (United States)

    Hasmi, Laila; Drukker, Marjan; Guloksuz, Sinan; Menne-Lothmann, Claudia; Decoster, Jeroen; van Winkel, Ruud; Collip, Dina; Delespaul, Philippe; De Hert, Marc; Derom, Catherine; Thiery, Evert; Jacobs, Nele; Rutten, Bart P F; Wichers, Marieke; van Os, Jim

    2017-01-01

    Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions "cheerful," "insecure," "relaxed," "anxious," "irritated," and "down"-collected using the ESM method. Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network. Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p < 0.001). The medium GL stratum also showed a directionally similar (in-between high and low GL strata) but statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021) and overall density (p = 0.042) in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing. Conclusions: The present findings demonstrate that

  4. Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis

    Directory of Open Access Journals (Sweden)

    Laila Hasmi

    2017-11-01

    Full Text Available Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL to psychopathology and childhood trauma (CT are associated with the network structure of the emotions “cheerful,” “insecure,” “relaxed,” “anxious,” “irritated,” and “down”—collected using the ESM method.Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals, we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network.Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018 and negative affect network density (p < 0.001. The medium GL stratum also showed a directionally similar (in-between high and low GL strata but statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021 and overall density (p = 0.042 in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing.Conclusions: The

  5. Network performance analysis

    CERN Document Server

    Bonald, Thomas

    2013-01-01

    The book presents some key mathematical tools for the performance analysis of communication networks and computer systems.Communication networks and computer systems have become extremely complex. The statistical resource sharing induced by the random behavior of users and the underlying protocols and algorithms may affect Quality of Service.This book introduces the main results of queuing theory that are useful for analyzing the performance of these systems. These mathematical tools are key to the development of robust dimensioning rules and engineering methods. A number of examples i

  6. Network systems security analysis

    Science.gov (United States)

    Yilmaz, Ä.°smail

    2015-05-01

    Network Systems Security Analysis has utmost importance in today's world. Many companies, like banks which give priority to data management, test their own data security systems with "Penetration Tests" by time to time. In this context, companies must also test their own network/server systems and take precautions, as the data security draws attention. Based on this idea, the study cyber-attacks are researched throughoutly and Penetration Test technics are examined. With these information on, classification is made for the cyber-attacks and later network systems' security is tested systematically. After the testing period, all data is reported and filed for future reference. Consequently, it is found out that human beings are the weakest circle of the chain and simple mistakes may unintentionally cause huge problems. Thus, it is clear that some precautions must be taken to avoid such threats like updating the security software.

  7. Analysis of computer networks

    CERN Document Server

    Gebali, Fayez

    2015-01-01

    This textbook presents the mathematical theory and techniques necessary for analyzing and modeling high-performance global networks, such as the Internet. The three main building blocks of high-performance networks are links, switching equipment connecting the links together, and software employed at the end nodes and intermediate switches. This book provides the basic techniques for modeling and analyzing these last two components. Topics covered include, but are not limited to: Markov chains and queuing analysis, traffic modeling, interconnection networks and switch architectures and buffering strategies.   ·         Provides techniques for modeling and analysis of network software and switching equipment; ·         Discusses design options used to build efficient switching equipment; ·         Includes many worked examples of the application of discrete-time Markov chains to communication systems; ·         Covers the mathematical theory and techniques necessary for ana...

  8. Insomnia and Personality—A Network Approach

    Directory of Open Access Journals (Sweden)

    Kim Dekker

    2017-03-01

    Full Text Available Studies on personality traits and insomnia have remained inconclusive about which traits show the most direct associations with insomnia severity. It has moreover hardly been explored how traits relate to specific characteristics of insomnia. We here used network analysis in a large sample (N = 2089 to obtain an integrated view on the associations of personality traits with both overall insomnia severity and different insomnia characteristics, while distinguishing direct from indirect associations. We first estimated a network describing the associations among the five factor model personality traits and overall insomnia severity. Overall insomnia severity was associated with neuroticism, agreeableness, and openness. Subsequently, we estimated a separate network describing the associations among the personality traits and each of the seven individual items of the Insomnia Severity Index. This revealed relatively separate clusters of daytime and nocturnal insomnia complaints, that both contributed to dissatisfaction with sleep, and were both most directly associated with neuroticism and conscientiousness. The approach revealed the strongest direct associations between personality traits and the severity of different insomnia characteristics and overall insomnia severity. Differentiating them from indirect associations identified the targets for improving Cognitive Behavioral Therapy for insomnia with the highest probability of effectively changing the network of associated complaints.

  9. Network growth approach to macroevolution

    OpenAIRE

    Qin, Shao-Meng; Chen, Yong; Zhang, Pan

    2006-01-01

    We propose a novel network growth model coupled with the competition interaction to simulate macroevolution. Our work shows that the competition plays an important role in macroevolution and it is more rational to describe the interaction between species by network structures. Our model presents a complete picture of the development of phyla and the splitting process. It is found that periodic mass extinction occurred in our networks without any extraterrestrial factors and the lifetime distr...

  10. A network approach to leadership

    DEFF Research Database (Denmark)

    Lewis, Jenny; Ricard, Lykke Margot

    Leaders’ ego-networks within an organization are pivotal as focal points that point to other organizational factors such as innovation capacity and leadership effectiveness. The aim of the paper is to provide a framework for exploring leaders’ ego-networks within the boundary of an organization. We...... redundancy and effective size, and the potential for either divide and conquer or distributed leadership strategies. The empirical testing of this framework adds to our knowledge of the micro level role of individuals within networks. This will be used to examine the relationships between leadership, network...

  11. Network growth approach to macroevolution

    Energy Technology Data Exchange (ETDEWEB)

    Qin Shaomeng; Chen Yong; Zhang Pan [Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000 (China)

    2007-07-15

    We propose a novel network growth model coupled with the competition interaction to simulate macroevolution. Our work shows that competition plays an important role in macroevolution and it is more rational to describe the interaction between species by network structures. Our model presents a complete picture of the development of phyla and the splitting process. It is found that periodic mass extinction occurred in our networks without any extraterrestrial factors and the lifetime distribution of species is very close to the fossil record. We also perturb networks with two scenarios of mass extinctions on different hierarchic levels in order to study their recovery.

  12. Network growth approach to macroevolution

    Science.gov (United States)

    Qin, Shao-Meng; Chen, Yong; Zhang, Pan

    2007-07-01

    We propose a novel network growth model coupled with the competition interaction to simulate macroevolution. Our work shows that competition plays an important role in macroevolution and it is more rational to describe the interaction between species by network structures. Our model presents a complete picture of the development of phyla and the splitting process. It is found that periodic mass extinction occurred in our networks without any extraterrestrial factors and the lifetime distribution of species is very close to the fossil record. We also perturb networks with two scenarios of mass extinctions on different hierarchic levels in order to study their recovery.

  13. Clustering: a neural network approach.

    Science.gov (United States)

    Du, K-L

    2010-01-01

    Clustering is a fundamental data analysis method. It is widely used for pattern recognition, feature extraction, vector quantization (VQ), image segmentation, function approximation, and data mining. As an unsupervised classification technique, clustering identifies some inherent structures present in a set of objects based on a similarity measure. Clustering methods can be based on statistical model identification (McLachlan & Basford, 1988) or competitive learning. In this paper, we give a comprehensive overview of competitive learning based clustering methods. Importance is attached to a number of competitive learning based clustering neural networks such as the self-organizing map (SOM), the learning vector quantization (LVQ), the neural gas, and the ART model, and clustering algorithms such as the C-means, mountain/subtractive clustering, and fuzzy C-means (FCM) algorithms. Associated topics such as the under-utilization problem, fuzzy clustering, robust clustering, clustering based on non-Euclidean distance measures, supervised clustering, hierarchical clustering as well as cluster validity are also described. Two examples are given to demonstrate the use of the clustering methods.

  14. Satellite image analysis using neural networks

    Science.gov (United States)

    Sheldon, Roger A.

    1990-01-01

    The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

  15. Analysis of neural networks

    CERN Document Server

    Heiden, Uwe

    1980-01-01

    The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica­ ted throughout the text. However, they are not explored in de­ tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev­ els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be­ havior of neurons or neuron pools. In this respect the essay is writt...

  16. From Microactions to Macrostructure and Back : A Structurational Approach to the Evolution of Organizational Networks

    NARCIS (Netherlands)

    Whitbred, Robert; Fonti, Fabio; Steglich, Christian; Contractor, Noshir

    Structuration theory (ST) and network analysis are promising approaches for studying the emergence of communication networks. We offer a model that integrates the conceptual richness of structuration with the precision of relevant concepts and mechanisms offered from communication network research.

  17. Which is the best laparoscopic approach for inguinal hernia repair: TEP or TAPP? A systematic review of the literature with a network meta-analysis.

    Science.gov (United States)

    Bracale, Umberto; Melillo, Paolo; Pignata, Giusto; Di Salvo, Enrico; Rovani, Marcella; Merola, Giovanni; Pecchia, Leandro

    2012-12-01

    Totally extraperitoneal (TEP) repair and transabdominal preperitoneal (TAPP) repair are the most used laparoscopic techniques for inguinal hernia treatment. However, many studies have shown that laparoscopic hernia repair compared with open hernia repair (OHR) may offer less pain and shorter convalescence. Few studies compared the clinical efficacy between TEP and TAPP technique. The purpose of this study is to provide a comparison between TEP and TAPP for inguinal hernia repair to show the best approach. We performed an indirect comparison between TEP and TAPP techniques by considering only randomized, controlled trials comparing TEP with OHR and TAPP with OHR in a network meta-analysis. We considered the following outcomes: operative time, postoperative complications, hospital stay, postoperative pain, time to return to work, and recurrences. The two techniques improved some short outcomes (such as time to return to work) with respect to OHR. In the network meta-analysis, TEP and TAPP were equivalent for operative time, postoperative complications, postoperative pain, time to return to work, and recurrences, whereas TAPP was associated with a slightly longer hospital stay compared with TEP. TEP and TAPP improved clinical outcomes compared with OHR, but the network meta-analysis showed that TEP and TAPP efficacy is equivalent. TAPP was associated with a slightly longer hospital stay compared with TEP.

  18. Network meta-analysis: development of a three-level hierarchical modeling approach incorporating dose-related constraints.

    Science.gov (United States)

    Owen, Rhiannon K; Tincello, Douglas G; Keith, R Abrams

    2015-01-01

    Network meta-analysis (NMA) is commonly used in evidence synthesis; however, in situations in which there are a large number of treatment options, which may be subdivided into classes, and relatively few trials, NMAs produce considerable uncertainty in the estimated treatment effects, and consequently, identification of the most beneficial intervention remains inconclusive. To develop and demonstrate the use of evidence synthesis methods to evaluate extensive treatment networks with a limited number of trials, making use of classes. Using Bayesian Markov chain Monte Carlo methods, we build on the existing work of a random effects NMA to develop a three-level hierarchical NMA model that accounts for the exchangeability between treatments within the same class as well as for the residual between-study heterogeneity. We demonstrate the application of these methods to a continuous and binary outcome, using a motivating example of overactive bladder. We illustrate methods for incorporating ordering constraints in increasing doses, model selection, and assessing inconsistency between the direct and indirect evidence. The methods were applied to a data set obtained from a systematic literature review of trials for overactive bladder, evaluating the mean reduction in incontinence episodes from baseline and the number of patients reporting one or more adverse events. The data set involved 72 trials comparing 34 interventions that were categorized into nine classes of interventions, including placebo. Bayesian three-level hierarchical NMAs have the potential to increase the precision in the effect estimates while maintaining the interpretability of the individual interventions for decision making. Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  19. Disaster risk management in prospect mining area Blitar district, East Java, using microtremor analysis and ANP (analytical network processing) approach

    Science.gov (United States)

    Parwatiningtyas, Diyan; Ambarsari, Erlin Windia; Marlina, Dwi; Wiratomo, Yogi

    2014-03-01

    Indonesia has a wealth of natural assets is so large to be managed and utilized, either from its own local government and local communities, especially in the mining sector. However, mining activities can change the state of the surface layer of the earth that have a high impact disaster risk. This could threaten the safety and disrupt human life, environmental damage, loss of property, and the psychological impact, sulking to the rule of law no 24 of 2007. That's why we strive to manage and minimize the risk of mine disasters in the region, how to use the method of calculation of Amplification Factor (AF) from the analysis based microtremor sulking Kanai and Nakamura, and decision systems were tested by analysis of ANP. Based on the amplification factor and Analytical Network Processing (ANP) obtained, some points showed instability in the surface layer of a mining area include the site of the TP-7, TP-8, TP-9, TP-10, (Birowo2). If in terms of structure, location indicated unstable due to have a sloping surface layer, resulting in the occurrence of landslides and earthquake risk is high. In the meantime, other areas of the mine site can be said to be a stable area.

  20. Disaster risk management in prospect mining area Blitar district, East Java, using microtremor analysis and ANP (analytical network processing) approach

    Energy Technology Data Exchange (ETDEWEB)

    Parwatiningtyas, Diyan, E-mail: diane.tyas@gmail.com, E-mail: erlinunindra@gmail.com; Ambarsari, Erlin Windia, E-mail: diane.tyas@gmail.com, E-mail: erlinunindra@gmail.com; Marlina, Dwi, E-mail: diane.tyas@gmail.com, E-mail: erlinunindra@gmail.com; Wiratomo, Yogi, E-mail: diane.tyas@gmail.com, E-mail: erlinunindra@gmail.com [Department of Physics, Faculty of Engineering, Mathematics and Natural Sciences, Indraprasta PGRI University, Nangka Street No. 58C Tanjung Barat, South Jakarta (Indonesia)

    2014-03-24

    Indonesia has a wealth of natural assets is so large to be managed and utilized, either from its own local government and local communities, especially in the mining sector. However, mining activities can change the state of the surface layer of the earth that have a high impact disaster risk. This could threaten the safety and disrupt human life, environmental damage, loss of property, and the psychological impact, sulking to the rule of law no 24 of 2007. That's why we strive to manage and minimize the risk of mine disasters in the region, how to use the method of calculation of Amplification Factor (AF) from the analysis based microtremor sulking Kanai and Nakamura, and decision systems were tested by analysis of ANP. Based on the amplification factor and Analytical Network Processing (ANP) obtained, some points showed instability in the surface layer of a mining area include the site of the TP-7, TP-8, TP-9, TP-10, (Birowo2). If in terms of structure, location indicated unstable due to have a sloping surface layer, resulting in the occurrence of landslides and earthquake risk is high. In the meantime, other areas of the mine site can be said to be a stable area.

  1. Models of network reliability analysis, combinatorics, and Monte Carlo

    CERN Document Server

    Gertsbakh, Ilya B

    2009-01-01

    Unique in its approach, Models of Network Reliability: Analysis, Combinatorics, and Monte Carlo provides a brief introduction to Monte Carlo methods along with a concise exposition of reliability theory ideas. From there, the text investigates a collection of principal network reliability models, such as terminal connectivity for networks with unreliable edges and/or nodes, network lifetime distribution in the process of its destruction, network stationary behavior for renewable components, importance measures of network elements, reliability gradient, and network optimal reliability synthesis

  2. Measuring Road Network Vulnerability with Sensitivity Analysis

    Science.gov (United States)

    Jun-qiang, Leng; Long-hai, Yang; Liu, Wei-yi; Zhao, Lin

    2017-01-01

    This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the change of traffic utility index due to capacity degradation. This method, compared to traditional traffic assignment, can improve calculation efficiency and make the application of vulnerability analysis to large actual road network possible. Finally, all the above models and calculation method is applied to actual road network evaluation to verify its efficiency and utility. This approach can be used as a decision-supporting tool for evaluating the performance of road network and identifying critical infrastructures in transportation planning and management, especially in the resource allocation for mitigation and recovery. PMID:28125706

  3. Adjoint-based sensitivity analysis of low-order thermoacoustic networks using a wave-based approach

    Science.gov (United States)

    Aguilar, José G.; Magri, Luca; Juniper, Matthew P.

    2017-07-01

    Strict pollutant emission regulations are pushing gas turbine manufacturers to develop devices that operate in lean conditions, with the downside that combustion instabilities are more likely to occur. Methods to predict and control unstable modes inside combustion chambers have been developed in the last decades but, in some cases, they are computationally expensive. Sensitivity analysis aided by adjoint methods provides valuable sensitivity information at a low computational cost. This paper introduces adjoint methods and their application in wave-based low order network models, which are used as industrial tools, to predict and control thermoacoustic oscillations. Two thermoacoustic models of interest are analyzed. First, in the zero Mach number limit, a nonlinear eigenvalue problem is derived, and continuous and discrete adjoint methods are used to obtain the sensitivities of the system to small modifications. Sensitivities to base-state modification and feedback devices are presented. Second, a more general case with non-zero Mach number, a moving flame front and choked outlet, is presented. The influence of the entropy waves on the computed sensitivities is shown.

  4. Maximum Entropy Approaches to Living Neural Networks

    Directory of Open Access Journals (Sweden)

    John M. Beggs

    2010-01-01

    Full Text Available Understanding how ensembles of neurons collectively interact will be a key step in developing a mechanistic theory of cognitive processes. Recent progress in multineuron recording and analysis techniques has generated tremendous excitement over the physiology of living neural networks. One of the key developments driving this interest is a new class of models based on the principle of maximum entropy. Maximum entropy models have been reported to account for spatial correlation structure in ensembles of neurons recorded from several different types of data. Importantly, these models require only information about the firing rates of individual neurons and their pairwise correlations. If this approach is generally applicable, it would drastically simplify the problem of understanding how neural networks behave. Given the interest in this method, several groups now have worked to extend maximum entropy models to account for temporal correlations. Here, we review how maximum entropy models have been applied to neuronal ensemble data to account for spatial and temporal correlations. We also discuss criticisms of the maximum entropy approach that argue that it is not generally applicable to larger ensembles of neurons. We conclude that future maximum entropy models will need to address three issues: temporal correlations, higher-order correlations, and larger ensemble sizes. Finally, we provide a brief list of topics for future research.

  5. Data analysis of Permanent GPS networks in Italy and surrounding region: application of a distributed processing approach

    Directory of Open Access Journals (Sweden)

    M. Anzidei

    2006-06-01

    Full Text Available We describe the procedures used to combine into a uniform velocity solution the observations of more than 80 continuous GPS stations operating in the central Mediterranean in the 1998-2004 time interval. We used a distributed processing approach, which makes efficient use of computer resources, while producing velocity estimates for all stations in one common reference frame, allowing for an effective merging of all the observations into a self-consistent network solution. We describe the CGPS data archiving and processing procedures, and provide main results in terms of position time-series and velocities for all stations that observed more than three years. We computed horizontal and vertical velocities accounting for the seasonal (annual and semi-annual signals, and considering the off-sets in the coordinate time-series caused by station equipment changes. Weighted post-fit RMS of the north, east and vertical velocity components are in the range of 1.57-2.08 mm, 1.31-3.28 mm, and 3.60-7.24 mm, respectively, which are reduced by solving for seasonal signals in the velocity estimates. The annual and semi-annual signals in the height components, with amplitudes up to 4.8 mm, are much stronger than those in the horizontal components. The mean amplitudes of annual and semi-annual signals are within 0.18-0.47 mm, 0.23-0.52 mm and 0.55-1.92 mm in the north, east and vertical components, respectively.

  6. A Transdiagnostic Network Approach to Psychosis

    NARCIS (Netherlands)

    Wigman, Johanna T. W.; de Vos, Stijn; Wichers, Marieke; van Os, Jim; Bartels-Velthuis, Agna A.

    Our ability to accurately predict development and outcome of early expression of psychosis is limited. To elucidate the mechanisms underlying psychopathology, a broader, transdiagnostic approach that acknowledges the complexity of mental illness is required. The upcoming network paradigm may be

  7. A Bayesian Network Approach to Ontology Mapping

    National Research Council Canada - National Science Library

    Pan, Rong; Ding, Zhongli; Yu, Yang; Peng, Yun

    2005-01-01

    .... In this approach, the source and target ontologies are first translated into Bayesian networks (BN); the concept mapping between the two ontologies are treated as evidential reasoning between the two translated BNs...

  8. Analysis of complex networks using aggressive abstraction.

    Energy Technology Data Exchange (ETDEWEB)

    Colbaugh, Richard; Glass, Kristin.; Willard, Gerald

    2008-10-01

    This paper presents a new methodology for analyzing complex networks in which the network of interest is first abstracted to a much simpler (but equivalent) representation, the required analysis is performed using the abstraction, and analytic conclusions are then mapped back to the original network and interpreted there. We begin by identifying a broad and important class of complex networks which admit abstractions that are simultaneously dramatically simplifying and property preserving we call these aggressive abstractions -- and which can therefore be analyzed using the proposed approach. We then introduce and develop two forms of aggressive abstraction: 1.) finite state abstraction, in which dynamical networks with uncountable state spaces are modeled using finite state systems, and 2.) onedimensional abstraction, whereby high dimensional network dynamics are captured in a meaningful way using a single scalar variable. In each case, the property preserving nature of the abstraction process is rigorously established and efficient algorithms are presented for computing the abstraction. The considerable potential of the proposed approach to complex networks analysis is illustrated through case studies involving vulnerability analysis of technological networks and predictive analysis for social processes.

  9. Network Analysis, Architecture, and Design

    CERN Document Server

    McCabe, James D

    2007-01-01

    Traditionally, networking has had little or no basis in analysis or architectural development, with designers relying on technologies they are most familiar with or being influenced by vendors or consultants. However, the landscape of networking has changed so that network services have now become one of the most important factors to the success of many third generation networks. It has become an important feature of the designer's job to define the problems that exist in his network, choose and analyze several optimization parameters during the analysis process, and then prioritize and evalua

  10. Computer networking a top-down approach

    CERN Document Server

    Kurose, James

    2017-01-01

    Unique among computer networking texts, the Seventh Edition of the popular Computer Networking: A Top Down Approach builds on the author’s long tradition of teaching this complex subject through a layered approach in a “top-down manner.” The text works its way from the application layer down toward the physical layer, motivating readers by exposing them to important concepts early in their study of networking. Focusing on the Internet and the fundamentally important issues of networking, this text provides an excellent foundation for readers interested in computer science and electrical engineering, without requiring extensive knowledge of programming or mathematics. The Seventh Edition has been updated to reflect the most important and exciting recent advances in networking.

  11. Approach of Complex Networks for the Determination of Brain Death

    Science.gov (United States)

    Sun, Wei-Gang; Cao, Jian-Ting; Wang, Ru-Bin

    2011-06-01

    In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death.

  12. 3rd International Conference on Network Analysis

    CERN Document Server

    Kalyagin, Valery; Pardalos, Panos

    2014-01-01

    This volume compiles the major results of conference participants from the "Third International Conference in Network Analysis" held at the Higher School of Economics, Nizhny Novgorod in May 2013, with the aim to initiate further joint research among different groups. The contributions in this book cover a broad range of topics relevant to the theory and practice of network analysis, including the reliability of complex networks, software, theory, methodology, and applications.  Network analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network has brought together researchers, practitioners from numerous fields such as operations research, computer science, transportation, energy, biomedicine, computational neuroscience and social sciences. In addition, new approaches and computer environments such as parallel computing, grid computing, cloud computing, and quantum computing have helped to solve large scale...

  13. Network topology analysis.

    Energy Technology Data Exchange (ETDEWEB)

    Kalb, Jeffrey L.; Lee, David S.

    2008-01-01

    Emerging high-bandwidth, low-latency network technology has made network-based architectures both feasible and potentially desirable for use in satellite payload architectures. The selection of network topology is a critical component when developing these multi-node or multi-point architectures. This study examines network topologies and their effect on overall network performance. Numerous topologies were reviewed against a number of performance, reliability, and cost metrics. This document identifies a handful of good network topologies for satellite applications and the metrics used to justify them as such. Since often multiple topologies will meet the requirements of the satellite payload architecture under development, the choice of network topology is not easy, and in the end the choice of topology is influenced by both the design characteristics and requirements of the overall system and the experience of the developer.

  14. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

    Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  15. Social Networks and Mourning: A Comparative Approach.

    Science.gov (United States)

    Rubin, Nissan

    1990-01-01

    Suggests using social network theory to explain varieties of mourning behavior in different societies. Compares participation in funeral ceremonies of members of different social circles in American society and Israeli kibbutz. Concludes that results demonstrated validity of concepts deriving from social network analysis in study of bereavement,…

  16. A Transfer Learning Approach for Network Modeling

    Science.gov (United States)

    Huang, Shuai; Li, Jing; Chen, Kewei; Wu, Teresa; Ye, Jieping; Wu, Xia; Yao, Li

    2012-01-01

    Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature. PMID:24526804

  17. Yeast Augmented Network Analysis (YANA: a new systems approach to identify therapeutic targets for human genetic diseases [v1; ref status: indexed, http://f1000r.es/3gk

    Directory of Open Access Journals (Sweden)

    David J. Wiley

    2014-06-01

    Full Text Available Genetic interaction networks that underlie most human diseases are highly complex and poorly defined. Better-defined networks will allow identification of a greater number of therapeutic targets. Here we introduce our Yeast Augmented Network Analysis (YANA approach and test it with the X-linked spinal muscular atrophy (SMA disease gene UBA1. First, we express UBA1 and a mutant variant in fission yeast and use high-throughput methods to identify fission yeast genetic modifiers of UBA1. Second, we analyze available protein-protein interaction network databases in both fission yeast and human to construct UBA1 genetic networks. Third, from these networks we identified potential therapeutic targets for SMA. Finally, we validate one of these targets in a vertebrate (zebrafish SMA model. This study demonstrates the power of combining synthetic and chemical genetics with a simple model system to identify human disease gene networks that can be exploited for treating human diseases.

  18. An efficient neural network approach to dynamic robot motion planning.

    Science.gov (United States)

    Yang, S X; Meng, M

    2000-03-01

    In this paper, a biologically inspired neural network approach to real-time collision-free motion planning of mobile robots or robot manipulators in a nonstationary environment is proposed. Each neuron in the topologically organized neural network has only local connections, whose neural dynamics is characterized by a shunting equation. Thus the computational complexity linearly depends on the neural network size. The real-time robot motion is planned through the dynamic activity landscape of the neural network without any prior knowledge of the dynamic environment, without explicitly searching over the free workspace or the collision paths, and without any learning procedures. Therefore it is computationally efficient. The global stability of the neural network is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.

  19. Social network analysis for program implementation.

    Science.gov (United States)

    Valente, Thomas W; Palinkas, Lawrence A; Czaja, Sara; Chu, Kar-Hai; Brown, C Hendricks

    2015-01-01

    This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues [1] and our experience in developing multi-sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach.

  20. Electrical spectrum & network analyzers a practical approach

    CERN Document Server

    Helfrick, Albert D

    1991-01-01

    This book presents fundamentals and the latest techniques of electrical spectrum analysis. It focuses on instruments and techniques used on spectrum and network analysis, rather than theory. The book covers the use of spectrum analyzers, tracking generators, and network analyzers. Filled with practical examples, the book presents techniques that are widely used in signal processing and communications applications, yet are difficult to find in most literature.Key Features* Presents numerous practical examples, including actual spectrum analyzer circuits* Instruction on how to us

  1. Working Group 3: Operations Analysis for Systems of System within a Networked C2 Context: Introduction, Purpose, and Approach

    Science.gov (United States)

    2012-01-01

    Objective 1: Understand the impact of the application of traditional operational research techniques to networked C2 systems. • Objective 2: Develop...discussion would be in the Host Layer ( Application , Presentation, Session, Transport) to describe behavior; however, the impact of lower layers will be...CredentialEmployee(EmployeeID) DisableRemoteSystem(systemID) RideShare (Share/Match, Dynamic Route/Schedule, ServiceCoordination) ManageFleet(AVL/CAD, Planning

  2. Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat Imagery and the Artificial Neural Networks Approach

    Directory of Open Access Journals (Sweden)

    Prima Riza Kadavi

    2017-09-01

    Full Text Available Volcanic eruptions cause pyroclastic flows, which can destroy plantations and settlements. We used image data from Landsat 7 Bands 7, 4 and 2 and Landsat 8 Bands 7, 5 and 3 to observe and analyze the distribution of pyroclastic flow deposits for two volcanos, Mount Sinabung and Merapi, over a period of 10 years (2001–2017. The satellite data are used in conjunction with an artificial neural network method to produce maps of pyroclastic precipitation for Landsat 7 and 8, then we calculated the pyroclastic precipitation area using an artificial neural network method after dividing the images into four classes based on color. Red, green, blue and yellow were used to indicate pyroclastic deposits, vegetation and forest, water and cloud, and farmland, respectively. The area affected by a volcanic eruption was deduced from the neural network processing, including calculating the area of pyroclastic deposits. The main differences between the pyroclastic flow deposits of Mount Sinabung and Mount Merapi are: the sediment deposits of the pyroclastic flows of Mount Sinabung tend to widen, whereas those of Merapi elongated; the direction of pyroclastic flow differed; and the area affected by an eruption was greater for Mount Merapi than Mount Sinabung because the VEI (Volcanic Explosivity Index during the last 10 years of Mount Merapi was larger than Mount Sinabung.

  3. Impact of different dietary approaches on blood pressure in hypertensive and prehypertensive patients: protocol for a systematic review and network meta-analysis.

    Science.gov (United States)

    Schwingshackl, Lukas; Chaimani, Anna; Hoffmann, Georg; Schwedhelm, Carolina; Boeing, Heiner

    2017-04-26

    Lifestyle modification is one of the cornerstones in the management of hypertension. According to the most recent guidelines by the American Heart Association, all patients with hypertension should adopt the following dietary advices: increased consumption of fresh fruits, vegetables, low-fat dairy products and sodium reduction. The aim of the present study is to assess the efficacy of different dietary approaches on systolic and diastolic blood pressure in patients with hypertension and high normal blood pressure in a systematic review including a pairwise and network meta-analysis of randomised trials. We conducted searches in Cochrane Central Register of Controlled Trials in the Cochrane Library, PubMed and Google Scholar until November 2016. Citations, abstracts and relevant papers were screened for eligibility by two reviewers independently. Randomised trials were included if they met the following criteria: (1) hypertension (as mean values ≥140 mm Hg systolic blood pressure and/or ≥90 mm Hg diastolic blood pressure) or high normal blood pressure (mean systolic blood pressure ≥130 mm Hg and/or mean diastolic blood pressure ≥85 mm Hg), (2) age ≥18 years, (3) intervention diets (different type of dietary approaches, eg, dietary approach to stop hypertension diet; Mediterranean diet, vegetarian diet, palaeolithic diet, low sodium diet) either hypocaloric, isocaloric or ad libitum diets, (4) intervention period ≥12 weeks. For each outcome measure of interest, random effects pairwise and network meta-analyses were performed in order to determine the pooled relative effect of each intervention relative to every other intervention in terms of the postintervention values (or change scores). Subgroup analyses were planned for hypertensive status, study length, sample size, age and sex. As this study is based solely on the published literature, no ethics approval was required. We published our network meta-analysis in a peer-reviewed scientific

  4. Social network analysis applied to team sports analysis

    CERN Document Server

    Clemente, Filipe Manuel; Mendes, Rui Sousa

    2016-01-01

    Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.

  5. Introduction to Social Network Analysis

    Science.gov (United States)

    Zaphiris, Panayiotis; Ang, Chee Siang

    Social Network analysis focuses on patterns of relations between and among people, organizations, states, etc. It aims to describe networks of relations as fully as possible, identify prominent patterns in such networks, trace the flow of information through them, and discover what effects these relations and networks have on people and organizations. Social network analysis offers a very promising potential for analyzing human-human interactions in online communities (discussion boards, newsgroups, virtual organizations). This Tutorial provides an overview of this analytic technique and demonstrates how it can be used in Human Computer Interaction (HCI) research and practice, focusing especially on Computer Mediated Communication (CMC). This topic acquires particular importance these days, with the increasing popularity of social networking websites (e.g., youtube, myspace, MMORPGs etc.) and the research interest in studying them.

  6. Interference statistics and capacity analysis for uplink transmission in two-tier small cell networks: A geometric probability approach

    KAUST Repository

    Tabassum, Hina

    2014-07-01

    This paper presents a novel framework to derive the statistics of the interference considering dedicated and shared spectrum access for uplink transmission in two-tier small cell networks such as the macrocell-femtocell networks. The framework exploits the distance distributions from geometric probability theory to characterize the uplink interference while considering a traditional grid-model set-up for macrocells along with the randomly deployed femtocells. The derived expressions capture the impact of path-loss, composite shadowing and fading, uniform and non-uniform traffic loads, spatial distribution of femtocells, and partial and full spectral reuse among femtocells. Considering dedicated spectrum access, first, we derive the statistics of co-tier interference incurred at both femtocell and macrocell base stations (BSs) from a single interferer by approximating generalized- K composite fading distribution with the tractable Gamma distribution. We then derive the distribution of the number of interferers considering partial spectral reuse and moment generating function (MGF) of the cumulative interference for both partial and full spectral reuse scenarios. Next, we derive the statistics of the cross-tier interference at both femtocell and macrocell BSs considering shared spectrum access. Finally, we utilize the derived expressions to analyze the capacity in both dedicated and shared spectrum access scenarios. The derived expressions are validated by the Monte Carlo simulations. Numerical results are generated to assess the feasibility of shared and dedicated spectrum access in femtocells under varying traffic load and spectral reuse scenarios. © 2014 IEEE.

  7. Tensor Fusion Network for Multimodal Sentiment Analysis

    OpenAIRE

    Zadeh, Amir; Chen, Minghai; Poria, Soujanya; Cambria, Erik; Morency, Louis-Philippe

    2017-01-01

    Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the vola...

  8. An Innovative Approach for Drainage Network Sizing

    Directory of Open Access Journals (Sweden)

    Luca Cozzolino

    2015-02-01

    Full Text Available In this paper, a procedure for the optimal design of rural drainage networks is presented and demonstrated. The suggested approach, exploring the potentialities offered by heuristic methods for the solution of complex optimization problems, is based on the use of a Genetic Algorithm (GA, coupled with a steady and uniform flow hydraulic module. In particular, this work has focused: on one hand, on the problems of a technical nature posed by the correct sizing of a drainage network; on the other hand, on the possibility to use a simple but nevertheless efficient GA to reach the minimal cost solution very quickly. The suitability of the approach is tested with reference to small and large scale drainage networks, already considered in the literature.

  9. Applications of social media and social network analysis

    CERN Document Server

    Kazienko, Przemyslaw

    2015-01-01

    This collection of contributed chapters demonstrates a wide range of applications within two overlapping research domains: social media analysis and social network analysis. Various methodologies were utilized in the twelve individual chapters including static, dynamic and real-time approaches to graph, textual and multimedia data analysis. The topics apply to reputation computation, emotion detection, topic evolution, rumor propagation, evaluation of textual opinions, friend ranking, analysis of public transportation networks, diffusion in dynamic networks, analysis of contributors to commun

  10. Flowshop Scheduling Using a Network Approach | Oladeinde ...

    African Journals Online (AJOL)

    In this paper, a network based formulation of a permutation flow shop problem is presented. Two nuances of flow shop problems with different levels of complexity are solved using different approaches to the linear programming formulation. Key flow shop parameters inclosing makespan of the flow shop problems were ...

  11. Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management.

    Science.gov (United States)

    Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing in...

  12. Social Network Analysis with sna

    Directory of Open Access Journals (Sweden)

    Carter T. Butts

    2007-12-01

    Full Text Available Modern social network analysis---the analysis of relational data arising from social systems---is a computationally intensive area of research. Here, we provide an overview of a software package which provides support for a range of network analytic functionality within the R statistical computing environment. General categories of currently supported functionality are described, and brief examples of package syntax and usage are shown.

  13. Stabilizing patterns in time: Neural network approach.

    Science.gov (United States)

    Ben-Shushan, Nadav; Tsodyks, Misha

    2017-12-01

    Recurrent and feedback networks are capable of holding dynamic memories. Nonetheless, training a network for that task is challenging. In order to do so, one should face non-linear propagation of errors in the system. Small deviations from the desired dynamics due to error or inherent noise might have a dramatic effect in the future. A method to cope with these difficulties is thus needed. In this work we focus on recurrent networks with linear activation functions and binary output unit. We characterize its ability to reproduce a temporal sequence of actions over its output unit. We suggest casting the temporal learning problem to a perceptron problem. In the discrete case a finite margin appears, providing the network, to some extent, robustness to noise, for which it performs perfectly (i.e. producing a desired sequence for an arbitrary number of cycles flawlessly). In the continuous case the margin approaches zero when the output unit changes its state, hence the network is only able to reproduce the sequence with slight jitters. Numerical simulation suggest that in the discrete time case, the longest sequence that can be learned scales, at best, as square root of the network size. A dramatic effect occurs when learning several short sequences in parallel, that is, their total length substantially exceeds the length of the longest single sequence the network can learn. This model easily generalizes to an arbitrary number of output units, which boost its performance. This effect is demonstrated by considering two practical examples for sequence learning. This work suggests a way to overcome stability problems for training recurrent networks and further quantifies the performance of a network under the specific learning scheme.

  14. Computational Social Network Analysis

    CERN Document Server

    Hassanien, Aboul-Ella

    2010-01-01

    Presents insight into the social behaviour of animals (including the study of animal tracks and learning by members of the same species). This book provides web-based evidence of social interaction, perceptual learning, information granulation and the behaviour of humans and affinities between web-based social networks

  15. Network value and optimum analysis on the mode of networked marketing in TV media

    Directory of Open Access Journals (Sweden)

    Xiao Dongpo

    2012-12-01

    Full Text Available Purpose: With the development of the networked marketing in TV media, it is important to do the research on network value and optimum analysis in this field.Design/methodology/approach: According to the research on the mode of networked marketing in TV media and Correlation theory, the essence of media marketing is creating, spreading and transferring values. The Participants of marketing value activities are in network, and value activities proceed in networked form. Network capability is important to TV media marketing activities.Findings: This article raises the direction of research of analysis and optimization about network based on the mode of networked marketing in TV media by studying TV media marketing Development Mechanism , network analysis and network value structure.

  16. Event-driven approach of layered multicast to network adaptation in RED-based IP networks

    Science.gov (United States)

    Nahm, Kitae; Li, Qing; Kuo, C.-C. J.

    2003-11-01

    In this work, we investigate the congestion control problem for layered video multicast in IP networks of active queue management (AQM) using a simple random early detection (RED) queue model. AQM support from networks improves the visual quality of video streaming but makes network adaptation more di+/-cult for existing layered video multicast proticols that use the event-driven timer-based approach. We perform a simplified analysis on the response of the RED algorithm to burst traffic. The analysis shows that the primary problem lies in the weak correlation between the network feedback and the actual network congestion status when the RED queue is driven by burst traffic. Finally, a design guideline of the layered multicast protocol is proposed to overcome this problem.

  17. Use of Time-Frequency Analysis and Neural Networks for Mode Identification in a Wireless Software-Defined Radio Approach

    Directory of Open Access Journals (Sweden)

    Matteo Gandetto

    2004-09-01

    Full Text Available The use of time-frequency distributions is proposed as a nonlinear signal processing technique that is combined with a pattern recognition approach to identify superimposed transmission modes in a reconfigurable wireless terminal based on software-defined radio techniques. In particular, a software-defined radio receiver is described aiming at the identification of two coexistent communication modes: frequency hopping code division multiple access and direct sequence code division multiple access. As a case study, two standards, based on the previous modes and operating in the same band (industrial, scientific, and medical, are considered: IEEE WLAN 802.11b (direct sequence and Bluetooth (frequency hopping. Neural classifiers are used to obtain identification results. A comparison between two different neural classifiers is made in terms of relative error frequency.

  18. Topological analysis of telecommunications networks

    Directory of Open Access Journals (Sweden)

    Milojko V. Jevtović

    2011-01-01

    Full Text Available A topological analysis of the structure of telecommunications networks is a very interesting topic in the network research, but also a key issue in their design and planning. Satisfying multiple criteria in terms of locations of switching nodes as well as their connectivity with respect to the requests for capacity, transmission speed, reliability, availability and cost are the main research objectives. There are three ways of presenting the topology of telecommunications networks: table, matrix or graph method. The table method is suitable for a network of a relatively small number of nodes in relation to the number of links. The matrix method involves the formation of a connection matrix in which its columns present source traffic nodes and its rows are the switching systems that belong to the destination. The method of the topology graph means that the network nodes are connected via directional or unidirectional links. We can thus easily analyze the structural parameters of telecommunications networks. This paper presents the mathematical analysis of the star-, ring-, fully connected loop- and grid (matrix-shaped topology as well as the topology based on the shortest path tree. For each of these topologies, the expressions for determining the number of branches, the middle level of reliability, the medium length and the average length of the link are given in tables. For the fully connected loop network with five nodes the values of all topological parameters are calculated. Based on the topological parameters, the relationships that represent integral and distributed indicators of reliability are given in this work as well as the values of the particular network. The main objectives of the topology optimization of telecommunications networks are: achieving the minimum complexity, maximum capacity, the shortest path message transfer, the maximum speed of communication and maximum economy. The performance of telecommunications networks is

  19. Network approach to patterns in stratocumulus clouds

    Science.gov (United States)

    Glassmeier, Franziska; Feingold, Graham

    2017-10-01

    Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth’s climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis’s Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav–Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.

  20. Network approach to patterns in stratocumulus clouds.

    Science.gov (United States)

    Glassmeier, Franziska; Feingold, Graham

    2017-10-03

    Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth's climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis's Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav-Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.

  1. Direct analysis of blood serum by total reflection X-ray fluorescence spectrometry and application of an artificial neural network approach for cancer diagnosis*1

    Science.gov (United States)

    Hernández-Caraballo, Edwin A.; Marcó-Parra, Lué M.

    2003-12-01

    Iron, copper, zinc and selenium were determined directly in serum samples from healthy individuals ( n=33) and cancer patients ( n=27) by total reflection X-ray fluorescence spectrometry using the Compton peak as internal standard [L.M. Marcó P. et al., Spectrochim. Acta Part B 54 (1999) 1469-1480]. The standardized concentrations of these elements were used as input data for two-layer artificial neural networks trained with the generalized delta rule in order to classify such individuals according to their health status. Various artificial neural networks, comprising a linear function in the input layer, a hyperbolic tangent function in the hidden layer and a sigmoid function in the output layer, were evaluated for such a purpose. Of the networks studied, the (4:4:1) gave the highest estimation (98%) and prediction rates (94%). The latter demonstrates the potential of the total reflection X-ray fluorescence spectrometry/artificial neural network approach in clinical chemistry.

  2. Large graph visualization of millions of connections in the CERN control system network traffic: analysis and design of routing and firewall rules with a new approach

    CERN Document Server

    Gallerani, Luigi

    2015-01-01

    Abstract The CERN Technical Network (TN) TN was intended to be a network for accelerator and infrastructure operations. However, today, more than 60 million IP packets are routed every hour between the General Purpose Network (GPN) and the TN, involving more than 6000 different hosts. In order to improve the security of the accelerator control system, it is fundamental to understand the network traffic between the two networks and to define new appropriate routing and firewall rules without impacting operations. The complexity and huge size of the infrastructure and the number of protocols and services involved, have discouraged for years any attempt to understand and control the network traffic between the GPN and the TN. In this paper, we show a new way to solve the problem graphically. Combining the network traffic analysis with the use of large graph visualization algorithms we produced usable 2D large color topology maps of the network identifying the inter-relations of the control system machines and s...

  3. Building a glaucoma interaction network using a text mining approach.

    Science.gov (United States)

    Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

    2016-01-01

    The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of

  4. Disorganization of Equilibrium Directional Interactions in the Brain Motor Network of Parkinson's disease: New Insight of Resting State Analysis Using Granger Causality and Graphical Approach

    OpenAIRE

    Ghasemi, Mahdieh; Mahloojifar, Ali

    2013-01-01

    Parkinson's disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Particular changes related to various pathological attacks in PD could result in causal interactions of the brain network from resting state functional magnetic resonance imaging (rs-fMRI) data. In this paper, we aimed to disclose the network structure of the directed influences over the brain using multivariate Granger causality analysis and graph theory in patients w...

  5. Computer methods in electric network analysis

    Energy Technology Data Exchange (ETDEWEB)

    Saver, P.; Hajj, I.; Pai, M.; Trick, T.

    1983-06-01

    The computational algorithms utilized in power system analysis have more than just a minor overlap with those used in electronic circuit computer aided design. This paper describes the computer methods that are common to both areas and highlights the differences in application through brief examples. Recognizing this commonality has stimulated the exchange of useful techniques in both areas and has the potential of fostering new approaches to electric network analysis through the interchange of ideas.

  6. Data Farming Process and Initial Network Analysis Capabilities

    Directory of Open Access Journals (Sweden)

    Gary Horne

    2016-01-01

    Full Text Available Data Farming, network applications and approaches to integrate network analysis and processes to the data farming paradigm are presented as approaches to address complex system questions. Data Farming is a quantified approach that examines questions in large possibility spaces using modeling and simulation. It evaluates whole landscapes of outcomes to draw insights from outcome distributions and outliers. Social network analysis and graph theory are widely used techniques for the evaluation of social systems. Incorporation of these techniques into the data farming process provides analysts examining complex systems with a powerful new suite of tools for more fully exploring and understanding the effect of interactions in complex systems. The integration of network analysis with data farming techniques provides modelers with the capability to gain insight into the effect of network attributes, whether the network is explicitly defined or emergent, on the breadth of the model outcome space and the effect of model inputs on the resultant network statistics.

  7. Insomnia and Personality-A Network Approach

    NARCIS (Netherlands)

    Dekker, Kim; Blanken, Tessa F; Van Someren, Eus J W

    2017-01-01

    Studies on personality traits and insomnia have remained inconclusive about which traits show the most direct associations with insomnia severity. It has moreover hardly been explored how traits relate to specific characteristics of insomnia. We here used network analysis in a large sample (N =

  8. Hybrid Localization Approach for Underwater Sensor Networks

    Directory of Open Access Journals (Sweden)

    Pei-Hsuan Tsai

    2017-01-01

    Full Text Available Underwater Wireless Sensor Networks (UWSNs are widely used to collect data in the marine environment. Location and time are essential aspects when sensors collect data, particularly in the case of location-aware data. Many studies on terrestrial sensor networks consider sensor locations as the locations where data is collected and focus on sensor positioning when sensors are fixed. However, underwater sensors are mobile networks and the sensor locations change continuously. Localization schemes designed for static sensor networks need to run periodically to update locations and consume considerable sensor power and increase the communication overhead; hence, they cannot be applied to UWSNs. This paper presents a hybrid localization approach with data-location correction, called Data Localization Correction Approach (DLCA, which positions data without additional communication overhead and power consumption on sensors. Without loss of generality, we simulate the ocean environment based on a kinematic model and meandering current mobility model and conduct extensive simulations. Our results show that DLCA can significantly reduce communication costs, while maintaining relatively high localization accuracy.

  9. Robustness Analysis of Real Network Topologies Under Multiple Failure Scenarios

    DEFF Research Database (Denmark)

    Manzano, M.; Marzo, J. L.; Calle, E.

    2012-01-01

    on topological characteristics. Recently approaches also consider the services supported by such networks. In this paper we carry out a robustness analysis of five real backbone telecommunication networks under defined multiple failure scenarios, taking into account the consequences of the loss of established......Nowadays the ubiquity of telecommunication networks, which underpin and fulfill key aspects of modern day living, is taken for granted. Significant large-scale failures have occurred in the last years affecting telecommunication networks. Traditionally, network robustness analysis has been focused...... connections. Results show which networks are more robust in response to a specific type of failure....

  10. Functional stoichiometric analysis of metabolic networks.

    Science.gov (United States)

    Urbanczik, R; Wagner, C

    2005-11-15

    An important tool in Systems Biology is the stoichiometric modeling of metabolic networks, where the stationary states of the network are described by a high-dimensional polyhedral cone, the so-called flux cone. Exhaustive descriptions of the metabolism can be obtained by computing the elementary vectors of this cone but, owing to a combinatorial explosion of the number of elementary vectors, this approach becomes computationally intractable for genome scale networks. Hence, we propose to instead focus on the conversion cone, a projection of the flux cone, which describes the interaction of the metabolism with its external chemical environment. We present a direct method for calculating the elementary vectors of this cone and, by studying the metabolism of Saccharomyces cerevisiae, we demonstrate that such an analysis is computationally feasible even for genome scale networks.

  11. Disorganization of Equilibrium Directional Interactions in the Brain Motor Network of Parkinson's disease: New Insight of Resting State Analysis Using Granger Causality and Graphical Approach.

    Science.gov (United States)

    Ghasemi, Mahdieh; Mahloojifar, Ali

    2013-04-01

    Parkinson's disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Particular changes related to various pathological attacks in PD could result in causal interactions of the brain network from resting state functional magnetic resonance imaging (rs-fMRI) data. In this paper, we aimed to disclose the network structure of the directed influences over the brain using multivariate Granger causality analysis and graph theory in patients with PD as compared with control group. rs-fMRI at rest from 10 PD patients and 10 controls were analyzed. Topological properties of the networks showed that information flow in PD is smaller than that in healthy individuals. We found that there is a balanced local network in healthy control group, including positive pair-wise cross connections between caudate and cerebellum and reciprocal connections between motor cortex and caudate in the left and right hemispheres. The results showed that this local network is disrupted in PD due to disturbance of the interactions in the motor networks. These findings suggested alteration of the functional organization of the brain in the resting state that affects the information transmission from and to other brain regions related to both primary dysfunctions and higher-level cognition impairments in PD. Furthermore, we showed that regions with high degree values could be detected as betweenness centrality nodes. Our results demonstrate that properties of small-world connectivity could also recognize and quantify the characteristics of directed influence brain networks in PD.

  12. Multivariate analysis: A statistical approach for computations

    Science.gov (United States)

    Michu, Sachin; Kaushik, Vandana

    2014-10-01

    Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

  13. Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach

    Science.gov (United States)

    2016-03-30

    Undergraduate Student Paper Postgraduate Student Paper Parametric Identification of Aircraft Loads: An Artificial Neural Network Approach...monitoring, flight parameter, nonlinear modeling, Artificial Neural Network , typical loadcase. Introduction Aircraft load monitoring is an... Neural Networks (ANN), i.e. the BP network and Kohonen Clustering Network , are applied and revised by Kalman Filter and Genetic Algorithm to build

  14. Innovation Networks New Approaches in Modelling and Analyzing

    CERN Document Server

    Pyka, Andreas

    2009-01-01

    The science of graphs and networks has become by now a well-established tool for modelling and analyzing a variety of systems with a large number of interacting components. Starting from the physical sciences, applications have spread rapidly to the natural and social sciences, as well as to economics, and are now further extended, in this volume, to the concept of innovations, viewed broadly. In an abstract, systems-theoretical approach, innovation can be understood as a critical event which destabilizes the current state of the system, and results in a new process of self-organization leading to a new stable state. The contributions to this anthology address different aspects of the relationship between innovation and networks. The various chapters incorporate approaches in evolutionary economics, agent-based modeling, social network analysis and econophysics and explore the epistemic tension between insights into economics and society-related processes, and the insights into new forms of complex dynamics.

  15. Multifractal analysis of mobile social networks

    Science.gov (United States)

    Zheng, Wei; Zhang, Zifeng; Deng, Yufan

    2017-09-01

    As Wireless Fidelity (Wi-Fi)-enabled handheld devices have been widely used, the mobile social networks (MSNs) has been attracting extensive attention. Fractal approaches have also been widely applied to characterierize natural networks as useful tools to depict their spatial distribution and scaling properties. Moreover, when the complexity of the spatial distribution of MSNs cannot be properly charaterized by single fractal dimension, multifractal analysis is required. For further research, we introduced a multifractal analysis method based on box-covering algorithm to describe the structure of MSNs. Using this method, we find that the networks are multifractal at different time interval. The simulation results demonstrate that the proposed method is efficient for analyzing the multifractal characteristic of MSNs, which provides a distribution of singularities adequately describing both the heterogeneity of fractal patterns and the statistics of measurements across spatial scales in MSNs.

  16. Flood estimation: a neural network approach

    Energy Technology Data Exchange (ETDEWEB)

    Swain, P.C.; Seshachalam, C.; Umamahesh, N.V. [Regional Engineering Coll., Warangal (India). Water and Environment Div.

    2000-07-01

    The artificial neural network (ANN) approach described in this study aims at predicting the flood flow into a reservoir. This differs from the traditional methods of flow prediction in the sense that it belongs to a class of data driven approaches, where as the traditional methods are model driven. Physical processes influencing the occurrences of streamflow in a river are highly complex, and are very difficult to be modelled by available statistical or deterministic models. ANNs provide model free solutions and hence can be expected to be appropriate in these conditions. Non-linearity, adaptivity, evidential response and fault tolerance are additional properties and capabilities of the neural networks. This paper highlights the applicability of neural networks for predicting daily flood flow taking the Hirakud reservoir on river Mahanadi in Orissa, India as the case study. The correlation between the observed and predicted flows and the relative error are considered to measure the performance of the model. The correlation between the observed and the modelled flows are computed to be 0.9467 in testing phase of the model. (orig.)

  17. NEAT : an efficient network enrichment analysis test

    NARCIS (Netherlands)

    Signorelli, Mirko; Vinciotti, Veronica; Wit, Ernst C

    2016-01-01

    BACKGROUND: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be

  18. Analysis of Layered Social Networks

    Science.gov (United States)

    2006-09-01

    xiii List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv I. Introduction ...Islamiya JP Joint Publication JTC Joint Targeting Cycle KPP Key Player Problem MCDM Multi-Criteria Decision Making MP Mathematical Programming MST...ANALYSIS OF LAYERED SOCIAL NETWORKS I. Introduction “To know them means to eliminate them” - Colonel Mathieu in the movie, Battle of Algiers

  19. Statistical analysis of network data with R

    CERN Document Server

    Kolaczyk, Eric D

    2014-01-01

    Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

  20. A Network Traffic Control Enhancement Approach over Bluetooth Networks

    DEFF Research Database (Denmark)

    Son, L.T.; Schiøler, Henrik; Madsen, Ole Brun

    2003-01-01

    This paper analyzes network traffic control issues in Bluetooth data networks as convex optimization problem. We formulate the problem of maximizing of total network flows and minimizing the costs of flows. An adaptive distributed network traffic control scheme is proposed as an approximated...... solution of the stated optimization problem that satisfies quality of service requirements and topologically induced constraints in Bluetooth networks, such as link capacity and node resource limitations. The proposed scheme is decentralized and complies with frequent changes of topology as well...... as capacity limitations and flow requirements in the network. Simulation shows that the performance of Bluetooth networks could be improved by applying the adaptive distributed network traffic control scheme...

  1. A novel network analysis approach reveals DNA damage, oxidative stress and calcium/cAMP homeostasis-associated biomarkers in frontotemporal dementia.

    Directory of Open Access Journals (Sweden)

    Fernando Palluzzi

    Full Text Available Frontotemporal Dementia (FTD is the form of neurodegenerative dementia with the highest prevalence after Alzheimer's disease, equally distributed in men and women. It includes several variants, generally characterized by behavioural instability and language impairments. Although few mendelian genes (MAPT, GRN, and C9orf72 have been associated to the FTD phenotype, in most cases there is only evidence of multiple risk loci with relatively small effect size. To date, there are no comprehensive studies describing FTD at molecular level, highlighting possible genetic interactions and signalling pathways at the origin FTD-associated neurodegeneration. In this study, we designed a broad FTD genetic interaction map of the Italian population, through a novel network-based approach modelled on the concepts of disease-relevance and interaction perturbation, combining Steiner tree search and Structural Equation Model (SEM analysis. Our results show a strong connection between Calcium/cAMP metabolism, oxidative stress-induced Serine/Threonine kinases activation, and postsynaptic membrane potentiation, suggesting a possible combination of neuronal damage and loss of neuroprotection, leading to cell death. In our model, Calcium/cAMP homeostasis and energetic metabolism impairments are primary causes of loss of neuroprotection and neural cell damage, respectively. Secondly, the altered postsynaptic membrane potentiation, due to the activation of stress-induced Serine/Threonine kinases, leads to neurodegeneration. Our study investigates the molecular underpinnings of these processes, evidencing key genes and gene interactions that may account for a significant fraction of unexplained FTD aetiology. We emphasized the key molecular actors in these processes, proposing them as novel FTD biomarkers that could be crucial for further epidemiological and molecular studies.

  2. Identifying Geographic Clusters: A Network Analytic Approach

    CERN Document Server

    Catini, Roberto; Penner, Orion; Riccaboni, Massimo

    2015-01-01

    In recent years there has been a growing interest in the role of networks and clusters in the global economy. Despite being a popular research topic in economics, sociology and urban studies, geographical clustering of human activity has often studied been by means of predetermined geographical units such as administrative divisions and metropolitan areas. This approach is intrinsically time invariant and it does not allow one to differentiate between different activities. Our goal in this paper is to present a new methodology for identifying clusters, that can be applied to different empirical settings. We use a graph approach based on k-shell decomposition to analyze world biomedical research clusters based on PubMed scientific publications. We identify research institutions and locate their activities in geographical clusters. Leading areas of scientific production and their top performing research institutions are consistently identified at different geographic scales.

  3. THE NETWORKS IN TOURISM: A THEORETICAL APPROACH

    Directory of Open Access Journals (Sweden)

    Maria TĂTĂRUȘANU

    2016-12-01

    Full Text Available The economic world in which tourism companies act today is in a continuous changing process. The most important factor of these changes is the globalization of their environment, both in economic, social, natural and cultural aspects. The tourism companies can benefit from the opportunities brought by globalization, but also could be menaced by the new context. How could react the companies to these changes in order to create and maintain long term competitive advantage for their business? In the present paper we make a literature review of the new tourism companies´ business approach: the networks - a result and/or a reason for exploiting the opportunities or, on the contrary, for keeping their actual position on the market. It’s a qualitative approach and the research methods used are analyses, synthesis, abstraction, which are considered the most appropriate to achieve the objective of the paper.

  4. Transmission analysis in WDM networks

    DEFF Research Database (Denmark)

    Rasmussen, Christian Jørgen

    1999-01-01

    This thesis describes the development of a computer-based simulator for transmission analysis in optical wavelength division multiplexing networks. A great part of the work concerns fundamental optical network simulator issues. Among these issues are identification of the versatility and user......-friendliness demands which such a simulator must meet, development of the "spectral window representation" for representation of the optical signals and finding an effective way of handling the optical signals in the computer memory. One important issue more is the rules for the determination of the order in which...... the different component models are invoked during the simulation of a system. A simple set of rules which makes it possible to simulate any network architectures is laid down. The modelling of the nonlinear fibre and the optical receiver is also treated. The work on the fibre concerns the numerical solution...

  5. From Microactions to Macrostructure and Back: A Structurational Approach to the Evolution of Organizational Networks

    Science.gov (United States)

    Whitbred, Robert; Fonti, Fabio; Steglich, Christian; Contractor, Noshir

    2011-01-01

    Structuration theory (ST) and network analysis are promising approaches for studying the emergence of communication networks. We offer a model that integrates the conceptual richness of structuration with the precision of relevant concepts and mechanisms offered from communication network research. We leverage methodological advancements (i.e.,…

  6. Identifying changes in the support networks of end-of-life carers using social network analysis.

    Science.gov (United States)

    Leonard, Rosemary; Horsfall, Debbie; Noonan, Kerrie

    2015-06-01

    End-of-life caring is often associated with reduced social networks for both the dying person and for the carer. However, those adopting a community participation and development approach, see the potential for the expansion and strengthening of networks. This paper uses Knox, Savage and Harvey's definitions of three generations social network analysis to analyse the caring networks of people with a terminal illness who are being cared for at home and identifies changes in these caring networks that occurred over the period of caring. Participatory network mapping of initial and current networks was used in nine focus groups. The analysis used key concepts from social network analysis (size, density, transitivity, betweenness and local clustering) together with qualitative analyses of the group's reflections on the maps. The results showed an increase in the size of the networks and that ties between the original members of the network strengthened. The qualitative data revealed the importance between core and peripheral network members and the diverse contributions of the network members. The research supports the value of third generation social network analysis and the potential for end-of-life caring to build social capital. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  7. Investigating communication networks contextually: Qualitative network analysis as cross-media research

    Directory of Open Access Journals (Sweden)

    Andreas Hepp

    2016-06-01

    Full Text Available This article introduces the approach of contextualised communication network analysis as a qualitative procedure for researching communicative relationships realised through the media. It combines qualitative interviews on media appropriation, egocentric network maps, and media diaries. Through the triangulation of these methods of data collection, it is possible to gain a differentiated insight into the specific meanings, structures and processes of communication networks across a variety of media. The approach is illustrated using a recent study dealing with the mediatisation of community building among young people. In this context, the qualitative communication network analysis has been applied to distinguish “localists” from “centrists”, “multilocalists”, and “pluralists”. These different “horizons of mediatised communitisation” are connected to distinct communication networks. Since this involves today a variety of different media, the contextual analysis of communication networks necessarily has to imply a cross-media perspective.

  8. Social network analysis and network connectedness analysis for industrial symbiotic systems: model development and case study

    Science.gov (United States)

    Zhang, Yan; Zheng, Hongmei; Chen, Bin; Yang, Naijin

    2013-06-01

    An important and practical pattern of industrial symbiosis is rapidly developing: eco-industrial parks. In this study, we used social network analysis to study the network connectedness (i.e., the proportion of the theoretical number of connections that had been achieved) and related attributes of these hybrid ecological and industrial symbiotic systems. This approach provided insights into details of the network's interior and analyzed the overall degree of connectedness and the relationships among the nodes within the network. We then characterized the structural attributes of the network and subnetwork nodes at two levels (core and periphery), thereby providing insights into the operational problems within each eco-industrial park. We chose ten typical ecoindustrial parks in China and around the world and compared the degree of network connectedness of these systems that resulted from exchanges of products, byproducts, and wastes. By analyzing the density and nodal degree, we determined the relative power and status of the nodes in these networks, as well as other structural attributes such as the core-periphery structure and the degree of sub-network connectedness. The results reveal the operational problems created by the structure of the industrial networks and provide a basis for improving the degree of completeness, thereby increasing their potential for sustainable development and enriching the methods available for the study of industrial symbiosis.

  9. A novel function prediction approach using protein overlap networks.

    Science.gov (United States)

    Liang, Shide; Zheng, Dandan; Standley, Daron M; Guo, Huarong; Zhang, Chi

    2013-07-17

    Construction of a reliable network remains the bottleneck for network-based protein function prediction. We built an artificial network model called protein overlap network (PON) for the entire genome of yeast, fly, worm, and human, respectively. Each node of the network represents a protein, and two proteins are connected if they share a domain according to InterPro database. The function of a protein can be predicted by counting the occurrence frequency of GO (gene ontology) terms associated with domains of direct neighbors. The average success rate and coverage were 34.3% and 43.9%, respectively, for the test genomes, and were increased to 37.9% and 51.3% when a composite PON of the four species was used for the prediction. As a comparison, the success rate was 7.0% in the random control procedure. We also made predictions with GO term annotations of the second layer nodes using the composite network and obtained an impressive success rate (>30%) and coverage (>30%), even for small genomes. Further improvement was achieved by statistical analysis of manually annotated GO terms for each neighboring protein. The PONs are composed of dense modules accompanied by a few long distance connections. Based on the PONs, we developed multiple approaches effective for protein function prediction.

  10. Spectral Analysis of Rich Network Topology in Social Networks

    Science.gov (United States)

    Wu, Leting

    2013-01-01

    Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few…

  11. Wireless Sensor Networks Formation: Approaches and Techniques

    Directory of Open Access Journals (Sweden)

    Miriam Carlos-Mancilla

    2016-01-01

    Full Text Available Nowadays, wireless sensor networks (WSNs emerge as an active research area in which challenging topics involve energy consumption, routing algorithms, selection of sensors location according to a given premise, robustness, efficiency, and so forth. Despite the open problems in WSNs, there are already a high number of applications available. In all cases for the design of any application, one of the main objectives is to keep the WSN alive and functional as long as possible. A key factor in this is the way the network is formed. This survey presents most recent formation techniques and mechanisms for the WSNs. In this paper, the reviewed works are classified into distributed and centralized techniques. The analysis is focused on whether a single or multiple sinks are employed, nodes are static or mobile, the formation is event detection based or not, and network backbone is formed or not. We focus on recent works and present a discussion of their advantages and drawbacks. Finally, the paper overviews a series of open issues which drive further research in the area.

  12. Artificial neural network based approach to EEG signal simulation.

    Science.gov (United States)

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2012-06-01

    In this paper a new approach to the electroencephalogram (EEG) signal simulation based on the artificial neural networks (ANN) is proposed. The aim was to simulate the spontaneous human EEG background activity based solely on the experimentally acquired EEG data. Therefore, an EEG measurement campaign was conducted on a healthy awake adult in order to obtain an adequate ANN training data set. As demonstration of the performance of the ANN based approach, comparisons were made against autoregressive moving average (ARMA) filtering based method. Comprehensive quantitative and qualitative statistical analysis showed clearly that the EEG process obtained by the proposed method was in satisfactory agreement with the one obtained by measurements.

  13. Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

    Science.gov (United States)

    Castet, Jean-Francois; Saleh, Joseph H

    2013-01-01

    This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the

  14. Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

    Directory of Open Access Journals (Sweden)

    Jean-Francois Castet

    Full Text Available This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also

  15. Anomaly Detection Approaches for Communication Networks

    Science.gov (United States)

    Thottan, Marina; Liu, Guanglei; Ji, Chuanyi

    In recent years, network anomaly detection has become an important area for both commercial interests as well as academic research. Applications of anomaly detection typically stem from the perspectives of network monitoring and network security. In network monitoring, a service provider is often interested in capturing such network characteristics as heavy flows, flow size distributions, and the number of distinct flows. In network security, the interest lies in characterizing known or unknown anomalous patterns of an attack or a virus.

  16. State of the art applications of social network analysis

    CERN Document Server

    Can, Fazli; Polat, Faruk

    2014-01-01

    Social network analysis increasingly bridges the discovery of patterns in diverse areas of study as more data becomes available and complex. Yet the construction of huge networks from large data often requires entirely different approaches for analysis including; graph theory, statistics, machine learning and data mining. This work covers frontier studies on social network analysis and mining from different perspectives such as social network sites, financial data, e-mails, forums, academic research funds, XML technology, blog content, community detection and clique finding, prediction of user

  17. COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks

    NARCIS (Netherlands)

    Sie, Rory

    2012-01-01

    Sie, R. L. L. (2012). COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks (Unpublished doctoral dissertation). September, 28, 2012, Open Universiteit in the Netherlands (CELSTEC), Heerlen, The Netherlands.

  18. Networks and network analysis for defence and security

    CERN Document Server

    Masys, Anthony J

    2014-01-01

    Networks and Network Analysis for Defence and Security discusses relevant theoretical frameworks and applications of network analysis in support of the defence and security domains. This book details real world applications of network analysis to support defence and security. Shocks to regional, national and global systems stemming from natural hazards, acts of armed violence, terrorism and serious and organized crime have significant defence and security implications. Today, nations face an uncertain and complex security landscape in which threats impact/target the physical, social, economic

  19. An integrated approach (CLuster Analysis Integration Method) to combine expression data and protein-protein interaction networks in agrigenomics: application on Arabidopsis thaliana.

    Science.gov (United States)

    Santoni, Daniele; Swiercz, Aleksandra; Zmieńko, Agnieszka; Kasprzak, Marta; Blazewicz, Marek; Bertolazzi, Paola; Felici, Giovanni

    2014-02-01

    Experimental co-expression data and protein-protein interaction networks are frequently used to analyze the interactions among genes or proteins. Recent studies have investigated methods to integrate these two sources of information. We propose a new method to integrate co-expression data obtained through DNA microarray analysis (MA) and protein-protein interaction (PPI) network data, and apply it to Arabidopsis thaliana. The proposed method identifies small subsets of highly interacting proteins. Based on the analysis of the basis of co-localization and mRNA developmental expression, we show that these groups provide important biological insights; additionally, these subsets are significantly enriched with respect to KEGG Pathways and can be used to predict successfully whether proteins belong to known pathways. Thus, the method is able to provide relevant biological information and support the functional identification of complex genetic traits of economic value in plant agrigenomics research. The method has been implemented in a prototype software tool named CLAIM (CLuster Analysis Integration Method) and can be downloaded from http://bio.cs.put.poznan.pl/research_fields . CLAIM is based on the separate clustering of MA and PPI data; the clusters are merged in a special graph; cliques of this graph are subsets of strongly connected proteins. The proposed method was successfully compared with existing methods. CLAIM appears to be a useful semi-automated tool for protein functional analysis and warrants further evaluation in agrigenomics research.

  20. Investigating meta-approaches for reconstructing gene networks in a mammalian cellular context.

    Directory of Open Access Journals (Sweden)

    Azree Nazri

    Full Text Available The output of state-of-the-art reverse-engineering methods for biological networks is often based on the fitting of a mathematical model to the data. Typically, different datasets do not give single consistent network predictions but rather an ensemble of inconsistent networks inferred under the same reverse-engineering method that are only consistent with the specific experimentally measured data. Here, we focus on an alternative approach for combining the information contained within such an ensemble of inconsistent gene networks called meta-analysis, to make more accurate predictions and to estimate the reliability of these predictions. We review two existing meta-analysis approaches; the Fisher transformation combined coefficient test (FTCCT and Fisher's inverse combined probability test (FICPT; and compare their performance with five well-known methods, ARACNe, Context Likelihood or Relatedness network (CLR, Maximum Relevance Minimum Redundancy (MRNET, Relevance Network (RN and Bayesian Network (BN. We conducted in-depth numerical ensemble simulations and demonstrated for biological expression data that the meta-analysis approaches consistently outperformed the best gene regulatory network inference (GRNI methods in the literature. Furthermore, the meta-analysis approaches have a low computational complexity. We conclude that the meta-analysis approaches are a powerful tool for integrating different datasets to give more accurate and reliable predictions for biological networks.

  1. Applying temporal network analysis to the venture capital market

    Science.gov (United States)

    Zhang, Xin; Feng, Ling; Zhu, Rongqian; Stanley, H. Eugene

    2015-10-01

    Using complex network theory to study the investment relationships of venture capital firms has produced a number of significant results. However, previous studies have often neglected the temporal properties of those relationships, which in real-world scenarios play a pivotal role. Here we examine the time-evolving dynamics of venture capital investment in China by constructing temporal networks to represent (i) investment relationships between venture capital firms and portfolio companies and (ii) the syndication ties between venture capital investors. The evolution of the networks exhibits rich variations in centrality, connectivity and local topology. We demonstrate that a temporal network approach provides a dynamic and comprehensive analysis of real-world networks.

  2. Proposing an Integrative Approach for Efficiency Evaluation of Network Structures Including Tour and Allocation Link

    Directory of Open Access Journals (Sweden)

    reza hejazi

    2012-02-01

    Full Text Available Data envelopment analysis (DEA is known as one of the most common approaches for efficiency evaluation. Network models are new subjects in which, a DMU with all its subunits and links is considered as a network structure. One of the most widely used DEA methods for network data is the suggested approach of Lewis and Sexton. In this approach, performance of each DMU is measured compared to a similar DMU by moving on the effective paths and then computing the final outputs and classic primary inputs . In reality, many cases can be found that an original input or an intermediate product allocates to several subunits or forms a tour in a network. In such networks, the approach of Lewis and Sexton is not able to calculate efficiency. Therefore, in this paper, an approach has been proposed for solving such problems and computing the efficiency of such networks.

  3. Methodological Approaches to Locating Outlets of the Franchise Retail Network

    Directory of Open Access Journals (Sweden)

    Grygorenko Tetyana M.

    2016-08-01

    Full Text Available Methodical approaches to selecting strategic areas of managing the future location of franchise retail network outlets are presented. The main stages in the assessment of strategic areas of managing the future location of franchise retail network outlets have been determined and the evaluation criteria have been suggested. Since such selection requires consideration of a variety of indicators and directions of the assessment, the author proposes a scale of evaluation, which allows generalizing and organizing the research data and calculations of the previous stages of the analysis. The most important criteria and sequence of the selection of the potential franchisees for the franchise retail network have been identified, the technique for their evaluation has been proposed. The use of the suggested methodological approaches will allow the franchiser making sound decisions on the selection of potential target markets, minimizing expenditures of time and efforts on the selection of franchisees and hence optimizing the process of development of the franchise retail network, which will contribute to the formation of its structure.

  4. Unraveling protein networks with power graph analysis.

    Science.gov (United States)

    Royer, Loïc; Reimann, Matthias; Andreopoulos, Bill; Schroeder, Michael

    2008-07-11

    Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks.

  5. 6th International Conference on Network Analysis

    CERN Document Server

    Nikolaev, Alexey; Pardalos, Panos; Prokopyev, Oleg

    2017-01-01

    This valuable source for graduate students and researchers provides a comprehensive introduction to current theories and applications in optimization methods and network models. Contributions to this book are focused on new efficient algorithms and rigorous mathematical theories, which can be used to optimize and analyze mathematical graph structures with massive size and high density induced by natural or artificial complex networks. Applications to social networks, power transmission grids, telecommunication networks, stock market networks, and human brain networks are presented. Chapters in this book cover the following topics: Linear max min fairness Heuristic approaches for high-quality solutions Efficient approaches for complex multi-criteria optimization problems Comparison of heuristic algorithms New heuristic iterative local search Power in network structures Clustering nodes in random graphs Power transmission grid structure Network decomposition problems Homogeneity hypothesis testing Network analy...

  6. Network analysis of unstructured EHR data for clinical research.

    Science.gov (United States)

    Bauer-Mehren, Anna; Lependu, Paea; Iyer, Srinivasan V; Harpaz, Rave; Leeper, Nicholas J; Shah, Nigam H

    2013-01-01

    In biomedical research, network analysis provides a conceptual framework for interpreting data from high-throughput experiments. For example, protein-protein interaction networks have been successfully used to identify candidate disease genes. Recently, advances in clinical text processing and the increasing availability of clinical data have enabled analogous analyses on data from electronic medical records. We constructed networks of diseases, drugs, medical devices and procedures using concepts recognized in clinical notes from the Stanford clinical data warehouse. We demonstrate the use of the resulting networks for clinical research informatics in two ways-cohort construction and outcomes analysis-by examining the safety of cilostazol in peripheral artery disease patients as a use case. We show that the network-based approaches can be used for constructing patient cohorts as well as for analyzing differences in outcomes by comparing with standard methods, and discuss the advantages offered by network-based approaches.

  7. Micro-macro analysis of complex networks.

    Science.gov (United States)

    Marchiori, Massimo; Possamai, Lino

    2015-01-01

    Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a "classic" approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail ("micro") to a different scale level ("macro"), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability.

  8. Signed Link Analysis in Social Media Networks

    OpenAIRE

    Beigi, Ghazaleh; Tang, Jiliang; Liu, Huan

    2016-01-01

    Numerous real-world relations can be represented by signed networks with positive links (e.g., trust) and negative links (e.g., distrust). Link analysis plays a crucial role in understanding the link formation and can advance various tasks in social network analysis such as link prediction. The majority of existing works on link analysis have focused on unsigned social networks. The existence of negative links determines that properties and principles of signed networks are substantially dist...

  9. Social network analysis in medical education

    OpenAIRE

    Isba, Rachel; Woolf, Katherine; Hanneman, Robert

    2016-01-01

    Content\\ud Humans are fundamentally social beings. The social systems within which we live our lives (families, schools, workplaces, professions, friendship groups) have a significant influence on our health, success and well-being. These groups can be characterised as networks and analysed using social network analysis.\\ud \\ud Social Network Analysis\\ud Social network analysis is a mainly quantitative method for analysing how relationships between individuals form and affect those individual...

  10. Different approaches in Partial Least Squares and Artificial Neural Network models applied for the analysis of a ternary mixture of Amlodipine, Valsartan and Hydrochlorothiazide

    Science.gov (United States)

    Darwish, Hany W.; Hassan, Said A.; Salem, Maissa Y.; El-Zeany, Badr A.

    2014-03-01

    Different chemometric models were applied for the quantitative analysis of Amlodipine (AML), Valsartan (VAL) and Hydrochlorothiazide (HCT) in ternary mixture, namely, Partial Least Squares (PLS) as traditional chemometric model and Artificial Neural Networks (ANN) as advanced model. PLS and ANN were applied with and without variable selection procedure (Genetic Algorithm GA) and data compression procedure (Principal Component Analysis PCA). The chemometric methods applied are PLS-1, GA-PLS, ANN, GA-ANN and PCA-ANN. The methods were used for the quantitative analysis of the drugs in raw materials and pharmaceutical dosage form via handling the UV spectral data. A 3-factor 5-level experimental design was established resulting in 25 mixtures containing different ratios of the drugs. Fifteen mixtures were used as a calibration set and the other ten mixtures were used as validation set to validate the prediction ability of the suggested methods. The validity of the proposed methods was assessed using the standard addition technique.

  11. The gene expression profiling of hepatocellular carcinoma by a network analysis approach shows a dominance of intrinsically disordered proteins (IDPs) between hub nodes.

    Science.gov (United States)

    Singh, Sakshi; Colonna, Giovanni; Di Bernardo, Giovanni; Bergantino, Francesca; Cammarota, Marcella; Castello, Giuseppe; Costantini, Susan

    2015-11-01

    We have analyzed the transcriptomic data from patients with hepatocellular carcinoma (HCC) after viral HCV infection at the various stages of the disease by means of a networking analysis using the publicly available E-MTAB-950 dataset. The data was compared with those obtained in our group from HepG2 cells, a cancer cell line that lacks the viral infection. By sequential pruning of data, and also taking into account the data from cells of healthy patients as blanks, we were able to obtain a distribution of hub genes for the various stages that characterize the disease and finally, we isolated a metabolic sub-net specific to HCC alone. The general picture is that the basic organization to energetically and metabolically sustain the cells in both the normal and diseased conditions is the same, but a complex cluster of sub-networks controlled by hub genes drives the HCC progression with high metabolic flexibility and plasticity. In particular, we have extracted a sub-net of genes strictly correlated to other hub genes of the network from HepG2 cells, but specific for the HCC and mainly devoted to: (i) control at chromatin levels of cell division; (ii) control of ergastoplasmatic stress through protein degradation and misfolding; (iii) control of the immune response also through an increase of mature T-cells in the thymus. This sub-net is characterized by 26 hub genes coding for intrinsically disordered proteins with a high ability to interact with numerous molecular partners. Moreover, we have also noted that periphery molecules, that is, with one or very few interactions (e.g., cytokines or post-translational enzymes), which do not have a central role in the clusters that make up the global metabolic network, essentially have roles as information transporters. The results evidence a strong presence of intrinsically disordered proteins with key roles as hubs in the sub-networks that characterize the various stages of the disease, conferring a structural plasticity to

  12. Network Intrusion Detection System – A Novel Approach

    Directory of Open Access Journals (Sweden)

    Krish Pillai

    2013-08-01

    Full Text Available Network intrusion starts off with a series of unsuccessful breakin attempts and results eventually with the permanent or transient failure of an authentication or authorization system. Due to the current complexity of authentication systems, clandestine attempts at intrusion generally take considerable time before the system gets compromised or damaging change is affected to the system giving administrators a window of opportunity to proactively detect and prevent intrusion. Therefore maintaining a high level of sensitivity to abnormal access patterns is a very effective way of preventing possible break-ins. Under normal circumstances, gross errors on the part of the user can cause authentication and authorization failures on all systems. A normal distribution of failed attempts should be tolerated while abnormal attempts should be recognized as such and flagged. But one cannot manage what one cannot measure. This paper proposes a method that can efficiently quantify the behaviour of users on a network so that transient changes in usage can be detected, categorized based on severity, and closely investigated for possible intrusion. The author proposes the identification of patterns in protocol usage within a network to categorize it for surveillance. Statistical anomaly detection, under which category this approach falls, generally uses simple statistical tests such as mean and standard deviation to detect behavioural changes. The author proposes a novel approach using spectral density as opposed to using time domain data, allowing a clear separation or access patterns based on periodicity. Once a spectral profile has been identified for network, deviations from this profile can be used as an indication of a destabilized or compromised network. Spectral analysis of access patterns is done using the Fast Fourier Transform (FFT, which can be computed in Θ(N log N operations. The paper justifies the use of this approach and presents preliminary

  13. Quantitative analysis of access strategies to remoteinformation in network services

    DEFF Research Database (Denmark)

    Olsen, Rasmus Løvenstein; Schwefel, Hans-Peter; Hansen, Martin Bøgsted

    2006-01-01

    Remote access to dynamically changing information elements is a required functionality for various network services, including routing and instances of context-sensitive networking. Three fundamentally different strategies for such access are investigated in this paper: (1) a reactive approach in......, network delay characterization) and specific requirements on mismatch probability, traffic overhead, and access delay. Finally, the analysis is applied to the use-case of context-sensitive service discovery....

  14. Tissue segmentation-assisted analysis of fMRI for human motor response: an approach combining artificial neural network and fuzzy C means

    OpenAIRE

    Chiu, MJ; Lin, CC; Chuang, KH; Chen, JH; Huang, KM

    2001-01-01

    The authors have developed an automated algorithm for segmentation of magnetic resonance images (MRI) of the human brain. They investigated the quantitative analysis of tissue-specific human motor response through an approach combining gradient echo functional MRI and automated segmentation analysis. Fifteen healthy volunteers, placed in a 1.5 T clinical MR imager, performed a self-paced finger opposition throughout the activation periods. T1-weighted images (WI), T2WI, and proton density WI ...

  15. An Examination of Not-For-Profit Stakeholder Networks for Relationship Management: A Small-Scale Analysis on Social Media

    National Research Council Canada - National Science Library

    Wyllie, Jessica; Lucas, Benjamin; Carlson, Jamie; Kitchens, Brent; Kozary, Ben; Zaki, Mohamed

    2016-01-01

    Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholder...

  16. Service network design of bike sharing systems analysis and optimization

    CERN Document Server

    Vogel, Patrick

    2016-01-01

    This monograph presents a tactical planning approach for service network design in metropolitan areas. Designing the service network requires the suitable aggregation of demand data as well as the anticipation of operational relocation decisions. To this end, an integrated approach of data analysis and mathematical optimization is introduced. The book also includes a case study based on real-world data to demonstrate the benefit of the proposed service network design approach. The target audience comprises primarily research experts in the field of traffic engineering, but the book may also be beneficial for graduate students.

  17. Structural Analysis of Complex Networks

    CERN Document Server

    Dehmer, Matthias

    2011-01-01

    Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science,

  18. Balancing of Network Energy using Observer Approach

    OpenAIRE

    Patharlapati, Sai Ram Charan

    2016-01-01

    Efficient energy use is primarily for any sensor networks to function for a longer time period. There have been many efficient schemes with various progress levels proposed by many researchers. Yet, there still more improvements are needed. This thesis is an attempt to make wireless sensor networks with further efficient on energy usage in the network with respect to rate of delivery of the messages. In sensor network architecture radio, sensing and actuators have influence over the power ...

  19. Open home networks: the TEAHA approach

    NARCIS (Netherlands)

    van Dijk, H.W.; Scholten, Johan; Tobalina, Alvaro; García Muñoz, Victor; Milanini, Stephane; Kung, Antonio

    2006-01-01

    The current trend for home appliances is networking. Although more and more of these appliances are networked, there is not a standard way of interaction, which restrains the development of services for in-home networks. The lack of standardisation is partly due to a legacy of business interests;

  20. Open Home Networks: the TEAHA Approach

    NARCIS (Netherlands)

    van Dijk, H.W.; Scholten, Johan; Tobalina, Alvaro; García Muñoz, Victor; Milanini, Stephane; Kung, Antonio; Dini, C.; Smekal, Z.; Lochin, E.; Verma, P.

    2007-01-01

    The current trend for home appliances is networking. Although more and more of these appliances are networked, there is not a standard way of interaction, which restrains the development of services for in-home networks. The lack of standardisation is partly due to a legacy of business interests;

  1. An artificial immune system algorithm approach for reconfiguring distribution network

    Science.gov (United States)

    Syahputra, Ramadoni; Soesanti, Indah

    2017-08-01

    This paper proposes an artificial immune system (AIS) algorithm approach for reconfiguring distribution network with the presence distributed generators (DG). The distribution network with high-performance is a network that has a low power loss, better voltage profile, and loading balance among feeders. The task for improving the performance of the distribution network is optimization of network configuration. The optimization has become a necessary study with the presence of DG in entire networks. In this work, optimization of network configuration is based on an AIS algorithm. The methodology has been tested in a model of 33 bus IEEE radial distribution networks with and without DG integration. The results have been showed that the optimal configuration of the distribution network is able to reduce power loss and to improve the voltage profile of the distribution network significantly.

  2. Social sciences via network analysis and computation

    CERN Document Server

    Kanduc, Tadej

    2015-01-01

    In recent years information and communication technologies have gained significant importance in the social sciences. Because there is such rapid growth of knowledge, methods and computer infrastructure, research can now seamlessly connect interdisciplinary fields such as business process management, data processing and mathematics. This study presents some of the latest results, practices and state-of-the-art approaches in network analysis, machine learning, data mining, data clustering and classifications in the contents of social sciences. It also covers various real-life examples such as t

  3. Computer networks ISE a systems approach

    CERN Document Server

    Peterson, Larry L

    2007-01-01

    Computer Networks, 4E is the only introductory computer networking book written by authors who have had first-hand experience with many of the protocols discussed in the book, who have actually designed some of them as well, and who are still actively designing the computer networks today. This newly revised edition continues to provide an enduring, practical understanding of networks and their building blocks through rich, example-based instruction. The authors' focus is on the why of network design, not just the specifications comprising today's systems but how key technologies and p

  4. Structural factoring approach for analyzing stochastic networks

    Science.gov (United States)

    Hayhurst, Kelly J.; Shier, Douglas R.

    1991-01-01

    The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.

  5. Topological Analysis of Wireless Networks (TAWN)

    Science.gov (United States)

    2016-05-31

    19b. TELEPHONE NUMBER (Include area code) 31-05-2016 FINAL REPORT 12-02-2015 -- 31-05-2016 Topological Analysis of Wireless Networks (TAWN) Robinson...mathematical literature on sheaves that describes how to draw global ( network -wide) inferences from them. Wireless network , local homology, sheaf...topology U U U UU 32 Michael Robinson 202-885-3681 Final Report: May 2016 Topological Analysis of Wireless Networks Principal Investigator: Prof. Michael

  6. ADHD classification using bag of words approach on network features

    Science.gov (United States)

    Solmaz, Berkan; Dey, Soumyabrata; Rao, A. Ravishankar; Shah, Mubarak

    2012-02-01

    Attention Deficit Hyperactivity Disorder (ADHD) is receiving lots of attention nowadays mainly because it is one of the common brain disorders among children and not much information is known about the cause of this disorder. In this study, we propose to use a novel approach for automatic classification of ADHD conditioned subjects and control subjects using functional Magnetic Resonance Imaging (fMRI) data of resting state brains. For this purpose, we compute the correlation between every possible voxel pairs within a subject and over the time frame of the experimental protocol. A network of voxels is constructed by representing a high correlation value between any two voxels as an edge. A Bag-of-Words (BoW) approach is used to represent each subject as a histogram of network features; such as the number of degrees per voxel. The classification is done using a Support Vector Machine (SVM). We also investigate the use of raw intensity values in the time series for each voxel. Here, every subject is represented as a combined histogram of network and raw intensity features. Experimental results verified that the classification accuracy improves when the combined histogram is used. We tested our approach on a highly challenging dataset released by NITRC for ADHD-200 competition and obtained promising results. The dataset not only has a large size but also includes subjects from different demography and edge groups. To the best of our knowledge, this is the first paper to propose BoW approach in any functional brain disorder classification and we believe that this approach will be useful in analysis of many brain related conditions.

  7. Review Essay: Does Qualitative Network Analysis Exist?

    Directory of Open Access Journals (Sweden)

    Rainer Diaz-Bone

    2007-01-01

    Full Text Available Social network analysis was formed and established in the 1970s as a way of analyzing systems of social relations. In this review the theoretical-methodological standpoint of social network analysis ("structural analysis" is introduced and the different forms of social network analysis are presented. Structural analysis argues that social actors and social relations are embedded in social networks, meaning that action and perception of actors as well as the performance of social relations are influenced by the network structure. Since the 1990s structural analysis has integrated concepts such as agency, discourse and symbolic orientation and in this way structural analysis has opened itself. Since then there has been increasing use of qualitative methods in network analysis. They are used to include the perspective of the analyzed actors, to explore networks, and to understand network dynamics. In the reviewed book, edited by Betina HOLLSTEIN and Florian STRAUS, the twenty predominantly empirically orientated contributions demonstrate the possibilities of combining quantitative and qualitative methods in network analyses in different research fields. In this review we examine how the contributions succeed in applying and developing the structural analysis perspective, and the self-positioning of "qualitative network analysis" is evaluated. URN: urn:nbn:de:0114-fqs0701287

  8. An analytical approach to optical burst switched networks

    CERN Document Server

    Venkatesh, T

    2010-01-01

    This book presents the latest results on modeling and analysis of OBS networks. It classifies all the literature on the topic, and its scope extends to include discussion of high-speed communication networks with limited or no buffers.

  9. Google matrix analysis of directed networks

    Science.gov (United States)

    Ermann, Leonardo; Frahm, Klaus M.; Shepelyansky, Dima L.

    2015-10-01

    In the past decade modern societies have developed enormous communication and social networks. Their classification and information retrieval processing has become a formidable task for the society. Because of the rapid growth of the World Wide Web, and social and communication networks, new mathematical methods have been invented to characterize the properties of these networks in a more detailed and precise way. Various search engines extensively use such methods. It is highly important to develop new tools to classify and rank a massive amount of network information in a way that is adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency using various examples including the World Wide Web, Wikipedia, software architectures, world trade, social and citation networks, brain neural networks, DNA sequences, and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos, and random matrix theory.

  10. Unified Approach to Modeling and Simulation of Space Communication Networks and Systems

    Science.gov (United States)

    Barritt, Brian; Bhasin, Kul; Eddy, Wesley; Matthews, Seth

    2010-01-01

    Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks

  11. Introduction to stream network habitat analysis

    Science.gov (United States)

    Bartholow, John M.; Waddle, Terry J.

    1986-01-01

    Increasing demands on stream resources by a variety of users have resulted in an increased emphasis on studies that evaluate the cumulative effects of basinwide water management programs. Network habitat analysis refers to the evaluation of an entire river basin (or network) by predicting its habitat response to alternative management regimes. The analysis principally focuses on the biological and hydrological components of the riv er basin, which include both micro- and macrohabitat. (The terms micro- and macrohabitat are further defined and discussed later in this document.) Both conceptual and analytic models are frequently used for simplifying and integrating the various components of the basin. The model predictions can be used in developing management recommendations to preserve, restore, or enhance instream fish habitat. A network habitat analysis should begin with a clear and concise statement of the study objectives and a thorough understanding of the institutional setting in which the study results will be applied. This includes the legal, social, and political considerations inherent in any water management setting. The institutional environment may dictate the focus and level of detail required of the study to a far greater extent than the technical considerations. After the study objectives, including species on interest, and institutional setting are collectively defined, the technical aspects should be scoped to determine the spatial and temporal requirements of the analysis. A macro level approach should be taken first to identify critical biological elements and requirements. Next, habitat availability is quantified much as in a "standard" river segment analysis, with the likely incorporation of some macrohabitat components, such as stream temperature. Individual river segments may be aggregated to represent the networkwide habitat response of alternative water management schemes. Things learned about problems caused or opportunities generated may

  12. Social network analysis community detection and evolution

    CERN Document Server

    Missaoui, Rokia

    2015-01-01

    This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edit

  13. An Approach for Detecting Attacks in Mobile Adhoc Networks

    OpenAIRE

    V. M. Viswanatham; A. A. Chari

    2008-01-01

    The security of data becomes more important with the increased use of commercial applications over wireless network environments. We presented an approach to handle various attacks for wireless networks. There were several problems of security in wireless networks due to intruders and different type of attacks such as Node Isolation, Route Disruption and Resource Consumption. There were better methods and intruder handling procedures available for fixed networks but it was difficult to analyz...

  14. Social network analysis and dual rover communications

    Science.gov (United States)

    Litaker, Harry L.; Howard, Robert L.

    2013-10-01

    Social network analysis (SNA) refers to the collection of techniques, tools, and methods used in sociometry aiming at the analysis of social networks to investigate decision making, group communication, and the distribution of information. Human factors engineers at the National Aeronautics and Space Administration (NASA) conducted a social network analysis on communication data collected during a 14-day field study operating a dual rover exploration mission to better understand the relationships between certain network groups such as ground control, flight teams, and planetary science. The analysis identified two communication network structures for the continuous communication and Twice-a-Day Communication scenarios as a split network and negotiated network respectfully. The major nodes or groups for the networks' architecture, transmittal status, and information were identified using graphical network mapping, quantitative analysis of subjective impressions, and quantified statistical analysis using Sociometric Statue and Centrality. Post-questionnaire analysis along with interviews revealed advantages and disadvantages of each network structure with team members identifying the need for a more stable continuous communication network, improved robustness of voice loops, and better systems training/capabilities for scientific imagery data and operational data during Twice-a-Day Communications.

  15. Error performance analysis in downlink cellular networks with interference management

    KAUST Repository

    Afify, Laila H.

    2015-05-01

    Modeling aggregate network interference in cellular networks has recently gained immense attention both in academia and industry. While stochastic geometry based models have succeeded to account for the cellular network geometry, they mostly abstract many important wireless communication system aspects (e.g., modulation techniques, signal recovery techniques). Recently, a novel stochastic geometry model, based on the Equivalent-in-Distribution (EiD) approach, succeeded to capture the aforementioned communication system aspects and extend the analysis to averaged error performance, however, on the expense of increasing the modeling complexity. Inspired by the EiD approach, the analysis developed in [1] takes into consideration the key system parameters, while providing a simple tractable analysis. In this paper, we extend this framework to study the effect of different interference management techniques in downlink cellular network. The accuracy of the proposed analysis is verified via Monte Carlo simulations.

  16. Implementing the Fussy Baby Network[R] Approach

    Science.gov (United States)

    Gilkerson, Linda; Hofherr, Jennifer; Heffron, Mary Claire; Sims, Jennifer Murphy; Jalowiec, Barbara; Bromberg, Stacey R.; Paul, Jennifer J.

    2012-01-01

    Erikson Institute Fussy Baby Network[R] (FBN) developed an approach to engaging parents around their urgent concerns about their baby's crying, sleeping, or feeding in a way which builds their longer-term capacities as parents. This approach, called the FAN, is now in place in new Fussy Baby Network programs around the country and is being infused…

  17. A Network Coding Approach to Loss Tomography

    DEFF Research Database (Denmark)

    Sattari, Pegah; Markopoulou, Athina; Fragouli, Christina

    2013-01-01

    multicast and/or unicast end-to-end probes. Independently, recent advances in network coding have shown that there are several advantages from allowing intermediate nodes to process and combine, in addition to just forward, packets. In this paper, we pose the problem of loss tomography in networks that have...... network coding capabilities. We design a framework for estimating link loss rates, which leverages network coding capabilities and we show that it improves several aspects of tomography, including the identifiability of links, the tradeoff between estimation accuracy and bandwidth efficiency...... and multiple paths between sources and receivers. This work was the first to make the connection between active network tomography and network coding, and thus opened a new research direction....

  18. Mobile social networking an innovative approach

    CERN Document Server

    Zhang, Daqing

    2014-01-01

    The use of contextually aware, pervasive, distributed computing, and sensor networks to bridge the gap between the physical and online worlds is the basis of mobile social networking. This book shows how applications can be built to provide mobile social networking, the research issues that need to be solved to enable this vision, and how mobile social networking can be used to provide computational intelligence that will improve daily life. With contributions from the fields of sociology, computer science, human-computer interaction and design, this book demonstrates how mobile social networks can be inferred from users' physical interactions both with the environment and with others, as well as how users behave around them and how their behavior differs on mobile vs. traditional online social networks.

  19. Design Criteria For Networked Image Analysis System

    Science.gov (United States)

    Reader, Cliff; Nitteberg, Alan

    1982-01-01

    Image systems design is currently undergoing a metamorphosis from the conventional computing systems of the past into a new generation of special purpose designs. This change is motivated by several factors, notably among which is the increased opportunity for high performance with low cost offered by advances in semiconductor technology. Another key issue is a maturing in understanding of problems and the applicability of digital processing techniques. These factors allow the design of cost-effective systems that are functionally dedicated to specific applications and used in a utilitarian fashion. Following an overview of the above stated issues, the paper presents a top-down approach to the design of networked image analysis systems. The requirements for such a system are presented, with orientation toward the hospital environment. The three main areas are image data base management, viewing of image data and image data processing. This is followed by a survey of the current state of the art, covering image display systems, data base techniques, communications networks and software systems control. The paper concludes with a description of the functional subystems and architectural framework for networked image analysis in a production environment.

  20. Software defined networks a comprehensive approach

    CERN Document Server

    Goransson, Paul

    2014-01-01

    Software Defined Networks discusses the historical networking environment that gave rise to SDN, as well as the latest advances in SDN technology. The book gives you the state of the art knowledge needed for successful deployment of an SDN, including: How to explain to the non-technical business decision makers in your organization the potential benefits, as well as the risks, in shifting parts of a network to the SDN modelHow to make intelligent decisions about when to integrate SDN technologies in a networkHow to decide if your organization should be developing its own SDN applications or

  1. Applications of Social Network Analysis

    Science.gov (United States)

    Thilagam, P. Santhi

    A social network [2] is a description of the social structure between actors, mostly persons, groups or organizations. It indicates the ways in which they are connected with each other by some relationship such as friendship, kinship, finance exchange etc. In a nutshell, when the person uses already known/unknown people to create new contacts, it forms social networking. The social network is not a new concept rather it can be formed when similar people interact with each other directly or indirectly to perform particular task. Examples of social networks include a friendship networks, collaboration networks, co-authorship networks, and co-employees networks which depict the direct interaction among the people. There are also other forms of social networks, such as entertainment networks, business Networks, citation networks, and hyperlink networks, in which interaction among the people is indirect. Generally, social networks operate on many levels, from families up to the level of nations and assists in improving interactive knowledge sharing, interoperability and collaboration.

  2. Unification of theoretical approaches for epidemic spreading on complex networks.

    Science.gov (United States)

    Wang, Wei; Tang, Ming; Eugene Stanley, H; Braunstein, Lidia A

    2017-03-01

    Models of epidemic spreading on complex networks have attracted great attention among researchers in physics, mathematics, and epidemiology due to their success in predicting and controlling scenarios of epidemic spreading in real-world scenarios. To understand the interplay between epidemic spreading and the topology of a contact network, several outstanding theoretical approaches have been developed. An accurate theoretical approach describing the spreading dynamics must take both the network topology and dynamical correlations into consideration at the expense of increasing the complexity of the equations. In this short survey we unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean-field, the quench mean-field, dynamical message-passing, link percolation, and pairwise approximation. We build connections among these approaches to provide new insights into developing an accurate theoretical approach to spreading dynamics on complex networks.

  3. Understanding complex interactions using social network analysis.

    Science.gov (United States)

    Pow, Janette; Gayen, Kaberi; Elliott, Lawrie; Raeside, Robert

    2012-10-01

    The aim of this paper is to raise the awareness of social network analysis as a method to facilitate research in nursing research. The application of social network analysis in assessing network properties has allowed greater insight to be gained in many areas including sociology, politics, business organisation and health care. However, the use of social networks in nursing has not received sufficient attention. Review of literature and illustration of the application of the method of social network analysis using research examples. First, the value of social networks will be discussed. Then by using illustrative examples, the value of social network analysis to nursing will be demonstrated. The method of social network analysis is found to give greater insights into social situations involving interactions between individuals and has particular application to the study of interactions between nurses and between nurses and patients and other actors. Social networks are systems in which people interact. Two quantitative techniques help our understanding of these networks. The first is visualisation of the network. The second is centrality. Individuals with high centrality are key communicators in a network. Applying social network analysis to nursing provides a simple method that helps gain an understanding of human interaction and how this might influence various health outcomes. It allows influential individuals (actors) to be identified. Their influence on the formation of social norms and communication can determine the extent to which new interventions or ways of thinking are accepted by a group. Thus, working with key individuals in a network could be critical to the success and sustainability of an intervention. Social network analysis can also help to assess the effectiveness of such interventions for the recipient and the service provider. © 2012 Blackwell Publishing Ltd.

  4. Gene network analysis: from heart development to cardiac therapy.

    Science.gov (United States)

    Ferrazzi, Fulvia; Bellazzi, Riccardo; Engel, Felix B

    2015-03-01

    Networks offer a flexible framework to represent and analyse the complex interactions between components of cellular systems. In particular gene networks inferred from expression data can support the identification of novel hypotheses on regulatory processes. In this review we focus on the use of gene network analysis in the study of heart development. Understanding heart development will promote the elucidation of the aetiology of congenital heart disease and thus possibly improve diagnostics. Moreover, it will help to establish cardiac therapies. For example, understanding cardiac differentiation during development will help to guide stem cell differentiation required for cardiac tissue engineering or to enhance endogenous repair mechanisms. We introduce different methodological frameworks to infer networks from expression data such as Boolean and Bayesian networks. Then we present currently available temporal expression data in heart development and discuss the use of network-based approaches in published studies. Collectively, our literature-based analysis indicates that gene network analysis constitutes a promising opportunity to infer therapy-relevant regulatory processes in heart development. However, the use of network-based approaches has so far been limited by the small amount of samples in available datasets. Thus, we propose to acquire high-resolution temporal expression data to improve the mathematical descriptions of regulatory processes obtained with gene network inference methodologies. Especially probabilistic methods that accommodate the intrinsic variability of biological systems have the potential to contribute to a deeper understanding of heart development.

  5. Network meta-analysis, electrical networks and graph theory.

    Science.gov (United States)

    Rücker, Gerta

    2012-12-01

    Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

  6. Understanding resilience in industrial symbiosis networks: insights from network analysis.

    Science.gov (United States)

    Chopra, Shauhrat S; Khanna, Vikas

    2014-08-01

    Industrial symbiotic networks are based on the principles of ecological systems where waste equals food, to develop synergistic networks. For example, industrial symbiosis (IS) at Kalundborg, Denmark, creates an exchange network of waste, water, and energy among companies based on contractual dependency. Since most of the industrial symbiotic networks are based on ad-hoc opportunities rather than strategic planning, gaining insight into disruptive scenarios is pivotal for understanding the balance of resilience and sustainability and developing heuristics for designing resilient IS networks. The present work focuses on understanding resilience as an emergent property of an IS network via a network-based approach with application to the Kalundborg Industrial Symbiosis (KIS). Results from network metrics and simulated disruptive scenarios reveal Asnaes power plant as the most critical node in the system. We also observe a decrease in the vulnerability of nodes and reduction in single points of failure in the system, suggesting an increase in the overall resilience of the KIS system from 1960 to 2010. Based on our findings, we recommend design strategies, such as increasing diversity, redundancy, and multi-functionality to ensure flexibility and plasticity, to develop resilient and sustainable industrial symbiotic networks. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Considerations for Software Defined Networking (SDN): Approaches and use cases

    Science.gov (United States)

    Bakshi, K.

    Software Defined Networking (SDN) is an evolutionary approach to network design and functionality based on the ability to programmatically modify the behavior of network devices. SDN uses user-customizable and configurable software that's independent of hardware to enable networked systems to expand data flow control. SDN is in large part about understanding and managing a network as a unified abstraction. It will make networks more flexible, dynamic, and cost-efficient, while greatly simplifying operational complexity. And this advanced solution provides several benefits including network and service customizability, configurability, improved operations, and increased performance. There are several approaches to SDN and its practical implementation. Among them, two have risen to prominence with differences in pedigree and implementation. This paper's main focus will be to define, review, and evaluate salient approaches and use cases of the OpenFlow and Virtual Network Overlay approaches to SDN. OpenFlow is a communication protocol that gives access to the forwarding plane of a network's switches and routers. The Virtual Network Overlay relies on a completely virtualized network infrastructure and services to abstract the underlying physical network, which allows the overlay to be mobile to other physical networks. This is an important requirement for cloud computing, where applications and associated network services are migrated to cloud service providers and remote data centers on the fly as resource demands dictate. The paper will discuss how and where SDN can be applied and implemented, including research and academia, virtual multitenant data center, and cloud computing applications. Specific attention will be given to the cloud computing use case, where automated provisioning and programmable overlay for scalable multi-tenancy is leveraged via the SDN approach.

  8. Statistical Network Analysis for Functional MRI: Mean Networks and Group Comparisons.

    Directory of Open Access Journals (Sweden)

    Cedric E Ginestet

    2014-05-01

    Full Text Available Comparing networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges of that network. This problem arises in the analysis of both weighted and unweighted networks. The term density is often used in this context, in order to refer to the mean edge weight of a weighted network, or to the number of edges in an unweighted one. Comparing families of networks is therefore statistically difficult because differences in topology are necessarily associated with differences in density. In this review paper, we consider this problem from two different perspectives, which include (i the construction of summary networks, such as how to compute and visualize the mean network from a sample of network-valued data points; and (ii how to test for topological differences, when two families of networks also exhibit significant differences in density. In the first instance, we show that the issue of summarizing a family of networks can be conducted by either adopting a mass-univariate approach, which produces a statistical parametric network (SPN, or by directly computing the mean network, provided that a metric has been specified on the space of all networks with a given number of nodes. In the second part of this review, we then highlight the inherent problems associated with the comparison of topological functions of families of networks that differ in density. In particular, we show that a wide range of topological summaries, such as global efficiency and network modularity are highly sensitive to differences in density. Moreover, these problems are not restricted to unweighted metrics, as we demonstrate that the same issues remain present when considering the weighted versions of these metrics. We conclude by encouraging caution, when reporting such statistical comparisons, and by emphasizing the importance of constructing summary networks.

  9. Neural network approach to parton distributions fitting

    CERN Document Server

    Piccione, Andrea; Forte, Stefano; Latorre, Jose I.; Rojo, Joan; Piccione, Andrea; Rojo, Joan

    2006-01-01

    We will show an application of neural networks to extract information on the structure of hadrons. A Monte Carlo over experimental data is performed to correctly reproduce data errors and correlations. A neural network is then trained on each Monte Carlo replica via a genetic algorithm. Results on the proton and deuteron structure functions, and on the nonsinglet parton distribution will be shown.

  10. Statistical Analysis of Bus Networks in India

    CERN Document Server

    Chatterjee, Atanu; Ramadurai, Gitakrishnan

    2015-01-01

    Through the past decade the field of network science has established itself as a common ground for the cross-fertilization of exciting inter-disciplinary studies which has motivated researchers to model almost every physical system as an interacting network consisting of nodes and links. Although public transport networks such as airline and railway networks have been extensively studied, the status of bus networks still remains in obscurity. In developing countries like India, where bus networks play an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer some of the basic questions on its evolution, growth, robustness and resiliency. In this paper, we model the bus networks of major Indian cities as graphs in \\textit{L}-space, and evaluate their various statistical properties using concepts from network science. Our analysis reveals a wide spectrum of network topology with the common underlying feature of small-world property. We observe tha...

  11. Discovering the Network Topology: An Efficient Approach for SDN

    Directory of Open Access Journals (Sweden)

    Leonardo OCHOA-ADAY

    2016-11-01

    Full Text Available Network topology is a physical description of the overall resources in the network. Collecting this information using efficient mechanisms becomes a critical task for important network functions such as routing, network management, quality of service (QoS, among many others. Recent technologies like Software-Defined Networks (SDN have emerged as promising approaches for managing the next generation networks. In order to ensure a proficient topology discovery service in SDN, we propose a simple agents-based mechanism. This mechanism improves the overall efficiency of the topology discovery process. In this paper, an algorithm for a novel Topology Discovery Protocol (SD-TDP is described. This protocol will be implemented in each switch through a software agent. Thus, this approach will provide a distributed solution to solve the problem of network topology discovery in a more simple and efficient way.

  12. Systemic Analysis Approaches for Air Transportation

    Science.gov (United States)

    Conway, Sheila

    2005-01-01

    Air transportation system designers have had only limited success using traditional operations research and parametric modeling approaches in their analyses of innovations. They need a systemic methodology for modeling of safety-critical infrastructure that is comprehensive, objective, and sufficiently concrete, yet simple enough to be used with reasonable investment. The methodology must also be amenable to quantitative analysis so issues of system safety and stability can be rigorously addressed. However, air transportation has proven itself an extensive, complex system whose behavior is difficult to describe, no less predict. There is a wide range of system analysis techniques available, but some are more appropriate for certain applications than others. Specifically in the area of complex system analysis, the literature suggests that both agent-based models and network analysis techniques may be useful. This paper discusses the theoretical basis for each approach in these applications, and explores their historic and potential further use for air transportation analysis.

  13. Using a Network Approach to Transform from a Municipality to Municipal Community

    NARCIS (Netherlands)

    van de Wijngaert, Lidwien; Wimmer, Maria A.; Janssen, Marijn; Macintosh, Ann; Scholl, Hans Jochen; Tambouris, Efthimios

    2013-01-01

    This paper presents a social network approach to help the government in their transformation to a leaner organization. We will first explain how social network analysis can contribute to this transformation. After that we will present some results from a pilot study that was conducted in Enschede

  14. Predicting Peer Nominations Among Medical Students: A Social Network Approach.

    Science.gov (United States)

    Michalec, Barret; Grbic, Douglas; Veloski, J Jon; Cuddy, Monica M; Hafferty, Frederic W

    2016-06-01

    Minimal attention has been paid to what factors may predict peer nomination or how peer nominations might exhibit a clustering effect. Focusing on the homophily principle that "birds of a feather flock together," and using a social network analysis approach, the authors investigated how certain student- and/or school-based factors might predict the likelihood of peer nomination, and the clusters, if any, that occur among those nominations. In 2013, the Jefferson Longitudinal Study of Medical Education included a special instrument to evaluate peer nominations. A total of 211 (81%) of 260 graduating medical students from the Sidney Kimmel Medical College responded to the peer nomination question. Data were analyzed using a relational contingency table and an ANOVA density model. Although peer nominations did not cluster around gender, age, or class rank, those students within an accelerated program, as well as those entering certain specialties, were more likely to nominate each other. The authors suggest that clerkships in certain specialties, as well as the accelerated program, may provide structured opportunities for students to connect and integrate, and that these opportunities may have an impact on peer nomination. The findings suggest that social network analysis is a useful approach to examine various aspects of peer nomination processes. The authors discuss implications regarding harnessing social cohesion within clinical clerkships, the possible development of siloed departmental identity and in-group favoritism, and future research possibilities.

  15. Egocentric social network analysis of pathological gambling.

    Science.gov (United States)

    Meisel, Matthew K; Clifton, Allan D; Mackillop, James; Miller, Joshua D; Campbell, W Keith; Goodie, Adam S

    2013-03-01

    To apply social network analysis (SNA) to investigate whether frequency and severity of gambling problems were associated with different network characteristics among friends, family and co-workers is an innovative way to look at relationships among individuals; the current study was the first, to our knowledge, to apply SNA to gambling behaviors. Egocentric social network analysis was used to characterize formally the relationships between social network characteristics and gambling pathology. Laboratory-based questionnaire and interview administration. Forty frequent gamblers (22 non-pathological gamblers, 18 pathological gamblers) were recruited from the community. The SNA revealed significant social network compositional differences between the two groups: pathological gamblers (PGs) had more gamblers, smokers and drinkers in their social networks than did non-pathological gamblers (NPGs). PGs had more individuals in their network with whom they personally gambled, smoked and drank than those with who were NPG. Network ties were closer to individuals in their networks who gambled, smoked and drank more frequently. Associations between gambling severity and structural network characteristics were not significant. Pathological gambling is associated with compositional but not structural differences in social networks. Pathological gamblers differ from non-pathological gamblers in the number of gamblers, smokers and drinkers in their social networks. Homophily within the networks also indicates that gamblers tend to be closer with other gamblers. This homophily may serve to reinforce addictive behaviors, and may suggest avenues for future study or intervention. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.

  16. Evaluating the impact of interdisciplinary research: a multilayer network approach

    CERN Document Server

    Omodei, Elisa; Arenas, Alex

    2016-01-01

    Nowadays, scientific challenges usually require approaches that cross traditional boundaries between academic disciplines, driving many researchers towards interdisciplinarity. Despite its obvious importance, there is a lack of studies on how to quantify the influence of interdisciplinarity on the research impact, posing uncertainty in a proper evaluation for hiring and funding purposes. Here we propose a method based on the analysis of bipartite interconnected multilayer networks of citations and disciplines, to assess scholars, institutions and countries interdisciplinary importance. Using data about physics publications and US patents, we show that our method allows to reveal, using a quantitative approach, that being more interdisciplinary causes -- in the Granger sense -- benefits in scientific productivity and impact. The proposed method could be used by funding agencies, universities and scientific policy decision makers for hiring and funding purposes, and to complement existing methods to rank univer...

  17. Network attacks and defenses a hands-on approach

    CERN Document Server

    Trabelsi, Zouheir; Al Braiki, Arwa; Mathew, Sujith Samuel

    2012-01-01

    The attacks on computers and business networks are growing daily, and the need for security professionals who understand how malfeasants perform attacks and compromise networks is a growing requirement to counter the threat. Network security education generally lacks appropriate textbooks with detailed, hands-on exercises that include both offensive and defensive techniques. Using step-by-step processes to build and generate attacks using offensive techniques, Network Attacks and Defenses: A Hands-on Approach enables students to implement appropriate network security solutions within a laborat

  18. Using social network analysis to understand actor participation and ...

    African Journals Online (AJOL)

    Sustainable management of wetland is complex due competing interests and require the participation of different actors. However, there is little attention on systematic analysis of actor participation in wetland management. This paper uses Social Network Analysis (SNA) approach to analyse how actors with different ...

  19. SCIENTIFIC COLLABORATION NETWORK AMONG BRAZILIAN UNIVERSITIES: AN ANALYSIS IN DENTISTRY AREA

    National Research Council Canada - National Science Library

    Maria Isabel Escalona-Fernandez; Antonio Pulgarin-Guerrero; Ely Francina Tannuri de Oliveira; Maria Cláudia Cabrini Gracio

    2012-01-01

    ... (ten) years. It is used social network analysis as the best methodological approach to visualize the capacity for collaboration, dissemination and transmission of new knowledge among universities...

  20. Characterizing Social Interaction in Tobacco-Oriented Social Networks: An Empirical Analysis

    National Research Council Canada - National Science Library

    Liang, Yunji; Zheng, Xiaolong; Zeng, Daniel Dajun; Zhou, Xingshe; Leischow, Scott James; Chung, Wingyan

    2015-01-01

    .... To reveal the impact of tobacco-related user-generated content, this paper characterizes user interaction and social influence utilizing social network analysis and information theoretic approaches...

  1. CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data.

    Science.gov (United States)

    Penfold, Christopher A; Shifaz, Ahmed; Brown, Paul E; Nicholson, Ann; Wild, David L

    2015-06-01

    Here we introduce the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data. The standard CSI approach infers a single GRN via joint learning from multiple time series datasets; the hierarchical approach (HCSI) infers a separate GRN for each dataset, albeit with the networks constrained to favor similar structures, allowing for the identification of context specific networks. The software is implemented in MATLAB and includes a graphical user interface (GUI) for user friendly inference. Finally the GUI can be connected to high performance computer clusters to facilitate analysis of large genomic datasets.

  2. Social Network Analysis and informal trade

    DEFF Research Database (Denmark)

    Walther, Olivier

    networks can be applied to better understand informal trade in developing countries, with a particular focus on Africa. The paper starts by discussing some of the fundamental concepts developed by social network analysis. Through a number of case studies, we show how social network analysis can...... illuminate the relevant causes of social patterns, the impact of social ties on economic performance, the diffusion of resources and information, and the exercise of power. The paper then examines some of the methodological challenges of social network analysis and how it can be combined with other...

  3. Social network analysis and supply chain management

    Directory of Open Access Journals (Sweden)

    Raúl Rodríguez Rodríguez

    2016-01-01

    Full Text Available This paper deals with social network analysis and how it could be integrated within supply chain management from a decision-making point of view. Even though the benefits of using social analysis have are widely accepted at both academic and industry/services context, there is still a lack of solid frameworks that allow decision-makers to connect the usage and obtained results of social network analysis – mainly both information and knowledge flows and derived results- with supply chain management objectives and goals. This paper gives an overview of social network analysis, the main social network analysis metrics, supply chain performance and, finally, it identifies how future frameworks could close the gap and link the results of social network analysis with the supply chain management decision-making processes.

  4. Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network.

    Science.gov (United States)

    Petrescu-Prahova, Miruna; Belza, Basia; Leith, Katherine; Allen, Peg; Coe, Norma B; Anderson, Lynda A

    2015-08-20

    Addressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA). We invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network. Sixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations. SNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level.

  5. Network Medicine: A Network-based Approach to Human Disease

    Science.gov (United States)

    Barabási, Albert-László; Gulbahce, Natali; Loscalzo, Joseph

    2011-01-01

    Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction are essential to identify new diseases genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases. PMID:21164525

  6. 4th International Conference in Network Analysis

    CERN Document Server

    Koldanov, Petr; Pardalos, Panos

    2016-01-01

    The contributions in this volume cover a broad range of topics including maximum cliques, graph coloring, data mining, brain networks, Steiner forest, logistic and supply chain networks. Network algorithms and their applications to market graphs, manufacturing problems, internet networks and social networks are highlighted. The "Fourth International Conference in Network Analysis," held at the Higher School of Economics, Nizhny Novgorod in May 2014, initiated joint research between scientists, engineers and researchers from academia, industry and government; the major results of conference participants have been reviewed and collected in this Work. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis.

  7. Tissue segmentation-assisted analysis of fMRI for human motor response: an approach combining artificial neural network and fuzzy C means.

    Science.gov (United States)

    Chiu, M J; Lin, C C; Chuang, K H; Chen, J H; Huang, K M

    2001-03-01

    The authors have developed an automated algorithm for segmentation of magnetic resonance images (MRI) of the human brain. They investigated the quantitative analysis of tissue-specific human motor response through an approach combining gradient echo functional MRI and automated segmentation analysis. Fifteen healthy volunteers, placed in a 1.5 T clinical MR imager, performed a self-paced finger opposition throughout the activation periods. T1-weighted images (WI), T2WI, and proton density WI were acquired for segmentation analysis. Single-slice axial T2* fast low-angle shot (FLASH) images were obtained during the functional study. Pixelwise cross-correlation analysis was performed to obtain an activation map. A cascaded algorithm, combining Kohonen feature maps and fuzzy C means, was applied for segmentation. After processing, masks for gray matter, white matter, small vessels, and large vessels were generated. Tissue-specific analysis showed a signal change rate of 4.53% in gray matter, 2.98% in white matter, 5.79% in small vessels, and 7.24% in large vessels. Different temporal patterns as well as different levels of activation were identified in the functional response from various types of tissue. High correlation exists between cortical gray matter and subcortical white matter (r = 0.957), while the vessel behaves somewhat different temporally. The cortical gray matter fits best to the assumed input function (r = 0.957) followed by subcortical white matter (r = 0.829) and vessels (r = 0.726). The automated algorithm of tissue-specific analysis thus can assist functional MRI studies with different modalities of response in different brain regions.

  8. Library Network Statistics and Performance Measures: Approaches and Issues

    Directory of Open Access Journals (Sweden)

    John Carlo Bertot

    2001-07-01

    Full Text Available Library networked statistics and performance measures are important indicators of the use, uses, and users of networked services that libraries offer their patrons. This article focuses on three efforts to develop and standardize library network statistics and performance measures. In particular, the article discusses, compares, and contrasts selected aspects of the International Standards Organization (ISO, U.S. public library network statistics, and Association of Research Library (ARL efforts. The three approaches attempt to capture, describe, and present library networked activities in similar ways through similar approaches – yet they differ in key areas. It is important to note that there are a number of national and international efforts underway that continue to research the library network statistics and performance measure environment.

  9. Sampling of Complex Networks: A Datamining Approach

    Science.gov (United States)

    Loecher, Markus; Dohrmann, Jakob; Bauer, Gernot

    2007-03-01

    Efficient and accurate sampling of big complex networks is still an unsolved problem. As the degree distribution is one of the most commonly used attributes to characterize a network, there have been many attempts in recent papers to derive the original degree distribution from the data obtained during a traceroute- like sampling process. This talk describes a strategy for predicting the original degree of a node using the data obtained from a network by traceroute-like sampling making use of datamining techniques. Only local quantities (the sampled degree k, the redundancy of node detection r, the time of the first discovery of a node t and the distance to the sampling source d) are used as input for the datamining models. Global properties like the betweenness centrality are ignored. These local quantities are examined theoretically and in simulations to increase their value for the predictions. The accuracy of the models is discussed as a function of the number of sources used in the sampling process and the underlying topology of the network. The purpose of this work is to introduce the techniques of the relatively young field of datamining to the discussion on network sampling.

  10. METHODOLOGY OF MATHEMATICAL ANALYSIS IN POWER NETWORK

    OpenAIRE

    Jerzy Szkutnik; Mariusz Kawecki

    2008-01-01

    Power distribution network analysis is taken into account. Based on correlation coefficient authors establish methodology of mathematical analysis useful in finding substations bear responsibility for power stoppage. Also methodology of risk assessment will be carried out.

  11. IPTV inter-destination synchronization: A network-based approach

    NARCIS (Netherlands)

    Stokking, H.M.; Deventer, M.O. van; Niamut, O.A.; Walraven, F.A.; Mekuria, R.N.

    2010-01-01

    This paper introduces a novel network-based approach to inter-destination media synchronization. The approach meets the need for synchronization in advanced TV concepts like social TV and offers high scalability, unlike conventional end-point based approaches. The solution for interdestination media

  12. A network analysis of leadership theory : the infancy of integration.

    OpenAIRE

    Meuser, J. D.; Gardner, W. L.; Dinh, J. E.; Hu, J.; Liden, R. C.; Lord, R. G.

    2016-01-01

    We investigated the status of leadership theory integration by reviewing 14 years of published research (2000 through 2013) in 10 top journals (864 articles). The authors of these articles examined 49 leadership approaches/theories, and in 293 articles, 3 or more of these leadership approaches were included in their investigations. Focusing on these articles that reflected relatively extensive integration, we applied an inductive approach and used graphic network analysis as a guide for drawi...

  13. Identification of important nodes in directed biological networks: a network motif approach.

    Directory of Open Access Journals (Sweden)

    Pei Wang

    Full Text Available Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA, this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

  14. Constructing an Intelligent Patent Network Analysis Method

    OpenAIRE

    Chao-Chan Wu; Ching-Bang Yao

    2012-01-01

    Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks...

  15. SOCIAL POLARIZATION AND CONFLICT: A NETWORK APPROACH

    Directory of Open Access Journals (Sweden)

    Ernesto Cárdenas

    2013-12-01

    Full Text Available Theoretically, polarization is associated with a higher probability of social conflict. This paper, in a microeconomic model based on the theory of social networks, analyses how changes in the network's structure affect the level of some basic parameters associated with the concept of polarization. This study shows that under upward monotonic preferences, longer sets of affiliations for each individual reduce polarization, whereas under downward monotonic preferences, longer sets of the so-called bad affiliations increase polarization. Finally, in the case of a non-monotonic system of preferences, an expansion of the affiliations set will alter the resulting polarization order in different ways depending on the preferences themselves

  16. Outline of a multilevel approach of the network society

    NARCIS (Netherlands)

    van Dijk, Johannes A.G.M.

    2005-01-01

    Social and media networks, the Internet in particular, increasingly link interpersonal, organizational and mass communication. It is argued that this gives a cause for an interdisciplinary and multilevel approach of the network society. This will have to link traditional micro- and meso-level

  17. A Complex Network Approach to Distributional Semantic Models.

    Directory of Open Access Journals (Sweden)

    Akira Utsumi

    Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.

  18. A Gaussian graphical model approach to climate networks

    Energy Technology Data Exchange (ETDEWEB)

    Zerenner, Tanja, E-mail: tanjaz@uni-bonn.de [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Friederichs, Petra; Hense, Andreas [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany); Lehnertz, Klaus [Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn (Germany); Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany)

    2014-06-15

    Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.

  19. Workplace English: Approach and Analysis.

    Science.gov (United States)

    Prince, David

    1984-01-01

    Describes two approaches to teaching vocational English as a second language: (1) describing work activities in terms of processes and procedures and (2) describing work activities in terms of specific human behaviors. Suggests a goal analysis as an initial step before deciding which approach to take in any training project. (SED)

  20. ALOHA networks : A game-theoretic approach

    NARCIS (Netherlands)

    Marban, S.; van de Ven, P.; Borm, P.E.M.; Hamers, H.J.M.

    2013-01-01

    In this paper we consider a wireless network consisting of various nodes, where transmissions are regulated by the slotted ALOHA protocol. Nodes using the protocol behave autonomously, and decide at random whether to transmit in a particular time slot. Simultaneous transmissions by multiple nodes

  1. ALOHA networks: A game-theoretic approach

    NARCIS (Netherlands)

    Marbán, S.; Ven, P. van de; Borm, P.; Hamers, H.

    2013-01-01

    In this paper we consider a wireless network consisting of various nodes, where transmissions are regulated by the slotted ALOHA protocol. Nodes using the protocol behave autonomously, and decide at random whether to transmit in a particular time slot. Simultaneous transmissions by multiple nodes

  2. Dobrushin's approach to queueing network theory

    Directory of Open Access Journals (Sweden)

    F. I. Karpelevich

    1996-01-01

    Full Text Available R.L. Dobrushin (1929-1995 made substantial contributions to Queueing Network Theory (QNT. A review of results from QNT which arose from his ideas or were connected to him in other ways is given. We also comment on various related open problems.

  3. Economic Institutions and Stability : A Network Approach

    NARCIS (Netherlands)

    Gilles, R.P.; Lazarova, E.A.; Ruys, P.H.M.

    2011-01-01

    We consider a network economy in which economic agents are connected within a structure of value-generating relationships. Agents are assumed to be able to participate in three types of economic activities: autarkic self-provision; binary matching interactions; and multi-person cooperative

  4. A Network Text Analysis of David Ayer’s Fury

    Directory of Open Access Journals (Sweden)

    Starling David Hunter

    2015-12-01

    Full Text Available Network Text Analysis (NTA involves the creation of networks of words and/or concepts from linguistic data. Its key insight is that the position of words and concepts in a text network provides vital clues to the central and underlying themes of the text as a whole. Recent research has relied on inductive approaches to identify these themes. In this study we demonstrate a deductive approach that we apply to the screenplay of the 2014 World War II-era film Fury. Specifically, we first use genre expectations theory to establish prior expectations as to the key themes associated with war films. We then empirically test whether words and concepts associated with the most influentially-positioned nodes are consistent with themes common to the war-film genre. As predicted, we find that words and concepts associated with the least constrained nodes in the text network were significantly more likely to be associated with the war, action, and biography genres and significantly less likely to be associated with the mystery, science-fiction, fantasy, and film-noir genres. Keywords: content analysis, text analysis, network text analysis, semantic network analysis, film studies, screenplay, screenwriting, war movies, World War II, tanks

  5. Inferring signalling networks from longitudinal data using sampling based approaches in the R-package 'ddepn'

    Directory of Open Access Journals (Sweden)

    Korf Ulrike

    2011-07-01

    Full Text Available Abstract Background Network inference from high-throughput data has become an important means of current analysis of biological systems. For instance, in cancer research, the functional relationships of cancer related proteins, summarised into signalling networks are of central interest for the identification of pathways that influence tumour development. Cancer cell lines can be used as model systems to study the cellular response to drug treatments in a time-resolved way. Based on these kind of data, modelling approaches for the signalling relationships are needed, that allow to generate hypotheses on potential interference points in the networks. Results We present the R-package 'ddepn' that implements our recent approach on network reconstruction from longitudinal data generated after external perturbation of network components. We extend our approach by two novel methods: a Markov Chain Monte Carlo method for sampling network structures with two edge types (activation and inhibition and an extension of a prior model that penalises deviances from a given reference network while incorporating these two types of edges. Further, as alternative prior we include a model that learns signalling networks with the scale-free property. Conclusions The package 'ddepn' is freely available on R-Forge and CRAN http://ddepn.r-forge.r-project.org, http://cran.r-project.org. It allows to conveniently perform network inference from longitudinal high-throughput data using two different sampling based network structure search algorithms.

  6. An adaptive neural swarm approach for intrusion defense in ad hoc networks

    Science.gov (United States)

    Cannady, James

    2011-06-01

    Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical applications due to the flexibility and extensibility of the technology. While these networks possess numerous advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the autonomic processes of biological systems. Each component of the network recognizes activity in its local environment and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network. The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network attacks.

  7. Weighted Complex Network Analysis of Pakistan Highways

    Directory of Open Access Journals (Sweden)

    Yasir Tariq Mohmand

    2013-01-01

    Full Text Available The structure and properties of public transportation networks have great implications in urban planning, public policies, and infectious disease control. This study contributes a weighted complex network analysis of travel routes on the national highway network of Pakistan. The network is responsible for handling 75 percent of the road traffic yet is largely inadequate, poor, and unreliable. The highway network displays small world properties and is assortative in nature. Based on the betweenness centrality of the nodes, the most important cities are identified as this could help in identifying the potential congestion points in the network. Keeping in view the strategic location of Pakistan, such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the highway network.

  8. Predictive structural dynamic network analysis.

    Science.gov (United States)

    Chen, Rong; Herskovits, Edward H

    2015-04-30

    Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data. Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models. We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity. For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002). We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. NEAT: an efficient network enrichment analysis test.

    Science.gov (United States)

    Signorelli, Mirko; Vinciotti, Veronica; Wit, Ernst C

    2016-09-05

    Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).

  10. Heuristic urban transportation network design method, a multilayer coevolution approach

    Science.gov (United States)

    Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun

    2017-08-01

    The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.

  11. A Dynamic Neural Network Approach to CBM

    Science.gov (United States)

    2011-03-15

    Therefore post-processing is needed to extract the time difference between corresponding events from which to calculate the crankshaft rotational speed...potentially already available from existing sensors (such as a crankshaft timing device) and a Neural Network processor to carry out the calculation . As...files are designated with the “_genmod” suffix. These files were the sources for the training and testing sets and made the extraction process easy

  12. Reaction network analysis in biochemical signaling pathways

    OpenAIRE

    Martinez-Forero, I. (Iván); Pelaez, A. (Antonio); Villoslada, P. (Pablo)

    2010-01-01

    The aim of this thesis is to improve the understanding of signaling pathways through a theoretical study of chemical reaction networks. The equilibirum solution to the equations derived from chemical networks will be analytically resolved using tools from algebraic geometry. The chapters are organized as follows: 1. An introduction to chemical dynamics in biological systems with a special emphasis on steady state analysis 2. Complete description of the chemical reaction network theor...

  13. A Bayesian Networks approach to Operational Risk

    Science.gov (United States)

    Aquaro, V.; Bardoscia, M.; Bellotti, R.; Consiglio, A.; De Carlo, F.; Ferri, G.

    2010-04-01

    A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.

  14. Social network analysis of public health programs to measure partnership.

    Science.gov (United States)

    Schoen, Martin W; Moreland-Russell, Sarah; Prewitt, Kim; Carothers, Bobbi J

    2014-12-01

    In order to prevent chronic diseases, community-based programs are encouraged to take an ecological approach to public health promotion and involve many diverse partners. Little is known about measuring partnership in implementing public health strategies. We collected data from 23 Missouri communities in early 2012 that received funding from three separate programs to prevent obesity and/or reduce tobacco use. While all of these funding programs encourage partnership, only the Social Innovation for Missouri (SIM) program included a focus on building community capacity and enhancing collaboration. Social network analysis techniques were used to understand contact and collaboration networks in community organizations. Measurements of average degree, density, degree centralization, and betweenness centralization were calculated for each network. Because of the various sizes of the networks, we conducted comparative analyses with and without adjustment for network size. SIM programs had increased measurements of average degree for partner collaboration and larger networks. When controlling for network size, SIM groups had higher measures of network density and lower measures of degree centralization and betweenness centralization. SIM collaboration networks were more dense and less centralized, indicating increased partnership. The methods described in this paper can be used to compare partnership in community networks of various sizes. Further research is necessary to define causal mechanisms of partnership development and their relationship to public health outcomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Industrial entrepreneurial network: Structural and functional analysis

    Science.gov (United States)

    Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

    2016-12-01

    Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

  16. Traffic networks as information systems a viability approach

    CERN Document Server

    Aubin, Jean-Pierre

    2017-01-01

    This authored monograph covers a viability to approach to traffic management by advising to vehicles circulated on the network the velocity they should follow for satisfying global traffic conditions;. It presents an investigation of three structural innovations: The objective is to broadcast at each instant and at each position the advised celerity to vehicles, which could be read by auxiliary speedometers or used by cruise control devices. Namely, 1. Construct regulation feedback providing at each time and position advised velocities (celerities) for minimizing congestion or other requirements. 2. Taking into account traffic constraints of different type, the first one being to remain on the roads, to stop at junctions, etc. 3. Use information provided by the probe vehicles equipped with GPS to the traffic regulator; 4. Use other global traffic measures of vehicles provided by different types of sensors; These results are based on convex analysis, intertemporal optimization and viability theory as mathemati...

  17. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Science.gov (United States)

    Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang

    2008-12-01

    Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation). The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  18. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Yang Dan

    2008-12-01

    Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  19. A full bayesian approach for boolean genetic network inference.

    Directory of Open Access Journals (Sweden)

    Shengtong Han

    Full Text Available Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.

  20. Approaches to modeling gene regulatory networks: a gentle introduction.

    Science.gov (United States)

    Schlitt, Thomas

    2013-01-01

    This chapter is split into two main sections; first, I will present an introduction to gene networks. Second, I will discuss various approaches to gene network modeling which will include some examples for using different data sources. Computational modeling has been used for many different biological systems and many approaches have been developed addressing the different needs posed by the different application fields. The modeling approaches presented here are not limited to gene regulatory networks and occasionally I will present other examples. The material covered here is an update based on several previous publications by Thomas Schlitt and Alvis Brazma (FEBS Lett 579(8),1859-1866, 2005; Philos Trans R Soc Lond B Biol Sci 361(1467), 483-494, 2006; BMC Bioinformatics 8(suppl 6), S9, 2007) that formed the foundation for a lecture on gene regulatory networks at the In Silico Systems Biology workshop series at the European Bioinformatics Institute in Hinxton.

  1. Exploratory Social Network Analysis of Management Faculty students on social media Facebook

    OpenAIRE

    Loušová, Petra

    2013-01-01

    The aim of the bachelor thesis is the introduction of modern approaches in analysis social networks. In theoretical part there is characterized various social media used in both the Czech Republic and in the world. Additionally discussed are fundamental elements of social networking and the ability of analysis using software. In practical part are these basic approaches applied for an exploratory analysis of the social network of students of University of Economics, Faculty of Management in s...

  2. Social network analysis in medical education.

    Science.gov (United States)

    Isba, Rachel; Woolf, Katherine; Hanneman, Robert

    2017-01-01

    Humans are fundamentally social beings. The social systems within which we live our lives (families, schools, workplaces, professions, friendship groups) have a significant influence on our health, success and well-being. These groups can be characterised as networks and analysed using social network analysis. Social network analysis is a mainly quantitative method for analysing how relationships between individuals form and affect those individuals, but also how individual relationships build up into wider social structures that influence outcomes at a group level. Recent increases in computational power have increased the accessibility of social network analysis methods for application to medical education research. Social network analysis has been used to explore team-working, social influences on attitudes and behaviours, the influence of social position on individual success, and the relationship between social cohesion and power. This makes social network analysis theories and methods relevant to understanding the social processes underlying academic performance, workplace learning and policy-making and implementation in medical education contexts. Social network analysis is underused in medical education, yet it is a method that could yield significant insights that would improve experiences and outcomes for medical trainees and educators, and ultimately for patients. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

  3. Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach**

    Science.gov (United States)

    Haynie, Dana L.; Doogan, Nathan J.; Soller, Brian

    2014-01-01

    Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties. PMID:26097241

  4. Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach.

    Science.gov (United States)

    Haynie, Dana L; Doogan, Nathan J; Soller, Brian

    2014-11-01

    Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties.

  5. Functional gene pyrosequencing and network analysis: an approach to examine the response of denitrifying bacteria to increased nitrogen supply in salt marsh sediments

    Directory of Open Access Journals (Sweden)

    Jennifer L. Bowen

    2013-11-01

    Full Text Available Functional gene pyrosequencing is emerging as a useful tool to examine the diversity and abundance of microbes that facilitate key biogeochemical processes. One such process, denitrification, is of particular importance because it converts fixed nitrate (NO3- to N2 gas, which returns to the atmosphere. In N limited salt marshes, removal of NO3- prior to entering adjacent waters helps prevent eutrophication. Understanding the dynamics of salt marsh microbial denitrification is thus imperative for the maintenance of healthy coastal ecosystems. We used pyrosequencing of the nirS gene to examine the denitrifying community response to fertilization in experimentally enriched marsh plots. A key challenge in the analysis of sequence data derived from pyrosequencing is understanding whether small differences in gene sequences are ecologically meaningful. We apply a novel approach from information theory that determined that the optimal similarity level for clustering DNA sequences into OTUs, while still capturing the ecological complexity of the system, was 88% similarity. With this clustering, phylogenetic analysis yielded 6 dominant clades of denitrifiers, the largest of which, accounting for more than half of all the sequences collected, had no close cultured representatives. Of the 638 OTUs identified, only 11 were present in all plots and no single OTU was dominant. We did, however, find a large number of specialist OTUs that were present only in a single plot. The high degree of endemic OTUs, while accounting for a large proportion of the nirS diversity in the plots, were found in lower abundance than the generalist taxa. The proportion of specialist taxa increased with increasing supply of nutrients, suggesting that addition of fertilizer may create conditions that expand the niche space for denitrifying organisms and may enhance the genetic capacity for denitrification.

  6. Functional gene pyrosequencing and network analysis: an approach to examine the response of denitrifying bacteria to increased nitrogen supply in salt marsh sediments.

    Science.gov (United States)

    Bowen, Jennifer L; Byrnes, Jarrett E K; Weisman, David; Colaneri, Cory

    2013-01-01

    Functional gene pyrosequencing is emerging as a useful tool to examine the diversity and abundance of microbes that facilitate key biogeochemical processes. One such process, denitrification, is of particular importance because it converts fixed nitrate (NO(-) 3) to N2 gas, which returns to the atmosphere. In nitrogen limited salt marshes, removal of NO(-) 3 prior to entering adjacent waters helps prevent eutrophication. Understanding the dynamics of salt marsh microbial denitrification is thus imperative for the maintenance of healthy coastal ecosystems. We used pyrosequencing of the nirS gene to examine the denitrifying community response to fertilization in experimentally enriched marsh plots. A key challenge in the analysis of sequence data derived from pyrosequencing is understanding whether small differences in gene sequences are ecologically meaningful. We applied a novel approach from information theory to determine that the optimal similarity level for clustering DNA sequences into OTUs, while still capturing the ecological complexity of the system, was 88%. With this clustering, phylogenetic analysis yielded 6 dominant clades of denitrifiers, the largest of which, accounting for more than half of all the sequences collected, had no close cultured representatives. Of the 638 OTUs identified, only 11 were present in all plots and no single OTU was dominant. We did, however, find a large number of specialist OTUs that were present only in a single plot. The high degree of endemic OTUs, while accounting for a large proportion of the nirS diversity in the plots, were found in lower abundance than the generalist taxa. The proportion of specialist taxa increased with increasing supply of nutrients, suggesting that addition of fertilizer may create conditions that expand the niche space for denitrifying organisms and may enhance the genetic capacity for denitrification.

  7. Functional Interaction Network Construction and Analysis for Disease Discovery.

    Science.gov (United States)

    Wu, Guanming; Haw, Robin

    2017-01-01

    Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

  8. A Constructive Neural-Network Approach to Modeling Psychological Development

    Science.gov (United States)

    Shultz, Thomas R.

    2012-01-01

    This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…

  9. The Islands Approach to Nearest Neighbor Querying in Spatial Networks

    DEFF Research Database (Denmark)

    Huang, Xuegang; Jensen, Christian Søndergaard; Saltenis, Simonas

    2005-01-01

    , and versatile approach to k nearest neighbor computation that obviates the need for using several k nearest neighbor approaches for supporting a single service scenario. The experimental comparison with the existing techniques uses real-world road network data and considers both I/O and CPU performance...

  10. A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

    Science.gov (United States)

    Röhl, Annika; Bockmayr, Alexander

    2017-01-03

    Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

  11. Spectrum-Based and Collaborative Network Topology Analysis and Visualization

    Science.gov (United States)

    Hu, Xianlin

    2013-01-01

    Networks are of significant importance in many application domains, such as World Wide Web and social networks, which often embed rich topological information. Since network topology captures the organization of network nodes and links, studying network topology is very important to network analysis. In this dissertation, we study networks by…

  12. DNA sequence analysis using hierarchical ART-based classification networks

    Energy Technology Data Exchange (ETDEWEB)

    LeBlanc, C.; Hruska, S.I. [Florida State Univ., Tallahassee, FL (United States); Katholi, C.R.; Unnasch, T.R. [Univ. of Alabama, Birmingham, AL (United States)

    1994-12-31

    Adaptive resonance theory (ART) describes a class of artificial neural network architectures that act as classification tools which self-organize, work in real-time, and require no retraining to classify novel sequences. We have adapted ART networks to provide support to scientists attempting to categorize tandem repeat DNA fragments from Onchocerca volvulus. In this approach, sequences of DNA fragments are presented to multiple ART-based networks which are linked together into two (or more) tiers; the first provides coarse sequence classification while the sub- sequent tiers refine the classifications as needed. The overall rating of the resulting classification of fragments is measured using statistical techniques based on those introduced to validate results from traditional phylogenetic analysis. Tests of the Hierarchical ART-based Classification Network, or HABclass network, indicate its value as a fast, easy-to-use classification tool which adapts to new data without retraining on previously classified data.

  13. Automatic Distribution Network Reconfiguration: An Event-Driven Approach

    Energy Technology Data Exchange (ETDEWEB)

    Ding, Fei; Jiang, Huaiguang; Tan, Jin

    2016-11-14

    This paper proposes an event-driven approach for reconfiguring distribution systems automatically. Specifically, an optimal synchrophasor sensor placement (OSSP) is used to reduce the number of synchrophasor sensors while keeping the whole system observable. Then, a wavelet-based event detection and location approach is used to detect and locate the event, which performs as a trigger for network reconfiguration. With the detected information, the system is then reconfigured using the hierarchical decentralized approach to seek for the new optimal topology. In this manner, whenever an event happens the distribution network can be reconfigured automatically based on the real-time information that is observable and detectable.

  14. Complex Network Analysis of Guangzhou Metro

    Directory of Open Access Journals (Sweden)

    Yasir Tariq Mohmand

    2015-11-01

    Full Text Available The structure and properties of public transportation networks can provide suggestions for urban planning and public policies. This study contributes a complex network analysis of the Guangzhou metro. The metro network has 236 kilometers of track and is the 6th busiest metro system of the world. In this paper topological properties of the network are explored. We observed that the network displays small world properties and is assortative in nature. The network possesses a high average degree of 17.5 with a small diameter of 5. Furthermore, we also identified the most important metro stations based on betweenness and closeness centralities. These could help in identifying the probable congestion points in the metro system and provide policy makers with an opportunity to improve the performance of the metro system.

  15. Extending Stochastic Network Calculus to Loss Analysis

    Directory of Open Access Journals (Sweden)

    Chao Luo

    2013-01-01

    Full Text Available Loss is an important parameter of Quality of Service (QoS. Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor.

  16. Constructing an Intelligent Patent Network Analysis Method

    Directory of Open Access Journals (Sweden)

    Chao-Chan Wu

    2012-11-01

    Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.

  17. Statistical Analysis of Bus Networks in India.

    Science.gov (United States)

    Chatterjee, Atanu; Manohar, Manju; Ramadurai, Gitakrishnan

    2016-01-01

    In this paper, we model the bus networks of six major Indian cities as graphs in L-space, and evaluate their various statistical properties. While airline and railway networks have been extensively studied, a comprehensive study on the structure and growth of bus networks is lacking. In India, where bus transport plays an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer basic questions on its evolution, growth, robustness and resiliency. Although the common feature of small-world property is observed, our analysis reveals a wide spectrum of network topologies arising due to significant variation in the degree-distribution patterns in the networks. We also observe that these networks although, robust and resilient to random attacks are particularly degree-sensitive. Unlike real-world networks, such as Internet, WWW and airline, that are virtual, bus networks are physically constrained. Our findings therefore, throw light on the evolution of such geographically and constrained networks that will help us in designing more efficient bus networks in the future.

  18. Optimizing Groundwater Monitoring Networks Using Integrated Statistical and Geostatistical Approaches

    Directory of Open Access Journals (Sweden)

    Jay Krishna Thakur

    2015-08-01

    Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.

  19. A simple network agreement-based approach for combining evidences in a heterogeneous sensor network

    Directory of Open Access Journals (Sweden)

    Raúl Eusebio-Grande

    2015-12-01

    Full Text Available In this research we investigate how the evidences provided by both static and mobile nodes that are part of a heterogenous sensor network can be combined to have trustworthy results. A solution relying on a network agreement-based approach was implemented and tested.

  20. Stochastic modeling and analysis of telecoms networks

    CERN Document Server

    Decreusefond, Laurent

    2012-01-01

    This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an

  1. Network Reverse Engineering Approach in Synthetic Biology

    Science.gov (United States)

    Zhang, Haoqian; Liu, Ao; Lu, Yuheng; Sheng, Ying; Wu, Qianzhu; Yin, Zhenzhen; Chen, Yiwei; Liu, Zairan; Pan, Heng; Ouyang, Qi

    2013-12-01

    Synthetic biology is a new branch of interdisciplinary science that has been developed in recent years. The main purpose of synthetic biology is to apply successful principles that have been developed in electronic and chemical engineering to develop basic biological functional modules, and through rational design, develop man-made biological systems that have predicted useful functions. Here, we discuss an important principle in rational design of functional biological circuits: the reverse engineering design. We will use a research project that was conducted at Peking University for the International Genetic Engineering Machine Competition (iGEM) to illustrate the principle: synthesis a cell which has a semi-log dose-response to the environment. Through this work we try to demonstrate the potential application of network engineering in synthetic biology.

  2. Multilayer motif analysis of brain networks

    Science.gov (United States)

    Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito

    2017-04-01

    In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.

  3. An overview of data routing approaches for wireless sensor networks.

    Science.gov (United States)

    Anisi, Mohammad Hossein; Abdullah, Abdul Hanan; Razak, Shukor Abd; Ngadi, Md Asri

    2012-03-27

    Recent years have witnessed a growing interest in deploying large populations of microsensors that collaborate in a distributed manner to gather and process sensory data and deliver them to a sink node through wireless communications systems. Currently, there is a lot of interest in data routing for Wireless Sensor Networks (WSNs) due to their unique challenges compared to conventional routing in wired networks. In WSNs, each data routing approach follows a specific goal (goals) according to the application. Although the general goal of every data routing approach in WSNs is to extend the network lifetime and every approach should be aware of the energy level of the nodes, data routing approaches may focus on one (or some) specific goal(s) depending on the application. Thus, existing approaches can be categorized according to their routing goals. In this paper, the main goals of data routing approaches in sensor networks are described. Then, the best known and most recent data routing approaches in WSNs are classified and studied according to their specific goals.

  4. Capturing complexity: Mixing methods in the analysis of a European tobacco control policy network.

    Science.gov (United States)

    Weishaar, Heide; Amos, Amanda; Collin, Jeff

    Social network analysis (SNA), a method which can be used to explore networks in various contexts, has received increasing attention. Drawing on the development of European smoke-free policy, this paper explores how a mixed method approach to SNA can be utilised to investigate a complex policy network. Textual data from public documents, consultation submissions and websites were extracted, converted and analysed using plagiarism detection software and quantitative network analysis, and qualitative data from public documents and 35 interviews were thematically analysed. While the quantitative analysis enabled understanding of the network's structure and components, the qualitative analysis provided in-depth information about specific actors' positions, relationships and interactions. The paper establishes that SNA is suited to empirically testing and analysing networks in EU policymaking. It contributes to methodological debates about the antagonism between qualitative and quantitative approaches and demonstrates that qualitative and quantitative network analysis can offer a powerful tool for policy analysis.

  5. Impact of different dietary approaches on glycemic control and cardiovascular risk factors in patients with type 2 diabetes: a protocol for a systematic review and network meta-analysis.

    Science.gov (United States)

    Schwingshackl, Lukas; Chaimani, Anna; Hoffmann, Georg; Schwedhelm, Carolina; Boeing, Heiner

    2017-03-20

    Dietary advice is one of the cornerstones in the management of type 2 diabetes mellitus. The American Diabetes Association recommended a hypocaloric diet for overweight or obese adults with type 2 diabetes in order to induce weight loss. However, there is limited evidence on the optimal approaches to control hyperglycemia in type 2 diabetes patients. The aim of the present study is to assess the comparative efficacy of different dietary approaches on glycemic control and blood lipids in patients with type 2 diabetes mellitus in a systematic review including a standard pairwise and network meta-analysis of randomized trials. We will conduct searches in Cochrane Central Register of Controlled Trials (CENTRAL) on the Cochrane Library, PubMed (from 1966), and Google Scholar. Citations, abstracts, and relevant papers will be screened for eligibility by two reviewers independently. Randomized controlled trials (with a control group or randomized trials with at least two intervention groups) will be included if they meet the following criteria: (1) include type 2 diabetes mellitus, (2) include patients aged ≥18 years, (3) include dietary intervention (different type of diets: e.g., Mediterranean dietary pattern, low-carbohydrate diet, low-fat diet, vegetarian diet, high protein diet); either hypo, iso-caloric, or ad libitum diets, (4) minimum intervention period of 12 weeks. For each outcome measure of interest, random effects pairwise and network meta-analyses will be performed in order to determine the pooled relative effect of each intervention relative to every other intervention in terms of the post-intervention values (or mean differences between the changes from baseline value scores). Subgroup analyses are planned for study length, sample size, age, and sex. This systematic review will synthesize the available evidence on the comparative efficacy of different dietary approaches in the management of glycosylated hemoglobin (primary outcome), fasting glucose

  6. Developing intelligent sensor networks —a technological convergence approach

    OpenAIRE

    Vassev, Emil; Hinchey, Mike; Nixon, Paddy

    2010-01-01

    peer-reviewed We present a technological convergence approach to developing sensor networks capable of self-management. We use ASSL (Autonomic System Specification Language) to formally develop autonomous intelligent sensor nodes and DMF (Demand Migration Framework) to connect these nodes in a sensor network. ASSL provides constructs for modeling special self-management policies that drive the sensor nodes’ behavior and control the communication mechanism provided by DMF.

  7. 1st International Conference on Network Analysis

    CERN Document Server

    Kalyagin, Valery; Pardalos, Panos

    2013-01-01

    This volume contains a selection of contributions from the "First International Conference in Network Analysis," held at the University of Florida, Gainesville, on December 14-16, 2011. The remarkable diversity of fields that take advantage of Network Analysis makes the endeavor of gathering up-to-date material in a single compilation a useful, yet very difficult, task. The purpose of this volume is to overcome this difficulty by collecting the major results found by the participants and combining them in one easily accessible compilation. Network analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network is bringing together researchers, practitioners and other scientific communities from numerous fields such as Operations Research, Computer Science, Transportation, Energy, Social Sciences, and more. The contributions not only come from different fields, but also cover a broad range of topics relevant to the...

  8. F-MAP: A Bayesian approach to infer the gene regulatory network using external hints.

    Science.gov (United States)

    Shahdoust, Maryam; Pezeshk, Hamid; Mahjub, Hossein; Sadeghi, Mehdi

    2017-01-01

    The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches.

  9. Actor Network Theory Approach and its Application in Investigating Agricultural Climate Information System

    Directory of Open Access Journals (Sweden)

    Maryam Sharifzadeh

    2013-03-01

    Full Text Available Actor network theory as a qualitative approach to study complex social factors and process of socio-technical interaction provides new concepts and ideas to understand socio-technical nature of information systems. From the actor network theory viewpoint, agricultural climate information system is a network consisting of actors, actions and information related processes (production, transformation, storage, retrieval, integration, diffusion and utilization, control and management, and system mechanisms (interfaces and networks. Analysis of such systemsembody the identification of basic components and structure of the system (nodes –thedifferent sources of information production, extension, and users, and the understanding of how successfully the system works (interaction and links – in order to promote climate knowledge content and improve system performance to reach agricultural development. The present research attempted to introduce actor network theory as research framework based on network view of agricultural climate information system.

  10. Dynamic network-based epistasis analysis: Boolean examples

    Directory of Open Access Journals (Sweden)

    Eugenio eAzpeitia

    2011-12-01

    Full Text Available In this review we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the topologies of gene interactions infered. This has been acknowledged in several previous papers and reviews, but here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson (herein, classical epistasis, defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus. Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct gene interaction topologies are hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our review complements previous accounts, not

  11. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

    Energy Technology Data Exchange (ETDEWEB)

    Shi, Cindy

    2015-07-17

    The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

  12. Mathematical Analysis, A Straightforward Approach

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 4; Issue 4. Mathematical Analysis, A Straightforward Approach. K Rama Murthy. Book Review Volume 4 Issue 4 April 1999 pp 96-96. Fulltext. Click here to view fulltext PDF. Permanent link: http://www.ias.ac.in/article/fulltext/reso/004/04/0096-0096 ...

  13. Emulation Platform for Cyber Analysis of Wireless Communication Network Protocols

    Energy Technology Data Exchange (ETDEWEB)

    Van Leeuwen, Brian P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Eldridge, John M. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    Wireless networking and mobile communications is increasing around the world and in all sectors of our lives. With increasing use, the density and complexity of the systems increase with more base stations and advanced protocols to enable higher data throughputs. The security of data transported over wireless networks must also evolve with the advances in technologies enabling more capable wireless networks. However, means for analysis of the effectiveness of security approaches and implementations used on wireless networks are lacking. More specifically a capability to analyze the lower-layer protocols (i.e., Link and Physical layers) is a major challenge. An analysis approach that incorporates protocol implementations without the need for RF emissions is necessary. In this research paper several emulation tools and custom extensions that enable an analysis platform to perform cyber security analysis of lower layer wireless networks is presented. A use case of a published exploit in the 802.11 (i.e., WiFi) protocol family is provided to demonstrate the effectiveness of the described emulation platform.

  14. Non-coherent Network Coding: An Arbitrarily Varying Channel Approach

    OpenAIRE

    Jafari Siavoshani, Mahdi; Yang, Shenghao; Yeung, Raymond

    2012-01-01

    In this paper, we propose an “arbitrarily varying channel” (AVC) approach to study the capacity of non-coherent transmission in a network that employs randomized linear network coding. The network operation is modeled by a matrix channel over a finite field where the transfer matrix changes arbitrarily from time-slot to time-slot but up to a known distribution over its rank. By extending the AVC results to this setup, we characterize the capacity of such a non-coherent transmission scheme and s...

  15. Contingent approach to Internet-based supply network integration

    Science.gov (United States)

    Ho, Jessica; Boughton, Nick; Kehoe, Dennis; Michaelides, Zenon

    2001-10-01

    The Internet is playing an increasingly important role in enhancing the operations of supply networks as many organizations begin to recognize the benefits of Internet- enabled supply arrangements. However, the developments and applications to-date do not extend significantly beyond the dyadic model, whereas the real advantages are to be made with the external and network models to support a coordinated and collaborative based approach. The DOMAIN research group at the University of Liverpool is currently defining new Internet- enabled approaches to enable greater collaboration across supply chains. Different e-business models and tools are focusing on different applications. Using inappropriate e- business models, tools or techniques will bring negative results instead of benefits to all the tiers in the supply network. Thus there are a number of issues to be considered before addressing Internet based supply network integration, in particular an understanding of supply chain management, the emergent business models and evaluating the effects of deploying e-business to the supply network or a particular tier. It is important to utilize a contingent approach to selecting the right e-business model to meet the specific supply chain requirements. This paper addresses the issues and provides a case study on the indirect materials supply networks.

  16. A dynamical approach to identify vertices' centrality in complex networks

    Science.gov (United States)

    Guo, Long; Zhang, Wen-Yao; Luo, Zhong-Jie; Gao, Fu-Juan; Zhang, Yi-Cheng

    2017-12-01

    In this paper, we proposed a dynamical approach to assess vertices' centrality according to the synchronization process of the Kuramoto model. In our approach, the vertices' dynamical centrality is calculated based on the Difference of vertices' Synchronization Abilities (DSA), which are different from traditional centrality measurements that are related to the topological properties. Through applying our approach to complex networks with a clear community structure, we have calculated all vertices' dynamical centrality and found that vertices at the end of weak links have higher dynamical centrality. Meanwhile, we analyzed the robustness and efficiency of our dynamical approach through testing the probabilities that some known vital vertices were recognized. Finally, we applied our dynamical approach to identify community due to its satisfactory performance in assessing overlapping vertices. Our present work provides a new perspective and tools to understand the crucial role of heterogeneity in revealing the interplay between the dynamics and structure of complex networks.

  17. Real Analysis A Historical Approach

    CERN Document Server

    Stahl, Saul

    2011-01-01

    A provocative look at the tools and history of real analysis This new edition of Real Analysis: A Historical Approach continues to serve as an interesting read for students of analysis. Combining historical coverage with a superb introductory treatment, this book helps readers easily make the transition from concrete to abstract ideas. The book begins with an exciting sampling of classic and famous problems first posed by some of the greatest mathematicians of all time. Archimedes, Fermat, Newton, and Euler are each summoned in turn, illuminating the utility of infinite, power, and trigonome

  18. Simulated, Emulated, and Physical Investigative Analysis (SEPIA) of networked systems.

    Energy Technology Data Exchange (ETDEWEB)

    Burton, David P.; Van Leeuwen, Brian P.; McDonald, Michael James; Onunkwo, Uzoma A.; Tarman, Thomas David; Urias, Vincent E.

    2009-09-01

    This report describes recent progress made in developing and utilizing hybrid Simulated, Emulated, and Physical Investigative Analysis (SEPIA) environments. Many organizations require advanced tools to analyze their information system's security, reliability, and resilience against cyber attack. Today's security analysis utilize real systems such as computers, network routers and other network equipment, computer emulations (e.g., virtual machines) and simulation models separately to analyze interplay between threats and safeguards. In contrast, this work developed new methods to combine these three approaches to provide integrated hybrid SEPIA environments. Our SEPIA environments enable an analyst to rapidly configure hybrid environments to pass network traffic and perform, from the outside, like real networks. This provides higher fidelity representations of key network nodes while still leveraging the scalability and cost advantages of simulation tools. The result is to rapidly produce large yet relatively low-cost multi-fidelity SEPIA networks of computers and routers that let analysts quickly investigate threats and test protection approaches.

  19. Disconnected by design: analytic approach in treatment networks having no common comparator.

    Science.gov (United States)

    Goring, S M; Gustafson, P; Liu, Y; Saab, S; Cline, S K; Platt, R W

    2016-12-01

    In a network meta-analysis, comparators of interest are ideally connected either directly or via one or more common comparators. However, in some therapeutic areas, the evidence base can produce networks that are disconnected, in which there is neither direct evidence nor an indirect route for comparing certain treatments within the network. Disconnected networks may occur when there is no accepted standard of care, when there has been a major paradigm shift in treatment, when use of a standard of care or placebo is debated, when a product receives orphan drug designation, or when there is a large number of available treatments and many accepted standards of care. These networks pose a challenge to decision makers and clinicians who want to estimate the relative efficacy and safety of newly available agents against alternatives. A currently recommended approach is to insert a distribution for the unknown treatment effect(s) into a network meta-analysis model of treatment effect. In this paper, we describe this approach along with two alternative Bayesian models that can accommodate disconnected networks. Additionally, we present a theoretical framework to guide the choice between modeling approaches. This paper presents researchers with the tools and framework for selecting appropriate models for indirect comparison of treatment efficacies when challenged with a disconnected framework. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  20. Study of co-authorship network of papers in the Journal of Research in Medical Sciences using social network analysis

    Directory of Open Access Journals (Sweden)

    Firoozeh Zare-Farashbandi

    2014-01-01

    Full Text Available Background: Co-authorship is one of the most tangible forms of research collaboration. A co-authorship network is a social network in which the authors through participation in one or more publication through an indirect path have linked to each other. The present research using the social network analysis studied co-authorship network of 681 articles published in Journal of Research in Medical Sciences (JRMS during 2008-2012. Materials and Methods: The study was carried out with the scientometrics approach and using co-authorship network analysis of authors. The topology of the co-authorship network of 681 published articles in JRMS between 2008 and 2012 was analyzed using macro-level metrics indicators of network analysis such as density, clustering coefficient, components and mean distance. In addition, in order to evaluate the performance of each authors and countries in the network, the micro-level indicators such as degree centrality, closeness centrality and betweenness centrality as well as productivity index were used. The UCINET and NetDraw softwares were used to draw and analyze the co-authorship network of the papers. Results: The assessment of the authors productivity in this journal showed that the first ranks were belonged to only five authors, respectively. Furthermore, analysis of the co-authorship of the authors in the network demonstrated that in the betweenness centrality index, three authors of them had the good position in the network. They can be considered as the network leaders able to control the flow of information in the network compared with the other members based on the shortest paths. On the other hand, the key role of the network according to the productivity and centrality indexes was belonged to Iran, Malaysia and United States of America. Conclusion: Co-authorship network of JRMS has the characteristics of a small world network. In addition, the theory of 6° separation is valid in this network was also true.

  1. A systems biology-based approach to uncovering the molecular mechanisms underlying the effects of dragon's blood tablet in colitis, involving the integration of chemical analysis, ADME prediction, and network pharmacology.

    Science.gov (United States)

    Xu, Haiyu; Zhang, Yanqiong; Lei, Yun; Gao, Xiumei; Zhai, Huaqiang; Lin, Na; Tang, Shihuan; Liang, Rixin; Ma, Yan; Li, Defeng; Zhang, Yi; Zhu, Guangrong; Yang, Hongjun; Huang, Luqi

    2014-01-01

    Traditional Chinese medicine (TCM) is one of the oldest East Asian medical systems. The present study adopted a systems biology-based approach to provide new insights relating to the active constituents and molecular mechanisms underlying the effects of dragon's blood (DB) tablets for the treatment of colitis. This study integrated chemical analysis, prediction of absorption, distribution, metabolism, and excretion (ADME), and network pharmacology. Firstly, a rapid, reliable, and accurate ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry method was employed to identify 48 components of DB tablets. In silico prediction of the passive absorption of these compounds, based on Caco-2 cell permeability, and their P450 metabolism enabled the identification of 22 potentially absorbed components and 8 metabolites. Finally, networks were constructed to analyze interactions between these DB components/metabolites absorbed and their putative targets, and between the putative DB targets and known therapeutic targets for colitis. This study provided a great opportunity to deepen the understanding of the complex pharmacological mechanisms underlying the effects of DB in colitis treatment.

  2. A systems biology-based approach to uncovering the molecular mechanisms underlying the effects of dragon's blood tablet in colitis, involving the integration of chemical analysis, ADME prediction, and network pharmacology.

    Directory of Open Access Journals (Sweden)

    Haiyu Xu

    Full Text Available Traditional Chinese medicine (TCM is one of the oldest East Asian medical systems. The present study adopted a systems biology-based approach to provide new insights relating to the active constituents and molecular mechanisms underlying the effects of dragon's blood (DB tablets for the treatment of colitis. This study integrated chemical analysis, prediction of absorption, distribution, metabolism, and excretion (ADME, and network pharmacology. Firstly, a rapid, reliable, and accurate ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry method was employed to identify 48 components of DB tablets. In silico prediction of the passive absorption of these compounds, based on Caco-2 cell permeability, and their P450 metabolism enabled the identification of 22 potentially absorbed components and 8 metabolites. Finally, networks were constructed to analyze interactions between these DB components/metabolites absorbed and their putative targets, and between the putative DB targets and known therapeutic targets for colitis. This study provided a great opportunity to deepen the understanding of the complex pharmacological mechanisms underlying the effects of DB in colitis treatment.

  3. Trimming of mammalian transcriptional networks using network component analysis

    Directory of Open Access Journals (Sweden)

    Liao James C

    2010-10-01

    Full Text Available Abstract Background Network Component Analysis (NCA has been used to deduce the activities of transcription factors (TFs from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies. Results We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network. Conclusions The advantage of our new algorithm

  4. NEXCADE: perturbation analysis for complex networks.

    Directory of Open Access Journals (Sweden)

    Gitanjali Yadav

    Full Text Available Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the 'robust, yet fragile' nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html.

  5. Social network approaches to leadership: an integrative conceptual review.

    Science.gov (United States)

    Carter, Dorothy R; DeChurch, Leslie A; Braun, Michael T; Contractor, Noshir S

    2015-05-01

    Contemporary definitions of leadership advance a view of the phenomenon as relational, situated in specific social contexts, involving patterned emergent processes, and encompassing both formal and informal influence. Paralleling these views is a growing interest in leveraging social network approaches to study leadership. Social network approaches provide a set of theories and methods with which to articulate and investigate, with greater precision and rigor, the wide variety of relational perspectives implied by contemporary leadership theories. Our goal is to advance this domain through an integrative conceptual review. We begin by answering the question of why-Why adopt a network approach to study leadership? Then, we offer a framework for organizing prior research. Our review reveals 3 areas of research, which we term: (a) leadership in networks, (b) leadership as networks, and (c) leadership in and as networks. By clarifying the conceptual underpinnings, key findings, and themes within each area, this review serves as a foundation for future inquiry that capitalizes on, and programmatically builds upon, the insights of prior work. Our final contribution is to advance an agenda for future research that harnesses the confluent ideas at the intersection of leadership in and as networks. Leadership in and as networks represents a paradigm shift in leadership research-from an emphasis on the static traits and behaviors of formal leaders whose actions are contingent upon situational constraints, toward an emphasis on the complex and patterned relational processes that interact with the embedding social context to jointly constitute leadership emergence and effectiveness. (c) 2015 APA, all rights reserved.

  6. Neural Network Approach To Sensory Fusion

    Science.gov (United States)

    Pearson, John C.; Gelfand, Jack J.; Sullivan, W. E.; Peterson, Richard M.; Spence, Clay D.

    1988-08-01

    We present a neural network model for sensory fusion based on the design of the visual/acoustic target localiza-tion system of the barn owl. This system adaptively fuses its separate visual and acoustic representations of object position into a single joint representation used for head orientation. The building block in this system, as in much of the brain, is the neuronal map. Neuronal maps are large arrays of locally interconnected neurons that represent information in a map-like form, that is, parameter values are systematically encoded by the position of neural activation in the array. The computational load is distributed to a hierarchy of maps, and the computation is performed in stages by transforming the representation from map to map via the geometry of the projections between the maps and the local interactions within the maps. For example, azimuthal position is computed from the frequency and binaural phase information encoded in the signals of the acoustic sensors, while elevation is computed in a separate stream using binaural intensity information. These separate streams are merged in their joint projection onto the external nucleus of the inferior colliculus, a two dimensional array of cells which contains a map of acoustic space. This acoustic map, and the visual map of the retina, jointly project onto the optic tectum, creating a fused visual/acoustic representation of position in space that is used for object localization. In this paper we describe our mathematical model of the stage of visual/acoustic fusion in the optic tectum. The model assumes that the acoustic projection from the external nucleus onto the tectum is roughly topographic and one-to-many, while the visual projection from the retina onto the tectum is topographic and one-to-one. A simple process of self-organization alters the strengths of the acoustic connections, effectively forming a focused beam of strong acoustic connections whose inputs are coincident with the visual inputs

  7. Network graph analysis of category fluency testing.

    Science.gov (United States)

    Lerner, Alan J; Ogrocki, Paula K; Thomas, Peter J

    2009-03-01

    Category fluency is impaired early in Alzheimer disease (AD). Graph theory is a technique to analyze complex relationships in networks. Features of interest in network analysis include the number of nodes and edges, and variables related to their interconnectedness. Other properties important in network analysis are "small world properties" and "scale-free" properties. The small world property (popularized as the so-called "6 degrees of separation") arises when the majority of connections are local, but a number of connections are to distant nodes. Scale-free networks are characterized by the presence of a few nodes with many connections, and many more nodes with fewer connections. To determine if category fluency data can be analyzed using graph theory. To compare normal elderly, mild cognitive impairment (MCI) and AD network graphs, and characterize changes seen with increasing cognitive impairment. Category fluency results ("animals" recorded over 60 s) from normals (n=38), MCI (n=33), and AD (n=40) completing uniform data set evaluations were converted to network graphs of all unique cooccurring neighbors, and compared for network variables. For Normal, MCI and AD, mean clustering coefficients were 0.21, 0.22, 0.30; characteristic path lengths were 3.27, 3.17, and 2.65; small world properties decreased with increasing cognitive impairment, and all graphs showed scale-free properties. Rank correlations of the 25 commonest items ranged from 0.75 to 0.83. Filtering of low-degree nodes in normal and MCI graphs resulted in properties similar to the AD network graph. Network graph analysis is a promising technique for analyzing changes in category fluency. Our technique results in nonrandom graphs consistent with well-characterized properties for these types of graphs.

  8. Real analysis a constructive approach

    CERN Document Server

    Bridger, Mark

    2012-01-01

    A unique approach to analysis that lets you apply mathematics across a range of subjects This innovative text sets forth a thoroughly rigorous modern account of the theoretical underpinnings of calculus: continuity, differentiability, and convergence. Using a constructive approach, every proof of every result is direct and ultimately computationally verifiable. In particular, existence is never established by showing that the assumption of non-existence leads to a contradiction. The ultimate consequence of this method is that it makes sense-not just to math majors but also to students from a

  9. Integration of a systems biological network analysis and QTL results for biomass heterosis in Arabidopsis thaliana

    National Research Council Canada - National Science Library

    Andorf, Sandra; Meyer, Rhonda C; Selbig, Joachim; Altmann, Thomas; Repsilber, Dirk

    2012-01-01

    To contribute to a further insight into heterosis we applied an integrative analysis to a systems biological network approach and a quantitative genetics analysis towards biomass heterosis in early...

  10. A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

    Science.gov (United States)

    Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

    2015-04-01

    Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

  11. A Hybrid Heuristic Optimization Approach for Leak Detection in Pipe Networks Using Ordinal Optimization Approach and the Symbiotic Organism Search

    Directory of Open Access Journals (Sweden)

    Chao-Chih Lin

    2017-10-01

    Full Text Available A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA and the symbiotic organism search (SOS to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN, are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS. Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.

  12. Transcription regulatory networks analysis using CAGE

    KAUST Repository

    Tegnér, Jesper N.

    2009-10-01

    Mapping out cellular networks in general and transcriptional networks in particular has proved to be a bottle-neck hampering our understanding of biological processes. Integrative approaches fusing computational and experimental technologies for decoding transcriptional networks at a high level of resolution is therefore of uttermost importance. Yet, this is challenging since the control of gene expression in eukaryotes is a complex multi-level process influenced by several epigenetic factors and the fine interplay between regulatory proteins and the promoter structure governing the combinatorial regulation of gene expression. In this chapter we review how the CAGE data can be integrated with other measurements such as expression, physical interactions and computational prediction of regulatory motifs, which together can provide a genome-wide picture of eukaryotic transcriptional regulatory networks at a new level of resolution. © 2010 by Pan Stanford Publishing Pte. Ltd. All rights reserved.

  13. Making Connections: Using Social Network Analysis for Program Evaluation. Issue Brief. Number 1

    Science.gov (United States)

    Honeycutt, Todd

    2009-01-01

    Social network analysis (SNA) is a methodological approach to measuring and mapping relationships. It can be used to study whole networks, all of the ties within a defined group, or connections that individuals have in their personal communities. The resulting graph-based structures illustrate the composition and effectiveness of networks on a…

  14. Performance Analysis of 3G Communication Network

    Directory of Open Access Journals (Sweden)

    Toni Anwar

    2013-09-01

    Full Text Available In this project, third generation (3G technologies research had been carried out to design and optimization conditions for 3G network. The 3G wireless mobile communication networks are growing at an ever faster rate, and this is likely to continue in the foreseeable future. Some services such as e-mail, web browsing etc allow the transition of the network from circuit switched to packet switched operation, resulting in increased overall network performance. Higher reliability, better coverage and services, higher capacity, mobility management, and wireless multimedia are all parts of the network performance. Throughput and spectral efficiency are fundamental parameters in capacity planning for 3G cellular network deployments. This project investigates also the downlink (DL and uplink (UL throughput and spectral efficiency performance of the standard Universal Mobile Telecommunications system (UMTS system for different scenarios of user and different technologies. Power consumption comparison for different mobile technology is also discussed. The analysis can significantly help system engineers to obtain crucial performance characteristics of 3G network. At the end of the paper, coverage area of 3G from one of the mobile network in Malaysia is presented.

  15. Medical image analysis with artificial neural networks.

    Science.gov (United States)

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  16. Fast network centrality analysis using GPUs

    Directory of Open Access Journals (Sweden)

    Shi Zhiao

    2011-05-01

    Full Text Available Abstract Background With the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the key in leveraging the power of GPUs. Results We proposed an efficient data parallel formulation of the All-Pairs Shortest Path problem, which is the key component for shortest path-based centrality computation. A betweenness centrality algorithm built upon this formulation was developed and benchmarked against the most recent GPU-based algorithm. Speedup between 11 to 19% was observed in various simulated scale-free networks. We further designed three algorithms based on this core component to compute closeness centrality, eccentricity centrality and stress centrality. To make all these algorithms available to the research community, we developed a software package gpu-fan (GPU-based Fast Analysis of Networks for CUDA enabled GPUs. Speedup of 10-50× compared with CPU implementations was observed for simulated scale-free networks and real world biological networks. Conclusions gpu-fan provides a significant performance improvement for centrality computation in large-scale networks. Source code is available under the GNU Public License (GPL at http://bioinfo.vanderbilt.edu/gpu-fan/.

  17. Kinetic analysis of complex metabolic networks

    Energy Technology Data Exchange (ETDEWEB)

    Stephanopoulos, G. [MIT, Cambridge, MA (United States)

    1996-12-31

    A new methodology is presented for the analysis of complex metabolic networks with the goal of metabolite overproduction. The objective is to locate a small number of reaction steps in a network that have maximum impact on network flux amplification and whose rate can also be increased without functional network derangement. This method extends the concepts of Metabolic Control Analysis to groups of reactions and offers the means for calculating group control coefficients as measures of the control exercised by groups of reactions on the overall network fluxes and intracellular metabolite pools. It is further demonstrated that the optimal strategy for the effective increase of network fluxes, while maintaining an uninterrupted supply of intermediate metabolites, is through the coordinated amplification of multiple (as opposed to a single) reaction steps. Satisfying this requirement invokes the concept of the concentration control to coefficient, which emerges as a critical parameter in the identification of feasible enzymatic modifications with maximal impact on the network flux. A case study of aromatic aminoacid production is provided to illustrate these concepts.

  18. Network-based approaches to climate knowledge discovery

    Science.gov (United States)

    Budich, Reinhard; Nyberg, Per; Weigel, Tobias

    2011-11-01

    Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.

  19. Modeling pedestrian's conformity violation behavior: a complex network based approach.

    Science.gov (United States)

    Zhou, Zhuping; Hu, Qizhou; Wang, Wei

    2014-01-01

    Pedestrian injuries and fatalities present a problem all over the world. Pedestrian conformity violation behaviors, which lead to many pedestrian crashes, are common phenomena at the signalized intersections in China. The concepts and metrics of complex networks are applied to analyze the structural characteristics and evolution rules of pedestrian network about the conformity violation crossings. First, a network of pedestrians crossing the street is established, and the network's degree distributions are analyzed. Then, by using the basic idea of SI model, a spreading model of pedestrian illegal crossing behavior is proposed. Finally, through simulation analysis, pedestrian's illegal crossing behavior trends are obtained in different network structures and different spreading rates. Some conclusions are drawn: as the waiting time increases, more pedestrians will join in the violation crossing once a pedestrian crosses on red firstly. And pedestrian's conformity violation behavior will increase as the spreading rate increases.

  20. Green pathways: Metabolic network analysis of plant systems.

    Science.gov (United States)

    Dersch, Lisa Maria; Beckers, Veronique; Wittmann, Christoph

    2016-03-01

    Metabolic engineering of plants with enhanced crop yield and value-added compositional traits is particularly challenging as they probably exhibit the highest metabolic network complexity of all living organisms. Therefore, approaches of plant metabolic network analysis, which can provide systems-level understanding of plant physiology, appear valuable as guidance for plant metabolic engineers. Strongly supported by the sequencing of plant genomes, a number of different experimental and computational methods have emerged in recent years to study plant systems at various levels: from heterotrophic cell cultures to autotrophic entire plants. The present review presents a state-of-the-art toolbox for plant metabolic network analysis. Among the described approaches are different in silico modeling techniques, including flux balance analysis, elementary flux mode analysis and kinetic flux profiling, as well as different variants of experiments with plant systems which use radioactive and stable isotopes to determine in vivo plant metabolic fluxes. The fundamental principles of these techniques, the required data input and the obtained flux information are enriched by technical advices, specific to plants. In addition, pioneering and high-impacting findings of plant metabolic network analysis highlight the potential of the field. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

  1. Hierarchical polynomial network approach to automated target recognition

    Science.gov (United States)

    Kim, Richard Y.; Drake, Keith C.; Kim, Tony Y.

    1994-02-01

    A hierarchical recognition methodology using abductive networks at several levels of object recognition is presented. Abductive networks--an innovative numeric modeling technology using networks of polynomial nodes--results from nearly three decades of application research and development in areas including statistical modeling, uncertainty management, genetic algorithms, and traditional neural networks. The systems uses pixel-registered multisensor target imagery provided by the Tri-Service Laser Radar sensor. Several levels of recognition are performed using detection, classification, and identification, each providing more detailed object information. Advanced feature extraction algorithms are applied at each recognition level for target characterization. Abductive polynomial networks process feature information and situational data at each recognition level, providing input for the next level of processing. An expert system coordinates the activities of individual recognition modules and enables employment of heuristic knowledge to overcome the limitations provided by a purely numeric processing approach. The approach can potentially overcome limitations of current systems such as catastrophic degradation during unanticipated operating conditions while meeting strict processing requirements. These benefits result from implementation of robust feature extraction algorithms that do not take explicit advantage of peculiar characteristics of the sensor imagery, and the compact, real-time processing capability provided by abductive polynomial networks.

  2. Social Network Analyses and Nutritional Behavior: An Integrated Modeling Approach

    Directory of Open Access Journals (Sweden)

    Alistair McNair Senior

    2016-01-01

    Full Text Available Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent advances in nutrition research, combining state-space models of nutritional geometry with agent-based models of systems biology, show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a tangible and practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit agent-based models that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition. Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interaction in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.

  3. Exploration Knowledge Sharing Networks Using Social Network Analysis Methods

    Directory of Open Access Journals (Sweden)

    Győző Attila Szilágyi

    2017-10-01

    Full Text Available Knowledge sharing within organization is one of the key factor for success. The organization, where knowledge sharing takes place faster and more efficiently, is able to adapt to changes in the market environment more successfully, and as a result, it may obtain a competitive advantage. Knowledge sharing in an organization is carried out through formal and informal human communication contacts during work. This forms a multi-level complex network whose quantitative and topological characteristics largely determine how quickly and to what extent the knowledge travels within organization. The study presents how different networks of knowledge sharing in the organization can be explored by means of network analysis methods through a case study, and which role play the properties of these networks in fast and sufficient spread of knowledge in organizations. The study also demonstrates the practical applications of our research results. Namely, on the basis of knowledge sharing educational strategies can be developed in an organization, and further, competitiveness of an organization may increase due to those strategies’ application.

  4. Using Granular-Evidence-Based Adaptive Networks for Sensitivity Analysis

    OpenAIRE

    Vališevskis, A.

    2002-01-01

    This paper considers the possibility of using adaptive networks for sensitivity analysis. Adaptive network that processes fuzzy granules is described. The adaptive network training algorithm can be used for sensitivity analysis of decision making models. Furthermore, a case study concerning sensitivity analysis is described, which shows in what way the adaptive network can be used for sensitivity analysis.

  5. Architecture Analysis of an FPGA-Based Hopfield Neural Network

    Directory of Open Access Journals (Sweden)

    Miguel Angelo de Abreu de Sousa

    2014-01-01

    Full Text Available Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.

  6. Analysis and Comparison of Typical Models within Distribution Network Design

    DEFF Research Database (Denmark)

    Jørgensen, Hans Jacob; Larsen, Allan; Madsen, Oli B.G.

    This paper investigates the characteristics of typical optimisation models within Distribution Network Design. During the paper fourteen models known from the literature will be thoroughly analysed. Through this analysis a schematic approach to categorisation of distribution network design models...... for educational purposes. Furthermore, the paper can be seen as a practical introduction to network design modelling as well as a being an art manual or recipe when constructing such a model....... are covered in the categorisation include fixed vs. general networks, specialised vs. general nodes, linear vs. nonlinear costs, single vs. multi commodity, uncapacitated vs. capacitated activities, single vs. multi modal and static vs. dynamic. The models examined address both strategic and tactical planning...

  7. A Novel Modulation Classification Approach Using Gabor Filter Network

    Science.gov (United States)

    Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

    2014-01-01

    A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603

  8. A Novel Modulation Classification Approach Using Gabor Filter Network

    Directory of Open Access Journals (Sweden)

    Sajjad Ahmed Ghauri

    2014-01-01

    Full Text Available A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN. The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR on AWGN channel.

  9. Jamming in Mobile Networks: A Game-Theoretic Approach

    Science.gov (United States)

    2013-03-01

    general treatment of multiplayer differential games was presented by Starr and Ho [16], Leitmann [36], Vaisbord and Zhukovskiy [65], Zhukovskiy and...REPORT Jamming in mobile networks: A game -theoretic approach. 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: In this paper, we address the problem of...model the intrusion as a pursuit-evasion game between a mobile jammer and a team of agents. First, we consider a differential game -theoretic approach

  10. A Cognitive Approach to Network Monitoring in Heterogeneous Environments

    DEFF Research Database (Denmark)

    Mihovska, Albena D.

    2007-01-01

    of information (QoI). QoI means QoS while all the requirements for dependability, security, privacy and trust are satisfied at the highest possible level. This work proposes and describes an approach to network monitoring in a heterogeneous communication environment based on use of cognitive techniques...... for efficient resource allocation, provisioning of network resources or for detection of security violations into the traditional network monitoring approach. The paper describes the cognitive monitoring architecture, the required physical and logical entities, and their functionalities. Further, the paper......Abstract— Introducing intelligence by means of cognition for managing, protecting, processing, and delivering of information in mobile communication systems is the way towards ubiquitous, converged and secure communications. In this context, this paper introduces the concept of quality...

  11. Using artificial neural network approach for modelling rainfall–runoff ...

    Indian Academy of Sciences (India)

    Home; Journals; Journal of Earth System Science; Volume 122; Issue 2. Using artificial neural network approach for ... In Taiwan, owing to the nonuniform temporal and spatial distribution of rainfall and high mountains all over the country, hydrologic systems are very complex. Therefore, preventing and controlling flood ...

  12. Stochastic approaches for product recovery network design: a case study

    NARCIS (Netherlands)

    O.L. Listes (Ovidiu); R. Dekker (Rommert)

    2001-01-01

    textabstractIncreased uncertainty is one of the characteristics of product recovery networks. In particular the strategic design of their logistic infrastructure has to take uncertain information into account. In this paper we present stochastic programming based approaches by which a deterministic

  13. Approaches in Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, Sandro

    Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at

  14. Evaluating Action Learning: A Critical Realist Complex Network Theory Approach

    Science.gov (United States)

    Burgoyne, John G.

    2010-01-01

    This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating. This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around…

  15. Combining network analysis with Cognitive Work Analysis: insights into social organisational and cooperation analysis.

    Science.gov (United States)

    Houghton, Robert J; Baber, Chris; Stanton, Neville A; Jenkins, Daniel P; Revell, Kirsten

    2015-01-01

    Cognitive Work Analysis (CWA) allows complex, sociotechnical systems to be explored in terms of their potential configurations. However, CWA does not explicitly analyse the manner in which person-to-person communication is performed in these configurations. Consequently, the combination of CWA with Social Network Analysis provides a means by which CWA output can be analysed to consider communication structure. The approach is illustrated through a case study of a military planning team. The case study shows how actor-to-actor and actor-to-function mapping can be analysed, in terms of centrality, to produce metrics of system structure under different operating conditions. In this paper, a technique for building social network diagrams from CWA is demonstrated.The approach allows analysts to appreciate the potential impact of organisational structure on a command system.

  16. Social network analysis of study environment

    Directory of Open Access Journals (Sweden)

    Blaženka Divjak

    2010-06-01

    Full Text Available Student working environment influences student learning and achievement level. In this respect social aspects of students’ formal and non-formal learning play special role in learning environment. The main research problem of this paper is to find out if students' academic performance influences their position in different students' social networks. Further, there is a need to identify other predictors of this position. In the process of problem solving we use the Social Network Analysis (SNA that is based on the data we collected from the students at the Faculty of Organization and Informatics, University of Zagreb. There are two data samples: in the basic sample N=27 and in the extended sample N=52. We collected data on social-demographic position, academic performance, learning and motivation styles, student status (full-time/part-time, attitudes towards individual and teamwork as well as informal cooperation. Afterwards five different networks (exchange of learning materials, teamwork, informal communication, basic and aggregated social network were constructed. These networks were analyzed with different metrics and the most important were betweenness, closeness and degree centrality. The main result is, firstly, that the position in a social network cannot be forecast only by academic success and, secondly, that part-time students tend to form separate groups that are poorly connected with full-time students. In general, position of a student in social networks in study environment can influence student learning as well as her/his future employability and therefore it is worthwhile to be investigated.

  17. What binds us when with whom? Content and structure in social network analysis

    NARCIS (Netherlands)

    Stokman, Frans N.

    2004-01-01

    I first introduce the reader to the concept of social capital and the different exchange approaches in social network analysis. Exchange approaches make explicit that there is always both cooperation and competition in and between relationships as well as between networks. Subsequently I review a

  18. Timescale analysis of rule-based biochemical reaction networks.

    Science.gov (United States)

    Klinke, David J; Finley, Stacey D

    2012-01-01

    The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed on reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of interleukin-12 (IL-12) signaling in naïve CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based on the available data. The analysis correctly predicted that reactions associated with Janus Kinase 2 and Tyrosine Kinase 2 binding to their corresponding receptor exist at a pseudo-equilibrium. By contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. Copyright © 2011 American Institute of Chemical Engineers (AIChE).

  19. Network analysis of eight industrial symbiosis systems

    Science.gov (United States)

    Zhang, Yan; Zheng, Hongmei; Shi, Han; Yu, Xiangyi; Liu, Gengyuan; Su, Meirong; Li, Yating; Chai, Yingying

    2016-06-01

    Industrial symbiosis is the quintessential characteristic of an eco-industrial park. To divide parks into different types, previous studies mostly focused on qualitative judgments, and failed to use metrics to conduct quantitative research on the internal structural or functional characteristics of a park. To analyze a park's structural attributes, a range of metrics from network analysis have been applied, but few researchers have compared two or more symbioses using multiple metrics. In this study, we used two metrics (density and network degree centralization) to compare the degrees of completeness and dependence of eight diverse but representative industrial symbiosis networks. Through the combination of the two metrics, we divided the networks into three types: weak completeness, and two forms of strong completeness, namely "anchor tenant" mutualism and "equality-oriented" mutualism. The results showed that the networks with a weak degree of completeness were sparse and had few connections among nodes; for "anchor tenant" mutualism, the degree of completeness was relatively high, but the affiliated members were too dependent on core members; and the members in "equality-oriented" mutualism had equal roles, with diverse and flexible symbiotic paths. These results revealed some of the systems' internal structure and how different structures influenced the exchanges of materials, energy, and knowledge among members of a system, thereby providing insights into threats that may destabilize the network. Based on this analysis, we provide examples of the advantages and effectiveness of recent improvement projects in a typical Chinese eco-industrial park (Shandong Lubei).

  20. Using Network Analysis to Understand Knowledge Mobilization in a Community-based Organization

    OpenAIRE

    Gainforth, Heather L.; Latimer-Cheung, Amy E.; Moore, Spencer; Athanasopoulos, Peter; Martin Ginis, Kathleen A.

    2014-01-01

    Background Knowledge mobilization (KM) has been described as putting research in the hands of research users. Network analysis is an empirical approach that has potential for examining the complex process of knowledge mobilization within community-based organizations (CBOs). Yet, conducting a network analysis in a CBO presents challenges. Purpose The purpose of this paper is to demonstrate the value and feasibility of using network analysis as a method for understanding knowledge mob...

  1. Introduction to Focus Issue: Quantitative Approaches to Genetic Networks

    Science.gov (United States)

    Albert, Réka; Collins, James J.; Glass, Leon

    2013-06-01

    All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

  2. Automated Analysis of Security in Networking Systems

    DEFF Research Database (Denmark)

    Buchholtz, Mikael

    2004-01-01

    It has for a long time been a challenge to built secure networking systems. One way to counter this problem is to provide developers of software applications for networking systems with easy-to-use tools that can check security properties before the applications ever reach the marked. These tools...... will both help raise the general level of awareness of the problems and prevent the most basic flaws from occurring. This thesis contributes to the development of such tools. Networking systems typically try to attain secure communication by applying standard cryptographic techniques. In this thesis...... attacks, and attacks launched by insiders. Finally, the perspectives for the application of the analysis techniques are discussed, thereby, coming a small step closer to providing developers with easy- to-use tools for validating the security of networking applications....

  3. A statistical analysis of UK financial networks

    Science.gov (United States)

    Chu, J.; Nadarajah, S.

    2017-04-01

    In recent years, with a growing interest in big or large datasets, there has been a rise in the application of large graphs and networks to financial big data. Much of this research has focused on the construction and analysis of the network structure of stock markets, based on the relationships between stock prices. Motivated by Boginski et al. (2005), who studied the characteristics of a network structure of the US stock market, we construct network graphs of the UK stock market using same method. We fit four distributions to the degree density of the vertices from these graphs, the Pareto I, Fréchet, lognormal, and generalised Pareto distributions, and assess the goodness of fit. Our results show that the degree density of the complements of the market graphs, constructed using a negative threshold value close to zero, can be fitted well with the Fréchet and lognormal distributions.

  4. Visualization and Analysis of Complex Covert Networks

    DEFF Research Database (Denmark)

    Memon, Bisharat

    This report discusses and summarize the results of my work so far in relation to my Ph.D. project entitled "Visualization and Analysis of Complex Covert Networks". The focus of my research is primarily on development of methods and supporting tools for visualization and analysis of networked...... systems that are covert and hence inherently complex. My Ph.D. is positioned within the wider framework of CrimeFighter project. The framework envisions a number of key knowledge management processes that are involved in the workflow, and the toolbox provides supporting tools to assist human end...

  5. Sentiment analysis on smoking in social networks.

    Science.gov (United States)

    Sofean, Mustafa; Smith, Matthew

    2013-01-01

    Online social networks play a vital role in daily life to share the opinions or behaviors on different topics. The data of social networks can be used to understand health-related behaviors. In this work, we used Twitter status updates to survey of smoking behaviors among the users. We introduce approach to classify the sentiment of smoke-related tweets into positive and negative tweets. The classifier is based on the Support Vector Machines (SVMs) and can achieve high accuracy up to 86%.

  6. Hydraulic Analysis of Water Distribution Network Using Shuffled Complex Evolution

    Directory of Open Access Journals (Sweden)

    Naser Moosavian

    2014-01-01

    Full Text Available Hydraulic analysis of water distribution networks is an important problem in civil engineering. A widely used approach in steady-state analysis of water distribution networks is the global gradient algorithm (GGA. However, when the GGA is applied to solve these networks, zero flows cause a computation failure. On the other hand, there are different mathematical formulations for hydraulic analysis under pressure-driven demand and leakage simulation. This paper introduces an optimization model for the hydraulic analysis of water distribution networks using a metaheuristic method called shuffled complex evolution (SCE algorithm. In this method, applying if-then rules in the optimization model is a simple way in handling pressure-driven demand and leakage simulation, and there is no need for an initial solution vector which must be chosen carefully in many other procedures if numerical convergence is to be achieved. The overall results indicate that the proposed method has the capability of handling various pipe networks problems without changing in model or mathematical formulation. Application of SCE in optimization model can lead to accurate solutions in pipes with zero flows. Finally, it can be concluded that the proposed method is a suitable alternative optimizer challenging other methods especially in terms of accuracy.

  7. A Monte Carlo EM approach for partially observable diffusion processes: theory and applications to neural networks.

    Science.gov (United States)

    Movellan, Javier R; Mineiro, Paul; Williams, R J

    2002-07-01

    We present a Monte Carlo approach for training partially observable diffusion processes. We apply the approach to diffusion networks, a stochastic version of continuous recurrent neural networks. The approach is aimed at learning probability distributions of continuous paths, not just expected values. Interestingly, the relevant activation statistics used by the learning rule presented here are inner products in the Hilbert space of square integrable functions. These inner products can be computed using Hebbian operations and do not require backpropagation of error signals. Moreover, standard kernel methods could potentially be applied to compute such inner products. We propose that the main reason that recurrent neural networks have not worked well in engineering applications (e.g., speech recognition) is that they implicitly rely on a very simplistic likelihood model. The diffusion network approach proposed here is much richer and may open new avenues for applications of recurrent neural networks. We present some analysis and simulations to support this view. Very encouraging results were obtained on a visual speech recognition task in which neural networks outperformed hidden Markov models.

  8. In silico Biochemical Reaction Network Analysis (IBRENA): a package for simulation and analysis of reaction networks.

    Science.gov (United States)

    Liu, Gang; Neelamegham, Sriram

    2008-04-15

    We present In silico Biochemical Reaction Network Analysis (IBRENA), a software package which facilitates multiple functions including cellular reaction network simulation and sensitivity analysis (both forward and adjoint methods), coupled with principal component analysis, singular-value decomposition and model reduction. The software features a graphical user interface that aids simulation and plotting of in silico results. While the primary focus is to aid formulation, testing and reduction of theoretical biochemical reaction networks, the program can also be used for analysis of high-throughput genomic and proteomic data. The software package, manual and examples are available at http://www.eng.buffalo.edu/~neel/ibrena

  9. Network analysis shining light on parasite ecology and diversity.

    Science.gov (United States)

    Poulin, Robert

    2010-10-01

    The vast number of species making up natural communities, and the myriad interactions among them, pose great difficulties for the study of community structure, dynamics and stability. Borrowed from other fields, network analysis is making great inroads in community ecology and is only now being applied to host-parasite interactions. It allows a complex system to be examined in its entirety, as opposed to one or a few components at a time. This review explores what network analysis is and how it can be used to investigate parasite ecology. It also summarizes the first findings to emerge from network analyses of host-parasite interactions and identifies promising future directions made possible by this approach. Copyright © 2010 Elsevier Ltd. All rights reserved.

  10. On a Registration-Based Approach to Sensor Network Localization

    Science.gov (United States)

    Sanyal, Rajat; Jaiswal, Monika; Chaudhury, Kunal Narayan

    2017-10-01

    We consider a registration-based approach for localizing sensor networks from range measurements. This is based on the assumption that one can find overlapping cliques spanning the network. That is, for each sensor, one can identify geometric neighbors for which all inter-sensor ranges are known. Such cliques can be efficiently localized using multidimensional scaling. However, since each clique is localized in some local coordinate system, we are required to register them in a global coordinate system. In other words, our approach is based on transforming the localization problem into a problem of registration. In this context, the main contributions are as follows. First, we describe an efficient method for partitioning the network into overlapping cliques. Second, we study the problem of registering the localized cliques, and formulate a necessary rigidity condition for uniquely recovering the global sensor coordinates. In particular, we present a method for efficiently testing rigidity, and a proposal for augmenting the partitioned network to enforce rigidity. A recently proposed semidefinite relaxation of global registration is used for registering the cliques. We present simulation results on random and structured sensor networks to demonstrate that the proposed method compares favourably with state-of-the-art methods in terms of run-time, accuracy, and scalability.

  11. Neural network approaches to dynamic collision-free trajectory generation.

    Science.gov (United States)

    Yang, S X; Meng, M

    2001-01-01

    In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.

  12. Organizational network analysis for two networks in the Washington State Department of Transportation.

    Science.gov (United States)

    2010-10-01

    Organizational network analysis (ONA) consists of gathering data on information sharing and : connectivity in a group, calculating network measures, creating network maps, and using this : information to analyze and improve the functionality of the g...

  13. Developing an intelligence analysis process through social network analysis

    Science.gov (United States)

    Waskiewicz, Todd; LaMonica, Peter

    2008-04-01

    Intelligence analysts are tasked with making sense of enormous amounts of data and gaining an awareness of a situation that can be acted upon. This process can be extremely difficult and time consuming. Trying to differentiate between important pieces of information and extraneous data only complicates the problem. When dealing with data containing entities and relationships, social network analysis (SNA) techniques can be employed to make this job easier. Applying network measures to social network graphs can identify the most significant nodes (entities) and edges (relationships) and help the analyst further focus on key areas of concern. Strange developed a model that identifies high value targets such as centers of gravity and critical vulnerabilities. SNA lends itself to the discovery of these high value targets and the Air Force Research Laboratory (AFRL) has investigated several network measures such as centrality, betweenness, and grouping to identify centers of gravity and critical vulnerabilities. Using these network measures, a process for the intelligence analyst has been developed to aid analysts in identifying points of tactical emphasis. Organizational Risk Analyzer (ORA) and Terrorist Modus Operandi Discovery System (TMODS) are the two applications used to compute the network measures and identify the points to be acted upon. Therefore, the result of leveraging social network analysis techniques and applications will provide the analyst and the intelligence community with more focused and concentrated analysis results allowing them to more easily exploit key attributes of a network, thus saving time, money, and manpower.

  14. [Causal analysis approaches in epidemiology].

    Science.gov (United States)

    Dumas, O; Siroux, V; Le Moual, N; Varraso, R

    2014-02-01

    Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the

  15. Phylodynamic analysis of a viral infection network

    Directory of Open Access Journals (Sweden)

    Teiichiro eShiino

    2012-07-01

    Full Text Available Viral infections by sexual and droplet transmission routes typically spread through a complex host-to-host contact network. Clarifying the transmission network and epidemiological parameters affecting the variations and dynamics of a specific pathogen is a major issue in the control of infectious diseases. However, conventional methods such as interview and/or classical phylogenetic analysis of viral gene sequences have inherent limitations and often fail to detect infectious clusters and transmission connections. Recent improvements in computational environments now permit the analysis of large datasets. In addition, novel analytical methods have been developed that serve to infer the evolutionary dynamics of virus genetic diversity using sample date information and sequence data. This type of framework, termed phylodynamics, helps connect some of the missing links on viral transmission networks, which are often hard to detect by conventional methods of epidemiology. With sufficient number of sequences available, one can use this new inference method to estimate theoretical epidemiological parameters such as temporal distributions of the primary infection, fluctuation of the pathogen population size, basic reproductive number, and the mean time span of disease infectiousness. Transmission networks estimated by this framework often have the properties of a scale-free network, which are characteristic of infectious and social communication processes. Network analysis based on phylodynamics has alluded to various suggestions concerning the infection dynamics associated with a given community and/or risk behavior. In this review, I will summarize the current methods available for identifying the transmission network using phylogeny, and present an argument on the possibilities of applying the scale-free properties to these existing frameworks.

  16. Link removal for the control of stochastically evolving epidemics over networks: A comparison of approaches

    Science.gov (United States)

    Brandeau, Margaret L.

    2015-01-01

    For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two “preventive” approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two “reactive” approaches (S-I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdős-Rényi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S-I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S-I edge centrality algorithm is a function of both network structure (higher for unstructured Erdős-Rényi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing

  17. Multiple neural network approaches to clinical expert systems

    Science.gov (United States)

    Stubbs, Derek F.

    1990-08-01

    We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

  18. A comparative performance evaluation of neural network based approach for sentiment classification of online reviews

    Directory of Open Access Journals (Sweden)

    G. Vinodhini

    2016-01-01

    Full Text Available The aim of sentiment classification is to efficiently identify the emotions expressed in the form of text messages. Machine learning methods for sentiment classification have been extensively studied, due to their predominant classification performance. Recent studies suggest that ensemble based machine learning methods provide better performance in classification. Artificial neural networks (ANNs are rarely being investigated in the literature of sentiment classification. This paper compares neural network based sentiment classification methods (back propagation neural network (BPN, probabilistic neural network (PNN & homogeneous ensemble of PNN (HEN using varying levels of word granularity as features for feature level sentiment classification. They are validated using a dataset of product reviews collected from the Amazon reviews website. An empirical analysis is done to compare results of ANN based methods with two statistical individual methods. The methods are evaluated using five different quality measures and results show that the homogeneous ensemble of the neural network method provides better performance. Among the two neural network approaches used, probabilistic neural networks (PNNs outperform in classifying the sentiment of the product reviews. The integration of neural network based sentiment classification methods with principal component analysis (PCA as a feature reduction technique provides superior performance in terms of training time also.

  19. Neighborhoods and adolescent health-risk behavior: an ecological network approach.

    Science.gov (United States)

    Browning, Christopher R; Soller, Brian; Jackson, Aubrey L

    2015-01-01

    This study integrates insights from social network analysis, activity space perspectives, and theories of urban and spatial processes to present an novel approach to neighborhood effects on health-risk behavior among youth. We suggest spatial patterns of neighborhood residents' non-home routines may be conceptualized as ecological, or "eco"-networks, which are two-mode networks that indirectly link residents through socio-spatial overlap in routine activities. We further argue structural configurations of eco-networks are consequential for youth's behavioral health. In this study we focus on a key structural feature of eco-networks--the neighborhood-level extent to which household dyads share two or more activity locations, or eco-network reinforcement--and its association with two dimensions of health-risk behavior, substance use and delinquency/sexual activity. Using geographic data on non-home routine activity locations among respondents from the Los Angeles Family and Neighborhood Survey (L.A.FANS), we constructed neighborhood-specific eco-networks by connecting sampled households to "activity clusters," which are sets of spatially-proximate activity locations. We then measured eco-network reinforcement and examined its association with dimensions of adolescent health risk behavior employing a sample of 830 youth ages 12-17 nested in 65 census tracts. We also examined whether neighborhood-level social processes (collective efficacy and intergenerational closure) mediate the association between eco-network reinforcement and the outcomes considered. Results indicated eco-network reinforcement exhibits robust negative associations with both substance use and delinquency/sexual activity scales. Eco-network reinforcement effects were not explained by potential mediating variables. In addition to introducing a novel theoretical and empirical approach to neighborhood effects on youth, our findings highlight the importance of intersecting conventional routines for

  20. Classification and Analysis of Computer Network Traffic

    DEFF Research Database (Denmark)

    Bujlow, Tomasz

    2014-01-01

    for traffic classification, which can be used for nearly real-time processing of big amounts of data using affordable CPU and memory resources. Other questions are related to methods for real-time estimation of the application Quality of Service (QoS) level based on the results obtained by the traffic......Traffic monitoring and analysis can be done for multiple different reasons: to investigate the usage of network resources, assess the performance of network applications, adjust Quality of Service (QoS) policies in the network, log the traffic to comply with the law, or create realistic models...... classifier. This thesis is focused on topics connected with traffic classification and analysis, while the work on methods for QoS assessment is limited to defining the connections with the traffic classification and proposing a general algorithm. We introduced the already known methods for traffic...

  1. Bandwidth Analysis of Smart Meter Network Infrastructure

    DEFF Research Database (Denmark)

    Balachandran, Kardi; Olsen, Rasmus Løvenstein; Pedersen, Jens Myrup

    2014-01-01

    Advanced Metering Infrastructure (AMI) is a net-work infrastructure in Smart Grid, which links the electricity customers to the utility company. This network enables smart services by making it possible for the utility company to get an overview of their customers power consumption and also control...... to utilize smart meters and which existing broadband network technologies can facilitate this smart meter service. Initially, scenarios for smart meter infrastructure are identified. The paper defines abstraction models which cover the AMI scenarios. When the scenario has been identified a general overview...... of the bandwidth requirements are analysed. For this analysis the assumptions and limitations are defined. The results obtained by the analysis show, that the amount of data collected and transferred by a smart meter is very low compared to the available bandwidth of most internet connections. The results show...

  2. Co-morbid obsessive-compulsive disorder and depression: a Bayesian network approach.

    Science.gov (United States)

    McNally, R J; Mair, P; Mugno, B L; Riemann, B C

    2017-05-01

    Obsessive-compulsive disorder (OCD) is often co-morbid with depression. Using the methods of network analysis, we computed two networks that disclose the potentially causal relationships among symptoms of these two disorders in 408 adult patients with primary OCD and co-morbid depression symptoms. We examined the relationship between the symptoms constituting these syndromes by computing a (regularized) partial correlation network via the graphical LASSO procedure, and a directed acyclic graph (DAG) via a Bayesian hill-climbing algorithm. The results suggest that the degree of interference and distress associated with obsessions, and the degree of interference associated with compulsions, are the chief drivers of co-morbidity. Moreover, activation of the depression cluster appears to occur solely through distress associated with obsessions activating sadness - a key symptom that 'bridges' the two syndromic clusters in the DAG. Bayesian analysis can expand the repertoire of network analytic approaches to psychopathology. We discuss clinical implications and limitations of our findings.

  3. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Science.gov (United States)

    Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

    2017-01-01

    Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  4. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Directory of Open Access Journals (Sweden)

    Shameng Wen

    Full Text Available Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  5. Behavioral modeling approach for optical communication network design

    Science.gov (United States)

    Vuorinen, Kimmo; Jacquemod, Gilles; Gaffiot, Frederic; Seassal, Christian

    1997-12-01

    An optical communication networks can be divided in two levels: communication level, which defines the protocols, the control and the management of the networks and physical level formed by photonic and electronic components in order to transmit and receive the data between different nodes of the network. Traditionally, these two levels are considered separately in the optical communication network design process. This can lead to an erroneous or non-ideal networks implementation, due to the fact that the communication and physical levels are not independent. For example, in WDM communication network the maximum achievable data rate is limited not only by the networks protocol, but depends also on the implementation of the physical level: tuning delay of the optical multiplexers. Also the lack of the possibilities for co-verification of the communication and the physical levels together could lead to misinterpretations between the designers of the different levels and thus induce design faults. Since the prototyping is extremely expensive and time consuming, an integrated simulation of both communication and physical levels is necessary, at least in some extend. In this paper, a behavioral modeling approach that allows a co- simulation of the communication and the physical levels is presented. It is based on the use of a VHDL-AMS-like hardware description language, dedicated to electronic system modeling, but also suitable for modeling and simulation of non- electronic and mixed-domain systems. The behavioral models for photonic and electronic components, as well as the software are integrated in a unique simulator in order to co-simulate the communication (control) and the physical level (data path) of a WDM optical communication network.

  6. PROACTIVE APPROACH TO THE INCIDENT AND PROBLEM MANAGEMENT IN COMMUNICATION NETWORKS

    Directory of Open Access Journals (Sweden)

    Vjeran Strahonja

    2007-06-01

    Full Text Available Proactive approach to communication network maintenance has the capability of enhancing the integrity and reliability of communication networks, as well as of reducing maintenance costs and overall number of incidents. This paper presents approaches to problem and incident prevention with the help of root-cause analysis, aligning that with the goal to foresee software performance. Implementation of proactive approach requires recognition of enterprise's current level of maintenance better insights into available approaches and tools, as well as their comparison, interoperability, integration and further development. The approach we are proposing and elaborating in this paper lies on the construction of a metamodel of the problem management of information technology, particularly the proactive problem management. The metamodel is derived from the original ITIL specification and presented in an object-oriented fashion by using structure (class diagrams conform to UML notation. Based on current research, appropriate metrics based on the concept of Key Performance Indicators is suggested.

  7. Social Network Analysis: Applied Tool to Enhance Effective Collaboration between Child Protection Organisations by Revealing and Strengthening Work Relationships

    National Research Council Canada - National Science Library

    Beáta Dávid

    2013-01-01

    .... The qualitative approach was complemented by social network analysis. Revealing the mechanism based on the actors' perception on how the child protection network operates, we identifi ed and named the strengths and weaknesses of its structure...

  8. Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study

    National Research Council Canada - National Science Library

    Edge, Rhiannon; Heath, Joseph; Rowlingson, Barry; Keegan, Thomas J; Isba, Rachel

    2015-01-01

    .... We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students' beliefs about vaccination behaviours...

  9. Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study: e0140085

    National Research Council Canada - National Science Library

    Rhiannon Edge; Joseph Heath; Barry Rowlingson; Thomas J Keegan; Rachel Isba

    2015-01-01

    .... Methods We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students' beliefs...

  10. Diversity Performance Analysis on Multiple HAP Networks

    Directory of Open Access Journals (Sweden)

    Feihong Dong

    2015-06-01

    Full Text Available One of the main design challenges in wireless sensor networks (WSNs is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV. In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF and cumulative distribution function (CDF of the received signal-to-noise ratio (SNR are derived. In addition, the average symbol error rate (ASER with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques.

  11. An Iterative Approach for the Optimization of Pavement Maintenance Management at the Network Level

    Science.gov (United States)

    Torres-Machí, Cristina; Chamorro, Alondra; Videla, Carlos; Yepes, Víctor

    2014-01-01

    Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic) and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods) have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed. Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners on when and how to apply available methods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach. PMID:24741352

  12. An Iterative Approach for the Optimization of Pavement Maintenance Management at the Network Level

    Directory of Open Access Journals (Sweden)

    Cristina Torres-Machí

    2014-01-01

    Full Text Available Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed. Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners on when and how to apply available methods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach.

  13. Mixed Methods Analysis of Enterprise Social Networks

    DEFF Research Database (Denmark)

    Behrendt, Sebastian; Richter, Alexander; Trier, Matthias

    2014-01-01

    The increasing use of enterprise social networks (ESN) generates vast amounts of data, giving researchers and managerial decision makers unprecedented opportunities for analysis. However, more transparency about the available data dimensions and how these can be combined is needed to yield accurate...

  14. Nonlinear Time Series Analysis via Neural Networks

    Science.gov (United States)

    Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

    This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

  15. Combining morphological analysis and Bayesian networks for ...

    African Journals Online (AJOL)

    Morphological analysis (MA) and Bayesian networks (BN) are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of a qualitative ...

  16. A graph-based network-vulnerability analysis system

    Energy Technology Data Exchange (ETDEWEB)

    Swiler, L.P.; Phillips, C. [Sandia National Labs., Albuquerque, NM (United States); Gaylor, T. [3M, Austin, TX (United States). Visual Systems Div.

    1998-01-01

    This report presents a graph-based approach to network vulnerability analysis. The method is flexible, allowing analysis of attacks from both outside and inside the network. It can analyze risks to a specific network asset, or examine the universe of possible consequences following a successful attack. The analysis system requires as input a database of common attacks, broken into atomic steps, specific network configuration and topology information, and an attacker profile. The attack information is matched with the network configuration information and an attacker profile to create a superset attack graph. Nodes identify a stage of attack, for example the class of machines the attacker has accessed and the user privilege level he or she has compromised. The arcs in the attack graph represent attacks or stages of attacks. By assigning probabilities of success on the arcs or costs representing level-of-effort for the attacker, various graph algorithms such as shortest-path algorithms can identify the attack paths with the highest probability of success.

  17. A graph-based system for network-vulnerability analysis

    Energy Technology Data Exchange (ETDEWEB)

    Swiler, L.P.; Phillips, C.

    1998-06-01

    This paper presents a graph-based approach to network vulnerability analysis. The method is flexible, allowing analysis of attacks from both outside and inside the network. It can analyze risks to a specific network asset, or examine the universe of possible consequences following a successful attack. The graph-based tool can identify the set of attack paths that have a high probability of success (or a low effort cost) for the attacker. The system could be used to test the effectiveness of making configuration changes, implementing an intrusion detection system, etc. The analysis system requires as input a database of common attacks, broken into atomic steps, specific network configuration and topology information, and an attacker profile. The attack information is matched with the network configuration information and an attacker profile to create a superset attack graph. Nodes identify a stage of attack, for example the class of machines the attacker has accessed and the user privilege level he or she has compromised. The arcs in the attack graph represent attacks or stages of attacks. By assigning probabilities of success on the arcs or costs representing level-of-effort for the attacker, various graph algorithms such as shortest-path algorithms can identify the attack paths with the highest probability of success.

  18. A graph-based network-vulnerability analysis system

    Energy Technology Data Exchange (ETDEWEB)

    Swiler, L.P.; Phillips, C.; Gaylor, T.

    1998-05-03

    This paper presents a graph based approach to network vulnerability analysis. The method is flexible, allowing analysis of attacks from both outside and inside the network. It can analyze risks to a specific network asset, or examine the universe of possible consequences following a successful attack. The analysis system requires as input a database of common attacks, broken into atomic steps, specific network configuration and topology information, and an attacker profile. The attack information is matched with the network configuration information and an attacker profile to create a superset attack graph. Nodes identify a stage of attack, for example the class of machines the attacker has accessed and the user privilege level he or she has compromised. The arcs in the attack graph represent attacks or stages of attacks. By assigning probabilities of success on the arcs or costs representing level of effort for the attacker, various graph algorithms such as shortest path algorithms can identify the attack paths with the highest probability of success.

  19. Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools.

    Science.gov (United States)

    Miryala, Sravan Kumar; Anbarasu, Anand; Ramaiah, Sudha

    2017-11-09

    Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks. Copyright © 2017. Published by Elsevier B.V.

  20. Socio-technical networks: how a technology studies approach may help to solve problems related to technical change

    NARCIS (Netherlands)

    Elzen, Boelem; Enserink, Bert; Enserink, B.; Smit, Willem A.

    1996-01-01

    This paper is motivated by a desire to deal with the problematic aspects of technical development. To achieve this, we need a new approach to the analysis of socio-technical change. In this paper we develop such an approach, called the `Socio-Technical Networks' (STN) approach. The basic concepts of

  1. Exploration of Online Culture Through Network Analysis of Wikipedia.

    Science.gov (United States)

    Park, Sung Joo; Kim, Jong Woo; Lee, Hong Joo; Park, Hyunjung; Han, Deugcheon; Gloor, Peter

    2015-11-01

    Understanding online culture is becoming crucial in the global and connected world. Contrary to conventional attitudinal surveys used in cultural research, this study uses the approach of directly observing culture-specific behavior that emerges from online collaboration on the Internet. The editing data of Wikipedia were analyzed in 12 languages. Distinctive cultural dimensions were identified, including collectivism, extraversion, boldness, and egalitarianism. Using network analysis, the language-framed cultural factors were extracted as an emergent phenomenon in the virtual world.

  2. Analysis of complex networks from biology to linguistics

    CERN Document Server

    Dehmer, Matthias

    2009-01-01

    Mathematical problems such as graph theory problems are of increasing importance for the analysis of modelling data in biomedical research such as in systems biology, neuronal network modelling etc. This book follows a new approach of including graph theory from a mathematical perspective with specific applications of graph theory in biomedical and computational sciences. The book is written by renowned experts in the field and offers valuable background information for a wide audience.

  3. Signal propagation in cortical networks: a digital signal processing approach.

    Science.gov (United States)

    Rodrigues, Francisco Aparecido; da Fontoura Costa, Luciano

    2009-01-01

    This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.

  4. Large-Scale Road Network Vulnerability Analysis

    OpenAIRE

    Jenelius, Erik

    2010-01-01

    Disruptions in the transport system can have severe impacts for affected individuals, businesses and the society as a whole. In this research, vulnerability is seen as the risk of unplanned system disruptions, with a focus on large, rare events. Vulnerability analysis aims to provide decision support regarding preventive and restorative actions, ideally as an integrated part of the planning process.The thesis specifically develops the methodology for vulnerability analysis of road networks an...

  5. Time series analysis of temporal networks

    Science.gov (United States)

    Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

    2016-01-01

    A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

  6. Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach.

    Science.gov (United States)

    Cheng, Feixiong; Liu, Chuang; Shen, Bairong; Zhao, Zhongming

    2016-08-26

    Cancer is increasingly recognized as a cellular system phenomenon that is attributed to the accumulation of genetic or epigenetic alterations leading to the perturbation of the molecular network architecture. Elucidation of network properties that can characterize tumor initiation and progression, or pinpoint the molecular targets related to the drug sensitivity or resistance, is therefore of critical importance for providing systems-level insights into tumorigenesis and clinical outcome in the molecularly targeted cancer therapy. In this study, we developed a network-based framework to quantitatively examine cellular network heterogeneity and modularity in cancer. Specifically, we constructed gene co-expressed protein interaction networks derived from large-scale RNA-Seq data across 8 cancer types generated in The Cancer Genome Atlas (TCGA) project. We performed gene network entropy and balanced versus unbalanced motif analysis to investigate cellular network heterogeneity and modularity in tumor versus normal tissues, different stages of progression, and drug resistant versus sensitive cancer cell lines. We found that tumorigenesis could be characterized by a significant increase of gene network entropy in all of the 8 cancer types. The ratio of the balanced motifs in normal tissues is higher than that of tumors, while the ratio of unbalanced motifs in tumors is higher than that of normal tissues in all of the 8 cancer types. Furthermore, we showed that network entropy could be used to characterize tumor progression and anticancer drug responses. For example, we found that kinase inhibitor resistant cancer cell lines had higher entropy compared to that of sensitive cell lines using the integrative analysis of microarray gene expression and drug pharmacological data collected from the Genomics of Drug Sensitivity in Cancer database. In addition, we provided potential network-level evidence that smoking might increase cancer cellular network heterogeneity and

  7. Coauthorship networks: A directed network approach considering the order and number of coauthors

    CERN Document Server

    Kim, Jinseok

    2015-01-01

    In many scientific fields, the order of coauthors on a paper conveys information about each individual's contribution to a piece of joint work. We argue that in prior network analyses of coauthorship networks, the information on ordering has been insufficiently considered because ties between authors are typically symmetrized. This is basically the same as assuming that each co-author has contributed equally to a paper. We introduce a solution to this problem by adopting a coauthorship credit allocation model proposed by Kim and Diesner (2014), which in its core conceptualizes co-authoring as a directed, weighted, and self-looped network. We test and validate our application of the adopted framework based on a sample data of 861 authors who have published in the journal Psychometrika. Results suggest that this novel sociometric approach can complement traditional measures based on undirected networks and expand insights into coauthoring patterns such as the hierarchy of collaboration among scholars. As anothe...

  8. Patients with Schizophrenia Fail to Up-Regulate Task-Positive and Down-Regulate Task-Negative Brain Networks: An fMRI Study Using an ICA Analysis Approach.

    Science.gov (United States)

    Nygård, Merethe; Eichele, Tom; Løberg, Else-Marie; Jørgensen, Hugo A; Johnsen, Erik; Kroken, Rune A; Berle, Jan Øystein; Hugdahl, Kenneth

    2012-01-01

    Recent research suggests that the cerebral correlates of cognitive deficits in schizophrenia are nested in the activity of widespread, inter-regional networks rather than being restricted to any specific brain location. One of the networks that have received focus lately is the default mode network. Parts of this network have been reported as hyper-activated in schizophrenia patients (SZ) during rest and during task performance compared to healthy controls (HC), although other parts have been found to be hypo-activated. In contrast to this network, task-positive networks have been reported as hypo-activated compared in SZ during task performance. However, the results are mixed, with, e.g., the dorsolateral prefrontal cortex showing both hyper- and hypo-activation in SZ. In this study we were interested in signal increase and decrease differences between a group of SZ and HC in cortical networks, assuming that the regulatory dynamics of alternating task-positive and task-negative neuronal processes are aberrant in SZ. We compared 31 SZ to age- and gender-matched HC, and used fMRI and independent component analysis (ICA) in order to identify relevant networks. We selected the independent components (ICs) with the largest signal intensity increases (STG, insula, supplementary motor cortex, anterior cingulate cortex, and MTG) and decreases (fusiform gyri, occipital lobe, PFC, cingulate, precuneus, and angular gyrus) in response to a dichotic auditory cognitive task. These ICs were then tested for group differences. Our findings showed deficient up-regulation of the executive network and a corresponding deficit in the down-regulation of the anterior default mode, or effort network during task performance in SZ when compared with HC. These findings may indicate a deficit in the dynamics of alternating task-dependent and task-independent neuronal processes in SZ. The results may cast new light on the mechanisms underlying cognitive deficits in schizophrenia, and may be of

  9. A Spatial Clustering Approach for Stochastic Fracture Network Modelling

    Science.gov (United States)

    Seifollahi, S.; Dowd, P. A.; Xu, C.; Fadakar, A. Y.

    2014-07-01

    Fracture network modelling plays an important role in many application areas in which the behaviour of a rock mass is of interest. These areas include mining, civil, petroleum, water and environmental engineering and geothermal systems modelling. The aim is to model the fractured rock to assess fluid flow or the stability of rock blocks. One important step in fracture network modelling is to estimate the number of fractures and the properties of individual fractures such as their size and orientation. Due to the lack of data and the complexity of the problem, there are significant uncertainties associated with fracture network modelling in practice. Our primary interest is the modelling of fracture networks in geothermal systems and, in this paper, we propose a general stochastic approach to fracture network modelling for this application. We focus on using the seismic point cloud detected during the fracture stimulation of a hot dry rock reservoir to create an enhanced geothermal system; these seismic points are the conditioning data in the modelling process. The seismic points can be used to estimate the geographical extent of the reservoir, the amount of fracturing and the detailed geometries of fractures within the reservoir. The objective is to determine a fracture model from the conditioning data by minimizing the sum of the distances of the points from the fitted fracture model. Fractures are represented as line segments connecting two points in two-dimensional applications or as ellipses in three-dimensional (3D) cases. The novelty of our model is twofold: (1) it comprises a comprehensive fracture modification scheme based on simulated annealing and (2) it introduces new spatial approaches, a goodness-of-fit measure for the fitted fracture model, a measure for fracture similarity and a clustering technique for proposing a locally optimal solution for fracture parameters. We use a simulated dataset to demonstrate the application of the proposed approach

  10. Coordination between Subway and Urban Space: A Networked Approach

    Directory of Open Access Journals (Sweden)

    Lei Mao

    2014-05-01

    Full Text Available This paper selects Changsha as a case study and constructs the models of the subway network and the urban spatial network by using planning data. In the network models, the districts of Changsha are regarded as nodes and the connections between each pair of districts are regarded as edges. The method is based on quantitative analysis of the node weights and the edge weights, which are defined in the complex network theory. And the structures of subway and urban space are visualized in the form of networks. Then, through analyzing the discrepancy coefficients of the corresponding nodes and edges, the paper carries out a comparison between the two networks to evaluate the coordination. The results indicate that only 21.4% of districts and 13.2% of district connections have a rational coordination. Finally, the strategies are put forward for optimization, which suggest adjusting subway transit density, regulating land-use intensity and planning new mass transits for the uncoordinated parts.

  11. Semantic network analysis of vaccine sentiment in online social media.

    Science.gov (United States)

    Kang, Gloria J; Ewing-Nelson, Sinclair R; Mackey, Lauren; Schlitt, James T; Marathe, Achla; Abbas, Kaja M; Swarup, Samarth

    2017-06-22

    To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines. Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood. We constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment. The semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits. Semantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage

  12. Social network analysis to cluster sociobibliometric information

    Directory of Open Access Journals (Sweden)

    Jorge Ricardo Vivas

    Full Text Available This paper examines the benefits of using Social Network Analysis in the field of sociobibliometric exploration. There are considered practical and conceptual limits and reaches. The proposal is illustrated through a study about a journals network of behavior modification by Peiró and Carpintero (1981. In this context it is shown the utility of using reticular properties of Density, Centrality, Betweenness, Power and Clusterig as indicators that allow obtaining novel and complementary information to the one extracted by the classic methods of bibliometric exploration.

  13. Capacity analysis of vehicular communication networks

    CERN Document Server

    Lu, Ning

    2013-01-01

    This SpringerBrief focuses on the network capacity analysis of VANETs, a key topic as fundamental guidance on design and deployment of VANETs is very limited. Moreover, unique characteristics of VANETs impose distinguished challenges on such an investigation. This SpringerBrief first introduces capacity scaling laws for wireless networks and briefly reviews the prior arts in deriving the capacity of VANETs. It then studies the unicast capacity considering the socialized mobility model of VANETs. With vehicles communicating based on a two-hop relaying scheme, the unicast capacity bound is deriv

  14. Historical Network Analysis of the Web

    DEFF Research Database (Denmark)

    Brügger, Niels

    2013-01-01

    of the online web has for a number of years gained currency within Internet studies. However, the combination of these two phenomena—historical network analysis of material in web archives—can at best be characterized as an emerging new area of study. Most of the methodological challenges within this new area...... at the Danish parliamentary elections in 2011, 2007, and 2001. As the Internet grows older historical studies of networks on the web will probably become more widespread and therefore it may be about time to begin debating the methodological challenges within this emerging field....

  15. Network analysis: A new way of understanding psychopathology?

    Science.gov (United States)

    Fonseca-Pedrero, Eduardo

    Current taxonomic systems are based on a descriptive and categorical approach where psychopathological symptoms and signs are caused by a hypothetical underlying mental disorder. In order to circumvent the limitations of classification systems, it is necessary to incorporate new conceptual and psychometric models that allow to understand, analyze and intervene in psychopathological phenomena from another perspective. The main goal was to present a new approach called network analysis for its application in the field of psychopathology. First of all, a brief introduction where psychopathological disorders are conceived as complex dynamic systems was carried out. Key concepts, as well as the different types of networks and the procedures for their estimation, are discussed. Following this, centrality measures, important for the understanding of the network as well as to examine the relevance of the variables within the network were addressed. These factors were then exemplified by estimating a network of self-reported psychopathological symptoms in a representative sample of adolescents. Finally, a brief recapitulation is made and future lines of research are discussed. Copyright © 2017 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

  16. Neural Network Approach to Locating Cryptography in Object Code

    Energy Technology Data Exchange (ETDEWEB)

    Jason L. Wright; Milos Manic

    2009-09-01

    Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (Neural Net for Locating Cryptography) is presented and results of applying this system to various libraries are described.

  17. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  18. Toward a Behavioral Approach to Privacy for Online Social Networks

    Science.gov (United States)

    Banks, Lerone D.; Wu, S. Felix

    We examine the correlation between user interactions and self reported information revelation preferences for users of the popular Online Social Network (OSN), Facebook. Our primary goal is to explore the use of indicators of tie strength to inform localized, per-user privacy preferences for users and their ties within OSNs. We examine the limitations of such an approach and discuss future plans to incorporate this approach into the development of an automated system for helping users define privacy policy. As part of future work, we discuss how to define/expand policy to the entire social network. We also present additional collected data similar to other studies such as perceived tie strength and information revelation preferences for OSN users.

  19. MACD-Based Motion Detection Approach in Heterogeneous Networks

    Directory of Open Access Journals (Sweden)

    Chen Yung-Mu

    2008-01-01

    Full Text Available Abstract Optimizing the balance between handoff quality and power consumption is a great challenge for seamless mobile communications in wireless networks. Traditional proactive schemes continuously monitor available access networks and exercise handoff. Although such schemes achieve good handoff quality, they consume much power because all interfaces must remain on all the time. To save power, the reactive schemes use fixed RSS thresholds to determine when to search for a new available access network. However, since they do not consider user motion, these approaches require that all interfaces be turned on even when a user is stationary, and they tend initiate excessive unnecessary handoffs. To address this problem, this research presents a novel motion-aware scheme called network discovery with motion detection (NDMD to improve handoff quality and minimize power consumption. The NDMD first applies a moving average convergence divergence (MACD scheme to analyze received signal strength (RSS samples of the current active interface. These results are then used to estimate user's motion. The proposed NDMD scheme adds very little computing overhead to a mobile terminal (MT and can be easily incorporated into existing schemes. The simulation results in this study showed that NDMD can quickly track user motion state without a positioning system and perform network discovery rapidly enough to achieve a much lower handoff-dropping rate with less power consumption.

  20. MACD-Based Motion Detection Approach in Heterogeneous Networks

    Directory of Open Access Journals (Sweden)

    Chih-Hung Hsu

    2008-09-01

    Full Text Available Optimizing the balance between handoff quality and power consumption is a great challenge for seamless mobile communications in wireless networks. Traditional proactive schemes continuously monitor available access networks and exercise handoff. Although such schemes achieve good handoff quality, they consume much power because all interfaces must remain on all the time. To save power, the reactive schemes use fixed RSS thresholds to determine when to search for a new available access network. However, since they do not consider user motion, these approaches require that all interfaces be turned on even when a user is stationary, and they tend initiate excessive unnecessary handoffs. To address this problem, this research presents a novel motion-aware scheme called network discovery with motion detection (NDMD to improve handoff quality and minimize power consumption. The NDMD first applies a moving average convergence divergence (MACD scheme to analyze received signal strength (RSS samples of the current active interface. These results are then used to estimate user's motion. The proposed NDMD scheme adds very little computing overhead to a mobile terminal (MT and can be easily incorporated into existing schemes. The simulation results in this study showed that NDMD can quickly track user motion state without a positioning system and perform network discovery rapidly enough to achieve a much lower handoff-dropping rate with less power consumption.

  1. An activities-based approach to network management : An explorative study

    NARCIS (Netherlands)

    Manser, Kristina; Hillebrand, Bas; Klein Woolthuis, R.J.A.; Ziggers, Gerrit Willem; Driessen, Paul H.; Bloemer, Josée

    2016-01-01

    Over the last few decades, the industrial marketing literature and the business network literature have promoted a holistic approach to marketing and provided a framework for understanding interorganizational networks. However, our understanding of how interorganizational networks govern themselves

  2. An activities-based approach to network management: An explorative study

    NARCIS (Netherlands)

    Manser, K.; Hillebrand, B.; Klein Woolthuis, R.J.A.; Ziggers, G.W.; Driessen, P.H.; Bloemer, J.M.M.; Klein Woolthuis, R.

    2016-01-01

    Over the last few decades, the industrial marketing literature and the business network literature have promoted a holistic approach to marketing and provided a framework for understanding interorganizational networks. However, our understanding of how interorganizational networks govern themselves

  3. Livelihood diversification in tropical coastal communities: a network-based approach to analyzing 'livelihood landscapes'.

    Science.gov (United States)

    Cinner, Joshua E; Bodin, Orjan

    2010-08-11

    Diverse livelihood portfolios are frequently viewed as a critical component of household economies in developing countries. Within the context of natural resources governance in particular, the capacity of individual households to engage in multiple occupations has been shown to influence important issues such as whether fishers would exit a declining fishery, how people react to policy, the types of resource management systems that may be applicable, and other decisions about natural resource use. This paper uses network analysis to provide a novel methodological framework for detailed systemic analysis of household livelihood portfolios. Paying particular attention to the role of natural resource-based occupations such as fisheries, we use network analyses to map occupations and their interrelationships- what we refer to as 'livelihood landscapes'. This network approach allows for the visualization of complex information about dependence on natural resources that can be aggregated at different scales. We then examine how the role of natural resource-based occupations changes along spectra of socioeconomic development and population density in 27 communities in 5 western Indian Ocean countries. Network statistics, including in- and out-degree centrality, the density of the network, and the level of network centralization are compared along a multivariate index of community-level socioeconomic development and a gradient of human population density. The combination of network analyses suggests an increase in household-level specialization with development for most occupational sectors, including fishing and farming, but that at the community-level, economies remained diversified. The novel modeling approach introduced here provides for various types of livelihood portfolio analyses at different scales of social aggregation. Our livelihood landscapes approach provides insights into communities' dependencies and usages of natural resources, and shows how patterns of

  4. Shared Leadership In Work Teams: A Social Network Approach

    OpenAIRE

    JUAN CARLOS PASTOR; MARGARITA MAYO

    2002-01-01

    (WP10/02 Clave pdf) In the past few years, the concept of leadership has shifted from the solitary leader to the team as a potential source of leadership. This shift from a single person to a "shared leadership" model requires new concepts and methods to capture the nature and structure of leadership by teams (Yukl, 1998). In this chapter, we argue that a social network approach helps to provide the conceptual framework and methodological tools to support a shared leadership perspective.

  5. Evolutionary Trends of Developer Coordination: A Network Approach

    OpenAIRE

    Joblin, Mitchell; Apel, Sven; Mauerer, Wolfgang

    2015-01-01

    Software evolution is a fundamental process that transcends the realm of technical artifacts and permeates the entire organizational structure of a software project. By means of a longitudinal empirical study of 18 large open-source projects, we examine and discuss the evolutionary principles that govern the coordination of developers. By applying a network-analytic approach, we found that the implicit and self-organizing structure of developer coordination is ubiquitously described by non-ra...

  6. Mathematical Analysis of Urban Spatial Networks

    CERN Document Server

    Blanchard, Philippe

    2009-01-01

    Cities can be considered to be among the largest and most complex artificial networks created by human beings. Due to the numerous and diverse human-driven activities, urban network topology and dynamics can differ quite substantially from that of natural networks and so call for an alternative method of analysis. The intent of the present monograph is to lay down the theoretical foundations for studying the topology of compact urban patterns, using methods from spectral graph theory and statistical physics. These methods are demonstrated as tools to investigate the structure of a number of real cities with widely differing properties: medieval German cities, the webs of city canals in Amsterdam and Venice, and a modern urban structure such as found in Manhattan. Last but not least, the book concludes by providing a brief overview of possible applications that will eventually lead to a useful body of knowledge for architects, urban planners and civil engineers.

  7. GEOMORPHOLOGIC ANALYSIS OF DRAINAGE NETWORKS ON MARS

    Directory of Open Access Journals (Sweden)

    KERESZTURI ÁKOS

    2012-06-01

    Full Text Available Altogether 327 valleys and their 314 cross-sectional profiles were analyzed on Mars, including width, depth, length, eroded volume, drainage and spatial density, as well as the network structure.According to this systematic analysis, five possible drainage network types were identified such as (a small valleys, (b integrated small valleys, (c individual, medium-sized valleys, (d unconfined,anastomosing outflow valleys, and (e confined outflow valleys. Measuring their various morphometric parameters, these five networks differ from each other in terms of parameters of the eroded volume, drainage density and depth values. This classification is more detailed than those described in the literature previously and correlated to several numerical parameters for the first time.These different types were probably formed during different periods of the evolution of Mars, and sprung from differently localized water sources, and they could be correlated to similar fluvialnetwork types from the Earth.

  8. A network analysis of Sibiu County, Romania

    CERN Document Server

    Grama, Cristina-Nicol

    2013-01-01

    Network science methods have proved to be able to provide useful insights from both a theoretical and a practical point of view in that they can better inform governance policies in complex dynamic environments. The tourism research community has provided an increasing number of works that analyse destinations from a network science perspective. However, most of the studies refer to relatively small samples of actors and linkages. With this note we provide a full network study, although at a preliminary stage, that reports a complete analysis of a Romanian destination (Sibiu). Our intention is to increase the set of similar studies with the aim of supporting the investigations in structural and dynamical characteristics of tourism destinations.

  9. Intentional risk management through complex networks analysis

    CERN Document Server

    Chapela, Victor; Moral, Santiago; Romance, Miguel

    2015-01-01

    This book combines game theory and complex networks to examine intentional technological risk through modeling. As information security risks are in constant evolution,  the methodologies and tools to manage them must evolve to an ever-changing environment. A formal global methodology is explained  in this book, which is able to analyze risks in cyber security based on complex network models and ideas extracted from the Nash equilibrium. A risk management methodology for IT critical infrastructures is introduced which provides guidance and analysis on decision making models and real situations. This model manages the risk of succumbing to a digital attack and assesses an attack from the following three variables: income obtained, expense needed to carry out an attack, and the potential consequences for an attack. Graduate students and researchers interested in cyber security, complex network applications and intentional risk will find this book useful as it is filled with a number of models, methodologies a...

  10. Hierarchical brain networks active in approach and avoidance goal pursuit

    Directory of Open Access Journals (Sweden)

    Jeffrey Martin Spielberg

    2013-06-01

    Full Text Available Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal pursuit processes (e.g., motivation has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity vital to goal pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.

  11. Reconstructing networks of pathways via significance analysis of their intersections

    Directory of Open Access Journals (Sweden)

    Francesconi Mirko

    2008-04-01

    Full Text Available Abstract Background Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statistical test. This problem is typically approached by applying post hoc corrections to control the false discovery rate, without taking into account prior biological knowledge. Pathway or gene ontology analysis can provide an alternative way to relax the significance threshold applied to single genes and may lead to a better biological interpretation. Results Here we propose a new analysis method based on the study of networks of pathways. These networks are reconstructed considering both the significance of single pathways (network nodes and the intersection between them (links. We apply this method for the reconstruction of networks of pathways to two gene expression datasets: the first one obtained from a c-Myc rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein; the second one obtained from the comparison of Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia derived from bone marrow samples. Conclusion Our method extends statistical models that have been recently adopted for the significance analysis of functional groups of genes to infer links between these groups. We show that groups of genes at the interface between different pathways can be considered as relevant even if the pathways they belong to are not significant by themselves.

  12. Network Analysis of Human Genes Influencing Susceptibility to Mycobacterial Infections.

    Directory of Open Access Journals (Sweden)

    Ettie M Lipner

    Full Text Available Tuberculosis and nontuberculous mycobacterial infections constitute a high burden of pulmonary disease in humans, resulting in over 1.5 million deaths per year. Building on the premise that genetic factors influence the instance, progression, and defense of infectious disease, we undertook a systems biology approach to investigate relationships among genetic factors that may play a role in increased susceptibility or control of mycobacterial infections. We combined literature and database mining with network analysis and pathway enrichment analysis to examine genes, pathways, and networks, involved in the human response to Mycobacterium tuberculosis and nontuberculous mycobacterial infections. This approach allowed us to examine functional relationships among reported genes, and to identify novel genes and enriched pathways that may play a role in mycobacterial susceptibility or control. Our findings suggest that the primary pathways and genes influencing mycobacterial infection control involve an interplay between innate and adaptive immune proteins and pathways. Signaling pathways involved in autoimmune disease were significantly enriched as revealed in our networks. Mycobacterial disease susceptibility networks were also examined within the context of gene-chemical relationships, in order to identify putative drugs and nutrients with potential beneficial immunomodulatory or anti-mycobacterial effects.

  13. Singular Perturbation Analysis and Gene Regulatory Networks with Delay

    Science.gov (United States)

    Shlykova, Irina; Ponosov, Arcady

    2009-09-01

    There are different ways of how to model gene regulatory networks. Differential equations allow for a detailed description of the network's dynamics and provide an explicit model of the gene concentration changes over time. Production and relative degradation rate functions used in such models depend on the vector of steeply sloped threshold functions which characterize the activity of genes. The most popular example of the threshold functions comes from the Boolean network approach, where the threshold functions are given by step functions. The system of differential equations becomes then piecewise linear. The dynamics of this system can be described very easily between the thresholds, but not in the switching domains. For instance this approach fails to analyze stationary points of the system and to define continuous solutions in the switching domains. These problems were studied in [2], [3], but the proposed model did not take into account a time delay in cellular systems. However, analysis of real gene expression data shows a considerable number of time-delayed interactions suggesting that time delay is essential in gene regulation. Therefore, delays may have a great effect on the dynamics of the system presenting one of the critical factors that should be considered in reconstruction of gene regulatory networks. The goal of this work is to apply the singular perturbation analysis to certain systems with delay and to obtain an analog of Tikhonov's theorem, which provides sufficient conditions for constracting the limit system in the delay case.

  14. Risk prediction model: Statistical and artificial neural network approach

    Science.gov (United States)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  15. A neural network approach to dynamic task assignment of multirobots.

    Science.gov (United States)

    Zhu, Anmin; Yang, Simon X

    2006-09-01

    In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.

  16. Network and adaptive system of systems modeling and analysis.

    Energy Technology Data Exchange (ETDEWEB)

    Lawton, Craig R.; Campbell, James E. Dr. (.; .); Anderson, Dennis James; Eddy, John P.

    2007-05-01

    This report documents the results of an LDRD program entitled ''Network and Adaptive System of Systems Modeling and Analysis'' that was conducted during FY 2005 and FY 2006. The purpose of this study was to determine and implement ways to incorporate network communications modeling into existing System of Systems (SoS) modeling capabilities. Current SoS modeling, particularly for the Future Combat Systems (FCS) program, is conducted under the assumption that communication between the various systems is always possible and occurs instantaneously. A more realistic representation of these communications allows for better, more accurate simulation results. The current approach to meeting this objective has been to use existing capabilities to model network hardware reliability and adding capabilities to use that information to model the impact on the sustainment supply chain and operational availability.

  17. Analysis of feeder bus network design and scheduling problems.

    Science.gov (United States)

    Almasi, Mohammad Hadi; Mirzapour Mounes, Sina; Koting, Suhana; Karim, Mohamed Rehan

    2014-01-01

    A growing concern for public transit is its inability to shift passenger's mode from private to public transport. In order to overcome this problem, a more developed feeder bus network and matched schedules will play important roles. The present paper aims to review some of the studies performed on Feeder Bus Network Design and Scheduling Problem (FNDSP) based on three distinctive parts of the FNDSP setup, namely, problem description, problem characteristics, and solution approaches. The problems consist of different subproblems including data preparation, feeder bus network design, route generation, and feeder bus scheduling. Subsequently, descriptive analysis and classification of previous works are presented to highlight the main characteristics and solution methods. Finally, some of the issues and trends for future research are identified. This paper is targeted at dealing with the FNDSP to exhibit strategic and tactical goals and also contributes to the unification of the field which might be a useful complement to the few existing reviews.

  18. A Framework for Security Analysis of Mobile Wireless Networks

    DEFF Research Database (Denmark)

    Nanz, Sebastian; Hankin, Chris

    2006-01-01

    We present a framework for specification and security analysis of communication protocols for mobile wireless networks. This setting introduces new challenges which are not being addressed by classical protocol analysis techniques. The main complication stems from the fact that the actions...... processes and the network's connectivity graph, which may change independently from protocol actions. We identify a property characterising an important aspect of security in this setting and express it using behavioural equivalences of the calculus. We complement this approach with a control flow analysis...... of intermediate nodes and their connectivity can no longer be abstracted into a single unstructured adversarial environment as they form an inherent part of the system's security. In order to model this scenario faithfully, we present a broadcast calculus which makes a clear distinction between the protocol...

  19. Analysis of cascading failure in gene networks

    Directory of Open Access Journals (Sweden)

    Shudong eWang

    2012-12-01

    Full Text Available It is an important subject to research the functional mechanism of cancer-related genes make in formation and development of cancers. The modern methodology of data analysis plays a very important role for deducing the relationship between cancers and cancer-related genes and analyzing functional mechanism of genome. In this research, we construct mutual information networks using gene expression profiles of glioblast and renal in normal condition and cancer conditions. We investigate the relationship between structure and robustness in gene networks of the two tissues using a cascading failure model based on betweenness centrality. Define some important parameters such as the percentage of failure nodes of the network, the average size-ratio of cascading failure and the cumulative probability of size-ratio of cascading failure to measure the robustness of the networks. By comparing control group and experiment groups, we find that the networks of experiment groups are more robust than that of control group. The gene that can cause large scale failure is called structural key gene (SKG. Some of them have been confirmed to be closely related to the formation and development of glioma and renal cancer respectively. Most of them are predicted to play important roles during the formation of glioma and renal cancer, maybe the oncogenes, suppressor genes, and other cancer candidate genes in the glioma and renal cancer cells. However, these studies provide little information about the detailed roles of identified cancer genes.

  20. Virtual networks pluralistic approach for the next generation of Internet

    CERN Document Server

    Duarte, Otto Carlos M B

    2013-01-01

    The first chapter of this title concerns virtualization techniques that allow sharing computational resources basically, slicing a real computational environment into virtual computational environments that are isolated from one another.The Xen and OpenFlow virtualization platforms are then presented in Chapter 2 and a performance analysis of both is provided. This chapter also defines the primitives that the network virtualization infrastructure must provide for allowing the piloting plane to manage virtual network elements.Following this, interfaces for system management of the two platform

  1. Multiplex network analysis of employee performance and employee social relationships

    Science.gov (United States)

    Cai, Meng; Wang, Wei; Cui, Ying; Stanley, H. Eugene

    2018-01-01

    In human resource management, employee performance is strongly affected by both formal and informal employee networks. Most previous research on employee performance has focused on monolayer networks that can represent only single categories of employee social relationships. We study employee performance by taking into account the entire multiplex structure of underlying employee social networks. We collect three datasets consisting of five different employee relationship categories in three firms, and predict employee performance using degree centrality and eigenvector centrality in a superimposed multiplex network (SMN) and an unfolded multiplex network (UMN). We use a quadratic assignment procedure (QAP) analysis and a regression analysis to demonstrate that the different categories of relationship are mutually embedded and that the strength of their impact on employee performance differs. We also use weighted/unweighted SMN/UMN to measure the predictive accuracy of this approach and find that employees with high centrality in a weighted UMN are more likely to perform well. Our results shed new light on how social structures affect employee performance.

  2. Back to the core: A network approach to bolster harm reduction among persons who inject drugs.

    Science.gov (United States)

    Bouchard, Martin; Hashimi, Sadaf; Tsai, Kristen; Lampkin, Hugh; Jozaghi, Ehsan

    2017-12-08

    Injecting drugs safely almost always includes the presence of one's social network, especially for the prevention of overdose. Yet, the systematic analysis of users' social networks has yet to be established as a focal method in harm reduction research, and interventions. This study draws from 200 interviews with persons who inject drugs recruited from North America's first sanctioned supervised injection facility and a drug user's advocacy group. Respondents were asked about the individuals they personally considered as facilitators of harm reduction, and the relations between them. Collectively, these 200 respondents provided over 900 individuals whom they considered as members of their harm reduction network. The aim was to locate individuals that would potentially make the network denser (harm reduction champions) and users that were situated in the "periphery" of the network, and in practice, further away from the harm reduction core. Of the 1135 network members, 63 individuals formed the "core" of the harm reduction network, collectively reaching approximately 70% of individuals in the network. We also uncovered 31 individuals that acted as "articulation points"- these individuals were not as connected, but were more effective at reaching peripheral individuals. Former or current injecting drug users that were sampled were surrounded by a relatively rich harm reduction network, but the network approach showed that only a minority of individuals were true harm reduction "champions". Recruitment of a combination of well-connected harm reduction champions, and strategically connected articulation points, would be most effective in planning network interventions that encourage harm reduction behaviors among this population. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Multimode Equivalent Networks for the Design and Analysis of Frequency Selective Surfaces

    NARCIS (Netherlands)

    Neto, A.; Tijhuis, A.G.; Monni, S.; Gerini, G.

    2007-01-01

    A modular technique originally proposed for waveguide junctions, the multimode equivalent network approach based on the integral equation formulation (IEMEN), is extended to the analysis of multilayer Frequency Selective Surfaces integrated with waveguide array antennas. This technique represents

  4. Intelligent future wireless networks for energy efficiency: overall analysis and standardization activities

    CSIR Research Space (South Africa)

    Kliks, A

    2013-10-01

    Full Text Available This chapter addresses a number of issues related to standardization and regulatory policies aiming at promoting energy-efficient communications and networking, highlighting the need of synergic approach. It encompasses the analysis of various...

  5. Analysis of network architecture reveals phylogenetic constraints on mycorrhizal specificity in the genus Orchis (Orchidaceae)

    National Research Council Canada - National Science Library

    Hans Jacquemyn; Vincent Merckx; Rein Brys; Daniel Tyteca; Bruno P. A. Cammue; Olivier Honnay; Bart Lievens

    2011-01-01

    ... of the mycorrhizal specificity of orchids. Here, we used a network analysis approach to investigate orchid mycorrhizal associations in 16 species of the genus Orchis sampled across 11 different regions in Europe...

  6. Multiple perspective vulnerability analysis of the power network

    Science.gov (United States)

    Wang, Shuliang; Zhang, Jianhua; Duan, Na

    2018-02-01

    To understand the vulnerability of the power network from multiple perspectives, multi-angle and multi-dimensional vulnerability analysis as well as community based vulnerability analysis are proposed in this paper. Taking into account of central China power grid as an example, correlation analysis of different vulnerability models is discussed. Then, vulnerabilities produced by different vulnerability metrics under the given vulnerability models and failure scenarios are analyzed. At last, applying the community detecting approach, critical areas of central China power grid are identified, Vulnerable and robust communities on both topological and functional perspective are acquired and analyzed. The approach introduced in this paper can be used to help decision makers develop optimal protection strategies. It will be also useful to give a multiple vulnerability analysis of the other infrastructure systems.

  7. A statistical framework for differential network analysis from microarray data

    Directory of Open Access Journals (Sweden)

    Datta Somnath

    2010-02-01

    Full Text Available Abstract Background It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types. Results We provide a recipe for conducting a differential analysis of networks constructed from microarray data under two experimental settings. At the core of our approach lies a connectivity score that represents the strength of genetic association or interaction between two genes. We use this score to propose formal statistical tests for each of following queries: (i whether the overall modular structures of the two networks are different, (ii whether the connectivity of a particular set of "interesting genes" has changed between the two networks, and (iii whether the connectivity of a given single gene has changed between the two networks. A number of examples of this score is provided. We carried out our method on two types of simulated data: Gaussian networks and networks based on differential equations. We show that, for appropriate choices of the connectivity scores and tuning parameters, our method works well on simulated data. We also analyze a real data set involving normal versus heavy mice and identify an interesting set of genes that may play key roles in obesity. Conclusions Examining changes in network structure can provide valuable information about the

  8. Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

    Directory of Open Access Journals (Sweden)

    Christian L Barrett

    2006-05-01

    Full Text Available The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12 that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.

  9. Malware Analysis;: A Systematic Approach

    OpenAIRE

    Wedum, Petter Langeland

    2008-01-01

    An almost incomprehensible amount of data and information is stored on millions and millions of computers worldwide. The computers, interconnected in local, national and international networks, use and share a high number of various software programs. Individuals, corporations, hospitals, communication networks, authorities among others are totally dependent on the reliability and accessibility of the data and information stored, and on the correct and predictable operation of the soft ware p...

  10. Resolving Weak Sources within a Dense Array using a Network Approach

    CERN Document Server

    Riahi, Nima

    2015-01-01

    A non-parametric technique to identify weak sources within dense sensor arrays is developed using a network approach. No knowledge about the propagation medium is needed except that signal strengths decay to insignificant levels within a scale that is shorter than the aperture. We then reinterpret the spatial covariance matrix of a wave field as a matrix whose support is a connectivity matrix of a network of vertices (sensors) connected into communities. These communities correspond to sensor clusters associated with individual sources. We estimate the support of the covariance matrix from limited-time data using a robust hypothesis test combined with a physical distance criterion. The latter ensures sufficient network sparsity to prevent vertex communities from forming by chance. We verify the approach on simulated data and quantify its reliability. The method is then applied to data from a dense 5200 element geophone array that blanketed 7$\\times$10 km of the city of Long Beach (CA). The analysis exposes a ...

  11. Network worlds : from link analysis to virtual places.

    Energy Technology Data Exchange (ETDEWEB)

    Joslyn, C. (Cliff)

    2002-01-01

    Significant progress is being made in knowledge systems through recent advances in the science of very large networks. Attention is now turning in many quarters to the potential impact on counter-terrorism methods. After reviewing some of these advances, we will discuss the difference between such 'network analytic' approaches, which focus on large, homogeneous graph strucures, and what we are calling 'link analytic' approaches, which focus on somewhat smaller graphs with heterogeneous link types. We use this venue to begin the process of rigorously defining link analysis methods, especially the concept of chaining of views of multidimensional databases. We conclude with some speculation on potential connections to virtual world architectures.

  12. Using Network Analysis to Understand Knowledge Mobilization in a Community-based Organization.

    Science.gov (United States)

    Gainforth, Heather L; Latimer-Cheung, Amy E; Moore, Spencer; Athanasopoulos, Peter; Martin Ginis, Kathleen A

    2015-06-01

    Knowledge mobilization (KM) has been described as putting research in the hands of research users. Network analysis is an empirical approach that has potential for examining the complex process of knowledge mobilization within community-based organizations (CBOs). Yet, conducting a network analysis in a CBO presents challenges. The purpose of this paper is to demonstrate the value and feasibility of using network analysis as a method for understanding knowledge mobilization within a CBO by (1) presenting challenges and solutions to conducting a network analysis in a CBO, (2) examining the feasibility of our methodology, and (3) demonstrating the utility of this methodology through an example of a network analysis conducted in a CBO engaging in knowledge mobilization activities. The final method used by the partnership team to conduct our network analysis of a CBO is described. An example of network analysis results of a CBO engaging in knowledge mobilization is presented. In total, 81 participants completed the network survey. All of the feasibility benchmarks set by the CBO were met. Results of the network analysis are highlighted and discussed as a means of identifying (1) prominent and influential individuals in the knowledge mobilization process and (2) areas for improvement in future knowledge mobilization initiatives. Findings demonstrate that network analysis can be feasibly used to provide a rich description of a CBO engaging in knowledge mobilization activities.

  13. A Passive Testing Approach for Protocols in Wireless Sensor Networks.

    Science.gov (United States)

    Che, Xiaoping; Maag, Stephane; Tan, Hwee-Xian; Tan, Hwee-Pink; Zhou, Zhangbing

    2015-11-19

    Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN). However, one stage that is often neglected before their deployment is the conformance testing process, a crucial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works propose to specify the protocol with state models or to analyze them with simulators and emulators, we here propose a logic-based approach for formally specifying some functional requirements of a novel WSN routing protocol. We provide an algorithm to evaluate these properties on collected protocol execution traces. Further, we demonstrate the efficiency and suitability of our approach by its application into common WSN functional properties, as well as specific ones designed from our own routing protocol. We provide relevant testing verdicts through a real indoor testbed and the implementation of our protocol. Furthermore, the flexibility, genericity and practicability of our approach have been proven by the experimental results.

  14. A Dynamic Resilience Approach for WDM Optical Networks

    Science.gov (United States)

    Garg, Amit Kumar

    2017-12-01

    Optical fibres have been developed as a transmission medium to carry traffic in order to provide various services in telecommunications platform. Failure of this fibre caused loss of data which can interrupt communication services. This paper has been focused only on survivable schemes in order to guarantee both protection and restoration in WDM optical networks. In this paper, a dynamic resilience approach has been proposed whose objective is to route the flows in a way which minimizes the total amount of bandwidth used for working and protection paths. In the proposed approach, path-based protection is utilized because it yields lower overhead and is also suitable for global optimization where, in case of a single link failure, all the flows utilizing the failed link are re-routed to a pre-computed set of paths. The simulation results demonstrate that proposed approach is much more efficient as it provides better quality of services (QoS) in terms of network resource utilization, blocking probability etc. as compared to conventional protection and restoration schemes. The proposed approach seems to offer an attractive combination of features, with both ring like speed and mesh-like efficiency.

  15. A Passive Testing Approach for Protocols in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xiaoping Che

    2015-11-01

    Full Text Available Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN. However, one stage that is often neglected before their deployment is the conformance testing process, a crucial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works propose to specify the protocol with state models or to analyze them with simulators and emulators, we here propose a logic-based approach for formally specifying some functional requirements of a novel WSN routing protocol. We provide an algorithm to evaluate these properties on collected protocol execution traces. Further, we demonstrate the efficiency and suitability of our approach by its application into common WSN functional properties, as well as specific ones designed from our own routing protocol. We provide relevant testing verdicts through a real indoor testbed and the implementation of our protocol. Furthermore, the flexibility, genericity and practicability of our approach have been proven by the experimental results.

  16. Principal component analysis networks and algorithms

    CERN Document Server

    Kong, Xiangyu; Duan, Zhansheng

    2017-01-01

    This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.

  17. Service network analysis for agricultural mental health

    Directory of Open Access Journals (Sweden)

    Fuller Jeffrey D

    2009-05-01

    Full Text Available Abstract Background Farmers represent a subgroup of rural and remote communities at higher risk of suicide attributed to insecure economic futures, self-reliant cultures and poor access to health services. Early intervention models are required that tap into existing farming networks. This study describes service networks in rural shires that relate to the mental health needs of farming families. This serves as a baseline to inform service network improvements. Methods A network survey of mental health related links between agricultural support, health and other human services in four drought declared shires in comparable districts in rural New South Wales, Australia. Mental health links covered information exchange, referral recommendations and program development. Results 87 agencies from 111 (78% completed a survey. 79% indicated that two thirds of their clients needed assistance for mental health related problems. The highest mean number of interagency links concerned information exchange and the frequency of these links between sectors was monthly to three monthly. The effectiveness of agricultural support and health sector links were rated as less effective by the agricultural support sector than by the health sector (p Conclusion Aligning with agricultural agencies is important to build effective mental health service pathways to address the needs of farming populations. Work is required to ensure that these agricultural support agencies have operational and effective links to primary mental health care services. Network analysis provides a baseline to inform this work. With interventions such as local mental health training and joint service planning to promote network development we would expect to see over time an increase in the mean number of links, the frequency in which these links are used and the rated effectiveness of these links.

  18. A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic

    Directory of Open Access Journals (Sweden)

    Meng Fan-Bo

    2016-01-01

    Full Text Available Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture time-frequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.

  19. Chemical reaction network approaches to Biochemical Systems Theory.

    Science.gov (United States)

    Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R

    2015-11-01

    This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Network Analysis and Modeling in Systems Biology

    OpenAIRE

    Bosque Chacón, Gabriel

    2017-01-01

    This thesis is dedicated to the study and comprehension of biological networks at the molecular level. The objectives were to analyse their topology, integrate it in a genotype-phenotype analysis, develop richer mathematical descriptions for them, study their community structure and compare different methodologies for estimating their internal fluxes. The work presented in this document moves around three main axes. The first one is the biological. Which organisms were studied in this ...

  1. The use of nodes attributes in social network analysis with an application to an international trade network

    Science.gov (United States)

    de Andrade, Ricardo Lopes; Rêgo, Leandro Chaves

    2018-02-01

    The social network analysis (SNA) studies the interactions among actors in a network formed through some relationship (friendship, cooperation, trade, among others). The SNA is constantly approached from a binary point of view, i.e., it is only observed if a link between two actors is present or not regardless of the strength of this link. It is known that different information can be obtained in weighted and unweighted networks and that the information extracted from weighted networks is more accurate and detailed. Another rarely discussed approach in the SNA is related to the individual attributes of the actors (nodes), because such analysis is usually focused on the topological structure of networks. Features of the nodes are not incorporated in the SNA what implies that there is some loss or misperception of information in those analyze. This paper aims at exploring more precisely the complexities of a social network, initially developing a method that inserts the individual attributes in the topological structure of the network and then analyzing the network in four different ways: unweighted, edge-weighted and two methods for using both edge-weights and nodes' attributes. The international trade network was chosen in the application of this approach, where the nodes represent the countries, the links represent the cash flow in the trade transactions and countries' GDP were chosen as nodes' attributes. As a result, it is possible to observe which countries are most connected in the world economy and with higher cash flows, to point out the countries that are central to the intermediation of the wealth flow and those that are most benefited from being included in this network. We also made a principal component analysis to study which metrics are more influential in describing the data variability, which turn out to be mostly the weighted metrics which include the nodes' attributes.

  2. A user’s guide to network analysis in R

    CERN Document Server

    Luke, Douglas

    2015-01-01

    Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.

  3. Network-analysis-guided synthesis of weisaconitine D and liljestrandinine

    Science.gov (United States)

    Marth, C. J.; Gallego, G. M.; Lee, J. C.; Lebold, T. P.; Kulyk, S.; Kou, K. G. M.; Qin, J.; Lilien, R.; Sarpong, R.

    2015-12-01

    General strategies for the chemical synthesis of organic compounds, especially of architecturally complex natural products, are not easily identified. Here we present a method to establish a strategy for such syntheses, which uses network analysis. This approach has led to the identification of a versatile synthetic intermediate that facilitated syntheses of the diterpenoid alkaloids weisaconitine D and liljestrandinine, and the core of gomandonine. We also developed a web-based graphing program that allows network analysis to be easily performed on molecules with complex frameworks. The diterpenoid alkaloids comprise some of the most architecturally complex and functional-group-dense secondary metabolites isolated. Consequently, they present a substantial challenge for chemical synthesis. The synthesis approach described here is a notable departure from other single-target-focused strategies adopted for the syntheses of related structures. Specifically, it affords not only the targeted natural products, but also intermediates and derivatives in the three subfamilies of diterpenoid alkaloids (C-18, C-19 and C-20), and so provides a unified synthetic strategy for these natural products. This work validates the utility of network analysis as a starting point for identifying strategies for the syntheses of architecturally complex secondary metabolites.

  4. A neural network approach to smarter sensor networks for water quality monitoring.

    Science.gov (United States)

    O'Connor, Edel; Smeaton, Alan F; O'Connor, Noel E; Regan, Fiona

    2012-01-01

    Environmental monitoring is evolving towards large-scale and low-cost sensor networks operating reliability and autonomously over extended periods of time. Sophisticated analytical instrumentation such as chemo-bio sensors present inherent limitations because of the number of samples that they can take. In order to maximize their deployment lifetime, we propose the coordination of multiple heterogeneous information sources. We use rainfall radar images and information from a water depth sensor as input to a neural network (NN) to dictate the sampling frequency of a phosphate analyzer at the River Lee in Cork, Ireland. This approach shows varied performance for different times of the year but overall produces output that is very satisfactory for the application context in question. Our study demonstrates that even with limited training data, a system for controlling the sampling rate of the nutrient sensor can be set up and can improve the efficiency of the more sophisticated nodes of the sensor network.

  5. Social network analysis of duplicative prescriptions: One-month analysis of medical facilities in Japan.

    Science.gov (United States)

    Takahashi, Yoshimitsu; Ishizaki, Tatsuro; Nakayama, Takeo; Kawachi, Ichiro

    2016-03-01

    Duplicative prescriptions refer to situations in which patients receive medications for the same condition from two or more sources. Health officials in Japan have expressed concern about medical "waste" resulting from this practices. We sought to conduct descriptive analysis of duplicative prescriptions using social network analysis and to report their prevalence across ages. We analyzed a health insurance claims database including 1.24 million people from December 2012. Through social network analysis, we examined the duplicative prescription networks, representing each medical facility as nodes, and individual prescriptions for patients as edges. The prevalence of duplicative prescription for any drug class was strongly correlated with its frequency of prescription (r=0.90). Among patients aged 0-19, cough and colds drugs showed the highest prevalence of duplicative prescriptions (10.8%). Among people aged 65 and over, antihypertensive drugs had the highest frequency of prescriptions, but the prevalence of duplicative prescriptions was low (0.2-0.3%). Social network analysis revealed clusters of facilities connected via duplicative prescriptions, e.g., psychotropic drugs showed clustering due to a few patients receiving drugs from 10 or more facilities. Overall, the prevalence of duplicative prescriptions was quite low - less than 10% - although the extent of the problem varied by drug class and age group. Our approach illustrates the potential utility of using a social network approach to understand these practices. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  6. Architectural approach for quality and safety aware healthcare social networks.

    Science.gov (United States)

    López, Diego M; Blobel, Bernd; González, Carolina

    2012-01-01

    Quality of information and privacy and safety issues are frequently identified as main limitations to make most benefit from social media in healthcare. The objective of the paper is to contribute to the analysis of healthcare social networks (SN), and online healthcare social network services (SNS) by proposing a formal architectural analysis of healthcare SN and SNS, considering the complexity of both systems, but stressing on quality, safety and usability aspects. Quality policies are necessary to control the quality of content published by experts and consumers. Privacy and safety policies protect against inappropriate use of information and users responsibility for sharing information. After the policies are established and documented, a proof of concept online SNS supporting primary healthcare promotion is presented in the paper.

  7. An Enhanced Probabilistic Neural Network Approach Applied to Text Classification

    Science.gov (United States)

    Marques Ciarelli, Patrick; Oliveira, Elias

    Text classification is still a quite difficult problem to be dealt with both by the academia and by the industrial areas. On the top of that, the importance of aggregating a set of related amount of text documents is steadily growing in importance these days. The presence of multi-labeled texts and great quantity of classes turn this problem even more challenging. In this article we present an enhanced version of Probabilistic Neural Network using centroids to tackle the multi-label classification problem. We carried out some experiments comparing our proposed classifier against the other well known classifiers in the literature which were specially designed to treat this type of problem. By the achieved results, we observed that our novel approach were superior to the other classifiers and faster than the Probabilistic Neural Network without the use of centroids.

  8. Structural Approaches to Sequence Evolution Molecules, Networks, Populations

    CERN Document Server

    Bastolla, Ugo; Roman, H. Eduardo; Vendruscolo, Michele

    2007-01-01

    Structural requirements constrain the evolution of biological entities at all levels, from macromolecules to their networks, right up to populations of biological organisms. Classical models of molecular evolution, however, are focused at the level of the symbols - the biological sequence - rather than that of their resulting structure. Now recent advances in understanding the thermodynamics of macromolecules, the topological properties of gene networks, the organization and mutation capabilities of genomes, and the structure of populations make it possible to incorporate these key elements into a broader and deeply interdisciplinary view of molecular evolution. This book gives an account of such a new approach, through clear tutorial contributions by leading scientists specializing in the different fields involved.

  9. Benchmarking and supplier networking--best practice approaches.

    Science.gov (United States)

    Waixel, B; Laidlaw, J

    1996-01-01

    This article examines the approach adopted by a health service to benchmark outside the health industry and to network with its own suppliers in its quest for best practice. The Maryborough District Health Service was selected for funding under the Commonwealth Government's Best Practice in the Health Sector Program. This rural health service is setting a fine example of how generic benchmarking can be used to increase efficiency and improve outcomes in an environment of change, increasing demands, and contracting resources. The organisation has networked with its suppliers with a view to ensuring that, as a customer, it has access to the best quality goods and services. The objective is to improve the services and quality of patient care provided by the health service and to minimise its cost structures.

  10. The Application of Social Network Analysis to Team Sports

    Science.gov (United States)

    Lusher, Dean; Robins, Garry; Kremer, Peter

    2010-01-01

    This article reviews how current social network analysis might be used to investigate individual and group behavior in sporting teams. Social network analysis methods permit researchers to explore social relations between team members and their individual-level qualities simultaneously. As such, social network analysis can be seen as augmenting…

  11. Syphilis Networks in Louisiana: An Analysis of Network Configuration and Disease Transmission

    Science.gov (United States)

    Desmarais, Catherine Theresa

    Background: In 2009, Louisiana had the highest rate of primary and secondary syphilis in the country. Recent partner notification approaches have been insufficient in addressing Louisiana's deeply entrenched areas of syphilis infection. Prior researchers have suggested that surveillance systems may benefit from utilizing social and spatial network analysis in syphilis control efforts. Objective: To expand the understanding of the spread of syphilis in Louisiana, and to add new tools to the state's case finding resources through the description of the characteristics of cases of early syphilis and their partners in Louisiana, the socio-sexual networks of these cases, and the geospatial clustering of cases and partners. Methods: Utilizing state surveillance data, all cases of primary, secondary, and early latent syphilis that were diagnosed in 2009 and data on their sexual or needle sharing partners were analyzed using a combination of descriptive, network, and geospatial measures. Results: In 2009, Louisiana experienced a high rate of heterosexual syphilis transmission. Within syphilis transmission networks, 50.8% of all cases were female and 84.2% of all cases were black. The average and median ages of males with reactive syphilis tests were higher than that of females in Louisiana, and in 88.9% of regions, older individuals were more likely to have a syphilis test than no test. A greater proportion of males (11.4%) refused to discuss partners than females (7.4%) and a greater proportion of males (5.5%) refused testing and prophylactic treatment than females (2.8%). No distinct patterns were seen in disease prevalence between regions based upon demographic data. Classic summary network measures such as density, degree, centrality, and betweenness provided little information on similarities and differences between the different regions in Louisiana. All measures indicated low density and extreme fragmentation of networks in Louisiana. The majority of network

  12. Analysis and visualization of citation networks

    CERN Document Server

    Zhao, Dangzhi

    2015-01-01

    Citation analysis-the exploration of reference patterns in the scholarly and scientific literature-has long been applied in a number of social sciences to study research impact, knowledge flows, and knowledge networks. It has important information science applications as well, particularly in knowledge representation and in information retrieval.Recent years have seen a burgeoning interest in citation analysis to help address research, management, or information service issues such as university rankings, research evaluation, or knowledge domain visualization. This renewed and growing interest

  13. Applying DNA computation to intractable problems in social network analysis.

    Science.gov (United States)

    Chen, Rick C S; Yang, Stephen J H

    2010-09-01

    From ancient times to the present day, social networks have played an important role in the formation of various organizations for a range of social behaviors. As such, social networks inherently describe the complicated relationships between elements around the world. Based on mathematical graph theory, social network analysis (SNA) has been developed in and applied to various fields such as Web 2.0 for Web applications and product developments in industries, etc. However, some definitions of SNA, such as finding a clique, N-clique, N-clan, N-club and K-plex, are NP-complete problems, which are not easily solved via traditional computer architecture. These challenges have restricted the uses of SNA. This paper provides DNA-computing-based approaches with inherently high information density and massive parallelism. Using these approaches, we aim to solve the three primary problems of social networks: N-clique, N-clan, and N-club. Their accuracy and feasible time complexities discussed in the paper will demonstrate that DNA computing can be used to facilitate the development of SNA. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

  14. Social Network Analysis and Qualitative Interviews for Assessing Geographic Characteristics of Tourism Business Networks.

    Science.gov (United States)

    Kelman, Ilan; Luthe, Tobias; Wyss, Romano; Tørnblad, Silje H; Evers, Yvette; Curran, Marina Martin; Williams, Richard J; Berlow, Eric L

    2016-01-01

    This study integrates quantitative social network analysis (SNA) and qualitative interviews for understanding tourism business links in isolated communities through analysing spatial characteristics. Two case studies are used, the Surselva-Gotthard region in the Swiss Alps and Longyearbyen in the Arctic archipelago of Svalbard, to test the spatial characteristics of physical proximity, isolation, and smallness for understanding tourism business links. In the larger Surselva-Gotthard region, we found a strong relationship between geographic separation of the three communities on compartmentalization of the collaboration network. A small set of businesses played a central role in steering collaborative decisions for this community, while a group of structurally 'peripheral' actors were less influential. By contrast, the business community in Svalbard showed compartmentalization that was independent of geographic distance between actors. Within towns of similar size and governance scale, Svalbard is more compartmentalized, and those compartments are not driven by geographic separation of the collaboration clusters. This compartmentalization in Svalbard was reflected in a lower density of formal business collaboration ties compared to the communities of the Alps. We infer that the difference is due to Svalbard having higher cultural diversity and population turnover than the Alps communities. We propose that integrating quantitative network analysis from simple surveys with qualitative interviews targeted from the network results is an efficient general approach to identify regionally specific constraints and opportunities for effective governance.

  15. Social Network Analysis and Qualitative Interviews for Assessing Geographic Characteristics of Tourism Business Networks.

    Directory of Open Access Journals (Sweden)

    Ilan Kelman

    Full Text Available This study integrates quantitative social network analysis (SNA and qualitative interviews for understanding tourism business links in isolated communities through analysing spatial characteristics. Two case studies are used, the Surselva-Gotthard region in the Swiss Alps and Longyearbyen in the Arctic archipelago of Svalbard, to test the spatial characteristics of physical proximity, isolation, and smallness for understanding tourism business links. In the larger Surselva-Gotthard region, we found a strong relationship between geographic separation of the three communities on compartmentalization of the collaboration network. A small set of businesses played a central role in steering collaborative decisions for this community, while a group of structurally 'peripheral' actors were less influential. By contrast, the business community in Svalbard showed compartmentalization that was independent of geographic distance between actors. Within towns of similar size and governance scale, Svalbard is more compartmentalized, and those compartments are not driven by geographic separation of the collaboration clusters. This compartmentalization in Svalbard was reflected in a lower density of formal business collaboration ties compared to the communities of the Alps. We infer that the difference is due to Svalbard having higher cultural diversity and population turnover than the Alps communities. We propose that integrating quantitative network analysis from simple surveys with qualitative interviews targeted from the network results is an efficient general approach to identify regionally specific constraints and opportunities for effective governance.

  16. Computational analysis of protein interaction networks for infectious diseases.

    Science.gov (United States)

    Pan, Archana; Lahiri, Chandrajit; Rajendiran, Anjana; Shanmugham, Buvaneswari

    2016-05-01

    Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host-pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  17. Logical Modeling and Dynamical Analysis of Cellular Networks.

    Science.gov (United States)

    Abou-Jaoudé, Wassim; Traynard, Pauline; Monteiro, Pedro T; Saez-Rodriguez, Julio; Helikar, Tomáš; Thieffry, Denis; Chaouiya, Claudine

    2016-01-01

    The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.

  18. A network theory approach for a better understanding of overland flow connectivity

    Science.gov (United States)

    Masselink, Rens; Heckmann, Tobias; Temme, Arnaud; Anders, Niels; Keesstra, Saskia

    2016-04-01

    Hydrological connectivity describes the physical coupling, or linkages of different elements within a landscape regarding (sub)surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for e.g. habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has been recognised within the scientific community as well, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterise overland flow connectivity dynamics on hillslopes in a humid sub-Mediterranean environment by using a combination of high-resolution digital-terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow on agricultural areas and semi-natural shrubs areas. Positive correlations between connectivity and precipitation characteristics were found, while negative correlations between connectivity and soil moisture were found, probably due to soil water repellency. The combination of a structural network to determine potential connectivity with dynamic networks to determine the actual connectivity proved a powerful tool in analysing overland flow connectivity.

  19. A cloud-based data network approach for translational cancer research.

    Science.gov (United States)

    Xing, Wei; Tsoumakos, Dimitrios; Ghanem, Moustafa

    2015-01-01

    We develop a new model and associated technology for constructing and managing self-organizing data to support translational cancer research studies. We employ a semantic content network approach to address the challenges of managing cancer research data. Such data is heterogeneous, large, decentralized, growing and continually being updated. Moreover, the data originates from different information sources that may be partially overlapping, creating redundancies as well as contradictions and inconsistencies. Building on the advantages of elasticity of cloud computing, we deploy the cancer data networks on top of the CELAR Cloud platform to enable more effective processing and analysis of Big cancer data.

  20. A statistical mechanics approach to autopoietic immune networks

    Science.gov (United States)

    Barra, Adriano; Agliari, Elena

    2010-07-01

    In this work we aim to bridge theoretical immunology and disordered statistical mechanics. We introduce a model for the behavior of B-cells which naturally merges the clonal selection theory and the autopoietic network theory as a whole. From the analysis of its features we recover several basic phenomena such as low-dose tolerance, dynamical memory of antigens and self/non-self discrimination.