WorldWideScience

Sample records for network structure running

  1. Evolving production network structures

    DEFF Research Database (Denmark)

    Grunow, Martin; Gunther, H.O.; Burdenik, H.

    2007-01-01

    When deciding about future production network configurations, the current structures have to be taken into account. Further, core issues such as the maturity of the products and the capacity requirements for test runs and ramp-ups must be incorporated. Our approach is based on optimization...... modelling and assigns products and capacity expansions to production sites under the above constraints. It also considers the production complexity at the individual sites and the flexibility of the network. Our implementation results for a large manufacturing network reveal substantial possible cost...

  2. Neural network-based run-to-run controller using exposure and resist thickness adjustment

    Science.gov (United States)

    Geary, Shane; Barry, Ronan

    2003-06-01

    This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

  3. Designing Green Networks and Network Operations Saving Run-the-Engine Costs

    CERN Document Server

    Minoli, Daniel

    2011-01-01

    In recent years the confluence of socio-political trends toward environmental responsibility and the pressing need to reduce Run-the-Engine (RTE) costs has given birth to a nascent discipline of Green IT. A clear and concise introduction to green networks and green network operations, this book examines analytical measures and discusses virtualization, network computing, and web services as approaches for green data centers and networks. It identifies some strategies for green appliance and end devices and examines the methodical steps that can be taken over time to achieve a seamless migratio

  4. Advanced Polymer Network Structures

    Science.gov (United States)

    2016-02-01

    attractive interaction (n = 2.0) and a neutral interaction (n = 1.0); n is equal to unity for self-interactions among the monomers of first network and...... Network Structures by Robert Lambeth, Joseph Lenhart, and Tim Sirk Weapons and Materials Research Directorate, ARL Yelena Sliozberg TKC Global

  5. Patchworking Network Structures

    DEFF Research Database (Denmark)

    Norus, Jesper

    2004-01-01

    analyzes fourdifferent managerial strategies of how to create network structures to deal with theinterfaces between industry, university and public institutions. The research-orientedstrategy, the incubator strategy, the industrial-partnering strategy, and the policyorientedstrategy. The research...... groups has been treated as a contingent factor.However, little attention has been given to the managerial efforts that entrepreneurshave make to establish the fit between small firms, university research, and publicpolicies such as regulatory policies and R&D policies through network-type structures.......New biotechnology organizations are perfect objects to study these relationshipsbecause new biotechnologies and techniques predominantly come from the universitysector (Kenney, 1986; Yoxen; 1984; Zucker & Darby, 1997; Robbins-Roth, 2001).From the perspective of the small biotechnology firms (SBFs,) this paper...

  6. A Decomposition Algorithm for Learning Bayesian Network Structures from Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Cordero Hernandez, Jorge

    2008-01-01

    It is a challenging task of learning a large Bayesian network from a small data set. Most conventional structural learning approaches run into the computational as well as the statistical problems. We propose a decomposition algorithm for the structure construction without having to learn...... the complete network. The new learning algorithm firstly finds local components from the data, and then recover the complete network by joining the learned components. We show the empirical performance of the decomposition algorithm in several benchmark networks....

  7. [Network structures in biological systems].

    Science.gov (United States)

    Oleskin, A V

    2013-01-01

    Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.

  8. vhv supply networks, problems of network structure

    Energy Technology Data Exchange (ETDEWEB)

    Raimbault, J

    1966-04-01

    The present and future power requirements of the Paris area and the structure of the existing networks are discussed. The various limitations that will have to be allowed for to lay down the structure of a regional transmission network leading in the power of the large national transmission network to within the Paris built up area are described. The theoretical solution that has been adopted, and the features of its final achievement, which is planned for about the year 2000, and the intermediate stages are given. The problem of the structure of the National Power Transmission network which is to supply the regional network was studied. To solve this problem, a 730 kV voltage network will have to be introduced.

  9. The Model of the Software Running on a Computer Equipment Hardware Included in the Grid network

    Directory of Open Access Journals (Sweden)

    T. A. Mityushkina

    2012-12-01

    Full Text Available A new approach to building a cloud computing environment using Grid networks is proposed in this paper. The authors describe the functional capabilities, algorithm, model of software running on a computer equipment hardware included in the Grid network, that will allow to implement cloud computing environment using Grid technologies.

  10. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Directory of Open Access Journals (Sweden)

    Pengfei Li

    2014-01-01

    Full Text Available The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  11. A red-light running prevention system based on artificial neural network and vehicle trajectory data.

    Science.gov (United States)

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

  12. A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

    Science.gov (United States)

    Li, Pengfei; Li, Yan; Guo, Xiucheng

    2014-01-01

    The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems. PMID:25435870

  13. Steady and transient coordination structures of walking and running

    NARCIS (Netherlands)

    Lamoth, C.J.C.; Daffertshofer, A.; Huys, R.; Beek, P.J.

    2009-01-01

    We studied multisegmental coordination and stride characteristics in nine participants while walking and running on a treadmill. The study's main aim was to evaluate the coordination patterns of walking and running and their variance as a function of locomotion speed, with a specific focus on gait

  14. Steady and transient coordination structures of walking and running

    NARCIS (Netherlands)

    Lamoth, C. J. C.; Daffertshofer, A.; Huys, R.; Beek, P. J.

    We studied multisegmental coordination and stride characteristics in nine participants while walking and running on a treadmill. The study's main aim was to evaluate the coordination patterns of walking and running and their variance as a function of locomotion speed, with a specific focus on gait

  15. The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code

    Directory of Open Access Journals (Sweden)

    Susanne Kunkel

    2017-06-01

    Full Text Available NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.

  16. Metagovernance, network structure, and legitimacy

    DEFF Research Database (Denmark)

    Daugbjerg, Carsten; Fawcett, Paul

    2017-01-01

    This article develops a heuristic for comparative governance analysis. The heuristic depicts four network types by combining network structure with the state’s capacity to metagovern. It suggests that each network type produces a particular combination of input and output legitimacy. We illustrate...... the heuristic and its utility using a comparative study of agri-food networks (organic farming and land use) in four countries, which each exhibit different combinations of input and output legitimacy respectively. The article concludes by using a fifth case study to illustrate what a network type that produces...... high levels of input and output legitimacy might look like....

  17. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2012-01-01

    Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose...... a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....

  18. Collective network for computer structures

    Science.gov (United States)

    Blumrich, Matthias A [Ridgefield, CT; Coteus, Paul W [Yorktown Heights, NY; Chen, Dong [Croton On Hudson, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk [Ossining, NY; Takken, Todd E [Brewster, NY; Steinmacher-Burow, Burkhard D [Wernau, DE; Vranas, Pavlos M [Bedford Hills, NY

    2011-08-16

    A system and method for enabling high-speed, low-latency global collective communications among interconnected processing nodes. The global collective network optimally enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices ate included that interconnect the nodes of the network via links to facilitate performance of low-latency global processing operations at nodes of the virtual network and class structures. The global collective network may be configured to provide global barrier and interrupt functionality in asynchronous or synchronized manner. When implemented in a massively-parallel supercomputing structure, the global collective network is physically and logically partitionable according to needs of a processing algorithm.

  19. Long-running telemedicine networks delivering humanitarian services: experience, performance and scientific output

    Science.gov (United States)

    Geissbuhler, Antoine; Jethwani, Kamal; Kovarik, Carrie; Person, Donald A; Vladzymyrskyy, Anton; Zanaboni, Paolo; Zolfo, Maria

    2012-01-01

    Abstract Objective To summarize the experience, performance and scientific output of long-running telemedicine networks delivering humanitarian services. Methods Nine long-running networks – those operating for five years or more– were identified and seven provided detailed information about their activities, including performance and scientific output. Information was extracted from peer-reviewed papers describing the networks’ study design, effectiveness, quality, economics, provision of access to care and sustainability. The strength of the evidence was scored as none, poor, average or good. Findings The seven networks had been operating for a median of 11 years (range: 5–15). All networks provided clinical tele-consultations for humanitarian purposes using store-and-forward methods and five were also involved in some form of education. The smallest network had 15 experts and the largest had more than 500. The clinical caseload was 50 to 500 cases a year. A total of 59 papers had been published by the networks, and 44 were listed in Medline. Based on study design, the strength of the evidence was generally poor by conventional standards (e.g. 29 papers described non-controlled clinical series). Over half of the papers provided evidence of sustainability and improved access to care. Uncertain funding was a common risk factor. Conclusion Improved collaboration between networks could help attenuate the lack of resources reported by some networks and improve sustainability. Although the evidence base is weak, the networks appear to offer sustainable and clinically useful services. These findings may interest decision-makers in developing countries considering starting, supporting or joining similar telemedicine networks. PMID:22589567

  20. European networks in structural integrity

    International Nuclear Information System (INIS)

    Crutzen, S.; Davies, M.; Hemsworth, B.; Hurst, R.; Kussmaul, K.

    1994-01-01

    Several institutions and electrical utilities in Europe, including the Joint Research Centre (JRC) have the capability to deal problems posed by the operation and ageing of structural components and with their structural integrity assessment. These institutions and the JRC have developed cooperative programmes now organised in networks. They include utilities, engineering companies, R and D laboratories and Regulatory Bodies. Networks are organised and managed like the successful PISC programme: The Institute for Advanced Materials of JRC plays the role of Operating Agent and Manager of these networks: ENIQ, AMES, NESC, each of them dealing with a specific aspect of fitness for purpose of materials in structural components. There exist strong links between the networks and EC Working Groups on Structural Integrity Codes and Standards. (orig.)

  1. Development of Fast-Running Simulation Methodology Using Neural Networks for Load Follow Operation

    International Nuclear Information System (INIS)

    Seong, Seung-Hwan; Park, Heui-Youn; Kim, Dong-Hoon; Suh, Yong-Suk; Hur, Seop; Koo, In-Soo; Lee, Un-Chul; Jang, Jin-Wook; Shin, Yong-Chul

    2002-01-01

    A new fast-running analytic model has been developed for analyzing the load follow operation. The new model was based on the neural network theory, which has the capability of modeling the input/output relationships of a nonlinear system. The new model is made up of two error back-propagation neural networks and procedures to calculate core parameters, such as the distributions and density of xenon in a quasi-steady-state core like load follow operation. One neural network is designed to retrieve the axial offset of power distribution, and the other is for reactivity corresponding to a given core condition. The training data sets for learning the neural networks in the new model are generated with a three-dimensional nodal code and, also, the measured data of the first-day test of load follow operation. Using the new model, the simulation results of the 5-day load follow test in a pressurized water reactor show a good agreement between the simulation data and the actual measured data. Required computing time for simulating a load follow operation is comparable to that of a fast-running lumped model. Moreover, the new model does not require additional engineering factors to compensate for the difference between the actual measurements and analysis results because the neural network has the inherent learning capability of neural networks to new situations

  2. Inferring network structure from cascades

    Science.gov (United States)

    Ghonge, Sushrut; Vural, Dervis Can

    2017-07-01

    Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

  3. Experience and Lessons learnt from running High Availability Databases on Network Attached Storage

    CERN Document Server

    Guijarro, Manuel

    2008-01-01

    The Database and Engineering Services Group of CERN's Information Technology Department supplies the Oracle Central Database services used in many activities at CERN. In order to provide High Availability and ease management for those services, a NAS (Network Attached Storage) based infrastructure has been setup. It runs several instances of the Oracle RAC (Real Application Cluster) using NFS (Network File System) as shared disk space for RAC purposes and Data hosting. It is composed of two private LANs (Local Area Network), one to provide access to the NAS filers and a second to implement the Oracle RAC private interconnect, both using Network Bonding. NAS filers are configured in partnership to prevent having single points of failure and to provide automatic NAS filer fail-over.

  4. Experience and lessons learnt from running high availability databases on network attached storage

    International Nuclear Information System (INIS)

    Guijarro, M; Gaspar, R

    2008-01-01

    The Database and Engineering Services Group of CERN's Information Technology Department supplies the Oracle Central Database services used in many activities at CERN. In order to provide High Availability and ease management for those services, a NAS (Network Attached Storage) based infrastructure has been setup. It runs several instances of the Oracle RAC (Real Application Cluster) using NFS (Network File System) as shared disk space for RAC purposes and Data hosting. It is composed of two private LANs (Local Area Network), one to provide access to the NAS filers and a second to implement the Oracle RAC private interconnect, both using Network Bonding. NAS filers are configured in partnership to prevent having single points of failure and to provide automatic NAS filer fail-over

  5. Structural change and forecasting long-run energy prices

    International Nuclear Information System (INIS)

    Bernard, J.T.; Khalaf, L.

    2004-01-01

    Fluctuating energy prices have a significant impact on the economies of industrialized nations. A recent study has shown a strong non-linear relationship between changes in oil prices and growth in gross domestic product (GDP). In order to forecast the behaviour of energy prices, a complete model must take into account domestic and international supply and demand conditions, market regulations, technological advances and geopolitics. In 1999, Pindyck suggested that for long-term forecasting, a simple model should be adopted where prices grow in real terms and at a fixed rate. This paper tests the statistical significance of Pindyck's suggested class of econometric equations that model the behaviour of long-run real energy prices. The models assume mean-reverting prices with continuous and random changes in their level and trend. They are estimated using Kalman filtering. The authors used simulation-based procedures to address the issue of non-standard test statistics and nuisance parameters. Results were reported for a standard Monte Carlo test and a maximized Monte Carlo test. Results shown statistically significant instabilities for coal and natural gas prices, but not for crude oil prices. Various models were differentiated using out-of-sample forecasting exercises. 25 refs., 3 tabs

  6. Robust Learning of Fixed-Structure Bayesian Networks

    OpenAIRE

    Diakonikolas, Ilias; Kane, Daniel; Stewart, Alistair

    2016-01-01

    We investigate the problem of learning Bayesian networks in an agnostic model where an $\\epsilon$-fraction of the samples are adversarially corrupted. Our agnostic learning model is similar to -- in fact, stronger than -- Huber's contamination model in robust statistics. In this work, we study the fully observable Bernoulli case where the structure of the network is given. Even in this basic setting, previous learning algorithms either run in exponential time or lose dimension-dependent facto...

  7. Structural principles in network glasses

    International Nuclear Information System (INIS)

    Boolchand, P.

    1986-01-01

    Substantial progress in decoding the structure of network glasses has taken place in the past few years. Crucial insights into the molecular structure of glasses have emerged by application of Raman bond and Moessbauer site spectroscopy. In this context, the complimentary role of each spectroscopy as a check on the interpretation of the other, is perhaps one of the more significant developments in the field. New advances in the theory of the subject have also taken place. It is thus appropriate to inquire what general principles if any, have emerged on the structure of real glasses. The author reviews some of the principal ideas on the structure of inorganic network glasses with the aid of specific examples. (Auth.)

  8. Adaptation of coordination mechanisms to network structures

    Directory of Open Access Journals (Sweden)

    Herwig Mittermayer

    2008-12-01

    Full Text Available The coordination efficiency of Supply Chain Management is determined by two opposite poles: benefit from improved planning results and associated coordination cost. The centralization grade, applied coordination mechanisms and IT support have influence on both categories. Therefore three reference types are developed and subsequently detailed in business process models for different network structures. In a simulation study the performance of these organization forms are compared in a process plant network. Coordination benefit is observed if the planning mode is altered by means of a demand planning IT tool. Coordination cost is divided into structural and activity-dependent cost. The activity level rises when reactive planning iterations become necessary as a consequence of inconsistencies among planning levels. Some characteristic influence factors are considered to be a reason for uninfeasible planning. In this study the effect of capacity availability and stochastic machine downtimes is investigated in an uncertain demand situation. Results that if the network runs with high overcapacity, central planning is less likely to increase benefit enough to outweigh associated cost. Otherwise, if capacity constraints are crucial, a central planning mode is recommendable. When also unforeseen machine downtimes are low, the use of sophisticated IT tools is most profitable.

  9. Running a network on a shoestring: the Global Invasive Species Information Network

    Science.gov (United States)

    Jarnevich, Catherine S.; Simpson, Annie; Graham, James J; Newman, Gregory J.; Bargeron, Chuck T.

    2015-01-01

    The Global Invasive Species Information Network (GISIN) was conceptualized in 2004 to aggregate and disseminate invasive species data in a standardized way. A decade later the GISIN community has implemented a data portal and three of six GISIN data aggregation models in the GISIN data exchange Protocol, including invasive species status information, resource URLs, and occurrence data. The portal is based on a protocol developed by representatives from 15 countries and 27 organizations of the global invasive species information management community. The GISIN has 19 data providers sharing 34,343 species status records, 1,693,073 occurrences, and 15,601 resource URLs. While the GISIN's goal is to be global, much of its data and funding are provided by the United States. Several initiatives use the GISIN as their information backbone, such as the Great Lakes Early Detection Network (GLEDN) and the North American Invasive Species Network (NAISN). Here we share several success stories and organizational challenges that remain.

  10. Rapid Alterations in Perirenal Adipose Tissue Transcriptomic Networks with Cessation of Voluntary Running.

    Directory of Open Access Journals (Sweden)

    Gregory N Ruegsegger

    Full Text Available In maturing rats, the growth of abdominal fat is attenuated by voluntary wheel running. After the cessation of running by wheel locking, a rapid increase in adipose tissue growth to a size that is similar to rats that have never run (i.e. catch-up growth has been previously reported by our lab. In contrast, diet-induced increases in adiposity have a slower onset with relatively delayed transcriptomic responses. The purpose of the present study was to identify molecular pathways associated with the rapid increase in adipose tissue after ending 6 wks of voluntary running at the time of puberty. Age-matched, male Wistar rats were given access to running wheels from 4 to 10 weeks of age. From the 10th to 11th week of age, one group of rats had continued wheel access, while the other group had one week of wheel locking. Perirenal adipose tissue was extracted, RNA sequencing was performed, and bioinformatics analyses were executed using Ingenuity Pathway Analysis (IPA. IPA was chosen to assist in the understanding of complex 'omics data by integrating data into networks and pathways. Wheel locked rats gained significantly more fat mass and significantly increased body fat percentage between weeks 10-11 despite having decreased food intake, as compared to rats with continued wheel access. IPA identified 646 known transcripts differentially expressed (p < 0.05 between continued wheel access and wheel locking. In wheel locked rats, IPA revealed enrichment of transcripts for the following functions: extracellular matrix, macrophage infiltration, immunity, and pro-inflammatory. These findings suggest that increases in visceral adipose tissue that accompanies the cessation of pubertal physical activity are associated with the alteration of multiple pathways, some of which may potentiate the development of pubertal obesity and obesity-associated systemic low-grade inflammation that occurs later in life.

  11. Experience and Lessons learnt from running high availability databases on Network Attached Storage

    CERN Document Server

    Guijarro, Juan Manuel; Segura Chinchilla, Nilo

    2008-01-01

    The Database and Engineering Services Group of CERN's Information Technology Department provides the Oracle based Central Data Base services used in many activities at CERN. In order to provide High Availability and ease management for those services, a NAS (Network Attached Storage) based infrastructure has been set up. It runs several instances of the Oracle RAC (Real Application Cluster) using NFS as share disk space for RAC purposes and Data hosting. It is composed of two private LAN's to provide access to the NAS file servers and Oracle RAC interconnect, both using network bonding. NAS nodes are configured in partnership to prevent having single points of failure and to provide automatic NAS fail-over. This presentation describes that infrastructure and gives some advice on how to automate its management and setup using a Fabric Management framework such as Quattor. It also covers aspects related with NAS Performance and Monitoring as well Data Backup and Archive of such facility using already existing i...

  12. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Introduction. Over the past few years, network science has drawn attention from a large number of ... The qualitative properties of biological networks cannot ... Here, we study the underlying undirected structure of empirical biological networks.

  13. From network structure to network reorganization: implications for adult neurogenesis

    International Nuclear Information System (INIS)

    Schneider-Mizell, Casey M; Zochowski, Michal R; Sander, Leonard M; Parent, Jack M; Ben-Jacob, Eshel

    2010-01-01

    Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells

  14. Structural constraints in complex networks

    International Nuclear Information System (INIS)

    Zhou, S; Mondragon, R J

    2007-01-01

    We present a link rewiring mechanism to produce surrogates of a network where both the degree distribution and the rich-club connectivity are preserved. We consider three real networks, the autonomous system (AS)-Internet, protein interaction and scientific collaboration. We show that for a given degree distribution, the rich-club connectivity is sensitive to the degree-degree correlation, and on the other hand the degree-degree correlation is constrained by the rich-club connectivity. In particular, in the case of the Internet, the assortative coefficient is always negative and a minor change in its value can reverse the network's rich-club structure completely; while fixing the degree distribution and the rich-club connectivity restricts the assortative coefficient to such a narrow range, that a reasonable model of the Internet can be produced by considering mainly the degree distribution and the rich-club connectivity. We also comment on the suitability of using the maximal random network as a null model to assess the rich-club connectivity in real networks

  15. 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,

  16. Social structure of Facebook networks

    Science.gov (United States)

    Traud, Amanda L.; Mucha, Peter J.; Porter, Mason A.

    2012-08-01

    We study the social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes-gender, class year, major, high school, and residence-at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on user characteristics. We thereby examine the relative importance of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.

  17. Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach

    Directory of Open Access Journals (Sweden)

    Andrea N. Onodera

    2017-02-01

    Full Text Available Background Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis. Methods Twenty-seven experienced male runners (63 ± 44 km/week run ran in four-shoe design that combined two resilience-cushioning materials (low and high and two uppers (minimalist and structured. Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features. Results The applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16% biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%. Discussion The discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects

  18. Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach.

    Science.gov (United States)

    Onodera, Andrea N; Gavião Neto, Wilson P; Roveri, Maria Isabel; Oliveira, Wagner R; Sacco, Isabel Cn

    2017-01-01

    Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis. Twenty-seven experienced male runners (63 ± 44 km/week run) ran in four-shoe design that combined two resilience-cushioning materials (low and high) and two uppers (minimalist and structured). Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects) × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features). The applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16%) biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%. The discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects of different uppers were distributed along the

  19. Network structure exploration via Bayesian nonparametric models

    International Nuclear Information System (INIS)

    Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z

    2015-01-01

    Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)

  20. An evolving network model with community structure

    International Nuclear Information System (INIS)

    Li Chunguang; Maini, Philip K

    2005-01-01

    Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

  1. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from different domains may vary quite significantly. As there is an interplay between network architecture and dynamics, structure plays an important role in communication and spreading of ...

  2. True Nature of Supply Network Communication Structure

    Directory of Open Access Journals (Sweden)

    Lokhman Hakim bin Osman

    2016-04-01

    Full Text Available Globalization of world economy has altered the definition of organizational structure. Global supply chain can no longer be viewed as an arm-length structure. It has become more complex. The complexity demands deeper research and understanding. This research analyzed a structure of supply network in an attempt to elucidate the true structure of the supply network. Using the quantitative Social Network Analysis methodology, findings of this study indicated that, the structure of the supply network differs depending on the types of network relations. An important implication of these findings would be a more focus resource management upon network relationship development that is based on firms’ positions in the different network structure. This research also contributes to the various strategies of effective and efficient supply chain management.

  3. Network structure exploration in networks with node attributes

    Science.gov (United States)

    Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

    2016-05-01

    Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

  4. Global Electricity Trade Network: Structures and Implications

    Science.gov (United States)

    Ji, Ling; Jia, Xiaoping; Chiu, Anthony S. F.; Xu, Ming

    2016-01-01

    Nations increasingly trade electricity, and understanding the structure of the global power grid can help identify nations that are critical for its reliability. This study examines the global grid as a network with nations as nodes and international electricity trade as links. We analyze the structure of the global electricity trade network and find that the network consists of four sub-networks, and provide a detailed analysis of the largest network, Eurasia. Russia, China, Ukraine, and Azerbaijan have high betweenness measures in the Eurasian sub-network, indicating the degrees of centrality of the positions they hold. The analysis reveals that the Eurasian sub-network consists of seven communities based on the network structure. We find that the communities do not fully align with geographical proximity, and that the present international electricity trade in the Eurasian sub-network causes an approximately 11 million additional tons of CO2 emissions. PMID:27504825

  5. Managing Network Partitions in Structured P2P Networks

    Science.gov (United States)

    Shafaat, Tallat M.; Ghodsi, Ali; Haridi, Seif

    Structured overlay networks form a major class of peer-to-peer systems, which are touted for their abilities to scale, tolerate failures, and self-manage. Any long-lived Internet-scale distributed system is destined to face network partitions. Consequently, the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems. This makes it a crucial requirement for building any structured peer-to-peer systems to be resilient to network partitions. Although the problem of network partitions and mergers is highly related to fault-tolerance and self-management in large-scale systems, it has hardly been studied in the context of structured peer-to-peer systems. Structured overlays have mainly been studied under churn (frequent joins/failures), which as a side effect solves the problem of network partitions, as it is similar to massive node failures. Yet, the crucial aspect of network mergers has been ignored. In fact, it has been claimed that ring-based structured overlay networks, which constitute the majority of the structured overlays, are intrinsically ill-suited for merging rings. In this chapter, we motivate the problem of network partitions and mergers in structured overlays. We discuss how a structured overlay can automatically detect a network partition and merger. We present an algorithm for merging multiple similar ring-based overlays when the underlying network merges. We examine the solution in dynamic conditions, showing how our solution is resilient to churn during the merger, something widely believed to be difficult or impossible. We evaluate the algorithm for various scenarios and show that even when falsely detecting a merger, the algorithm quickly terminates and does not clutter the network with many messages. The algorithm is flexible as the tradeoff between message complexity and time complexity can be adjusted by a parameter.

  6. Robustness and structure of complex networks

    Science.gov (United States)

    Shao, Shuai

    This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks

  7. Immunization of networks with community structure

    International Nuclear Information System (INIS)

    Masuda, Naoki

    2009-01-01

    In this study, an efficient method to immunize modular networks (i.e. networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, protection against intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.

  8. Impact of constrained rewiring on network structure and node dynamics

    Science.gov (United States)

    Rattana, P.; Berthouze, L.; Kiss, I. Z.

    2014-11-01

    In this paper, we study an adaptive spatial network. We consider a susceptible-infected-susceptible (SIS) epidemic on the network, with a link or contact rewiring process constrained by spatial proximity. In particular, we assume that susceptible nodes break links with infected nodes independently of distance and reconnect at random to susceptible nodes available within a given radius. By systematically manipulating this radius we investigate the impact of rewiring on the structure of the network and characteristics of the epidemic. We adopt a step-by-step approach whereby we first study the impact of rewiring on the network structure in the absence of an epidemic, then with nodes assigned a disease status but without disease dynamics, and finally running network and epidemic dynamics simultaneously. In the case of no labeling and no epidemic dynamics, we provide both analytic and semianalytic formulas for the value of clustering achieved in the network. Our results also show that the rewiring radius and the network's initial structure have a pronounced effect on the endemic equilibrium, with increasingly large rewiring radiuses yielding smaller disease prevalence.

  9. Epidemics in adaptive networks with community structure

    Science.gov (United States)

    Shaw, Leah; Tunc, Ilker

    2010-03-01

    Models for epidemic spread on static social networks do not account for changes in individuals' social interactions. Recent studies of adaptive networks have modeled avoidance behavior, as non-infected individuals try to avoid contact with infectives. Such models have not generally included realistic social structure. Here we study epidemic spread on an adaptive network with community structure. We model the effect of heterogeneous communities on infection levels and epidemic extinction. We also show how an epidemic can alter the community structure.

  10. Airline network structure in competitive market

    Directory of Open Access Journals (Sweden)

    Babić Danica D.

    2014-01-01

    Full Text Available Airline's network is the key element of its business strategy and selected network structure will not have influence only on the airline's costs but could gain some advantage in revenues, too. Network designing implies that an airline has to make decisions about markets that it will serve and how to serve those markets. Network choice raises the following questions for an airline: a what markets to serve, b how to serve selected markets, c what level of service to offer, d what are the benefits/cost of the that decisions and e what is the influence of the competition. We analyzed the existing airline business models and corresponding network structure. The paper highlights the relationship between the network structures and the airline business strategies. Using a simple model we examine the relationship between the network structure and service quality in deregulated market.

  11. Pinning Control Strategy of Multicommunity Structure Networks

    Directory of Open Access Journals (Sweden)

    Chao Ding

    2017-01-01

    Full Text Available In order to investigate the effects of community structure on synchronization, a pinning control strategy is researched in a class of complex networks with community structure in this paper. A feedback control law is designed based on the network community structure information. The stability condition is given and proved by using Lyapunov stability theory. Our research shows that as to community structure networks, there being a threshold hT≈5, when coupling strength bellows this threshold, the stronger coupling strength corresponds to higher synchronizability; vice versa, the stronger coupling strength brings lower synchronizability. In addition the synchronizability of overlapping and nonoverlapping community structure networks was simulated and analyzed; while the nodes were controlled randomly and intensively, the results show that intensive control strategy is better than the random one. The network will reach synchronization easily when the node with largest betweenness was controlled. Furthermore, four difference networks’ synchronizability, such as Barabási-Albert network, Watts-Strogatz network, Erdös-Rényi network, and community structure network, are simulated; the research shows that the community structure network is more easily synchronized under the same control strength.

  12. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    structures, protein–protein interaction networks, social interactions, the Internet, and so on can be described by complex networks [1–5]. Recent developments in the understanding of complex networks has led to deeper insights about their origin and other properties [1–5]. One common realization that emerges from these ...

  13. Exploring biological network structure with clustered random networks

    Directory of Open Access Journals (Sweden)

    Bansal Shweta

    2009-12-01

    Full Text Available Abstract Background Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions and the extent of clustering (the tendency for a set of three nodes to be interconnected are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in

  14. STRUCTURE AND COOPTATION IN ORGANIZATION NETWORK

    Directory of Open Access Journals (Sweden)

    Valéria Riscarolli

    2007-10-01

    Full Text Available Business executive are rethinking business concept, based on horizontalization principles. As so, most organizational functions are outsourced, leading the enterprise to build business through a network of organizations. Here we study the case of Cia Hering’s network of organizations, a leader in knit apparel segment in Latin America (IEMI, 2004, looking at the network’s structure and levels of cooptation. A theoretical model was used using Quinn et al. (2001 “sun ray” network structure as basis to analyze the case study. Main results indicate higher degree of structural conformity, but incipient degree of coopetation in the network.

  15. Cross-linked structure of network evolution

    Energy Technology Data Exchange (ETDEWEB)

    Bassett, Danielle S., E-mail: dsb@seas.upenn.edu [Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Department of Physics, University of California, Santa Barbara, California 93106 (United States); Sage Center for the Study of the Mind, University of California, Santa Barbara, California 93106 (United States); Wymbs, Nicholas F.; Grafton, Scott T. [Department of Psychology and UCSB Brain Imaging Center, University of California, Santa Barbara, California 93106 (United States); Porter, Mason A. [Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford OX2 6GG (United Kingdom); CABDyN Complexity Centre, University of Oxford, Oxford, OX1 1HP (United Kingdom); Mucha, Peter J. [Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina 27599 (United States); Department of Applied Physical Sciences, University of North Carolina, Chapel Hill, North Carolina 27599 (United States)

    2014-03-15

    We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks.

  16. Cross-linked structure of network evolution

    International Nuclear Information System (INIS)

    Bassett, Danielle S.; Wymbs, Nicholas F.; Grafton, Scott T.; Porter, Mason A.; Mucha, Peter J.

    2014-01-01

    We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice. Subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks

  17. A typical wave wake from high-speed vessels: its group structure and run-up

    Directory of Open Access Journals (Sweden)

    I. Didenkulova

    2013-02-01

    Full Text Available High-amplitude water waves induced by high-speed vessels are regularly observed in Tallinn Bay, the Baltic Sea, causing intense beach erosion and disturbing marine habitants in the coastal zone. Such a strong impact on the coast may be a result of a certain group structure of the wave wake. In order to understand it, here we present an experimental study of the group structure of these wakes at Pikakari beach, Tallinn Bay. The most energetic vessel waves at this location (100 m from the coast at the water depth 2.7 m have amplitudes of about 1 m and periods of 8–10 s and cause maximum run-up heights on a beach up to 1.4 m. These waves represent frequency modulated packets where the largest and longest waves propagate ahead of other smaller amplitude and period waves. Sometimes the groups of different heights and periods can be separated even within one wave wake event. The wave heights within a wake are well described by the Weibull distribution, which has different parameters for wakes from different vessels. Wave run-up heights can also be described by Weibull distribution and its parameters can be connected to the parameters of the distribution of wave heights 100 m from the coast. Finally, the run-up of individual waves within a packet is studied. It is shown that the specific structure of frequency modulated wave packets, induced by high-speed vessels, leads to a sequence of high wave run-ups at the coast, even when the original wave heights are rather moderate. This feature can be a key to understanding the significant impact on coasts caused by fast vessels.

  18. Network structure and travel time perception.

    Science.gov (United States)

    Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

    2013-01-01

    The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.

  19. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  20. Who runs public health? A mixed-methods study combining qualitative and network analyses.

    Science.gov (United States)

    Oliver, Kathryn; de Vocht, Frank; Money, Annemarie; Everett, Martin

    2013-09-01

    Persistent health inequalities encourage researchers to identify new ways of understanding the policy process. Informal relationships are implicated in finding evidence and making decisions for public health policy (PHP), but few studies use specialized methods to identify key actors in the policy process. We combined network and qualitative data to identify the most influential individuals in PHP in a UK conurbation and describe their strategies to influence policy. Network data were collected by asking for nominations of powerful and influential people in PHP (n = 152, response rate 80%), and 23 semi-structured interviews were analysed using a framework approach. The most influential PHP makers in this conurbation were mid-level managers in the National Health Service and local government, characterized by managerial skills: controlling policy processes through gate keeping key organizations, providing policy content and managing selected experts and executives to lead on policies. Public health professionals and academics are indirectly connected to policy via managers. The most powerful individuals in public health are managers, not usually considered targets for research. As we show, they are highly influential through all stages of the policy process. This study shows the importance of understanding the daily activities of influential policy individuals.

  1. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...

  2. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Stea, Diego; Soda, Giuseppe; Pedersen, Torben

    2016-01-01

    Network research has yet to determine whether bonding ties or bridging ties are more beneficial for individual creativity, but the debate has mostly overlooked the organizational context in which such ties are formed. In particular, the causal chain connecting network structures and individual...... with the network’s organizational context. Thus, actors in dense network structures acquire more knowledge and eventually become more creative in organizational contexts where collaboration is high. Conversely, brokers who arbitrage information across disconnected network contacts acquire more valuable knowledge...

  3. Information transfer in community structured multiplex networks

    Directory of Open Access Journals (Sweden)

    Albert eSolé Ribalta

    2015-08-01

    Full Text Available The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.. The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  4. Information transfer in community structured multiplex networks

    Science.gov (United States)

    Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

    2015-08-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  5. Network Ecology and Adolescent Social Structure.

    Science.gov (United States)

    McFarland, Daniel A; Moody, James; Diehl, David; Smith, Jeffrey A; Thomas, Reuben J

    2014-12-01

    Adolescent societies-whether arising from weak, short-term classroom friendships or from close, long-term friendships-exhibit various levels of network clustering, segregation, and hierarchy. Some are rank-ordered caste systems and others are flat, cliquish worlds. Explaining the source of such structural variation remains a challenge, however, because global network features are generally treated as the agglomeration of micro-level tie-formation mechanisms, namely balance, homophily, and dominance. How do the same micro-mechanisms generate significant variation in global network structures? To answer this question we propose and test a network ecological theory that specifies the ways features of organizational environments moderate the expression of tie-formation processes, thereby generating variability in global network structures across settings. We develop this argument using longitudinal friendship data on schools (Add Health study) and classrooms (Classroom Engagement study), and by extending exponential random graph models to the study of multiple societies over time.

  6. 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.

  7. The Structure of Online Consumer Communication Networks

    NARCIS (Netherlands)

    B.G.C. Dellaert (Benedict); M.J.W. Harmsen-van Hout (Marjolein); P.J.J. Herings (Jean-Jacques)

    2006-01-01

    textabstractIn this paper we study the structure of the bilateral communication links within Online Consumer Communication Networks (OCCNs), such as virtual communities. Compared to the offline world, consumers in online networks are highly flexible to choose their communication partners and little

  8. The global structure of knowledge network

    NARCIS (Netherlands)

    Angelopoulos, Spyros; Lomi, Alessandro

    2017-01-01

    In this paper, we treat patent citations as knowledge networks connecting pieces of formalized knowledge and people, and focus on how ideas are connected, rather than how they are protected. We focus on the global structural properties of formalized knowledge network, and more specifically on the

  9. Insect community structure and function in Upper Three Runs, Savannah River Site, South Carolina

    Energy Technology Data Exchange (ETDEWEB)

    Morse, J.C.; English, W.R. [Clemson Univ., SC (United States). Dept. of Entomology; Looney, B.B. [Westinghouse Savannah River Co., Aiken, SC (United States)

    1993-07-08

    A project to document the insect species in the upper reaches of Upper Three Runs at the Savannah River site was recently completed. This research was supported by the US Department of Energy under the National Environmental Research Park Program. The work was performed by the Department of Entomology at Clemson University in clemson, SC, by John C. Morse (principal investigator), William R. English and their colleagues. The major output from this study was the dissertation of Dr. William R. English entitled ``Ecosystem Dynamics of a South Carolina Sandhills Stream.`` He investigated selected environmental resources and determined their dynamics and the dynamics of the aquatic invertebrate community structure in response to them.

  10. Network Centric Information Structure - Crisis Information Management

    National Research Council Canada - National Science Library

    Aarholt, Eldar; Berg, Olav

    2004-01-01

    This paper presents a generic Network Centric Information Structure (NCIS) that can be used by civilian, military and public sectors, and that supports information handling applied to crises management and emergency response...

  11. NCI National Clinical Trials Network Structure

    Science.gov (United States)

    Learn about how the National Clinical Trials Network (NCTN) is structured. The NCTN is a program of the National Cancer Institute that gives funds and other support to cancer research organizations to conduct cancer clinical trials.

  12. Nonparametric inference of network structure and dynamics

    Science.gov (United States)

    Peixoto, Tiago P.

    The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

  13. Nuclear Structure and Decay Data (NSDD) network

    International Nuclear Information System (INIS)

    Pronyaev, V.G.

    2001-02-01

    This report provides a brief description of the Nuclear Structure and Decay Data (NSDD) Network in response to a request from the Advisory Group Meeting on ''Co-ordination of the International Network of Nuclear Structure and Decay Data Evaluators'' (IAEA, Vienna, 14-17 December 1998, report IAEA(NDS)-399 (1999)). This report supersedes the special issue of the Nuclear Data Newsletter No. 20 published in November 1994. (author)

  14. Modelling the structure of complex networks

    DEFF Research Database (Denmark)

    Herlau, Tue

    networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

  15. The Deep Structure of Organizational Online Networking

    DEFF Research Database (Denmark)

    Trier, Matthias; Richter, Alexander

    2015-01-01

    While research on organizational online networking recently increased significantly, most studies adopt quantitative research designs with a focus on the consequences of social network configurations. Very limited attention is paid to comprehensive theoretical conceptions of the complex phenomenon...... of organizational online networking. We address this gap by adopting a theoretical framework of the deep structure of organizational online networking with a focus on their emerging meaning for the employees. We apply and assess the framework in a qualitative case study of a large-scale implementation...... of a corporate social network site (SNS) in a global organization. We reveal organizational online networking as a multi-dimensional phenomenon with multiplex relationships that are unbalanced, primarily consist of weak ties and are subject to temporal change. Further, we identify discourse drivers...

  16. Universal data access for run-time resource management in resource constrained wireless networks

    OpenAIRE

    Rerkrai, Krisakorn

    2012-01-01

    Resource-constrainedwireless networks, e.g.wireless sensor networks (WSNs), small embedded devices with limited computational power and energy, have been the subject of intense research in the past decade. Moreover, recent technological advances and growing demand for better efficiency have led to a great number of link and network protocols for WSNs. The protocols depend on specific interfaces to exchange necessary information. Unfortunately these interfaces are often proprietary and highly ...

  17. On Adding Structure to Unstructured Overlay Networks

    Science.gov (United States)

    Leitão, João; Carvalho, Nuno A.; Pereira, José; Oliveira, Rui; Rodrigues, Luís

    Unstructured peer-to-peer overlay networks are very resilient to churn and topology changes, while requiring little maintenance cost. Therefore, they are an infrastructure to build highly scalable large-scale services in dynamic networks. Typically, the overlay topology is defined by a peer sampling service that aims at maintaining, in each process, a random partial view of peers in the system. The resulting random unstructured topology is suboptimal when a specific performance metric is considered. On the other hand, structured approaches (for instance, a spanning tree) may optimize a given target performance metric but are highly fragile. In fact, the cost for maintaining structures with strong constraints may easily become prohibitive in highly dynamic networks. This chapter discusses different techniques that aim at combining the advantages of unstructured and structured networks. Namely we focus on two distinct approaches, one based on optimizing the overlay and another based on optimizing the gossip mechanism itself.

  18. Network structure of subway passenger flows

    Science.gov (United States)

    Xu, Q.; Mao, B. H.; Bai, Y.

    2016-03-01

    The results of transportation infrastructure network analyses have been used to analyze complex networks in a topological context. However, most modeling approaches, including those based on complex network theory, do not fully account for real-life traffic patterns and may provide an incomplete view of network functions. This study utilizes trip data obtained from the Beijing Subway System to characterize individual passenger movement patterns. A directed weighted passenger flow network was constructed from the subway infrastructure network topology by incorporating trip data. The passenger flow networks exhibit several properties that can be characterized by power-law distributions based on flow size, and log-logistic distributions based on the fraction of boarding and departing passengers. The study also characterizes the temporal patterns of in-transit and waiting passengers and provides a hierarchical clustering structure for passenger flows. This hierarchical flow organization varies in the spatial domain. Ten cluster groups were identified, indicating a hierarchical urban polycentric structure composed of large concentrated flows at urban activity centers. These empirical findings provide insights regarding urban human mobility patterns within a large subway network.

  19. Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach

    OpenAIRE

    Onodera, Andrea N.; Gavi?o Neto, Wilson P.; Roveri, Maria Isabel; Oliveira, Wagner R.; Sacco, Isabel CN

    2017-01-01

    Background Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial...

  20. How structure determines correlations in neuronal networks.

    Directory of Open Access Journals (Sweden)

    Volker Pernice

    2011-05-01

    Full Text Available Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.

  1. Beyond E-business : towards networked structures

    NARCIS (Netherlands)

    Grefen, P.W.P.J.

    2015-01-01

    In Beyond E-Business: Towards Networked Structures Paul Grefen returns with his tried and tested BOAT framework for e-business, now fully expanded and updated with the very latest overview of digitally connected business; from business models, organization structures and architecture, to information

  2. Structural Connectivity Networks of Transgender People

    NARCIS (Netherlands)

    Hahn, Andreas; Kranz, Georg S.; Küblböck, Martin; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F.; Lanzenberger, Rupert

    2015-01-01

    Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF)

  3. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Soda, Giuseppe; Stea, Diego; Pedersen, Torben

    2017-01-01

    The debate on whether bonding or bridging ties are more beneficial for acquiring knowledge that is conducive to individual creativity has mostly overlooked the context in which such ties are formed. We challenge the widespread assumption that closed, heavily bonded networks imply a collaborative...... attitude on the part of the embedded actors and propose that the level of collaboration in a network can be independent from that network’s structural characteristics, such that it moderates the effects of closed and brokering network positions on the acquisition of knowledge that supports creativity....... Individuals embedded in closed networks acquire more knowledge and become more creative when the level of collaboration in their network is high. Brokers who arbitrage information across disconnected contacts acquire more knowledge and become more creative when collaboration is low. An analysis of employee...

  4. NAPS: Network Analysis of Protein Structures

    Science.gov (United States)

    Chakrabarty, Broto; Parekh, Nita

    2016-01-01

    Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue–residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein–protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201

  5. Structure and growth of weighted networks

    Energy Technology Data Exchange (ETDEWEB)

    Riccaboni, Massimo [Department of Computer and Management Sciences, University of Trento, Trento (Italy); Schiavo, Stefano [Department of Economics, University of Trento, Trento (Italy)], E-mail: massimo.riccaboni@unitn.it, E-mail: stefano.schiavo@unitn.it

    2010-02-15

    We develop a simple theoretical framework for the evolution of weighted networks that is consistent with a number of stylized features of real-world data. In our framework, the Barabasi-Albert model of network evolution is extended by assuming that link weights evolve according to a geometric Brownian motion. Our model is verified by means of simulations and real-world trade data. We show that the model correctly predicts the intensity and growth distribution of links, the size-variance relationship of the growth of link weights, the relationship between the degree and strength of nodes, and the scale-free structure of the network.

  6. Polarized DIS Structure Functions from Neural Networks

    International Nuclear Information System (INIS)

    Del Debbio, L.; Guffanti, A.; Piccione, A.

    2007-01-01

    We present a parametrization of polarized Deep-Inelastic-Scattering (DIS) structure functions based on Neural Networks. The parametrization provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. As an example we discuss the application of this method to the study of the structure function g 1 p (x,Q 2 )

  7. Community Structure in Online Collegiate Social Networks

    Science.gov (United States)

    Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

    2009-03-01

    Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

  8. Networks: structure and action : steering in and steering by policy networks

    NARCIS (Netherlands)

    Dassen, A.

    2010-01-01

    This thesis explores the opportunities to build a structural policy network model that is rooted in social network theories. By making a distinction between a process of steering in networks, and a process of steering by networks, it addresses the effects of network structures on network dynamics as

  9. OGS improvements in 2012 in running the Northeastern Italy Seismic Network: the Ferrara VBB borehole seismic station

    Science.gov (United States)

    Pesaresi, Damiano; Romanelli, Marco; Barnaba, Carla; Bragato, Pier Luigi; Durì, Giorgio

    2013-04-01

    The Centro di Ricerche Sismologiche (CRS, Seismological Research Center) of the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS, Italian National Institute for Oceanography and Experimental Geophysics) in Udine (Italy) after the strong earthquake of magnitude M=6.4 occurred in 1976 in the Italian Friuli-Venezia Giulia region, started to operate the Northeastern Italy Seismic Network: it currently consists of 17 very sensitive broad band and 18 simpler short period seismic stations, all telemetered to and acquired in real time at the OGS-CRS data center in Udine. Real time data exchange agreements in place with other Italian, Slovenian, Austrian and Swiss seismological institutes lead to a total number of about 100 seismic stations acquired in real time, which makes the OGS the reference institute for seismic monitoring of Northeastern Italy. The southwestern edge of the OGS seismic network stands on the Po alluvial basin: earthquake localization and characterization in this area is affected by the presence of soft alluvial deposits. OGS ha already experience in running a local seismic network in high noise conditions making use of borehole installations in the case of the micro-seismicity monitoring of a local gas storage site for a private company. Following the ML=5.9 earthquake that struck the Emilia region around Ferrara in Northern Italy on May 20, 2012 at 02:03:53 UTC, a cooperation of Istituto Nazionale di Geofisica e Vulcanologia, OGS, the Comune di Ferrara and the University of Ferrara lead to the reinstallation of a previously existing very broad band (VBB) borehole seismic station in Ferrara. The aim of the OGS intervention was on one hand to extend its real time seismic monitoring capabilities toward South-West, including Ferrara and its surroundings, and on the other hand to evaluate the seismic response at the site. We will describe improvements in running the Northeastern Italy Seismic Network, including details of the Ferrara VBB

  10. RENEB - Running the European Network of biological dosimetry and physical retrospective dosimetry.

    Science.gov (United States)

    Kulka, Ulrike; Abend, Michael; Ainsbury, Elizabeth; Badie, Christophe; Barquinero, Joan Francesc; Barrios, Lleonard; Beinke, Christina; Bortolin, Emanuela; Cucu, Alexandra; De Amicis, Andrea; Domínguez, Inmaculada; Fattibene, Paola; Frøvig, Anne Marie; Gregoire, Eric; Guogyte, Kamile; Hadjidekova, Valeria; Jaworska, Alicja; Kriehuber, Ralf; Lindholm, Carita; Lloyd, David; Lumniczky, Katalin; Lyng, Fiona; Meschini, Roberta; Mörtl, Simone; Della Monaca, Sara; Monteiro Gil, Octávia; Montoro, Alegria; Moquet, Jayne; Moreno, Mercedes; Oestreicher, Ursula; Palitti, Fabrizio; Pantelias, Gabriel; Patrono, Clarice; Piqueret-Stephan, Laure; Port, Matthias; Prieto, María Jesus; Quintens, Roel; Ricoul, Michelle; Romm, Horst; Roy, Laurence; Sáfrány, Géza; Sabatier, Laure; Sebastià, Natividad; Sommer, Sylwester; Terzoudi, Georgia; Testa, Antonella; Thierens, Hubert; Turai, Istvan; Trompier, François; Valente, Marco; Vaz, Pedro; Voisin, Philippe; Vral, Anne; Woda, Clemens; Zafiropoulos, Demetre; Wojcik, Andrzej

    2017-01-01

    A European network was initiated in 2012 by 23 partners from 16 European countries with the aim to significantly increase individualized dose reconstruction in case of large-scale radiological emergency scenarios. The network was built on three complementary pillars: (1) an operational basis with seven biological and physical dosimetric assays in ready-to-use mode, (2) a basis for education, training and quality assurance, and (3) a basis for further network development regarding new techniques and members. Techniques for individual dose estimation based on biological samples and/or inert personalized devices as mobile phones or smart phones were optimized to support rapid categorization of many potential victims according to the received dose to the blood or personal devices. Communication and cross-border collaboration were also standardized. To assure long-term sustainability of the network, cooperation with national and international emergency preparedness organizations was initiated and links to radiation protection and research platforms have been developed. A legal framework, based on a Memorandum of Understanding, was established and signed by 27 organizations by the end of 2015. RENEB is a European Network of biological and physical-retrospective dosimetry, with the capacity and capability to perform large-scale rapid individualized dose estimation. Specialized to handle large numbers of samples, RENEB is able to contribute to radiological emergency preparedness and wider large-scale research projects.

  11. Structural health monitoring using wireless sensor networks

    Science.gov (United States)

    Sreevallabhan, K.; Nikhil Chand, B.; Ramasamy, Sudha

    2017-11-01

    Monitoring and analysing health of large structures like bridges, dams, buildings and heavy machinery is important for safety, economical, operational, making prior protective measures, and repair and maintenance point of view. In recent years there is growing demand for such larger structures which in turn make people focus more on safety. By using Microelectromechanical Systems (MEMS) Accelerometer we can perform Structural Health Monitoring by studying the dynamic response through measure of ambient vibrations and strong motion of such structures. By using Wireless Sensor Networks (WSN) we can embed these sensors in wireless networks which helps us to transmit data wirelessly thus we can measure the data wirelessly at any remote location. This in turn reduces heavy wiring which is a cost effective as well as time consuming process to lay those wires. In this paper we developed WSN based MEMS-accelerometer for Structural to test the results in the railway bridge near VIT University, Vellore campus.

  12. Structural covariance networks in the mouse brain.

    Science.gov (United States)

    Pagani, Marco; Bifone, Angelo; Gozzi, Alessandro

    2016-04-01

    The presence of networks of correlation between regional gray matter volume as measured across subjects in a group of individuals has been consistently described in several human studies, an approach termed structural covariance MRI (scMRI). Complementary to prevalent brain mapping modalities like functional and diffusion-weighted imaging, the approach can provide precious insights into the mutual influence of trophic and plastic processes in health and pathological states. To investigate whether analogous scMRI networks are present in lower mammal species amenable to genetic and experimental manipulation such as the laboratory mouse, we employed high resolution morphoanatomical MRI in a large cohort of genetically-homogeneous wild-type mice (C57Bl6/J) and mapped scMRI networks using a seed-based approach. We show that the mouse brain exhibits robust homotopic scMRI networks in both primary and associative cortices, a finding corroborated by independent component analyses of cortical volumes. Subcortical structures also showed highly symmetric inter-hemispheric correlations, with evidence of distributed antero-posterior networks in diencephalic regions of the thalamus and hypothalamus. Hierarchical cluster analysis revealed six identifiable clusters of cortical and sub-cortical regions corresponding to previously described neuroanatomical systems. Our work documents the presence of homotopic cortical and subcortical scMRI networks in the mouse brain, thus supporting the use of this species to investigate the elusive biological and neuroanatomical underpinnings of scMRI network development and its derangement in neuropathological states. The identification of scMRI networks in genetically homogeneous inbred mice is consistent with the emerging view of a key role of environmental factors in shaping these correlational networks. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Robustness and modular structure in networks

    DEFF Research Database (Denmark)

    Bagrow, James P.; Lehmann, Sune; Ahn, Yong-Yeol

    2015-01-01

    -12]. Many complex systems, from power grids and the Internet to the brain and society [13-15], can be modeled using modular networks comprised of small, densely connected groups of nodes [16, 17]. These modules often overlap, with network elements belonging to multiple modules [18, 19]. Yet existing work...... on robustness has not considered the role of overlapping, modular structure. Here we study the robustness of these systems to the failure of elements. We show analytically and empirically that it is possible for the modules themselves to become uncoupled or non-overlapping well before the network disintegrates....... If overlapping modular organization plays a role in overall functionality, networks may be far more vulnerable than predicted by conventional percolation theory....

  14. Social Network Structures among Groundnut Farmers

    Science.gov (United States)

    Thuo, Mary; Bell, Alexandra A.; Bravo-Ureta, Boris E.; Okello, David K.; Okoko, Evelyn Nasambu; Kidula, Nelson L.; Deom, C. Michael; Puppala, Naveen

    2013-01-01

    Purpose: Groundnut farmers in East Africa have experienced declines in production despite research and extension efforts to increase productivity. This study examined how social network structures related to acquisition of information about new seed varieties and productivity among groundnut farmers in Uganda and Kenya.…

  15. Decentralized Networked Control of Building Structures

    Czech Academy of Sciences Publication Activity Database

    Bakule, Lubomír; Rehák, Branislav; Papík, Martin

    2016-01-01

    Roč. 31, č. 11 (2016), s. 871-886 ISSN 1093-9687 R&D Projects: GA ČR GA13-02149S Institutional support: RVO:67985556 Keywords : decentralized control * networked control * building structures Subject RIV: BC - Control Systems Theory Impact factor: 5.786, year: 2016

  16. Running the running

    OpenAIRE

    Cabass, Giovanni; Di Valentino, Eleonora; Melchiorri, Alessandro; Pajer, Enrico; Silk, Joseph

    2016-01-01

    We use the recent observations of Cosmic Microwave Background temperature and polarization anisotropies provided by the Planck satellite experiment to place constraints on the running $\\alpha_\\mathrm{s} = \\mathrm{d}n_{\\mathrm{s}} / \\mathrm{d}\\log k$ and the running of the running $\\beta_{\\mathrm{s}} = \\mathrm{d}\\alpha_{\\mathrm{s}} / \\mathrm{d}\\log k$ of the spectral index $n_{\\mathrm{s}}$ of primordial scalar fluctuations. We find $\\alpha_\\mathrm{s}=0.011\\pm0.010$ and $\\beta_\\mathrm{s}=0.027\\...

  17. Short run hydrothermal coordination with network constraints using an interior point method

    International Nuclear Information System (INIS)

    Lopez Lezama, Jesus Maria; Gallego Pareja, Luis Alfonso; Mejia Giraldo, Diego

    2008-01-01

    This paper presents a lineal optimization model to solve the hydrothermal coordination problem. The main contribution of this work is the inclusion of the network constraints to the hydrothermal coordination problem and its solution using an interior point method. The proposed model allows working with a system that can be completely hydraulic, thermal or mixed. Results are presented on the IEEE 14 bus test system

  18. Network structure shapes spontaneous functional connectivity dynamics.

    Science.gov (United States)

    Shen, Kelly; Hutchison, R Matthew; Bezgin, Gleb; Everling, Stefan; McIntosh, Anthony R

    2015-04-08

    The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture. Copyright © 2015 the authors 0270-6474/15/355579-10$15.00/0.

  19. Epidemic spreading on complex networks with community structures

    NARCIS (Netherlands)

    Stegehuis, C.; van der Hofstad, R.W.; van Leeuwaarden, J.S.H.

    2016-01-01

    Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities

  20. Effect of direct reciprocity and network structure on continuing prosperity of social networking services.

    Science.gov (United States)

    Osaka, Kengo; Toriumi, Fujio; Sugawara, Toshihauru

    2017-01-01

    Social networking services (SNSs) are widely used as communicative tools for a variety of purposes. SNSs rely on the users' individual activities associated with some cost and effort, and thus it is not known why users voluntarily continue to participate in SNSs. Because the structures of SNSs are similar to that of the public goods (PG) game, some studies have focused on why voluntary activities emerge as an optimal strategy by modifying the PG game. However, their models do not include direct reciprocity between users, even though reciprocity is a key mechanism that evolves and sustains cooperation in human society. We developed an abstract SNS model called the reciprocity rewards and meta-rewards games that include direct reciprocity by extending the existing models. Then, we investigated how direct reciprocity in an SNS facilitates cooperation that corresponds to participation in SNS by posting articles and comments and how the structure of the networks of users exerts an influence on the strategies of users using the reciprocity rewards game. We run reciprocity rewards games on various complex networks and an instance network of Facebook and found that two types of stable cooperation emerged. First, reciprocity slightly improves the rate of cooperation in complete graphs but the improvement is insignificant because of the instability of cooperation. However, this instability can be avoided by making two assumptions: high degree of fun, i.e. articles are read with high probability, and different attitudes to reciprocal and non-reciprocal agents. We then propose the concept of half free riders to explain what strategy sustains cooperation-dominant situations. Second, we indicate that a certain WS network structure affects users' optimal strategy and facilitates stable cooperation without any extra assumptions. We give a detailed analysis of the different characteristics of the two types of cooperation-dominant situations and the effect of the memory of

  1. Modeling Insurgent Network Structure and Dynamics

    Science.gov (United States)

    Gabbay, Michael; Thirkill-Mackelprang, Ashley

    2010-03-01

    We present a methodology for mapping insurgent network structure based on their public rhetoric. Indicators of cooperative links between insurgent groups at both the leadership and rank-and-file levels are used, such as joint policy statements or joint operations claims. In addition, a targeting policy measure is constructed on the basis of insurgent targeting claims. Network diagrams which integrate these measures of insurgent cooperation and ideology are generated for different periods of the Iraqi and Afghan insurgencies. The network diagrams exhibit meaningful changes which track the evolution of the strategic environment faced by insurgent groups. Correlations between targeting policy and network structure indicate that insurgent targeting claims are aimed at establishing a group identity among the spectrum of rank-and-file insurgency supporters. A dynamical systems model of insurgent alliance formation and factionalism is presented which evolves the relationship between insurgent group dyads as a function of their ideological differences and their current relationships. The ability of the model to qualitatively and quantitatively capture insurgent network dynamics observed in the data is discussed.

  2. Information diffusion in structured online social networks

    Science.gov (United States)

    Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

    2015-05-01

    Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

  3. Network structure of multivariate time series.

    Science.gov (United States)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-21

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  4. An examination of a reciprocal relationship between network governance and network structure

    DEFF Research Database (Denmark)

    Bergenholtz, Carsten; Goduscheit, René Chester

    2011-01-01

    In the present article, we examine the network structure and governance of inter-organisational innovation networks over time. Network governance refers to the issue of how to manage and coordinate the relational activities and processes in the network while research on network structure deals...

  5. Soy protein is beneficial but high-fat diet and voluntary running are detrimental to bone structure in mice.

    Science.gov (United States)

    Yan, Lin; Graef, George L; Nielsen, Forrest H; Johnson, LuAnn K; Cao, Jay

    2015-06-01

    Physical activity and soy protein isolate (SPI) augmentation have been reported to be beneficial for bone health. We hypothesized that combining voluntary running and SPI intake would alleviate detrimental changes in bone induced by a high-fat diet. A 2 × 2 × 2 experiment was designed with diets containing 16% or 45% of energy as corn oil and 20% SPI or casein fed to sedentary or running male C57BL/6 mice for 14 weeks. Distal femurs were assessed for microstructural changes. The high-fat diet significantly decreased trabecular number (Tb.N) and bone mineral density (BMD) and increased trabecular separation (Tb.Sp). Soy protein instead of casein, regardless of fat content, in the diet significantly increased bone volume fraction, Tb.N, connectivity density, and BMD and decreased Tb.Sp. Voluntary running, regardless of fat content, significantly decreased bone volume fraction, Tb.N, connectivity density, and BMD and increased Tb.Sp. The high-fat diet significantly decreased osteocalcin and increased tartrate-resistant acid phosphatase 5b (TRAP 5b) concentrations in plasma. Plasma concentrations of osteocalcin were increased by both SPI and running. Running alleviated the increase in TRAP 5b induced by the high-fat diet. These findings demonstrate that a high-fat diet is deleterious, and SPI is beneficial to trabecular bone properties. The deleterious effect of voluntary running on trabecular structural characteristics indicates that there may be a maximal threshold of running beyond which beneficial effects cease and detrimental effects occur. Increases in plasma osteocalcin and decreases in plasma TRAP 5b in running mice suggest that a compensatory response occurs to counteract the detrimental effects of excessive running. Published by Elsevier Inc.

  6. Economic consequences of extra by-passes in district heating networks. Investment-, running- and maintenance costs

    International Nuclear Information System (INIS)

    Herbert, P.

    1995-02-01

    For various reasons, extra by-passes are installed in district heating networks to ensure a high flow temperature when the water circulation is insufficient. By 'extra by-pass' we here mean a connection between the distribution pipe and the return pipe. This study mainly deals with extra by-passes to prevent freezing. The estimation of the extra by-pass costs is based on the district heating rates. Our assumption is that an extra by-pass can be regarded as a substation in the district heating network, with regard to the demand for the water flow, heat and power. The reason is the difficulty to obtain available facts to estimate the real costs concerning extra by-passes. Therefore, the method can not claim that the information about the costs is exact but gives an indication of the size of them. The valves in an extra by-pass can be set more or less open. We assume that manual valves in extra by-passes are wide open. Thermostatic valves are, however, assumed to be adjusted in order to cause a very small water flow. 2 refs, 16 figs, 9 tabs, 6 appendices

  7. Effect of treadmill versus overground running on the structure of variability of stride timing.

    Science.gov (United States)

    Lindsay, Timothy R; Noakes, Timothy D; McGregor, Stephen J

    2014-04-01

    Gait timing dynamics of treadmill and overground running were compared. Nine trained runners ran treadmill and track trials at 80, 100, and 120% of preferred pace for 8 min. each. Stride time series were generated for each trial. To each series, detrended fluctuation analysis (DFA), power spectral density (PSD), and multiscale entropy (MSE) analysis were applied to infer the regime of control along the randomness-regularity axis. Compared to overground running, treadmill running exhibited a higher DFA and PSD scaling exponent, as well as lower entropy at non-preferred speeds. This indicates a more ordered control for treadmill running, especially at non-preferred speeds. The results suggest that the treadmill itself brings about greater constraints and requires increased voluntary control. Thus, the quantification of treadmill running gait dynamics does not necessarily reflect movement in overground settings.

  8. Self-Healing Networks: Redundancy and Structure

    Science.gov (United States)

    Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

    2014-01-01

    We introduce the concept of self-healing in the field of complex networks modelling; in particular, self-healing capabilities are implemented through distributed communication protocols that exploit redundant links to recover the connectivity of the system. We then analyze the effect of the level of redundancy on the resilience to multiple failures; in particular, we measure the fraction of nodes still served for increasing levels of network damages. Finally, we study the effects of redundancy under different connectivity patterns—from planar grids, to small-world, up to scale-free networks—on healing performances. Small-world topologies show that introducing some long-range connections in planar grids greatly enhances the resilience to multiple failures with performances comparable to the case of the most resilient (and least realistic) scale-free structures. Obvious applications of self-healing are in the important field of infrastructural networks like gas, power, water, oil distribution systems. PMID:24533065

  9. Approaches in highly parameterized inversion-PESTCommander, a graphical user interface for file and run management across networks

    Science.gov (United States)

    Karanovic, Marinko; Muffels, Christopher T.; Tonkin, Matthew J.; Hunt, Randall J.

    2012-01-01

    Models of environmental systems have become increasingly complex, incorporating increasingly large numbers of parameters in an effort to represent physical processes on a scale approaching that at which they occur in nature. Consequently, the inverse problem of parameter estimation (specifically, model calibration) and subsequent uncertainty analysis have become increasingly computation-intensive endeavors. Fortunately, advances in computing have made computational power equivalent to that of dozens to hundreds of desktop computers accessible through a variety of alternate means: modelers have various possibilities, ranging from traditional Local Area Networks (LANs) to cloud computing. Commonly used parameter estimation software is well suited to take advantage of the availability of such increased computing power. Unfortunately, logistical issues become increasingly important as an increasing number and variety of computers are brought to bear on the inverse problem. To facilitate efficient access to disparate computer resources, the PESTCommander program documented herein has been developed to provide a Graphical User Interface (GUI) that facilitates the management of model files ("file management") and remote launching and termination of "slave" computers across a distributed network of computers ("run management"). In version 1.0 described here, PESTCommander can access and ascertain resources across traditional Windows LANs: however, the architecture of PESTCommander has been developed with the intent that future releases will be able to access computing resources (1) via trusted domains established in Wide Area Networks (WANs) in multiple remote locations and (2) via heterogeneous networks of Windows- and Unix-based operating systems. The design of PESTCommander also makes it suitable for extension to other computational resources, such as those that are available via cloud computing. Version 1.0 of PESTCommander was developed primarily to work with the

  10. OGS improvements in 2012 in running the North-eastern Italy Seismic Network: the Ferrara VBB borehole seismic station

    Science.gov (United States)

    Pesaresi, D.; Romanelli, M.; Barnaba, C.; Bragato, P. L.; Durì, G.

    2014-07-01

    The Centro di Ricerche Sismologiche (CRS, Seismological Research Centre) of the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS, Italian National Institute for Oceanography and Experimental Geophysics) in Udine (Italy) after the strong earthquake of magnitude M=6.4 occurred in 1976 in the Italian Friuli-Venezia Giulia region, started to operate the North-eastern Italy Seismic Network: it currently consists of 17 very sensitive broad band and 18 simpler short period seismic stations, all telemetered to and acquired in real time at the OGS-CRS data centre in Udine. Real time data exchange agreements in place with other Italian, Slovenian, Austrian and Swiss seismological institutes lead to a total number of about 100 seismic stations acquired in real time, which makes the OGS the reference institute for seismic monitoring of North-eastern Italy. The south-western edge of the OGS seismic network (Fig. 1) stands on the Po alluvial basin: earthquake localization and characterization in this area is affected by the presence of soft alluvial deposits. OGS ha already experience in running a local seismic network in high noise conditions making use of borehole installations in the case of the micro-seismicity monitoring of a local gas storage site for a private company. Following the ML = 5.9 earthquake that struck the Emilia region around Ferrara in Northern Italy on 20 May 2012 at 02:03:53 UTC, a cooperation of Istituto Nazionale di Geofisica e Vulcanologia, OGS, the Comune di Ferrara and the University of Ferrara lead to the reinstallation of a previously existing very broad band (VBB) borehole seismic station in Ferrara. The aim of the OGS intervention was on one hand to extend its real time seismic monitoring capabilities toward South-West, including Ferrara and its surroundings, and on the other hand to evaluate the seismic response at the site. We will describe improvements in running the North-eastern Italy Seismic Network, including details of

  11. Structure of the human chromosome interaction network.

    Directory of Open Access Journals (Sweden)

    Sergio Sarnataro

    Full Text Available New Hi-C technologies have revealed that chromosomes have a complex network of spatial contacts in the cell nucleus of higher organisms, whose organisation is only partially understood. Here, we investigate the structure of such a network in human GM12878 cells, to derive a large scale picture of nuclear architecture. We find that the intensity of intra-chromosomal interactions is power-law distributed. Inter-chromosomal interactions are two orders of magnitude weaker and exponentially distributed, yet they are not randomly arranged along the genomic sequence. Intra-chromosomal contacts broadly occur between epigenomically homologous regions, whereas inter-chromosomal contacts are especially associated with regions rich in highly expressed genes. Overall, genomic contacts in the nucleus appear to be structured as a network of networks where a set of strongly individual chromosomal units, as envisaged in the 'chromosomal territory' scenario derived from microscopy, interact with each other via on average weaker, yet far from random and functionally important interactions.

  12. OGS improvements in the year 2011 in running the Northeastern Italy Seismic Network

    Science.gov (United States)

    Bragato, P. L.; Pesaresi, D.; Saraò, A.; Di Bartolomeo, P.; Durı, G.

    2012-04-01

    The Centro di Ricerche Sismologiche (CRS, Seismological Research Center) of the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS, Italian National Institute for Oceanography and Experimental Geophysics) in Udine (Italy) after the strong earthquake of magnitude M=6.4 occurred in 1976 in the Italian Friuli-Venezia Giulia region, started to operate the Northeastern Italy Seismic Network: it currently consists of 15 very sensitive broad band and 21 simpler short period seismic stations, all telemetered to and acquired in real time at the OGS-CRS data center in Udine. Real time data exchange agreements in place with other Italian, Slovenian, Austrian and Swiss seismological institutes lead to a total number of about 100 seismic stations acquired in real time, which makes the OGS the reference institute for seismic monitoring of Northeastern Italy. Since 2002 OGS-CRS is using the Antelope software suite on several workstations plus a SUN Cluster as the main tool for collecting, analyzing, archiving and exchanging seismic data, initially in the framework of the EU Interreg IIIA project "Trans-national seismological networks in the South-Eastern Alps". SeisComP is also used as a real time data exchange server tool. In order to improve the seismological monitoring of the Northeastern Italy area, at OGS-CRS we tuned existing programs and created ad hoc ones like: a customized web server named PickServer to manually relocate earthquakes, a script for automatic moment tensor determination, scripts for web publishing of earthquake parametric data, waveforms, state of health parameters and shaking maps, noise characterization by means of automatic spectra analysis, and last but not least scripts for email/SMS/fax alerting. The OGS-CRS Real Time Seismological website (RTS, http://rts.crs.inogs.it/) operative since several years was initially developed in the framework of the Italian DPC-INGV S3 Project: the RTS website shows classic earthquake locations

  13. Characteristic imsets for learning Bayesian network structure

    Czech Academy of Sciences Publication Activity Database

    Hemmecke, R.; Lindner, S.; Studený, Milan

    2012-01-01

    Roč. 53, č. 9 (2012), s. 1336-1349 ISSN 0888-613X R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope Subject RIV: BA - General Mathematics Impact factor: 1.729, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf

  14. Run-up on a structure due to second-order waves and current in a numerical wave tank

    DEFF Research Database (Denmark)

    Buchmann, Bjarne; Skourup, Jesper; Cheung, Kwok Fai

    1998-01-01

    order in current speed. The boundary-value problem is separated into a known incident wave field and an unknown scattered wave field, the latter being absorbed at the radiation boundaries using active wave absorption. The present paper focuses on the wave run-up on a structure in waves and current...

  15. An examination of a reciprocal relationship between network governance and network structure

    DEFF Research Database (Denmark)

    Bergenholtz, Carsten; Goduscheit, René Chester

    The present article examines the network structure and governance of inter-organisational innovation networks. Network governance refers to the issue of how to manage and coordinate the relational activities and processes in the network while research on network structure deals with the overall...... structural relations between the actors in the network. These streams of research do contain references to each other but mostly rely on a static conception of the relationship between network structure and the applied network governance. The paper is based on a primarily qualitative case study of a loosely...... coupled Danish inter-organisational innovation network. The proposition is that a reciprocal relation between network governance and network structure can be identified....

  16. Measuring structural similarity in large online networks.

    Science.gov (United States)

    Shi, Yongren; Macy, Michael

    2016-09-01

    Structural similarity based on bipartite graphs can be used to detect meaningful communities, but the networks have been tiny compared to massive online networks. Scalability is important in applications involving tens of millions of individuals with highly skewed degree distributions. Simulation analysis holding underlying similarity constant shows that two widely used measures - Jaccard index and cosine similarity - are biased by the distribution of out-degree in web-scale networks. However, an alternative measure, the Standardized Co-incident Ratio (SCR), is unbiased. We apply SCR to members of Congress, musical artists, and professional sports teams to show how massive co-following on Twitter can be used to map meaningful affiliations among cultural entities, even in the absence of direct connections to one another. Our results show how structural similarity can be used to map cultural alignments and demonstrate the potential usefulness of social media data in the study of culture, politics, and organizations across the social and behavioral sciences. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Structure Learning in Power Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-01-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as these related to demand response, outage detection and management, and improved load-monitoring. Here, inspired by proliferation of the metering technology, we discuss statistical estimation problems in structurally loopy but operationally radial distribution grids consisting in learning operational layout of the network from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time – which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  18. The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

    Directory of Open Access Journals (Sweden)

    Rutger Goekoop

    Full Text Available INTRODUCTION: Human personality is described preferentially in terms of factors (dimensions found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. AIM: To directly compare the ability of network community detection (NCD and principal component factor analysis (PCA to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R. METHODS: 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. RESULTS: At facet level, NCS showed a best match (96.2% with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80% with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. CONCLUSION: We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

  19. Community detection for networks with unipartite and bipartite structure

    Science.gov (United States)

    Chang, Chang; Tang, Chao

    2014-09-01

    Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.

  20. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  1. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  2. Principal component structure and sport-specific differences in the running one-leg vertical jump.

    Science.gov (United States)

    Laffaye, G; Bardy, B G; Durey, A

    2007-05-01

    The aim of this study is to identify the kinetic principal components involved in one-leg running vertical jumps, as well as the potential differences between specialists from different sports. The sample was composed of 25 regional skilled athletes who play different jumping sports (volleyball players, handball players, basketball players, high jumpers and novices), who performed a running one-leg jump. A principal component analysis was performed on the data obtained from the 200 tested jumps in order to identify the principal components summarizing the six variables extracted from the force-time curve. Two principal components including six variables accounted for 78 % of the variance in jump height. Running one-leg vertical jump performance was predicted by a temporal component (that brings together impulse time, eccentric time and vertical displacement of the center of mass) and a force component (who brings together relative peak of force and power, and rate of force development). A comparison made among athletes revealed a temporal-prevailing profile for volleyball players, and a force-dominant profile for Fosbury high jumpers. Novices showed an ineffective utilization of the force component, while handball and basketball players showed heterogeneous and neutral component profiles. Participants will use a jumping strategy in which variables related to either the magnitude or timing of force production will be closely coupled; athletes from different sporting backgrounds will use a jumping strategy that reflects the inherent demands of their chosen sport.

  3. Towards structural controllability of local-world networks

    International Nuclear Information System (INIS)

    Sun, Shiwen; Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi

    2016-01-01

    Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

  4. Towards structural controllability of local-world networks

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Shiwen, E-mail: sunsw80@126.com [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China); Ma, Yilin; Wu, Yafang; Wang, Li; Xia, Chengyi [Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384 (China); Key Laboratory of Computer Vision and System (Tianjin University of Technology), Ministry of Education, Tianjin 300384 (China)

    2016-05-20

    Controlling complex networks is of vital importance in science and engineering. Meanwhile, local-world effect is an important ingredient which should be taken into consideration in the complete description of real-world complex systems. In this letter, structural controllability of a class of local-world networks is investigated. Through extensive numerical simulations, firstly, effects of local world size M and network size N on structural controllability are examined. For local-world networks with sparse topological configuration, compared to network size, local-world size can induce stronger influence on controllability, however, for dense networks, controllability is greatly affected by network size and local-world effect can be neglected. Secondly, relationships between controllability and topological properties are analyzed. Lastly, the robustness of local-world networks under targeted attacks regarding structural controllability is discussed. These results can help to deepen the understanding of structural complexity and connectivity patterns of complex systems. - Highlights: • Structural controllability of a class of local-world networks is investigated. • For sparse local-world networks, compared to network size, local-world size can bring stronger influence on controllability. • For dense networks, controllability is greatly affected by network size and the effect of local-world size can be neglected. • Structural controllability against targeted node attacks is discussed.

  5. Phase synchronization on small-world networks with community structure

    International Nuclear Information System (INIS)

    Xiao-Hua, Wang; Li-Cheng, Jiao; Jian-She, Wu

    2010-01-01

    In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by the ratio of inter- to intra-community connectivity, and a smaller r corresponds to a stronger community structure. The structure properties, including the degree distribution, clustering, the communication efficiency and modularity are also analysed for the network. In addition, by using the Kuramoto model, we investigated the phase synchronization on this network, and found that increasing the fuzziness of community structure will markedly enhance the network synchronizability; however, in an abnormal region (r ≤ 0.001), the network has even worse synchronizability than the case of isolated communities (r = 0). Furthermore, this network exhibits a remarkable synchronization behaviour in topological scales: the oscillators of high densely interconnected communities synchronize more easily, and more rapidly than the whole network. (general)

  6. Social inheritance can explain the structure of animal social networks

    Science.gov (United States)

    Ilany, Amiyaal; Akçay, Erol

    2016-01-01

    The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance. PMID:27352101

  7. PROSPECTS OF REGIONAL NETWORK STRUCTURES IN THE SOUTHERN FEDERAL DISTRICT

    Directory of Open Access Journals (Sweden)

    I. V. Morozov

    2014-01-01

    Full Text Available The article reveals the possibility of the Southern Federal District to form regional network structures. The prospects for the formation of networks in the region in relation to the Olympic Winter Games Sochi 2014.

  8. The effect of aging on network structure

    OpenAIRE

    Zhu, Han; Wang, Xin-Ran; Zhu, Jian-Yang

    2003-01-01

    In network evolution, the effect of aging is universal: in scientific collaboration network, scientists have a finite time span of being active; in movie actors network, once popular stars are retiring from stage; devices on the Internet may become outmoded with techniques developing so rapidly. Here we find in citation networks that this effect can be represented by an exponential decay factor, $e^{-\\beta \\tau}$, where $\\tau $ is the node age, while other evolving networks (the Internet for ...

  9. Structural equation models from paths to networks

    CERN Document Server

    Westland, J Christopher

    2015-01-01

    This compact reference surveys the full range of available structural equation modeling (SEM) methodologies.  It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable.  This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method.  This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow in importance in the near future.  SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists.  Latent variable theory and application are comprehensively explained, and methods are presented for extending their power, including guidelines for data preparation, sample size calculation, and the special treatment of Likert scale data.  Tables of software, methodologies and fit st...

  10. A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL.

    Science.gov (United States)

    Brown, Stephen; Hutton, Brian; Clifford, Tammy; Coyle, Doug; Grima, Daniel; Wells, George; Cameron, Chris

    2014-09-29

    The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent

  11. Clustering coefficient and community structure of bipartite networks

    Science.gov (United States)

    Zhang, Peng; Wang, Jinliang; Li, Xiaojia; Li, Menghui; Di, Zengru; Fan, Ying

    2008-12-01

    Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. In order to detect community structures in bipartite networks, two different edge clustering coefficients LC4 and LC3 of bipartite networks are defined, which are based on squares and triples respectively. With the algorithm of cutting the edge with the least clustering coefficient, communities in artificial and real world networks are identified. The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks.

  12. Developing a network-level structural capacity index for structural evaluation of pavements.

    Science.gov (United States)

    2013-03-01

    The objective of this project was to develop a structural index for use in network-level pavement evaluation to facilitate : the inclusion of the pavements structural condition in pavement management applications. The primary goal of network-level...

  13. Distance metric learning for complex networks: Towards size-independent comparison of network structures

    Science.gov (United States)

    Aliakbary, Sadegh; Motallebi, Sadegh; Rashidian, Sina; Habibi, Jafar; Movaghar, Ali

    2015-02-01

    Real networks show nontrivial topological properties such as community structure and long-tail degree distribution. Moreover, many network analysis applications are based on topological comparison of complex networks. Classification and clustering of networks, model selection, and anomaly detection are just some applications of network comparison. In these applications, an effective similarity metric is needed which, given two complex networks of possibly different sizes, evaluates the amount of similarity between the structural features of the two networks. Traditional graph comparison approaches, such as isomorphism-based methods, are not only too time consuming but also inappropriate to compare networks with different sizes. In this paper, we propose an intelligent method based on the genetic algorithms for integrating, selecting, and weighting the network features in order to develop an effective similarity measure for complex networks. The proposed similarity metric outperforms state of the art methods with respect to different evaluation criteria.

  14. Interconnection Structures, Management and Routing Challenges in Cloud-Service Data Center Networks: A Survey

    Directory of Open Access Journals (Sweden)

    Ahmad Nahar Quttoum

    2018-01-01

    Full Text Available Today’s data center networks employ expensive networking equipments in associated structures that were not designed to meet the increasing requirements of the current large-scale data center services. Limitations that vary between reliability, resource utilization, and high costs are challenging. The era of cloud computing represents a promise to enable large-scale data centers. Computing platforms of such cloud service data centers consist of large number of commodity low-price servers that, with a theme of virtualization on top, can meet the performance of the expensive high-level servers at only a fraction of the price. Recently, the research in data center networks started to evolve rapidly. This opened the path for addressing many of its design and management challenges, these like scalability, reliability, bandwidth capacities, virtual machines’ migration, and cost. Bandwidth resource fragmentation limits the network agility, and leads to low utilization rates, not only for the bandwidth resources, but also for the servers that run the applications. With Traffic Engineering methods, managers of such networks can adapt for rapid changes in the network traffic among their servers, this can help to provide better resource utilization and lower costs. The market is going through exciting changes, and the need to run demanding-scale services drives the work toward cloud networks. These networks that are enabled by the notation of autonomic management, and the availability of commodity low-price network equipments. This work provides the readers with a survey that presents the management challenges, design and operational constraints of the cloud-service data center networks

  15. The prisoner's dilemma in structured scale-free networks

    International Nuclear Information System (INIS)

    Li Xing; Wu Yonghui; Zhang Zhongzhi; Zhou Shuigeng; Rong Zhihai

    2009-01-01

    The conventional wisdom is that scale-free networks are prone to cooperation spreading. In this paper we investigate the cooperative behavior on the structured scale-free network. In contrast to the conventional wisdom that scale-free networks are prone to cooperation spreading, the evolution of cooperation is inhibited on the structured scale-free network when the prisoner's dilemma (PD) game is modeled. First, we demonstrate that neither the scale-free property nor the high clustering coefficient is responsible for the inhibition of cooperation spreading on the structured scale-free network. Then we provide one heuristic method to argue that the lack of age correlations and its associated 'large-world' behavior in the structured scale-free network inhibit the spread of cooperation. These findings may help enlighten further studies on the evolutionary dynamics of the PD game in scale-free networks

  16. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  17. Joint Modelling of Structural and Functional Brain Networks

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten

    -parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...

  18. Adding run history to CLIPS

    Science.gov (United States)

    Tuttle, Sharon M.; Eick, Christoph F.

    1991-01-01

    To debug a C Language Integrated Production System (CLIPS) program, certain 'historical' information about a run is needed. It would be convenient for system builders to have the capability to request such information. We will discuss how historical Rete networks can be used for answering questions that help a system builder detect the cause of an error in a CLIPS program. Moreover, the cost of maintaining a historical Rete network is compared with that for a classical Rete network. We will demonstrate that the cost for assertions is only slightly higher for a historical Rete network. The cost for handling retraction could be significantly higher; however, we will show that by using special data structures that rely on hashing, it is also possible to implement retractions efficiently.

  19. Influence of degree correlations on network structure and stability in protein-protein interaction networks

    Directory of Open Access Journals (Sweden)

    Zimmer Ralf

    2007-08-01

    Full Text Available Abstract Background The existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs. Results For each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks. Conclusion Considerable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree

  20. Ames and other European networks in integrity of ageing structures

    International Nuclear Information System (INIS)

    Davies, L.M.; Von Estorff, U.; Crutzen, S.

    1996-01-01

    Several European institutions and organisations and the Joint Research Centre have developed co-operative programmes now organised into Networks for mutual benefit. They include utilities, engineering companies, Research and Development laboratories and regulatory bodies. Networks are organised and managed like the successful Programme for the Inspection of Steel Components (PISC). The JRC's Institute for Advanced Materials of the European Commission plays the role of Operating Agent and manager of these Networks: ENIQ. AMES, NESC, each of them dealing with specific aspect of fitness for purpose of materials in structural components. This paper describes the structure and the objectives of these networks. Particular emphasis is given to the network AMES

  1. 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.

  2. Structural Behavioral Study on the General Aviation Network Based on Complex Network

    Science.gov (United States)

    Zhang, Liang; Lu, Na

    2017-12-01

    The general aviation system is an open and dissipative system with complex structures and behavioral features. This paper has established the system model and network model for general aviation. We have analyzed integral attributes and individual attributes by applying the complex network theory and concluded that the general aviation network has influential enterprise factors and node relations. We have checked whether the network has small world effect, scale-free property and network centrality property which a complex network should have by applying degree distribution of functions and proved that the general aviation network system is a complex network. Therefore, we propose to achieve the evolution process of the general aviation industrial chain to collaborative innovation cluster of advanced-form industries by strengthening network multiplication effect, stimulating innovation performance and spanning the structural hole path.

  3. Structural analysis of behavioral networks from the Internet

    International Nuclear Information System (INIS)

    Meiss, M R; Menczer, F; Vespignani, A

    2008-01-01

    In spite of the Internet's phenomenal growth and social impact, many aspects of the collective communication behavior of its users are largely unknown. Understanding the structure and dynamics of the behavioral networks that connect users with each other and with services across the Internet is key to modeling the network and designing future applications. We present a characterization of the properties of the behavioral networks generated by several million users of the Abilene (Internet2) network. Structural features of these networks offer new insights into scaling properties of network activity and ways of distinguishing particular patterns of traffic. For example, we find that the structure of the behavioral network associated with Web activity is characterized by such extreme heterogeneity as to challenge any simple attempt to model Web server traffic

  4. Structural analysis of behavioral networks from the Internet

    Energy Technology Data Exchange (ETDEWEB)

    Meiss, M R; Menczer, F [Department of Computer Science, Indiana University, Bloomington, IN 47405 (United States); Vespignani, A [Department of Informatics, Indiana University, Bloomington, IN 47408 (United States)], E-mail: mmeiss@indiana.edu

    2008-06-06

    In spite of the Internet's phenomenal growth and social impact, many aspects of the collective communication behavior of its users are largely unknown. Understanding the structure and dynamics of the behavioral networks that connect users with each other and with services across the Internet is key to modeling the network and designing future applications. We present a characterization of the properties of the behavioral networks generated by several million users of the Abilene (Internet2) network. Structural features of these networks offer new insights into scaling properties of network activity and ways of distinguishing particular patterns of traffic. For example, we find that the structure of the behavioral network associated with Web activity is characterized by such extreme heterogeneity as to challenge any simple attempt to model Web server traffic.

  5. A new hierarchical method to find community structure in networks

    Science.gov (United States)

    Saoud, Bilal; Moussaoui, Abdelouahab

    2018-04-01

    Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

  6. Dynamical community structure of populations evolving on genotype networks

    International Nuclear Information System (INIS)

    Capitán, José A.; Aguirre, Jacobo; Manrubia, Susanna

    2015-01-01

    Neutral evolutionary dynamics of replicators occurs on large and heterogeneous networks of genotypes. These networks, formed by all genotypes that yield the same phenotype, have a complex architecture that conditions the molecular composition of populations and their movements on genome spaces. Here we consider as an example the case of populations evolving on RNA secondary structure neutral networks and study the community structure of the network revealed through dynamical properties of the population at equilibrium and during adaptive transients. We unveil a rich hierarchical community structure that, eventually, can be traced back to the non-trivial relationship between RNA secondary structure and sequence composition. We demonstrate that usual measures of modularity that only take into account the static, topological structure of networks, cannot identify the community structure disclosed by population dynamics

  7. A Hierarchical Dispatch Structure for Distribution Network Pricing

    OpenAIRE

    Yuan, Zhao; Hesamzadeh, Mohammad Reza

    2015-01-01

    This paper presents a hierarchical dispatch structure for efficient distribution network pricing. The dispatch coordination problem in the context of hierarchical network operators are addressed. We formulate decentralized generation dispatch into a bilevel optimization problem in which main network operator and the connected distribution network operator optimize their costs in two levels. By using Karush-Kuhn-Tucker conditions and Fortuny-Amat McCarl linearization, the bilevel optimization ...

  8. Influence of choice of null network on small-world parameters of structural correlation networks.

    Directory of Open Access Journals (Sweden)

    S M Hadi Hosseini

    Full Text Available In recent years, coordinated variations in brain morphology (e.g., volume, thickness have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1 networks constructed by topology randomization (TOP, 2 networks matched to the distributional properties of the observed covariance matrix (HQS, and 3 networks generated from correlation of randomized input data (COR. The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.

  9. Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

    Science.gov (United States)

    Hosseini, S. M. Hadi; Kesler, Shelli R.

    2013-01-01

    In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672

  10. ShakeNet: a portable wireless sensor network for instrumenting large civil structures

    Science.gov (United States)

    Kohler, Monica D.; Hao, Shuai; Mishra, Nilesh; Govindan, Ramesh; Nigbor, Robert

    2015-08-03

    We report our findings from a U.S. Geological Survey (USGS) National Earthquake Hazards Reduction Program-funded project to develop and test a wireless, portable, strong-motion network of up to 40 triaxial accelerometers for structural health monitoring. The overall goal of the project was to record ambient vibrations for several days from USGS-instrumented structures. Structural health monitoring has important applications in fields like civil engineering and the study of earthquakes. The emergence of wireless sensor networks provides a promising means to such applications. However, while most wireless sensor networks are still in the experimentation stage, very few take into consideration the realistic earthquake engineering application requirements. To collect comprehensive data for structural health monitoring for civil engineers, high-resolution vibration sensors and sufficient sampling rates should be adopted, which makes it challenging for current wireless sensor network technology in the following ways: processing capabilities, storage limit, and communication bandwidth. The wireless sensor network has to meet expectations set by wired sensor devices prevalent in the structural health monitoring community. For this project, we built and tested an application-realistic, commercially based, portable, wireless sensor network called ShakeNet for instrumentation of large civil structures, especially for buildings, bridges, or dams after earthquakes. Two to three people can deploy ShakeNet sensors within hours after an earthquake to measure the structural response of the building or bridge during aftershocks. ShakeNet involved the development of a new sensing platform (ShakeBox) running a software suite for networking, data collection, and monitoring. Deployments reported here on a tall building and a large dam were real-world tests of ShakeNet operation, and helped to refine both hardware and software. 

  11. Wireless sensor networks for structural health monitoring

    CERN Document Server

    Cao, Jiannong

    2016-01-01

    This brief covers the emerging area of wireless sensor network (WSN)-based structural health monitoring (SHM) systems, and introduces the authors’ WSN-based platform called SenetSHM. It helps the reader differentiate specific requirements of SHM applications from other traditional WSN applications, and demonstrates how these requirements are addressed by using a series of systematic approaches. The brief serves as a practical guide, explaining both the state-of-the-art technologies in domain-specific applications of WSNs, as well as the methodologies used to address the specific requirements for a WSN application. In particular, the brief offers instruction for problem formulation and problem solving based on the authors’ own experiences implementing SenetSHM. Seven concise chapters cover the development of hardware and software design of SenetSHM, as well as in-field experiments conducted while testing the platform. The brief’s exploration of the SenetSHM platform is a valuable feature for civil engine...

  12. Network versus portfolio structure in financial systems

    Science.gov (United States)

    Kobayashi, Teruyoshi

    2013-10-01

    The question of how to stabilize financial systems has attracted considerable attention since the global financial crisis of 2007-2009. Recently, Beale et al. [Proc. Natl. Acad. Sci. USA 108, 12647 (2011)] demonstrated that higher portfolio diversity among banks would reduce systemic risk by decreasing the risk of simultaneous defaults at the expense of a higher likelihood of individual defaults. In practice, however, a bank default has an externality in that it undermines other banks’ balance sheets. This paper explores how each of these different sources of risk, simultaneity risk and externality, contributes to systemic risk. The results show that the allocation of external assets that minimizes systemic risk varies with the topology of the financial network as long as asset returns have negative correlations. In the model, a well-known centrality measure, PageRank, reflects an appropriately defined “infectiveness” of a bank. An important result is that the most infective bank needs not always to be the safest bank. Under certain circumstances, the most infective node should act as a firewall to prevent large-scale collective defaults. The introduction of a counteractive portfolio structure will significantly reduce systemic risk.

  13. Exploring network structure, dynamics, and function using networkx

    Energy Technology Data Exchange (ETDEWEB)

    Hagberg, Aric [Los Alamos National Laboratory; Swart, Pieter [Los Alamos National Laboratory; S Chult, Daniel [COLGATE UNIV

    2008-01-01

    NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility mades NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for eash exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdoes-Renyi, Small World, and Barabasi-Albert models, are included. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.

  14. Optimal neural networks for protein-structure prediction

    International Nuclear Information System (INIS)

    Head-Gordon, T.; Stillinger, F.H.

    1993-01-01

    The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

  15. Complex network perspective on structure and function of ...

    Indian Academy of Sciences (India)

    of community social networks, which are dense node–node links within modules, but have sparser links between ... 3.2 Bow tie structure. The whole metabolic network of S. aureus is then decomposed into four parts based on the 'bow tie' structure (figure 2, table 2). It should be noted that most nodes in S, P and IS parts are ...

  16. Changing organizational structures of jihadist networks in the Netherlands

    NARCIS (Netherlands)

    de Bie, Jasper L.; de Poot, Christianne J.; Freilich, Joshua D.; Chermak, Steven M.

    2017-01-01

    This paper uses Social Network Analysis to study and compare the organizational structures and division of roles of three jihadist networks in the Netherlands. It uses unique longitudinal Dutch police data covering the 2000–2013 period. This study demonstrates how the organizational structures

  17. Online Social Networks: Essays on Membership, Privacy, and Structure

    NARCIS (Netherlands)

    Hofstra, B.

    2017-01-01

    The structure of social networks is crucial for obtaining social support, for meaningful connections to unknown social groups, and to overcome prejudice. Yet, we know little about the structure of social networks beyond those contacts that stand closest to us. This lack of knowledge results from a

  18. Reconstructing consensus Bayesian network structures with application to learning molecular interaction networks

    NARCIS (Netherlands)

    Fröhlich, H.; Klau, G.W.

    2013-01-01

    Bayesian Networks are an established computational approach for data driven network inference. However, experimental data is limited in its availability and corrupted by noise. This leads to an unavoidable uncertainty about the correct network structure. Thus sampling or bootstrap based strategies

  19. Functional clustering in hippocampal cultures: relating network structure and dynamics

    International Nuclear Information System (INIS)

    Feldt, S; Dzakpasu, R; Olariu, E; Żochowski, M; Wang, J X; Shtrahman, E

    2010-01-01

    In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures

  20. Running in the family? : structural brain abnormalities in first-degree relatives of patients with schizophrenia

    NARCIS (Netherlands)

    Boos, H.B.M.

    2011-01-01

    The studies conducted in this thesis explored brain structures in first-degree relatives of patients with schizophrenia. The meta-analysis that Boos and colleagues performed showed that relatives of patients with schizophrenia had smaller hippocampal volumes, smaller gray matter volumes and larger

  1. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    We study the effect of learning dynamics on network topology. Firstly, a network of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates ...

  2. Wireless Sensor Networks : Structure and Algorithms

    NARCIS (Netherlands)

    van Dijk, T.C.

    2014-01-01

    In this thesis we look at various problems in wireless networking. First we consider two problems in physical-model networks. We introduce a new model for localisation. The model is based on a range-free model of radio transmissions. The first scheme is randomised and we analyse its expected

  3. Fundamental structures of dynamic social networks

    DEFF Research Database (Denmark)

    Sekara, Vedran; Stopczynski, Arkadiusz; Jørgensen, Sune Lehmann

    2016-01-01

    Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships...... and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection......, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals...

  4. Structural and robustness properties of smart-city transportation networks

    Science.gov (United States)

    Zhang, Zhen-Gang; Ding, Zhuo; Fan, Jing-Fang; Meng, Jun; Ding, Yi-Min; Ye, Fang-Fu; Chen, Xiao-Song

    2015-09-01

    The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities. Project supported by the Major Projects of the China National Social Science Fund (Grant No. 11 & ZD154).

  5. Structural and robustness properties of smart-city transportation networks

    International Nuclear Information System (INIS)

    Zhang Zhen-Gang; Ding Zhuo; Fan Jing-Fang; Chen Xiao-Song; Meng Jun; Ye Fang-Fu; Ding Yi-Min

    2015-01-01

    The concept of smart city gives an excellent resolution to construct and develop modern cities, and also demands infrastructure construction. How to build a safe, stable, and highly efficient public transportation system becomes an important topic in the process of city construction. In this work, we study the structural and robustness properties of transportation networks and their sub-networks. We introduce a complementary network model to study the relevance and complementarity between bus network and subway network. Our numerical results show that the mutual supplement of networks can improve the network robustness. This conclusion provides a theoretical basis for the construction of public traffic networks, and it also supports reasonable operation of managing smart cities. (rapid communication)

  6. Adapting Bayes Network Structures to Non-stationary Domains

    DEFF Research Database (Denmark)

    Nielsen, Søren Holbech; Nielsen, Thomas Dyhre

    2008-01-01

    When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit a sequential stream of observations, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN is gradu...

  7. Resolving structural variability in network models and the brain.

    Directory of Open Access Journals (Sweden)

    Florian Klimm

    2014-03-01

    Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful

  8. Covariance, correlation matrix, and the multiscale community structure of networks.

    Science.gov (United States)

    Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

    2010-07-01

    Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

  9. A Web-Based, Social Networking Beginners’ Running Intervention for Adults Aged 18 to 50 Years Delivered via a Facebook Group: Randomized Controlled Trial

    Science.gov (United States)

    Boshoff, Kobie; Maher, Carol

    2018-01-01

    Background Online social networks continue to grow in popularity, with 1.7 billion users worldwide accessing Facebook each month. The use of social networking sites such as Facebook for the delivery of health behavior programs is relatively new. Objective The primary aim of this study was to determine the effectiveness of a Web-based beginners’ running program for adults aged 18 to 50 years, delivered via a Facebook group, in increasing physical activity (PA) and cardiorespiratory fitness. Methods A total of 89 adults with a mean age of 35.2 years (SD 10.9) were recruited online and via print media. Participants were randomly allocated to receive the UniSA Run Free program, an 8-week Web-based beginners’ running intervention, delivered via a closed Facebook group (n=41) that included daily interactive posts (information with links, motivational quotes, opinion polls, or questions) and details of the running sessions; or to the control group who received a hard copy of the running program (n=48). Assessments were completed online at baseline, 2 months, and 5 months. The primary outcome measures were self-reported weekly moderate to vigorous physical activity (MVPA) and objectively measured cardiorespiratory fitness. Secondary outcomes were social support, exercise attitudes, and self-efficacy. Analyses were undertaken using random effects mixed modeling. Compliance with the running program and engagement with the Facebook group were analyzed descriptively. Results Both groups significantly increased MVPA across the study period (P=.004); however, this was significantly higher in the Facebook group (P=.04). The Facebook group increased their MVPA from baseline by 140 min/week versus 91 min for the control at 2 months. MVPA remained elevated for the Facebook group (from baseline) by 129 min/week versus a 50 min/week decrease for the control at 5 months. Both groups had significant increases in social support scores at 2 months (P=.02); however, there were no group

  10. Structural and Infrastructural Underpinnings of International R&D Networks

    DEFF Research Database (Denmark)

    Niang, Mohamed; Sørensen, Brian Vejrum

    2009-01-01

    This paper explores the process of globally distributing R&D activities with an emphasis on the effects of network maturity. It discusses emerging configurations by asking how the structure and infrastructure of international R&D networks evolve along with the move from a strong R&D center...... to dispersed development. Drawing from case studies of two international R&D networks, it presents a capability maturity model and argues that understanding the interaction between new structures and infrastructures of the dispersed networks has become a key requirement for developing organizational...

  11. Conversation practices and network structure in Twitter

    DEFF Research Database (Denmark)

    Rossi, Luca; Magnani, Matteo

    2012-01-01

    that this double nature of Twitter is widely recognized among scholars there is still little literature facing the relationships between these two networks. This paper presents the results of an empirical research aimed at discovering how the Twitter network is affected by what happens on the topical network. Does...... the participation in the same hashtag based conversation change the follower list of the participants? Is it possible to point out specific social behaviors that would produce a major gain of followers? Our conclusions are based on real data concerning the popular TV show Xfactor, that largely used Twitter...

  12. A Microsoft-Excel-based tool for running and critically appraising network meta-analyses—an overview and application of NetMetaXL

    Science.gov (United States)

    2014-01-01

    Background The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. Methods We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL’s interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. Results We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. Conclusions Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows

  13. Implications of network structure on public health collaboratives.

    Science.gov (United States)

    Retrum, Jessica H; Chapman, Carrie L; Varda, Danielle M

    2013-10-01

    Interorganizational collaboration is an essential function of public health agencies. These partnerships form social networks that involve diverse types of partners and varying levels of interaction. Such collaborations are widely accepted and encouraged, yet very little comparative research exists on how public health partnerships develop and evolve, specifically in terms of how subsequent network structures are linked to outcomes. A systems science approach, that is, one that considers the interdependencies and nested features of networks, provides the appropriate methods to examine the complex nature of these networks. Applying Mays and Scutchfields's categorization of "structural signatures" (breadth, density, and centralization), this research examines how network structure influences the outcomes of public health collaboratives. Secondary data from the Program to Analyze, Record, and Track Networks to Enhance Relationships (www.partnertool.net) data set are analyzed. This data set consists of dyadic (N = 12,355), organizational (N = 2,486), and whole network (N = 99) data from public health collaborations around the United States. Network data are used to calculate structural signatures and weighted least squares regression is used to examine how network structures can predict selected intermediary outcomes (resource contributions, overall value and trust rankings, and outcomes) in public health collaboratives. Our findings suggest that network structure may have an influence on collaborative-related outcomes. The structural signature that had the most significant relationship to outcomes was density, with higher density indicating more positive outcomes. Also significant was the finding that more breadth creates new challenges such as difficulty in reaching consensus and creating ties with other members. However, assumptions that these structural components lead to improved outcomes for public health collaboratives may be slightly premature. Implications of

  14. Conversation practices and network structure in Twitter

    DEFF Research Database (Denmark)

    Rossi, Luca; Magnani, Matteo

    2012-01-01

    The public by default nature of Twitter messages, together with the adoption of the #hashtag convention led, in few years, to the creation of a digital space able to host worldwide conversation on almost every kind of topic. From major TV shows to Natural disasters there is no contemporary event...... that does not have its own #hashtag to gather together the ongoing Twitter conversation. These topical discussions take place outside of the Twitter network made of followers and friends. Nevertheless this topical network is where many of the most studied phenomena take place. Therefore Twitter based...... communication exists on two almost autonomous levels: the Twitter network made of followers and friends that shows a certain level of stability and the topical network, characterized by a high level of contingency, that appears and disappears following the rhythm of a worldwide conversation. Despite the fact...

  15. Network Properties of the Ensemble of RNA Structures

    Science.gov (United States)

    Clote, Peter; Bayegan, Amir

    2015-01-01

    We describe the first dynamic programming algorithm that computes the expected degree for the network, or graph G = (V, E) of all secondary structures of a given RNA sequence a = a 1, …, a n. Here, the nodes V correspond to all secondary structures of a, while an edge exists between nodes s, t if the secondary structure t can be obtained from s by adding, removing or shifting a base pair. Since secondary structure kinetics programs implement the Gillespie algorithm, which simulates a random walk on the network of secondary structures, the expected network degree may provide a better understanding of kinetics of RNA folding when allowing defect diffusion, helix zippering, and related conformation transformations. We determine the correlation between expected network degree, contact order, conformational entropy, and expected number of native contacts for a benchmarking dataset of RNAs. Source code is available at http://bioinformatics.bc.edu/clotelab/RNAexpNumNbors. PMID:26488894

  16. Structure of Retail Services in the Brazilian Hosting Network

    Directory of Open Access Journals (Sweden)

    Claudio Zancan

    2015-08-01

    Full Text Available this research has identified Brazilian hosting networks through infrastructure services indicators that it was sold to tourists in organizations that form these networks. The theory consulted the discussion of structural techniques present in Social Network Analysis. The study has three stages: documental research, creation of Tourism database and interviews. The results identified three networks with the highest expression in Brazil formed by hotels, lodges, and resorts. Different char-acteristics of infrastructure and services were observed between hosting networks. Future studies suggest a comparative analysis of structural indicators present in other segments of tourism services, as well as the existing international influ-ence on the development of the Brazilian hosting networks.

  17. Structure of the macrozoobenthos as an indicator of different types of pollution in running waters

    Directory of Open Access Journals (Sweden)

    Simić Vladica M.

    2002-01-01

    Full Text Available Hydrobiological research on streams with different dominant forms of pollution (anthropogenic eutrophication, organic pollution, toxic pollution, trans-saprobic pollution in the central part of the Balkan Peninsula (territory of Serbia and Montenegro indicates that these different forms of pollution can be detected on the basis of structure of the macrozoobenthos community and individual indicator taxa. It is established from the obtained results that the greatest community diversity occurs in the case of anthropogenic eutrophication or weak organic pollution as a dominant process, where phytophilous and/or detritophagic forms (Cloeon, Polypedilum, Asellus, Stylaria, Erpobdella, Gammarus are dominant in the community. In the cases of very strong organic pollution, the macrozoobenthos community is characterized by mass development of certain taxa of the group Oligochaeta (Tubificidae and Chironomidae larvae {Chironomus gr. thummi. Depending on the strength and contribution of organic, toxic, and/or transsaprobic components, toxic pollution and mixed pollution arc characterized by a more or less heterogeneous and nonspecific population. However, a common feature is the presence of a small number of taxa with populations of a relatively low density, or the occurrence of individual specimens belonging to different groups of animals.

  18. The running fine structure constant α(E) via the Adler function

    International Nuclear Information System (INIS)

    Jegerlehner, F.

    2008-06-01

    We present an up-to-date analysis for a precise determination of the effective fine structure constant and discuss the prospects for future improvements. We advocate to use a determination monitored by the Adler function which allows us to exploit perturbative QCD in an optimal well controlled way. Together with a long term program of hadronic cross section measurements at energies up to a few GeV, a determination of α(M Z ) at a precision comparable to the one of the Z mass M Z should be feasible. Presently α(E) at E >1 GeV is the least precisely known of the fundamental parameters of the SM. Since, in spite of substantial progress due to new BaBar exclusive data, the region 1.4 to 2.4 GeV remains the most problematic one a major step in the reduction of the uncertainties are expected from VEPP-2000 and from a possible ''high-energy'' option DAFNE-2 at Frascati. The up-to-date evaluation reads Δ (5) had (M 2 Z )=0.027515±0.000149 or α -1 (M 2 Z )=128.957±0.020. (orig.)

  19. Liquidity Runs

    NARCIS (Netherlands)

    Matta, R.; Perotti, E.

    2016-01-01

    Can the risk of losses upon premature liquidation produce bank runs? We show how a unique run equilibrium driven by asset liquidity risk arises even under minimal fundamental risk. To study the role of illiquidity we introduce realistic norms on bank default, such that mandatory stay is triggered

  20. Displacement and deformation measurement for large structures by camera network

    Science.gov (United States)

    Shang, Yang; Yu, Qifeng; Yang, Zhen; Xu, Zhiqiang; Zhang, Xiaohu

    2014-03-01

    A displacement and deformation measurement method for large structures by a series-parallel connection camera network is presented. By taking the dynamic monitoring of a large-scale crane in lifting operation as an example, a series-parallel connection camera network is designed, and the displacement and deformation measurement method by using this series-parallel connection camera network is studied. The movement range of the crane body is small, and that of the crane arm is large. The displacement of the crane body, the displacement of the crane arm relative to the body and the deformation of the arm are measured. Compared with a pure series or parallel connection camera network, the designed series-parallel connection camera network can be used to measure not only the movement and displacement of a large structure but also the relative movement and deformation of some interesting parts of the large structure by a relatively simple optical measurement system.

  1. Density perturbations for running vacuum: a successful approach to structure formation and to the σ8-tension

    Science.gov (United States)

    Gómez-Valent, Adrià; Peracaula, Joan Solà

    2018-04-01

    Recent studies suggest that dynamical dark energy (DDE) provides a better fit to the rising affluence of modern cosmological observations than the concordance model (ΛCDM) with a rigid cosmological constant, Λ. Such is the case with the running vacuum models (RVMs) and to some extent also with a simple XCDM parametrization. Apart from the cosmic microwave background (CMB) anisotropies, the most crucial datasets potentially carrying the DDE signature are: i) baryonic acoustic oscillations (BAO), and ii) direct large scale structure (LSS) formation data (i.e. the observations on f(z)σ8(z) at different redshifts). As it turns out, analyses mainly focusing on CMB and with insufficient BAO+LSS input, or those just making use of gravitational weak-lensing data for the description of structure formation, generally fail to capture the DDE signature, whereas the few existing studies using a rich set of CMB+BAO+LSS data (see in particular Solà, Gómez-Valent & de Cruz Pérez 2015, 2017; and Zhao et al. 2017) do converge to the remarkable conclusion that DDE might well be encoded in the current cosmological observations. Being the issue so pressing, here we explain both analytically and numerically the origin of the possible hints of DDE in the context of RVMs, which arise at a significance level of 3 - 4σ. By performing a detailed study on the matter and vacuum perturbations within the RVMs, and comparing with the XCDM, we show why the running vacuum fully relaxes the existing σ8-tension and accounts for the LSS formation data much better than the concordance model.

  2. Analyzing the Wheeled Vehicle Gearbox Structures for Running in Harsh Conditions

    Directory of Open Access Journals (Sweden)

    V. V. Vasiliev

    2015-01-01

    Full Text Available In domestic automotive industry the need for transition from transmission with manual control to automatic gearboxes (GB emerged long ago. Regarding the leading foreign manufacturers (Allison, ZF, Eaton, etc., an experience in design of automatic transmissions and the level of technological development is incomparably small. Thus, to have an informed choice of the gearbox structure types and control system is a relevant problem. Application efficiency of this or other gearbox depends both on its adaptability to the actual operating conditions and on the quality of transition refinement. This paper analyzes the special features of the gear change process in the most common types of automatic gearboxes. Constructive feasibilities of an "ideal" gear change (without power loss and strategies for gear shifting are compared. The paper offers an overview of common problems when achieving these goals and the ways to solve them. An automatic transmission of the particular type used in the wheeled vehicles under off-road conditions determines the probability of maintaining their mobility. This is due to the phenomenon of wheel`s breakdown to slipping caused by sharp increase of torque on the wheel. Planetary hydromechanical transmissions (AT provide continuous input torque to the wheels, but they are expensive and difficult to manufacture and use. Besides, to provide a high number of density ratios in them is more complicated than in the automated and manual transmissions (AMT. This is important when moving long in the lower gears under difficult conditions. Compared with AT dual clutch transmissions due to design features require even more precise and fast control system of actuators and engine. Automation of constant-mesh or synchromesh transmissions provides a significant reduction in the duration of interruption in torque delivery. If it is not enough to increase mobility in the harsh conditions, a rational choice is to use a transmission type of TCCT

  3. Synchronization in complex networks with a modular structure.

    Science.gov (United States)

    Park, Kwangho; Lai, Ying-Cheng; Gupte, Saurabh; Kim, Jong-Won

    2006-03-01

    Networks with a community (or modular) structure arise in social and biological sciences. In such a network individuals tend to form local communities, each having dense internal connections. The linkage among the communities is, however, much more sparse. The dynamics on modular networks, for instance synchronization, may be of great social or biological interest. (Here by synchronization we mean some synchronous behavior among the nodes in the network, not, for example, partially synchronous behavior in the network or the synchronizability of the network with some external dynamics.) By using a recent theoretical framework, the master-stability approach originally introduced by Pecora and Carroll in the context of synchronization in coupled nonlinear oscillators, we address synchronization in complex modular networks. We use a prototype model and develop scaling relations for the network synchronizability with respect to variations of some key network structural parameters. Our results indicate that random, long-range links among distant modules is the key to synchronization. As an application we suggest a viable strategy to achieve synchronous behavior in social networks.

  4. Structuring evolution: biochemical networks and metabolic diversification in birds.

    Science.gov (United States)

    Morrison, Erin S; Badyaev, Alexander V

    2016-08-25

    Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a "global" carotenoid network - comprising of all known enzymatic reactions among naturally occurring carotenoids - with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network - compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.

  5. Information Propagation in Complex Networks : Structures and Dynamics

    NARCIS (Netherlands)

    Märtens, M.

    2018-01-01

    This thesis is a contribution to a deeper understanding of how information propagates and what this process entails. At its very core is the concept of the network: a collection of nodes and links, which describes the structure of the systems under investigation. The network is a mathematical model

  6. Structural dimensions of knowledge-action networks for sustainability

    Science.gov (United States)

    Tischa A. Munoz; B.B. Cutts

    2016-01-01

    Research on the influence of social network structure over flows of knowledge in support of sustainability governance and action has recently flourished. These studies highlight three challenges to evaluating knowledge-action networks: first, defining boundaries; second, characterizing power distributions; and third, identifying obstacles to knowledge sharing and...

  7. The macroecology of phylogenetically structured hummingbird-plant networks

    DEFF Research Database (Denmark)

    González, Ana M. Martín; Dalsgaard, Bo; Nogues, David Bravo

    2015-01-01

    Aim To investigate the association between hummingbird–plant network structure and species richness, phylogenetic signal on species' interaction pattern, insularity and historical and current climate. Location Fifty-four communities along a c. 10,000 km latitudinal gradient across the Americas (39...... approach, we examined the influence of species richness, phylogenetic signal, insularity and current and historical climate conditions on network structure (null-model-corrected specialization and modularity). Results Phylogenetically related species, especially plants, showed a tendency to interact...... with a similar array of mutualistic partners. The spatial variation in network structure exhibited a constant association with species phylogeny (R2 = 0.18–0.19); however, network structure showed the strongest association with species richness and environmental factors (R2 = 0.20–0.44 and R2 = 0...

  8. Mesoscopic structure conditions the emergence of cooperation on social networks.

    Directory of Open Access Journals (Sweden)

    Sergi Lozano

    Full Text Available BACKGROUND: We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. METHODOLOGY/PRINCIPAL FINDINGS: We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates. CONCLUSION: Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.

  9. Mesoscopic structure conditions the emergence of cooperation on social networks

    Energy Technology Data Exchange (ETDEWEB)

    Lozano, S.; Arenas, A.; Sanchez, A.

    2008-12-01

    We study the evolutionary Prisoner's Dilemma on two social networks substrates obtained from actual relational data. We find very different cooperation levels on each of them that cannot be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding a good agreement with the observations in both real substrates. Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. Further, the study allows us to define new quantitative parameters that summarize the mesoscopic structure of any network. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.

  10. Structural Changes in Online Discussion Networks

    DEFF Research Database (Denmark)

    Yang, Yang; Medaglia, Rony

    2014-01-01

    Social networking platforms in China provide a hugely interesting and relevant source for understanding dynamics of online discussions in a unique socio-cultural and institutional environment. This paper investigates the evolution of patterns of similar-minded and different-minded interactions ov...

  11. Hourly Comparison of GPM-IMERG-Final-Run and IMERG-Real-Time (V-03) over a Dense Surface Network in Northeastern Austria

    Science.gov (United States)

    Sharifi, Ehsan; Steinacker, Reinhold; Saghafian, Bahram

    2017-04-01

    Accurate quantitative daily precipitation estimation is key to meteorological and hydrological applications in hazards forecast and management. In-situ observations over mountainous areas are mostly limited, however, currently available satellite precipitation products can potentially provide the precipitation estimation needed for meteorological and hydrological applications. Over the years, blended methods that use multi-satellites and multi-sensors have been developed for estimating of global precipitation. One of the latest satellite precipitation products is GPM-IMERG (Global Precipitation Measurement with 30-minute temporal and 0.1-degree spatial resolutions) which consists of three products: Final-Run (aimed for research), Real-Time early run, and Real-Time late run. The Integrated Multisatellite Retrievals for GPM (IMERG) products built upon the success of TRMM's Multisatellite Precipitation Analysis (TMPA) products continue to make improvements in spatial and temporal resolutions and snowfall estimates. Recently, researchers who evaluated IMERG-Final-Run V-03 and other precipitation products indicated better performance for IMERG-Final-Run against other similar products. In this study two GPM-IMERG products, namely final run and real time-late run, were evaluated against a dense synoptic stations network (62 stations) over Northeastern Austria for mid-March 2015 to end of January 2016 period at hourly time-scale. Both products were examined against the reference data (stations) in capturing the occurrence of precipitation and statistical characteristics of precipitation intensity. Both satellite precipitation products underestimated precipitation events of 0.1 mm/hr to 0.4 mm/hr in intensity. For precipitations 0.4 mm/hr and greater, the trend was reversed and both satellite products overestimated than station recorded data. IMERG-RT outperformed IMERG-FR for precipitation intensity in the range of 0.1 mm/hr to 0.4 mm/hr while in the range of 1.1 to 1.8 mm

  12. Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation.

    Science.gov (United States)

    Jablonski, Piotr; Poe, Gina; Zochowski, Michal

    2007-03-01

    The hippocampus has the capacity for reactivating recently acquired memories and it is hypothesized that one of the functions of sleep reactivation is the facilitation of consolidation of novel memory traces. The dynamic and network processes underlying such a reactivation remain, however, unknown. We show that such a reactivation characterized by local, self-sustained activity of a network region may be an inherent property of the recurrent excitatory-inhibitory network with a heterogeneous structure. The entry into the reactivation phase is mediated through a physiologically feasible regulation of global excitability and external input sources, while the reactivated component of the network is formed through induced network heterogeneities during learning. We show that structural changes needed for robust reactivation of a given network region are well within known physiological parameters.

  13. Structural stability of interaction networks against negative external fields

    Science.gov (United States)

    Yoon, S.; Goltsev, A. V.; Mendes, J. F. F.

    2018-04-01

    We explore structural stability of weighted and unweighted networks of positively interacting agents against a negative external field. We study how the agents support the activity of each other to confront the negative field, which suppresses the activity of agents and can lead to collapse of the whole network. The competition between the interactions and the field shape the structure of stable states of the system. In unweighted networks (uniform interactions) the stable states have the structure of k -cores of the interaction network. The interplay between the topology and the distribution of weights (heterogeneous interactions) impacts strongly the structural stability against a negative field, especially in the case of fat-tailed distributions of weights. We show that apart from critical slowing down there is also a critical change in the system structure that precedes the network collapse. The change can serve as an early warning of the critical transition. To characterize changes of network structure we develop a method based on statistical analysis of the k -core organization and so-called "corona" clusters belonging to the k -cores.

  14. Disentangling bipartite and core-periphery structure in financial networks

    International Nuclear Information System (INIS)

    Barucca, Paolo; Lillo, Fabrizio

    2016-01-01

    A growing number of systems are represented as networks whose architecture conveys significant information and determines many of their properties. Examples of network architecture include modular, bipartite, and core-periphery structures. However inferring the network structure is a non trivial task and can depend sometimes on the chosen null model. Here we propose a method for classifying network structures and ranking its nodes in a statistically well-grounded fashion. The method is based on the use of Belief Propagation for learning through Entropy Maximization on both the Stochastic Block Model (SBM) and the degree-corrected Stochastic Block Model (dcSBM). As a specific application we show how the combined use of the two ensembles—SBM and dcSBM—allows to disentangle the bipartite and the core-periphery structure in the case of the e-MID interbank network. Specifically we find that, taking into account the degree, this interbank network is better described by a bipartite structure, while using the SBM the core-periphery structure emerges only when data are aggregated for more than a week.

  15. Cognitive and Social Structure of the Elite Collaboration Network of Astrophysics: A Case Study on Shifting Network Structures

    Science.gov (United States)

    Heidler, Richard

    2011-01-01

    Scientific collaboration can only be understood along the epistemic and cognitive grounding of scientific disciplines. New scientific discoveries in astrophysics led to a major restructuring of the elite network of astrophysics. To study the interplay of the epistemic grounding and the social network structure of a discipline, a mixed-methods…

  16. Approximating spectral impact of structural perturbations in large networks

    CERN Document Server

    Milanese, A; Nishikawa, Takashi; Sun, Jie

    2010-01-01

    Determining the effect of structural perturbations on the eigenvalue spectra of networks is an important problem because the spectra characterize not only their topological structures, but also their dynamical behavior, such as synchronization and cascading processes on networks. Here we develop a theory for estimating the change of the largest eigenvalue of the adjacency matrix or the extreme eigenvalues of the graph Laplacian when small but arbitrary set of links are added or removed from the network. We demonstrate the effectiveness of our approximation schemes using both real and artificial networks, showing in particular that we can accurately obtain the spectral ranking of small subgraphs. We also propose a local iterative scheme which computes the relative ranking of a subgraph using only the connectivity information of its neighbors within a few links. Our results may not only contribute to our theoretical understanding of dynamical processes on networks, but also lead to practical applications in ran...

  17. Exponential random graph models for networks with community structure.

    Science.gov (United States)

    Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

    2013-09-01

    Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

  18. Community structures and role detection in music networks

    Science.gov (United States)

    Teitelbaum, T.; Balenzuela, P.; Cano, P.; Buldú, Javier M.

    2008-12-01

    We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes.

  19. Structural and functional networks in complex systems with delay.

    Science.gov (United States)

    Eguíluz, Víctor M; Pérez, Toni; Borge-Holthoefer, Javier; Arenas, Alex

    2011-05-01

    Functional networks of complex systems are obtained from the analysis of the temporal activity of their components, and are often used to infer their unknown underlying connectivity. We obtain the equations relating topology and function in a system of diffusively delay-coupled elements in complex networks. We solve exactly the resulting equations in motifs (directed structures of three nodes) and in directed networks. The mean-field solution for directed uncorrelated networks shows that the clusterization of the activity is dominated by the in-degree of the nodes, and that the locking frequency decreases with increasing average degree. We find that the exponent of a power law degree distribution of the structural topology γ is related to the exponent of the associated functional network as α=(2-γ)(-1) for γ<2. © 2011 American Physical Society

  20. Modeling Temporal Evolution and Multiscale Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2013-01-01

    Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change......-point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights...

  1. The structure of complex networks theory and applications

    CERN Document Server

    Estrada, Ernesto

    2012-01-01

    This book deals with the analysis of the structure of complex networks by combining results from graph theory, physics, and pattern recognition. The book is divided into two parts. 11 chapters are dedicated to the development of theoretical tools for the structural analysis of networks, and 7 chapters are illustrating, in a critical way, applications of these tools to real-world scenarios. The first chapters provide detailed coverage of adjacency and metric and topologicalproperties of networks, followed by chapters devoted to the analysis of individual fragments and fragment-based global inva

  2. Structure and Evolution of the Foreign Exchange Networks

    Science.gov (United States)

    Kwapień, J.; Gworek, S.; Drożdż, S.

    2009-01-01

    We investigate topology and temporal evolution of the foreign currency exchange market viewed from a weighted network perspective. Based on exchange rates for a set of 46 currencies (including precious metals), we construct different representations of the FX network depending on a choice of the base currency. Our results show that the network structure is not stable in time, but there are main clusters of currencies, which persist for a long period of time despite the fact that their size and content are variable. We find a long-term trend in the network's evolution which affects the USD and EUR nodes. In all the network representations, the USD node gradually loses its centrality, while, on contrary, the EUR node has become slightly more central than it used to be in its early years. Despite this directional trend, the overall evolution of the network is noisy.

  3. Imaging structural and functional brain networks in temporal lobe epilepsy

    Science.gov (United States)

    Bernhardt, Boris C.; Hong, SeokJun; Bernasconi, Andrea; Bernasconi, Neda

    2013-01-01

    Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy. PMID:24098281

  4. Imaging structural and functional brain networks in temporal lobe epilepsy

    Directory of Open Access Journals (Sweden)

    Boris eBernhardt

    2013-10-01

    Full Text Available Early imaging studies in temporal lobe epilepsy (TLE focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

  5. Imaging structural and functional brain networks in temporal lobe epilepsy.

    Science.gov (United States)

    Bernhardt, Boris C; Hong, Seokjun; Bernasconi, Andrea; Bernasconi, Neda

    2013-10-01

    Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

  6. Fragmented Romanian sociology: growth and structure of the collaboration network.

    Science.gov (United States)

    Hâncean, Marian-Gabriel; Perc, Matjaž; Vlăsceanu, Lazăr

    2014-01-01

    Structural patterns in collaboration networks are essential for understanding how new ideas, research practices, innovation or cooperation circulate and develop within academic communities and between and within university departments. In our research, we explore and investigate the structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania. We show that the collaboration network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field. Although recent years have witnessed an increase in the productivity of Romanian sociologists, there is still ample room for improvement in terms of the interaction infrastructure that ought to link individuals together so that they could maximize their potentials. We also fail to discern evidence in favor of the Matthew effect governing the growth of the network, which suggests scientific success and productivity are not rewarded. Instead, the structural properties of the collaboration network are partly those of a core-periphery network, where the spread of innovation and change can be explained by structural equivalence rather than by interpersonal influence models. We also provide support for the idea that, within the observed network, collaboration is the product of homophily rather than prestige effects. Further research on the subject based on data from other countries in the region is needed to place our results in a comparative framework, in particular to discern whether the behavior of the Romanian sociologist community is unique or rather common.

  7. Unifying Inference of Meso-Scale Structures in Networks.

    Science.gov (United States)

    Tunç, Birkan; Verma, Ragini

    2015-01-01

    Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities) of the brain, as well as its auxiliary characteristics (core-periphery).

  8. Unifying Inference of Meso-Scale Structures in Networks.

    Directory of Open Access Journals (Sweden)

    Birkan Tunç

    Full Text Available Networks are among the most prevalent formal representations in scientific studies, employed to depict interactions between objects such as molecules, neuronal clusters, or social groups. Studies performed at meso-scale that involve grouping of objects based on their distinctive interaction patterns form one of the main lines of investigation in network science. In a social network, for instance, meso-scale structures can correspond to isolated social groupings or groups of individuals that serve as a communication core. Currently, the research on different meso-scale structures such as community and core-periphery structures has been conducted via independent approaches, which precludes the possibility of an algorithmic design that can handle multiple meso-scale structures and deciding which structure explains the observed data better. In this study, we propose a unified formulation for the algorithmic detection and analysis of different meso-scale structures. This facilitates the investigation of hybrid structures that capture the interplay between multiple meso-scale structures and statistical comparison of competing structures, all of which have been hitherto unavailable. We demonstrate the applicability of the methodology in analyzing the human brain network, by determining the dominant organizational structure (communities of the brain, as well as its auxiliary characteristics (core-periphery.

  9. Validation of network communicability metrics for the analysis of brain structural networks.

    Directory of Open Access Journals (Sweden)

    Jennifer Andreotti

    Full Text Available Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

  10. Improving the Reliability of Network Metrics in Structural Brain Networks by Integrating Different Network Weighting Strategies into a Single Graph

    Directory of Open Access Journals (Sweden)

    Stavros I. Dimitriadis

    2017-12-01

    Full Text Available Structural brain networks estimated from diffusion MRI (dMRI via tractography have been widely studied in healthy controls and patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost fully-weighted. Here, we scanned five healthy participants five times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROI from the Automated Anatomical Labeling (AAL template. The edges were weighted according to nine different methods. We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an Integrated Weighted Structural Brain Network (ISWBN. Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of: (a intra-class correlation coefficient (ICC of well-known network metrics, both node-wise and per network level; and (b the recognition accuracy of each subject compared to the remainder of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject, after first applying our proposed topological filtering scheme. Based on a threshold where the network level ICC should be >0.90, our findings revealed that six out of nine NWS lead to unreliable results at the network level, while all nine NWS were unreliable at the node level. In comparison, our proposed ISWBN performed as well as the best performing individual NWS at the network level, and the ICC was higher compared to all individual NWS at the node

  11. Structure of a randomly grown 2-d network

    DEFF Research Database (Denmark)

    Ajazi, Fioralba; Napolitano, George M.; Turova, Tatyana

    2015-01-01

    We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes in...... in time different phases of the structure. We conclude with a possible explanation of some empirical data on the connections between neurons.......We introduce a growing random network on a plane as a model of a growing neuronal network. The properties of the structure of the induced graph are derived. We compare our results with available data. In particular, it is shown that depending on the parameters of the model the system undergoes...

  12. Structural Quality of Service in Large-Scale Networks

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup

    , telephony and data. To meet the requirements of the different applications, and to handle the increased vulnerability to failures, the ability to design robust networks providing good Quality of Service is crucial. However, most planning of large-scale networks today is ad-hoc based, leading to highly...... complex networks lacking predictability and global structural properties. The thesis applies the concept of Structural Quality of Service to formulate desirable global properties, and it shows how regular graph structures can be used to obtain such properties.......Digitalization has created the base for co-existence and convergence in communications, leading to an increasing use of multi service networks. This is for example seen in the Fiber To The Home implementations, where a single fiber is used for virtually all means of communication, including TV...

  13. Detecting the overlapping and hierarchical community structure in complex networks

    International Nuclear Information System (INIS)

    Lancichinetti, Andrea; Fortunato, Santo; Kertesz, Janos

    2009-01-01

    Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.

  14. Global tree network for computing structures enabling global processing operations

    Science.gov (United States)

    Blumrich; Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Hoenicke, Dirk; Steinmacher-Burow, Burkhard D.; Takken, Todd E.; Vranas, Pavlos M.

    2010-01-19

    A system and method for enabling high-speed, low-latency global tree network communications among processing nodes interconnected according to a tree network structure. The global tree network enables collective reduction operations to be performed during parallel algorithm operations executing in a computer structure having a plurality of the interconnected processing nodes. Router devices are included that interconnect the nodes of the tree via links to facilitate performance of low-latency global processing operations at nodes of the virtual tree and sub-tree structures. The global operations performed include one or more of: broadcast operations downstream from a root node to leaf nodes of a virtual tree, reduction operations upstream from leaf nodes to the root node in the virtual tree, and point-to-point message passing from any node to the root node. The global tree network is configurable to provide global barrier and interrupt functionality in asynchronous or synchronized manner, and, is physically and logically partitionable.

  15. Road Network Selection Based on Road Hierarchical Structure Control

    Directory of Open Access Journals (Sweden)

    HE Haiwei

    2015-04-01

    Full Text Available A new road network selection method based on hierarchical structure is studied. Firstly, road network is built as strokes which are then classified into hierarchical collections according to the criteria of betweenness centrality value (BC value. Secondly, the hierarchical structure of the strokes is enhanced using structural characteristic identification technique. Thirdly, the importance calculation model was established according to the relationships among the hierarchical structure of the strokes. Finally, the importance values of strokes are got supported with the model's hierarchical calculation, and with which the road network is selected. Tests are done to verify the advantage of this method by comparing it with other common stroke-oriented methods using three kinds of typical road network data. Comparision of the results show that this method had few need to semantic data, and could eliminate the negative influence of edge strokes caused by the criteria of BC value well. So, it is better to maintain the global hierarchical structure of road network, and suitable to meet with the selection of various kinds of road network at the same time.

  16. Structure of Small World Innovation Network and Learning Performance

    Directory of Open Access Journals (Sweden)

    Shuang Song

    2014-01-01

    Full Text Available This paper examines the differences of learning performance of 5 MNCs (multinational corporations that filed the largest number of patents in China. We establish the innovation network with the patent coauthorship data by these 5 MNCs and classify the networks by the tail of distribution curve of connections. To make a comparison of the learning performance of these 5 MNCs with differing network structures, we develop an organization learning model by regarding the reality as having m dimensions, which denotes the heterogeneous knowledge about the reality. We further set n innovative individuals that are mutually interactive and own unique knowledge about the reality. A longer (shorter distance between the knowledge of the individual and the reality denotes a lower (higher knowledge level of that individual. Individuals interact with and learn from each other within the small-world network. By making 1,000 numerical simulations and averaging the simulated results, we find that the differing structure of the small-world network leads to the differences of learning performance between these 5 MNCs. The network monopolization negatively impacts and network connectivity positively impacts learning performance. Policy implications in the conclusion section suggest that to improve firm learning performance, it is necessary to establish a flat and connective network.

  17. An automated approach to network features of protein structure ensembles

    Science.gov (United States)

    Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi

    2013-01-01

    Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html. PMID:23934896

  18. Comparison and validation of community structures in complex networks

    Science.gov (United States)

    Gustafsson, Mika; Hörnquist, Michael; Lombardi, Anna

    2006-07-01

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

  19. Developing a robust wireless sensor network structure for environmental sensing

    Science.gov (United States)

    Zhang, Z.; Oroza, C.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

    2013-12-01

    The American River Hydrologic Observatory is being strategically deployed as a real-time ground-based measurement network that delivers accurate and timely information on snow conditions and other hydrologic attributes with a previously unheard of granularity of time and space. The basin-scale network involves 18 sub-networks set out at physiographically representative locations spanning the seasonally snow-covered half of the 5000 km2 American river basin. Each sub-network, covering about a 1-km2 area, consists of 10 wirelessly networked sensing nodes that continuously measure and telemeter temperature, and snow depth; plus selected locations are equipped with sensors for relative humidity, solar radiation, and soil moisture at several depths. The sensor locations were chosen to maximize the variance sampled for snow depth within the basin. Network design and deployment involves an iterative but efficient process. After sensor-station locations are determined, a robust network of interlinking sensor stations and signal repeaters must be constructed to route sensor data to a central base station with a two-way communicable data uplink. Data can then be uploaded from site to remote servers in real time through satellite and cell modems. Signal repeaters are placed for robustness of a self-healing network with redundant signal paths to the base station. Manual, trial-and-error heuristic approaches for node placement are inefficient and labor intensive. In that approach field personnel must restructure the network in real time and wait for new network statistics to be calculated at the base station before finalizing a placement, acting without knowledge of the global topography or overall network structure. We show how digital elevation plus high-definition aerial photographs to give foliage coverage can optimize planning of signal repeater placements and guarantee a robust network structure prior to the physical deployment. We can also 'stress test' the final network

  20. Dynamics of Networks if Everyone Strives for Structural Holes

    NARCIS (Netherlands)

    Buskens, Vincent; Rijt, Arnout van de

    2008-01-01

    When entrepreneurs enter structural holes in networks, they can exploit the related benefits. Evidence for these benefits has steadily accumulated. The authors ask whether those who strive for such structural advantages can maintain them if others follow their example. Burt speculates that they

  1. A Mapping Between Structural and Functional Brain Networks.

    Science.gov (United States)

    Meier, Jil; Tewarie, Prejaas; Hillebrand, Arjan; Douw, Linda; van Dijk, Bob W; Stufflebeam, Steven M; Van Mieghem, Piet

    2016-05-01

    The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.

  2. Structural parameter identifiability analysis for dynamic reaction networks

    DEFF Research Database (Denmark)

    Davidescu, Florin Paul; Jørgensen, Sten Bay

    2008-01-01

    method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...... where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models....... The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...

  3. The overlapping community structure of structural brain network in young healthy individuals.

    Directory of Open Access Journals (Sweden)

    Kai Wu

    2011-05-01

    Full Text Available Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of information through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain.

  4. A Web-Based, Social Networking Beginners' Running Intervention for Adults Aged 18 to 50 Years Delivered via a Facebook Group: Randomized Controlled Trial.

    Science.gov (United States)

    Looyestyn, Jemma; Kernot, Jocelyn; Boshoff, Kobie; Maher, Carol

    2018-02-26

    Online social networks continue to grow in popularity, with 1.7 billion users worldwide accessing Facebook each month. The use of social networking sites such as Facebook for the delivery of health behavior programs is relatively new. The primary aim of this study was to determine the effectiveness of a Web-based beginners' running program for adults aged 18 to 50 years, delivered via a Facebook group, in increasing physical activity (PA) and cardiorespiratory fitness. A total of 89 adults with a mean age of 35.2 years (SD 10.9) were recruited online and via print media. Participants were randomly allocated to receive the UniSA Run Free program, an 8-week Web-based beginners' running intervention, delivered via a closed Facebook group (n=41) that included daily interactive posts (information with links, motivational quotes, opinion polls, or questions) and details of the running sessions; or to the control group who received a hard copy of the running program (n=48). Assessments were completed online at baseline, 2 months, and 5 months. The primary outcome measures were self-reported weekly moderate to vigorous physical activity (MVPA) and objectively measured cardiorespiratory fitness. Secondary outcomes were social support, exercise attitudes, and self-efficacy. Analyses were undertaken using random effects mixed modeling. Compliance with the running program and engagement with the Facebook group were analyzed descriptively. Both groups significantly increased MVPA across the study period (P=.004); however, this was significantly higher in the Facebook group (P=.04). The Facebook group increased their MVPA from baseline by 140 min/week versus 91 min for the control at 2 months. MVPA remained elevated for the Facebook group (from baseline) by 129 min/week versus a 50 min/week decrease for the control at 5 months. Both groups had significant increases in social support scores at 2 months (P=.02); however, there were no group by time differences (P=.16). There were

  5. Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

    Science.gov (United States)

    Rich, Scott; Zochowski, Michal; Booth, Victoria

    2018-01-01

    Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

  6. Struttura produttiva e sviluppo regionale di lungo periodo in Italia (Production Structure and Long-Run Regional Development in Italy

    Directory of Open Access Journals (Sweden)

    Giulio Caianelli

    2012-04-01

    Full Text Available During the 1990s the link between specialisation and/or variety of the production structure and long-run local development has been widely studied both at theoretical and empirical level. Following this research line, the aim of this paper is twofold. First of all, we analyse, by means of transition matrices, the evolution of the model of specialisation/variety of manufacturing industries at regional level in Italy during the twentieth century. Secondly, we investigate, from an econometric point of view, the potential impact of specialisation and/or of variety on the different growth rates of manufacturing employment of the Italian regions during the period 1927-91. In order to carry out this kind of empirical investigation, we have built up a new and original historical data set which, using statistical information drawn from the Italian Industrial Census carried out from 1911 to 1991, provides data on the number of employees for 15 manufacturing industries and for 18 regions.           JEL Codes: R12, L60, R11, R23Keywords: Manufacturing, Regional Development, Regional

  7. Brain networks that track musical structure.

    Science.gov (United States)

    Janata, Petr

    2005-12-01

    As the functional neuroimaging literature grows, it becomes increasingly apparent that music and musical activities engage diverse regions of the brain. In this paper I discuss two studies to illustrate that exactly which brain areas are observed to be responsive to musical stimuli and tasks depends on the tasks and the methods used to describe the tasks and the stimuli. In one study, subjects listened to polyphonic music and were asked to either orient their attention selectively to individual instruments or in a divided or holistic manner across multiple instruments. The network of brain areas that was recruited changed subtly with changes in the task instructions. The focus of the second study was to identify brain regions that follow the pattern of movement of a continuous melody through the tonal space defined by the major and minor keys of Western tonal music. Such an area was identified in the rostral medial prefrontal cortex. This observation is discussed in the context of other neuroimaging studies that implicate this region in inwardly directed mental states involving decisions about the self, autobiographical memory, the cognitive regulation of emotion, affective responses to musical stimuli, and familiarity judgments about musical stimuli. Together with observations that these regions are among the last to atrophy in Alzheimer disease, and that these patients appear to remain responsive to autobiographically salient musical stimuli, very early evidence is emerging from the literature for the hypothesis that the rostral medial prefrontal cortex is a node that is important for binding music with memories within a broader music-responsive network.

  8. Structure-based control of complex networks with nonlinear dynamics.

    Science.gov (United States)

    Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

    2017-07-11

    What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

  9. Modeling structure and resilience of the dark network.

    Science.gov (United States)

    De Domenico, Manlio; Arenas, Alex

    2017-02-01

    While the statistical and resilience properties of the Internet are no longer changing significantly across time, the Darknet, a network devoted to keep anonymous its traffic, still experiences rapid changes to improve the security of its users. Here we study the structure of the Darknet and find that its topology is rather peculiar, being characterized by a nonhomogeneous distribution of connections, typical of scale-free networks; very short path lengths and high clustering, typical of small-world networks; and lack of a core of highly connected nodes. We propose a model to reproduce such features, demonstrating that the mechanisms used to improve cybersecurity are responsible for the observed topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet much more resilient than the Internet (used as a benchmark for comparison at a descriptive level) to random failures, targeted attacks, and cascade failures, as a result of adaptive changes in response to the attempts of dismantling the network across time.

  10. Offspring social network structure predicts fitness in families.

    Science.gov (United States)

    Royle, Nick J; Pike, Thomas W; Heeb, Philipp; Richner, Heinz; Kölliker, Mathias

    2012-12-22

    Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively.

  11. Resistance and Security Index of Networks: Structural Information Perspective of Network Security

    Science.gov (United States)

    Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

    2016-01-01

    Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks. PMID:27255783

  12. Resistance and Security Index of Networks: Structural Information Perspective of Network Security.

    Science.gov (United States)

    Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

    2016-06-03

    Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks.

  13. Resistance and Security Index of Networks: Structural Information Perspective of Network Security

    Science.gov (United States)

    Li, Angsheng; Hu, Qifu; Liu, Jun; Pan, Yicheng

    2016-06-01

    Recently, Li and Pan defined the metric of the K-dimensional structure entropy of a structured noisy dataset G to be the information that controls the formation of the K-dimensional structure of G that is evolved by the rules, order and laws of G, excluding the random variations that occur in G. Here, we propose the notion of resistance of networks based on the one- and two-dimensional structural information of graphs. Given a graph G, we define the resistance of G, written , as the greatest overall number of bits required to determine the code of the module that is accessible via random walks with stationary distribution in G, from which the random walks cannot escape. We show that the resistance of networks follows the resistance law of networks, that is, for a network G, the resistance of G is , where and are the one- and two-dimensional structure entropies of G, respectively. Based on the resistance law, we define the security index of a network G to be the normalised resistance of G, that is, . We show that the resistance and security index are both well-defined measures for the security of the networks.

  14. Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure.

    Science.gov (United States)

    Carnegie, Nicole Bohme

    2018-01-30

    Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable. To this end, we vary the network size, mean degree, and transmissibility of the pathogen, as well as the network feature of interest: clustering, degree assortativity, or attribute-based preferential mixing. We record several standard measures of the size and spread of the epidemic, as well as measures that describe the shape of the transmission tree in order to ascertain whether there are detectable signals in the final data from the outbreak. The results suggest that there is potential to estimate contact network features from transmission trees or pure epidemic data, particularly for diseases with high transmissibility or for which the relevant contact network is of low mean degree. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  15. Launching applications on compute and service processors running under different operating systems in scalable network of processor boards with routers

    Science.gov (United States)

    Tomkins, James L [Albuquerque, NM; Camp, William J [Albuquerque, NM

    2009-03-17

    A multiple processor computing apparatus includes a physical interconnect structure that is flexibly configurable to support selective segregation of classified and unclassified users. The physical interconnect structure also permits easy physical scalability of the computing apparatus. The computing apparatus can include an emulator which permits applications from the same job to be launched on processors that use different operating systems.

  16. Structural controllability and controlling centrality of temporal networks.

    Science.gov (United States)

    Pan, Yujian; Li, Xiang

    2014-01-01

    Temporal networks are such networks where nodes and interactions may appear and disappear at various time scales. With the evidence of ubiquity of temporal networks in our economy, nature and society, it's urgent and significant to focus on its structural controllability as well as the corresponding characteristics, which nowadays is still an untouched topic. We develop graphic tools to study the structural controllability as well as its characteristics, identifying the intrinsic mechanism of the ability of individuals in controlling a dynamic and large-scale temporal network. Classifying temporal trees of a temporal network into different types, we give (both upper and lower) analytical bounds of the controlling centrality, which are verified by numerical simulations of both artificial and empirical temporal networks. We find that the positive relationship between aggregated degree and controlling centrality as well as the scale-free distribution of node's controlling centrality are virtually independent of the time scale and types of datasets, meaning the inherent robustness and heterogeneity of the controlling centrality of nodes within temporal networks.

  17. Sampling from complex networks with high community structures.

    Science.gov (United States)

    Salehi, Mostafa; Rabiee, Hamid R; Rajabi, Arezo

    2012-06-01

    In this paper, we propose a novel link-tracing sampling algorithm, based on the concepts from PageRank vectors, to sample from networks with high community structures. Our method has two phases; (1) Sampling the closest nodes to the initial nodes by approximating personalized PageRank vectors and (2) Jumping to a new community by using PageRank vectors and unknown neighbors. Empirical studies on several synthetic and real-world networks show that the proposed method improves the performance of network sampling compared to the popular link-based sampling methods in terms of accuracy and visited communities.

  18. Completely random measures for modelling block-structured sparse networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten

    2016-01-01

    Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks...... have a power-law distribution of the vertices which in turn implies the number of edges scale slower than quadratically in the number of vertices. These assumptions are fundamentally irreconcilable as the Aldous-Hoover theorem implies quadratic scaling of the number of edges. Recently Caron and Fox...

  19. Mapping human whole-brain structural networks with diffusion MRI.

    Directory of Open Access Journals (Sweden)

    Patric Hagmann

    Full Text Available Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.

  20. Structure and Cooptition in Urban Networks

    NARCIS (Netherlands)

    M.J. Burger (Martijn)

    2011-01-01

    textabstractOver the past decades, demographic changes, advances in transportation and communication technology, and the growth of the services sector have had a significant impact on the spatial structure of regions. Monocentric cities are disappearing and developing into polycentric metropolitan

  1. 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.

  2. News and Views: Research council resource allocations: managing demand; e-MERLIN radio telescope network is up and running

    Science.gov (United States)

    2011-02-01

    The research councils discovered in December the allocation of money from the UK government's Comprehensive Spending Review, and have set out their delivery plans outlining how they will spend it. Details and decisions will follow consultation in the coming months. The first image from eMerlin, the UK's national radio astronomy facility, shows the power of the enhanced network of radio telescopes spread over 220 km and now linked by fibre optics. These links and advanced receivers will allow astronomers to see in a single day what would have previously taken them more than a year of observations.

  3. Running Linux

    CERN Document Server

    Dalheimer, Matthias Kalle

    2006-01-01

    The fifth edition of Running Linux is greatly expanded, reflecting the maturity of the operating system and the teeming wealth of software available for it. Hot consumer topics such as audio and video playback applications, groupware functionality, and spam filtering are covered, along with the basics in configuration and management that always made the book popular.

  4. RUN COORDINATION

    CERN Multimedia

    C. Delaere

    2013-01-01

    Since the LHC ceased operations in February, a lot has been going on at Point 5, and Run Coordination continues to monitor closely the advance of maintenance and upgrade activities. In the last months, the Pixel detector was extracted and is now stored in the pixel lab in SX5; the beam pipe has been removed and ME1/1 removal has started. We regained access to the vactank and some work on the RBX of HB has started. Since mid-June, electricity and cooling are back in S1 and S2, allowing us to turn equipment back on, at least during the day. 24/7 shifts are not foreseen in the next weeks, and safety tours are mandatory to keep equipment on overnight, but re-commissioning activities are slowly being resumed. Given the (slight) delays accumulated in LS1, it was decided to merge the two global runs initially foreseen into a single exercise during the week of 4 November 2013. The aim of the global run is to check that we can run (parts of) CMS after several months switched off, with the new VME PCs installed, th...

  5. Topological structure and mechanics of glassy polymer networks.

    Science.gov (United States)

    Elder, Robert M; Sirk, Timothy W

    2017-11-22

    The influence of chain-level network architecture (i.e., topology) on mechanics was explored for unentangled polymer networks using a blend of coarse-grained molecular simulations and graph-theoretic concepts. A simple extension of the Watts-Strogatz model is proposed to control the graph properties of the network such that the corresponding physical properties can be studied with simulations. The architecture of polymer networks assembled with a dynamic curing approach were compared with the extended Watts-Strogatz model, and found to agree surprisingly well. The final cured structures of the dynamically-assembled networks were nearly an intermediate between lattice and random connections due to restrictions imposed by the finite length of the chains. Further, the uni-axial stress response, character of the bond breaking, and non-affine displacements of fully-cured glassy networks were analyzed as a function of the degree of disorder in the network architecture. It is shown that the architecture strongly affects the network stability, flow stress, onset of bond breaking, and ultimate stress while leaving the modulus and yield point nearly unchanged. The results show that internal restrictions imposed by the network architecture alter the chain-level response through changes to the crosslink dynamics in the flow regime and through the degree of coordinated chain failure at the ultimate stress. The properties considered here are shown to be sensitive to even incremental changes to the architecture and, therefore, the overall network architecture, beyond simple defects, is predicted to be a meaningful physical parameter in the mechanics of glassy polymer networks.

  6. Complex modular structure of large-scale brain networks

    Science.gov (United States)

    Valencia, M.; Pastor, M. A.; Fernández-Seara, M. A.; Artieda, J.; Martinerie, J.; Chavez, M.

    2009-06-01

    Modular structure is ubiquitous among real-world networks from related proteins to social groups. Here we analyze the modular organization of brain networks at a large scale (voxel level) extracted from functional magnetic resonance imaging signals. By using a random-walk-based method, we unveil the modularity of brain webs and show modules with a spatial distribution that matches anatomical structures with functional significance. The functional role of each node in the network is studied by analyzing its patterns of inter- and intramodular connections. Results suggest that the modular architecture constitutes the structural basis for the coexistence of functional integration of distant and specialized brain areas during normal brain activities at rest.

  7. Coevolution of game and network structure with adjustable linking

    Science.gov (United States)

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

    2009-12-01

    Most papers about the evolutionary game on graph assume the statistic network structure. However, in the real world, social interaction could change the relationship among people. And the change of social structure will also affect people’s strategies. We build a coevolution model of prisoner’s dilemma game and network structure to study the dynamic interaction in the real world. Differing from other coevolution models, players rewire their network connections according to the density of cooperation and other players’ payoffs. We use a parameter α to control the effect of payoff in the process of rewiring. Based on the asynchronous update rule and Monte Carlo simulation, we find that, when players prefer to rewire their links to those who are richer, the temptation can increase the cooperation density.

  8. Topological properties of complex networks in protein structures

    Science.gov (United States)

    Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik

    2014-03-01

    We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).

  9. Research on Community Structure in Bus Transport Networks

    International Nuclear Information System (INIS)

    Yang Xuhua; Wang Bo; Sun Youxian

    2009-01-01

    We abstract the bus transport networks (BTNs) to two kinds of complex networks with space L and space P methods respectively. Using improved community detecting algorithm (PKM agglomerative algorithm), we analyze the community property of two kinds of BTNs graphs. The results show that the BTNs graph described with space L method have obvious community property, but the other kind of BTNs graph described with space P method have not. The reason is that the BTNs graph described with space P method have the intense overlapping community property and general community division algorithms can not identify this kind of community structure. To overcome this problem, we propose a novel community structure called N-depth community and present a corresponding community detecting algorithm, which can detect overlapping community. Applying the novel community structure and detecting algorithm to a BTN evolution model described with space P, whose network property agrees well with real BTNs', we get obvious community property. (general)

  10. Mapping structural covariance networks of facial emotion recognition in early psychosis: A pilot study.

    Science.gov (United States)

    Buchy, Lisa; Barbato, Mariapaola; Makowski, Carolina; Bray, Signe; MacMaster, Frank P; Deighton, Stephanie; Addington, Jean

    2017-11-01

    People with psychosis show deficits recognizing facial emotions and disrupted activation in the underlying neural circuitry. We evaluated associations between facial emotion recognition and cortical thickness using a correlation-based approach to map structural covariance networks across the brain. Fifteen people with an early psychosis provided magnetic resonance scans and completed the Penn Emotion Recognition and Differentiation tasks. Fifteen historical controls provided magnetic resonance scans. Cortical thickness was computed using CIVET and analyzed with linear models. Seed-based structural covariance analysis was done using the mapping anatomical correlations across the cerebral cortex methodology. To map structural covariance networks involved in facial emotion recognition, the right somatosensory cortex and bilateral fusiform face areas were selected as seeds. Statistics were run in SurfStat. Findings showed increased cortical covariance between the right fusiform face region seed and right orbitofrontal cortex in controls than early psychosis subjects. Facial emotion recognition scores were not significantly associated with thickness in any region. A negative effect of Penn Differentiation scores on cortical covariance was seen between the left fusiform face area seed and right superior parietal lobule in early psychosis subjects. Results suggest that facial emotion recognition ability is related to covariance in a temporal-parietal network in early psychosis. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Development of human brain structural networks through infancy and childhood.

    Science.gov (United States)

    Huang, Hao; Shu, Ni; Mishra, Virendra; Jeon, Tina; Chalak, Lina; Wang, Zhiyue J; Rollins, Nancy; Gong, Gaolang; Cheng, Hua; Peng, Yun; Dong, Qi; He, Yong

    2015-05-01

    During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  12. Romanian network for structural integrity assessment of nuclear components

    International Nuclear Information System (INIS)

    Roth, Maria; Constantinescu, Dan Mihai; Brad, Sebastian; Ducu, Catalin

    2008-01-01

    Full text: Based of the Romanian option to develop and operate nuclear facilities, using as model the networks created at European level and taking into account the international importance of the structural integrity assessments for lifetime extension of the nuclear components, a national Project started since 2005 in the framework of the National Program 'Research of Excellence', Modulus I 2006-2008, managed by the Ministry of Education and Research. Entitled 'Integrated Network for Structural Integrity Monitoring of Critical Components in Nuclear Facilities', with the acronym RIMIS, the Project had two main objectives: - to elaborate a procedure applicable to the structural integrity assessment of the critical components used in Romanian nuclear facilities; - to integrate the national networking in a similar one, at European level, to enhance the scientific significance of Romanian R and D organizations as well as to increase the contribution to solving one of the major issue of the nuclear field. The paper aimed to present the activities performed in the Romanian institutes, involved in the Project, the final results obtained as part of the R and D activities, including experimental, theoretical and modeling ones regarding structural integrity assessment of nuclear components employed in CANDU type reactors. Also the activity carried out in the framework of the NULIFE network, created at European level of the FP6 Program and sustained by the RIMIS network will be described. (authors)

  13. Structural covariance networks across healthy young adults and their consistency.

    Science.gov (United States)

    Guo, Xiaojuan; Wang, Yan; Guo, Taomei; Chen, Kewei; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

    2015-08-01

    To investigate structural covariance networks (SCNs) as measured by regional gray matter volumes with structural magnetic resonance imaging (MRI) from healthy young adults, and to examine their consistency and stability. Two independent cohorts were included in this study: Group 1 (82 healthy subjects aged 18-28 years) and Group 2 (109 healthy subjects aged 20-28 years). Structural MRI data were acquired at 3.0T and 1.5T using a magnetization prepared rapid-acquisition gradient echo sequence for these two groups, respectively. We applied independent component analysis (ICA) to construct SCNs and further applied the spatial overlap ratio and correlation coefficient to evaluate the spatial consistency of the SCNs between these two datasets. Seven and six independent components were identified for Group 1 and Group 2, respectively. Moreover, six SCNs including the posterior default mode network, the visual and auditory networks consistently existed across the two datasets. The overlap ratios and correlation coefficients of the visual network reached the maximums of 72% and 0.71. This study demonstrates the existence of consistent SCNs corresponding to general functional networks. These structural covariance findings may provide insight into the underlying organizational principles of brain anatomy. © 2014 Wiley Periodicals, Inc.

  14. Development of Human Brain Structural Networks Through Infancy and Childhood

    Science.gov (United States)

    Huang, Hao; Shu, Ni; Mishra, Virendra; Jeon, Tina; Chalak, Lina; Wang, Zhiyue J.; Rollins, Nancy; Gong, Gaolang; Cheng, Hua; Peng, Yun; Dong, Qi; He, Yong

    2015-01-01

    During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers. PMID:24335033

  15. LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

    Science.gov (United States)

    Berahmand, Kamal; Bouyer, Asgarali

    2018-03-01

    Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

  16. Metropolitian area network services comprised of virtual local area networks running over hybrid fiber-coax and asynchronous transfer mode technologies

    Science.gov (United States)

    Biedron, William S.

    1995-11-01

    Since 1990 there has been a rapid increase in the demand for communication services, especially local and wide area network (LAN/WAN) oriented services. With the introduction of the DFB laser transmitter, hybrid-fiber-coax (HFC) cable plant designs, ATM transport technologies and rf modems, new LAN/WAN services can now be defined and marketed to residential and business customers over existing cable TV systems. The term metropolitan area network (MAN) can be used to describe this overall network. This paper discusses the technical components needed to provision these services as well as provides some perspectives on integration issues. Architecture at the headend and in the backbone is discussed, as well as specific service definitions and the technology issues associated with each. The TCP/IP protocol is suggested as a primary protocol to be used throughout the MAN.

  17. Network dynamics and its relationships to topology and coupling structure in excitable complex networks

    International Nuclear Information System (INIS)

    Zhang Li-Sheng; Mi Yuan-Yuan; Gu Wei-Feng; Hu Gang

    2014-01-01

    All dynamic complex networks have two important aspects, pattern dynamics and network topology. Discovering different types of pattern dynamics and exploring how these dynamics depend on network topologies are tasks of both great theoretical importance and broad practical significance. In this paper we study the oscillatory behaviors of excitable complex networks (ECNs) and find some interesting dynamic behaviors of ECNs in oscillatory probability, the multiplicity of oscillatory attractors, period distribution, and different types of oscillatory patterns (e.g., periodic, quasiperiodic, and chaotic). In these aspects, we further explore strikingly sharp differences among network dynamics induced by different topologies (random or scale-free topologies) and different interaction structures (symmetric or asymmetric couplings). The mechanisms behind these differences are explained physically. (interdisciplinary physics and related areas of science and technology)

  18. Science advancements key to increasing management value of life stage monitoring networks for endangered Sacramento River winter-run Chinook salmon in California

    Science.gov (United States)

    Johnson, Rachel C.; Windell, Sean; Brandes, Patricia L.; Conrad, J. Louise; Ferguson, John; Goertler, Pascale A. L.; Harvey, Brett N.; Heublein, Joseph; Isreal, Joshua A.; Kratville, Daniel W.; Kirsch, Joseph E.; Perry, Russell W.; Pisciotto, Joseph; Poytress, William R.; Reece, Kevin; Swart, Brycen G.

    2017-01-01

    A robust monitoring network that provides quantitative information about the status of imperiled species at key life stages and geographic locations over time is fundamental for sustainable management of fisheries resources. For anadromous species, management actions in one geographic domain can substantially affect abundance of subsequent life stages that span broad geographic regions. Quantitative metrics (e.g., abundance, movement, survival, life history diversity, and condition) at multiple life stages are needed to inform how management actions (e.g., hatcheries, harvest, hydrology, and habitat restoration) influence salmon population dynamics. The existing monitoring network for endangered Sacramento River winterrun Chinook Salmon (SRWRC, Oncorhynchus tshawytscha) in California’s Central Valley was compared to conceptual models developed for each life stage and geographic region of the life cycle to identify relevant SRWRC metrics. We concluded that the current monitoring network was insufficient to diagnose when (life stage) and where (geographic domain) chronic or episodic reductions in SRWRC cohorts occur, precluding within- and among-year comparisons. The strongest quantitative data exist in the Upper Sacramento River, where abundance estimates are generated for adult spawners and emigrating juveniles. However, once SRWRC leave the upper river, our knowledge of their identity, abundance, and condition diminishes, despite the juvenile monitoring enterprise. We identified six system-wide recommended actions to strengthen the value of data generated from the existing monitoring network to assess resource management actions: (1) incorporate genetic run identification; (2) develop juvenile abundance estimates; (3) collect data for life history diversity metrics at multiple life stages; (4) expand and enhance real-time fish survival and movement monitoring; (5) collect fish condition data; and (6) provide timely public access to monitoring data in open data

  19. High eccentric hip abduction strength reduces the risk of developing patellofemoral pain among novice runners initiating a self-structured running program

    DEFF Research Database (Denmark)

    Ramskov, Daniel; Barton, Christian; Nielsen, Rasmus O

    2015-01-01

    Study Design Observational prospective cohort study with 1-year follow-up. Objectives To investigate the relationship between eccentric hip abduction strength and the development of patellofemoral pain (PFP) in novice runners, during a self-structured running regime. Background Recent research...

  20. Network structure detection and analysis of Shanghai stock market

    Directory of Open Access Journals (Sweden)

    Sen Wu

    2015-04-01

    Full Text Available Purpose: In order to investigate community structure of the component stocks of SSE (Shanghai Stock Exchange 180-index, a stock correlation network is built to find the intra-community and inter-community relationship. Design/methodology/approach: The stock correlation network is built taking the vertices as stocks and edges as correlation coefficients of logarithm returns of stock price. It is built as undirected weighted at first. GN algorithm is selected to detect community structure after transferring the network into un-weighted with different thresholds. Findings: The result of the network community structure analysis shows that the stock market has obvious industrial characteristics. Most of the stocks in the same industry or in the same supply chain are assigned to the same community. The correlation of the internal stock prices’ fluctuation is closer than in different communities. The result of community structure detection also reflects correlations among different industries. Originality/value: Based on the analysis of the community structure in Shanghai stock market, the result reflects some industrial characteristics, which has reference value to relationship among industries or sub-sectors of listed companies.

  1. Sensitivity analysis of human brain structural network construction

    Directory of Open Access Journals (Sweden)

    Kuang Wei

    2017-12-01

    Full Text Available Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP, we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography streamlines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes. Diffusion tractography has been proven to be a promising noninvasive technique to study the network properties of the human brain. However, how various tractography and network construction parameters affect network properties has not been studied using a large cohort of high-quality data. We utilize data provided by the Human Connectome Project to characterize the changes to network properties induced by varying the brain parcellation atlas scales, the number of reconstructed tractography tracks, and the degree of grey

  2. The structure and resilience of financial market networks.

    Science.gov (United States)

    Peron, Thomas Kaue Dal'Maso; Costa, Luciano da Fontoura; Rodrigues, Francisco A

    2012-03-01

    Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience.

  3. Global network structure of dominance hierarchy of ant workers.

    Science.gov (United States)

    Shimoji, Hiroyuki; Abe, Masato S; Tsuji, Kazuki; Masuda, Naoki

    2014-10-06

    Dominance hierarchy among animals is widespread in various species and believed to serve to regulate resource allocation within an animal group. Unlike small groups, however, detection and quantification of linear hierarchy in large groups of animals are a difficult task. Here, we analyse aggression-based dominance hierarchies formed by worker ants in Diacamma sp. as large directed networks. We show that the observed dominance networks are perfect or approximate directed acyclic graphs, which are consistent with perfect linear hierarchy. The observed networks are also sparse and random but significantly different from networks generated through thinning of the perfect linear tournament (i.e. all individuals are linearly ranked and dominance relationship exists between every pair of individuals). These results pertain to global structure of the networks, which contrasts with the previous studies inspecting frequencies of different types of triads. In addition, the distribution of the out-degree (i.e. number of workers that the focal worker attacks), not in-degree (i.e. number of workers that attack the focal worker), of each observed network is right-skewed. Those having excessively large out-degrees are located near the top, but not the top, of the hierarchy. We also discuss evolutionary implications of the discovered properties of dominance networks. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  4. The structure and resilience of financial market networks

    Science.gov (United States)

    Kauê Dal'Maso Peron, Thomas; da Fontoura Costa, Luciano; Rodrigues, Francisco A.

    2012-03-01

    Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience.

  5. Running Club

    CERN Multimedia

    Running Club

    2011-01-01

    The cross country running season has started well this autumn with two events: the traditional CERN Road Race organized by the Running Club, which took place on Tuesday 5th October, followed by the ‘Cross Interentreprises’, a team event at the Evaux Sports Center, which took place on Saturday 8th October. The participation at the CERN Road Race was slightly down on last year, with 65 runners, however the participants maintained the tradition of a competitive yet friendly atmosphere. An ample supply of refreshments before the prize giving was appreciated by all after the race. Many thanks to all the runners and volunteers who ensured another successful race. The results can be found here: https://espace.cern.ch/Running-Club/default.aspx CERN participated successfully at the cross interentreprises with very good results. The teams succeeded in obtaining 2nd and 6th place in the Mens category, and 2nd place in the Mixed category. Congratulations to all. See results here: http://www.c...

  6. RUN COORDINATION

    CERN Multimedia

    Christophe Delaere

    2013-01-01

    The focus of Run Coordination during LS1 is to monitor closely the advance of maintenance and upgrade activities, to smooth interactions between subsystems and to ensure that all are ready in time to resume operations in 2015 with a fully calibrated and understood detector. After electricity and cooling were restored to all equipment, at about the time of the last CMS week, recommissioning activities were resumed for all subsystems. On 7 October, DCS shifts began 24/7 to allow subsystems to remain on to facilitate operations. That culminated with the Global Run in November (GriN), which   took place as scheduled during the week of 4 November. The GriN has been the first centrally managed operation since the beginning of LS1, and involved all subdetectors but the Pixel Tracker presently in a lab upstairs. All nights were therefore dedicated to long stable runs with as many subdetectors as possible. Among the many achievements in that week, three items may be highlighted. First, the Strip...

  7. RUN COORDINATION

    CERN Multimedia

    M. Chamizo

    2012-01-01

      On 17th January, as soon as the services were restored after the technical stop, sub-systems started powering on. Since then, we have been running 24/7 with reduced shift crew — Shift Leader and DCS shifter — to allow sub-detectors to perform calibration, noise studies, test software upgrades, etc. On 15th and 16th February, we had the first Mid-Week Global Run (MWGR) with the participation of most sub-systems. The aim was to bring CMS back to operation and to ensure that we could run after the winter shutdown. All sub-systems participated in the readout and the trigger was provided by a fraction of the muon systems (CSC and the central RPC wheel). The calorimeter triggers were not available due to work on the optical link system. Initial checks of different distributions from Pixels, Strips, and CSC confirmed things look all right (signal/noise, number of tracks, phi distribution…). High-rate tests were done to test the new CSC firmware to cure the low efficiency ...

  8. Learning Orthographic Structure With Sequential Generative Neural Networks.

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-04-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

  9. Age structure and cooperation in coevolutionary games on dynamic network

    Science.gov (United States)

    Qin, Zilong; Hu, Zhenhua; Zhou, Xiaoping; Yi, Jingzhang

    2015-04-01

    Our proposed model imitates the growth of a population and describes the age structure and the level of cooperation in games on dynamic network with continuous changes of structure and topology. The removal of nodes and links caused by age-dependent attack, together with the nodes addition standing for the newborns of population, badly ruins Matthew effect in this coevolutionary process. Though the network is generated by growth and preferential attachment, it degenerates into random network and it is no longer heterogeneous. When the removal of nodes and links is equal to the addition of nodes and links, the size of dynamic network is maintained in steady-state, so is the low level of cooperation. Severe structure variation, homogeneous topology and continuous invasion of new defection jointly make dynamic network unsuitable for the survival of cooperator even when the probability with which the newborn players initially adopt the strategy cooperation is high, while things change slightly when the connections of newborn players are restricted. Fortunately, moderate interactions in a generation trigger an optimal recovering process to encourage cooperation. The model developed in this paper outlines an explanation of the cohesion changes in the development process of an organization. Some suggestions for cooperative behavior improvement are given in the end.

  10. Brain Structure Network Analysis in Patients with Obstructive Sleep Apnea.

    Directory of Open Access Journals (Sweden)

    Yun-Gang Luo

    Full Text Available Childhood obstructive sleep apnea (OSA is a sleeping disorder commonly affecting school-aged children and is characterized by repeated episodes of blockage of the upper airway during sleep. In this study, we performed a graph theoretical analysis on the brain morphometric correlation network in 25 OSA patients (OSA group; 5 female; mean age, 10.1 ± 1.8 years and investigated the topological alterations in global and regional properties compared with 20 healthy control individuals (CON group; 6 females; mean age, 10.4 ± 1.8 years. A structural correlation network based on regional gray matter volume was constructed respectively for each group. Our results revealed a significantly decreased mean local efficiency in the OSA group over the density range of 0.32-0.44 (p < 0.05. Regionally, the OSAs showed a tendency of decreased betweenness centrality in the left angular gyrus, and a tendency of decreased degree in the right lingual and inferior frontal (orbital part gyrus (p < 0.005, uncorrected. We also found that the network hubs in OSA and controls were distributed differently. To the best of our knowledge, this is the first study that characterizes the brain structure network in OSA patients and invests the alteration of topological properties of gray matter volume structural network. This study may help to provide new evidence for understanding the neuropathophysiology of OSA from a topological perspective.

  11. Brain Structure Network Analysis in Patients with Obstructive Sleep Apnea.

    Science.gov (United States)

    Luo, Yun-Gang; Wang, Defeng; Liu, Kai; Weng, Jian; Guan, Yuefeng; Chan, Kate C C; Chu, Winnie C W; Shi, Lin

    2015-01-01

    Childhood obstructive sleep apnea (OSA) is a sleeping disorder commonly affecting school-aged children and is characterized by repeated episodes of blockage of the upper airway during sleep. In this study, we performed a graph theoretical analysis on the brain morphometric correlation network in 25 OSA patients (OSA group; 5 female; mean age, 10.1 ± 1.8 years) and investigated the topological alterations in global and regional properties compared with 20 healthy control individuals (CON group; 6 females; mean age, 10.4 ± 1.8 years). A structural correlation network based on regional gray matter volume was constructed respectively for each group. Our results revealed a significantly decreased mean local efficiency in the OSA group over the density range of 0.32-0.44 (p gyrus, and a tendency of decreased degree in the right lingual and inferior frontal (orbital part) gyrus (p < 0.005, uncorrected). We also found that the network hubs in OSA and controls were distributed differently. To the best of our knowledge, this is the first study that characterizes the brain structure network in OSA patients and invests the alteration of topological properties of gray matter volume structural network. This study may help to provide new evidence for understanding the neuropathophysiology of OSA from a topological perspective.

  12. The structural and functional brain networks that support human social networks.

    Science.gov (United States)

    Noonan, M P; Mars, R B; Sallet, J; Dunbar, R I M; Fellows, L K

    2018-02-20

    Social skills rely on a specific set of cognitive processes, raising the possibility that individual differences in social networks are related to differences in specific brain structural and functional networks. Here, we tested this hypothesis with multimodality neuroimaging. With diffusion MRI (DMRI), we showed that differences in structural integrity of particular white matter (WM) tracts, including cingulum bundle, extreme capsule and arcuate fasciculus were associated with an individual's social network size (SNS). A voxel-based morphology analysis demonstrated correlations between gray matter (GM) volume and SNS in limbic and temporal lobe regions. These structural changes co-occured with functional network differences. As a function of SNS, dorsomedial and dorsolateral prefrontal cortex showed altered resting-state functional connectivity with the default mode network (DMN). Finally, we integrated these three complementary methods, interrogating the relationship between social GM clusters and specific WM and resting-state networks (RSNs). Probabilistic tractography seeded in these GM nodes utilized the SNS-related WM pathways. Further, the spatial and functional overlap between the social GM clusters and the DMN was significantly closer than other control RSNs. These integrative analyses provide convergent evidence of the role of specific circuits in SNS, likely supporting the adaptive behavior necessary for success in extensive social environments. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

  13. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  14. The Impact of Drainage Network Structure on Flooding in a Small Urban Watershed in Metropolitan Baltimore, MD

    Science.gov (United States)

    Meierdiercks, K. L.; Smith, J. A.; Miller, A. J.

    2006-12-01

    The impact of urban development on watershed-scale hydrology is examined in a small urban watershed in the Metropolitan Baltimore area. Analyses focus on Dead Run, a 14.3 km2 tributary of the Gwynns Falls, which is the principal study watershed of the Baltimore Ecosystem Study. Field observations of rainfall and discharge have been collected for storms occurring in the 2003, 2004, and 2005 warm seasons including the flood of record for the USGS Dead Run at Franklintown gage (7 July 2004), in which 5 inches of rain fell in less than 4 hours. Dead Run has stream gages at 6 locations with drainage areas ranging from 1.2 to 14.3 km2. Hydrologic response to storm events varies greatly in each of the subwatersheds due to the diverse development types located there. These subwatersheds range in land use from medium-density residential, with and without stormwater management control, to commercial/light industrial with large impervious lots and an extensive network of stormwater management ponds. The unique response of each subwatershed is captured using field observations in conjunction with the EPA Stormwater Management Model (SWMM), which routes storm runoff over the land surface and through the drainage network of a watershed. Of particular importance to flood response is the structure of the drainage network (both surface channels and storm drain network) and its connectivity to preferential flow paths within the watershed. The Dead Run drainage network has been delineated using geospatial data derived from aerial photography and engineering planning drawings. Model analyses are used to examine the characteristics of flow paths that control flood response in urban watersheds. These analyses aim to identify patterns in urban flow pathways and use those patterns to predict response in other urban watersheds.

  15. Mass media influence spreading in social networks with community structure

    Science.gov (United States)

    Candia, Julián; Mazzitello, Karina I.

    2008-07-01

    We study an extension of Axelrod's model for social influence, in which cultural drift is represented as random perturbations, while mass media are introduced by means of an external field. In this scenario, we investigate how the modular structure of social networks affects the propagation of mass media messages across a society. The community structure of social networks is represented by coupled random networks, in which two random graphs are connected by intercommunity links. Considering inhomogeneous mass media fields, we study the conditions for successful message spreading and find a novel phase diagram in the multidimensional parameter space. These findings show that social modularity effects are of paramount importance for designing successful, cost-effective advertising campaigns.

  16. Analyzing the multilevel structure of the European airport network

    Directory of Open Access Journals (Sweden)

    Oriol Lordan

    2017-04-01

    Full Text Available The multilayered structure of the European airport network (EAN, composed of connections and flights between European cities, is analyzed through the k-core decomposition of the connections network. This decomposition allows to identify the core, bridge and periphery layers of the EAN. The core layer includes the best-connected cities, which include important business air traffic destinations. The periphery layer includes cities with lesser connections, which serve low populated areas where air travel is an economic alternative. The remaining cities form the bridge of the EAN, including important leisure travel origins and destinations. The multilayered structure of the EAN affects network robustness, as the EAN is more robust to isolation of nodes of the core, than to the isolation of a combination of core and bridge nodes.

  17. Finding the core : Network structure in interbank markets

    NARCIS (Netherlands)

    in 't Veld, Daan; van Lelyveld, Iman

    2014-01-01

    This paper investigates the network structure of interbank markets. Using a dataset of interbank exposures in the Netherlands, we corroborate the recent hypothesis that the core periphery model is a 'stylised fact' of interbank markets. We find a core of highly connected banks intermediating between

  18. Refining a Heuristic for Constructing Bayesian Networks from Structured Arguments

    NARCIS (Netherlands)

    Wieten, G.M.; Bex, F.J.; van der Gaag, L.C.; Prakken, H.; Renooij, S.

    2018-01-01

    Recently, a heuristic was proposed for constructing Bayesian networks (BNs) from structured arguments. This heuristic helps domain experts who are accustomed to argumentation to transform their reasoning into a BN and subsequently weigh their case evidence in a probabilistic manner. While the

  19. Discussion Tool Effects on Collaborative Learning and Social Network Structure

    Science.gov (United States)

    Tomsic, Astrid; Suthers, Daniel D.

    2006-01-01

    This study investigated the social network structure of booking officers at the Honolulu Police Department and how the introduction of an online discussion tool affected knowledge about operation of a booking module. Baseline data provided evidence for collaboration among officers in the same district using e-mail, telephone and face-to-face media…

  20. The structural, connectomic and network covariance of the human brain.

    Science.gov (United States)

    Irimia, Andrei; Van Horn, John D

    2013-02-01

    Though it is widely appreciated that complex structural, functional and morphological relationships exist between distinct areas of the human cerebral cortex, the extent to which such relationships coincide remains insufficiently appreciated. Here we determine the extent to which correlations between brain regions are modulated by either structural, connectomic or network-theoretic properties using a structural neuroimaging data set of magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) volumes acquired from N=110 healthy human adults. To identify the linear relationships between all available pairs of regions, we use canonical correlation analysis to test whether a statistically significant correlation exists between each pair of cortical parcels as quantified via structural, connectomic or network-theoretic measures. In addition to this, we investigate (1) how each group of canonical variables (whether structural, connectomic or network-theoretic) contributes to the overall correlation and, additionally, (2) whether each individual variable makes a significant contribution to the test of the omnibus null hypothesis according to which no correlation between regions exists across subjects. We find that, although region-to-region correlations are extensively modulated by structural and connectomic measures, there are appreciable differences in how these two groups of measures drive inter-regional correlation patterns. Additionally, our results indicate that the network-theoretic properties of the cortex are strong modulators of region-to-region covariance. Our findings are useful for understanding the structural and connectomic relationship between various parts of the brain, and can inform theoretical and computational models of cortical information processing. Published by Elsevier Inc.

  1. Automated analysis of Physarum network structure and dynamics

    Science.gov (United States)

    Fricker, Mark D.; Akita, Dai; Heaton, Luke LM; Jones, Nick; Obara, Boguslaw; Nakagaki, Toshiyuki

    2017-06-01

    We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray’s law. This work was presented at PhysNet 2015.

  2. Hemispheric lateralization of topological organization in structural brain networks.

    Science.gov (United States)

    Caeyenberghs, Karen; Leemans, Alexander

    2014-09-01

    The study on structural brain asymmetries in healthy individuals plays an important role in our understanding of the factors that modulate cognitive specialization in the brain. Here, we used fiber tractography to reconstruct the left and right hemispheric networks of a large cohort of 346 healthy participants (20-86 years) and performed a graph theoretical analysis to investigate this brain laterality from a network perspective. Findings revealed that the left hemisphere is significantly more "efficient" than the right hemisphere, whereas the right hemisphere showed higher values of "betweenness centrality" and "small-worldness." In particular, left-hemispheric networks displayed increased nodal efficiency in brain regions related to language and motor actions, whereas the right hemisphere showed an increase in nodal efficiency in brain regions involved in memory and visuospatial attention. In addition, we found that hemispheric networks decrease in efficiency with age. Finally, we observed significant gender differences in measures of global connectivity. By analyzing the structural hemispheric brain networks, we have provided new insights into understanding the neuroanatomical basis of lateralized brain functions. Copyright © 2014 Wiley Periodicals, Inc.

  3. Automated analysis of Physarum network structure and dynamics

    International Nuclear Information System (INIS)

    Fricker, Mark D; Heaton, Luke LM; Akita, Dai; Jones, Nick; Obara, Boguslaw; Nakagaki, Toshiyuki

    2017-01-01

    We evaluate different ridge-enhancement and segmentation methods to automatically extract the network architecture from time-series of Physarum plasmodia withdrawing from an arena via a single exit. Whilst all methods gave reasonable results, judged by precision-recall analysis against a ground-truth skeleton, the mean phase angle (Feature Type) from intensity-independent, phase-congruency edge enhancement and watershed segmentation was the most robust to variation in threshold parameters. The resultant single pixel-wide segmented skeleton was converted to a graph representation as a set of weighted adjacency matrices containing the physical dimensions of each vein, and the inter-vein regions. We encapsulate the complete image processing and network analysis pipeline in a downloadable software package, and provide an extensive set of metrics that characterise the network structure, including hierarchical loop decomposition to analyse the nested structure of the developing network. In addition, the change in volume for each vein and intervening plasmodial sheet was used to predict the net flow across the network. The scaling relationships between predicted current, speed and shear force with vein radius were consistent with predictions from Murray’s law. This work was presented at PhysNet 2015. (paper)

  4. Probabilistic diffusion tractography reveals improvement of structural network in musicians.

    Directory of Open Access Journals (Sweden)

    Jianfu Li

    Full Text Available PURPOSE: Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. METHODS: Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. RESULTS: Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. CONCLUSIONS: We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.

  5. Structure and evolution of the global seafood trade network

    Science.gov (United States)

    Gephart, Jessica A.; Pace, Michael L.

    2015-12-01

    The food production system is increasingly global and seafood is among the most highly traded commodities. Global trade can improve food security by providing access to a greater variety of foods, increasing wealth, buffering against local supply shocks, and benefit the environment by increasing overall use efficiency for some resources. However, global trade can also expose countries to external supply shocks and degrade the environment by increasing resource demand and loosening feedbacks between consumers and the impacts of food production. As a result, changes in global food trade can have important implications for both food security and the environmental impacts of production. Measurements of globalization and the environmental impacts of food production require data on both total trade and the origin and destination of traded goods (the network structure). While the global trade network of agricultural and livestock products has previously been studied, seafood products have been excluded. This study describes the structure and evolution of the global seafood trade network, including metrics quantifying the globalization of seafood, shifts in bilateral trade flows, changes in centrality and comparisons of seafood to agricultural and industrial trade networks. From 1994 to 2012 the number of countries trading in the network remained relatively constant, while the number of trade partnerships increased by over 65%. Over this same period, the total quantity of seafood traded increased by 58% and the value increased 85% in real terms. These changes signify the increasing globalization of seafood products. Additionally, the trade patterns in the network indicate: increased influence of Thailand and China, strengthened intraregional trade, and increased exports from South America and Asia. In addition to characterizing these network changes, this study identifies data needs in order to connect seafood trade with environmental impacts and food security outcomes.

  6. Protein enriched pasta: structure and digestibility of its protein network.

    Science.gov (United States)

    Laleg, Karima; Barron, Cécile; Santé-Lhoutellier, Véronique; Walrand, Stéphane; Micard, Valérie

    2016-02-01

    Wheat (W) pasta was enriched in 6% gluten (G), 35% faba (F) or 5% egg (E) to increase its protein content (13% to 17%). The impact of the enrichment on the multiscale structure of the pasta and on in vitro protein digestibility was studied. Increasing the protein content (W- vs. G-pasta) strengthened pasta structure at molecular and macroscopic scales but reduced its protein digestibility by 3% by forming a higher covalently linked protein network. Greater changes in the macroscopic and molecular structure of the pasta were obtained by varying the nature of protein used for enrichment. Proteins in G- and E-pasta were highly covalently linked (28-32%) resulting in a strong pasta structure. Conversely, F-protein (98% SDS-soluble) altered the pasta structure by diluting gluten and formed a weak protein network (18% covalent link). As a result, protein digestibility in F-pasta was significantly higher (46%) than in E- (44%) and G-pasta (39%). The effect of low (55 °C, LT) vs. very high temperature (90 °C, VHT) drying on the protein network structure and digestibility was shown to cause greater molecular changes than pasta formulation. Whatever the pasta, a general strengthening of its structure, a 33% to 47% increase in covalently linked proteins and a higher β-sheet structure were observed. However, these structural differences were evened out after the pasta was cooked, resulting in identical protein digestibility in LT and VHT pasta. Even after VHT drying, F-pasta had the best amino acid profile with the highest protein digestibility, proof of its nutritional interest.

  7. Algorithmic Complexity and Reprogrammability of Chemical Structure Networks

    KAUST Repository

    Zenil, Hector; Kiani, Narsis A.; Shang, Ming-mei; Tegner, Jesper

    2018-01-01

    Here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective. We explore the potential of applying a computational interventional calculus based on the principles of algorithmic probability to chemical structure networks. We profile the sensitivity of the elements and covalent bonds in a chemical structure network algorithmically, asking whether reprogrammability affords information about thermodynamic and chemical processes involved in the transformation of different compound classes. We arrive at numerical results suggesting a correspondence between some physical, structural and functional properties. Our methods are capable of separating chemical classes that reflect functional and natural differences without considering any information about atomic and molecular properties. We conclude that these methods, with their links to chemoinformatics via algorithmic, probability hold promise for future research.

  8. Algorithmic Complexity and Reprogrammability of Chemical Structure Networks

    KAUST Repository

    Zenil, Hector

    2018-04-02

    Here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective. We explore the potential of applying a computational interventional calculus based on the principles of algorithmic probability to chemical structure networks. We profile the sensitivity of the elements and covalent bonds in a chemical structure network algorithmically, asking whether reprogrammability affords information about thermodynamic and chemical processes involved in the transformation of different compound classes. We arrive at numerical results suggesting a correspondence between some physical, structural and functional properties. Our methods are capable of separating chemical classes that reflect functional and natural differences without considering any information about atomic and molecular properties. We conclude that these methods, with their links to chemoinformatics via algorithmic, probability hold promise for future research.

  9. Algorithmic Complexity and Reprogrammability of Chemical Structure Networks

    KAUST Repository

    Zenil, Hector

    2018-02-16

    Here we address the challenge of profiling causal properties and tracking the transformation of chemical compounds from an algorithmic perspective. We explore the potential of applying a computational interventional calculus based on the principles of algorithmic probability to chemical structure networks. We profile the sensitivity of the elements and covalent bonds in a chemical structure network algorithmically, asking whether reprogrammability affords information about thermodynamic and chemical processes involved in the transformation of different compound classes. We arrive at numerical results suggesting a correspondence between some physical, structural and functional properties. Our methods are capable of separating chemical classes that reflect functional and natural differences without considering any information about atomic and molecular properties. We conclude that these methods, with their links to chemoinformatics via algorithmic, probability hold promise for future research.

  10. 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.

  11. RUN COORDINATION

    CERN Multimedia

    G. Rakness.

    2013-01-01

    After three years of running, in February 2013 the era of sub-10-TeV LHC collisions drew to an end. Recall, the 2012 run had been extended by about three months to achieve the full complement of high-energy and heavy-ion physics goals prior to the start of Long Shutdown 1 (LS1), which is now underway. The LHC performance during these exciting years was excellent, delivering a total of 23.3 fb–1 of proton-proton collisions at a centre-of-mass energy of 8 TeV, 6.2 fb–1 at 7 TeV, and 5.5 pb–1 at 2.76 TeV. They also delivered 170 μb–1 lead-lead collisions at 2.76 TeV/nucleon and 32 nb–1 proton-lead collisions at 5 TeV/nucleon. During these years the CMS operations teams and shift crews made tremendous strides to commission the detector, repeatedly stepping up to meet the challenges at every increase of instantaneous luminosity and energy. Although it does not fully cover the achievements of the teams, a way to quantify their success is the fact that that...

  12. Memory functions reveal structural properties of gene regulatory networks

    Science.gov (United States)

    Perez-Carrasco, Ruben

    2018-01-01

    Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. For this reason, methods that simplify and aid exploration of complex networks are necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori projection. By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. The influence of the rest of the network, the bulk, is captured by memory functions that describe how the subnetwork reacts to its own past state via components in the bulk. These memory functions provide probes of near-steady state dynamics, revealing information not easily accessible otherwise. We illustrate the method on a simple cross-repressive transcriptional motif to show that memory functions not only simplify the analysis of the subnetwork but also have a natural interpretation. We then apply the approach to a GRN from the vertebrate neural tube, a well characterised developmental transcriptional network composed of four interacting transcription factors. The memory functions reveal the function of specific links within the neural tube network and identify features of the regulatory structure that specifically increase the robustness of the network to initial conditions. Taken together, the study provides evidence that Zwanzig-Mori projections offer powerful and effective tools for simplifying and exploring the behaviour of GRNs. PMID:29470492

  13. Structure constrained by metadata in networks of chess players.

    Science.gov (United States)

    Almeira, Nahuel; Schaigorodsky, Ana L; Perotti, Juan I; Billoni, Orlando V

    2017-11-09

    Chess is an emblematic sport that stands out because of its age, popularity and complexity. It has served to study human behavior from the perspective of a wide number of disciplines, from cognitive skills such as memory and learning, to aspects like innovation and decision-making. Given that an extensive documentation of chess games played throughout history is available, it is possible to perform detailed and statistically significant studies about this sport. Here we use one of the most extensive chess databases in the world to construct two networks of chess players. One of the networks includes games that were played over-the-board and the other contains games played on the Internet. We study the main topological characteristics of the networks, such as degree distribution and correlations, transitivity and community structure. We complement the structural analysis by incorporating players' level of play as node metadata. Although both networks are topologically different, we show that in both cases players gather in communities according to their expertise and that an emergent rich-club structure, composed by the top-rated players, is also present.

  14. Linking structure and activity in nonlinear spiking networks.

    Directory of Open Access Journals (Sweden)

    Gabriel Koch Ocker

    2017-06-01

    Full Text Available Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

  15. Linking structure and activity in nonlinear spiking networks.

    Science.gov (United States)

    Ocker, Gabriel Koch; Josić, Krešimir; Shea-Brown, Eric; Buice, Michael A

    2017-06-01

    Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

  16. A key heterogeneous structure of fractal networks based on inverse renormalization scheme

    Science.gov (United States)

    Bai, Yanan; Huang, Ning; Sun, Lina

    2018-06-01

    Self-similarity property of complex networks was found by the application of renormalization group theory. Based on this theory, network topologies can be classified into universality classes in the space of configurations. In return, through inverse renormalization scheme, a given primitive structure can grow into a pure fractal network, then adding different types of shortcuts, it exhibits different characteristics of complex networks. However, the effect of primitive structure on networks structural property has received less attention. In this paper, we introduce a degree variance index to measure the dispersion of nodes degree in the primitive structure, and investigate the effect of the primitive structure on network structural property quantified by network efficiency. Numerical simulations and theoretical analysis show a primitive structure is a key heterogeneous structure of generated networks based on inverse renormalization scheme, whether or not adding shortcuts, and the network efficiency is positively correlated with degree variance of the primitive structure.

  17. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  18. Dynamics Analysis of Fluid-Structure Interaction for a Biologically-Inspired Biped Robot Running on Water

    Directory of Open Access Journals (Sweden)

    Linsen Xu

    2013-10-01

    Full Text Available A kinematics analysis of a biologically-inspired biped robot is carried out, and the trajectory of the robot foot is understood. For calculating the pressure distribution across a robot foot before touching the surface of water, the compression flow of air and the depression motion of the water surface are considered. The pressure model after touching the water surface has been built according to the theory of rigid body planar motion. The multi-material ALE algorithm is applied to emulate the course of the foot slapping water. The simulation results indicate that the model of the bionic robot can satisfy the water-running function. The real prototype of the robot is manufactured to test its function of running on water. When the biped robot is running on water, the average force generated by the propulsion mechanism is about 1.3N. The experimental results show that the propulsion system can satisfy the requirement of biped robot running on water.

  19. Genomic analysis of the hierarchical structure of regulatory networks

    Science.gov (United States)

    Yu, Haiyuan; Gerstein, Mark

    2006-01-01

    A fundamental question in biology is how the cell uses transcription factors (TFs) to coordinate the expression of thousands of genes in response to various stimuli. The relationships between TFs and their target genes can be modeled in terms of directed regulatory networks. These relationships, in turn, can be readily compared with commonplace “chain-of-command” structures in social networks, which have characteristic hierarchical layouts. Here, we develop algorithms for identifying generalized hierarchies (allowing for various loop structures) and use these approaches to illuminate extensive pyramid-shaped hierarchical structures existing in the regulatory networks of representative prokaryotes (Escherichia coli) and eukaryotes (Saccharomyces cerevisiae), with most TFs at the bottom levels and only a few master TFs on top. These masters are situated near the center of the protein–protein interaction network, a different type of network from the regulatory one, and they receive most of the input for the whole regulatory hierarchy through protein interactions. Moreover, they have maximal influence over other genes, in terms of affecting expression-level changes. Surprisingly, however, TFs at the bottom of the regulatory hierarchy are more essential to the viability of the cell. Finally, one might think master TFs achieve their wide influence through directly regulating many targets, but TFs with most direct targets are in the middle of the hierarchy. We find, in fact, that these midlevel TFs are “control bottlenecks” in the hierarchy, and this great degree of control for “middle managers” has parallels in efficient social structures in various corporate and governmental settings. PMID:17003135

  20. Disentangling the co-structure of multilayer interaction networks: degree distribution and module composition in two-layer bipartite networks.

    Science.gov (United States)

    Astegiano, Julia; Altermatt, Florian; Massol, François

    2017-11-13

    Species establish different interactions (e.g. antagonistic, mutualistic) with multiple species, forming multilayer ecological networks. Disentangling network co-structure in multilayer networks is crucial to predict how biodiversity loss may affect the persistence of multispecies assemblages. Existing methods to analyse multilayer networks often fail to consider network co-structure. We present a new method to evaluate the modular co-structure of multilayer networks through the assessment of species degree co-distribution and network module composition. We focus on modular structure because of its high prevalence among ecological networks. We apply our method to two Lepidoptera-plant networks, one describing caterpillar-plant herbivory interactions and one representing adult Lepidoptera nectaring on flowers, thereby possibly pollinating them. More than 50% of the species established either herbivory or visitation interactions, but not both. These species were over-represented among plants and lepidopterans, and were present in most modules in both networks. Similarity in module composition between networks was high but not different from random expectations. Our method clearly delineates the importance of interpreting multilayer module composition similarity in the light of the constraints imposed by network structure to predict the potential indirect effects of species loss through interconnected modular networks.

  1. Detailed temporal structure of communication networks in groups of songbirds.

    Science.gov (United States)

    Stowell, Dan; Gill, Lisa; Clayton, David

    2016-06-01

    Animals in groups often exchange calls, in patterns whose temporal structure may be influenced by contextual factors such as physical location and the social network structure of the group. We introduce a model-based analysis for temporal patterns of animal call timing, originally developed for networks of firing neurons. This has advantages over cross-correlation analysis in that it can correctly handle common-cause confounds and provides a generative model of call patterns with explicit parameters for the influences between individuals. It also has advantages over standard Markovian analysis in that it incorporates detailed temporal interactions which affect timing as well as sequencing of calls. Further, a fitted model can be used to generate novel synthetic call sequences. We apply the method to calls recorded from groups of domesticated zebra finch (Taeniopygia guttata) individuals. We find that the communication network in these groups has stable structure that persists from one day to the next, and that 'kernels' reflecting the temporal range of influence have a characteristic structure for a calling individual's effect on itself, its partner and on others in the group. We further find characteristic patterns of influences by call type as well as by individual. © 2016 The Authors.

  2. Developmental changes in organization of structural brain networks.

    Science.gov (United States)

    Khundrakpam, Budhachandra S; Reid, Andrew; Brauer, Jens; Carbonell, Felix; Lewis, John; Ameis, Stephanie; Karama, Sherif; Lee, Junki; Chen, Zhang; Das, Samir; Evans, Alan C

    2013-09-01

    Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8-8.4 year; late childhood: 8.5-11.3 year; early adolescence: 11.4-14.7 year; late adolescence: 14.8-18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces.

  3. [Challenges of implementing a geriatric trauma network : A regional structure].

    Science.gov (United States)

    Schoeneberg, Carsten; Hussmann, Bjoern; Wesemann, Thomas; Pientka, Ludger; Vollmar, Marie-Christin; Bienek, Christine; Steinmann, Markus; Buecking, Benjamin; Lendemans, Sven

    2018-04-01

    At present, there is a high percentage and increasing tendency of patients presenting with orthogeriatric injuries. Moreover, significant comorbidities often exist, requiring increased interdisciplinary treatment. These developments have led the German Society of Trauma Surgery, in cooperation with the German Society of Geriatrics, to establish geriatric trauma centers. As a conglomerate hospital at two locations, we are cooperating with two external geriatric clinics. In 2015, a geriatric trauma center certification in the form of a conglomerate network structure was agreed upon for the first time in Germany. For this purpose, the requirements for certification were observed. Both structure and organization were defined in a manual according to DIN EN ISO 9001:2015. Between 2008 and 2016, an increase of 70% was seen in geriatric trauma cases in our hospital, with a rise of up to 360% in specific diagnoses. The necessary standards and regulations were compiled and evaluated from our hospitals. After successful certification, improvements were necessary, followed by a planned re-audit. These were prepared by multiprofessional interdisciplinary teams and implemented at all locations. A network structure can be an alternative to classical cooperation between trauma and geriatric units in one clinic and help reduce possible staffing shortage. Due to the lack of scientific evidence, future evaluations of the geriatric trauma register should reveal whether network structures in geriatric trauma surgery lead to a valid improvement in medical care.

  4. The Network Structure Underlying the Earth Observation Assessment

    Science.gov (United States)

    Vitkin, S.; Doane, W. E. J.; Mary, J. C.

    2017-12-01

    The Earth Observations Assessment (EOA 2016) is a multiyear project designed to assess the effectiveness of civil earth observation data sources (instruments, sensors, models, etc.) on societal benefit areas (SBAs) for the United States. Subject matter experts (SMEs) provided input and scored how data sources inform products, product groups, key objectives, SBA sub-areas, and SBAs in an attempt to quantify the relationships between data sources and SBAs. The resulting data were processed by Integrated Applications Incorporated (IAI) using MITRE's PALMA software to create normalized relative impact scores for each of these relationships. However, PALMA processing obscures the natural network representation of the data. Any network analysis that might identify patterns of interaction among data sources, products, and SBAs is therefore impossible. Collaborating with IAI, we cleaned and recreated a network from the original dataset. Using R and Python we explore the underlying structure of the network and apply frequent itemset mining algorithms to identify groups of data sources and products that interact. We reveal interesting patterns and relationships in the EOA dataset that were not immediately observable from the EOA 2016 report and provide a basis for further exploration of the EOA network dataset.

  5. Novel indexes based on network structure to indicate financial market

    Science.gov (United States)

    Zhong, Tao; Peng, Qinke; Wang, Xiao; Zhang, Jing

    2016-02-01

    There have been various achievements to understand and to analyze the financial market by complex network model. However, current studies analyze the financial network model but seldom present quantified indexes to indicate or forecast the price action of market. In this paper, the stock market is modeled as a dynamic network, in which the vertices refer to listed companies and edges refer to their rank-based correlation based on price series. Characteristics of the network are analyzed and then novel indexes are introduced into market analysis, which are calculated from maximum and fully-connected subnets. The indexes are compared with existing ones and the results confirm that our indexes perform better to indicate the daily trend of market composite index in advance. Via investment simulation, the performance of our indexes is analyzed in detail. The results indicate that the dynamic complex network model could not only serve as a structural description of the financial market, but also work to predict the market and guide investment by indexes.

  6. Global and local targeted immunization in networks with community structure

    International Nuclear Information System (INIS)

    Yan, Shu; Tang, Shaoting; Pei, Sen; Zheng, Zhiming; Fang, Wenyi

    2015-01-01

    Immunization plays an important role in the field of epidemic spreading in complex networks. In previous studies, targeted immunization has been proved to be an effective strategy. However, when extended to networks with community structure, it is unknown whether the superior strategy is to vaccinate the nodes who have the most connections in the entire network (global strategy), or those in the original community where epidemic starts to spread (local strategy). In this work, by using both analytic approaches and simulations, we observe that the answer depends on the closeness between communities. If communities are tied closely, the global strategy is superior to the local strategy. Otherwise, the local targeted immunization is advantageous. The existence of a transitional value of closeness implies that we should adopt different strategies. Furthermore, we extend our investigation from two-community networks to multi-community networks. We consider the mode of community connection and the location of community where epidemic starts to spread. Both simulation results and theoretical predictions show that local strategy is a better option for immunization in most cases. But if the epidemic begins from a core community, global strategy is superior in some cases. (paper)

  7. Structure, function, and control of the human musculoskeletal network.

    Directory of Open Access Journals (Sweden)

    Andrew C Murphy

    2018-01-01

    Full Text Available The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle's role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.

  8. Structural properties of the Chinese air transportation multilayer network

    International Nuclear Information System (INIS)

    Hong, Chen; Zhang, Jun; Cao, Xian-Bin; Du, Wen-Bo

    2016-01-01

    Highlights: • We investigate the structural properties of the Chinese air transportation multilayer network (ATMN). • We compare two main types of layers corresponding to major and low-cost airlines. • It is found that small-world property and rich-club effect of the Chinese ATMN are mainly caused by major airlines. - Abstract: Recently multilayer networks are attracting great attention because the properties of many real-world systems cannot be well understood without considering their different layers. In this paper, we investigate the structural properties of the Chinese air transportation multilayer network (ATMN) by progressively merging layers together, where each commercial airline (company) defines a layer. The results show that the high clustering coefficient, short characteristic path length and large collection of reachable destinations of the Chinese ATMN can only emerge when several layers are merged together. Moreover, we compare two main types of layers corresponding to major and low-cost airlines. It is found that the small-world property and the rich-club effect of the Chinese ATMN are mainly caused by those layers corresponding to major airlines. Our work will highlight a better understanding of the Chinese air transportation network.

  9. Epidemic spreading on dual-structure networks with mobile agents

    Science.gov (United States)

    Yao, Yiyang; Zhou, Yinzuo

    2017-02-01

    The rapid development of modern society continually transforms the social structure which leads to an increasingly distinct dual structure of higher population density in urban areas and lower density in rural areas. Such structure may induce distinctive spreading behavior of epidemics which does not happen in a single type structure. In this paper, we study the epidemic spreading of mobile agents on dual structure networks based on SIRS model. First, beyond the well known epidemic threshold for generic epidemic model that when the infection rate is below the threshold a pertinent infectious disease will die out, we find the other epidemic threshold which appears when the infection rate of a disease is relatively high. This feature of two thresholds for the SIRS model may lead to the elimination of infectious disease when social network has either high population density or low population density. Interestingly, however, we find that when a high density area is connected to a low density may cause persistent spreading of the infectious disease, even though the same disease will die out when it spreads in each single area. This phenomenon indicates the critical role of the connection between the two areas which could radically change the behavior of spreading dynamics. Our findings, therefore, provide new understanding of epidemiology pertinent to the characteristic modern social structure and have potential to develop controlling strategies accordingly.

  10. Measurement of the running of the fine structure constant below 1 GeV with the KLOE detector

    Directory of Open Access Journals (Sweden)

    A. Anastasi

    2017-04-01

    Full Text Available We have measured the running of the effective QED coupling constant α(s in the time-like region 0.6running of α(s in this energy region. Our results show a more than 5σ significance of the hadronic contribution to the running of α(s, which is the strongest direct evidence both in time- and space-like regions achieved in a single measurement. By using the e+e−→π+π− cross section measured by KLOE, the real and imaginary parts of the shift Δα(s have been extracted. From a fit of the real part of Δα(s and assuming the lepton universality the branching ratio BR(ω→μ+μ−=(6.6±1.4stat±1.7syst⋅10−5 has been determined.

  11. An Algebraic Approach to Inference in Complex Networked Structures

    Science.gov (United States)

    2015-07-09

    44], [45],[46] where the shift is the elementary non-trivial filter that generates, under an appropriate notion of shift invariance, all linear ... elementary filter, and its output is a graph signal with the value at vertex n of the graph given approximately by a weighted linear combination of...AFRL-AFOSR-VA-TR-2015-0265 An Algebraic Approach to Inference in Complex Networked Structures Jose Moura CARNEGIE MELLON UNIVERSITY Final Report 07

  12. Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring

    Science.gov (United States)

    2016-02-02

    Virginia 22203 Air Force Research Laboratory Air Force Materiel Command 1 Final Performance Report: AFOSR T.C. Henderson , V.J. Mathews, and D...AFRL-AFOSR-VA-TR-2016-0094 Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring. Thomas Henderson UNIVERSITY OF UTAH SALT...The people who worked on this project include: Thomas C. Henderson , John Mathews, Jingru Zhou, Daimei Zhij, Ahmad Zoubi, Sabita Nahata, Dan Adams

  13. Fracture network topology and characterization of structural permeability

    Science.gov (United States)

    Hansberry, Rowan; King, Rosalind; Holford, Simon

    2017-04-01

    There are two fundamental requirements for successful geothermal development: elevated temperatures at accessible depths, and a reservoir from which fluids can be extracted. The Australian geothermal sector has successfully targeted shallow heat, however, due in part to the inherent complexity of targeting permeability, obtaining adequate flow rates for commercial production has been problematic. Deep sedimentary aquifers are unlikely to be viable geothermal resources due to the effects of diagenetic mineral growth on rock permeability. Therefore, it is likely structural permeability targets, exploiting natural or induced fracture networks will provide the primary means for fluid flow in geothermal, as well as unconventional gas, reservoirs. Recent research has focused on the pattern and generation of crustal stresses across Australia, while less is known about the resultant networks of faults, joints, and veins that can constitute interconnected sub-surface permeability pathways. The ability of a fracture to transmit fluid is controlled by the orientation and magnitude of the in-situ stress field that acts on the fracture walls, rock strength, and pore pressure, as well as fracture properties such as aperture, orientation, and roughness. Understanding the distribution, orientation and character of fractures is key to predicting structural permeability. This project focuses on extensive mapping of fractures over various scales in four key Australian basins (Cooper, Otway, Surat and Perth) with the potential to host geothermal resources. Seismic attribute analysis is used in concert with image logs from petroleum wells, and field mapping to identify fracture networks that are usually not resolved in traditional seismic interpretation. We use fracture network topology to provide scale-invariant characterisation of fracture networks from multiple data sources to assess similarity between data sources, and fracture network connectivity. These results are compared with

  14. Parallel protein secondary structure prediction based on neural networks.

    Science.gov (United States)

    Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

    2004-01-01

    Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

  15. Scalable, ultra-resistant structural colors based on network metamaterials

    KAUST Repository

    Galinski, Henning

    2017-05-05

    Structural colors have drawn wide attention for their potential as a future printing technology for various applications, ranging from biomimetic tissues to adaptive camouflage materials. However, an efficient approach to realize robust colors with a scalable fabrication technique is still lacking, hampering the realization of practical applications with this platform. Here, we develop a new approach based on large-scale network metamaterials that combine dealloyed subwavelength structures at the nanoscale with lossless, ultra-thin dielectric coatings. By using theory and experiments, we show how subwavelength dielectric coatings control a mechanism of resonant light coupling with epsilon-near-zero regions generated in the metallic network, generating the formation of saturated structural colors that cover a wide portion of the spectrum. Ellipsometry measurements support the efficient observation of these colors, even at angles of 70°. The network-like architecture of these nanomaterials allows for high mechanical resistance, which is quantified in a series of nano-scratch tests. With such remarkable properties, these metastructures represent a robust design technology for real-world, large-scale commercial applications.

  16. Analysis of structure-function network decoupling in the brain systems of spastic diplegic cerebral palsy.

    Science.gov (United States)

    Lee, Dongha; Pae, Chongwon; Lee, Jong Doo; Park, Eun Sook; Cho, Sung-Rae; Um, Min-Hee; Lee, Seung-Koo; Oh, Maeng-Keun; Park, Hae-Jeong

    2017-10-01

    Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure-function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural-functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure-function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure-function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292-5306, 2017. © 2017 Wiley Periodicals

  17. The Network Structure of Human Personality According to the NEO-PI-R: Matching Network Community Structure to Factor Structure

    NARCIS (Netherlands)

    Goekoop, R.; Goekoop, J.G.; Scholte, H.S.

    2012-01-01

    Introduction: Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. Aim: To directly compare the ability of network

  18. Running Club

    CERN Multimedia

    Running Club

    2010-01-01

    The 2010 edition of the annual CERN Road Race will be held on Wednesday 29th September at 18h. The 5.5km race takes place over 3 laps of a 1.8 km circuit in the West Area of the Meyrin site, and is open to everyone working at CERN and their families. There are runners of all speeds, with times ranging from under 17 to over 34 minutes, and the race is run on a handicap basis, by staggering the starting times so that (in theory) all runners finish together. Children (< 15 years) have their own race over 1 lap of 1.8km. As usual, there will be a “best family” challenge (judged on best parent + best child). Trophies are awarded in the usual men’s, women’s and veterans’ categories, and there is a challenge for the best age/performance. Every adult will receive a souvenir prize, financed by a registration fee of 10 CHF. Children enter free (each child will receive a medal). More information, and the online entry form, can be found at http://cern.ch/club...

  19. RUN COORDINATION

    CERN Multimedia

    Christophe Delaere

    2012-01-01

      On Wednesday 14 March, the machine group successfully injected beams into LHC for the first time this year. Within 48 hours they managed to ramp the beams to 4 TeV and proceeded to squeeze to β*=0.6m, settings that are used routinely since then. This brought to an end the CMS Cosmic Run at ~Four Tesla (CRAFT), during which we collected 800k cosmic ray events with a track crossing the central Tracker. That sample has been since then topped up to two million, allowing further refinements of the Tracker Alignment. The LHC started delivering the first collisions on 5 April with two bunches colliding in CMS, giving a pile-up of ~27 interactions per crossing at the beginning of the fill. Since then the machine has increased the number of colliding bunches to reach 1380 bunches and peak instantaneous luminosities around 6.5E33 at the beginning of fills. The average bunch charges reached ~1.5E11 protons per bunch which results in an initial pile-up of ~30 interactions per crossing. During the ...

  20. RUN COORDINATION

    CERN Multimedia

    C. Delaere

    2012-01-01

      With the analysis of the first 5 fb–1 culminating in the announcement of the observation of a new particle with mass of around 126 GeV/c2, the CERN directorate decided to extend the LHC run until February 2013. This adds three months to the original schedule. Since then the LHC has continued to perform extremely well, and the total luminosity delivered so far this year is 22 fb–1. CMS also continues to perform excellently, recording data with efficiency higher than 95% for fills with the magnetic field at nominal value. The highest instantaneous luminosity achieved by LHC to date is 7.6x1033 cm–2s–1, which translates into 35 interactions per crossing. On the CMS side there has been a lot of work to handle these extreme conditions, such as a new DAQ computer farm and trigger menus to handle the pile-up, automation of recovery procedures to minimise the lost luminosity, better training for the shift crews, etc. We did suffer from a couple of infrastructure ...

  1. Effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons

    Energy Technology Data Exchange (ETDEWEB)

    Li, Chun-Hsien, E-mail: chli@nknucc.nknu.edu.tw [Department of Mathematics, National Kaohsiung Normal University, Yanchao District, Kaohsiung City 82444, Taiwan (China); Yang, Suh-Yuh, E-mail: syyang@math.ncu.edu.tw [Department of Mathematics, National Central University, Jhongli District, Taoyuan City 32001, Taiwan (China)

    2015-10-23

    This work is devoted to investigate the effects of network structure on the synchronizability of nonlinearly coupled dynamical network of Hindmarsh–Rose neurons with a sigmoidal coupling function. We mainly focus on the networks that exhibit the small-world character or scale-free property. By checking the first nonzero eigenvalue of the outer-coupling matrix, which is closely related to the synchronization threshold, the synchronizabilities of three specific network ensembles with prescribed network structures are compared. Interestingly, we find that networks with more connections will not necessarily result in better synchronizability. - Highlights: • We investigate the effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons. • We mainly consider the networks that exhibit the small-world character or scale-free property. • The synchronizability of three specific network ensembles with prescribed network structures are compared. • Networks with more connections will not necessarily result in better synchronizability.

  2. Effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons

    International Nuclear Information System (INIS)

    Li, Chun-Hsien; Yang, Suh-Yuh

    2015-01-01

    This work is devoted to investigate the effects of network structure on the synchronizability of nonlinearly coupled dynamical network of Hindmarsh–Rose neurons with a sigmoidal coupling function. We mainly focus on the networks that exhibit the small-world character or scale-free property. By checking the first nonzero eigenvalue of the outer-coupling matrix, which is closely related to the synchronization threshold, the synchronizabilities of three specific network ensembles with prescribed network structures are compared. Interestingly, we find that networks with more connections will not necessarily result in better synchronizability. - Highlights: • We investigate the effects of network structure on the synchronizability of nonlinearly coupled Hindmarsh–Rose neurons. • We mainly consider the networks that exhibit the small-world character or scale-free property. • The synchronizability of three specific network ensembles with prescribed network structures are compared. • Networks with more connections will not necessarily result in better synchronizability

  3. Dynamics of cluster structures in a financial market network

    Science.gov (United States)

    Kocheturov, Anton; Batsyn, Mikhail; Pardalos, Panos M.

    2014-11-01

    In the course of recent fifteen years the network analysis has become a powerful tool for studying financial markets. In this work we analyze stock markets of the USA and Sweden. We study cluster structures of a market network constructed from a correlation matrix of returns of the stocks traded in each of these markets. Such cluster structures are obtained by means of the P-Median Problem (PMP) whose objective is to maximize the total correlation between a set of stocks called medians of size p and other stocks. Every cluster structure is an undirected disconnected weighted graph in which every connected component (cluster) is a star, or a tree with one central node (called a median) and several leaf nodes connected with the median by weighted edges. Our main observation is that in non-crisis periods of time cluster structures change more chaotically, while during crises they show more stable behavior and fewer changes. Thus an increasing stability of a market graph cluster structure obtained via the PMP could be used as an indicator of a coming crisis.

  4. Optimal map of the modular structure of complex networks

    International Nuclear Information System (INIS)

    Arenas, A; Borge-Holthoefer, J; Gomez, S; Zamora-Lopez, G

    2010-01-01

    The modular structure is pervasive in many complex networks of interactions observed in natural, social and technological sciences. Its study sheds light on the relation between the structure and the function of complex systems. Generally speaking, modules are islands of highly connected nodes separated by a relatively small number of links. Every module can have the contributions of links from any node in the network. The challenge is to disentangle these contributions to understand how the modular structure is built. The main problem is that the analysis of a certain partition into modules involves, in principle, as much data as the number of modules times the number of nodes. To confront this challenge, here we first define the contribution matrix, the mathematical object containing all the information about the partition of interest, and then we use truncated singular value decomposition to extract the best representation of this matrix in a plane. The analysis of this projection allows us to scrutinize the skeleton of the modular structure, revealing the structure of individual modules and their interrelations.

  5. Sandwich node architecture for agile wireless sensor networks for real-time structural health monitoring applications

    Science.gov (United States)

    Wang, Zi; Pakzad, Shamim; Cheng, Liang

    2012-04-01

    In recent years, wireless sensor network (WSN), as a powerful tool, has been widely applied to structural health monitoring (SHM) due to its low cost of deployment. Several commercial hardware platforms of wireless sensor networks (WSN) have been developed and used for structural monitoring applications [1,2]. A typical design of a node includes a sensor board and a mote connected to it. Sensing units, analog filters and analog-to-digital converters (ADCs) are integrated on the sensor board and the mote consists of a microcontroller and a wireless transceiver. Generally, there are a set of sensor boards compatible with the same model of mote and the selection of the sensor board depends on the specific applications. A WSN system based on this node lacks the capability of interrupting its scheduled task to start a higher priority task. This shortcoming is rooted in the hardware architecture of the node. The proposed sandwich-node architecture is designed to remedy the shortcomings of the existing one for task preemption. A sandwich node is composed of a sensor board and two motes. The first mote is dedicated to managing the sensor board and processing acquired data. The second mote controls the first mote via commands. A prototype has been implemented using Imote2 and verified by an emulation in which one mote is triggered by a remote base station and then preempts the running task at the other mote for handling an emergency event.

  6. Network Structure as a Modulator of Disturbance Impacts in Streams

    Science.gov (United States)

    Warner, S.; Tullos, D. D.

    2017-12-01

    This study examines how river network structure affects the propagation of geomorphic and anthropogenic disturbances through streams. Geomorphic processes such as debris flows can alter channel morphology and modify habitat for aquatic biota. Anthropogenic disturbances such as road construction can interact with the geomorphology and hydrology of forested watersheds to change sediment and water inputs to streams. It was hypothesized that the network structure of streams within forested watersheds would influence the location and magnitude of the impacts of debris flows and road construction on sediment size and channel width. Longitudinal surveys were conducted every 50 meters for 11 kilometers of third-to-fifth order streams in the H.J. Andrews Experimental Forest in the Western Cascade Range of Oregon. Particle counts and channel geometry measurements were collected to characterize the geomorphic impacts of road crossings and debris flows as disturbances. Sediment size distributions and width measurements were plotted against the distance of survey locations through the network to identify variations in longitudinal trends of channel characteristics. Thresholds for the background variation in sediment size and channel width, based on the standard deviations of sample points, were developed for sampled stream segments characterized by location as well as geomorphic and land use history. Survey locations were classified as "disturbed" when they deviated beyond the reference thresholds in expected sediment sizes and channel widths, as well as flow-connected proximity to debris flows and road crossings. River network structure was quantified by drainage density and centrality of nodes upstream of survey locations. Drainage density and node centrality were compared between survey locations with similar channel characteristic classifications. Cluster analysis was used to assess the significance of survey location, proximity of survey location to debris flows and road

  7. Radiation synthesis and characterization of network structure of natural/synthetic double-network superabsorbent polymers

    International Nuclear Information System (INIS)

    Sen, M.; Hayrabolulu, H.

    2011-01-01

    Complete text of publication follows. Superabsorbent polymers (SAPs) are moderately cross linked, 3-D, hydrophilic network polymers that can absorb and conserve considerable amounts of aqueous fluids even under certain heat or pressure. Because of the unique properties superior to conventional absorbents, SAPs have found potential application in many fields such as hygienic products, disposable diapers, horticulture, gel actuators, drug-delivery systems, as well as water-blocking tapes coal dewatering, water managing materials for the renewal of arid and desert environment, etc. In recent years, naturally available resources, such as polysaccharides have drawn considerable attention for the preparation of SAPs. Since the mechanical properties of polysaccharide based natural polymers are low, researchers have mostly focused on natural/synthetic polymer/monomer mixtures to obtain novel SAPs. The aim of this study is to synthesize and characterization of network structure of novel double-network (DN) hydrogels as a SAP. Hydrogels with high mechanical strength have been prepared by radiation induced polymerization and crosslink of acrylic acid sodium salt in the presence of natural polymer locust bean gum. Liquid retention capacities and absorbency under load (AUL) analysis of synthesized SAPs was performed at different temperatures in water and synthetic urine solution, in order to determine their SAP character. For the characterization of network structure of the semi-IPN hydrogels, the average molecular weight between cross links (M c ) were evaluated by using uniaxial compression and oscillatory dynamical mechanical analyses and the advantage and disadvantage of these two technique for the characterization of network structures were compared.

  8. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  9. LHCb: Time structure analysis of the LHCb Online network

    CERN Multimedia

    Antichi, G; Campora Perez, D H; Liu, G; Neufeld, N; Giordano, S; Owezarski, P; Moore, A

    2013-01-01

    The LHCb Online Network is a real time high performance network, in which 350 data sources send data over a Gigabit Ethernet LAN to more than 1500 receiving nodes. The aggregated throughput of the application, called Event Building, is more than 60 GB/s. The protocol employed by LHCb makes the sending nodes transmit simultaneously portions of events to one receiving node at a time, which is selected using a credit-token scheme. The resulting traffic is very bursty and sensitive to irregularities in the temporal distribution of packet-bursts to the same destination or region of the network. In order to study the relevant properties of such a dataflow, a non-disruptive monitoring setup based on a networking capable FPGA (NetFPGA) has been deployed. The NetFPGA allows order of hundred nano-second precise time-stamping of packets. We study in detail the timing structure of the Event Building communication, and we identify potential effects of micro-bursts like buffer packet drops or jitter.

  10. Dynamical Structure of a Traditional Amazonian Social Network

    Directory of Open Access Journals (Sweden)

    Paul L. Hooper

    2013-11-01

    Full Text Available Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha; Tsimane’: shocdye’ drinking events in a Tsimane’ village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone.

  11. PHYSICAL AND CHEMICAL PROPERTIES AND STRUCTURAL-GROUP COMPOSITION OF STRAIGHT-RUN FRACTIONS OF IRAQI OILFIELDS

    Directory of Open Access Journals (Sweden)

    С.В. Бойченко

    2012-10-01

    Full Text Available  Iraq is one of the richest countries of the Near East in terms of oil reserves. Only Saudi Arabia has greater hydrocarbon reserves. Despite the fact that commercial oil production is implemented since 1927, the harsh social and political and economic situation added with long-time military conflict had totally destroyed           oil processing infrastructure. Considering this factor, one of the key tasks for this country is building of  a new, contemporary oil processing infrastructure. Thus, complex study of oils as well as their  straight-run fractions is a requirement for preparation of the projects of oil processing plants that are consistent with current hard requirements. This material contents results of physical and chemical studies  of straight-run oil fractions as gasoline, Diesel oil, and heavy oils. Properties of fractions shown below, their potential related to export oil products are essential data for preparation of process flow diagram of the oil processing plant.

  12. Structural differences in basal ganglia of elite running versus martial arts athletes: a diffusion tensor imaging study.

    Science.gov (United States)

    Chang, Yu-Kai; Tsai, Jack Han-Chao; Wang, Chun-Chih; Chang, Erik Chihhung

    2015-07-01

    The aim of this study was to use diffusion tensor imaging (DTI) to characterize and compare microscopic differences in white matter integrity in the basal ganglia between elite professional athletes specializing in running and martial arts. Thirty-three young adults with sport-related skills as elite professional runners (n = 11) or elite professional martial artists (n = 11) were recruited and compared with non-athletic and healthy controls (n = 11). All participants underwent health- and skill-related physical fitness assessments. Fractional anisotropy (FA) and mean diffusivity (MD), the primary indices derived from DTI, were computed for five regions of interest in the bilateral basal ganglia, including the caudate nucleus, putamen, globus pallidus internal segment (GPi), globus pallidus external segment (GPe), and subthalamic nucleus. Results revealed that both athletic groups demonstrated better physical fitness indices compared with their control counterparts, with the running group exhibiting the highest cardiovascular fitness and the martial arts group exhibiting the highest muscular endurance and flexibility. With respect to the basal ganglia, both athletic groups showed significantly lower FA and marginally higher MD values in the GPi compared with the healthy control group. These findings suggest that professional sport or motor skill training is associated with changes in white matter integrity in specific regions of the basal ganglia, although these positive changes did not appear to depend on the type of sport-related motor skill being practiced.

  13. Measurement of the running of the fine structure constant below 1 GeV with the KLOE detector

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Precision physics requires appropriate inclusion of higher order effects and the knowledge of very precise input parameters of the electroweak Standard Model. One of the basic input parameters is the effective QED coupling constant α(s) which depends on the energy scale because of charge screening by vacuum polarization. Hadronic non-perturbative effects limits the accuracy of α(s) from low energy to the Z mass scale. We present the measurement of the running of the QED coupling constant in the time-like region 0.6 < √s < 0.975 GeV with the KLOE detector at DAΦNE , using the ISR differential cross section dσ(e+e− → μ+μ− γ)/d√s. The result shows a clear contribution of the ρ−ω resonances to the photon propagator with a significance of the hadronic contribution to the running of α(s) of more than 5σ. It represents the first measurement of th...

  14. Group composition and network structure in school classes : a multilevel application of the p* model

    NARCIS (Netherlands)

    Lubbers, Miranda J.

    2003-01-01

    This paper describes the structure of social networks of students within school classes and examines differences in network structure between classes. In order to examine the network structure within school classes, we focused in particular on the principle of homophily, i.e. the tendency that

  15. Investigation of Wireless Sensor Networks for Structural Health Monitoring

    Directory of Open Access Journals (Sweden)

    Ping Wang

    2012-01-01

    Full Text Available Wireless sensor networks (WSNs are one of the most able technologies in the structural health monitoring (SHM field. Through intelligent, self-organising means, the contents of this paper will test a variety of different objects and different working principles of sensor nodes connected into a network and integrated with data processing functions. In this paper the key issues of WSN applied in SHM are discussed, including the integration of different types of sensors with different operational modalities, sampling frequencies, issues of transmission bandwidth, real-time ability, and wireless transmitter frequency. Furthermore, the topology, data fusion, integration, energy saving, and self-powering nature of different systems will be investigated. In the FP7 project “Health Monitoring of Offshore Wind Farms,” the above issues are explored.

  16. Wireless sensor networks for active vibration control in automobile structures

    International Nuclear Information System (INIS)

    Mieyeville, Fabien; Navarro, David; Du, Wan; Ichchou, Mohamed; Scorletti, Gérard

    2012-01-01

    Wireless sensor networks (WSNs) are nowadays widely used in monitoring and tracking applications. This paper presents the feasibility of using WSNs in active vibration control strategies. The method employed here involves active-structural acoustic control using piezoelectric sensors distributed on a car structure. This system aims at being merged with a WSN whose head node collects data and processes control laws so as to command piezoelectric actuators wisely placed on the structure. We will study the feasibility of implementing WSNs in active vibration control and introduce a complete design methodology to optimize hardware/software and control law synergy in mechatronic systems. A design space exploration will be conducted so as to identify the best WSN platform and the resulting impact on control. (paper)

  17. Controlling nosocomial infection based on structure of hospital social networks.

    Science.gov (United States)

    Ueno, Taro; Masuda, Naoki

    2008-10-07

    Nosocomial infection (i.e. infection in healthcare facilities) raises a serious public health problem, as implied by the existence of pathogens characteristic to healthcare facilities such as methicillin-resistant Staphylococcus aureus and hospital-mediated outbreaks of influenza and severe acute respiratory syndrome. For general communities, epidemic modeling based on social networks is being recognized as a useful tool. However, disease propagation may occur in a healthcare facility in a manner different from that in a urban community setting due to different network architecture. We simulate stochastic susceptible-infected-recovered dynamics on social networks, which are based on observations in a hospital in Tokyo, to explore effective containment strategies against nosocomial infection. The observed social networks in the hospital have hierarchical and modular structure in which dense substructure such as departments, wards, and rooms, are globally but only loosely connected, and do not reveal extremely right-skewed distributions of the number of contacts per individual. We show that healthcare workers, particularly medical doctors, are main vectors (i.e. transmitters) of diseases on these networks. Intervention methods that restrict interaction between medical doctors and their visits to different wards shrink the final epidemic size more than intervention methods that directly protect patients, such as isolating patients in single rooms. By the same token, vaccinating doctors with priority rather than patients or nurses is more effective. Finally, vaccinating individuals with large betweenness centrality (frequency of mediating connection between pairs of individuals along the shortest paths) is superior to vaccinating ones with large connectedness to others or randomly chosen individuals, which was suggested by previous model studies.

  18. Structural Covariance Networks in Children with Autism or ADHD.

    Science.gov (United States)

    Bethlehem, R A I; Romero-Garcia, R; Mak, E; Bullmore, E T; Baron-Cohen, S

    2017-08-01

    While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics. Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69). Regional cortical thickness was used to construct the structural covariance networks. This was analysed in a theoretical framework examining potential differences in long and short-range connectivity, with a specific focus on relation between central graph measures and cortical thickness. We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance between centroids compared with a NT population. The 2 conditions also show divergence. Namely, there is less modular overlap between the 2 conditions than there is between each condition and the NT group. The ADHD group also showed reduced cortical thickness and lower degree in hub regions than the autism group. Lastly, the ADHD group also showed reduced wiring costs compared with the autism groups. Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Furthermore, autism and ADHD both showed alterations in the relation between inter-regional covariance and centroid distance, where both groups show a steeper decline in covariance as a function of distance. The 2 groups also diverge on modular organization, cortical thickness of hub regions and wiring cost of the covariance network. Thus, on some network features the

  19. Oligomeric protein structure networks: insights into protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Brinda KV

    2005-12-01

    Full Text Available Abstract Background Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues with special emphasis to protein interfaces. Results A variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb. A few predictions of interface hot

  20. Using structural equation modeling for network meta-analysis.

    Science.gov (United States)

    Tu, Yu-Kang; Wu, Yun-Chun

    2017-07-14

    Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM. We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM. For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison

  1. Human cancer protein-protein interaction network: a structural perspective.

    Directory of Open Access Journals (Sweden)

    Gozde Kar

    2009-12-01

    Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

  2. Searching for realism, structure and agency in Actor Network Theory.

    Science.gov (United States)

    Elder-Vass, Dave

    2008-09-01

    Superficially, Actor Network Theory (ANT) and critical realism (CR) are radically opposed research traditions. Written from a realist perspective, this paper asks whether there might be a basis for finding common ground between these two traditions. It looks in turn at the questions of realism, structure, and agency, analysing the differences between the two perspectives and seeking to identify what each might learn from the other. Overall, the paper argues that there is a great deal that realists can learn from actor network theory; yet ANT remains stunted by its lack of a depth ontology. It fails to recognize the significance of mechanisms, and of their dependence on emergence, and thus lacks both dimensions of the depth that is characteristic of critical realism's ontology. This prevents ANT from recognizing the role and powers of social structure; but on the other hand, realists would do well to heed ANT's call for us to trace the connections through which structures are constantly made and remade. A lack of ontological depth also underpins ANT's practice of treating human and non-human actors symmetrically, yet this remains a valuable provocation to sociologists who neglect non-human entities entirely.

  3. Ubuntu Up and Running

    CERN Document Server

    Nixon, Robin

    2010-01-01

    Ubuntu for everyone! This popular Linux-based operating system is perfect for people with little technical background. It's simple to install, and easy to use -- with a strong focus on security. Ubuntu: Up and Running shows you the ins and outs of this system with a complete hands-on tour. You'll learn how Ubuntu works, how to quickly configure and maintain Ubuntu 10.04, and how to use this unique operating system for networking, business, and home entertainment. This book includes a DVD with the complete Ubuntu system and several specialized editions -- including the Mythbuntu multimedia re

  4. True Nature of Supply Network Communication Structure (P.1-14

    Directory of Open Access Journals (Sweden)

    Lokhman Hakim bin Osman

    2017-02-01

    Full Text Available Globalization of world economy has altered the definition of organizational structure. Global supply chain can no longer be viewed as an arm-length structure. It has become more complex. The complexity demands deeper research and understanding. This research analyzed a structure of supply network in an attempt to elucidate the true structure of the supply network. Using the quantitative Social Network Analysis methodology, findings of this study indicated that, the structure of the supply network differs depending on the types of network relations. An important implication of these findings would be a more focus resource management upon network relationship development that is based on firms’ positions in the different network structure. This research also contributes to the various strategies of effective and efficient supply chain management.Keywords: Supply Chain Management, Network Studies, Inter-Organizational Relations, Social Capital

  5. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  6. Refinement of Bayesian Network Structures upon New Data

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Xiang, Yanping; Pacekajus, Saulius

    2010-01-01

    Refinement of Bayesian network (BN) structures using new data becomes more and more relevant. Some work has been done there; however, one problem has not been considered yet – what to do when new data have fewer or more attributes than the existing model. In both cases, data contain important...... knowledge and every effort must be made in order to extract it. In this paper, we propose a general merging algorithm to deal with situations when new data have different set of attributes. The merging algorithm updates sufficient statistics when new data are received. It expands the flexibility of BN...

  7. Structural studies of novel coordination compounds run in the Nesmeyanov Institute of Organoelement Compounds, Russian Academy of Sciences

    International Nuclear Information System (INIS)

    Antipin, M.Yu.; Starikova, Z.A.; Yanovskij, A.I.; Dolgushin, F.M.; Lysenko, K.A.; Khrustalev, V.N.; Vorontsov, I.I.; Korlyukov, A.A.; Andreev, G.B.; Neretin, I.S.

    2001-01-01

    The results of the investigation into structural chemistry of coordination compounds taking place in the X-ray Laboratory of the Nesmeyanov Institute of Organoelement Compounds, Russian Academy of Sciences are given. The review gives an idea on the tendencies to structural researches of complexes of varying categories of coordination compounds, among which are lithium, strontium, cadmium compounds, rare earth compounds, transuranium compounds, transition element compounds, carboranes, fullerenes. An attempt was made to prove the structure and reveal the novel structural and crystallochemical regularities in the studied series of relative compounds. The outlooks for the following progress of studies on this field are determined [ru

  8. Demonstration and Methodology of Structural Monitoring of Stringer Runs out Composite Areas by Embedded Optical Fiber Sensors and Connectors Integrated during Production in a Composite Plant.

    Science.gov (United States)

    Miguel Giraldo, Carlos; Zúñiga Sagredo, Juan; Sánchez Gómez, José; Corredera, Pedro

    2017-07-21

    Embedding optical fibers sensors into composite structures for Structural Health Monitoring purposes is not just one of the most attractive solutions contributing to smart structures, but also the optimum integration approach that insures maximum protection and integrity of the fibers. Nevertheless this intended integration level still remains an industrial challenge since today there is no mature integration process in composite plants matching all necessary requirements. This article describes the process developed to integrate optical fiber sensors in the Production cycle of a test specimen. The sensors, Bragg gratings, were integrated into the laminate during automatic tape lay-up and also by a secondary bonding process, both in the Airbus Composite Plant. The test specimen, completely representative of the root joint of the lower wing cover of a real aircraft, is comprised of a structural skin panel with the associated stringer run out. The ingress-egress was achieved through the precise design and integration of miniaturized optical connectors compatible with the manufacturing conditions and operational test requirements. After production, the specimen was trimmed, assembled and bolted to metallic plates to represent the real triform and buttstrap, and eventually installed into the structural test rig. The interrogation of the sensors proves the effectiveness of the integration process; the analysis of the strain results demonstrate the good correlation between fiber sensors and electrical gauges in those locations where they are installed nearby, and the curvature and load transfer analysis in the bolted stringer run out area enable demonstration of the consistency of the fiber sensors measurements. In conclusion, this work presents strong evidence of the performance of embedded optical sensors for structural health monitoring purposes, where in addition and most importantly, the fibers were integrated in a real production environment and the ingress

  9. Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

    Science.gov (United States)

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

    Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

  10. CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Jing Wang

    2017-01-01

    Full Text Available Many structural variations (SVs detection methods have been proposed due to the popularization of next-generation sequencing (NGS. These SV calling methods use different SV-property-dependent features; however, they all suffer from poor accuracy when running on low coverage sequences. The union of results from these tools achieves fairly high sensitivity but still produces low accuracy on low coverage sequence data. That is, these methods contain many false positives. In this paper, we present CNNdel, an approach for calling deletions from paired-end reads. CNNdel gathers SV candidates reported by multiple tools and then extracts features from aligned BAM files at the positions of candidates. With labeled feature-expressed candidates as a training set, CNNdel trains convolutional neural networks (CNNs to distinguish true unlabeled candidates from false ones. Results show that CNNdel works well with NGS reads from 26 low coverage genomes of the 1000 Genomes Project. The paper demonstrates that convolutional neural networks can automatically assign the priority of SV features and reduce the false positives efficaciously.

  11. Involuntary wheel running improves but does not fully reverse the deterioration of bone structure of obese rats despite decreasing adiposity

    Science.gov (United States)

    Excessive adiposity induced by a high-fat diet is detrimental to bone structure and strength in various animal models. This study investigated whether exercise or anti-oxidant supplementation with vitamin C and E during exercise counteracts bone structure deterioration at different skeletal sites an...

  12. A modular structure to accident identification using neural networks

    International Nuclear Information System (INIS)

    Duque Estrada, Cassius Rodrigo

    2005-01-01

    This work uses the accident identification method based on Artificial Neural Networks (ANN) as basic blocks of a modular structure, allowing the inclusion of new accidents to be identified without modifying the ANN already trained. This structure comprises several modules for accident identification and one module for analysis. Each identification module follows the structure of the basic block. The identification modules are responsible for the recognition of an accident belonging to the specific set of events for which it were trained. The analysis module processes the output from the identification module to determine the system response. In order to test this structure it was proposed a transient identification problem comprising fifty accidents distributed in five identification modules. The results have demonstrated that the accident identification method used as basic block of a modular structure allows the inclusion of new sets of accidents, or variations of a same accident, without modifying the ANN already trained. For this, it is enough to include into the system an specific module for this new set of accidents. (author)

  13. Combining structure, governance and context : A configurational approach to network effectiveness

    NARCIS (Netherlands)

    Raab, J.; Mannak, R.S.; Cambré, B.

    2015-01-01

    This study explores the way in which network structure (network integration), network context (resource munificence and stability), and network governance mode relate to net -work effectiveness. The model by Provan and Milward (Provan, Keith G., and H. Brinton Milward. 1995. A preliminary theory of

  14. Industry Consolidation and Future Airline Network Structures in Europe

    Science.gov (United States)

    Dennis, Nigel

    2003-01-01

    In the current downturn in demand for air travel, major airlines are revising and rationalizing their networks in an attempt to improve financial performance and strengthen their defences against both new entrants and traditional rivals. Expansion of commercial agreements or alliances with other airlines has become a key reaction to the increasingly competitive marketplace. In the absence, for regulatory reasons, of cross-border mergers these are the principal means by which the industry can consolidate internationally. This paper analyzes the developments which have been taking place and attempts to itentify the implications for airline network structures and the function of different hub airports. The range of services available to passengers in long-haul markets to/from Europe is evaluated before and after recent industry reorganization. Hubs are crucial to interlink the route networks of parmers in an alliance. However, duplication between nearby hub airports that find themselves within the same airline alliance can lead to loss of service at the weaker locations. The extent to which the alliance hubs in Europe duplicate or complement each other in terms of network coverage is assessed and this methodology also enables the optimal partnerships for "unattached" airlines to be identified. The future role of the various European hubs is considered under different scenarios of global alliance development. The paper concludes by considering possible longer-term developments. In an environment where the low-cost carriers will provide a major element of customer choice, it is suggested that the traditional airlines will retrench around their hubs, surrendering many secondary cities to the low-cost sector. Further reduction in the number of alliances could threaten more of the European hubs. For both regulatory and commercial reasons, the end result may be just one airline alliance - so recreating in the deregulated market the historic rule of IATA.

  15. Cascaded bidirectional recurrent neural networks for protein secondary structure prediction.

    Science.gov (United States)

    Chen, Jinmiao; Chaudhari, Narendra

    2007-01-01

    Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.

  16. Virality Prediction and Community Structure in Social Networks

    Science.gov (United States)

    Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

    2013-08-01

    How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

  17. Density-based and transport-based core-periphery structures in networks.

    Science.gov (United States)

    Lee, Sang Hoon; Cucuringu, Mihai; Porter, Mason A

    2014-03-01

    Networks often possess mesoscale structures, and studying them can yield insights into both structure and function. It is most common to study community structure, but numerous other types of mesoscale structures also exist. In this paper, we examine core-periphery structures based on both density and transport. In such structures, core network components are well-connected both among themselves and to peripheral components, which are not well-connected to anything. We examine core-periphery structures in a wide range of examples of transportation, social, and financial networks-including road networks in large urban areas, a rabbit warren, a dolphin social network, a European interbank network, and a migration network between counties in the United States. We illustrate that a recently developed transport-based notion of node coreness is very useful for characterizing transportation networks. We also generalize this notion to examine core versus peripheral edges, and we show that the resulting diagnostic is also useful for transportation networks. To examine the properties of transportation networks further, we develop a family of generative models of roadlike networks. We illustrate the effect of the dimensionality of the embedding space on transportation networks, and we demonstrate that the correlations between different measures of coreness can be very different for different types of networks.

  18. Brain gray matter structural network in myotonic dystrophy type 1.

    Directory of Open Access Journals (Sweden)

    Atsuhiko Sugiyama

    Full Text Available This study aimed to investigate abnormalities in structural covariance network constructed from gray matter volume in myotonic dystrophy type 1 (DM1 patients by using graph theoretical analysis for further clarification of the underlying mechanisms of central nervous system involvement. Twenty-eight DM1 patients (4 childhood onset, 10 juvenile onset, 14 adult onset, excluding three cases from 31 consecutive patients who underwent magnetic resonance imaging in a certain period, and 28 age- and sex- matched healthy control subjects were included in this study. The normalized gray matter images of both groups were subjected to voxel based morphometry (VBM and Graph Analysis Toolbox for graph theoretical analysis. VBM revealed extensive gray matter atrophy in DM1 patients, including cortical and subcortical structures. On graph theoretical analysis, there were no significant differences between DM1 and control groups in terms of the global measures of connectivity. Betweenness centrality was increased in several regions including the left fusiform gyrus, whereas it was decreased in the right striatum. The absence of significant differences between the groups in global network measurements on graph theoretical analysis is consistent with the fact that the general cognitive function is preserved in DM1 patients. In DM1 patients, increased connectivity in the left fusiform gyrus and decreased connectivity in the right striatum might be associated with impairment in face perception and theory of mind, and schizotypal-paranoid personality traits, respectively.

  19. Complex Network Structure Influences Processing in Long-Term and Short-Term Memory

    Science.gov (United States)

    Vitevitch, Michael S.; Chan, Kit Ying; Roodenrys, Steven

    2012-01-01

    Complex networks describe how entities in systems interact; the structure of such networks is argued to influence processing. One measure of network structure, clustering coefficient, C, measures the extent to which neighbors of a node are also neighbors of each other. Previous psycholinguistic experiments found that the C of phonological…

  20. The formation of a core-periphery structure in heterogeneous financial networks

    NARCIS (Netherlands)

    van der Leij, M.; in 't Veld, D.; Hommes, C.

    2016-01-01

    Recent empirical evidence suggests that financial networks exhibit a core-periphery network structure. This paper aims at giving an explanation for the emergence of such a structure using network formation theory. We propose a simple model of the overnight interbank lending market, in which banks

  1. Composition and structure of a large online social network in The Netherlands.

    Directory of Open Access Journals (Sweden)

    Rense Corten

    Full Text Available Limitations in data collection have long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization. The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. This study uses complete network data from Hyves, a popular online social networking service in The Netherlands, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for The Netherlands, I examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. Results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities.

  2. Composition and structure of a large online social network in The Netherlands.

    Science.gov (United States)

    Corten, Rense

    2012-01-01

    Limitations in data collection have long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization). The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. This study uses complete network data from Hyves, a popular online social networking service in The Netherlands, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for The Netherlands, I examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. Results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities.

  3. Radiation-Induced Topological Disorder in Irradiated Network Structures

    International Nuclear Information System (INIS)

    Hobbs, Linn W.

    2002-12-01

    This report summarizes results of a research program investigating the fundamental principles underlying the phenomenon of topological disordering in a radiation environment. This phenomenon is known popularly as amorphization, but is more formally described as a process of radiation-induced structural arrangement that leads in crystals to loss of long-range translational and orientational correlations and in glasses to analogous alteration of connectivity topologies. The program focus has been on a set compound ceramic solids with directed bonding exhibiting structures that can be described as networks. Such solids include SiO2, Si3N4, SiC, which are of interest to applications in fusion energy production, nuclear waste storage, and device manufacture involving ion implantation or use in radiation fields. The principal investigative tools comprise a combination of experimental diffraction-based techniques, topological modeling, and molecular-dynamics simulations that have proven a rich source of information in the preceding support period. The results from the present support period fall into three task areas. The first comprises enumeration of the rigidity constraints applying to (1) more complex ceramic structures (such as rutile, corundum, spinel and olivine structures) that exhibit multiply polytopic coordination units or multiple modes of connecting such units, (2) elemental solids (such as graphite, silicon and diamond) for which a correct choice of polytope is necessary to achieve correct representation of the constraints, and (3) compounds (such as spinel and silicon carbide) that exhibit chemical disorder on one or several sublattices. With correct identification of the topological constraints, a unique correlation is shown to exist between constraint and amorphizability which demonstrates that amorphization occurs at a critical constraint loss. The second task involves the application of molecular dynamics (MD) methods to topologically-generated models

  4. Modular structure of functional networks in olfactory memory.

    Science.gov (United States)

    Meunier, David; Fonlupt, Pierre; Saive, Anne-Lise; Plailly, Jane; Ravel, Nadine; Royet, Jean-Pierre

    2014-07-15

    Graph theory enables the study of systems by describing those systems as a set of nodes and edges. Graph theory has been widely applied to characterize the overall structure of data sets in the social, technological, and biological sciences, including neuroscience. Modular structure decomposition enables the definition of sub-networks whose components are gathered in the same module and work together closely, while working weakly with components from other modules. This processing is of interest for studying memory, a cognitive process that is widely distributed. We propose a new method to identify modular structure in task-related functional magnetic resonance imaging (fMRI) networks. The modular structure was obtained directly from correlation coefficients and thus retained information about both signs and weights. The method was applied to functional data acquired during a yes-no odor recognition memory task performed by young and elderly adults. Four response categories were explored: correct (Hit) and incorrect (False alarm, FA) recognition and correct and incorrect rejection. We extracted time series data for 36 areas as a function of response categories and age groups and calculated condition-based weighted correlation matrices. Overall, condition-based modular partitions were more homogeneous in young than elderly subjects. Using partition similarity-based statistics and a posteriori statistical analyses, we demonstrated that several areas, including the hippocampus, caudate nucleus, and anterior cingulate gyrus, belonged to the same module more frequently during Hit than during all other conditions. Modularity values were negatively correlated with memory scores in the Hit condition and positively correlated with bias scores (liberal/conservative attitude) in the Hit and FA conditions. We further demonstrated that the proportion of positive and negative links between areas of different modules (i.e., the proportion of correlated and anti-correlated areas

  5. 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.

  6. Fragmentation alters stream fish community structure in dendritic ecological networks.

    Science.gov (United States)

    Perkin, Joshuah S; Gido, Keith B

    2012-12-01

    Effects of fragmentation on the ecology of organisms occupying dendritic ecological networks (DENs) have recently been described through both conceptual and mathematical models, but few hypotheses have been tested in complex, real-world ecosystems. Stream fishes provide a model system for assessing effects of fragmentation on the structure of communities occurring within DENs, including how fragmentation alters metacommunity dynamics and biodiversity. A recently developed habitat-availability measure, the "dendritic connectivity index" (DCI), allows for assigning quantitative measures of connectivity in DENs regardless of network extent or complexity, and might be used to predict fish community response to fragmentation. We characterized stream fish community structure in 12 DENs in the Great Plains, USA, during periods of dynamic (summer) and muted (fall) discharge regimes to test the DCI as a predictive model of fish community response to fragmentation imposed by road crossings. Results indicated that fish communities in stream segments isolated by road crossings had reduced species richness (alpha diversity) relative to communities that maintained connectivity with the surrounding DEN during summer and fall. Furthermore, isolated communities had greater dissimilarity (beta diversity) to downstream sites notisolated by road crossings during summer and fall. Finally, dissimilarity among communities within DENs decreased as a function of increased habitat connectivity (measured using the DCI) for summer and fall, suggesting that communities within highly connected DENs tend to be more homogeneous. Our results indicate that the DCI is sensitive to community effects of fragmentation in riverscapes and might be used by managers to predict ecological responses to changes in habitat connectivity. Moreover, our findings illustrate that relating structural connectivity of riverscapes to functional connectivity among communities might aid in maintaining metacommunity

  7. Identifying the Critical Links in Road Transportation Networks: Centrality-based approach utilizing structural properties

    Energy Technology Data Exchange (ETDEWEB)

    Chinthavali, Supriya [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2016-04-01

    Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) and the criticality index is found to be effective for one test network to identify the vulnerable nodes.

  8. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    Energy Technology Data Exchange (ETDEWEB)

    Bornholdt, S. [Heidelberg Univ., (Germany). Inst., fuer Theoretische Physik; Graudenz, D. [Lawrence Berkeley Lab., CA (United States)

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  9. Data envelopment analysis a handbook of modeling internal structure and network

    CERN Document Server

    Cook, Wade D

    2014-01-01

    This comprehensive handbook on state-of-the-art topics in DEA modeling of internal structures and networks presents work by leading researchers who share their results on subjects including additive efficiency decomposition and slacks-based network DEA.

  10. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    International Nuclear Information System (INIS)

    Bornholdt, S.

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback

  11. Obesity-related changes in bone structural and material properties in hyperphagic OLETF rats and protection by voluntary wheel running

    Science.gov (United States)

    We conducted a study to examine how the development of obesity and the associated insulin resistance affect bone structural and material properties, and bone formation and resorption markers in the Otsuka Long-Evans Tokushima Fatty (OLETF) rat model. This was a 36-week study of sedentary, hyperphag...

  12. Similarity between community structures of different online social networks and its impact on underlying community detection

    Science.gov (United States)

    Fan, W.; Yeung, K. H.

    2015-03-01

    As social networking services are popular, many people may register in more than one online social network. In this paper we study a set of users who have accounts of three online social networks: namely Foursquare, Facebook and Twitter. Community structure of this set of users may be reflected in these three online social networks. Therefore, high correlation between these reflections and the underlying community structure may be observed. In this work, community structures are detected in all three online social networks. Also, we investigate the similarity level of community structures across different networks. It is found that they show strong correlation with each other. The similarity between different networks may be helpful to find a community structure close to the underlying one. To verify this, we propose a method to increase the weights of some connections in networks. With this method, new networks are generated to assist community detection. By doing this, value of modularity can be improved and the new community structure match network's natural structure better. In this paper we also show that the detected community structures of online social networks are correlated with users' locations which are identified on Foursquare. This information may also be useful for underlying community detection.

  13. Structure identification and adaptive synchronization of uncertain general complex dynamical networks

    Energy Technology Data Exchange (ETDEWEB)

    Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)

    2009-12-28

    This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

  14. Structure identification and adaptive synchronization of uncertain general complex dynamical networks

    International Nuclear Information System (INIS)

    Xu Yuhua; Zhou Wuneng; Fang Jian'an; Lu Hongqian

    2009-01-01

    This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

  15. Structure-function relationships in elderly resting-state-networks : influence of age and cognitive performance

    OpenAIRE

    Jockwitz, Christiane

    2016-01-01

    The aim of this work was to investigate the structure-function relationship in cognitive resting state networks in a large population-based elderly sample. The first study characterized the functional connectivity in four cognitive resting state networks with respect to age, gender and cognitive performance: Default Mode Network (DMN), executive, and left and right frontoparietal resting state networks. The second study assessed the structural correlates of the functional reorganization of th...

  16. A wireless sensor network design and evaluation for large structural strain field monitoring

    International Nuclear Information System (INIS)

    Qiu, Zixue; Wu, Jian; Yuan, Shenfang

    2011-01-01

    Structural strain changes under external environmental or mechanical loads are the main monitoring parameters in structural health monitoring or mechanical property tests. This paper presents a wireless sensor network designed for monitoring large structural strain field variation. First of all, a precision strain sensor node is designed for multi-channel strain gauge signal conditioning and wireless monitoring. In order to establish a synchronous strain data acquisition network, the cluster-star network synchronization method is designed in detail. To verify the functionality of the designed wireless network for strain field monitoring capability, a multi-point network evaluation system is developed for an experimental aluminum plate structure for load variation monitoring. Based on the precision wireless strain nodes, the wireless data acquisition network is deployed to synchronously gather, process and transmit strain gauge signals and monitor results under concentrated loads. This paper shows the efficiency of the wireless sensor network for large structural strain field monitoring

  17. Structural Properties of the Brazilian Air Transportation Network

    Directory of Open Access Journals (Sweden)

    GUILHERME S. COUTO

    2015-09-01

    Full Text Available The air transportation network in a country has a great impact on the local, national and global economy. In this paper, we analyze the air transportation network in Brazil with complex network features to better understand its characteristics. In our analysis, we built networks composed either by national or by international flights. We also consider the network when both types of flights are put together. Interesting conclusions emerge from our analysis. For instance, Viracopos Airport (Campinas City is the most central and connected airport on the national flights network. Any operational problem in this airport separates the Brazilian national network into six distinct subnetworks. Moreover, the Brazilian air transportation network exhibits small world characteristics and national connections network follows a power law distribution. Therefore, our analysis sheds light on the current Brazilian air transportation infrastructure, bringing a novel understanding that may help face the recent fast growth in the usage of the Brazilian transport network.

  18. Structural Properties of the Brazilian Air Transportation Network.

    Science.gov (United States)

    Couto, Guilherme S; da Silva, Ana Paula Couto; Ruiz, Linnyer B; Benevenuto, Fabrício

    2015-09-01

    The air transportation network in a country has a great impact on the local, national and global economy. In this paper, we analyze the air transportation network in Brazil with complex network features to better understand its characteristics. In our analysis, we built networks composed either by national or by international flights. We also consider the network when both types of flights are put together. Interesting conclusions emerge from our analysis. For instance, Viracopos Airport (Campinas City) is the most central and connected airport on the national flights network. Any operational problem in this airport separates the Brazilian national network into six distinct subnetworks. Moreover, the Brazilian air transportation network exhibits small world characteristics and national connections network follows a power law distribution. Therefore, our analysis sheds light on the current Brazilian air transportation infrastructure, bringing a novel understanding that may help face the recent fast growth in the usage of the Brazilian transport network.

  19. Energy Harvesting for Structural Health Monitoring Sensor Networks

    Energy Technology Data Exchange (ETDEWEB)

    Park, G.; Farrar, C. R.; Todd, M. D.; Hodgkiss, T.; Rosing, T.

    2007-02-26

    This report has been developed based on information exchanges at a 2.5-day workshop on energy harvesting for embedded structural health monitoring (SHM) sensing systems that was held June 28-30, 2005, at Los Alamos National Laboratory. The workshop was hosted by the LANL/UCSD Engineering Institute (EI). This Institute is an education- and research-focused collaboration between Los Alamos National Laboratory (LANL) and the University of California, San Diego (UCSD), Jacobs School of Engineering. A Statistical Pattern Recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized, followed by a discussion of SHM sensor network paradigms, power requirements for these networks and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. This discussion also addresses current energy harvesting applications and system integration issues. The report concludes by defining some future research directions and possible technology demonstrations that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes.

  20. Structure Learning and Statistical Estimation in Distribution Networks - Part I

    Energy Technology Data Exchange (ETDEWEB)

    Deka, Deepjyoti [Univ. of Texas, Austin, TX (United States); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-02-13

    Traditionally power distribution networks are either not observable or only partially observable. This complicates development and implementation of new smart grid technologies, such as those related to demand response, outage detection and management, and improved load-monitoring. In this two part paper, inspired by proliferation of the metering technology, we discuss estimation problems in structurally loopy but operationally radial distribution grids from measurements, e.g. voltage data, which are either already available or can be made available with a relatively minor investment. In Part I, the objective is to learn the operational layout of the grid. Part II of this paper presents algorithms that estimate load statistics or line parameters in addition to learning the grid structure. Further, Part II discusses the problem of structure estimation for systems with incomplete measurement sets. Our newly suggested algorithms apply to a wide range of realistic scenarios. The algorithms are also computationally efficient – polynomial in time– which is proven theoretically and illustrated computationally on a number of test cases. The technique developed can be applied to detect line failures in real time as well as to understand the scope of possible adversarial attacks on the grid.

  1. Building alternate protein structures using the elastic network model.

    Science.gov (United States)

    Yang, Qingyi; Sharp, Kim A

    2009-02-15

    We describe a method for efficiently generating ensembles of alternate, all-atom protein structures that (a) differ significantly from the starting structure, (b) have good stereochemistry (bonded geometry), and (c) have good steric properties (absence of atomic overlap). The method uses reconstruction from a series of backbone framework structures that are obtained from a modified elastic network model (ENM) by perturbation along low-frequency normal modes. To ensure good quality backbone frameworks, the single force parameter ENM is modified by introducing two more force parameters to characterize the interaction between the consecutive carbon alphas and those within the same secondary structure domain. The relative stiffness of the three parameters is parameterized to reproduce B-factors, while maintaining good bonded geometry. After parameterization, violations of experimental Calpha-Calpha distances and Calpha-Calpha-Calpha pseudo angles along the backbone are reduced to less than 1%. Simultaneously, the average B-factor correlation coefficient improves to R = 0.77. Two applications illustrate the potential of the approach. (1) 102,051 protein backbones spanning a conformational space of 15 A root mean square deviation were generated from 148 nonredundant proteins in the PDB database, and all-atom models with minimal bonded and nonbonded violations were produced from this ensemble of backbone structures using the SCWRL side chain building program. (2) Improved backbone templates for homology modeling. Fifteen query sequences were each modeled on two targets. For each of the 30 target frameworks, dozens of improved templates could be produced In all cases, improved full atom homology models resulted, of which 50% could be identified blind using the D-Fire statistical potential. (c) 2008 Wiley-Liss, Inc.

  2. Fundamentals of complex networks models, structures and dynamics

    CERN Document Server

    Chen, Guanrong; Li, Xiang

    2014-01-01

    Complex networks such as the Internet, WWW, transportationnetworks, power grids, biological neural networks, and scientificcooperation networks of all kinds provide challenges for futuretechnological development. In particular, advanced societies havebecome dependent on large infrastructural networks to an extentbeyond our capability to plan (modeling) and to operate (control).The recent spate of collapses in power grids and ongoing virusattacks on the Internet illustrate the need for knowledge aboutmodeling, analysis of behaviors, optimized planning and performancecontrol in such networks. F

  3. Interacting with Networks : How Does Structure Relate to Controllability in Single-Leader, Consensus Networks?

    NARCIS (Netherlands)

    Egerstedt, Magnus; Martini, Simone; Cao, Ming; Camlibel, Kanat; Bicchi, Antonio

    As networked dynamical systems appear around us at an increasing rate, questions concerning how to manage and control such systems are becoming more important. Examples include multiagent robotics, distributed sensor networks, interconnected manufacturing chains, and data networks. In response to

  4. Effect of synapse dilution on the memory retrieval in structured attractor neural networks

    Science.gov (United States)

    Brunel, N.

    1993-08-01

    We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

  5. Trichomes: different regulatory networks lead to convergent structures.

    Science.gov (United States)

    Serna, Laura; Martin, Cathie

    2006-06-01

    Sometimes, proteins, biological structures or even organisms have similar functions and appearances but have evolved through widely divergent pathways. There is experimental evidence to suggest that different developmental pathways have converged to produce similar outgrowths of the aerial plant epidermis, referred to as trichomes. The emerging picture suggests that trichomes in Arabidopsis thaliana and, perhaps, in cotton develop through a transcriptional regulatory network that differs from those regulating trichome formation in Antirrhinum and Solanaceous species. Several lines of evidence suggest that the duplication of a gene controlling anthocyanin production and subsequent divergence might be the major force driving trichome formation in Arabidopsis, whereas the multicellular trichomes of Antirrhinum and Solanaceous species appear to have a different regulatory origin.

  6. Interbank lending, network structure and default risk contagion

    Science.gov (United States)

    Zhang, Minghui; He, Jianmin; Li, Shouwei

    2018-03-01

    This paper studies the default risk contagion in banking systems based on a dynamic network model with two different kinds of lenders' selecting mechanisms, namely, endogenous selecting (ES) and random selecting (RS). From sensitivity analysis, we find that higher risk premium, lower initial proportion of net assets, higher liquid assets threshold, larger size of liquidity shocks, higher proportion of the initial investments and higher Central Bank interest rates all lead to severer default risk contagion. Moreover, the autocorrelation of deposits and lenders' selecting probability have non-monotonic effects on the default risk contagion, and the effects differ under two mechanisms. Generally, the default risk contagion is much severer under RS mechanism than that of ES, because the multi-money-center structure generated by ES mechanism enables borrowers to borrow from more liquid banks with lower interest rates.

  7. Structure and external factors of chinese city airline network

    Science.gov (United States)

    Liu, Hong-Kun; Zhang, Xiao-Li; Zhou, Tao

    2010-08-01

    Abstract We investigate the structural properties of Chinese city airline network (CCAN), where nodes and edges denote cities and direct flights. The degree distribution follows a double power law and a clear hierarchical layout is observed. The population exhibits a weakly positive correlation with the number of flights, yet it does not show obvious correlation with the transportation flow. The distance is an important parameter in CCAN, that is, the number of flights decays fast with the increasing of the distance. In comparison, the tertiary industry has the most important influence on the Chinese air passenger transportation. Statistically speaking, when the tertiary industry value increases by 1%, the next period's volume will increase by 0.73%.

  8. 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.

  9. Common neighbour structure and similarity intensity in complex networks

    Science.gov (United States)

    Hou, Lei; Liu, Kecheng

    2017-10-01

    Complex systems as networks always exhibit strong regularities, implying underlying mechanisms governing their evolution. In addition to the degree preference, the similarity has been argued to be another driver for networks. Assuming a network is randomly organised without similarity preference, the present paper studies the expected number of common neighbours between vertices. A symmetrical similarity index is accordingly developed by removing such expected number from the observed common neighbours. The developed index can not only describe the similarities between vertices, but also the dissimilarities. We further apply the proposed index to measure of the influence of similarity on the wring patterns of networks. Fifteen empirical networks as well as artificial networks are examined in terms of similarity intensity and degree heterogeneity. Results on real networks indicate that, social networks are strongly governed by the similarity as well as the degree preference, while the biological networks and infrastructure networks show no apparent similarity governance. Particularly, classical network models, such as the Barabási-Albert model, the Erdös-Rényi model and the Ring Lattice, cannot well describe the social networks in terms of the degree heterogeneity and similarity intensity. The findings may shed some light on the modelling and link prediction of different classes of networks.

  10. Uncovering the community structure associated with the diffusion dynamics on networks

    International Nuclear Information System (INIS)

    Cheng, Xue-Qi; Shen, Hua-Wei

    2010-01-01

    As two main focuses of the study of complex networks, the community structure and the dynamics on networks have both attracted much attention in various scientific fields. However, it is still an open question how the community structure is associated with the dynamics on complex networks. In this paper, through investigating the diffusion process taking place on networks, we demonstrate that the intrinsic community structure of networks can be revealed by the stable local equilibrium states of the diffusion process. Furthermore, we show that such community structure can be directly identified through the optimization of the conductance of the network, which measures how easily the diffusion among different communities occurs. Tests on benchmark networks indicate that the conductance optimization method significantly outperforms the modularity optimization methods in identifying the community structure of networks. Applications to real world networks also demonstrate the effectiveness of the conductance optimization method. This work provides insights into the multiple topological scales of complex networks, and the community structure obtained can naturally reflect the diffusion capability of the underlying network

  11. Multilabel user classification using the community structure of online networks.

    Science.gov (United States)

    Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

    2017-01-01

    We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  12. Multilabel user classification using the community structure of online networks.

    Directory of Open Access Journals (Sweden)

    Georgios Rizos

    Full Text Available We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE, an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  13. Convolution neural-network-based detection of lung structures

    Science.gov (United States)

    Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

    1994-05-01

    Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

  14. Structure Identification of Uncertain Complex Networks Based on Anticipatory Projective Synchronization.

    Directory of Open Access Journals (Sweden)

    Liu Heng

    Full Text Available This paper investigates a method to identify uncertain system parameters and unknown topological structure in general complex networks with or without time delay. A complex network, which has uncertain topology and unknown parameters, is designed as a drive network, and a known response complex network with an input controller is designed to identify the drive network. Under the proposed input controller, the drive network and the response network can achieve anticipatory projective synchronization when the system is steady. Lyapunov theorem and Barbǎlat's lemma guarantee the stability of synchronization manifold between two networks. When the synchronization is achieved, the system parameters and topology in response network can be changed to equal with the parameters and topology in drive network. A numerical example is given to show the effectiveness of the proposed method.

  15. Test and evaluation of the Malicious Activity Simulation Tool (MAST) in a Local Area Network (LAN) running the Common PC Operating System Environment (COMPOSE)

    OpenAIRE

    Littlejohn, Aaron M.; Makhlouf, Ehab

    2013-01-01

    APPLIED CYBER OPERATIONS CAPSTONE PROJECT REPORT Approved for public release; distribution is unlimited In the Department of the Navys layered defense approach to protecting a computer network, it is the networks administrators who provide the last layer of defense before the end user. Training network administrators is a rather expensive and time consuming process. However, this training needs to be provided on a regular basis in order to refresh their readiness and to train them to re...

  16. Inferring Structure and Forecasting Dynamics on Evolving Networks

    Science.gov (United States)

    2016-01-05

    Graphs ........................................................................................................................ 23 7. Sacred Values...5) Team Formation; (6) Games of Graphs; (7) Sacred Values and Legitimacy in Network Interactions; (8) Network processes in Geo-Social Context. 1...Authority, Cooperation and Competition in Religious Networks Key Papers: McBride 2015a [72] and McBride 2015b [73] McBride (2015a) examines

  17. Ethnicity and Population Structure in Personal Naming Networks

    Science.gov (United States)

    Mateos, Pablo; Longley, Paul A.; O'Sullivan, David

    2011-01-01

    Personal naming practices exist in all human groups and are far from random. Rather, they continue to reflect social norms and ethno-cultural customs that have developed over generations. As a consequence, contemporary name frequency distributions retain distinct geographic, social and ethno-cultural patterning that can be exploited to understand population structure in human biology, public health and social science. Previous attempts to detect and delineate such structure in large populations have entailed extensive empirical analysis of naming conventions in different parts of the world without seeking any general or automated methods of population classification by ethno-cultural origin. Here we show how ‘naming networks’, constructed from forename-surname pairs of a large sample of the contemporary human population in 17 countries, provide a valuable representation of cultural, ethnic and linguistic population structure around the world. This innovative approach enriches and adds value to automated population classification through conventional national data sources such as telephone directories and electoral registers. The method identifies clear social and ethno-cultural clusters in such naming networks that extend far beyond the geographic areas in which particular names originated, and that are preserved even after international migration. Moreover, one of the most striking findings of this approach is that these clusters simply ‘emerge’ from the aggregation of millions of individual decisions on parental naming practices for their children, without any prior knowledge introduced by the researcher. Our probabilistic approach to community assignment, both at city level as well as at a global scale, helps to reveal the degree of isolation, integration or overlap between human populations in our rapidly globalising world. As such, this work has important implications for research in population genetics, public health, and social science adding new

  18. ANALYSIS OF DEFORMED STATE STRUCTURES OF THE KYIV METRO RUNNING TUNNELS ON A TRANSITION ZONE FROM SPONDYLOV’S CLAY TO BUCHATSKIY SANDS

    Directory of Open Access Journals (Sweden)

    V. D. Petrenko

    2014-07-01

    Full Text Available Purpose. In the section of changes geotechnical conditions of spondylov’s clay to buchatskiy sands may have significant structural deformation of running tunnels. It is necessary to identify the cause of deformities develop ways to minimize and based modeling and calculations to prove the effectiveness of measures to reduce deformation.Methodology. To solve the analysis problem of the stress-strain state (SSS of the system «structure array» it was conducted the numerical simulation using the finite element method (FEM. On the basis of the obtained results the graphs were constructed and the dependencies were determined. Findings. The presence of weak water-saturated soils in tray of the tunnel on an area of transition from spondylov’s clay to buchatskiy sand causes significant increasing in strain construction of tunnels and general vibration liquefaction in soil basis. Also change the physical and mechanical characteristics of soils within the frames of tunnels influences on the level of strain state of most frames. Improved strain state settings of tunnels in areas of change soil characteristics of the array (especially at the bottom of casing can be achieved by chemical consolidation of weak soils. Composition of solutions for fixing the weak soils should be determined based on the study of grain size, porosity, and other parameters of physical and mechanical and physical and chemical characteristics of soils.Originality.The basic cause significant strain on transition zone from spondylov’s clay to buchatskiy sands is found, that is explained by saturated phenomenon vibration liquefaction basis under the tunnel.Practical value.The approaches to reduce the strain in the construction of running tunnels in the transition zone from spondylov’s clay to buchatskiy sands are developed, as well as in the area ofthe station «Glybochytska»the Kyiv Metro.

  19. Structure-function relationships during segregated and integrated network states of human brain functional connectivity.

    Science.gov (United States)

    Fukushima, Makoto; Betzel, Richard F; He, Ye; van den Heuvel, Martijn P; Zuo, Xi-Nian; Sporns, Olaf

    2018-04-01

    Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.

  20. Entrepreneur online social networks: structure, diversity and impact on start-up survival

    NARCIS (Netherlands)

    Song, Y.; Vinig, T.

    2012-01-01

    In this paper, we discuss the results of a pilot study in which we use a novel approach to collect entrepreneur online social network data from LinkedIn, Facebook and Twitter. We studied the size and structure of entrepreneur social networks by analysing the online network industry and location

  1. Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

    NARCIS (Netherlands)

    Sinke, Michel R T; Dijkhuizen, Rick M; Caimo, Alberto; Stam, Cornelis J; Otte, Wim

    2016-01-01

    Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as

  2. On Line Segment Length and Mapping 4-regular Grid Structures in Network Infrastructures

    DEFF Research Database (Denmark)

    Riaz, Muhammad Tahir; Nielsen, Rasmus Hjorth; Pedersen, Jens Myrup

    2006-01-01

    The paper focuses on mapping the road network into 4-regular grid structures. A mapping algorithm is proposed. To model the road network GIS data have been used. The Geographic Information System (GIS) data for the road network are composed with different size of line segment lengths...

  3. Optimal network structure in an open market environment

    International Nuclear Information System (INIS)

    2002-01-01

    The focus of this report is on network planning in the new environment of a liberalized electricity market. The development of the network is viewed from different stakeholders objectives. The stakeholders in the transmission network are groups or individuals who have a stake in, or an expectation of the development and performance of the network. An open network exists when all market players meet equal admission rights and obligations. This required that the grid be administered through a transparent set of rules such as a grid code. (author)

  4. A network of networks model to study phase synchronization using structural connection matrix of human brain

    Science.gov (United States)

    Ferrari, F. A. S.; Viana, R. L.; Reis, A. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.

    2018-04-01

    The cerebral cortex plays a key role in complex cortical functions. It can be divided into areas according to their function (motor, sensory and association areas). In this paper, the cerebral cortex is described as a network of networks (cortex network), we consider that each cortical area is composed of a network with small-world property (cortical network). The neurons are assumed to have bursting properties with the dynamics described by the Rulkov model. We study the phase synchronization of the cortex network and the cortical networks. In our simulations, we verify that synchronization in cortex network is not homogeneous. Besides, we focus on the suppression of neural phase synchronization. Synchronization can be related to undesired and pathological abnormal rhythms in the brain. For this reason, we consider the delayed feedback control to suppress the synchronization. We show that delayed feedback control is efficient to suppress synchronous behavior in our network model when an appropriate signal intensity and time delay are defined.

  5. Spectral properties of the temporal evolution of brain network structure.

    Science.gov (United States)

    Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

    2015-12-01

    The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

  6. Unraveling the disease consequences and mechanisms of modular structure in animal social networks

    Science.gov (United States)

    Sah, Pratha; Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta

    2017-01-01

    Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

  7. Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

    OpenAIRE

    Sinke, Michel R T; Dijkhuizen, Rick M; Caimo, Alberto; Stam, Cornelis J; Otte, Wim

    2016-01-01

    Descriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as in studies on neurodevelopment or brain diseases. Furthermore, descriptive neural network analyses lack an appropriate generic null model and a unifying framework. These issues may be solved with an...

  8. Impact of environmental inputs on reverse-engineering approach to network structures.

    Science.gov (United States)

    Wu, Jianhua; Sinfield, James L; Buchanan-Wollaston, Vicky; Feng, Jianfeng

    2009-12-04

    Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.

  9. Dr. Sheehan on Running.

    Science.gov (United States)

    Sheehan, George A.

    This book is both a personal and technical account of the experience of running by a heart specialist who began a running program at the age of 45. In its seventeen chapters, there is information presented on the spiritual, psychological, and physiological results of running; treatment of athletic injuries resulting from running; effects of diet…

  10. Optimal pinnate leaf-like network/matrix structure for enhanced conductive cooling

    International Nuclear Information System (INIS)

    Hu, Liguo; Zhou, Han; Zhu, Hanxing; Fan, Tongxiang; Zhang, Di

    2015-01-01

    Highlights: • We present a pinnate leaf-like network/matrix structure for conductive cooling. • We study the effect of matrix thickness on network conductive cooling performance. • Matrix thickness determines optimal distance between collection channels in network. • We determine the optimal network architecture from a global perspective. • Optimal network greatly reduces the maximum temperature difference in the network. - Abstract: Heat generated in electronic devices has to be effectively removed because excessive temperature strongly impairs their performance and reliability. Embedding a high thermal conductivity network into an electronic device is an effective method to conduct the generated heat to the outside. In this study, inspired by the pinnate leaf, we present a pinnate leaf-like network embedded in the matrix (i.e., electronic device) to cool the matrix by conduction and develop a method to construct the optimal network. In this method, we first investigate the effect of the matrix thickness on the conductive cooling performance of the network, and then optimize the network architecture from a global perspective so that to minimize the maximum temperature difference between the heat sink and the matrix. The results indicate that the matrix thickness determines the optimal distance of the neighboring collection channels in the network, which minimizes the maximum temperature difference between the matrix and the network, and that the optimal network greatly reduces the maximum temperature difference in the network. The results can serve as a design guide for efficient conductive cooling of electronic devices

  11. The scaling structure of the global road network.

    Science.gov (United States)

    Strano, Emanuele; Giometto, Andrea; Shai, Saray; Bertuzzo, Enrico; Mucha, Peter J; Rinaldo, Andrea

    2017-10-01

    Because of increasing global urbanization and its immediate consequences, including changes in patterns of food demand, circulation and land use, the next century will witness a major increase in the extent of paved roads built worldwide. To model the effects of this increase, it is crucial to understand whether possible self-organized patterns are inherent in the global road network structure. Here, we use the largest updated database comprising all major roads on the Earth, together with global urban and cropland inventories, to suggest that road length distributions within croplands are indistinguishable from urban ones, once rescaled to account for the difference in mean road length. Such similarity extends to road length distributions within urban or agricultural domains of a given area. We find two distinct regimes for the scaling of the mean road length with the associated area, holding in general at small and at large values of the latter. In suitably large urban and cropland domains, we find that mean and total road lengths increase linearly with their domain area, differently from earlier suggestions. Scaling regimes suggest that simple and universal mechanisms regulate urban and cropland road expansion at the global scale. As such, our findings bear implications for global road infrastructure growth based on land-use change and for planning policies sustaining urban expansions.

  12. Nafion–clay hybrids with a network structure

    KAUST Repository

    Burgaz, Engin; Lian, Huiqin; Alonso, Rafael Herrera; Estevez, Luis; Kelarakis, Antonios; Giannelis, Emmanuel P.

    2009-01-01

    Nafion-clay hybrid membranes with a unique microstructure were synthesized using a fundamentally new approach. The new approach is based on depletion aggregation of suspended particles - a well-known phenomenon in colloids. For certain concentrations of clay and polymer, addition of Nafion solution to clay suspensions in water leads to a gel. Using Cryo-TEM we show that the clay particles in the hybrid gels form a network structure with an average cell size in the order of 500 nm. The hybrid gels are subsequently cast to produce hybrid Nafion-clay membranes. Compared to pure Nafion the swelling of the hybrid membranes in water and methanol is dramatically reduced while their selectivity (ratio of conductivity over permeability) increases. The small decrease of ionic conductivity for the hybrid membranes is more than compensated by the large decrease in methanol permeability. Lastly the hybrid membranes are much stiffer and can withstand higher temperatures compared to pure Nafion. Both of these characteristics are highly desirable for use in fuel cell applications, since a) they will allow the use of a thinner membrane circumventing problems associated with the membrane resistance and b) enable high temperature applications. © 2009 Elsevier Ltd. All rights reserved.

  13. Nafion–clay hybrids with a network structure

    KAUST Repository

    Burgaz, Engin

    2009-05-01

    Nafion-clay hybrid membranes with a unique microstructure were synthesized using a fundamentally new approach. The new approach is based on depletion aggregation of suspended particles - a well-known phenomenon in colloids. For certain concentrations of clay and polymer, addition of Nafion solution to clay suspensions in water leads to a gel. Using Cryo-TEM we show that the clay particles in the hybrid gels form a network structure with an average cell size in the order of 500 nm. The hybrid gels are subsequently cast to produce hybrid Nafion-clay membranes. Compared to pure Nafion the swelling of the hybrid membranes in water and methanol is dramatically reduced while their selectivity (ratio of conductivity over permeability) increases. The small decrease of ionic conductivity for the hybrid membranes is more than compensated by the large decrease in methanol permeability. Lastly the hybrid membranes are much stiffer and can withstand higher temperatures compared to pure Nafion. Both of these characteristics are highly desirable for use in fuel cell applications, since a) they will allow the use of a thinner membrane circumventing problems associated with the membrane resistance and b) enable high temperature applications. © 2009 Elsevier Ltd. All rights reserved.

  14. How does network structure affect partnerships for promoting physical activity? Evidence from Brazil and Colombia.

    Science.gov (United States)

    Parra, Diana C; Dauti, Marsela; Harris, Jenine K; Reyes, Lissette; Malta, Deborah C; Brownson, Ross C; Quintero, Mario A; Pratt, Michael

    2011-11-01

    The objective of this study was to describe the network structure and factors associated with collaboration in two networks that promote physical activity (PA) in Brazil and Colombia. Organizations that focus on studying and promoting PA in Brazil (35) and Colombia (53) were identified using a modified one-step reputational snowball sampling process. Participants completed an on-line survey between December 2008 and March 2009 for the Brazil network, and between April and June 2009 for the Colombia network. Network stochastic modeling was used to investigate the likelihood of reported inter-organizational collaboration. While structural features of networks were significant predictors of collaboration within each network, the coefficients and other network characteristics differed. Brazil's PA network was decentralized with a larger number of shared partnerships. Colombia's PA network was centralized and collaboration was influenced by perceived importance of peer organizations. On average, organizations in the PA network of Colombia reported facing more barriers (1.5 vs. 2.5 barriers) for collaboration. Future studies should focus on how these different network structures affect the implementation and uptake of evidence-based PA interventions. Copyright © 2011 Elsevier Ltd. All rights reserved.

  15. Co-ordination of the international network of nuclear structure and decay data evaluators

    International Nuclear Information System (INIS)

    Lorenz, A.

    1986-10-01

    The seventh meeting of the International Network of Nuclear Structure and Decay Data (NSDD) Evaluators concentrated on the organizational aspects of the coordination of the NSDD network and on the presentation and discussion of papers related to the physics of evaluation of NSDD. The report contains short status reports from NSDD Network members, the status of the mass-chain and nuclear structure data, a discussion of evaluation rules and procedures and a short presentation of the next activities

  16. Scale-dependent genetic structure of the Idaho giant salamander (Dicamptodon aterrimus) in stream networks

    Science.gov (United States)

    Lindy B. Mullen; H. Arthur Woods; Michael K. Schwartz; Adam J. Sepulveda; Winsor H. Lowe

    2010-01-01

    The network architecture of streams and rivers constrains evolutionary, demographic and ecological processes of freshwater organisms. This consistent architecture also makes stream networks useful for testing general models of population genetic structure and the scaling of gene flow. We examined genetic structure and gene flow in the facultatively paedomorphic Idaho...

  17. Coordination of the international network of nuclear structure and decay data evaluators

    International Nuclear Information System (INIS)

    Lorenz, A.

    1984-09-01

    This meeting of the International NSDD (Nuclear Structure and Decay Data) Network dealt with problems related to both the coordination of the NSDD network of centres and groups and to physics questions related to the evaluation of NSDD. The status of the mass-chain and nuclear structure data is reviewed and the planned activities are presented

  18. Association between structural brain network efficiency and intelligence increases during adolescence

    NARCIS (Netherlands)

    Koenis, Marinka M G; Brouwer, Rachel M; Swagerman, Suzanne C; van Soelen, Inge L C; Boomsma, Dorret I; Hulshoff Pol, Hilleke E

    2018-01-01

    Adolescence represents an important period during which considerable changes in the brain take place, including increases in integrity of white matter bundles, and increasing efficiency of the structural brain network. A more efficient structural brain network has been associated with higher

  19. Structure and dynamics of the global financial network

    International Nuclear Information System (INIS)

    Silva, Thiago Christiano; Rubens Stancato de Souza, Sergio; Tabak, Benjamin Miranda

    2016-01-01

    In this paper, we study the evolution of the network topology for the global financial market. We evaluate the level of diversification and participation of developed and emerging economies in cross-border exposures and find that the gross exposure network is dense, the vulnerability matrix is sparse, and the network’s fragility changes over time. Prior to the financial crisis in 2008, the network was relatively fragile, whereas it became more resilient afterwards, showing a reduction in financial institutions’ risk appetite. Our results suggest that financial regulators should track down the network evolution in their systemic risk assessment.

  20. Graph analysis of structural brain networks in Alzheimer's disease: beyond small world properties.

    Science.gov (United States)

    John, Majnu; Ikuta, Toshikazu; Ferbinteanu, Janina

    2017-03-01

    Changes in brain connectivity in patients with early Alzheimer's disease (AD) have been investigated using graph analysis. However, these studies were based on small data sets, explored a limited range of network parameters, and did not focus on more restricted sub-networks, where neurodegenerative processes may introduce more prominent alterations. In this study, we constructed structural brain networks out of 87 regions using data from 135 healthy elders and 100 early AD patients selected from the Open Access Series of Imaging Studies (OASIS) database. We evaluated the graph properties of these networks by investigating metrics of network efficiency, small world properties, segregation, product measures of complexity, and entropy. Because degenerative processes take place at different rates in different brain areas, analysis restricted to sub-networks may reveal changes otherwise undetected. Therefore, we first analyzed the graph properties of a network encompassing all brain areas considered together, and then repeated the analysis after dividing the brain areas into two sub-networks constructed by applying a clustering algorithm. At the level of large scale network, the analysis did not reveal differences between AD patients and controls. In contrast, the same analysis performed on the two sub-networks revealed that small worldness diminished with AD only in the sub-network containing the areas of medial temporal lobe known to be heaviest and earliest affected. The second sub-network, which did not present significant AD-induced modifications of 'classical' small world parameters, nonetheless showed a trend towards an increase in small world propensity, a novel metric that unbiasedly quantifies small world structure. Beyond small world properties, complexity and entropy measures indicated that the intricacy of connection patterns and structural diversity decreased in both sub-networks. These results show that neurodegenerative processes impact volumetric

  1. Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

    Science.gov (United States)

    Nitta, Tohru

    2017-10-01

    We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

  2. NET European Network on Neutron Techniques Standardization for Structural Integrity

    International Nuclear Information System (INIS)

    Youtsos, A.

    2004-01-01

    Improved performance and safety of European energy production systems is essential for providing safe, clean and inexpensive electricity to the citizens of the enlarged EU. The state of the art in assessing internal stresses, micro-structure and defects in welded nuclear components -as well as their evolution due to complex thermo-mechanical loads and irradiation exposure -needs to be improved before relevant structural integrity assessment code requirements can safely become less conservative. This is valid for both experimental characterization techniques and predictive numerical algorithms. In the course of the last two decades neutron methods have proven to be excellent means for providing valuable information required in structural integrity assessment of advanced engineering applications. However, the European industry is hampered from broadly using neutron research due to lack of harmonised and standardized testing methods. 35 European major industrial and research/academic organizations have joined forces, under JRC coordination, to launch the NET European Network on Neutron Techniques Standardization for Structural Integrity in May 2002. The NET collaborative research initiative aims at further development and harmonisation of neutron scattering methods, in support of structural integrity assessment. This is pursued through a number of testing round robin campaigns on neutron diffraction and small angle neutron scattering - SANS and supported by data provided by other more conventional destructive and non-destructive methods, such as X-ray diffraction and deep and surface hole drilling. NET also strives to develop more reliable and harmonized simulation procedures for the prediction of residual stress and damage in steel welded power plant components. This is pursued through a number of computational round robin campaigns based on advanced FEM techniques, and on reliable data obtained by such novel and harmonized experimental methods. The final goal of

  3. Hierarchical modular structure enhances the robustness of self-organized criticality in neural networks

    International Nuclear Information System (INIS)

    Wang Shengjun; Zhou Changsong

    2012-01-01

    One of the most prominent architecture properties of neural networks in the brain is the hierarchical modular structure. How does the structure property constrain or improve brain function? It is thought that operating near criticality can be beneficial for brain function. Here, we find that networks with modular structure can extend the parameter region of coupling strength over which critical states are reached compared to non-modular networks. Moreover, we find that one aspect of network function—dynamical range—is highest for the same parameter region. Thus, hierarchical modularity enhances robustness of criticality as well as function. However, too much modularity constrains function by preventing the neural networks from reaching critical states, because the modular structure limits the spreading of avalanches. Our results suggest that the brain may take advantage of the hierarchical modular structure to attain criticality and enhanced function. (paper)

  4. A Microsoft-Excel-based tool for running and critically appraising network meta-analyses—an overview and application of NetMetaXL

    OpenAIRE

    Brown, Stephen; Hutton, Brian; Clifford, Tammy; Coyle, Doug; Grima, Daniel; Wells, George; Cameron, Chris

    2014-01-01

    This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. BACKGROUND: The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-ana...

  5. The structure and dynamics of knowledge networks: a proximity approach

    NARCIS (Netherlands)

    ter Wal, L.J.

    2009-01-01

    Local knowledge networks are often held responsible for the competitiveness and innovativeness of geographical clusters. However, the literature on spatial clustering tends to assume that firms in clusters have equal access to the knowledge that circulates in those networks and that this knowledge

  6. Formalization of the partnering structure for networked businesses

    NARCIS (Netherlands)

    Santana Tapia, R.G.; Zarvic, N.

    2007-01-01

    Rapidly changing market demands and increasing competitive pressure cause many businesses implement changes to the way they conduct business. One of these changes is the decision to collaborate with other businesses, forming what we call a 'networked business'. Networked businesses are formed by

  7. An Analysis of the Structure and Evolution of Networks

    Science.gov (United States)

    Hua, Guangying

    2011-01-01

    As network research receives more and more attention from both academic researchers and practitioners, network analysis has become a fast growing field attracting many researchers from diverse fields such as physics, computer science, and sociology. This dissertation provides a review of theory and research on different real data sets from the…

  8. Diagnostic Classifiers: Revealing how Neural Networks Process Hierarchical Structure

    NARCIS (Netherlands)

    Veldhoen, S.; Hupkes, D.; Zuidema, W.

    2016-01-01

    We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that

  9. Inventory theory, mode choice and network structure in freight transport

    NARCIS (Netherlands)

    Combes, F.; Tavasszy, L.A.

    2016-01-01

    In passenger transport, hub-and-spoke networks allow the transportation of small passenger flows with competitive frequencies, in a way that direct line networks cannot. Equivalently, in freight transport, it can be expected that small shipper-receiver flows of high added value commodities transit

  10. Structural properties and complexity of a new network class: Collatz step graphs.

    Directory of Open Access Journals (Sweden)

    Frank Emmert-Streib

    Full Text Available In this paper, we introduce a biologically inspired model to generate complex networks. In contrast to many other construction procedures for growing networks introduced so far, our method generates networks from one-dimensional symbol sequences that are related to the so called Collatz problem from number theory. The major purpose of the present paper is, first, to derive a symbol sequence from the Collatz problem, we call the step sequence, and investigate its structural properties. Second, we introduce a construction procedure for growing networks that is based on these step sequences. Third, we investigate the structural properties of this new network class including their finite scaling and asymptotic behavior of their complexity, average shortest path lengths and clustering coefficients. Interestingly, in contrast to many other network models including the small-world network from Watts & Strogatz, we find that CS graphs become 'smaller' with an increasing size.

  11. Finding the Sweet Spot: Network Structures and Processes for Increased Knowledge Mobilization

    Directory of Open Access Journals (Sweden)

    Patricia Briscoe

    2016-06-01

    Full Text Available The use of networks in public education is one of a number of knowledge mobilization (KMb strategies utilized to promote evidence-based research into practice. However, challenges exist in the ability to effectively mobilizing knowledge through external partnership networks. The purpose of this paper is to further explore how networks work. Data was collected from virtual discussions for an interim report for a province-wide government initiative. A secondary analysis of the data was performed. The findings present network structures and processes that partners were engaged in when building a network within education. The implications of this study show that building a network for successful outcomes is complex and metaphorically similar to finding the “sweet spot.” It is challenging but networks that used strategies to align structures and processes proved to achieve more success in mobilizing research to practice.

  12. The evolving network structure of US airline system during 1990-2010

    Science.gov (United States)

    Lin, Jingyi; Ban, Yifang

    2014-09-01

    This paper analyzes the growth and evolution of topological features of the US airline network over a 20-year period. It captures the change in the network system from different dimensions of complex networks such as centrality distribution and various structural properties of the network over time. We first illustrate the results of a set of measures, including degree, strength, betweenness centrality, and clustering structure. The geographic features of airport systems, spatial distance and network efficiency are also discussed in this section. In order to further capture the dynamics of the system, this paper also explores the correlation between different measures, and investigates various interactions inside the network. Overall this study offers a novel approach to understanding the growth and evolution of real physical networks.

  13. Structural integration and performance of inter-sectoral public health-related policy networks: An analysis across policy phases

    NARCIS (Netherlands)

    Peters, D. T. J. M.; Raab, J.; Grêaux, K. M.; Stronks, K.; Harting, J.

    2017-01-01

    Background: Inter-sectoral policy networks may be effective in addressing environmental determinants of health with interventions. However, contradictory results are reported on relations between structural network characteristics (i.e., composition and integration) and network performance, such as

  14. Structural integration and performance of inter-sectoral public health-related policy networks : An analysis across policy phases

    NARCIS (Netherlands)

    Peters, Dorothee; Raab, J.; Grêaux, Kimberley M.; Stronks, Karien; Harting, Janneke

    2017-01-01

    Background: Inter-sectoral policy networks may be effective in addressing environmental determinants of health with interventions. However, contradictory results are reported on relations between structure and network characteristics (i.e., composition and integration) and network performance, such

  15. Correlations in the degeneracy of structurally controllable topologies for networks

    Science.gov (United States)

    Campbell, Colin; Aucott, Steven; Ruths, Justin; Ruths, Derek; Shea, Katriona; Albert, Réka

    2017-04-01

    Many dynamic systems display complex emergent phenomena. By directly controlling a subset of system components (nodes) via external intervention it is possible to indirectly control every other component in the system. When the system is linear or can be approximated sufficiently well by a linear model, methods exist to identify the number and connectivity of a minimum set of external inputs (constituting a so-called minimal control topology, or MCT). In general, many MCTs exist for a given network; here we characterize a broad ensemble of empirical networks in terms of the fraction of nodes and edges that are always, sometimes, or never a part of an MCT. We study the relationships between the measures, and apply the methodology to the T-LGL leukemia signaling network as a case study. We show that the properties introduced in this report can be used to predict key components of biological networks, with potentially broad applications to network medicine.

  16. Google Wave Up and Running

    CERN Document Server

    Ferrate, Andres

    2010-01-01

    Catch Google Wave, the revolutionary Internet protocol and web service that lets you communicate and collaborate in realtime. With this book, you'll understand how Google Wave integrates email, instant messaging (IM), wiki, and social networking functionality into a powerful and extensible platform. You'll also learn how to use its features, customize its functions, and build sophisticated extensions with Google Wave's open APIs and network protocol. Written for everyone -- from non-techies to ninja coders -- Google Wave: Up and Running provides a complete tour of this complex platform. You'

  17. Temporal variation in bat-fruit interactions: Foraging strategies influence network structure over time

    Science.gov (United States)

    Zapata-Mesa, Natalya; Montoya-Bustamante, Sebastián; Murillo-García, Oscar E.

    2017-11-01

    Mutualistic interactions, such as seed dispersal, are important for the maintenance of structure and stability of tropical communities. However, there is a lack of information about spatial and temporal variation in plant-animal interaction networks. Thus, our goal was to assess the effect of bat's foraging strategies on temporal variation in the structure and robustness of bat-fruit networks in both a dry and a rain tropical forest. We evaluated monthly variation in bat-fruit networks by using seven structure metrics: network size, average path length, nestedness, modularity, complementary specialization, normalized degree and betweenness centrality. Seed dispersal networks showed variations in size, species composition and modularity; did not present nested structures and their complementary specialization was high compared to other studies. Both networks presented short path lengths, and a constantly high robustness, despite their monthly variations. Sedentary bat species were recorded during all the study periods and occupied more central positions than nomadic species. We conclude that foraging strategies are important structuring factors that affect the dynamic of networks by determining the functional roles of frugivorous bats over time; thus sedentary bats are more important than nomadic species for the maintenance of the network structure, and their conservation is a must.

  18. The structure of political elite networks in the Republic of Poland in 1993—2013

    Directory of Open Access Journals (Sweden)

    Fidrya Efim

    2013-12-01

    Full Text Available To identify the structure of network ties within Polish political elites; to study the features of network ties formation and the impact that both primary and labour socialisation periods and diaspora characteristics have on this process; to describe the structural features of the resultant network structures over different periods of time and analyse the structural dynamics of political elites for the purpose of forecasting major trends in the structural transformation of Polish political elites. In the course of the study, biographical data on the presidents, ministers, advisors, and party leaders of the Republic of Poland was collected and processed. The work follows the network analysis paradigm and identifies the dynamics of the key network parameters: distance, density, transitivity, and compactness. The author analyses the dynamics of representation in the structure of political territorial diaspora elites, business community members, and ‘moral politicians’. The article identifies two periods of formation of political party networks in Poland: the first period (1993—2007 saw a transition from rather weakly integrated systems to high density and cohesion networks as early as the second electoral cycle, after which a gradual decrease in the key indices of network integration was registered. A new peak of network cohesion and integration was reached in 2007—2011; however, the death of some key members of political elites in a plane crash resulted in a decrease in the network integration indices to the level of 2001—2005. On the whole, the network structure of Polish political elite is characterised by unstable dynamics relating to the crisis events of the past. However, it is established that the elites have a pronounced diaspora core and an unstable periphery; the share of businesspeople directly participating in political processes is decreasing, whereas ‘moral politicians’ usually take an active part in the formation of

  19. Association of structural global brain network properties with intelligence in normal aging.

    Directory of Open Access Journals (Sweden)

    Florian U Fischer

    Full Text Available Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60-85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience.

  20. Association of Structural Global Brain Network Properties with Intelligence in Normal Aging

    Science.gov (United States)

    Fischer, Florian U.; Wolf, Dominik; Scheurich, Armin; Fellgiebel, Andreas

    2014-01-01

    Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60–85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R) and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient) were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience. PMID:24465994

  1. Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network

    Science.gov (United States)

    Merrison-Hort, Robert; Soffe, Stephen R; Borisyuk, Roman

    2018-01-01

    Although, in most animals, brain connectivity varies between individuals, behaviour is often similar across a species. What fundamental structural properties are shared across individual networks that define this behaviour? We describe a probabilistic model of connectivity in the hatchling Xenopus tadpole spinal cord which, when combined with a spiking model, reliably produces rhythmic activity corresponding to swimming. The probabilistic model allows calculation of structural characteristics that reflect common network properties, independent of individual network realisations. We use the structural characteristics to study examples of neuronal dynamics, in the complete network and various sub-networks, and this allows us to explain the basis for key experimental findings, and make predictions for experiments. We also study how structural and functional features differ between detailed anatomical connectomes and those generated by our new, simpler, model (meta-model). PMID:29589828

  2. Multilayer networks reveal the spatial structure of seed-dispersal interactions across the Great Rift landscapes.

    Science.gov (United States)

    Timóteo, Sérgio; Correia, Marta; Rodríguez-Echeverría, Susana; Freitas, Helena; Heleno, Ruben

    2018-01-10

    Species interaction networks are traditionally explored as discrete entities with well-defined spatial borders, an oversimplification likely impairing their applicability. Using a multilayer network approach, explicitly accounting for inter-habitat connectivity, we investigate the spatial structure of seed-dispersal networks across the Gorongosa National Park, Mozambique. We show that the overall seed-dispersal network is composed by spatially explicit communities of dispersers spanning across habitats, functionally linking the landscape mosaic. Inter-habitat connectivity determines spatial structure, which cannot be accurately described with standard monolayer approaches either splitting or merging habitats. Multilayer modularity cannot be predicted by null models randomizing either interactions within each habitat or those linking habitats; however, as habitat connectivity increases, random processes become more important for overall structure. The importance of dispersers for the overall network structure is captured by multilayer versatility but not by standard metrics. Highly versatile species disperse many plant species across multiple habitats, being critical to landscape functional cohesion.

  3. Does where you stand depend on who you behave? Networking behavior as an alternative explanation for gender differences in network structure

    NARCIS (Netherlands)

    Gremmen, I.; Akkerman, A.; Benschop, Y.

    2013-01-01

    The purpose of this study is to gain insight into the relations between gender, networking behavior and network structure, in order to investigate the relevance of gender for organizational networks. Semi-structured interviews with 39 white, Dutch, women and men account managers were analyzed both

  4. Structural Brain Network Disturbances in the Psychosis Spectrum

    NARCIS (Netherlands)

    van Dellen, Edwin; Bohlken, Marc M; Draaisma, Laurijn; Tewarie, Prejaas K; van Lutterveld, Remko; Mandl, René; Stam, Cornelis J; Sommer, Iris E

    2016-01-01

    BACKGROUND: Individuals with subclinical psychotic symptoms provide a unique window on the pathophysiology of psychotic experiences as these individuals are free of confounders such as hospitalization, negative and cognitive symptoms and medication use. Brain network disturbances of white matter

  5. An object recognition using structured light and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Byeong Gab; Kim, Dong Gi; Kang, E Sok [Chungnam National Univ., Taejon (Korea, Republic of); Yoon, Ji Sup [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    This paper presents a 3D image processing which uses neural networks to combine a 2D vision camera and a laser slit beam. A laser slit beam from laser source is slitted by a set of cylindrical lenses and the line image of the networks allow to get the 3D image parameters such as the size, the position and the orientation from the line image without knowing the camera intrinsic parameters. (author). 7 refs., 3 tabs., 5 figs.

  6. An object recognition using structured light and neural networks

    International Nuclear Information System (INIS)

    Kim, Byeong Gab; Kim, Dong Gi; Kang, E Sok; Yoon, Ji Sup

    1997-01-01

    This paper presents a 3D image processing which uses neural networks to combine a 2D vision camera and a laser slit beam. A laser slit beam from laser source is slitted by a set of cylindrical lenses and the line image of the networks allow to get the 3D image parameters such as the size, the position and the orientation from the line image without knowing the camera intrinsic parameters. (author). 7 refs., 3 tabs., 5 figs

  7. An object recognition using structured light and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Byeong Gab; Kim, Dong Gi; Kang, E Sok [Chungnam National Univ., Taejon (Korea, Republic of); Yoon, Ji Sup [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1997-12-31

    This paper presents a 3D image processing which uses neural networks to combine a 2D vision camera and a laser slit beam. A laser slit beam from laser source is slitted by a set of cylindrical lenses and the line image of the networks allow to get the 3D image parameters such as the size, the position and the orientation from the line image without knowing the camera intrinsic parameters. (author). 7 refs., 3 tabs., 5 figs.

  8. On Backbone Structure for a Future Multipurpose Network

    DEFF Research Database (Denmark)

    Gutierrez Lopez, Jose Manuel; Cuevas, Ruben; Riaz, M. Tahir

    2008-01-01

    Telecommunications are evolving towards the unification of services and infrastructures. This unification must be achieved at the highest hierarchical level for a complete synergy of services. Therefore, one of the requirements is a multipurpose backbone network capable of supporting all the curr......Telecommunications are evolving towards the unification of services and infrastructures. This unification must be achieved at the highest hierarchical level for a complete synergy of services. Therefore, one of the requirements is a multipurpose backbone network capable of supporting all...

  9. A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

    Energy Technology Data Exchange (ETDEWEB)

    Jang, Jin-wook [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: Jinwook@kaeri.re.kr; Seong, Seung-Hwan [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: shseong@kaeri.re.kr; Lee, Un-Chul [Department of Nuclear Engineering, Seoul National University, Shinlim-Dong, Gwanak-Gu, Seoul 151-742 (Korea, Republic of)

    2007-09-15

    A fast prediction model for load-following operations in a soluble boron-free reactor has been proposed, which can predict the core status when three or more control rod groups are moved at a time. This prediction model consists of two multilayer feedforward neural network models to retrieve the axial offset and the reactivity, and compensation models to compensate for the reactivity and axial offset arising from the xenon transient. The neural network training data were generated by taking various overlaps among the control rod groups into consideration for training the neural network models, and the accuracy of the constructed neural network models was verified. Validation results of predicting load following operations for a soluble boron-free reactor show that this model has a good capability to predict the positions of the control rods for sustaining the criticality of a core during load-following operations to ensure that the tolerable axial offset band is not exceeded and it can provide enough corresponding time for the operators to take the necessary actions to prevent a deviation from the tolerable operating band.

  10. A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

    International Nuclear Information System (INIS)

    Jang, Jin-wook; Seong, Seung-Hwan; Lee, Un-Chul

    2007-01-01

    A fast prediction model for load-following operations in a soluble boron-free reactor has been proposed, which can predict the core status when three or more control rod groups are moved at a time. This prediction model consists of two multilayer feedforward neural network models to retrieve the axial offset and the reactivity, and compensation models to compensate for the reactivity and axial offset arising from the xenon transient. The neural network training data were generated by taking various overlaps among the control rod groups into consideration for training the neural network models, and the accuracy of the constructed neural network models was verified. Validation results of predicting load following operations for a soluble boron-free reactor show that this model has a good capability to predict the positions of the control rods for sustaining the criticality of a core during load-following operations to ensure that the tolerable axial offset band is not exceeded and it can provide enough corresponding time for the operators to take the necessary actions to prevent a deviation from the tolerable operating band

  11. Structural covariance networks across the life span, from 6 to 94 years of age

    Directory of Open Access Journals (Sweden)

    Elizabeth DuPre

    2017-10-01

    Full Text Available Structural covariance examines covariation of gray matter morphology between brain regions and across individuals. Despite significant interest in the influence of age on structural covariance patterns, no study to date has provided a complete life span perspective—bridging childhood with early, middle, and late adulthood—on the development of structural covariance networks. Here, we investigate the life span trajectories of structural covariance in six canonical neurocognitive networks: default, dorsal attention, frontoparietal control, somatomotor, ventral attention, and visual. By combining data from five open-access data sources, we examine the structural covariance trajectories of these networks from 6 to 94 years of age in a sample of 1,580 participants. Using partial least squares, we show that structural covariance patterns across the life span exhibit two significant, age-dependent trends. The first trend is a stable pattern whose integrity declines over the life span. The second trend is an inverted-U that differentiates young adulthood from other age groups. Hub regions, including posterior cingulate cortex and anterior insula, appear particularly influential in the expression of this second age-dependent trend. Overall, our results suggest that structural covariance provides a reliable definition of neurocognitive networks across the life span and reveal both shared and network-specific trajectories. The importance of life span perspectives is increasingly apparent in understanding normative interactions of large-scale neurocognitive networks. Although recent work has made significant strides in understanding the functional and structural connectivity of these networks, there has been comparatively little attention to life span trajectories of structural covariance networks. In this study we examine patterns of structural covariance across the life span for six neurocognitive networks. Our results suggest that networks exhibit

  12. Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

    Directory of Open Access Journals (Sweden)

    Ram Prahlad T

    2008-08-01

    Full Text Available Abstract Background In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior. Results We introduce PathwayOracle, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates PathwayOracle from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, PathwayOracle includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model. Conclusion PathwayOracle provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models

  13. Coevolution of network structure and cooperation in the public goods game

    International Nuclear Information System (INIS)

    Wang Lei; Xia Chengyi; Wang Juan

    2013-01-01

    Recently, the emergence of cooperation has become a central topic in the evolutionary game field, and coevolution of game dynamics and network topology structure can give us a fresh viewpoint of how the network evolves and cooperation arises. In this paper, we show in detail a picture of the co-evolutionary behaviors between the microscopic structure of the network and cooperation promotion in the public goods game (PGG). Based on a mechanism named after evolutionary preferential attachment (EPA), in which the growth of the network depends on the outcome of PGG dynamics, we explore the structural properties of networks and cooperative behaviors taking place on the networks created by EPA rules. Extensive simulation results indicate that the structure of the resulting networks displays a transition from homogeneous to heterogeneous properties as the selection strength ϵ increases, and the cooperative behaviors have a non-trivial state in which cooperators and defectors can simultaneously occupy the hub nodes in the network. Current results are of interest for us to further understand the cooperation persistence and structure evolution in many natural, social and economical systems. (paper)

  14. Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

    Science.gov (United States)

    Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

    2017-03-01

    Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

  15. The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

    Science.gov (United States)

    Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

    2018-01-01

    Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

  16. Exploiting The Brain’s Network Structure in Identifying ADHD

    Directory of Open Access Journals (Sweden)

    Soumyabrata eDey

    2012-11-01

    Full Text Available Attention Deficit Hyperactive Disorder (ADHD is a common behavioral problem affecting children. In this work, we investigate the automatic classification of ADHD subjects using the resting state Functional Magnetic Resonance Imaging (fMRI sequences of the brain. We show that brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects. We compute the pairwise correlation of brain voxels' activity over the time frame of the experimental protocol which helps to model the function of a brain as a network. Different network features are computed for each of the voxels constructing the network. The concatenation of the network features of all the voxels in a brain serves as the feature vector. Feature vectors from a set of subjects are then used to train a PCA-LDA (principal component analysis-linear discriminant analysis based classifier. We hypothesized that ADHD related differences lie in some specific regions of brain and using features only from those regions are sufficient to discriminate ADHD and control subjects. We propose a method to create a brain mask which includes the useful regions only and demonstrate that using the feature from the masked regions improves classification accuracy on the test data set. We train our classifier with 776 subjects, and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge. We demonstrate the utility of graph-motif features, specifically the maps that represent the frequency of participation of voxels in network cycles of length 3. The best classification performance (69.59% is achieved using 3-cycle map features with masking. Our proposed approach holds promise in being able to diagnose and understand the disorder.

  17. Error and attack tolerance of synchronization in Hindmarsh–Rose neural networks with community structure

    International Nuclear Information System (INIS)

    Li, Chun-Hsien; Yang, Suh-Yuh

    2014-01-01

    Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

  18. Brain networks, structural realism, and local approaches to the scientific realism debate.

    Science.gov (United States)

    Yan, Karen; Hricko, Jonathon

    2017-08-01

    We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates that structure (nonstructural knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Structure-Function Network Mapping and Its Assessment via Persistent Homology

    Science.gov (United States)

    2017-01-01

    Understanding the relationship between brain structure and function is a fundamental problem in network neuroscience. This work deals with the general method of structure-function mapping at the whole-brain level. We formulate the problem as a topological mapping of structure-function connectivity via matrix function, and find a stable solution by exploiting a regularization procedure to cope with large matrices. We introduce a novel measure of network similarity based on persistent homology for assessing the quality of the network mapping, which enables a detailed comparison of network topological changes across all possible thresholds, rather than just at a single, arbitrary threshold that may not be optimal. We demonstrate that our approach can uncover the direct and indirect structural paths for predicting functional connectivity, and our network similarity measure outperforms other currently available methods. We systematically validate our approach with (1) a comparison of regularized vs. non-regularized procedures, (2) a null model of the degree-preserving random rewired structural matrix, (3) different network types (binary vs. weighted matrices), and (4) different brain parcellation schemes (low vs. high resolutions). Finally, we evaluate the scalability of our method with relatively large matrices (2514x2514) of structural and functional connectivity obtained from 12 healthy human subjects measured non-invasively while at rest. Our results reveal a nonlinear structure-function relationship, suggesting that the resting-state functional connectivity depends on direct structural connections, as well as relatively parsimonious indirect connections via polysynaptic pathways. PMID:28046127

  20. Darlington up and running

    International Nuclear Information System (INIS)

    Show, Don

    1993-01-01

    We've built some of the largest and most successful generating stations in the world. Nonetheless, we cannot take our knowledge and understanding of the technology for granted. Although, I do believe that we are getting better, building safer, more efficient plants, and introducing significant improvements to our existing stations. Ontario Hydro is a large and technically rich organization. Even so, we realize that partnerships with others in the industry are absolutely vital. I am thinking particularly of Atomic Energy of Canada Limited. We enjoy a very close relationship with Aecl, and their support was never more important than during the N/A Investigations. In recent years, we've strengthened our relationship with Aecl considerably. For example, we recently signed an agreement with Aecl, making available all of the Darlington 900 MW e design. Much of the cooperation between Ontario Hydro and Aecl occurs through the CANDU Engineering Authority and the CANDU Owners Group (CO G). These organizations are helping both of US to greatly improve cooperation and efficiency, and they are helping ensure we get the biggest return on our CANDU investments. CO G also provides an important information network which links CANDU operators in Canada, here in Korea, Argentina, India, Pakistan and Romania. In many respects, it is helping to develop the strong partnerships to support CANDU technology worldwide. We all benefit in the long run form sharing information and resources

  1. Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks.

    Science.gov (United States)

    Morrison, Erin S; Badyaev, Alexander V

    2018-05-01

    Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

  2. A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain

    Directory of Open Access Journals (Sweden)

    Xiaojin Li

    2013-01-01

    Full Text Available Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY and scale-free gene duplication model (SF-GD, that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

  3. Network diversity structure, closeness and innovation of South African micro-entrepreneurs

    OpenAIRE

    Eliada Wosu Griffin-EL

    2014-01-01

    This study qualitatively explores the embeddedness of the innovation process of South African microbusinesses by investigating how small local entrepreneurs in the Greater Johannesburg area utilise their social networks to source entrepreneurial value. A comparative grounded theory analysis enabled the original conceptualisation of Network Diversity Structure and formulates the central proposition that the network dimensions of diversity and closeness enable the innovation process among manuf...

  4. Structure and Discourse: Mapping the Networked Public Sphere in the Arab Region

    OpenAIRE

    Faris, Robert M; Kelly, John; Noman, Helmi; Othman, Dalia

    2016-01-01

    In this study, we employ social network mapping techniques to analyze the shape and structure of the networked public sphere in the Arab region. The analysis is based on four distinct views of digitally connected communities: a regional map of the blogosphere and maps of Twitter networks in three countries: Egypt, Tunisia, and Bahrain. This media ecology mapping across these different platforms and regions offers a detailed view of social, cultural, religious, and political expression through...

  5. Low-cost airlines in Europe: Network structures after the enlargement of the European Union

    Directory of Open Access Journals (Sweden)

    Dudas Gabor

    2010-01-01

    Full Text Available The liberalization of the European air opened the strictly regulated European market, and contributed to the appearance and quick spread of the Low-Cost Carriers (LCCs. At the beginning of the 21st century the low cost traffic absolutely concentrated on the Western European market but after the enlargement of the European Union (EU LCCs started their operations in Eastern Europe enlarging and enriching the former evolved network structures. The aim of this paper is to trace the evolution of the route network as a result of EU expansion. During the study we came to the conclusion that in the time period after the EU enlargement the European LCC traffic showed dynamic development, route networks widened and the number of accessible destinations doubled. Comparing the LCCs network structures we defined three main characteristics, which represents the North-South flows, the West-East routes and the mixed network structure.

  6. Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning

    Directory of Open Access Journals (Sweden)

    Guangyi Liu

    2014-01-01

    Full Text Available Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues. Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper. We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are given as a result; after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network. Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.

  7. Epidemic spreading on complex networks with overlapping and non-overlapping community structure

    Science.gov (United States)

    Shang, Jiaxing; Liu, Lianchen; Li, Xin; Xie, Feng; Wu, Cheng

    2015-02-01

    Many real-world networks exhibit community structure where vertices belong to one or more communities. Recent studies show that community structure plays an import role in epidemic spreading. In this paper, we investigate how the extent of overlap among communities affects epidemics. In order to experiment on the characteristic of overlapping communities, we propose a rewiring algorithm that can change the community structure from overlapping to non-overlapping while maintaining the degree distribution of the network. We simulate the Susceptible-Infected-Susceptible (SIS) epidemic process on synthetic scale-free networks and real-world networks by applying our rewiring algorithm. Experiments show that epidemics spread faster on networks with higher level of overlapping communities. Furthermore, overlapping communities' effect interacts with the average degree's effect. Our work further illustrates the important role of overlapping communities in the process of epidemic spreading.

  8. Running and osteoarthritis.

    Science.gov (United States)

    Willick, Stuart E; Hansen, Pamela A

    2010-07-01

    The overall health benefits of cardiovascular exercise, such as running, are well established. However, it is also well established that in certain circumstances running can lead to overload injuries of muscle, tendon, and bone. In contrast, it has not been established that running leads to degeneration of articular cartilage, which is the hallmark of osteoarthritis. This article reviews the available literature on the association between running and osteoarthritis, with a focus on clinical epidemiologic studies. The preponderance of clinical reports refutes an association between running and osteoarthritis. Copyright 2010 Elsevier Inc. All rights reserved.

  9. Network Structure and Biased Variance Estimation in Respondent Driven Sampling.

    Science.gov (United States)

    Verdery, Ashton M; Mouw, Ted; Bauldry, Shawn; Mucha, Peter J

    2015-01-01

    This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.

  10. Epidemic outbreaks in growing scale-free networks with local structure

    Science.gov (United States)

    Ni, Shunjiang; Weng, Wenguo; Shen, Shifei; Fan, Weicheng

    2008-09-01

    The class of generative models has already attracted considerable interest from researchers in recent years and much expanded the original ideas described in BA model. Most of these models assume that only one node per time step joins the network. In this paper, we grow the network by adding n interconnected nodes as a local structure into the network at each time step with each new node emanating m new edges linking the node to the preexisting network by preferential attachment. This successfully generates key features observed in social networks. These include power-law degree distribution pk∼k, where μ=(n-1)/m is a tuning parameter defined as the modularity strength of the network, nontrivial clustering, assortative mixing, and modular structure. Moreover, all these features are dependent in a similar way on the parameter μ. We then study the susceptible-infected epidemics on this network with identical infectivity, and find that the initial epidemic behavior is governed by both of the infection scheme and the network structure, especially the modularity strength. The modularity of the network makes the spreading velocity much lower than that of the BA model. On the other hand, increasing the modularity strength will accelerate the propagation velocity.

  11. A review of structural and functional brain networks: small world and atlas.

    Science.gov (United States)

    Yao, Zhijun; Hu, Bin; Xie, Yuanwei; Moore, Philip; Zheng, Jiaxiang

    2015-03-01

    Brain networks can be divided into two categories: structural and functional networks. Many studies of neuroscience have reported that the complex brain networks are characterized by small-world or scale-free properties. The identification of nodes is the key factor in studying the properties of networks on the macro-, micro- or mesoscale in both structural and functional networks. In the study of brain networks, nodes are always determined by atlases. Therefore, the selection of atlases is critical, and appropriate atlases are helpful to combine the analyses of structural and functional networks. Currently, some problems still exist in the establishment or usage of atlases, which are often caused by the segmentation or the parcellation of the brain. We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent. In the process of building atlases, the influences of single subjects and groups should be balanced. In this article, we focused on the effects of atlases on the analysis of brain networks and the improved divisions based on the tractography or connectivity in the parcellation of atlases.

  12. Structural reliability calculation method based on the dual neural network and direct integration method.

    Science.gov (United States)

    Li, Haibin; He, Yun; Nie, Xiaobo

    2018-01-01

    Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

  13. Exploring hierarchical and overlapping modular structure in the yeast protein interaction network

    Directory of Open Access Journals (Sweden)

    Zhao Yi

    2010-12-01

    Full Text Available Abstract Background Developing effective strategies to reveal modular structures in protein interaction networks is crucial for better understanding of molecular mechanisms of underlying biological processes. In this paper, we propose a new density-based algorithm (ADHOC for clustering vertices of a protein interaction network using a novel subgraph density measurement. Results By statistically evaluating several independent criteria, we found that ADHOC could significantly improve the outcome as compared with five previously reported density-dependent methods. We further applied ADHOC to investigate the hierarchical and overlapping modular structure in the yeast PPI network. Our method could effectively detect both protein modules and the overlaps between them, and thus greatly promote the precise prediction of protein functions. Moreover, by further assaying the intermodule layer of the yeast PPI network, we classified hubs into two types, module hubs and inter-module hubs. Each type presents distinct characteristics both in network topology and biological functions, which could conduce to the better understanding of relationship between network architecture and biological implications. Conclusions Our proposed algorithm based on the novel subgraph density measurement makes it possible to more precisely detect hierarchical and overlapping modular structures in protein interaction networks. In addition, our method also shows a strong robustness against the noise in network, which is quite critical for analyzing such a high noise network.

  14. Consensus formation on coevolving networks: groups' formation and structure

    International Nuclear Information System (INIS)

    Kozma, Balazs; Barrat, Alain

    2008-01-01

    We study the effect of adaptivity on a social model of opinion dynamics and consensus formation. We analyse how the adaptivity of the network of contacts between agents to the underlying social dynamics affects the size and topological properties of groups and the convergence time to the stable final state. We find that, while on static networks these properties are determined by percolation phenomena, on adaptive networks the rewiring process leads to different behaviors: adaptive rewiring fosters group formation by enhancing communication between agents of similar opinion, though it also makes possible the division of clusters. We show how the convergence time is determined by the characteristic time of link rearrangement. We finally investigate how the adaptivity yields nontrivial correlations between the internal topology and the size of the groups of agreeing agents

  15. Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

    Science.gov (United States)

    Wiedermann, Marc; Donges, Jonathan F.; Kurths, Jürgen; Donner, Reik V.

    2016-04-01

    Networks with nodes embedded in a metric space have gained increasing interest in recent years. The effects of spatial embedding on the networks' structural characteristics, however, are rarely taken into account when studying their macroscopic properties. Here, we propose a hierarchy of null models to generate random surrogates from a given spatially embedded network that can preserve certain global and local statistics associated with the nodes' embedding in a metric space. Comparing the original network's and the resulting surrogates' global characteristics allows one to quantify to what extent these characteristics are already predetermined by the spatial embedding of the nodes and links. We apply our framework to various real-world spatial networks and show that the proposed models capture macroscopic properties of the networks under study much better than standard random network models that do not account for the nodes' spatial embedding. Depending on the actual performance of the proposed null models, the networks are categorized into different classes. Since many real-world complex networks are in fact spatial networks, the proposed approach is relevant for disentangling the underlying complex system structure from spatial embedding of nodes in many fields, ranging from social systems over infrastructure and neurophysiology to climatology.

  16. Inference of Transmission Network Structure from HIV Phylogenetic Trees.

    Science.gov (United States)

    Giardina, Federica; Romero-Severson, Ethan Obie; Albert, Jan; Britton, Tom; Leitner, Thomas

    2017-01-01

    Phylogenetic inference is an attractive means to reconstruct transmission histories and epidemics. However, there is not a perfect correspondence between transmission history and virus phylogeny. Both node height and topological differences may occur, depending on the interaction between within-host evolutionary dynamics and between-host transmission patterns. To investigate these interactions, we added a within-host evolutionary model in epidemiological simulations and examined if the resulting phylogeny could recover different types of contact networks. To further improve realism, we also introduced patient-specific differences in infectivity across disease stages, and on the epidemic level we considered incomplete sampling and the age of the epidemic. Second, we implemented an inference method based on approximate Bayesian computation (ABC) to discriminate among three well-studied network models and jointly estimate both network parameters and key epidemiological quantities such as the infection rate. Our ABC framework used both topological and distance-based tree statistics for comparison between simulated and observed trees. Overall, our simulations showed that a virus time-scaled phylogeny (genealogy) may be substantially different from the between-host transmission tree. This has important implications for the interpretation of what a phylogeny reveals about the underlying epidemic contact network. In particular, we found that while the within-host evolutionary process obscures the transmission tree, the diversification process and infectivity dynamics also add discriminatory power to differentiate between different types of contact networks. We also found that the possibility to differentiate contact networks depends on how far an epidemic has progressed, where distance-based tree statistics have more power early in an epidemic. Finally, we applied our ABC inference on two different outbreaks from the Swedish HIV-1 epidemic.

  17. Detecting overlapping community structure of networks based on vertex–vertex correlations

    International Nuclear Information System (INIS)

    Zarei, Mina; Izadi, Dena; Samani, Keivan Aghababaei

    2009-01-01

    Using the NMF (non-negative matrix factorization) method, the structure of overlapping communities in complex networks is investigated. For the feature matrix of the NMF method we introduce a vertex–vertex correlation matrix. The method is applied to some computer-generated and real-world networks. Simulations show that this feature matrix gives more reasonable results

  18. Structural Observability and Sensor Node Selection for Complex Networks Governed by Nonlinear Balance Equations

    NARCIS (Netherlands)

    Kawano, Yu; Cao, Ming

    2017-01-01

    We define and then study the structural observability for a class of complex networks whose dynamics are governed by the nonlinear balance equations. Although related notions of observability of such complex networks have been studied before and in particular, necessary conditions have been reported

  19. Network Analysis of Resting State EEG in the Developing Young Brain: Structure Comes With Maturation

    NARCIS (Netherlands)

    Boersma, M.; Smit, D.J.A.; de Bie, H.M.A.; van Baal, G.C.M.; Boomsma, D.I.; de Geus, E.J.C.; Delemarre-van de Waal, H.A.; Stam, C.J.

    2011-01-01

    During childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small-world networks, such as the brain, combine optimal properties of both ordered and random networks, i.e., high clustering and short path lengths.

  20. Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory

    Directory of Open Access Journals (Sweden)

    Joshua Rodewald

    2016-10-01

    Full Text Available Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure’s dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN’s self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system’s structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment.

  1. Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Rytter, A.

    In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorith as a non-destructive damage assessment technique to locate and quantify a damage in Civil Engineering structures is investigated. Since artificial neural networks are proving...

  2. Elastic properties and short-range structural order in mixed network former glasses.

    Science.gov (United States)

    Wang, Weimin; Christensen, Randilynn; Curtis, Brittany; Hynek, David; Keizer, Sydney; Wang, James; Feller, Steve; Martin, Steve W; Kieffer, John

    2017-06-21

    Elastic properties of alkali containing glasses are of great interest not only because they provide information about overall structural integrity but also they are related to other properties such as thermal conductivity and ion mobility. In this study, we investigate two mixed-network former glass systems, sodium borosilicate 0.2Na 2 O + 0.8[xBO 1.5 + (1 - x)SiO 2 ] and sodium borogermanate 0.2Na 2 O + 0.8[xBO 1.5 + (1 - x)GeO 2 ] glasses. By mixing network formers, the network topology can be changed while keeping the network modifier concentration constant, which allows for the effect of network structure on elastic properties to be analyzed over a wide parametric range. In addition to non-linear, non-additive mixed-glass former effects, maxima are observed in longitudinal, shear and Young's moduli with increasing atomic number density. By combining results from NMR spectroscopy and Brillouin light scattering with a newly developed statistical thermodynamic reaction equilibrium model, it is possible to determine the relative proportions of all network structural units. This new analysis reveals that the structural characteristic predominantly responsible for effective mechanical load transmission in these glasses is a high density of network cations coordinated by four or more bridging oxygens, as it provides for establishing a network of covalent bonds among these cations with connectivity in three dimensions.

  3. Chimera-like states in structured heterogeneous networks

    Science.gov (United States)

    Li, Bo; Saad, David

    2017-04-01

    Chimera-like states are manifested through the coexistence of synchronous and asynchronous dynamics and have been observed in various systems. To analyze the role of network topology in giving rise to chimera-like states, we study a heterogeneous network model comprising two groups of nodes, of high and low degrees of connectivity. The architecture facilitates the analysis of the system, which separates into a densely connected coherent group of nodes, perturbed by their sparsely connected drifting neighbors. It describes a synchronous behavior of the densely connected group and scaling properties of the induced perturbations.

  4. How transfer flights shape the structure of the airline network.

    Science.gov (United States)

    Ryczkowski, Tomasz; Fronczak, Agata; Fronczak, Piotr

    2017-07-17

    In this paper, we analyse the gravity model in the global passenger air-transport network. We show that in the standard form, the model is inadequate for correctly describing the relationship between passenger flows and typical geo-economic variables that characterize connected countries. We propose a model for transfer flights that allows exploitation of these discrepancies in order to discover hidden subflows in the network. We illustrate its usefulness by retrieving the distance coefficient in the gravity model, which is one of the determinants of the globalization process. Finally, we discuss the correctness of the presented approach by comparing the distance coefficient to several well-known economic events.

  5. EBG structures on high permittivity substrate to reduce noise in power distribution networks

    NARCIS (Netherlands)

    Tereshchenko, O.V.; Buesink, Frederik Johannes Karel; Leferink, Frank Bernardus Johannes

    2012-01-01

    The noise reduction effect in a Power Distribution Network (PDN) by implementing Electromagnetic Band Gap structures (EBG) on standard and high permittivity substrates has been investigated. Boards with different EBG structures have been modelled and designed. Using the EBG structures the Power

  6. The structural connectivity pattern of the default mode network and its association with memory and anxiety

    Directory of Open Access Journals (Sweden)

    Yan eTao

    2015-11-01

    Full Text Available The default mode network (DMN is one of the most widely studied resting state functional networks. The structural basis for the DMN is of particular interest and has been studied by several researchers using diffusion tensor imaging (DTI. Most of these previous studies focused on a few regions or white matter tracts of the DMN so that the global structural connectivity pattern and network properties of the DMN remain unclear. Moreover, evidences indicate that the DMN is involved in both memory and emotion, but how the DMN regulates memory and anxiety from the perspective of the whole DMN structural network remains unknown. We used multimodal neuroimaging methods to investigate the structural connectivity pattern of the DMN and the association of its network properties with memory and anxiety in 205 young healthy subjects. Using a probabilistic fiber tractography technique based on DTI data and graph theory methods, we constructed the global structural connectivity pattern of the DMN and found that memory quotient (MQ score was significantly positively correlated with the global and local efficiency of the DMN whereas anxiety was found to be negatively correlated with the efficiency. The strong structural connectivity between multiple brain regions within DMN may reflect that the DMN has certain structural basis. Meanwhile, we found the network efficiency of the DMN were related to memory and anxiety measures, which indicated that the DMN may play a role in the memory and anxiety.

  7. Multi-frequency complex network from time series for uncovering oil-water flow structure.

    Science.gov (United States)

    Gao, Zhong-Ke; Yang, Yu-Xuan; Fang, Peng-Cheng; Jin, Ning-De; Xia, Cheng-Yi; Hu, Li-Dan

    2015-02-04

    Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.

  8. Effects of the network structure and coupling strength on the noise-induced response delay of a neuronal network

    International Nuclear Information System (INIS)

    Ozer, Mahmut; Uzuntarla, Muhammet

    2008-01-01

    The Hodgkin-Huxley (H-H) neuron model driven by stimuli just above threshold shows a noise-induced response delay with respect to time to the first spike for a certain range of noise strengths, an effect called 'noise delayed decay' (NDD). We study the response time of a network of coupled H-H neurons, and investigate how the NDD can be affected by the connection topology of the network and the coupling strength. We show that the NDD effect exists for weak and intermediate coupling strengths, whereas it disappears for strong coupling strength regardless of the connection topology. We also show that although the network structure has very little effect on the NDD for a weak coupling strength, the network structure plays a key role for an intermediate coupling strength by decreasing the NDD effect with the increasing number of random shortcuts, and thus provides an additional operating regime, that is absent in the regular network, in which the neurons may also exploit a spike time code

  9. Embedded Efficiency: A Social Networks Approach to Popular Support and Dark Network Structure

    Science.gov (United States)

    2016-03-01

    Sean F. Everton, and Dan Cunningham. “Dark Network Resilience in a Hostile Environment: Optimizing Centralization and Density.” Criminology , Criminal...33 Sean F. Everton and Dan Cunningham, “Dark Network Resilience in a Hostile Environment: Optimizing Centralization and Density,” Criminology ...Centralization and Density” Criminology , Criminal Justice Law, & Society 16, no. 1 (2015): 1- 20. Gill, Paul, Jeongyoon Lee, Karl R. Rethemeyer, John

  10. A pilot study on diagnostic sensor networks for structure health monitoring.

    Science.gov (United States)

    2013-08-01

    The proposal was submitted in an effort to obtain some preliminary results on using sensor networks for real-time structure health : monitoring. The proposed work has twofold: to develop and validate an elective algorithm for the diagnosis of coupled...

  11. Retrofit of heat exchanger networks considering pressure drop and existing structure: a new targeting procedure

    International Nuclear Information System (INIS)

    PanjehShahi, M.H.; Nouzari, M.M.

    2002-01-01

    A new retrofit targeting procedure, based on pinch technology has been developed. The procedure considers existing structure and hydrodynamic system of a given network as two main constraint during targeting. The procedure uses a linear programming model to consider existing structure. The model finds a network structure that has maximum compatibility with existing structure. Furthermore, the procedure using the pressure drop equations, can consider decreasing the film coefficients of streams due to increasing network area. Good compatibility between old and new networks and non replacement of hydrodynamic equipment cause to the best use of capital in retrofit projects. The procedure has been checked by doing two case studies, in which the results compared to the established methods, and realized significant improvement

  12. A new metric method-improved structural holes researches on software networks

    Science.gov (United States)

    Li, Bo; Zhao, Hai; Cai, Wei; Li, Dazhou; Li, Hui

    2013-03-01

    The scale software systems quickly increase with the rapid development of software technologies. Hence, how to understand, measure, manage and control software structure is a great challenge for software engineering. there are also many researches on software networks metrics: C&K, MOOD, McCabe and etc, the aim of this paper is to propose a new and better method to metric software networks. The metric method structural holes are firstly introduced to in this paper, which can not directly be applied as a result of modular characteristics on software network. Hence, structural holes is redefined in this paper and improved, calculation process and results are described in detail. The results shows that the new method can better reflect bridge role of vertexes on software network and there is a significant correlation between degree and improved structural holes. At last, a hydropower simulation system is taken as an example to show validity of the new metric method.

  13. An addressable conducting network for autonomic structural health management of composite structures

    International Nuclear Information System (INIS)

    Takahashi, Kosuke; Park, Jong Se; Thomas Hahn, H

    2010-01-01

    The electrical resistance change method (ERCM) has long been an area of interest as an in-service health monitoring system. To apply the ERCM to existing structures, a new concept, the addressable conducting network (ACN), is proposed for autonomic structural health management of graphite/polymer composites. The ACN consists of two sets of conducting lines normal to each other, where one set resides on the top surface of the laminate and the other on the bottom surface. Damage can be detected by monitoring the resistance change 'through the laminate thickness' between two lines. By using a thermally mendable polymer as the matrix, the same conducting lines can be used to supply the electric current needed for resistive heating, thereby allowing the detected damage to be healed. As shown experimentally, the electrical resistance change method using an ACN distinguishes between laminates made of properly and improperly cured prepreg as well as revealing damage generated during three-point bending tests. Finite element analysis was performed to examine the feasibility of the ACN and indicated that the damage can be easily located from the spatial distribution of resistance changes and that the damaged area can be locally heated by supplying a large amount of current to selected conducting lines

  14. An addressable conducting network for autonomic structural health management of composite structures

    Science.gov (United States)

    Takahashi, Kosuke; Park, Jong Se; Hahn, H. Thomas

    2010-10-01

    The electrical resistance change method (ERCM) has long been an area of interest as an in-service health monitoring system. To apply the ERCM to existing structures, a new concept, the addressable conducting network (ACN), is proposed for autonomic structural health management of graphite/polymer composites. The ACN consists of two sets of conducting lines normal to each other, where one set resides on the top surface of the laminate and the other on the bottom surface. Damage can be detected by monitoring the resistance change 'through the laminate thickness' between two lines. By using a thermally mendable polymer as the matrix, the same conducting lines can be used to supply the electric current needed for resistive heating, thereby allowing the detected damage to be healed. As shown experimentally, the electrical resistance change method using an ACN distinguishes between laminates made of properly and improperly cured prepreg as well as revealing damage generated during three-point bending tests. Finite element analysis was performed to examine the feasibility of the ACN and indicated that the damage can be easily located from the spatial distribution of resistance changes and that the damaged area can be locally heated by supplying a large amount of current to selected conducting lines.

  15. Structure-function relationship in complex brain networks expressed by hierarchical synchronization

    International Nuclear Information System (INIS)

    Zhou Changsong; Zemanova, Lucia; Zamora-Lopez, Gorka; Hilgetag, Claus C; Kurths, Juergen

    2007-01-01

    The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks

  16. Structure-function relationship in complex brain networks expressed by hierarchical synchronization

    Energy Technology Data Exchange (ETDEWEB)

    Zhou Changsong [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany); Zemanova, Lucia [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany); Zamora-Lopez, Gorka [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany); Hilgetag, Claus C [Jacobs University Bremen, Campus Ring 6, Rm 116, D-28759 Bremen (Germany); Kurths, Juergen [Institute of Physics, University of Potsdam, PF 601553, 14415 Potsdam (Germany)

    2007-06-15

    The brain is one of the most complex systems in nature, with a structured complex connectivity. Recently, large-scale corticocortical connectivities, both structural and functional, have received a great deal of research attention, especially using the approach of complex network analysis. Understanding the relationship between structural and functional connectivity is of crucial importance in neuroscience. Here we try to illuminate this relationship by studying synchronization dynamics in a realistic anatomical network of cat cortical connectivity. We model the nodes (cortical areas) by a neural mass model (population model) or by a subnetwork of interacting excitable neurons (multilevel model). We show that if the dynamics is characterized by well-defined oscillations (neural mass model and subnetworks with strong couplings), the synchronization patterns are mainly determined by the node intensity (total input strengths of a node) and the detailed network topology is rather irrelevant. On the other hand, the multilevel model with weak couplings displays more irregular, biologically plausible dynamics, and the synchronization patterns reveal a hierarchical cluster organization in the network structure. The relationship between structural and functional connectivity at different levels of synchronization is explored. Thus, the study of synchronization in a multilevel complex network model of cortex can provide insights into the relationship between network topology and functional organization of complex brain networks.

  17. Structure and relationships within global manufacturing virtual networks

    Directory of Open Access Journals (Sweden)

    José Ramón Vilana

    2009-04-01

    Full Text Available Global Manufacturing Virtual Networks (GMVNs are dynamically changing organizations formed by Original Equipment Manufacturers (OEMs, Contract Manufacturers (CMs, turn-key and component suppliers, R+D centres and distributors. These networks establish a new type of vertical and horizontal relations between independent companies or even competitors where it is not needed to maintain internal manufacturing resources but to manage and share the network resources. The fluid relations that exist within the GMVNs allow them a very permeable organization easy to connect and disconnect from one to each other as well as to choose a set of partners with specific attributes. The result is a highly flexible system characterized by low barriers to entry and exit, geographic flexibility, low costs, rapid technological diffusion, high diversification through contract manufacturers and exceptional economies of scale. Anyhow, there are three major drawbacks in the GMVNs that should be considered at the beginning of this type of collaborations: 1 the risk of contract manufacturers to develop their own end-products in competition with their customers; 2 the technology transfer between competitors OEMs through other members of the GMVN and 3 the lose of process expertise by the OEMs the more they outsource manufacturing processes to the network.

  18. A structured approach to heat exchanger network retrofit design

    NARCIS (Netherlands)

    Van Reisen, J.L.B.

    2008-01-01

    Process plants have high energy consumption. Much energy can be saved by a proper design of the heat exchanger network, which contains the main heat transferring equipment of the plant. Existing plants can often be made more energy-efficient by a retrofit: the (physical) modification of the

  19. The missing part of seed dispersal networks: structure and robustness of bat-fruit interactions.

    Directory of Open Access Journals (Sweden)

    Marco Aurelio Ribeiro Mello

    2011-02-01

    Full Text Available Mutualistic networks are crucial to the maintenance of ecosystem services. Unfortunately, what we know about seed dispersal networks is based only on bird-fruit interactions. Therefore, we aimed at filling part of this gap by investigating bat-fruit networks. It is known from population studies that: (i some bat species depend more on fruits than others, and (ii that some specialized frugivorous bats prefer particular plant genera. We tested whether those preferences affected the structure and robustness of the whole network and the functional roles of species. Nine bat-fruit datasets from the literature were analyzed and all networks showed lower complementary specialization (H(2' = 0.37±0.10, mean ± SD and similar nestedness (NODF = 0.56±0.12 than pollination networks. All networks were modular (M = 0.32±0.07, and had on average four cohesive subgroups (modules of tightly connected bats and plants. The composition of those modules followed the genus-genus associations observed at population level (Artibeus-Ficus, Carollia-Piper, and Sturnira-Solanum, although a few of those plant genera were dispersed also by other bats. Bat-fruit networks showed high robustness to simulated cumulative removals of both bats (R = 0.55±0.10 and plants (R = 0.68±0.09. Primary frugivores interacted with a larger proportion of the plants available and also occupied more central positions; furthermore, their extinction caused larger changes in network structure. We conclude that bat-fruit networks are highly cohesive and robust mutualistic systems, in which redundancy is high within modules, although modules are complementary to each other. Dietary specialization seems to be an important structuring factor that affects the topology, the guild structure and functional roles in bat-fruit networks.

  20. The circadian rhythm induced by the heterogeneous network structure of the suprachiasmatic nucleus

    Science.gov (United States)

    Gu, Changgui; Yang, Huijie

    2016-05-01

    In mammals, the master clock is located in the suprachiasmatic nucleus (SCN), which is composed of about 20 000 nonidentical neuronal oscillators expressing different intrinsic periods. These neurons are coupled through neurotransmitters to form a network consisting of two subgroups, i.e., a ventrolateral (VL) subgroup and a dorsomedial (DM) subgroup. The VL contains about 25% SCN neurons that receive photic input from the retina, and the DM comprises the remaining 75% SCN neurons which are coupled to the VL. The synapses from the VL to the DM are evidently denser than that from the DM to the VL, in which the VL dominates the DM. Therefore, the SCN is a heterogeneous network where the neurons of the VL are linked with a large number of SCN neurons. In the present study, we mimicked the SCN network based on Goodwin model considering four types of networks including an all-to-all network, a Newman-Watts (NW) small world network, an Erdös-Rényi (ER) random network, and a Barabási-Albert (BA) scale free network. We found that the circadian rhythm was induced in the BA, ER, and NW networks, while the circadian rhythm was absent in the all-to-all network with weak cellular coupling, where the amplitude of the circadian rhythm is largest in the BA network which is most heterogeneous in the network structure. Our finding provides an alternative explanation for the induction or enhancement of circadian rhythm by the heterogeneity of the network structure.