WorldWideScience

Sample records for network structure characteristics

  1. Comparing Community Structure to Characteristics in Online Collegiate Social Networks

    OpenAIRE

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

    2008-01-01

    We study the structure of social networks of students by examining the graphs of Facebook "friendships" at five American universities at a single point in time. We investigate each single-institution network's community structure and employ graphical and quantitative tools, including standardized pair-counting methods, to measure the correlations between the network communities and a set of self-identified user characteristics (residence, class year, major, and high school). We review the bas...

  2. A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2016-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

  3. Effect of landslides on the structural characteristics of land-cover based on complex networks

    Science.gov (United States)

    He, Jing; Tang, Chuan; Liu, Gang; Li, Weile

    2017-09-01

    Landslides have been widely studied by geologists. However, previous studies mainly focused on the formation of landslides and never considered the effect of landslides on the structural characteristics of land-cover. Here we define the modeling of the graph topology for the land-cover, using the satellite images of the earth’s surface before and after the earthquake. We find that the land-cover network satisfies the power-law distribution, whether the land-cover contains landslides or not. However, landslides may change some parameters or measures of the structural characteristics of land-cover. The results show that the linear coefficient, modularity and area distribution are all changed after the occurence of landslides, which means the structural characteristics of the land-cover are changed.

  4. Understanding characteristics in multivariate traffic flow time series from complex network structure

    Science.gov (United States)

    Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

    2017-07-01

    Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

  5. Modelling and structural characteristics analysis of gene networks for prostate cancer.

    Science.gov (United States)

    Zhang, Yulin; Wang, Shudong; Meng, Dazhi

    2015-01-01

    Analysing structure of gene networks is an important way to understand regulatory mechanisms of organism at the molecular level. In this work, gene mutual information networks are constructed based on gene expression profiles in prostate tissues with and without cancer. In order to contrast structural difference of normal and diseased networks, curves of four structural parameters are given with the change of thresholds. Then threshold discrimination intervals and discrimination weights are defined. A method of finding structural key genes with significant degree-difference is proposed. The finding of key genes will help the biomedical scientists to further research the pathogenesis of prostate cancer. Finally randomisation test is performed to prove that these structural parameters can distinguish normal and prostate cancer in their structures compared with these results in real data.

  6. Epidemic Spreading Characteristics and Immunity Measures Based on Complex Network with Contact Strength and Community Structure

    Directory of Open Access Journals (Sweden)

    Xueting Zhang

    2015-01-01

    Full Text Available Middle East Respiratory Syndrome (MERS, bursting in South Korea from May 2015 and mainly spreading within the hospitals at the beginning, has caused a large scale of public panic. Aiming at this kind of epidemic spreading swiftly by intimate contact within community structure, we first established a spreading model based on contact strength and SI model, and a weighted network with community structure based on BBV network model. Meanwhile, the sufficient conditions were deduced to ensure the optimal community division. Next, after the verification by the real data of MERS, it is found that the spreading rate is closely related to the average weight of network but not the number of communities. Then, as the further study shows, the final infection proportion declines with the decreases both in isolation delay and in average weight; however, this proportion can only be postponed rather than decreased with respect to sole average weight reduction without isolation. Finally, the opportunities to take action can be found to restrain the epidemic spreading to the most extent.

  7. Socioeconomic analysis of patient-centric networks: effects of patients and hospitals' characteristics and network structure on hospitalization costs.

    Science.gov (United States)

    Abbasi, Alireza; Uddin, Shahadat; Hossain, Liaquat

    2012-06-01

    Improving operations and delivery of cost-effective healthcare services is considered to be an important area of investigation due to the challenges in allocation of resources in meeting the increasing cost of health care for the twenty-first century. To date, appropriate mechanisms for systematic evaluation of hospital operations and its impact of the delivery of cost-effective healthcare services are lacking. This is, perhaps, the first study, which focuses on using large insurance claims data to develop a social network-based model for exploring the effect of patient-doctor tie strength and patient socio-demographic factors for exploring the social structure of operations and delivery of cost-effective healthcare services. We suggest that delivery of cost-effective healthcare services and operation is embedded within the social structure of hospitals. By exploring the mode of hospital operations in terms of their patient-centric care network, we are able to develop a better understanding of the operation and delivery of cost-effective healthcare services.

  8. [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.

  9. Cost Characteristics of Telecom Networks and their Implications for Market Structures

    DEFF Research Database (Denmark)

    Falch, Morten

    1997-01-01

    The paper analyses the cost structure of telecom operators and test the hypotheses of economies of scale and economies of density. The impact of new technologies and the regulatory implications are analysed.......The paper analyses the cost structure of telecom operators and test the hypotheses of economies of scale and economies of density. The impact of new technologies and the regulatory implications are analysed....

  10. Characteristics of Effective Leadership Networks

    Science.gov (United States)

    Leithwood, Kenneth; Azah, Vera Ndifor

    2016-01-01

    Purpose: The purpose of this paper is to inquire about the characteristics of effective school leadership networks and the contribution of such networks to the development of individual leaders' professional capacities. Design/methodology/approach: The study used path-analytic techniques with survey data provided by 450 school and district leaders…

  11. Advanced Polymer Network Structures

    Science.gov (United States)

    2016-02-01

    characteristic time 02 /UmaLJ =τ . Topologically bound monomers interact through the sum of the purely repulsive LJ potential ( arc 6/12= ) or so-called Weeks...3 Content of the simulated polymer double network. Self- attraction coefficient between particles within a network (first or second) is fixed at 1...technique to the study the microscopic topology and dynamics of a wide variety of polymer networks and gels.5–8 The pair interaction between

  12. 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...... reductions compared to the traditional manual planning results of our industrial partner....

  13. A fibril-based structural constitutive theory reveals the dominant role of network characteristics on the mechanical behavior of fibroblast-compacted collagen gels.

    Science.gov (United States)

    Feng, Zhonggang; Ishiguro, Yuki; Fujita, Kyohei; Kosawada, Tadashi; Nakamura, Takao; Sato, Daisuke; Kitajima, Tatsuo; Umezu, Mitsuo

    2015-10-01

    In this paper, we present a general, fibril-based structural constitutive theory which accounts for three material aspects of crosslinked filamentous materials: the single fibrillar force response, the fibrillar network model, and the effects of alterations to the fibrillar network. In the case of the single fibrillar response, we develop a formula that covers the entropic and enthalpic deformation regions, and introduce the relaxation phase to explain the observed force decay after crosslink breakage. For the filamentous network model, we characterize the constituent element of the fibrillar network in terms its end-to-end distance vector and its contour length, then decompose the vector orientation into an isotropic random term and a specific alignment, paving the way for an expanded formalism from principal deformation to general 3D deformation; and, more important, we define a critical core quantity over which macroscale mechanical characteristics can be integrated: the ratio of the initial end-to-end distance to the contour length (and its probability function). For network alterations, we quantitatively treat changes in constituent elements and relate these changes to the alteration of network characteristics. Singular in its physical rigor and clarity, this constitutive theory can reproduce and predict a wide range of nonlinear mechanical behavior in materials composed of a crosslinked filamentous network, including: stress relaxation (with dual relaxation coefficients as typically observed in soft tissues); hysteresis with decreasing maximum stress under serial cyclic loading; strain-stiffening under uniaxial tension; the rupture point of the structure as a whole; various effects of biaxial tensile loading; strain-stiffening under simple shearing; the so-called "negative normal stress" phenomenon; and enthalpic elastic behaviors of the constituent element. Applied to compacted collagen gels, the theory demonstrates that collagen fibrils behave as enthalpic

  14. 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.

  15. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Stea, Diego; Soda, Giuseppe; Pedersen, Torben

    2016-01-01

    outcomes often assumes that different network structures embody specific individual behaviors. This paper challenges the widespread assumption that dense, heavily bonded network structures imply a collaborative attitude on the part of network actors. We propose that collaboration can also be contextual......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...... and exogenous to a network’s structural characteristics, such that it moderates the effects of both dense and brokered networks on individual creativity. Specifically, we argue that knowledge acquisition and, in turn, individual creativity are more likely when an individual’s network position has a good fit...

  16. Social Networks and Network Structures

    Science.gov (United States)

    2006-11-01

    Research in Command & Control • Latent Semantic Analysis – Team communication – Emergent team dynamics – Shared situation awareness • Dynamic Network...requirements – Information technology requirements 28 LSA Essentials of Latent Semantic Analysis 29 Communication Analysis • Goal: Automatically monitor and

  17. Network characteristics and open innovation in SMEs

    DEFF Research Database (Denmark)

    Pedersen, Maja; Søndergaard, Helle Alsted; Esbjerg, Lars

    The open innovation literature has primarily been concentrating on open innovation (OI) in large companies. Hence it is very unclear whether these findings can be generalized to small and medium-sized enterprises (SMEs). This paper begins filling in this knowledge gap. In doing so, we focus...... explicitly on understanding network characteristics and the unfolding of OI in SMEs. Case studies were used and empirical data was mainly generated through semi-structured interviews. Cases selected for study were companies in the Danish food industry with less than 499 employees. In this paper we report our...

  18. THE COMMERCIAL BANK AS NETWORK STRUCTURE

    Directory of Open Access Journals (Sweden)

    D. O. Dyl

    2010-05-01

    Full Text Available The article examines the problems of the modern enterprise as a network structure that meets the increasing processes of globalization and the rise of postmodern trends. The definition of the term «a network of commercial bank» and the main characteristics of such a definition are given.

  19. Characteristics of networks in energy efficiency research, development and demonstration – a comparison of actors, technological domains and network structure in seven research areas

    DEFF Research Database (Denmark)

    Ruby, Tobias Møller

    2013-01-01

    - but there is no such thing as “just” RD&D in energy efficiency as it encompasses a multitude of different sub-areas, institutions, actors, markets etc. Through the use of network analysis on unique RD&D project data from Denmark the study provides new insights into knowledge and inter-organisational networks in energy...

  20. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Stea, Diego; Soda, Giuseppe; Pedersen, Torben

    2016-01-01

    and exogenous to a network’s structural characteristics, such that it moderates the effects of both dense and brokered networks on individual creativity. Specifically, we argue that knowledge acquisition and, in turn, individual creativity are more likely when an individual’s network position has a good fit...... 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...

  1. Optimized null model for protein structure networks.

    Science.gov (United States)

    Milenković, Tijana; Filippis, Ioannis; Lappe, Michael; Przulj, Natasa

    2009-06-26

    Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by

  2. Optimized null model for protein structure networks.

    Directory of Open Access Journals (Sweden)

    Tijana Milenković

    Full Text Available Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model

  3. Network Structure, Collaborative Context, and Individual Creativity

    DEFF Research Database (Denmark)

    Soda, Giuseppe; Stea, Diego; Pedersen, Torben

    2017-01-01

    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. 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

  5. Exploring biological network structure with clustered random networks.

    Science.gov (United States)

    Bansal, Shweta; Khandelwal, Shashank; Meyers, Lauren Ancel

    2009-12-09

    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. 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. 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 unraveling the functional consequences of the structural

  6. Characteristics of networks of interventions: a description of a database of 186 published networks.

    Directory of Open Access Journals (Sweden)

    Adriani Nikolakopoulou

    Full Text Available Systematic reviews that employ network meta-analysis are undertaken and published with increasing frequency while related statistical methodology is evolving. Future statistical developments and evaluation of the existing methodologies could be motivated by the characteristics of the networks of interventions published so far in order to tackle real rather than theoretical problems. Based on the recently formed network meta-analysis literature we aim to provide an insight into the characteristics of networks in healthcare research. We searched PubMed until end of 2012 for meta-analyses that used any form of indirect comparison. We collected data from networks that compared at least four treatments regarding their structural characteristics as well as characteristics of their analysis. We then conducted a descriptive analysis of the various network characteristics. We included 186 networks of which 35 (19% were star-shaped (treatments were compared to a common comparator but not between themselves. The median number of studies per network was 21 and the median number of treatments compared was 6. The majority (85% of the non-star shaped networks included at least one multi-arm study. Synthesis of data was primarily done via network meta-analysis fitted within a Bayesian framework (113 (61% networks. We were unable to identify the exact method used to perform indirect comparison in a sizeable number of networks (18 (9%. In 32% of the networks the investigators employed appropriate statistical methods to evaluate the consistency assumption; this percentage is larger among recently published articles. Our descriptive analysis provides useful information about the characteristics of networks of interventions published the last 16 years and the methods for their analysis. Although the validity of network meta-analysis results highly depends on some basic assumptions, most authors did not report and evaluate them adequately. Reviewers and editors need

  7. Network structure of production

    Science.gov (United States)

    Atalay, Enghin; Hortaçsu, Ali; Roberts, James; Syverson, Chad

    2011-01-01

    Complex social networks have received increasing attention from researchers. Recent work has focused on mechanisms that produce scale-free networks. We theoretically and empirically characterize the buyer–supplier network of the US economy and find that purely scale-free models have trouble matching key attributes of the network. We construct an alternative model that incorporates realistic features of firms’ buyer–supplier relationships and estimate the model’s parameters using microdata on firms’ self-reported customers. This alternative framework is better able to match the attributes of the actual economic network and aids in further understanding several important economic phenomena. PMID:21402924

  8. Key brainstem structures activated during hypoxic exposure in one-day-old mice highlight characteristics for modelling breathing network in premature infants

    Directory of Open Access Journals (Sweden)

    Fanny JOUBERT

    2016-12-01

    Full Text Available We mapped and characterized changes in the activity of brainstem cell groups under hypoxia in one-day-old newborn mice, an animal model in which the central nervous system at birth is particularly immature. The classical biphasic respiratory response characterized by transient hyperventilation, followed by severe ventilation decline, was associated with increased c-FOS immunoreactivity in brainstem cell groups: the nucleus of the solitary tract, ventral reticular nucleus of the medulla, retrotrapezoid/parafacial region, parapyramidal group, raphe magnus nucleus, lateral and medial parabrachial nucleus, and dorsal subcoeruleus nucleus. In contrast, the hypoglossal nucleus displayed decreased c-FOS immunoreactivity. There were fewer or no activated catecholaminergic cells activated in the medulla oblongata, whereas approximately 45% of the c-FOS-positive cells in the dorsal subcoeruleus were co-labelled. Approximately 30% of the c-FOS-positive cells in the parapyramidal group were serotoninergic, whereas only a small portion were labelled for serotonin in the raphe magnus nucleus. None of the c-FOS-positive cells in the retrotrapezoid/parafacial region were co-labelled for PHOX2B. Thus, the hypoxia-activated brainstem neuronal network of one-day-old mice is characterized by i the activation of catecholaminergic cells of the dorsal subcoeruleus nucleus, a structure implicated in the strong depressive pontine influence previously reported in the fetus but not in newborns, ii the weak activation of catecholaminergic cells of the ventral reticular nucleus of the medulla, an area involved in hypoxic hyperventilation, and iii the absence of PHOX2B-positive cells activated in the retrotrapezoid/parafacial region. Based on these results, one-day-old mice could highlight characteristics for modelling the breathing network of premature infants.

  9. Complexity Characteristics of Currency Networks

    Science.gov (United States)

    Gorski, A. Z.; Drozdz, S.; Kwapien, J.; Oswiecimka, P.

    2006-11-01

    A large set of daily FOREX time series is analyzed. The corresponding correlation matrices (CM) are constructed for USD, EUR and PLN used as the base currencies. The triangle rule is interpreted as constraints reducing the number of independent returns. The CM spectrum is computed and compared with the cases of shuffled currencies and a fictitious random currency taken as a base currency. The Minimal Spanning Tree (MST) graphs are calculated and the clustering effects for strong currencies are found. It is shown that for MSTs the node rank has power like, scale free behavior. Finally, the scaling exponents are evaluated and found in the range analogous to those identified recently for various complex networks.

  10. 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.

  11. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

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

    2012-01-01

    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......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....... 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....

  12. Discovering network structure beyond communities.

    Science.gov (United States)

    Nishikawa, Takashi; Motter, Adilson E

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

  13. Discovering Network Structure Beyond Communities

    OpenAIRE

    Nishikawa, Takashi; Motter, Adilson E.

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes chara...

  14. Collective network for computer structures

    Energy Technology Data Exchange (ETDEWEB)

    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.

  15. Improving the Robustness of Complex Networks with Preserving Community Structure

    Science.gov (United States)

    Yang, Yang; Li, Zhoujun; Chen, Yan; Zhang, Xiaoming; Wang, Senzhang

    2015-01-01

    Complex networks are everywhere, such as the power grid network, the airline network, the protein-protein interaction network, and the road network. The networks are ‘robust yet fragile’, which means that the networks are robust against random failures but fragile under malicious attacks. The cascading failures, system-wide disasters and intentional attacks on these networks are deserving of in-depth study. Researchers have proposed many solutions to improve the robustness of these networks. However whilst many solutions preserve the degree distribution of the networks, little attention is paid to the community structure of these networks. We argue that the community structure of a network is a defining characteristic of a network which identifies its functionality and thus should be preserved. In this paper, we discuss the relationship between robustness and the community structure. Then we propose a 3-step strategy to improve the robustness of a network, while retaining its community structure, and also its degree distribution. With extensive experimentation on representative real-world networks, we demonstrate that our method is effective and can greatly improve the robustness of networks, while preserving community structure and degree distribution. Finally, we give a description of a robust network, which is useful not only for improving robustness, but also for designing robust networks and integrating networks. PMID:25674786

  16. 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.

  17. 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.

  18. Characteristics of road network forms in historic districts of Japan

    Directory of Open Access Journals (Sweden)

    Ze Liu

    2015-12-01

    Full Text Available This study focused on the structure and the platform of the road space of historic districts. We analyzed the road networks of 16 historic districts in Japan from the perspectives of circularity, accessibility and indirection based on graph theory. By calculating and comparing the indexes of each road network (NW1 and NW2 forms, we quantitatively describe the effects of the main prefectural roads (more than 4 m in width and narrow streets (less than 4 m in width on the spatial characteristics. And it turned out that we could divided the 16 objective historic districts into 4 types. Moreover, we qualitatively studied the characteristics of each type of historic districts based on their development background and the structure of road network.

  19. Confidence sets for network structure

    CERN Document Server

    Airoldi, Edoardo M; Wolfe, Patrick J

    2011-01-01

    Latent variable models are frequently used to identify structure in dichotomous network data, in part because they give rise to a Bernoulli product likelihood that is both well understood and consistent with the notion of exchangeable random graphs. In this article we propose conservative confidence sets that hold with respect to these underlying Bernoulli parameters as a function of any given partition of network nodes, enabling us to assess estimates of 'residual' network structure, that is, structure that cannot be explained by known covariates and thus cannot be easily verified by manual inspection. We demonstrate the proposed methodology by analyzing student friendship networks from the National Longitudinal Survey of Adolescent Health that include race, gender, and school year as covariates. We employ a stochastic expectation-maximization algorithm to fit a logistic regression model that includes these explanatory variables as well as a latent stochastic blockmodel component and additional node-specific...

  20. Gender differences in predicting loneliness from social network characteristics.

    Science.gov (United States)

    Stokes, J; Levin, I

    1986-11-01

    In two studies we examined gender differences in predicting loneliness from measures of social network structure and a measure of perceived social support. The results showed that social network characteristics, especially density, were consistently better predictors of perceived loneliness for men than for women. Study 1 used the traditional measure of network density in which the number of relationships among network members was determined. Study 2 used a newly developed index of density that assessed the extent of closeness of relationships between pairs of network members. Uniformly, male subjects with more highly interconnected, cohesive sets of friends reported themselves to be less lonely, whereas density had little relation to loneliness in female subjects. These results are discussed as possibly indicating that men and women use different standards in evaluating whether they are lonely. It is suggested that men may use more group-oriented criteria in evaluating loneliness, whereas women focus more on the qualities of dyadic relationships.

  1. Disease-associated pathophysiologic structures in pediatric rheumatic diseases show characteristics of scale-free networks seen in physiologic systems: implications for pathogenesis and treatment

    Directory of Open Access Journals (Sweden)

    McGhee Timothy

    2009-02-01

    Full Text Available Abstract Background While standard reductionist approaches have provided some insights into specific gene polymorphisms and molecular pathways involved in disease pathogenesis, our understanding of complex traits such as atherosclerosis or type 2 diabetes remains incomplete. Gene expression profiling provides an unprecedented opportunity to understand complex human diseases by providing a global view of the multiple interactions across the genome that are likely to contribute to disease pathogenesis. Thus, the goal of gene expression profiling is not to generate lists of differentially expressed genes, but to identify the physiologic or pathogenic processes and structures represented in the expression profile. Methods RNA was separately extracted from peripheral blood neutrophils and mononuclear leukocytes, labeled, and hybridized to genome level microarrays to generate expression profiles of children with polyarticular juvenile idiopathic arthritis, juvenile dermatomyositis relative to childhood controls. Statistically significantly differentially expressed genes were identified from samples of each disease relative to controls. Functional network analysis identified interactions between products of these differentially expressed genes. Results In silico models of both diseases demonstrated similar features with properties of scale-free networks previously described in physiologic systems. These networks were observable in both cells of the innate immune system (neutrophils and cells of the adaptive immune system (peripheral blood mononuclear cells. Conclusion Genome-level transcriptional profiling from childhood onset rheumatic diseases suggested complex interactions in two arms of the immune system in both diseases. The disease associated networks showed scale-free network patterns similar to those reported in normal physiology. We postulate that these features have important implications for therapy as such networks are relatively resistant

  2. Structurally Dynamic Spin Market Networks

    Science.gov (United States)

    Horváth, Denis; Kuscsik, Zoltán

    The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.

  3. Health and the Structure of Adolescent Social Networks

    Science.gov (United States)

    Haas, Steven A.; Schaefer, David R.; Kornienko, Olga

    2010-01-01

    Much research has explored the role of social networks in promoting health through the provision of social support. However, little work has examined how social networks themselves may be structured by health. This article investigates the link between individuals' health and the characteristics of their social network positions.We first develop…

  4. Rumor propagation on networks with community structure

    Science.gov (United States)

    Zhang, Ruixia; Li, Deyu

    2017-10-01

    In this paper, based on growth and preferential attachment mechanism, we give a network generation model aiming at generating networks with community structure. There are three characteristics for the networks generated by the generation model. The first is that the community sizes can be nonuniform. The second is that there are bridge hubs in each community. The third is that the strength of community structure is adjustable. Next, we investigate rumor propagation behavior on the generated networks by performing Monte Carlo simulations to reveal the influence of bridge hubs, nonuniformity of community sizes and the strength of community structure on the dynamic behavior of the rumor propagation. We find that bridge hubs have outstanding performance in propagation speed and propagation size, and larger modularity can reduce rumor propagation. Furthermore, when the decay rate of rumor spreading β is large, the final density of the stiflers is larger if the rumor originates in larger community. Additionally, when on networks with different strengths of community structure, rumor propagation exhibits greater difference in the density of stiflers and in the peak prevalence if the decay rate β is larger.

  5. Predictive structural dynamic network analysis.

    Science.gov (United States)

    Chen, Rong; Herskovits, Edward H

    2015-04-30

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

  6. Stability from Structure : Metabolic Networks Are Unlike Other Biological Networks

    NARCIS (Netherlands)

    Van Nes, P.; Bellomo, D.; Reinders, M.J.T.; De Ridder, D.

    2009-01-01

    In recent work, attempts have been made to link the structure of biochemical networks to their complex dynamics. It was shown that structurally stable network motifs are enriched in such networks. In this work, we investigate to what extent these findings apply to metabolic networks. To this end, we

  7. Research on complex networks' repairing characteristics due to cascading failure

    Science.gov (United States)

    Chaoqi, Fu; Ying, Wang; Xiaoyang, Wang

    2017-09-01

    In reality, most of the topological structures of complex networks are not ideal. Considering the restrictions from all aspects, we cannot timely adjust and improve network defects. Once complex networks collapse under cascading failure, an appropriate repair strategy must be implemented. This repair process is divided into 3 kinds of situations. Based on different types of opening times, we presented 2 repair modes, and researched 4 kinds of repair strategies. Results showed that network efficiency recovered faster when the repair strategies were arranged in descending order by parameters under the immediate opening condition. However, the risk of secondary failure and additional expansion capacity were large. On the contrary, when repair strategies were in ascending order, the demand for additional capacity caused by secondary failure was greatly saved, but the recovery of network efficiency was relatively slow. Compared to immediate opening, delayed opening alleviated the contradiction between network efficiency and additional expansion capacity, particularly to reduce the risk of secondary failure. Therefore, different repair methods have different repair characteristics. This paper investigates the impact of cascading effects on the network repair process, and by presenting a detailed description of the status of each repaired node, helps us understand the advantages and disadvantages of different repair strategies.

  8. Application of the artificial neural network for reconstructing the internal-structure image of a random medium by spatial characteristics of backscattered optical radiation

    Science.gov (United States)

    Veksler, B. A.; Meglinskii, I. V.

    2008-06-01

    The feasibility of using an artificial neural network (ANN), which is the standard Matlab tool, for non-invasive (based on the data of backscattering) diagnostics of macro-inhomogeneities, localised at subsurface layers of the turbid strongly scattering medium was shown. The spatial and angle distribution of the backscattered optical radiation was calculated by using the Monte-Carlo method combining the modelling of effective optical paths and the use of statistical weights. It was shown that application of the backscattering method together with the ANN allows solving inverse problems for determining the average radius of the scattering particles and for reconstructing the images of structural elements within the medium with a high accuracy.

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

    Science.gov (United States)

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

    2010-12-01

    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.

  10. Deformation Characteristics of Composite Structures

    Directory of Open Access Journals (Sweden)

    Theddeus T. AKANO

    2016-08-01

    Full Text Available The composites provide design flexibility because many of them can be moulded into complex shapes. The carbon fibre-reinforced epoxy composites exhibit excellent fatigue tolerance and high specific strength and stiffness which have led to numerous advanced applications ranging from the military and civil aircraft structures to the consumer products. However, the modelling of the beams undergoing the arbitrarily large displacements and rotations, but small strains, is a common problem in the application of these engineering composite systems. This paper presents a nonlinear finite element model which is able to estimate the deformations of the fibre-reinforced epoxy composite beams. The governing equations are based on the Euler-Bernoulli beam theory (EBBT with a von Kármán type of kinematic nonlinearity. The anisotropic elasticity is employed for the material model of the composite material. Moreover, the characterization of the mechanical properties of the composite material is achieved through a tensile test, while a simple laboratory experiment is used to validate the model. The results reveal that the composite fibre orientation, the type of applied load and boundary condition, affect the deformation characteristics of the composite structures. The nonlinearity is an important factor that should be taken into consideration in the analysis of the fibre-reinforced epoxy composites.

  11. Network Characteristics, Perceived Social Support, and Psychological Adjustment in Mothers of Children with Autism Spectrum Disorder

    Science.gov (United States)

    Benson, Paul R.

    2012-01-01

    This study examined the characteristics of the support networks of 106 mothers of children with ASD and their relationship to perceived social support, depressed mood, and subjective well-being. Using structural equation modeling, two competing sets of hypotheses were assessed: (1) that network characteristics would impact psychological adjustment…

  12. Network nestedness as generalized core-periphery structures

    CERN Document Server

    Lee, Sang Hoon

    2016-01-01

    The concept of nestedness, in particular for ecological and economical networks, has been introduced as a structural characteristic of real interacting systems. We suggest that the nestedness is in fact another way to express a mesoscale network property called the core-periphery structure. With real ecological mutualistic networks and synthetic model networks, we reveal the strong correlation between the nestedness and core-peripheriness, by defining the network-level measures for nestedness and core-peripheriness in case of weighted and bipartite networks. However, at the same time, via more sophisticated null-model analysis, we also discover that the degree (the number of connected neighbors of a node) distribution poses quite severe restrictions on the possible nestedness and core-peripheriness parameter space. Therefore, there must exist structurally interwoven properties in more fundamental levels of network formation, behind this seemingly obvious relation between nestedness and core-periphery structur...

  13. Community structure of complex networks based on continuous neural network

    Science.gov (United States)

    Dai, Ting-ting; Shan, Chang-ji; Dong, Yan-shou

    2017-09-01

    As a new subject, the research of complex networks has attracted the attention of researchers from different disciplines. Community structure is one of the key structures of complex networks, so it is a very important task to analyze the community structure of complex networks accurately. In this paper, we study the problem of extracting the community structure of complex networks, and propose a continuous neural network (CNN) algorithm. It is proved that for any given initial value, the continuous neural network algorithm converges to the eigenvector of the maximum eigenvalue of the network modularity matrix. Therefore, according to the stability of the evolution of the network symbol will be able to get two community structure.

  14. 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,

  15. On the topological structure of multinationals network

    Science.gov (United States)

    Joyez, Charlie

    2017-05-01

    This paper uses a weighted network analysis to examine the structure of multinationals' implantation countries network. Based on French firm-level dataset of multinational enterprises (MNEs) the network analysis provides information on each country position in the network and in internationalization strategies of French MNEs through connectivity preferences among the nodes. The paper also details network-wide features and their recent evolution toward a more decentralized structure. While much has been said on international trade network, this paper shows that multinational firms' studies would also benefit from network analysis, notably by investigating the sensitivity of the network construction to firm heterogeneity.

  16. Tensegrity II. How structural networks influence cellular information processing networks

    Science.gov (United States)

    Ingber, Donald E.

    2003-01-01

    The major challenge in biology today is biocomplexity: the need to explain how cell and tissue behaviors emerge from collective interactions within complex molecular networks. Part I of this two-part article, described a mechanical model of cell structure based on tensegrity architecture that explains how the mechanical behavior of the cell emerges from physical interactions among the different molecular filament systems that form the cytoskeleton. Recent work shows that the cytoskeleton also orients much of the cell's metabolic and signal transduction machinery and that mechanical distortion of cells and the cytoskeleton through cell surface integrin receptors can profoundly affect cell behavior. In particular, gradual variations in this single physical control parameter (cell shape distortion) can switch cells between distinct gene programs (e.g. growth, differentiation and apoptosis), and this process can be viewed as a biological phase transition. Part II of this article covers how combined use of tensegrity and solid-state mechanochemistry by cells may mediate mechanotransduction and facilitate integration of chemical and physical signals that are responsible for control of cell behavior. In addition, it examines how cell structural networks affect gene and protein signaling networks to produce characteristic phenotypes and cell fate transitions during tissue development.

  17. Distributed Structure-Searchable Toxicity Database Network

    Data.gov (United States)

    U.S. Environmental Protection Agency — The Distributed Structure-Searchable Toxicity (DSSTox) Database Network provides a public forum for search and publishing downloadable, structure-searchable,...

  18. Community structure in the phonological network

    Directory of Open Access Journals (Sweden)

    Cynthia S. Q. Siew

    2013-08-01

    Full Text Available Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009. Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008. Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935. The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

  19. [Numerical analysis on network characteristics of communities in herb-pairs network].

    Science.gov (United States)

    Cao, Jia; Xin, Juan-juan; Wang, Yun

    2015-06-01

    To interpret the traditional Chinese medicine (TCM) theory by the network technology, in order to promote the modernization and programming of studies on compatibility of TCMs. In this paper, efforts were made to express the direct interactions between drugs through the herb-pair network, analyze the community characteristics of the network and its relations with blood-Qi theory, and study the expression of blood-Qi theory on the herb-pair network through prescriptions. According to the findings, the herb-pairs network showed a strong community structure characteristics; Each community is composed of a series of herb pairs with close correlations, and either blood efficacy or Qi efficacy but not both of them. Based on that, the 386 single TCM ingredients involved by the herb-pair network were divided into three types of communities: Blood (B) community, Qi (Q) community and uncertain community. According to the statistical results of 262 prescriptions mapped onto the three types of communities, if a prescription contains single herbs of the Q community, the probability that it contains single herbs o the B community is 99.84%; Meanwhile, there are 140 prescriptions containing single herbs of both the Q community and the B community. The result is completely coincident with the TCM Blood-Qi theory that single herbs belong to both Q and B communities or the B community, because Qi regulation leads to blood regulation, but not vice versa. For example, a patient with hemorrhage due to trauma or blood-heat, Qi tonifying prescriptions may aggravate hemorrhage. In this paper, authors found high-recognition macroscopic network numerical characteristics to network data reference for judging rationality of new prescriptions, and proved human blood and Qi relations from the perspective of data analysis.

  20. Exploring the Community Structure of Complex Networks

    OpenAIRE

    Drago, Carlo

    2016-01-01

    Regarding complex networks, one of the most relevant problems is to understand and to explore community structure. In particular it is important to define the network organization and the functions associated to the different network partitions. In this context, the idea is to consider some new approaches based on interval data in order to represent the different relevant network components as communities. The method is also useful to represent the network community structure, especially the ...

  1. Evolving networks-Using past structure to predict the future

    Science.gov (United States)

    Shang, Ke-ke; Yan, Wei-sheng; Small, Michael

    2016-08-01

    Many previous studies on link prediction have focused on using common neighbors to predict the existence of links between pairs of nodes. More broadly, research into the structural properties of evolving temporal networks and temporal link prediction methods have recently attracted increasing attention. In this study, for the first time, we examine the use of links between a pair of nodes to predict their common neighbors and analyze the relationship between the weight and the structure in static networks, evolving networks, and in the corresponding randomized networks. We propose both new unweighted and weighted prediction methods and use six kinds of real networks to test our algorithms. In unweighted networks, we find that if a pair of nodes connect to each other in the current network, they will have a higher probability to connect common nodes both in the current and the future networks-and the probability will decrease with the increase of the number of neighbors. Furthermore, we find that the original networks have their particular structure and statistical characteristics which benefit link prediction. In weighted networks, the prediction algorithm performance of networks which are dominated by human factors decrease with the decrease of weight and are in general better in static networks. Furthermore, we find that geographical position and link weight both have significant influence on the transport network. Moreover, the evolving financial network has the lowest predictability. In addition, we find that the structure of non-social networks has more robustness than social networks. The structure of engineering networks has both best predictability and also robustness.

  2. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Abstract. 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 impor- tant role in communication and ...

  3. 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.

  4. Core-Periphery Structure in Networks

    OpenAIRE

    Rombach, M. Puck; Porter, Mason A.; Fowler, James H.; Mucha, Peter J

    2012-01-01

    Intermediate-scale (or `meso-scale') structures in networks have received considerable attention, as the algorithmic detection of such structures makes it possible to discover network features that are not apparent either at the local scale of nodes and edges or at the global scale of summary statistics. Numerous types of meso-scale structures can occur in networks, but investigations of such features have focused predominantly on the identification and study of community structure. In this p...

  5. STRUCTURE OF BOXERS´ MORPHOLOGICAL CHARACTERISTICS

    Directory of Open Access Journals (Sweden)

    Benin Murić

    2008-08-01

    Full Text Available We wanted to help solving the problems we meet every day, and through this research we did. Research was done on population of boxers 15-19 years old, who compete in Serbian Youth League. We keep being asked: “How big is the morphological characteristics influence on a boxer`s success? While selecting boys for boxing, on which morphological characteristics we need to pay our attention most?” Based on results we got in this research, boys must have a good relation of height and weight, and relatively long legs and arms.

  6. 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

  7. 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.

  8. Network characteristics, perceived social support, and psychological adjustment in mothers of children with autism spectrum disorder.

    Science.gov (United States)

    Benson, Paul R

    2012-12-01

    This study examined the characteristics of the support networks of 106 mothers of children with ASD and their relationship to perceived social support, depressed mood, and subjective well-being. Using structural equation modeling, two competing sets of hypotheses were assessed: (1) that network characteristics would impact psychological adjustment directly, and (2) that network effects on adjustment would be indirect, mediated by perceived social support. Results primarily lent support to the latter hypotheses, with measures of network structure (network size) and function (proportion of network members providing emotional support) predicting increased levels of perceived social support which, in turn, predicted decreased depressed mood and increased well-being. Results also indicated that increased interpersonal strain in the maternal network was directly and indirectly associated with increased maternal depression, while being indirectly linked to reduced well-being. Study limitations and implications are discussed.

  9. The relevance of network micro-structure for neural dynamics

    Directory of Open Access Journals (Sweden)

    Volker ePernice

    2013-06-01

    Full Text Available The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previousstudies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neuronsin recurrent networks. However, typically very simple random network models are considered in such studies. Here weuse a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much morevariable than commonly used network models, and which therefore promise to sample the space of recurrent networks ina more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology insimulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive datasetof networks and neuronal simulations we assess statistical relations between features of the network structure and the spikingactivity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics ofboth single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistentrelations between activity characteristics like spike-train irregularity or correlations and network properties, for example thedistributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that itis possible to estimate structural characteristics of the network from activity data. We also assess higher order correlationsof spiking activity in the various networks considered here, and find that their occurrence strongly depends on the networkstructure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpretspike train recordings from neural circuits.

  10. How Friendship Network Characteristics Influence Subjective Well-Being

    Science.gov (United States)

    van der Horst, Mariska; Coffe, Hilde

    2012-01-01

    This article explores how friendship network characteristics influence subjective well-being (SWB). Using data from the 2003 General Social Survey of Canada, three components of the friendship network are differentiated: number of friends, frequency of contact, and heterogeneity of friends. We argue that these characteristics shape SWB through the…

  11. Characteristics of Family/Friend Care Networks of Frail Seniors

    Science.gov (United States)

    Fast, Janet; Keating, Norah Christine; Derksen, Linda; Otfinowski, Pam

    2004-01-01

    This paper tests assumptions often made by policy makers and practitioners that networks of family, friends, and neighbours are able to provide sustained care to frail elderly Canadians. Using national survey data, we examined characteristics of the care networks of 1,104 seniors living with a long-term health problem. Care networks were found to…

  12. Taxonomies of networks from community structure

    Science.gov (United States)

    Onnela, Jukka-Pekka; Fenn, Daniel J.; Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

    2012-09-01

    The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

  13. Cost and Demand Characteristics of Telecom Networks

    DEFF Research Database (Denmark)

    Falch, Morten

    1997-01-01

    The Chapter presents the general characteristics of the cost and demand profiles for telecommunication services and identifies the barriers towards a free competitive market for telecommunication services related to these characteristics.......The Chapter presents the general characteristics of the cost and demand profiles for telecommunication services and identifies the barriers towards a free competitive market for telecommunication services related to these characteristics....

  14. 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

  15. Refining ensembles of predicted gene regulatory networks based on characteristic interaction sets.

    Directory of Open Access Journals (Sweden)

    Lukas Windhager

    Full Text Available Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate

  16. Refining Ensembles of Predicted Gene Regulatory Networks Based on Characteristic Interaction Sets

    Science.gov (United States)

    Windhager, Lukas; Zierer, Jonas; Küffner, Robert

    2014-01-01

    Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate predictions for a gene

  17. Immunization of networks with community structure

    Energy Technology Data Exchange (ETDEWEB)

    Masuda, Naoki [Graduate School of Information Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo 113-8656 (Japan); PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012 (Japan)], E-mail: masuda@mist.i.u-tokyo.ac.jp

    2009-12-15

    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.

  18. 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.

  19. Structure and properties of triolein-based polyurethane networks.

    Science.gov (United States)

    Zlatanić, Alisa; Petrović, Zoran S; Dusek, Karel

    2002-01-01

    Polyurethane networks based on vegetable oils have very heterogeneous composition, and it is difficult to find a close correlation between their structure and properties. To establish benchmark structure-properties relationships, we have prepared model polyurethane networks based on triolein and 4,4'-diphenylmethane diisocyanate (MDI). Cross-linking in the middle of fatty acid chains leaves significant parts of the triglyceride as dangling chains. To examine their effect on properties, we have synthesized another polyurethane network using triolein without dangling chains (removed by metathesis). The structure of polyols was studied in detail since it affects the structure of polyurethane networks. The network structure was analyzed from swelling and mechanical measurements and by applying network and rubber elasticity theories. The cross-linking density in both networks was found to be close to theoretical. The triolein-based model network displayed modulus (around 6 MPa), tensile strength (8.7 MPa), and elongation at break (136%), characteristic of hard rubbers. Glass transition temperatures of the networks from triolein and its metathesis analogue were 25 and 31.5 degrees C, respectively.

  20. 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.

  1. PARALLEL ALGORITHM FOR BAYESIAN NETWORK STRUCTURE LEARNING

    Directory of Open Access Journals (Sweden)

    S. A. Arustamov

    2013-03-01

    Full Text Available The article deals with implementation of a scalable parallel algorithm for structure learning of Bayesian network. Comparative analysis of sequential and parallel algorithms is done.

  2. Sensitive Dependence of Optimal Network Dynamics on Network Structure

    Directory of Open Access Journals (Sweden)

    Takashi Nishikawa

    2017-11-01

    Full Text Available The relation between network structure and dynamics is determinant for the behavior of complex systems in numerous domains. An important long-standing problem concerns the properties of the networks that optimize the dynamics with respect to a given performance measure. Here, we show that such optimization can lead to sensitive dependence of the dynamics on the structure of the network. Specifically, using diffusively coupled systems as examples, we demonstrate that the stability of a dynamical state can exhibit sensitivity to unweighted structural perturbations (i.e., link removals and node additions for undirected optimal networks and to weighted perturbations (i.e., small changes in link weights for directed optimal networks. As mechanisms underlying this sensitivity, we identify discontinuous transitions occurring in the complement of undirected optimal networks and the prevalence of eigenvector degeneracy in directed optimal networks. These findings establish a unified characterization of networks optimized for dynamical stability, which we illustrate using Turing instability in activator-inhibitor systems, synchronization in power-grid networks, network diffusion, and several other network processes. Our results suggest that the network structure of a complex system operating near an optimum can potentially be fine-tuned for a significantly enhanced stability compared to what one might expect from simple extrapolation. On the other hand, they also suggest constraints on how close to the optimum the system can be in practice. Finally, the results have potential implications for biophysical networks, which have evolved under the competing pressures of optimizing fitness while remaining robust against perturbations.

  3. Self-organization in neural networks - Applications in structural optimization

    Science.gov (United States)

    Hajela, Prabhat; Fu, B.; Berke, Laszlo

    1993-01-01

    The present paper discusses the applicability of ART (Adaptive Resonance Theory) networks, and the Hopfield and Elastic networks, in problems of structural analysis and design. A characteristic of these network architectures is the ability to classify patterns presented as inputs into specific categories. The categories may themselves represent distinct procedural solution strategies. The paper shows how this property can be adapted in the structural analysis and design problem. A second application is the use of Hopfield and Elastic networks in optimization problems. Of particular interest are problems characterized by the presence of discrete and integer design variables. The parallel computing architecture that is typical of neural networks is shown to be effective in such problems. Results of preliminary implementations in structural design problems are also included in the paper.

  4. Social Network Analysis and Qualitative Interviews for Assessing Geographic Characteristics of Tourism Business Networks.

    Science.gov (United States)

    Kelman, Ilan; Luthe, Tobias; Wyss, Romano; Tørnblad, Silje H; Evers, Yvette; Curran, Marina Martin; Williams, Richard J; Berlow, Eric L

    2016-01-01

    This study integrates quantitative social network analysis (SNA) and qualitative interviews for understanding tourism business links in isolated communities through analysing spatial characteristics. Two case studies are used, the Surselva-Gotthard region in the Swiss Alps and Longyearbyen in the Arctic archipelago of Svalbard, to test the spatial characteristics of physical proximity, isolation, and smallness for understanding tourism business links. In the larger Surselva-Gotthard region, we found a strong relationship between geographic separation of the three communities on compartmentalization of the collaboration network. A small set of businesses played a central role in steering collaborative decisions for this community, while a group of structurally 'peripheral' actors were less influential. By contrast, the business community in Svalbard showed compartmentalization that was independent of geographic distance between actors. Within towns of similar size and governance scale, Svalbard is more compartmentalized, and those compartments are not driven by geographic separation of the collaboration clusters. This compartmentalization in Svalbard was reflected in a lower density of formal business collaboration ties compared to the communities of the Alps. We infer that the difference is due to Svalbard having higher cultural diversity and population turnover than the Alps communities. We propose that integrating quantitative network analysis from simple surveys with qualitative interviews targeted from the network results is an efficient general approach to identify regionally specific constraints and opportunities for effective governance.

  5. Social Network Analysis and Qualitative Interviews for Assessing Geographic Characteristics of Tourism Business Networks.

    Directory of Open Access Journals (Sweden)

    Ilan Kelman

    Full Text Available This study integrates quantitative social network analysis (SNA and qualitative interviews for understanding tourism business links in isolated communities through analysing spatial characteristics. Two case studies are used, the Surselva-Gotthard region in the Swiss Alps and Longyearbyen in the Arctic archipelago of Svalbard, to test the spatial characteristics of physical proximity, isolation, and smallness for understanding tourism business links. In the larger Surselva-Gotthard region, we found a strong relationship between geographic separation of the three communities on compartmentalization of the collaboration network. A small set of businesses played a central role in steering collaborative decisions for this community, while a group of structurally 'peripheral' actors were less influential. By contrast, the business community in Svalbard showed compartmentalization that was independent of geographic distance between actors. Within towns of similar size and governance scale, Svalbard is more compartmentalized, and those compartments are not driven by geographic separation of the collaboration clusters. This compartmentalization in Svalbard was reflected in a lower density of formal business collaboration ties compared to the communities of the Alps. We infer that the difference is due to Svalbard having higher cultural diversity and population turnover than the Alps communities. We propose that integrating quantitative network analysis from simple surveys with qualitative interviews targeted from the network results is an efficient general approach to identify regionally specific constraints and opportunities for effective governance.

  6. Communication on the structure of biological networks

    Indian Academy of Sciences (India)

    Among all biological networks studied here, the undirected structure of neuronal networks not only possesses the small-world property but the same is also expressed remarkably to a higher degree compared to any randomly generated network which possesses the same degree sequence. A relatively high percentage of ...

  7. Network quotients: structural skeletons of complex systems.

    Science.gov (United States)

    Xiao, Yanghua; MacArthur, Ben D; Wang, Hui; Xiong, Momiao; Wang, Wei

    2008-10-01

    A defining feature of many large empirical networks is their intrinsic complexity. However, many networks also contain a large degree of structural repetition. An immediate question then arises: can we characterize essential network complexity while excluding structural redundancy? In this article we utilize inherent network symmetry to collapse all redundant information from a network, resulting in a coarse graining which we show to carry the essential structural information of the "parent" network. In the context of algebraic combinatorics, this coarse-graining is known as the "quotient." We systematically explore the theoretical properties of network quotients and summarize key statistics of a variety of "real-world" quotients with respect to those of their parent networks. In particular, we find that quotients can be substantially smaller than their parent networks yet typically preserve various key functional properties such as complexity (heterogeneity and hub vertices) and communication (diameter and mean geodesic distance), suggesting that quotients constitute the essential structural skeletons of their parent networks. We summarize with a discussion of potential uses of quotients in analysis of biological regulatory networks and ways in which using quotients can reduce the computational complexity of network algorithms.

  8. Structure of triadic relations in multiplex networks

    Science.gov (United States)

    Cozzo, Emanuele; Kivelä, Mikko; De Domenico, Manlio; Solé-Ribalta, Albert; Arenas, Alex; Gómez, Sergio; Porter, Mason A.; Moreno, Yamir

    2015-07-01

    Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different ‘layers’ that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.

  9. Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks

    Directory of Open Access Journals (Sweden)

    Javier Portela

    2016-11-01

    Full Text Available Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.

  10. Network repair based on community structure

    Science.gov (United States)

    Wang, Tianyu; Zhang, Jun; Sun, Xiaoqian; Wandelt, Sebastian

    2017-06-01

    Real-world complex systems are often fragile under disruptions. Accordingly, research on network repair has been studied intensively. Recently proposed efficient strategies for network disruption, based on collective influence, call for more research on efficient network repair strategies. Existing strategies are often designed to repair networks with local information only. However, the absence of global information impedes the creation of efficient repairs. Motivated by this limitation, we propose a concept of community-level repair, which leverages the community structure of the network during the repair process. Moreover, we devise a general framework of network repair, with in total six instances. Evaluations on real-world and random networks show the effectiveness and efficiency of the community-level repair approaches, compared to local and random repairs. Our study contributes to a better understanding of repair processes, and reveals that exploitation of the community structure improves the repair process on a disrupted network significantly.

  11. 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.

  12. Social Network Analysis and Qualitative Interviews for Assessing Geographic Characteristics of Tourism Business Networks

    National Research Council Canada - National Science Library

    Kelman, Ilan; Luthe, Tobias; Wyss, Romano; Tørnblad, Silje H; Evers, Yvette; Curran, Marina Martin; Williams, Richard J; Berlow, Eric L

    2016-01-01

    This study integrates quantitative social network analysis (SNA) and qualitative interviews for understanding tourism business links in isolated communities through analysing spatial characteristics...

  13. Network structure of inter-industry flows

    Science.gov (United States)

    McNerney, James; Fath, Brian D.; Silverberg, Gerald

    2013-12-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.

  14. Network structure of inter-industry flows

    CERN Document Server

    McNerney, James; Silverberg, Gerald

    2012-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, service industries, and extraction industries.

  15. Network structure of inter-industry flows

    OpenAIRE

    McNerney, J.; Fath, B.D.; G. Silverberg

    2012-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The community structure is hierarchical, with the top level of the hierarchy comprising five industry communities: food industries, chemical industries, manufacturing industries, servic...

  16. Constitutional characteristics of zones of prostate structure

    Directory of Open Access Journals (Sweden)

    Vinnik Y.Y.

    2012-06-01

    Full Text Available The research article is devoted to the study of structural characteristics of prostate according to the young men constitution. Materials and methods: 540 vertical and horizontal sections of prostate have been investigated. Results: Size characteristics of prostate have been established in men of different somatotypes

  17. Some Structural Characteristics of Music Television Videos.

    Science.gov (United States)

    Fry, Donald L.; Fry, Virginia H.

    1987-01-01

    Indicates, by analyzing two types of montage structures, that music television is a hybrid form of television programing displaying visual characteristics of both television commercials and drama. Argues that this amalgam of different characteristics gives music television its distinctive look and power as a promotional tool for the record…

  18. Network Structure of Inter-Industry Flows

    NARCIS (Netherlands)

    McNerney, J.; Fath, B.D.; Silverberg, G.P.

    2015-01-01

    We study the structure of inter-industry relationships using networks of money flows between industries in 45 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community

  19. Learning Latent Structure in Complex Networks

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    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...... prediction performance of the learning based approaches and other widely used link prediction approaches in 14 networks ranging from medium size to large networks with more than a million nodes. While link prediction is typically well above chance for all networks, we find that the learning based mixed...... membership stochastic block model of Airoldi et al., performs well and often best in our experiments. The added complexity of the LD model improves link predictions for four of the 14 networks....

  20. 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.

  1. Realization of Broadband Matched Filter Structures Based on Dual Networks

    Directory of Open Access Journals (Sweden)

    M. Gerding

    2005-01-01

    Full Text Available This paper deals with the basic electrical properties of dual networks and with their application in broadband matched filter structures. Starting with the main characteristics and different realization methods of dual networks, a filter structure is presented, which is based on a combination of dual networks and which provides a broadband matched input and two decoupled output ports. This filter synthesis focuses on the design of high pass filters, which are suitable to be used as differentiating stages in electrical pulse generators as a part of the so-called pulse shaping network. In order to achieve a proper pulse shape and for the prevention of multiple reflections between the switching circuit and the differentiating network, a broadband matched filter is a basic requirement.

  2. On the relationship between the structural and socioacademic communities of an interdisciplinary coauthorship network

    OpenAIRE

    Rodriguez, Marko A; Pepe, Alberto

    2008-01-01

    This article presents a study that compares detected structural communities in a coauthorship network to the socioacademic characteristics of the scholars that compose the network. The coauthorship network was created from the bibliographic record of an overt interdisciplinary research group focused on sensor networks and wireless communication. The popular leading eigenvector community detection algorithm was employed to assign a structural community to each scholar in the network. Socioacad...

  3. 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.

  4. Structure analysis of growing network based on partial differential equations

    Directory of Open Access Journals (Sweden)

    Junbo JIA

    2016-04-01

    Full Text Available The topological structure is one of the most important contents in the complex network research. Therein the node degree and the degree distribution are the most basic characteristic quantities to describe topological structure. In order to calculate the degree distribution, first of all, the node degree is considered as a continuous variable. Then, according to the Markov Property of growing network, the cumulative distribution function's evolution equation with time can be obtained. Finally, the partial differential equation (PDE model can be established through distortion processing. Taking the growing network with preferential and random attachment mechanism as an example, the PDE model is obtained. The analytic expression of degree distribution is obtained when this model is solved. Besides, the degree function over time is the same as the characteristic line of PDE. At last, the model is simulated. This PDE method of changing the degree distribution calculation into problem of solving PDE makes the structure analysis more accurate.

  5. How Friendship Network Characteristics Influence Subjective Well-Being

    OpenAIRE

    van der Horst, Mariska; Coffé, Hilde

    2011-01-01

    This article explores how friendship network characteristics influence subjective well-being (SWB). Using data from the 2003 General Social Survey of Canada, three components of the friendship network are differentiated: number of friends, frequency of contact, and heterogeneity of friends. We argue that these characteristics shape SWB through the benefits they bring. Benefits considered are more social trust, less stress, better health, and more social support. Results confirm that higher fr...

  6. Effect of mean network coordination number on dispersivity characteristics

    NARCIS (Netherlands)

    Vasilyev, L.; Raoof, A.; Nordbotten, J.M.

    2012-01-01

    In this study, we investigate the role of topology on the macroscopic (centimeter scale) dispersion characteristics derived from pore-network models.We consider 3D random porous networks extracted from a regular cubic lattice with coordination number distributed in accordance with real porous

  7. Public Cardiac Arrest Characteristics in Enclosed Pedestrian Networks

    NARCIS (Netherlands)

    Lee, Minha; Demirtas, Derya; Buick, Jason E.; Ng, Amy; Feldman, Michael J.; Cheskes, Sheldon; Morrison, Laurie J.; Chan, Timothy C.Y.

    2015-01-01

    Background: Cities around the world have underground or above-ground enclosed networks for pedestrian travel, representing unique environments for studying out-of-hospital cardiac arrest (OHCA) and resuscitation. The characteristics of OHCAs that occur in such networks are unknown. Objective: To

  8. 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...

  9. Exploring the structural regularities in networks

    CERN Document Server

    Shen, Hua-Wei; Guo, Jia-Feng

    2011-01-01

    In this paper, we consider the problem of exploring structural regularities of networks by dividing the nodes of a network into groups such that the members of each group have similar patterns of connections to other groups. Specifically, we propose a general statistical model to describe network structure. In this model, group is viewed as hidden or unobserved quantity and it is learned by fitting the observed network data using the expectation-maximization algorithm. Compared with existing models, the most prominent strength of our model is the high flexibility. This strength enables it to possess the advantages of existing models and overcomes their shortcomings in a unified way. As a result, not only broad types of structure can be detected without prior knowledge of what type of intrinsic regularities exist in the network, but also the type of identified structure can be directly learned from data. Moreover, by differentiating outgoing edges from incoming edges, our model can detect several types of stru...

  10. Research on Harmonic Characteristics Influence for Distribution Network with Renewable Energy

    Directory of Open Access Journals (Sweden)

    Huang Wei

    2016-01-01

    Full Text Available In order to study the influence on harmonic characteristics of distribution network with renewable energy, a simplified model of distribution network is established to analyse theory while considering the influence of cable on the system capacitive current. Establishing an actual distribution network model by Digsilent to research the harmonic characteristics impact when photovoltaic power with PV incorporated the distribution network in a variety of different access , while using constant current source model as harmonic source model which often been used in engineering practices. The simulation results show that: optimizing the access location and dispersion of PV can decrease the waveform distortion levels in distribution network and a certain number of high-order harmonic will magnify by a specific grid structure, deteriorating the distribution network power quality.

  11. Analyzing topological characteristics of neuronal functional networks in the rat brain

    Energy Technology Data Exchange (ETDEWEB)

    Lu, Hu [School of Computer Science and Communication Engineering, Jiangsu University, Jiangsu 212003 (China); School of Computer Science, Fudan University, Shanghai 200433 (China); Yang, Shengtao [Institutes of Brain Science, Fudan University, Shanghai 200433 (China); Song, Yuqing [School of Computer Science and Communication Engineering, Jiangsu University, Jiangsu 212003 (China); Wei, Hui [School of Computer Science, Fudan University, Shanghai 200433 (China)

    2014-08-28

    In this study, we recorded spike trains from brain cortical neurons of several behavioral rats in vivo by using multi-electrode recordings. An NFN was constructed in each trial, obtaining a total of 150 NFNs in this study. The topological characteristics of NFNs were analyzed by using the two most important characteristics of complex networks, namely, small-world structure and community structure. We found that the small-world properties exist in different NFNs constructed in this study. Modular function Q was used to determine the existence of community structure in NFNs, through which we found that community-structure characteristics, which are related to recorded spike train data sets, are more evident in the Y-maze task than in the DM-GM task. Our results can also be used to analyze further the relationship between small-world characteristics and the cognitive behavioral responses of rats. - Highlights: • We constructed the neuronal function networks based on the recorded neurons. • We analyzed the two main complex network characteristics, namely, small-world structure and community structure. • NFNs which were constructed based on the recorded neurons in this study exhibit small-world properties. • Some NFNs have community structure characteristics.

  12. 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.

  13. 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.

  14. Information transfer in community structured multiplex networks

    CERN Document Server

    Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex

    2015-01-01

    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 ...

  15. Network characteristics of individual pigments in cyanobacterial photosystem II core complexes

    Science.gov (United States)

    Lee, Eun; Holme, Petter

    2013-12-01

    Part of the excitation energy transfer (EET) characteristics of the photosystem II (PSII) comes from the interconnection between pigments. To understand the correlation between the EET and the pigments' interaction structure, we construct a network from the EET rates which are related to both the distance between the pigments (chlorophylls and pheophytins) and their spatial orientations. Especially, we investigate how well the PS II core complex's EET functionality can be explained by using only the network topology in Thermosynechococcus vulcanus 1.9 °A. Starting from the Förster theory, we construct a network of EET pathways. For an analysis of the network structure, we calculate common network-structural measures like betweenness centrality, eigenvector centrality and weighted clustering. These measures can reflect the role of individual pigments in the EET network. In our work, we found that some well-known properties were reproduced by the network analysis of the simplified network, which means that the topology of the network encodes functionally relevant information. For example, from the network structural analysis, we can infer that most of the chlorophyll molecules (clorophylls) in the pigment-protein complex CP47 have heightened probability to transfer energy compared with other chlorophylls. We also see that the active branch chlorophylls in the reaction center are characterized by a high eigenvector centrality, a high betweenness centrality and a low weighted clustering coefficient. This is indicative of functionally important vertices.

  16. Network structure classification and features of water distribution systems

    Science.gov (United States)

    Giustolisi, Orazio; Simone, Antonietta; Ridolfi, Luca

    2017-04-01

    The network connectivity structure of water distribution systems (WDSs) represents the domain where hydraulic processes occur, driving the emerging behavior of such systems, for example with respect to robustness and vulnerability. In complex network theory (CNT), a common way of classifying the network structure and connectivity is the association of the nodal degree distribution to specific probability distribution models, and during the last decades, researchers classified many real networks using the Poisson or Pareto distributions. In spite of the fact that degree-based network classification could play a crucial role to assess WDS vulnerability, this task is not easy because the network structure of WDSs is strongly constrained by spatial characteristics of the environment where they are constructed. The consequence of these spatial constraints is that the nodal degree spans very small ranges in WDSs hindering a reliable classification by the standard approach based on the nodal degree distribution. This work investigates the classification of the network structure of 22 real WDSs, built in different environments, demonstrating that the Poisson distribution generally models the degree distributions very well. In order to overcome the problem of the reliable classification based on the standard nodal degree, we define the "neighborhood" degree, equal to the sum of the nodal degrees of the nearest topological neighbors (i.e., the adjacent nodes). This definition of "neighborhood" degree is consistent with the fact that the degree of a single node is not significant for analysis of WDSs.

  17. 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.

  18. 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).

  19. Learning Bayesian Network Model Structure from Data

    National Research Council Canada - National Science Library

    Margaritis, Dimitris

    2003-01-01

    In this thesis I address the important problem of the determination of the structure of directed statistical models, with the widely used class of Bayesian network models as a concrete vehicle of my ideas...

  20. Brain connectivity dynamics during social interaction reflect social network structure.

    Science.gov (United States)

    Schmälzle, Ralf; Brook O'Donnell, Matthew; Garcia, Javier O; Cascio, Christopher N; Bayer, Joseph; Bassett, Danielle S; Vettel, Jean M; Falk, Emily B

    2017-05-16

    Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants' friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics.

  1. 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

  2. Characteristics and Prediction of RNA Structure

    Directory of Open Access Journals (Sweden)

    Hengwu Li

    2014-01-01

    Full Text Available RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is NP-hard. Most RNAs fold during transcription from DNA into RNA through a hierarchical pathway wherein secondary structures form prior to tertiary structures. Real RNA secondary structures often have local instead of global optimization because of kinetic reasons. The performance of RNA structure prediction may be improved by considering dynamic and hierarchical folding mechanisms. This study is a novel report on RNA folding that accords with the golden mean characteristic based on the statistical analysis of the real RNA secondary structures of all 480 sequences from RNA STRAND, which are validated by NMR or X-ray. The length ratios of domains in these sequences are approximately 0.382L, 0.5L, 0.618L, and L, where L is the sequence length. These points are just the important golden sections of sequence. With this characteristic, an algorithm is designed to predict RNA hierarchical structures and simulate RNA folding by dynamically folding RNA structures according to the above golden section points. The sensitivity and number of predicted pseudoknots of our algorithm are better than those of the Mfold, HotKnots, McQfold, ProbKnot, and Lhw-Zhu algorithms. Experimental results reflect the folding rules of RNA from a new angle that is close to natural folding.

  3. 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...

  4. 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......A complex network is a systems in which a discrete set of units interact in a quantifiable manner. Representing systems as complex networks have become increasingly popular in a variety of scientific fields including biology, social sciences and economics. Parallel to this development 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...

  5. Fundamental structures of dynamic social networks

    DEFF Research Database (Denmark)

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

    2016-01-01

    , 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...... 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...... a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework...

  6. Study on hydraulic characteristics of mine dust-proof water supply network

    Science.gov (United States)

    Deng, Quanlong; Jiang, Zhongan; Han, Shuo; Fu, Enqi

    2018-01-01

    In order to study the hydraulic characteristics of mine dust-proof water supply network and obtain the change rule of water consumption and water pressure, according to the similarity principle and the fluid continuity equation and energy equation, the similarity criterion of mine dust-proof water supply network is deduced, and a similar model of dust-proof water supply network is established based on the prototype of Kailuan Group, the characteristics of hydraulic parameters in water supply network are studied experimentally. The results show that water pressure at each point is a dynamic process, and there is a negative correlation between water pressure and water consumption. With the increase of water consumption, the pressure of water points show a decreasing trend. According to the structure of the pipe network and the location of the water point, the influence degree on the pressure of each point is different.

  7. 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.

  8. Structural measures for multiplex networks.

    Science.gov (United States)

    Battiston, Federico; Nicosia, Vincenzo; Latora, Vito

    2014-03-01

    Many real-world complex systems consist of a set of elementary units connected by relationships of different kinds. All such systems are better described in terms of multiplex networks, where the links at each layer represent a different type of interaction between the same set of nodes rather than in terms of (single-layer) networks. In this paper we present a general framework to describe and study multiplex networks, whose links are either unweighted or weighted. In particular, we propose a series of measures to characterize the multiplexicity of the systems in terms of (i) basic node and link properties such as the node degree, and the edge overlap and reinforcement, (ii) local properties such as the clustering coefficient and the transitivity, and (iii) global properties related to the navigability of the multiplex across the different layers. The measures we introduce are validated on a genuinely multiplex data set of Indonesian terrorists, where information among 78 individuals are recorded with respect to mutual trust, common operations, exchanged communications, and business relationships.

  9. Social network characteristics and body mass index in an elderly Korean population.

    Science.gov (United States)

    Lee, Won Joon; Youm, Yoosik; Rhee, Yumie; Park, Yeong-Ran; Chu, Sang Hui; Kim, Hyeon Chang

    2013-11-01

    Research has shown that obesity appears to spread through social ties. However, the association between other characteristics of social networks and obesity is unclear. This study aimed to identify the association between social network characteristics and body mass index (BMI, kg/m(2)) in an elderly Korean population. This cross-sectional study analyzed data from 657 Koreans (273 men, 384 women) aged 60 years or older who participated in the Korean Social Life, Health, and Aging Project. Network size is a count of the number of friends. Density of communication network is the number of connections in the social network reported as a fraction of the total links possible in the personal (ego-centric) network. Average frequency of communication (or meeting) measures how often network members communicate (or meet) each other. The association of each social network measure with BMI was investigated by multiple linear regression analysis. After adjusting for potential confounders, the men with lower density (0.83) and lower size (1-2), but not in the women (p=0.393). The lowest tertile of communication frequency was associated with higher BMI in the women (β=0.885, p=0.049), but not in the men (p=0.140). Our study suggests that social network structure (network size and density) and activation (communication frequency and meeting frequency) are associated with obesity among the elderly. There may also be gender differences in this association.

  10. Controlling congestion on complex networks: fairness, efficiency and network structure.

    Science.gov (United States)

    Buzna, Ľuboš; Carvalho, Rui

    2017-08-22

    We consider two elementary (max-flow and uniform-flow) and two realistic (max-min fairness and proportional fairness) congestion control schemes, and analyse how the algorithms and network structure affect throughput, the fairness of flow allocation, and the location of bottleneck edges. The more realistic proportional fairness and max-min fairness algorithms have similar throughput, but path flow allocations are more unequal in scale-free than in random regular networks. Scale-free networks have lower throughput than their random regular counterparts in the uniform-flow algorithm, which is favoured in the complex networks literature. We show, however, that this relation is reversed on all other congestion control algorithms for a region of the parameter space given by the degree exponent γ and average degree 〈k〉. Moreover, the uniform-flow algorithm severely underestimates the network throughput of congested networks, and a rich phenomenology of path flow allocations is only present in the more realistic α-fair family of algorithms. Finally, we show that the number of paths passing through an edge characterises the location of a wide range of bottleneck edges in these algorithms. Such identification of bottlenecks could provide a bridge between the two fields of complex networks and congestion control.

  11. 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.

  12. Social Network Characteristics and Salivary Cortisol in Healthy Older People

    Directory of Open Access Journals (Sweden)

    Julian C. L. Lai

    2012-01-01

    Full Text Available Psychobiological research on aging in humans has been confounded by individual differences that have not been adequately characterized in the literature. This paper is an attempt to shed light on this issue by examining the impact of social network characteristics predictive of successful aging on salivary cortisol among 78 older Chinese people in Hong Kong. Eight salivary cortisol samples were collected each day for two consecutive days from immediately after awakening to 12 hours later. Two components of the cortisol diurnal cycle, response to awakening and diurnal decline, were examined in relation to social network characteristics including size, emotional support, and cultivation. ANOVAs with repeated measured were run to examine influences of the three social network characteristics on the cortisol awakening response and diurnal decline, with the effects of gender, age, socioeconomic status, and waking time controlled. Results indicated that those who spent more time and effort in developing and strengthening their social ties (i.e., those high in “cultivation” exhibited a significantly greater rise in cortisol in the morning and a significantly steeper decline over the day, thus attesting to more effective activation and deactivation of the HPA axis. Network cultivation reflected a positive motivation to nurture social relationships more than the other two network characteristics. Its effect on cortisol might stem from the positivity underlying the motivation.

  13. Structure and function of complex brain networks

    Science.gov (United States)

    Sporns, Olaf

    2013-01-01

    An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a “rich club,” centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed. PMID:24174898

  14. 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.

  15. 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)

  16. A Topological Perspective of Neural Network Structure

    Science.gov (United States)

    Sizemore, Ann; Giusti, Chad; Cieslak, Matthew; Grafton, Scott; Bassett, Danielle

    The wiring patterns of white matter tracts between brain regions inform functional capabilities of the neural network. Indeed, densely connected and cyclically arranged cognitive systems may communicate and thus perform distinctly. However, previously employed graph theoretical statistics are local in nature and thus insensitive to such global structure. Here we present an investigation of the structural neural network in eight healthy individuals using persistent homology. An extension of homology to weighted networks, persistent homology records both circuits and cliques (all-to-all connected subgraphs) through a repetitive thresholding process, thus perceiving structural motifs. We report structural features found across patients and discuss brain regions responsible for these patterns, finally considering the implications of such motifs in relation to cognitive function.

  17. Structure formation in active networks

    CERN Document Server

    Köhler, Simone; Bausch, Andreas R

    2011-01-01

    Structure formation and constant reorganization of the actin cytoskeleton are key requirements for the function of living cells. Here we show that a minimal reconstituted system consisting of actin filaments, crosslinking molecules and molecular-motor filaments exhibits a generic mechanism of structure formation, characterized by a broad distribution of cluster sizes. We demonstrate that the growth of the structures depends on the intricate balance between crosslinker-induced stabilization and simultaneous destabilization by molecular motors, a mechanism analogous to nucleation and growth in passive systems. We also show that the intricate interplay between force generation, coarsening and connectivity is responsible for the highly dynamic process of structure formation in this heterogeneous active gel, and that these competing mechanisms result in anomalous transport, reminiscent of intracellular dynamics.

  18. Identifying community structure in complex networks

    Science.gov (United States)

    Shao, Chenxi; Duan, Yubing

    2015-07-01

    A wide variety of applications could be formulated to resolve the problem of finding all communities from a given network, ranging from social and biological network analysis to web mining and searching. In this study, we propose the concept of virtual attractive strength between each pair of node in networks, and then give the definition of community structure based on the proposed attractive strength. Furthermore, we present a community detection method by moving vertices to the clusters that produce the largest attractive strengths to them until the division of network reaches unchanged. Experimental results on synthetic and real networks indicate that the proposed approach has favorite effectiveness and fast convergence speed, which provides an efficient method for exploring and analyzing complex systems.

  19. Analysis of the Air Transport Network Characteristics of Major Airports

    Directory of Open Access Journals (Sweden)

    Min Geun Song

    2017-09-01

    Full Text Available The world's major airports are directly connected to hundreds of airports without intermediate routes. This connectivity can be described as the network in which the airport becomes a node and the route becomes a connection line. In this regard, this study analyzes the air transport network of 1,060 airports using the social network analysis (SNA methodology. We consolidated the data from three airline alliances and established a network of 1,060 airports and 5,580 routes in 173 countries. Many previous studies on air transport network examined several specific airports or regions and mainly utilized the internal indicators of airports. Conversely, this study conducted a comprehensive analysis covering 173 countries by using air route, which is an external indicator of airports. This study presented the general characteristics of major countries and regions from the perspective of SNA and compared the individual networks of the United States and China, which have the greatest influence on international air logistics within the scope of the entire network analysis. This study can aid in the understanding of air transport networks and logistics connectivity in inter-city and inter-country transport.

  20. Structural systems identification of genetic regulatory networks.

    Science.gov (United States)

    Xiong, Hao; Choe, Yoonsuck

    2008-02-15

    Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit. In this report, we extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles. We applied our method to synthetic data and to SOS data and showed that our method significantly outperforms the regular EM without structural constraints. The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.

  1. 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.

  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. PMID:22523557

  3. 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.

  4. Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks.

    Science.gov (United States)

    Park, Heewon; Shimamura, Teppei; Imoto, Seiya; Miyano, Satoru

    2017-10-20

    There is currently much discussion about sample (patient)-specific gene regulatory network identification, since the efficiently constructed sample-specific gene networks lead to effective personalized cancer therapy. Although statistical approaches have been proposed for inferring gene regulatory networks, the methods cannot reveal sample-specific characteristics because the existing methods, such as an L1-type regularization, provide averaged results for all samples. Thus, we cannot reveal sample-specific characteristics in transcriptional regulatory networks. To settle on this issue, the NetworkProfiler was proposed based on the kernel-based L1-type regularization. The NetworkProfiler imposes a weight on each sample based on the Gaussian kernal function for controlling effect of samples on modeling a target sample, where the amount of weight depends on similarity of cancer characteristics between samples. The method, however, cannot perform gene regulatory network identification well for a target sample in a sparse region (i.e., for a target sample, there are only a few samples having a similar characteristic of the target sample, where the characteristic is considered as a modulator in sample-specific gene network construction), since a constant bandwidth in the Gaussian kernel function cannot effectively group samples for modeling a target sample in sparse region. The cancer characteristics, such as an anti-cancer drug sensitivity, are usually nonuniformly distributed, and thus modeling for samples in a sparse region is also a crucial issue. We propose a novel kernel-based L1-type regularization method based on a modified k-nearest neighbor (KNN)-Gaussian kernel function, called an adaptive NetworkProfiler. By using the modified KNN-Gaussian kernel function, our method provides robust results against the distribution of modulators, and properly groups samples according to a cancer characteristic for sample-specific analysis. Furthermore, we propose a sample

  5. Characteristic flow patterns generated by macrozoobenthic structures

    Science.gov (United States)

    Friedrichs, M.; Graf, G.

    2009-02-01

    A laboratory flume channel, equipped with an acoustic Doppler flow sensor and a bottom scanning laser, was used for detailed, non-intrusive flow measurements (at 2 cm s - 1 and 10 cm s - 1 ) around solitary biogenic structures, combined with high-resolution mapping of the structure shape and position. The structures were replicates of typical macrozoobenthic species commonly found in the Mecklenburg Bight and with a presumed influence on both, the near-bed current regime and sediment transport dynamics: a worm tube, a snail shell, a mussel, a sand mound, a pit, and a cross-stream track furrow. The flow was considerably altered locally by the different protruding structures (worm tube, snail, mussel and mound). They reduced the horizontal approach velocity by 72% to 79% in the wake zone at about 1-2 cm height, and the flow was deflected around the structures with vertical and lateral velocities of up to 10% and 20% of the free-stream velocity respectively in a region adjacent to the structures. The resulting flow separation (at flow Reynolds number of about 4000 and 20,000 respectively) divided an outer deflection region from an inner region with characteristic vortices and the wake region. All protruding structures showed this general pattern, but also produced individual characteristics. Conversely, the depressions (track and pit) only had a weak influence on the local boundary layer flow, combined with a considerable flow reduction within their cavities (between 29% and 53% of the free-stream velocity). A longitudinal vortex formed, below which a stagnant space was found. The average height affected by the structure-related mass flow rate deficit for the two velocities was 1.6 cm and 1.3 cm respectively (80% of height and 64%) for the protruding structures and 0.6 cm and 0.9 cm (90% and 127% of depth) for the depressions. Marine benthic soft-bottom macrozoobenthos species are expected to benefit from the flow modifications they induce, particularly in terms of

  6. Supercritical flow characteristics at abrupt expansion structure

    Science.gov (United States)

    Lim, Jia Jun; Puay, How Tion; Zakaria, Nor Azazi

    2017-10-01

    When dealing with the design of a hydraulic structure, lateral expansion is often necessary for flow emerging at high velocity served as a cross-sectional transition. If the abrupt expansion structure is made to diverge rapidly, it will cause the major part of the flow fail to follow the boundaries. If the transition is too gradual, it will result in a waste of structural material. A preliminary study on the flow structure near the expansion and its relationship with flow parameter is carried out in this study. A two-dimensional depth-averaged model is developed to simulate the supercritical flow at the abrupt expansion structure. Constrained Interpolation Profile (CIP) scheme (which is of third order accuracy) is adopted in the numerical model. Results show that the flow structure and flow characteristics at the abrupt expansion can be reproduced numerically. The validation of numerical result is done against analytical studies. The result from numerical simulation showed good agreement with the analytical solution.

  7. Network characteristics emerging from agent interactions in balanced distributed system.

    Science.gov (United States)

    Salman, Mahdi Abed; Bertelle, Cyrille; Sanlaville, Eric

    2015-01-01

    A distributed computing system behaves like a complex network, the interactions between nodes being essential information exchanges and migrations of jobs or services to execute. These actions are performed by software agents, which behave like the members of social networks, cooperating and competing to obtain knowledge and services. The load balancing consists in distributing the load evenly between system nodes. It aims at enhancing the resource usage. A load balancing strategy specifies scenarios for the cooperation. Its efficiency depends on quantity, accuracy, and distribution of available information. Nevertheless, the distribution of information on the nodes, together with the initial network structure, may create different logical network structures. In this paper, different load balancing strategies are tested on different network structures using a simulation. The four tested strategies are able to distribute evenly the load so that the system reaches a steady state (the mean response time of the jobs is constant), but it is shown that a given strategy indeed behaves differently according to structural parameters and information spreading. Such a study, devoted to distributed computing systems (DCSs), can be useful to understand and drive the behavior of other complex systems.

  8. Robustness in Weighted Networks with Cluster Structure

    Directory of Open Access Journals (Sweden)

    Yi Zheng

    2014-01-01

    Full Text Available The vulnerability of complex systems induced by cascade failures revealed the comprehensive interaction of dynamics with network structure. The effect on cascade failures induced by cluster structure was investigated on three networks, small-world, scale-free, and module networks, of which the clustering coefficient is controllable by the random walk method. After analyzing the shifting process of load, we found that the betweenness centrality and the cluster structure play an important role in cascading model. Focusing on this point, properties of cascading failures were studied on model networks with adjustable clustering coefficient and fixed degree distribution. In the proposed weighting strategy, the path length of an edge is designed as the product of the clustering coefficient of its end nodes, and then the modified betweenness centrality of the edge is calculated and applied in cascade model as its weights. The optimal region of the weighting scheme and the size of the survival components were investigated by simulating the edge removing attack, under the rule of local redistribution based on edge weights. We found that the weighting scheme based on the modified betweenness centrality makes all three networks have better robustness against edge attack than the one based on the original betweenness centrality.

  9. A clustering algorithm for determining community structure in complex networks

    Science.gov (United States)

    Jin, Hong; Yu, Wei; Li, ShiJun

    2018-02-01

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

  10. 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.

  11. An Evaluation Framework for Large-Scale Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Knudsen, Thomas Phillip; Madsen, Ole Brun

    2004-01-01

    An evaluation framework for large-scale network structures is presented, which facilitates evaluations and comparisons of different physical network structures. A number of quantitative and qualitative parameters are presented, and their importance to networks discussed. Choosing a network...... is closed by an example of how the framework can be used. The framework supports network planners in decision-making and researchers in evaluation and development of network structures....

  12. THE GOVERNANCE STRUCTURE OF COOPERATIVE NETWORKS

    National Research Council Canada - National Science Library

    Rosileia Milagres

    2014-01-01

    .... The analysis shows that the governance structure is influenced by the objectives established, the partners' experience, the types of knowledge and the context where network is inserted. The case highlights the importance of learning during the process, but, although present, it can be negatively influenced by the context and the possibility of future partnerships.

  13. 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.…

  14. Structural network efficiency predicts conversion to dementia

    NARCIS (Netherlands)

    Tuladhar, A.; van Uden, I.W.M.; Rutten-Jacobs, L.C.A.; van der Holst, H.; van Norden, A.; de Laat, K.; Dijk, E.; Claassen, J.A.H.R.; Kessels, R.P.C.; Markus, H.S.; Norris, David Gordon; de Leeuw, F.E.

    2016-01-01

    Objective: To examine whether structural network connectivity at baseline predicts incident all-cause dementia in a prospective hospital-based cohort of elderly participants with MRI evidence of small vessel disease (SVD). Methods: A total of 436 participants from the Radboud University Nijmegen

  15. Structures and Statistics of Citation Networks

    Science.gov (United States)

    2011-05-01

    assignment procedure ( QAP ) (14) and its regression counterpart MRQAP (15) have been used to detect structural significance and compare networks in...Correcting Codes. Hamming, R.W. 2, s.l. : Bell System Technical Journal, 1950, Vol. 29, pp. 147--160. 14. QAP Partialling as a Test of Spuriousness* 1

  16. Social Network Characteristics and Daily Smoking among Young Adults in Sweden

    Directory of Open Access Journals (Sweden)

    Mikael Rostila

    2013-11-01

    Full Text Available A large number of studies have shown that friends’ smoking behavior is strongly associated with an individual’s own risk for smoking. However, few studies have examined whether other features of social networks, independently or conjointly with friends’ smoking behavior, may influence the risk for smoking. Because it is characterized by the growing importance of friendship networks, the transition from adolescence to young adulthood may constitute a particularly relevant period on which to focus our investigation of network influences on smoking behavior. The aim of this study was therefore to examine the consequences of peer smoking as well as other network characteristics (friends’ other health behaviors, relationship content, and structural aspects of the network on the risk for smoking among young adults. The data was based on a cross-sectional survey of Swedish 19-year-olds carried out in 2009 (n = 5,695 with a response rate of 51.6%. Logistic regression was the primary method of analysis. The results show that having a large percentage of smokers in one’s network was by far the most important risk factor for daily smoking. The risk of daily smoking was 21.20 (CI 14.24. 31.54 if 76%–100% of the network members smoked. Having a high percentage of physically active friends was inversely associated with daily smoking. The risk of smoking was 0.65 (CI 0.42. 1.00 if 76%–100% of the network members were physically active. No main associations between the other network characteristics (relationship content and structural aspects of the network and smoking were found. However, there was an interaction between the percentage of smokers in the network and relationship content (i.e., trust, relationship quality and propensity to discuss problems: positive relationship content in combination with peer smoking may increase the risk of smoking. Women with a high percentage of smokers in their networks were also at higher risk of daily

  17. 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.

  18. A Robust Method for Inferring Network Structures.

    Science.gov (United States)

    Yang, Yang; Luo, Tingjin; Li, Zhoujun; Zhang, Xiaoming; Yu, Philip S

    2017-07-12

    Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provides a great chance to solve the problem. In this paper, we propose an iterative smoothing algorithm with structure sparsity (ISSS) method. The elastic penalty in the model is introduced for the sparse solution, identifying group features and avoiding over-fitting, and the total variation (TV) penalty in the model can effectively utilize the structure information to identify the neighborhood of the vertices. Due to the non-smoothness of the elastic and structural TV penalties, an efficient algorithm with the Nesterov's smoothing optimization technique is proposed to solve the non-smooth problem. The experimental results on both synthetic and real-world networks show that the proposed model is robust against insufficient data and high noise. In addition, we investigate many factors that play important roles in identifying the performance of ISSS.

  19. Science collaboration networks in sociology and their general characteristics in Serbia

    Directory of Open Access Journals (Sweden)

    Lazar Zsolt

    2012-01-01

    Full Text Available Science collaboration networks occupy an important place in the analysis of social networks. There are several directions of concern in this kind of research: one attempts to explain the structure of science collaboration networks, the second one analyses the invisible scientific communities and the third one is dealing with the problems of different citation forms which function as a base from which many of the conclusions concerning these networks are being derived. However, the results of recent research in science collaboration networks indicate that there are a number of idiosyncratic characteristics of collaboration networks in sociology. In the first part of the paper the author analyzes the results of previous research of sociological social networks, published in scientific papers available in the following databases: JSTOR, ScienceDirect and SpringerLink. The authors then proceeds with the analysis of social networks of sociologists in Serbia and gives guidelines of the first research project dedicated to the analysis of social networks in Serbia.

  20. Network structure of multivariate time series

    Science.gov (United States)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-01

    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.

  1. The fundamental structures of dynamic social networks

    CERN Document Server

    Sekara, Vedran; Lehmann, Sune

    2015-01-01

    Networks provide a powerful mathematical framework for analyzing the structure and dynamics of complex systems (1-3). The study of group behavior has deep roots in the social science literature (4,5) and community detection is a central part of modern network science. Network communities have been found to be highly overlapping and organized in a hierarchical structure (6-9). Recent technological advances have provided a toolset for measuring the detailed social dynamics at scale (10,11). In spite of great progress, a quantitative description of the complex temporal behavior of social groups-with dynamics spanning from minute-by-minute changes to patterns expressed on the timescale of years-is still absent. Here we uncover a class of fundamental structures embedded within highly dynamic social networks. On the shortest time-scale, we find that social gatherings are fluid, with members coming and going, but organized via a stable core of individuals. We show that cores represent social contexts (9), with recur...

  2. Computational 3D imaging to quantify structural components and assembly of protein networks.

    Science.gov (United States)

    Asgharzadeh, Pouyan; Özdemir, Bugra; Reski, Ralf; Röhrle, Oliver; Birkhold, Annette I

    2018-03-15

    Traditionally, protein structures have been described by the secondary structure architecture and fold arrangement. However, the relatively novel method of 3D confocal microscopy of fluorescent-protein-tagged networks in living cells allows resolving the detailed spatial organization of these networks. This provides new possibilities to predict network functionality, as structure and function seem to be linked at various scales. Here, we propose a quantitative approach using 3D confocal microscopy image data to describe protein networks based on their nano-structural characteristics. This analysis is constructed in four steps: (i) Segmentation of the microscopic raw data into a volume model and extraction of a spatial graph representing the protein network. (ii) Quantifying protein network gross morphology using the volume model. (iii) Quantifying protein network components using the spatial graph. (iv) Linking these two scales to obtain insights into network assembly. Here, we quantitatively describe the filamentous temperature sensitive Z protein network of the moss Physcomitrella patens and elucidate relations between network size and assembly details. Future applications will link network structure and functionality by tracking dynamic structural changes over time and comparing different states or types of networks, possibly allowing more precise identification of (mal) functions or the design of protein-engineered biomaterials for applications in regenerative medicine. Protein networks are highly complex and dynamic structures that play various roles in biological environments. Analyzing the detailed spatial structure of these networks may lead to new insight into biological functions and malfunctions. Here, we propose a tool set that extracts structural information at two scales of the protein network and allows therefore to address questions such as "how is the network built?" or "how networks grow?". Copyright © 2018 Acta Materialia Inc. Published by

  3. Realization of Negative Group Delay Network Using Defected Microstrip Structure

    Directory of Open Access Journals (Sweden)

    Girdhari Chaudhary

    2014-01-01

    Full Text Available A design of negative group delay (NGD networks using a U-shaped defected microstrip structure (DMS and lumped elements is presented in this paper. The signal attenuation characteristics of DMS were utilized to get NGD time. The group delay (GD time and signal attenuation of the proposed networks are controlled by an external resistor connected across the DMS slot. For experimental validation, a single-stage and cascaded two-stage NGD networks were designed and fabricated. From experimental results, the GD of -8.24±1.1 ns with the maximum insertion loss of 37.84 dB was obtained over bandwidth of 40 MHz.

  4. Methods and applications for detecting structure in complex networks

    Science.gov (United States)

    Leicht, Elizabeth A.

    The use of networks to represent systems of interacting components is now common in many fields including the biological, physical, and social sciences. Network models are widely applicable due to their relatively simple framework of vertices and edges. Network structure, patterns of connection between vertices, impacts both the functioning of networks and processes occurring on networks. However, many aspects of network structure are still poorly understood. This dissertation presents a set of network analysis methods and applications to real-world as well as simulated networks. The methods are divided into two main types: linear algebra formulations and probabilistic mixture model techniques. Network models lend themselves to compact mathematical representation as matrices, making linear algebra techniques useful probes of network structure. We present methods for the detection of two distinct, but related, network structural forms. First, we derive a measure of vertex similarity based upon network structure. The method builds on existing ideas concerning calculation of vertex similarity, but generalizes and extends the scope to large networks. Second, we address the detection of communities or modules in a specific class of networks, directed networks. We propose a method for detecting community structure in directed networks, which is an extension of a community detection method previously only known for undirected networks. Moving away from linear algebra formulations, we propose two methods for network structure detection based on probabilistic techniques. In the first method, we use the machinery of the expectation-maximization (EM) algorithm to probe patterns of connection among vertices in static networks. The technique allows for the detection of a broad range of types of structure in networks. The second method focuses on time evolving networks. We propose an application of the EM algorithm to evolving networks that can reveal significant structural

  5. Structural determinants of criticality in biological networks.

    Science.gov (United States)

    Valverde, Sergi; Ohse, Sebastian; Turalska, Malgorzata; West, Bruce J; Garcia-Ojalvo, Jordi

    2015-01-01

    Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness, and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behavior in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organization can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system toward criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality.

  6. Structural Determinants of Criticality in Biological Networks

    Directory of Open Access Journals (Sweden)

    Sergi eValverde

    2015-05-01

    Full Text Available Many adaptive evolutionary systems display spatial and temporal features, such as long-range correlations, typically associated with the critical point of a phase transition in statistical physics. Empirical and theoretical studies suggest that operating near criticality enhances the functionality of biological networks, such as brain and gene networks, in terms for instance of information processing, robustness and evolvability. While previous studies have explained criticality with specific system features, we still lack a general theory of critical behaviour in biological systems. Here we look at this problem from the complex systems perspective, since in principle all critical biological circuits have in common the fact that their internal organisation can be described as a complex network. An important question is how self-similar structure influences self-similar dynamics. Modularity and heterogeneity, for instance, affect the location of critical points and can be used to tune the system towards criticality. We review and discuss recent studies on the criticality of neuronal and genetic networks, and discuss the implications of network theory when assessing the evolutionary features of criticality.

  7. Structure and mechanics of aegagropilae fiber network.

    Science.gov (United States)

    Verhille, Gautier; Moulinet, Sébastien; Vandenberghe, Nicolas; Adda-Bedia, Mokhtar; Le Gal, Patrice

    2017-05-02

    Fiber networks encompass a wide range of natural and manmade materials. The threads or filaments from which they are formed span a wide range of length scales: from nanometers, as in biological tissues and bundles of carbon nanotubes, to millimeters, as in paper and insulation materials. The mechanical and thermal behavior of these complex structures depends on both the individual response of the constituent fibers and the density and degree of entanglement of the network. A question of paramount importance is how to control the formation of a given fiber network to optimize a desired function. The study of fiber clustering of natural flocs could be useful for improving fabrication processes, such as in the paper and textile industries. Here, we use the example of aegagropilae that are the remains of a seagrass (Posidonia oceanica) found on Mediterranean beaches. First, we characterize different aspects of their structure and mechanical response, and second, we draw conclusions on their formation process. We show that these natural aggregates are formed in open sea by random aggregation and compaction of fibers held together by friction forces. Although formed in a natural environment, thus under relatively unconstrained conditions, the geometrical and mechanical properties of the resulting fiber aggregates are quite robust. This study opens perspectives for manufacturing complex fiber network materials.

  8. 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.

  9. 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.

  10. Community structure in introductory physics course networks

    CERN Document Server

    Traxler, Adrienne L

    2015-01-01

    Student-to-student interactions are foundational to many active learning environments, but are most often studied using qualitative methods. Network analysis tools provide a quantitative complement to this picture, allowing researchers to describe the social interactions of whole classrooms as systems. Past results from introductory physics courses have suggested a sharp division in the formation of social structure between large lecture sections and small studio classroom environments. Extending those results, this study focuses on calculus-based introductory physics courses at a large public university with a heavily commuter and nontraditional student population. Community detection network methods are used to characterize pre- and post-course collaborative structure in several sections, and differences are considered between small and large classes. These results are compared with expectations from earlier findings, and comment on implications for instruction and further study.

  11. Finding local community structure in networks

    Science.gov (United States)

    Clauset, Aaron

    2005-08-01

    Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring the graph one vertex at a time. This algorithm runs in time O(k2d) for general graphs when d is the mean degree and k is the number of vertices to be explored. For graphs where exploring a new vertex is time consuming, the running time is linear, O(k) . We show that on computer-generated graphs the average behavior of this technique approximates that of algorithms that require global knowledge. As an application, we use this algorithm to extract meaningful local clustering information in the large recommender network of an online retailer.

  12. Hierarchical Neural Network Structures for Phoneme Recognition

    CERN Document Server

    Vasquez, Daniel; Minker, Wolfgang

    2013-01-01

    In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are evaluated on the phoneme recognition task where a  Hybrid Hidden Markov Model/Artificial Neural Network paradigm is used. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron. Additionally, the output of the first level serves as a second level input. The computational speed of the phoneme recognizer can be substantially increased by removing redundant information still contained at the first level output. Several techniques based on temporal and phonetic criteria have been investigated to remove this redundant information. The computational time could be reduced by 57% whilst keeping the system accuracy comparable to the baseline hierarchical approach.

  13. Walk modularity and community structure in networks

    OpenAIRE

    Mehrle, David; Strosser, Amy; Harkin, Anthony

    2014-01-01

    Modularity maximization has been one of the most widely used approaches in the last decade for discovering community structure in networks of practical interest in biology, computing, social science, statistical mechanics, and more. Modularity is a quality function that measures the difference between the number of edges found within clusters minus the number of edges one would statistically expect to find based on random chance. We present a natural generalization of modularity based on the ...

  14. A network characteristic that correlates environmental and genetic robustness.

    Directory of Open Access Journals (Sweden)

    Zeina Shreif

    2014-02-01

    Full Text Available As scientific advances in perturbing biological systems and technological advances in data acquisition allow the large-scale quantitative analysis of biological function, the robustness of organisms to both transient environmental stresses and inter-generational genetic changes is a fundamental impediment to the identifiability of mathematical models of these functions. An approach to overcoming this impediment is to reduce the space of possible models to take into account both types of robustness. However, the relationship between the two is still controversial. This work uncovers a network characteristic, transient responsiveness, for a specific function that correlates environmental imperturbability and genetic robustness. We test this characteristic extensively for dynamic networks of ordinary differential equations ranging up to 30 interacting nodes and find that there is a power-law relating environmental imperturbability and genetic robustness that tends to linearity as the number of nodes increases. Using our methods, we refine the classification of known 3-node motifs in terms of their environmental and genetic robustness. We demonstrate our approach by applying it to the chemotaxis signaling network. In particular, we investigate plausible models for the role of CheV protein in biochemical adaptation via a phosphorylation pathway, testing modifications that could improve the robustness of the system to environmental and/or genetic perturbation.

  15. Database on wind characteristics - Structure and philosophy

    Energy Technology Data Exchange (ETDEWEB)

    Larsen, G.C.; Hansen, K.S.

    2001-11-01

    The main objective of IEA R and D Wind Annex XVII - Database on Wind Characteristics - is to provide wind energy planners and designers, as well as the international wind engineering community in general, with easy access to quality controlled measured wind field time series observed in a wide range of environments. The project partners are Sweden, Norway, U.S.A., The Netherlands, Japan and Denmark, with Denmark as the Operating Agent. The reporting of IEA R and D Annex XVII falls in three separate parts. Part one deals with the overall structure and philosophy behind the database, part two accounts in details for the available data in the established database bank and part three is the Users Manual describing the various ways to access and analyse the data. The present report constitutes the first part of the Annex XVII reporting, and it contains a detailed description of the database structure, the data quality control procedures, the selected indexing of the data and the hardware system. (au)

  16. Structure and dynamics of core-periphery networks

    CERN Document Server

    Csermely, Peter; Wu, Ling-Yun; Uzzi, Brian

    2013-01-01

    Recent studies uncovered important core/periphery network structures characterizing complex sets of cooperative and competitive interactions between network nodes, be they proteins, cells, species or humans. Better characterization of the structure, dynamics and function of core/periphery networks is a key step of our understanding cellular functions, species adaptation, social and market changes. Here we summarize the current knowledge of the structure and dynamics of "traditional" core/periphery networks, rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery structures with network modules, we discriminate between global and local cores. The core/periphery network organization lies in the middle of several extreme properties, such as random/condensed structures, clique/star configurations, network symmetry/asymmetry, network assortativity/disassortativity, as well as network hierarchy/anti-hierarchy. These properties of high complexity together with the large degeneracy of core pathways e...

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

    Indian Academy of Sciences (India)

    , uncovering complex network structure and function from these networks is becoming one of the most important topics in system biology. This work aims at studying the structure and function of Staphylococcus aureus (S. aureus) metabolic ...

  18. The geospatial characteristics of a social movement communication network.

    Directory of Open Access Journals (Sweden)

    Michael D Conover

    Full Text Available Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements' objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement's efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.

  19. The geospatial characteristics of a social movement communication network.

    Science.gov (United States)

    Conover, Michael D; Davis, Clayton; Ferrara, Emilio; McKelvey, Karissa; Menczer, Filippo; Flammini, Alessandro

    2013-01-01

    Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements' objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement's efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.

  20. Network characteristics for server selection in online games

    Science.gov (United States)

    Claypool, Mark

    2008-01-01

    Online gameplay is impacted by the network characteristics of players connected to the same server. Unfortunately, the network characteristics of online game servers are not well-understood, particularly for groups that wish to play together on the same server. As a step towards a remedy, this paper presents analysis of an extensive set of measurements of game servers on the Internet. Over the course of many months, actual Internet game servers were queried simultaneously by twenty-five emulated game clients, with both servers and clients spread out on the Internet. The data provides statistics on the uptime and populations of game servers over a month long period an an in-depth look at the suitability for game servers for multi-player server selection, concentrating on characteristics critical to playability--latency and fairness. Analysis finds most game servers have latencies suitable for third-person and omnipresent games, such as real-time strategy, sports and role-playing games, providing numerous server choices for game players. However, far fewer game servers have the low latencies required for first-person games, such as shooters or race games. In all cases, groups that wish to play together have a greatly reduced set of servers from which to choose because of inherent unfairness in server latencies and server selection is particularly limited as the group size increases. These results hold across different game types and even across different generations of games. The data should be useful for game developers and network researchers that seek to improve game server selection, whether for single or multiple players.

  1. Structural host-microbiota interaction networks.

    Science.gov (United States)

    Guven-Maiorov, Emine; Tsai, Chung-Jung; Nussinov, Ruth

    2017-10-01

    Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.

  2. Higher-order structure and epidemic dynamics in clustered networks.

    Science.gov (United States)

    Ritchie, Martin; Berthouze, Luc; House, Thomas; Kiss, Istvan Z

    2014-05-07

    Clustering is typically measured by the ratio of triangles to all triples regardless of whether open or closed. Generating clustered networks, and how clustering affects dynamics on networks, is reasonably well understood for certain classes of networks (Volz et al., 2011; Karrer and Newman, 2010), e.g. networks composed of lines and non-overlapping triangles. In this paper we show that it is possible to generate networks which, despite having the same degree distribution and equal clustering, exhibit different higher-order structure, specifically, overlapping triangles and other order-four (a closed network motif composed of four nodes) structures. To distinguish and quantify these additional structural features, we develop a new network metric capable of measuring order-four structure which, when used alongside traditional network metrics, allows us to more accurately describe a network׳s topology. Three network generation algorithms are considered: a modified configuration model and two rewiring algorithms. By generating homogeneous networks with equal clustering we study and quantify their structural differences, and using SIS (Susceptible-Infected-Susceptible) and SIR (Susceptible-Infected-Recovered) dynamics we investigate computationally how differences in higher-order structure impact on epidemic threshold, final epidemic or prevalence levels and time evolution of epidemics. Our results suggest that characterising and measuring higher-order network structure is needed to advance our understanding of the impact of network topology on dynamics unfolding on the networks. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  3. 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.

  4. 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.

  5. 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.

  6. The complex channel networks of bone structure

    CERN Document Server

    Costa, Luciano da Fontoura; Beletti, Marcelo E

    2006-01-01

    Bone structure in mammals involves a complex network of channels (Havers and Volkmann channels) required to nourish the bone marrow cells. This work describes how three-dimensional reconstructions of such systems can be obtained and represented in terms of complex networks. Three important findings are reported: (i) the fact that the channel branching density resembles a power law implies the existence of distribution hubs; (ii) the conditional node degree density indicates a clear tendency of connection between nodes with degrees 2 and 4; and (iii) the application of the recently introduced concept of hierarchical clustering coefficient allows the identification of typical scales of channel redistribution. A series of important biological insights is drawn and discussed

  7. Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks.

    Science.gov (United States)

    Chen, Xiaoguang; Jiang, Xu; Wu, Zhilu; Zhuang, Shufeng

    2017-02-16

    High throughput, low latency and reliable communication has always been a hot topic for wireless sensor networks (WSNs) in various applications. Multiuser detection is widely used to suppress the bad effect of multiple access interference in WSNs. In this paper, a novel multiuser detection method based on differential characteristics is proposed to suppress multiple access interference. The proposed iterative receive method consists of three stages. Firstly, a differential characteristics function is presented based on the optimal multiuser detection decision function; then on the basis of differential characteristics, a preliminary threshold detection is utilized to find the potential wrongly received bits; after that an error bit corrector is employed to correct the wrong bits. In order to further lower the bit error ratio (BER), the differential characteristics calculation, threshold detection and error bit correction process described above are iteratively executed. Simulation results show that after only a few iterations the proposed multiuser detection method can achieve satisfactory BER performance. Besides, BER and near far resistance performance are much better than traditional suboptimal multiuser detection methods. Furthermore, the proposed iterative multiuser detection method also has a large system capacity.

  8. 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.

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

    Science.gov (United States)

    Goekoop, Rutger; Goekoop, Jaap G.; Scholte, H. Steven

    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 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. PMID:23284713

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

    Science.gov (United States)

    Goekoop, Rutger; Goekoop, Jaap G; Scholte, H Steven

    2012-01-01

    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. 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). 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. 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. 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.

  11. Information diversity in structure and dynamics of simulated neuronal networks.

    Science.gov (United States)

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Nykter, Matti; Kesseli, Juha; Ruohonen, Keijo; Yli-Harja, Olli; Linne, Marja-Leena

    2011-01-01

    Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

  12. Information Diversity in Structure and Dynamics of Simulated Neuronal Networks

    Directory of Open Access Journals (Sweden)

    Tuomo eMäki-Marttunen

    2011-06-01

    Full Text Available Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance (NCD. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviours are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses.We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.

  13. Stable Matching with Incomplete Information in Structured Networks

    OpenAIRE

    Ling, Ying; Wan, Tao; Qin, Zengchang

    2015-01-01

    In this paper, we investigate stable matching in structured networks. Consider case of matching in social networks where candidates are not fully connected. A candidate on one side of the market gets acquaintance with which one on the heterogeneous side depends on the structured network. We explore four well-used structures of networks and define the social circle by the distance between each candidate. When matching within social circle, we have equilibrium distinguishes from each other sinc...

  14. Sex Differences in Virtual Network Characteristics and Sexual Risk Behavior among Emerging Adults

    Science.gov (United States)

    Cook, Stephanie H.; Bauermeister, José A.; Zimmerman, Marc A.

    2016-01-01

    Emerging adults (EAs)ages 18 to 24 account for a large proportion of all sexually transmitted infections (STIs), HIV infections, and unintended pregnancies in the United States. Given the increased influence of online media on decision-making, we examined how EA online networks were associated with sexual risk behaviors. We used egocentric network data collected from EAs aged 18 to 24 years old across the United States (N=1,687) to examine how online norms (e.g., acceptance of HIV infections, other STIs, and pregnancy) and network characteristics (i.e., network size and density; ties' closeness, race, age, and sex similarities) were associated with participants' unprotected vaginal intercourse (UVI) in the last 30 days. Findings suggested that in male EAs, there was a strong association between sexual norms, structural characteristics, and sexual risk behavior compared to females. Researchers and practitioners may wish to address online peer norms and EAs' online network composition when developing online sexual risk prevention tools. PMID:28083447

  15. Dismissing attachment characteristics dynamically modulate brain networks subserving social aversion.

    Directory of Open Access Journals (Sweden)

    Anna Linda eKrause

    2016-03-01

    prediction of neuronal responses by individual attachment and trauma characteristics and reversely prediction of subjective experience by intrinsic functional connections. We consider these findings of activation of within-network and between-network connectivity modulated by inter-individual differences as substantial for the understanding of interpersonal processes, particularly in clinical settings.

  16. 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.

  17. Fundamental structures of dynamic social networks

    DEFF Research Database (Denmark)

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

    2016-01-01

    unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit...... a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework......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...

  18. Dynamics and control of diseases in networks with community structure.

    Directory of Open Access Journals (Sweden)

    Marcel Salathé

    2010-04-01

    Full Text Available The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc. depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

  19. 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....

  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. STRUCTURE OF MORPHOLOGIC CHARACTERISTICS OF BODYBUILDERS

    Directory of Open Access Journals (Sweden)

    Vladimir Mutavdžić

    2007-05-01

    Full Text Available Today body-building as a spport is well known and accepted world-wide. When scientifi researches on this fi eld are in question, it is evident that they are very rare partly and probably due to its real interpretation as well as due to its insuffi cient affi rmation and popularization here. Reason for this is also the wrong interpretation of the sport’s essence among sports-people – the body-builders themselves and among the experts and scientists from this fi eld. Intrest in the research of morphological sphere of body-builders was inspired by the fact that exercising body-building is a very specifi c physical activity that, due to its nature, demands body-builders to have specifi c morphological characteristics that characterize and differ them from other sports-people. The goal of this research was determining the structure of the morhologicall sphere of a body-builder. It was performed on the sample of 30 selected sportsmen, bodybuilders, of chronological age between 17 and 19 ( 6 months, members of the Sports' Club Strength ''Leskovac'', the Weight Lifters' Club '' Dubočica'' and the Body-building Club '' Dubočica'' from Leskovac. All the examiees have been submitted to training processes during a period longer than a year. For the purpose of determining the structure of the morphological sphere the Factor Analysis has been applied. Based on the data from the matrix of the Factor Structure the isolated factors can be interpreted in the following manner: The fi rst isolated factor in the sphere of applied anthropometric variables can be defi ned as a dimension of the body-volume and the transversal dimensionality of the skeleton sine it’s best defi ned by the following variables: the chest-circumference (AOGKS, the upper-arm circumference (AONAD, the thigh circumference (AOBUT, the body-weight (AMAST and the shoulder-width (ASIRA. The second isolated factor in the sphere of applied anthropometric variables can be defi ned as the

  2. Social networks of people with mild intellectual disabilities: characteristics, satisfaction, wishes and quality of life.

    Science.gov (United States)

    van Asselt-Goverts, A E; Embregts, P J C M; Hendriks, A H C

    2015-05-01

    A supportive social network is crucial for facilitating social inclusion, which can, in turn, contribute to the quality of life (QOL) for people with intellectual disabilities (ID). In this study, we investigate how people with mild ID perceive their social networks and which network characteristics relate to satisfaction with the network and perceived QOL. Data were gathered from 33 young adults with a mild to borderline ID using structured questionnaires: the MSNA to map the social network, the IDQOL-16 to assess QOL, and a questionnaire to determine satisfaction and wishes with regard to the social network. The majority of the participants (73.1%) were satisfied with their social networks. Improvement in the area of strengthening existing ties (e.g. more frequent contact, better contact) was desired as opposed to expansion of the network. Affection--especially towards family and professionals--was most strongly related to perceived QOL. It appears to be essential that relatives live in the same town, can frequently meet up and provide both emotional and practical support. The significance of family and the importance of high-quality interpersonal relationships between professional and client in the lives of young adults with ID cannot be overestimated. Although measures of satisfaction and wishes can have limitations, in actual practice it is considered useful to assess the opinions of clients with respect to their social networks. Interventions can then be tailored to the needs and wishes of the persons themselves. © 2014 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

  3. 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.

  4. How structure determines correlations in neuronal networks

    National Research Council Canada - National Science Library

    Pernice, Volker; Staude, Benjamin; Cardanobile, Stefano; Rotter, Stefan

    2011-01-01

    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...

  5. 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.

  6. Fracture network topology and characterization of structural permeability

    Science.gov (United States)

    Hansberry, Rowan; King, Rosalind; Holford, Simon

    2017-04-01

    other permeability indicators such as drilling fluid losses, and pore pressure measurements. Initial work with these techniques has led to new developments in our ability to image subsurface faults and fractures at a variety of scales from independent datasets. We establish a strong relationship between features identified using seismic attribute analysis and interpreted natural fractures. However, care must be taken to use these methods in a case-by-case basis, as controls on fracture distribution and orientation can vary significantly with both regional and local influences. These results outline and effective method by which structural permeability can be assessed with existing petroleum datasets. However, unlike the broad stress field, mapping fracture orientation and characteristics within the Australian Continent is complicated as the distribution, geometry, areal extent and connectivity of fracture networks can vary significantly.

  7. 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

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

    Science.gov (United States)

    Ilany, Amiyaal; Akçay, Erol

    2016-06-28

    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.

  9. 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.

  10. Spectral characteristics of fiber couplers for FTTx networks

    Science.gov (United States)

    Vitasek, Jan; Latal, Jan; Koudelka, Petr; Vasinek, Vladimir; Bocheza, Jiri; Hejduk, Stanislav

    2012-06-01

    This article deals with spectral characteristics measurement of fiber couplers which are used for FTTx networks. Due to WDM systems we are able to communicate with several wavelengths at a time. In xPON systems the data transmission runs at wavelengths 1310 nm, 1490 and 1550 nm, in case of using singlemode fibers, or at 850 nm and 1300 nm in case of using multimode fibers. The target of this work is a testing how the individual parameters of fiber coupler behave whether broad spectrum light source is connected to the input. In sum it was measured four most often used fiber couplers, fiber coupler in port configuration 1x2 with coupling ratio 50/50%, fiber coupler in port configuration 1x2 with coupling ratio 30/70%, fiber coupler in port configuration 1x2 with coupling ratio 10/90% and fiber coupler in port configuration 1×4 with coupling ratio 4×25%. For these fiber couplers it was set insertion losses, coupling ratios, homogeneities and total losses by using a broad spectrum light source. The results are valuable information for companies which deal with optical networks.

  11. Community Structure in Time-Dependent, Multiscale, and Multiplex Networks

    OpenAIRE

    Mucha, Peter J; Richardson, Thomas; Macon, Kevin; Porter, Mason A.; Onnela, Jukka-Pekka

    2009-01-01

    Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly-connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that con...

  12. 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....

  13. Seasonal changes in the structure of rhesus macaque social networks.

    Science.gov (United States)

    Brent, Lauren J N; Maclarnon, Ann; Platt, Michael L; Semple, Stuart

    2013-03-01

    Social structure emerges from the patterning of interactions between individuals and plays a critical role in shaping some of the main characteristics of animal populations. The topological features of social structure, such as the extent to which individuals interact in clusters, can influence many biologically important factors, including the persistence of cooperation, and the rate of spread of disease. Yet the extent to which social structure topology fluctuates over relatively short periods of time in relation to social, demographic or environmental events remains unclear. Here, we use social network analysis to examine seasonal changes in the topology of social structures that emerge from socio-positive associations in adult female rhesus macaques ( Macaca mulatta ). Behavioral data for two different association types (grooming, spatial proximity) were collected for females in two free-ranging groups during two seasons: the mating and birth seasons. Stronger dyadic bonds resulted in social structures that were more tightly connected (i.e. of greater density) in the mating season compared to the birth season. Social structures were also more centralized around a subset of individuals, and were more clustered in the mating season than the birth season, although the latter differences were mostly driven by differences in density alone. Our results suggest a degree of temporal variation in the topological features of social structure in this population. Such variation may feed back on interactions, hence affecting the behaviors of individuals, and may therefore be important to take into account in studies of animal behavior.

  14. Phenology drives mutualistic network structure and diversity

    NARCIS (Netherlands)

    Encinas Viso, Francisco; Revilla, Tomas A; Etienne, Rampal S.

    Several network properties have been identified as determinants of the stability and complexity of mutualistic networks. However, it is unclear which mechanisms give rise to these network properties. Phenology seems important, because it shapes the topology of mutualistic networks, but its effects

  15. Analysis of Ego Network Structure in Online Social Networks

    OpenAIRE

    Arnaboldi, Valerio; Conti, Marco; Passarella, Andrea; Pezzoni, Fabio

    2012-01-01

    Results about offline social networks demonstrated that the social relationships that an individual (ego) maintains with other people (alters) can be organised into different groups according to the ego network model. In this model the ego can be seen as the centre of a series of layers of increasing size. Social relationships between ego and alters in layers close to ego are stronger than those belonging to more external layers. Online Social Networks are becoming a fundamental medium for hu...

  16. How the dynamics and structure of sexual contact networks shape pathogen phylogenies.

    Directory of Open Access Journals (Sweden)

    Katy Robinson

    Full Text Available The characteristics of the host contact network over which a pathogen is transmitted affect both epidemic spread and the projected effectiveness of control strategies. Given the importance of understanding these contact networks, it is unfortunate that they are very difficult to measure directly. This challenge has led to an interest in methods to infer information about host contact networks from pathogen phylogenies, because in shaping a pathogen's opportunities for reproduction, contact networks also shape pathogen evolution. Host networks influence pathogen phylogenies both directly, through governing opportunities for evolution, and indirectly by changing the prevalence and incidence. Here, we aim to separate these two effects by comparing pathogen evolution on different host networks that share similar epidemic trajectories. This approach allows use to examine the direct effects of network structure on pathogen phylogenies, largely controlling for confounding differences arising from population dynamics. We find that networks with more heterogeneous degree distributions yield pathogen phylogenies with more variable cluster numbers, smaller mean cluster sizes, shorter mean branch lengths, and somewhat higher tree imbalance than networks with relatively homogeneous degree distributions. However, in particular for dynamic networks, we find that these direct effects are relatively modest. These findings suggest that the role of the epidemic trajectory, the dynamics of the network and the inherent variability of metrics such as cluster size must each be taken into account when trying to use pathogen phylogenies to understand characteristics about the underlying host contact network.

  17. RESEARCH OF BRIDGE STRUCTURE VIBRATION CHARACTERISTICS

    Directory of Open Access Journals (Sweden)

    V.P. Babak

    2005-02-01

    Full Text Available  Bridge structure test results with using different types of dynamic force have been considered. It has been shown, that the developed technique of registering and processing vibration signals allows obtaining thin spectrum structure. The analysis of its change that is defined by the type of structure loading applied has been carried out. Key parameters of the vibration signals registered have been defined.

  18. Application of Bayesian Networks for Estimation of Individual Psychological Characteristics

    KAUST Repository

    Litvinenko, Alexander

    2017-07-19

    In this paper we apply Bayesian networks for developing more accurate final overall estimations of psychological characteristics of an individual, based on psychological test results. Psychological tests which identify how much an individual possesses a certain factor are very popular and quite common in the modern world. We call this value for a given factor -- the final overall estimation. Examples of factors could be stress resistance, the readiness to take a risk, the ability to concentrate on certain complicated work and many others. An accurate qualitative and comprehensive assessment of human potential is one of the most important challenges in any company or collective. The most common way of studying psychological characteristics of each single person is testing. Psychologists and sociologists are constantly working on improvement of the quality of their tests. Despite serious work, done by psychologists, the questions in tests often do not produce enough feedback due to the use of relatively poor estimation systems. The overall estimation is usually based on personal experiences and the subjective perception of a psychologist or a group of psychologists about the investigated psychological personality factors.

  19. 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...

  20. Epidemic spreading on complex networks with community structures

    CERN Document Server

    Stegehuis, Clara; van Leeuwaarden, Johan 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 and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both \\textit{enforce} as well as \\textit{inhibit} diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities.

  1. 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.

  2. Social Network Characteristics Associated with Weight Loss among Black and Hispanic Adults with Overweight and Obesity

    OpenAIRE

    Winston, Ginger; Phillips, Erica G.; Wethington, Elaine; Devine, Carol; Wells, Martin; Peterson, Janey C.; Hippolyte, Jessica; Ramos, Rosio; Martinez, Guillerma; Eldridge, Johanna; Charlson, Mary

    2015-01-01

    Objective To examine social network member characteristics associated with weight loss. Methods Cross-sectional examination of egocentric network data from 245 Black and Hispanic adults with BMI ? 25 kg/m2 enrolled in a small change weight loss study. The relationship between weight loss at 12 months and characteristics of helpful and harmful network members (relationship, contact frequency, living proximity and body size) were examined. Results There were 2,571 network members identified. Me...

  3. A mathematical model for networks with structures in the mesoscale

    OpenAIRE

    Criado, Regino; Flores, Julio; Gacia Del Amo, Alejandro Jose; Gómez, Jesus; Romance, Miguel

    2011-01-01

    Abstract The new concept of multilevel network is introduced in order to embody some topological properties of complex systems with structures in the mesoscale which are not completely captured by the classical models. This new model, which generalizes the hyper-network and hyper-structure models, fits perfectly with several real-life complex systems, including social and public transportation networks. We present an analysis of the structural properties of the mu...

  4. State network approach to characteristics of financial crises

    Science.gov (United States)

    Qiu, Lu; Gu, Changgui; Xiao, Qin; Yang, Huijie; Wu, Guolin

    2018-02-01

    Extensive works have reported that a financial crisis can induce significant changes to topological structure of a stock network constructed with cross-correlations between stocks. But there are still some problems to be answered, such as what is the relationship between different crises in history and how to classify them? In the present work, we propose a new network-based solution to extract and display the relationships between the crises. The Dow Jones stock market is investigated as a typical example. The cross-correlation matrix between stocks is used to measure the state of stock market, called state matrix. All the states cluster into six sub-categories. A state network is constructed further to display the relationships between all the states, which contains a total of nine communities. It is found that three crises C , D and E (refer to the Lehman's bankruptcy in 2008, the Euro-zone and International Monetary Fund decide the first bailout for Greece in 2010, and the European sovereign debt crisis in 2011, respectively) belong to a specific sub-category and cluster in a single community. The mid-stage of C is closely linked with E, while the other stages with D. The other two crises A and B (refer to the financial crisis in Asia in 1997, and the burst of "dot-com bubble" in 2002, respectively) belong to another sub-category and gather in a corner of another single community. A and B are linked directly with C and D by two edges. By this way, we give a clear picture of the relationships between the crises.

  5. Network-level structural covariance in the developing brain.

    Science.gov (United States)

    Zielinski, Brandon A; Gennatas, Efstathios D; Zhou, Juan; Seeley, William W

    2010-10-19

    Intrinsic or resting state functional connectivity MRI and structural covariance MRI have begun to reveal the adult human brain's multiple network architectures. How and when these networks emerge during development remains unclear, but understanding ontogeny could shed light on network function and dysfunction. In this study, we applied structural covariance MRI techniques to 300 children in four age categories (early childhood, 5-8 y; late childhood, 8.5-11 y; early adolescence, 12-14 y; late adolescence, 16-18 y) to characterize gray matter structural relationships between cortical nodes that make up large-scale functional networks. Network nodes identified from eight widely replicated functional intrinsic connectivity networks served as seed regions to map whole-brain structural covariance patterns in each age group. In general, structural covariance in the youngest age group was limited to seed and contralateral homologous regions. Networks derived using primary sensory and motor cortex seeds were already well-developed in early childhood but expanded in early adolescence before pruning to a more restricted topology resembling adult intrinsic connectivity network patterns. In contrast, language, social-emotional, and other cognitive networks were relatively undeveloped in younger age groups and showed increasingly distributed topology in older children. The so-called default-mode network provided a notable exception, following a developmental trajectory more similar to the primary sensorimotor systems. Relationships between functional maturation and structural covariance networks topology warrant future exploration.

  6. Video Game Structural Characteristics: A New Psychological Taxonomy

    Science.gov (United States)

    King, Daniel; Delfabbro, Paul; Griffiths, Mark

    2010-01-01

    Excessive video game playing behaviour may be influenced by a variety of factors including the structural characteristics of video games. Structural characteristics refer to those features inherent within the video game itself that may facilitate initiation, development and maintenance of video game playing over time. Numerous structural…

  7. [Do the structure and functioning of the elderly's social network influence functional health: a preliminary study].

    Science.gov (United States)

    Masse, Marie; Swine, Christian

    2015-06-01

    We examined structural and functional characteristics of social networks related to health and well-being among community-dwelling older adults. A survey was performed in Brussels, using an original name-generating network inventory, to explore the structure and types of social ties (e.g. children, friends, neighbors) which forms the elderly's network. Different kinds of support (instrumental, emotional, social) were assessed due to the multiple contents of social exchanges between the elderly and their network's members. Our results highlighted some important social network resources. Especially, social participation, contacts with friends of the same age and reciprocity of social relationships are likely to promote functional health and well-being in later life. We discuss our findings in relation to major social network's typologies referring to older adults.

  8. Modeling past and future structure of the global virtual water trade network

    Science.gov (United States)

    Dalin, C.; Suweis, S.; Konar, M.; Hanasaki, N.; Rodriguez-Iturbe, I.

    2012-12-01

    Climate change and socio-economic development place an increasing pressure on essential natural resources, such as arable land and freshwater. The international food trade can save water globally by redistributing commodities produced relatively more water-efficiently. We focus on the global virtual water trade network associated with international staple food trade from 1986-2008. This study aims to determine which variables control the network's structure and temporal evolution, and to estimate changes in the network under future scenarios. Our fitness model reproduces both the topological and weighted characteristics of the network for the whole period. Undirected and directed network properties are well reproduced in each year, assuming as sole controls each nation's GDP, mean annual rainfall, agricultural area and population. The future structure of the network is estimated using climate and socio-economic projections, showing that volumes of virtual water traded will become increasingly heterogeneous and the importance of dominant importing nations will further strengthen.

  9. 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...

  10. Form and function in gene regulatory networks: the structure of network motifs determines fundamental properties of their dynamical state space.

    Science.gov (United States)

    Ahnert, S E; Fink, T M A

    2016-07-01

    Network motifs have been studied extensively over the past decade, and certain motifs, such as the feed-forward loop, play an important role in regulatory networks. Recent studies have used Boolean network motifs to explore the link between form and function in gene regulatory networks and have found that the structure of a motif does not strongly determine its function, if this is defined in terms of the gene expression patterns the motif can produce. Here, we offer a different, higher-level definition of the 'function' of a motif, in terms of two fundamental properties of its dynamical state space as a Boolean network. One is the basin entropy, which is a complexity measure of the dynamics of Boolean networks. The other is the diversity of cyclic attractor lengths that a given motif can produce. Using these two measures, we examine all 104 topologically distinct three-node motifs and show that the structural properties of a motif, such as the presence of feedback loops and feed-forward loops, predict fundamental characteristics of its dynamical state space, which in turn determine aspects of its functional versatility. We also show that these higher-level properties have a direct bearing on real regulatory networks, as both basin entropy and cycle length diversity show a close correspondence with the prevalence, in neural and genetic regulatory networks, of the 13 connected motifs without self-interactions that have been studied extensively in the literature. © 2016 The Authors.

  11. CHARACTERISTIC STRUCTURE OF STAR-FORMING CLOUDS

    Energy Technology Data Exchange (ETDEWEB)

    Myers, Philip C., E-mail: pmyers@cfa.harvard.edu [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States)

    2015-06-20

    This paper presents a new method to diagnose the star-forming potential of a molecular cloud region from the probability density function of its column density (N-pdf). This method provides expressions for the column density and mass profiles of a symmetric filament having the same N-pdf as a filamentary region. The central concentration of this characteristic filament can distinguish regions and can quantify their fertility for star formation. Profiles are calculated for N-pdfs which are pure lognormal, pure power law, or a combination. In relation to models of singular polytropic cylinders, characteristic filaments can be unbound, bound, or collapsing depending on their central concentration. Such filamentary models of the dynamical state of N-pdf gas are more relevant to star-forming regions than are spherical collapse models. The star formation fertility of a bound or collapsing filament is quantified by its mean mass accretion rate when in radial free fall. For a given mass per length, the fertility increases with the filament mean column density and with its initial concentration. In selected regions the fertility of their characteristic filaments increases with the level of star formation.

  12. Kinematic Structural Modelling in Bayesian Networks

    Science.gov (United States)

    Schaaf, Alexander; de la Varga, Miguel; Florian Wellmann, J.

    2017-04-01

    We commonly capture our knowledge about the spatial distribution of distinct geological lithologies in the form of 3-D geological models. Several methods exist to create these models, each with its own strengths and limitations. We present here an approach to combine the functionalities of two modeling approaches - implicit interpolation and kinematic modelling methods - into one framework, while explicitly considering parameter uncertainties and thus model uncertainty. In recent work, we proposed an approach to implement implicit modelling algorithms into Bayesian networks. This was done to address the issues of input data uncertainty and integration of geological information from varying sources in the form of geological likelihood functions. However, one general shortcoming of implicit methods is that they usually do not take any physical constraints into consideration, which can result in unrealistic model outcomes and artifacts. On the other hand, kinematic structural modelling intends to reconstruct the history of a geological system based on physically driven kinematic events. This type of modelling incorporates simplified, physical laws into the model, at the cost of a substantial increment of usable uncertain parameters. In the work presented here, we show an integration of these two different modelling methodologies, taking advantage of the strengths of both of them. First, we treat the two types of models separately, capturing the information contained in the kinematic models and their specific parameters in the form of likelihood functions, in order to use them in the implicit modelling scheme. We then go further and combine the two modelling approaches into one single Bayesian network. This enables the direct flow of information between the parameters of the kinematic modelling step and the implicit modelling step and links the exclusive input data and likelihoods of the two different modelling algorithms into one probabilistic inference framework. In

  13. Strength characteristics for the structural assessment of existing concrete structures

    NARCIS (Netherlands)

    Vervuurt, A.; Courage, W.; Steenbergen, R.

    2013-01-01

    For the assessment of the in situ compressive strength in structures and precast concrete components EN 13791 applies. Among others, this standard may be adopted when doubt arises about the compressive strength in the structure. However, for assessing the structural safety of existing structures of

  14. Reverse Logistics Network Structures and Design

    NARCIS (Netherlands)

    M. Fleischmann (Moritz)

    2001-01-01

    textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network

  15. The structure of replicating kinetoplast DNA networks

    OpenAIRE

    1993-01-01

    Kinetoplast DNA (kDNA), the mitochondrial DNA of Crithidia fasciculata and related trypanosomatids, is a network containing approximately 5,000 covalently closed minicircles which are topologically interlocked. kDNA synthesis involves release of covalently closed minicircles from the network, and, after replication of the free minicircles, reattachment of the nicked or gapped progeny minicircles to the network periphery. We have investigated this process by electron microscopy of networks at ...

  16. Sexual network structure and the spread of HIV in Africa: evidence from Likoma Island, Malawi.

    Science.gov (United States)

    Helleringer, Stéphane; Kohler, Hans-Peter

    2007-11-12

    Whereas sexual relationships among low-risk individuals account for the majority of HIV infections in sub-Saharan Africa, limited knowledge exists about the structure and characteristics of sexual networks among the general population in sub-Saharan Africa. To investigate the population-level structure of sexual networks connecting the young adult population of several villages on Likoma Island (Malawi), and analyse the structural position of HIV-positive individuals within the sexual network. A cross-sectional sociocentric survey of sexual partnerships and biomarkers of prevalent HIV infections. The study documents the existence of a large and robust sexual network linking a substantial fraction of the island's young adult population: half of all sexually active respondents were connected in a giant network component, and more than a quarter were linked through multiple independent chains of sexual relationships. This high network connectivity emerges within short time frames. The prevalence of HIV also varied significantly across the network, with sparser regions having a higher HIV prevalence than densely connected components. Several risk factors related to sexual mixing patterns help explain differentials in HIV prevalence across network locations. Contrary to claims that sexual networks in rural sub-Saharan Africa are too sparse to sustain generalized HIV epidemics, the structure of the networks observed in Likoma appears compatible with a broad diffusion of HIV among lower-risk groups. The non-homogeneous distribution of HIV infection within the network suggests that network characteristics are an important determinant of the dynamics of HIV spread within a population.

  17. Structural Antecedents of Corporate Network Evolution

    NARCIS (Netherlands)

    F.H. Wijen (Frank); N. Noorderhaven (Niels); W. Vanhaverbeke (Wim)

    2011-01-01

    textabstractAbstract: While most network studies adopt a static view, we argue that corporate social networks are subject to endogenous dynamics of cognitive path dependence and self-reinforcing power relations. Over time, these dynamics drive corporate networks to become increasingly focused (i.e.,

  18. Stable and emergent network topologies : A structural approach

    NARCIS (Netherlands)

    Herman Monsuur

    2007-01-01

    Economic, social and military networks have at least one thing in common: they change over time. For various reasons, nodes form and terminate links, thereby rearranging the network. In this paper, we present a structural network mechanism that formalizes a possible incentive that guides nodes in

  19. A new cement slurry modified with chitosan/alginate interpenetrating networks and hydroxyapatite: structural characteristics after long-term contact with hyper-saline produced water from oil well operations

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Ivory Marcos Gomes dos; Santos, Danilo Oliveira; Cestari, Antonio Reinaldo, E-mail: ivorymarcos@hotmail.com, E-mail: danilo.quimico@yahoo.com.br, E-mail: rcestari@gmail.com [Universidade Federal de Sergipe (UFS), Sao Cristovao, SE (Brazil). Departamento de Ciencia dos Materiais; Ribeiro, Joenesson Filip Santos, E-mail: joenesson.joe@hotmail.com [Universidade Federal de Sergipe (UFS), Sao Cristovao, SE (Brazil). Lab, Materiais e Calorimetria; Alves, Jose do Patrocinio Hora; Ferreira, Angelica Baganha, E-mail: jphalves@uol.com.br, E-mail: angelica.bferreira@itps.se.gov.br [Instituto Tecnologico e de Pesquisas do Estado de Sergipe (ITPS), Aracaju, SE (Brazil)

    2017-01-15

    Oil is an important source of energy, mainly in developing countries. Important research has been conducted to find cementing procedures that guarantee safe and cost-effective oil exploration below pre-salt layers. This work aimed to make a new cement paste with cement, seawater, silica, biopolymers (chitosan and sodium alginate) and hydroxyapatite (HA), found in nature. For comparison purposes, slurry without additives was prepared and characterized. The HA used was extracted from fish scales (Cynoscion acoupa) in optimized condition NaOH concentration, temperature and reaction time. Both slurry were prepared with ratios water/cement (w/c) and silica/cement (s/c) equal to 0.50 and 0.35, respectively. The new cement slurry was obtained with proportions of 5% of each biopolymer and HA with respect to the total weight of the cement. In the immersion tests, specimens were immersed in samples of hyper production of saline water by 35°C for 15 days. Thereafter, they were washed, dried and its surface layers were scraped. Before, the resulting materials were characterized. The values of the ratios Ca/Si of new cement slurry (3.38 ± 0.06) were superior compared to standard (2.58 ± 0.05). The new slurry had high thermal stability and low amounts of small crystallite-type portlandite (35.70 nm). Conversely, a slurry standard formed larger crystals of about 50.3 nm. Significantly, after continuous long-term contact of both slurries with hyper-saline produced water from oil well fields operations, in comparison with standard slurry structural characteristics, the new slurry has practically maintained its pristine chemical structure, as well as has shown crystallite-type particles of NaCl and Friedel’s/Kuzel’s salts with lower proportion. The presence of the biopolymers and HA has driven the improved the self-healing properties observed in the new cement slurry. In this first study, the new slurry has shown adequate characteristics to contribute to cost effective and

  20. 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.

  1. Structural characteristics of hydration sites in lysozyme.

    Science.gov (United States)

    Soda, Kunitsugu; Shimbo, Yudai; Seki, Yasutaka; Taiji, Makoto

    2011-06-01

    A new method is presented for determining the hydration site of proteins, where the effect of structural fluctuations in both protein and hydration water is explicitly considered by using molecular dynamics simulation (MDS). The whole hydration sites (HS) of lysozyme are composed of 195 single HSs and 38 clustered ones (CHS), and divided into 231 external HSs (EHS) and 2 internal ones (IHS). The largest CHSs, 'Hg' and 'Lβ', are the IHSs having 2.54 and 1.35 mean internal hydration waters respectively. The largest EHS, 'Clft', is located in the cleft region. The real hydration structure of a CHS is an ensemble of multiple structures. The transition between two structures occurs through recombinations of some H-bonds. The number of the experimental X-ray crystal waters is nearly the same as that of the estimated MDS hydration waters for 70% of the HSs, but significantly different for the rest of HSs. Copyright © 2011 Elsevier B.V. All rights reserved.

  2. An Overview of Structural Characteristics in Problematic Video Game Playing

    OpenAIRE

    Griffiths, MD; Nuyens, F

    2017-01-01

    Purpose of Review: There are many different factors involved in how and why people develop problems with video game playing. One such set of factors concerns the structural characteristics of video games (i.e., the structure, elements, and components of the video games themselves). Much of the research examining the structural characteristics of video games was initially based on research and theorizing from the gambling studies field. The present review briefly overviews the key papers in th...

  3. The National Biomedical Communications Network as a Developing Structure *

    Science.gov (United States)

    Davis, Ruth M.

    1971-01-01

    The National Biomedical Communications Network has evolved both from a set of conceptual recommendations over the last twelve years and an accumulation of needs manifesting themselves in the requests of members of the medical community. With a short history of three years this network and its developing structure have exhibited most of the stresses of technology interfacing with customer groups, and of a structure attempting to build itself upon many existing fragmentary unconnected segments of a potentially viable resourcesharing capability. In addition to addressing these topics, the paper treats a design appropriate to any network devoted to information transfer in a special interest user community. It discusses fundamentals of network design, highlighting that network structure most appropriate to a national information network. Examples are given of cost analyses of information services and certain conjectures are offered concerning the roles of national networks. PMID:5542912

  4. Random field Ising model and community structure in complex networks

    Science.gov (United States)

    Son, S.-W.; Jeong, H.; Noh, J. D.

    2006-04-01

    We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)

  5. 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.

  6. Studies on the Structure and Dynamics of Urban Bus Networks in Indian Cities

    CERN Document Server

    Chatterjee, Atanu

    2015-01-01

    In recent times, the domain of network science has become extremely useful in understanding the underlying structure of various real-world networks and to answer non-trivial questions regarding them. In this study, we rigourously analyze the statistical properties of the bus networks of six major Indian cities as graphs in L- and P-space, using tools from network science. Although public transport networks, such as airline and railway networks have been extensively studied, a comprehensive study on the structure and growth of bus networks is lacking. In India, where bus networks play an important role in day-to-day commutation, it is of significant interest to analyze their topological structure, and answer some of the basic questions on their evolution, growth, robustness and resiliency. We start from an empirical analysis of these networks, and determine their principle characteristics in terms of the complex network theory. The common features of small-world property and heavy tails in degree-distribution ...

  7. Exploring community structure in biological networks with random graphs.

    Science.gov (United States)

    Sah, Pratha; Singh, Lisa O; Clauset, Aaron; Bansal, Shweta

    2014-06-25

    Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system's functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to identify the community structure in an empirical network can be difficult, however, as the many algorithms available are based on a variety of cost functions and are difficult to validate. Even when community structure is identified in an empirical system, disentangling the effect of community structure from other network properties such as clustering coefficient and assortativity can be a challenge. Here, we develop a generative model to produce undirected, simple, connected graphs with a specified degrees and pattern of communities, while maintaining a graph structure that is as random as possible. Additionally, we demonstrate two important applications of our model: (a) to generate networks that can be used to benchmark existing and new algorithms for detecting communities in biological networks; and (b) to generate null models to serve as random controls when investigating the impact of complex network features beyond the byproduct of degree and modularity in empirical biological networks. Our model allows for the systematic study of the presence of community structure and its impact on network function and dynamics. This process is a crucial step in unraveling the functional consequences of the structural properties of biological systems and uncovering the mechanisms that drive these systems.

  8. 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

  9. Association between Social Network Characteristics and Lifestyle Behaviours in Adults at Risk of Diabetes and Cardiovascular Disease.

    Science.gov (United States)

    Bot, Sandra D; Mackenbach, Joreintje D; Nijpels, Giel; Lakerveld, Jeroen

    2016-01-01

    In this exploratory study we examined the associations between several social network characteristics and lifestyle behaviours in adults at increased risk of diabetes and cardiovascular diseases. In addition, we explored whether similarities in lifestyle between individuals and their network members, or the level of social support perceived by these individuals, could explain these associations. From the control group of the Hoorn Prevention Study, participants with high and low educational attainment were approached for a structured interview between April and August 2010. Inclusion was stopped when fifty adults agreed to participate. Participants and a selection of their network members (e.g. spouses, best friends, neighbours, colleagues) completed a questionnaire on healthy lifestyle that included questions on fruit and vegetable intake, daily physical activity and leisure-time sedentary behaviour. We first examined associations between network characteristics and lifestyle using regression analyses. Second, we assessed associations between network characteristics and social support, social support and lifestyle, and compared the participants' lifestyles to those of their network members using concordance correlation coefficients. Fifty adults (50/83 x 100 = 62% response) and 170 of their network members (170/192 x 100 = 89% response) participated in the study. Individuals with more close-knit relationships, more friends who live nearby, and a larger and denser network showed higher levels of vegetable consumption and physical activity, and lower levels of sedentary behaviour. Perceived social norms or perceived support for behavioural change were not related to healthy lifestyle. Except for spousal concordance for vegetable intake, the lifestyle of individuals and their network members were not alike. Study results suggest that adults with a larger and denser social network have a healthier lifestyle. Underlying mechanisms for these associations should be

  10. Topological properties of four networks in protein structures

    Science.gov (United States)

    Min, Seungsik; Kim, Kyungsik; Chang, Ki-Ho; Ha, Deok-Ho; Lee, Jun-Ho

    2017-11-01

    In this paper, we investigate the complex networks of interacting amino acids in protein structures. The cellular networks and their random controls are treated for the four threshold distances between atoms. The numerical simulation and analysis are relevant to the topological properties of the complex networks in the structural classification of proteins, and we mainly estimate the network's metrics from the resultant network. The cellular network is shown to exhibit a small-world feature regardless of their structural class. The protein structure presents the positive assortative coefficients, when the topological property is described as a tendency for connectivity of high-degree nodes. We particularly show that both the modularity and the small-wordness are significantly followed the increasing function against nodes.

  11. 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.

  12. 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.

  13. Predicting Retweeting Behavior on Breast Cancer Social Networks: Network and Content Characteristics.

    Science.gov (United States)

    Kim, Eunkyung; Hou, Jiran; Han, Jeong Yeob; Himelboim, Itai

    2016-01-01

    This study explored how social media, especially Twitter, serves as a viable place for communicating about cancer. Using a 2-step analytic method that combined social network analysis and computer-aided content analysis, this study investigated (a) how different types of network structures explain retweeting behavior and (b) which types of tweets are retweeted and why some messages generate more interaction among users. The analysis revealed that messages written by users who had a higher number of followers, a higher level of personal influence over the interaction, and closer relationships and similarities with other users were retweeted. In addition, a tweet with a higher level of positive emotion was more likely to be retweeted, whereas a tweet with a higher level of tentative words was less likely to be retweeted. These findings imply that Twitter can be an effective tool for the dissemination of health information. Theoretical and practical implications for psychosocial interventions for people with health concerns are discussed.

  14. Measuring the robustness of network community structure using assortativity

    Science.gov (United States)

    Shizuka, Daizaburo; Farine, Damien R.

    2016-01-01

    The existence of discrete social clusters, or ‘communities’, is a common feature of social networks in human and nonhuman animals. The level of such community structure in networks is typically measured using an index of modularity, Q. While modularity quantifies the degree to which individuals associate within versus between social communities and provides a useful measure of structure in the social network, it assumes that the network has been well sampled. However, animal social network data is typically subject to sampling errors. In particular, the associations among individuals are often not sampled equally, and animal social network studies are often based on a relatively small set of observations. Here, we extend an existing framework for bootstrapping network metrics to provide a method for assessing the robustness of community assignment in social networks using a metric we call community assortativity (rcom). We use simulations to demonstrate that modularity can reliably detect the transition from random to structured associations in networks that differ in size and number of communities, while community assortativity accurately measures the level of confidence based on the detectability of associations. We then demonstrate the use of these metrics using three publicly available data sets of avian social networks. We suggest that by explicitly addressing the known limitations in sampling animal social network, this approach will facilitate more rigorous analyses of population-level structural patterns across social systems. PMID:26949266

  15. 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.

  16. Tensor Spectral Clustering for Partitioning Higher-order Network Structures.

    Science.gov (United States)

    Benson, Austin R; Gleich, David F; Leskovec, Jure

    2015-01-01

    Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.

  17. 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.

  18. 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...

  19. 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

  20. An Overview of Structural Characteristics in Problematic Video Game Playing.

    Science.gov (United States)

    Griffiths, Mark D; Nuyens, Filip

    2017-01-01

    There are many different factors involved in how and why people develop problems with video game playing. One such set of factors concerns the structural characteristics of video games (i.e., the structure, elements, and components of the video games themselves). Much of the research examining the structural characteristics of video games was initially based on research and theorizing from the gambling studies field. The present review briefly overviews the key papers in the field to date. The paper examines a number of areas including (i) similarities in structural characteristics of gambling and video gaming, (ii) structural characteristics in video games, (iii) narrative and flow in video games, (iv) structural characteristic taxonomies for video games, and (v) video game structural characteristics and game design ethics. Many of the studies carried out to date are small-scale, and comprise self-selected convenience samples (typically using self-report surveys or non-ecologically valid laboratory experiments). Based on the small amount of empirical data, it appears that structural features that take a long time to achieve in-game are the ones most associated with problematic video game play (e.g., earning experience points, managing in-game resources, mastering the video game, getting 100% in-game). The study of video games from a structural characteristic perspective is of benefit to many different stakeholders including academic researchers, video game players, and video game designers, as well as those interested in prevention and policymaking by making the games more socially responsible. It is important that researchers understand and recognize the psycho-social effects and impacts that the structural characteristics of video games can have on players, both positive and negative.

  1. Synthetic Event Reconstruction Experiments for Defining Sensor Network Characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Lundquist, J K; Kosovic, B; Belles, R

    2005-12-15

    An event reconstruction technology system has been designed and implemented at Lawrence Livermore National Laboratory (LLNL). This system integrates sensor observations, which may be sparse and/or conflicting, with transport and dispersion models via Bayesian stochastic sampling methodologies to characterize the sources of atmospheric releases of hazardous materials. We demonstrate the application of this event reconstruction technology system to designing sensor networks for detecting and responding to atmospheric releases of hazardous materials. The quantitative measure of the reduction in uncertainty, or benefit of a given network, can be utilized by policy makers to determine the cost/benefit of certain networks. Herein we present two numerical experiments demonstrating the utility of the event reconstruction methodology for sensor network design. In the first set of experiments, only the time resolution of the sensors varies between three candidate networks. The most ''expensive'' sensor network offers few advantages over the moderately-priced network for reconstructing the release examined here. The second set of experiments explores the significance of the sensors detection limit, which can have a significant impact on sensor cost. In this experiment, the expensive network can most clearly define the source location and source release rate. The other networks provide data insufficient for distinguishing between two possible clusters of source locations. When the reconstructions from all networks are aggregated into a composite plume, a decision-maker can distinguish the utility of the expensive sensor network.

  2. Robustness and modular structure in networks

    DEFF Research Database (Denmark)

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

    2015-01-01

    Complex networks have recently attracted much interest due to their prevalence in nature and our daily lives [1, 2]. A critical property of a network is its resilience to random breakdown and failure [3-6], typically studied as a percolation problem [7-9] or by modeling cascading failures[10....... If overlapping modular organization plays a role in overall functionality, networks may be far more vulnerable than predicted by conventional percolation theory....

  3. The structural characteristics of inflatable beams

    Science.gov (United States)

    Wicker, William J.

    The use of pneumatic architecture has historically been limited because of its poor performance in the areas of strength and rigidity. Yet the advent of new high strength fibers such as Allied Signal's "Spectra 1000" composed of ultra-high molecular weight polyethylene offer hope for the expanded use of inflatable structures. This thesis involved the design, construction and stiffness testing of two inflatable beams fashioned from Spectra cloth. The test data and calculations indicated that the prototype tubes were neither as strong or stiff as rigid composite or metal alloy tubes. The tubes did, however, show an improvement over the capabilities of conventional inflatable tubes fashioned from nylon or dacron fabrics. The data from the deflection tests offered insight on the non-linear and anisotropic behavior of fabrics. The data interpretation process was quite educational for the author, for he learned that nearly every assumption he held and theory postulated concerning the behavior of fabric beams - based on his knowledge of isotropic materials - varied from somewhat to quite inaccurate. This project has increased the author's interest in inflatable structures and has highlighted the importance of understanding the behavior of textiles for effective design efforts.

  4. Stochastic margin-based structure learning of Bayesian network classifiers.

    Science.gov (United States)

    Pernkopf, Franz; Wohlmayr, Michael

    2013-02-01

    The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.

  5. Fault structure, damage and acoustic emission characteristics

    Science.gov (United States)

    Dresen, G. H.; Göbel, T.; Stanchits, S.; Kwiatek, G.; Charalampidou, E. M.

    2011-12-01

    We investigate the evolution of faulting-related damage and acoustic emission activity in experiments performed on granite, quartzite and sandstone samples with 40-50 mm diameter and 100-125 mm length. Experiments were performed in a servo-controlled MTS loading frame in triaxial compression at confining pressures ranging from 20-140 MPa. We performed a series of fracture and stick-slip sliding experiments on prefractured samples. Acoustic emissions (AE) and ultrasonic velocities were monitored using up to 14 P-wave sensors glued to the cylindrical surface of the rock. Full waveforms were stored in a 16 channel transient recording system (Daxbox, PRÖKEL, Germany). Full moment tensor analysis and polarity of AE first motions were used to discriminate source types associated with tensile, shear and pore-collapse cracking. To monitor strain, two pairs of orthogonally oriented strain-gages were glued onto the specimen surface. Fracture nucleation and growth occurred from a nucleation patch mostly located at the specimen surface or at the tip of prefabricated notches inside the specimens. Irrespective of the rock type, fracture propagation is associated with formation of a damage zone surrounding the fracture surface as revealed by distribution of cracks and AE hypocenters displaying a logarithmic decay in microcrack damage with distance normal to the fault trace. The width of the damage zone varies along the fault. After fracturing, faults were locked by increasing confining pressure. Subsequent sliding was mostly induced by driving the piston at a constant displacement rate producing large single events or multiple stick-slips. With increasing sliding distance a corrugated and rough fault surface formed displaying displacement-parallel lineations. Microstructural analysis of fault surfaces and cross-sections revealed formation of multiple secondary shears progressively merging into an anastomosing 3D-network controlling damage evolution and AE activity in the fault

  6. Simulation and Experimental Investigation of Structural Dynamic Frequency Characteristics Control

    Directory of Open Access Journals (Sweden)

    Bing Li

    2012-04-01

    Full Text Available In general, mechanical equipment such as cars, airplanes, and machine tools all operate with constant frequency characteristics. These constant working characteristics should be controlled if the dynamic performance of the equipment demands improvement or the dynamic characteristics is intended to change with different working conditions. Active control is a stable and beneficial method for this, but current active control methods mainly focus on vibration control for reducing the vibration amplitudes in the time domain or frequency domain. In this paper, a new method of dynamic frequency characteristics active control (DFCAC is presented for a flat plate, which can not only accomplish vibration control but also arbitrarily change the dynamic characteristics of the equipment. The proposed DFCAC algorithm is based on a neural network including two parts of the identification implement and the controller. The effectiveness of the DFCAC method is verified by several simulation and experiments, which provide desirable results.

  7. Social Network Characteristics of Urban Adolescents in Brief Substance Abuse Treatment

    Science.gov (United States)

    Mason, Michael

    2009-01-01

    In this study, the social network characteristics of 102 urban adolescents in brief substance abuse treatment are described and analyzed longitudinally to examine risk and protective mechanisms. The treatment intervention had one session devoted to social support and networks. Social networks were conceptualized and measured along two dimensions…

  8. Topological effects of network structure on long-term social network dynamics in a wild mammal.

    Science.gov (United States)

    Ilany, Amiyaal; Booms, Andrew S; Holekamp, Kay E

    2015-07-01

    Social structure influences ecological processes such as dispersal and invasion, and affects survival and reproductive success. Recent studies have used static snapshots of social networks, thus neglecting their temporal dynamics, and focused primarily on a limited number of variables that might be affecting social structure. Here, instead we modelled effects of multiple predictors of social network dynamics in the spotted hyena, using observational data collected during 20 years of continuous field research in Kenya. We tested the hypothesis that the current state of the social network affects its long-term dynamics. We employed stochastic agent-based models that allowed us to estimate the contribution of multiple factors to network changes. After controlling for environmental and individual effects, we found that network density and individual centrality affected network dynamics, but that social bond transitivity consistently had the strongest effects. Our results emphasise the significance of structural properties of networks in shaping social dynamics. © 2015 John Wiley & Sons Ltd/CNRS.

  9. The Application of Social Characteristic and L1 Optimization in the Error Correction for Network Coding in Wireless Sensor Networks.

    Science.gov (United States)

    Zhang, Guangzhi; Cai, Shaobin; Xiong, Naixue

    2018-02-03

    One of the remarkable challenges about Wireless Sensor Networks (WSN) is how to transfer the collected data efficiently due to energy limitation of sensor nodes. Network coding will increase network throughput of WSN dramatically due to the broadcast nature of WSN. However, the network coding usually propagates a single original error over the whole network. Due to the special property of error propagation in network coding, most of error correction methods cannot correct more than C/2 corrupted errors where C is the max flow min cut of the network. To maximize the effectiveness of network coding applied in WSN, a new error-correcting mechanism to confront the propagated error is urgently needed. Based on the social network characteristic inherent in WSN and L1 optimization, we propose a novel scheme which successfully corrects more than C/2 corrupted errors. What is more, even if the error occurs on all the links of the network, our scheme also can correct errors successfully. With introducing a secret channel and a specially designed matrix which can trap some errors, we improve John and Yi's model so that it can correct the propagated errors in network coding which usually pollute exactly 100% of the received messages. Taking advantage of the social characteristic inherent in WSN, we propose a new distributed approach that establishes reputation-based trust among sensor nodes in order to identify the informative upstream sensor nodes. With referred theory of social networks, the informative relay nodes are selected and marked with high trust value. The two methods of L1 optimization and utilizing social characteristic coordinate with each other, and can correct the propagated error whose fraction is even exactly 100% in WSN where network coding is performed. The effectiveness of the error correction scheme is validated through simulation experiments.

  10. 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 ...

  11. Learning and structure of neuronal networks

    Indian Academy of Sciences (India)

    Corresponding author. E-mail: Kiran.Kolwankar@gmail.com. Abstract. We study the effect of learning dynamics on network topology. Firstly, a network of dis- crete 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 ...

  12. Wireless Sensor Networks : Structure and Algorithms

    NARCIS (Netherlands)

    van Dijk, T.C.|info:eu-repo/dai/nl/304841293

    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

  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. Structural network topology revealed by white matter tractography in cannabis users: a graph theoretical analysis.

    Science.gov (United States)

    Kim, Dae-Jin; Skosnik, Patrick D; Cheng, Hu; Pruce, Ben J; Brumbaugh, Margaret S; Vollmer, Jennifer M; Hetrick, William P; O'Donnell, Brian F; Sporns, Olaf; Puce, Aina; Newman, Sharlene D

    2011-01-01

    Endocannabinoid receptors modulate synaptic plasticity in the brain and may therefore impact cortical connectivity not only during development but also in response to substance abuse in later life. Such alterations may not be evident in volumetric measures utilized in brain imaging, but could affect the local and global organization of brain networks. To test this hypothesis, we used a novel computational approach to estimate network measures of structural brain connectivity derived from diffusion tensor imaging (DTI) and white matter tractography. Twelve adult cannabis (CB) users and 13 healthy subjects were evaluated using a graph theoretic analysis of both global and local brain network properties. Structural brain networks in both CB subjects and controls exhibited robust small-world network attributes in both groups. However, CB subjects showed significantly decreased global network efficiency and significantly increased clustering coefficients (degree to which nodes tend to cluster around individual nodes). CB subjects also exhibited altered patterns of local network organization in the cingulate region. Among all subjects, schizotypal and impulsive personality characteristics correlated with global efficiency but not with the clustering coefficient. Our data indicate that structural brain networks in CB subjects are less efficiently integrated and exhibit altered regional connectivity. These differences in network properties may reflect physiological processes secondary to substance abuse-induced synaptic plasticity, or differences in brain organization that increase vulnerability to substance use.

  15. Spatial Structure and Scaling of Agricultural Networks

    CERN Document Server

    Sousa, Daniel

    2016-01-01

    Considering agricultural landscapes as networks can provide information about spatial connectivity relevant for a wide range of applications including pollination, pest management, and ecology. Global agricultural networks are well-described by power law rank-size distributions. However, regional analyses capture only a subset of the total global network. Most analyses are regional. In this paper, we seek to address the following questions: Does the globally observed scale-free property of agricultural networks hold over smaller spatial domains? Can similar properties be observed at kilometer to meter scales? We analyze 9 intensively cultivated Landsat scenes on 5 continents with a wide range of vegetation distributions. We find that networks of vegetation fraction within the domain of each of these Landsat scenes exhibit substantial variability - but still possess similar scaling properties to the global distribution of agriculture. We also find similar results using a 39 km2 IKONOS image. To illustrate an a...

  16. NACP MsTMIP Summary of Model Structure and Characteristics

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set provides a summary of the model structure and characteristics of participating models in the North American Carbon Program (NACP) Multi-scale synthesis...

  17. On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia

    Science.gov (United States)

    Rendón de la Torre, Stephanie; Kalda, Jaan; Kitt, Robert; Engelbrecht, Jüri

    2016-09-01

    This paper presents the first topological analysis of the economic structure of an entire country based on payments data obtained from Swedbank. This data set is exclusive in its kind because around 80% of Estonia's bank transactions are done through Swedbank, hence, the economic structure of the country can be reconstructed. Scale-free networks are commonly observed in a wide array of different contexts such as nature and society. In this paper, the nodes are comprised by customers of the bank (legal entities) and the links are established by payments between these nodes. We study the scaling-free and structural properties of this network. We also describe its topology, components and behaviors. We show that this network shares typical structural characteristics known in other complex networks: degree distributions follow a power law, low clustering coefficient and low average shortest path length. We identify the key nodes of the network and perform simulations of resiliency against random and targeted attacks of the nodes with two different approaches. With this, we find that by identifying and studying the links between the nodes is possible to perform vulnerability analysis of the Estonian economy with respect to economic shocks.

  18. Adapting Bayes Network Structures to Non-stationary Domains

    DEFF Research Database (Denmark)

    Nielsen, Søren Holbech; Nielsen, Thomas Dyhre

    2006-01-01

    When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit observations, as they are read from a database, we call the process structural adaptation. Structural adaptation is useful when the learner is set to work in an unknown environment, where a BN...

  19. 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...

  20. Social Network Analysis of a Supply Network Structural Investigation of the South Korean Automotive Industry

    OpenAIRE

    Kim, Jin-Baek

    2015-01-01

    Part 3: Knowledge Based Production Management; International audience; In this paper, we analyzed the structure of the South Korean automotive industry using social network analysis (SNA) metrics. Based on the data collected from 275 companies, a social network model of the supply network was constructed. Centrality measures in the SNA field were used to interpret the result and identify key companies. The results show that SNA metrics can be useful to understand the structure of a supply net...

  1. Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

    Science.gov (United States)

    Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng

    2017-10-01

    So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

  2. 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...... [2014] proposed the use of a different notion of exchangeability due to Kallenberg [2006] and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re......-introduce the use of block-structure for network models obeying allenberg’s notion of exchangeability and thereby obtain a model which admits the inference of block-structure and edge inhomogeneity. We derive a simple expression for the likelihood and an efficient sampling method. The obtained model...

  3. Research on energy stock market associated network structure based on financial indicators

    Science.gov (United States)

    Xi, Xian; An, Haizhong

    2018-01-01

    A financial market is a complex system consisting of many interacting units. In general, due to the various types of information exchange within the industry, there is a relationship between the stocks that can reveal their clear structural characteristics. Complex network methods are powerful tools for studying the internal structure and function of the stock market, which allows us to better understand the stock market. Applying complex network methodology, a stock associated network model based on financial indicators is created. Accordingly, we set threshold value and use modularity to detect the community network, and we analyze the network structure and community cluster characteristics of different threshold situations. The study finds that the threshold value of 0.7 is the abrupt change point of the network. At the same time, as the threshold value increases, the independence of the community strengthens. This study provides a method of researching stock market based on the financial indicators, exploring the structural similarity of financial indicators of stocks. Also, it provides guidance for investment and corporate financial management.

  4. Analysis of networking characteristics of different personality types

    OpenAIRE

    Charilaos, Mylonas

    2014-01-01

    The MBTI personality test and a personal facebook network were used in order to gain some insights on the relationship of social network centrality and path length measures and different personality types. Although the personality classification data were scarce, there were some intuitive quantitative results supporting anecdotal statements, based on empirical observations, about the expected social behavior of personality types.

  5. Optimizing and Understanding Network Structure for Diffusion

    OpenAIRE

    Zhang, Yao

    2017-01-01

    Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given the Twitter following network and tweets, how to choose the best communities/groups to stop rumors from spreading? How to find the best accounts that bridge celebrities and ordinary users? These questions are related to diffusion (aka propagation) phenomena. Diffusion can be treated as a behavior of spreading contagion...

  6. Reverse Logistics Network Structures and Design

    OpenAIRE

    Fleischmann, Moritz

    2001-01-01

    textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network design for the particular case of closed-loop supply chains. We highlight key issues that companies are facing when deciding upon the logistics implementation of a product recovery initiative. In partic...

  7. Spontaneous functional network dynamics and associated structural substrates in the human brain

    Science.gov (United States)

    Liao, Xuhong; Yuan, Lin; Zhao, Tengda; Dai, Zhengjia; Shu, Ni; Xia, Mingrui; Yang, Yihong; Evans, Alan; He, Yong

    2015-01-01

    Recent imaging connectomics studies have demonstrated that the spontaneous human brain functional networks derived from resting-state functional MRI (R-fMRI) include many non-trivial topological properties, such as highly efficient small-world architecture and densely connected hub regions. However, very little is known about dynamic functional connectivity (D-FC) patterns of spontaneous human brain networks during rest and about how these spontaneous brain dynamics are constrained by the underlying structural connectivity. Here, we combined sub-second multiband R-fMRI data with graph-theoretical approaches to comprehensively investigate the dynamic characteristics of the topological organization of human whole-brain functional networks, and then employed diffusion imaging data in the same participants to further explore the associated structural substrates. At the connection level, we found that human whole-brain D-FC patterns spontaneously fluctuated over time, while homotopic D-FC exhibited high connectivity strength and low temporal variability. At the network level, dynamic functional networks exhibited time-varying but evident small-world and assortativity architecture, with several regions (e.g., insula, sensorimotor cortex and medial prefrontal cortex) emerging as functionally persistent hubs (i.e., highly connected regions) while possessing large temporal variability in their degree centrality. Finally, the temporal characteristics (i.e., strength and variability) of the connectional and nodal properties of the dynamic brain networks were significantly associated with their structural counterparts. Collectively, we demonstrate the economical, efficient, and flexible characteristics of dynamic functional coordination in large-scale human brain networks during rest, and highlight their relationship with underlying structural connectivity, which deepens our understandings of spontaneous brain network dynamics in humans. PMID:26388757

  8. Spontaneous Functional Network Dynamics and Associated Structural Substrates in the Human Brain

    Directory of Open Access Journals (Sweden)

    Xuhong eLiao

    2015-09-01

    Full Text Available Recent imaging connectomics studies have demonstrated that the spontaneous human brain functional networks derived from resting-state functional MRI (R-fMRI include many non-trivial topological properties, such as highly efficient small-world architecture and densely connected hub regions. However, very little is known about dynamic functional connectivity (D-FC patterns of spontaneous human brain networks during rest and about how these spontaneous brain dynamics are constrained by the underlying structural connectivity. Here, we combined sub-second multiband R-fMRI data with graph-theoretical approaches to comprehensively investigate the dynamic characteristics of the topological organization of human whole-brain functional networks, and then employed diffusion imaging data in the same participants to further explore the associated structural substrates. At the connection level, we found that human whole-brain D-FC patterns spontaneously fluctuated over time, while homotopic D-FC exhibited high connectivity strength and low temporal variability. At the network level, dynamic functional networks exhibited time-varying but evident small-world and assortativity architecture, with several regions (e.g., insula, sensorimotor cortex and medial prefrontal cortex emerging as functionally persistent hubs (i.e., highly connected regions while possessing large temporal variability in their degree centrality. Finally, the temporal characteristics (i.e., strength and variability of the connectional and nodal properties of the dynamic brain networks were significantly associated with their structural counterparts. Collectively, we demonstrate the economical, efficient and flexible characteristics of dynamic functional coordination in large-scale human brain networks during rest, and highlight their relationship with underlying structural connectivity, which deepens our understandings of spontaneous brain network dynamics in humans.

  9. Leveraging Structure to Improve Classification Performance in Sparsely Labeled Networks

    Energy Technology Data Exchange (ETDEWEB)

    Gallagher, B; Eliassi-Rad, T

    2007-10-22

    We address the problem of classification in a partially labeled network (a.k.a. within-network classification), with an emphasis on tasks in which we have very few labeled instances to start with. Recent work has demonstrated the utility of collective classification (i.e., simultaneous inferences over class labels of related instances) in this general problem setting. However, the performance of collective classification algorithms can be adversely affected by the sparseness of labels in real-world networks. We show that on several real-world data sets, collective classification appears to offer little advantage in general and hurts performance in the worst cases. In this paper, we explore a complimentary approach to within-network classification that takes advantage of network structure. Our approach is motivated by the observation that real-world networks often provide a great deal more structural information than attribute information (e.g., class labels). Through experiments on supervised and semi-supervised classifiers of network data, we demonstrate that a small number of structural features can lead to consistent and sometimes dramatic improvements in classification performance. We also examine the relative utility of individual structural features and show that, in many cases, it is a combination of both local and global network structure that is most informative.

  10. Supervised neural networks for the classification of structures.

    Science.gov (United States)

    Sperduti, A; Starita, A

    1997-01-01

    Standard neural networks and statistical methods are usually believed to be inadequate when dealing with complex structures because of their feature-based approach. In fact, feature-based approaches usually fail to give satisfactory solutions because of the sensitivity of the approach to the a priori selection of the features, and the incapacity to represent any specific information on the relationships among the components of the structures. However, we show that neural networks can, in fact, represent and classify structured patterns. The key idea underpinning our approach is the use of the so called "generalized recursive neuron", which is essentially a generalization to structures of a recurrent neuron. By using generalized recursive neurons, all the supervised networks developed for the classification of sequences, such as backpropagation through time networks, real-time recurrent networks, simple recurrent networks, recurrent cascade correlation networks, and neural trees can, on the whole, be generalized to structures. The results obtained by some of the above networks (with generalized recursive neurons) on the classification of logic terms are presented.

  11. Joint Modelling of Structural and Functional Brain Networks

    DEFF Research Database (Denmark)

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

    Functional and structural magnetic resonance imaging have become the most important noninvasive windows to the human brain. A major challenge in the analysis of brain networks is to establish the similarities and dissimilarities between functional and structural connectivity. We formulate a non......-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...... significant structures that are consistently shared across subjects and data splits. This provides an unsupervised approach for modeling of structure-function relations in the brain and provides a general framework for multimodal integration....

  12. Design of Beam-Forming Networks for Multibeam Antenna Arrays Using Coherently Radiating Periodic Structures

    Directory of Open Access Journals (Sweden)

    A. Arce

    2012-03-01

    Full Text Available The design of a beam-forming network (BFN for a multibeam-steerable antenna array using Coherently Radiating Periodic Structures (CORPS is presented. In this design, the input ports of the feeding network are optimized using the particle swarm optimization (PSO algorithm. A two-beam design configuration of CORPS-BFN for a multibeamsteerable linear array is proposed and analyzed. The results shown in this paper present certain interesting characteristics in the array factor response, in terms of sidelobe level (SLL and directivity (D, for the scannable multibeam linear array and the feeding network simplification for the design of BFN based on CORPS.

  13. Ion aggregation in high salt solutions. II. Spectral graph analysis of water hydrogen-bonding network and ion aggregate structures

    Science.gov (United States)

    Choi, Jun-Ho; Cho, Minhaeng

    2014-10-01

    Graph theory in mathematics and computer science is the study of graphs that are structures with pairwise connections between any objects. Here, the spectral graph theory and molecular dynamics simulation method are used to describe both morphological variation of ion aggregates in high salt solutions and ion effects on water hydrogen-bonding network structure. From the characteristic value analysis of the adjacency matrices that are graph theoretical representations of ion clusters, ion networks, and water H-bond structures, we obtained the ensemble average eigenvalue spectra revealing intricate connectivity and topology of ion aggregate structure that can be classified as either ion cluster or ion network. We further show that there is an isospectral relationship between the eigenvalue spectra of ion networks in high KSCN solutions and those of water H-bonding networks. This reveals the isomorphic relationship between water H-bond structure and ion-ion network structure in KSCN solution. On the other hand, the ion clusters formed in high NaCl solutions are shown to be graph-theoretically and morphologically different from the ion network structures in KSCN solutions. These observations support the bifurcation hypothesis on large ion aggregate growth mechanism via either ion cluster or ion network formation. We thus anticipate that the present spectral graph analyses of ion aggregate structures and their effects on water H-bonding network structures in high salt solutions can provide important information on the specific ion effects on water structures and possibly protein stability resulting from protein-water interactions.

  14. Ion aggregation in high salt solutions. II. Spectral graph analysis of water hydrogen-bonding network and ion aggregate structures.

    Science.gov (United States)

    Choi, Jun-Ho; Cho, Minhaeng

    2014-10-21

    Graph theory in mathematics and computer science is the study of graphs that are structures with pairwise connections between any objects. Here, the spectral graph theory and molecular dynamics simulation method are used to describe both morphological variation of ion aggregates in high salt solutions and ion effects on water hydrogen-bonding network structure. From the characteristic value analysis of the adjacency matrices that are graph theoretical representations of ion clusters, ion networks, and water H-bond structures, we obtained the ensemble average eigenvalue spectra revealing intricate connectivity and topology of ion aggregate structure that can be classified as either ion cluster or ion network. We further show that there is an isospectral relationship between the eigenvalue spectra of ion networks in high KSCN solutions and those of water H-bonding networks. This reveals the isomorphic relationship between water H-bond structure and ion-ion network structure in KSCN solution. On the other hand, the ion clusters formed in high NaCl solutions are shown to be graph-theoretically and morphologically different from the ion network structures in KSCN solutions. These observations support the bifurcation hypothesis on large ion aggregate growth mechanism via either ion cluster or ion network formation. We thus anticipate that the present spectral graph analyses of ion aggregate structures and their effects on water H-bonding network structures in high salt solutions can provide important information on the specific ion effects on water structures and possibly protein stability resulting from protein-water interactions.

  15. FLUXNET Research Network Site Characteristics, Investigators, and Bibliography, 2016

    Data.gov (United States)

    National Aeronautics and Space Administration — FLUXNET is a global network of micrometeorological tower sites that use eddy covariance methods to measure the exchanges of carbon dioxide, water vapor, and energy...

  16. 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...

  17. 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...

  18. The National Biomedical Communications Network as a Developing Structure.

    Science.gov (United States)

    Davis, Ruth M.

    The National Biomedical Communications Network has evolved both from a set of conceptual recommendations over the last twelve years and an accumulation of needs manifesting themselves in the requests of members of the medical community. With a short history of three years this Network and its developing structure have exhibited most of the…

  19. 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

  20. Chinese lexical networks: The structure, function and formation

    Science.gov (United States)

    Li, Jianyu; Zhou, Jie; Luo, Xiaoyue; Yang, Zhanxin

    2012-11-01

    In this paper Chinese phrases are modeled using complex networks theory. We analyze statistical properties of the networks and find that phrase networks display some important features: not only small world and the power-law distribution, but also hierarchical structure and disassortative mixing. These statistical traits display the global organization of Chinese phrases. The origin and formation of such traits are analyzed from a macroscopic Chinese culture and philosophy perspective. It is interesting to find that Chinese culture and philosophy may shape the formation and structure of Chinese phrases. To uncover the structural design principles of networks, network motif patterns are studied. It is shown that they serve as basic building blocks to form the whole phrase networks, especially triad 38 (feed forward loop) plays a more important role in forming most of the phrases and other motifs. The distinct structure may not only keep the networks stable and robust, but also be helpful for information processing. The results of the paper can give some insight into Chinese language learning and language acquisition. It strengthens the idea that learning the phrases helps to understand Chinese culture. On the other side, understanding Chinese culture and philosophy does help to learn Chinese phrases. The hub nodes in the networks show the close relationship with Chinese culture and philosophy. Learning or teaching the hub characters, hub-linking phrases and phrases which are meaning related based on motif feature should be very useful and important for Chinese learning and acquisition.

  1. Cognitive sensor networks for structure defect monitoring and classification using guided wave signals

    Science.gov (United States)

    Jin, Yuanwei; O'Donoughue, Nicholas; Moura, José M. F.; Harley, Joel; Garrett, James H.; Oppenheim, Irving J.; Soibelman, Lucio; Ying, Yujie; He, Lin

    2010-04-01

    This paper develops a framework of a cognitive sensor networks system for structure defect monitoring and classification using guided wave signals. Guided ultrasonic waves that can propagate long distances along civil structures have been widely studied for inspection and detection of structure damage. Smart ultrasonic sensors arranged as a spatially distributed cognitive sensor networks system can transmit and receive ultrasonic guided waves to interrogate structure defects such as cracks and corrosion. A distinguishing characteristic of the cognitive sensor networks system is that it adaptively probes and learns about the environment, which enables constant optimization in response to its changing understanding of the defect response. In this paper, we develop a sequential multiple hypothesis testing scheme combined with adaptive waveform transmission for defect monitoring and classification. The performance is verified using numerical simulations of guided elastic wave propagation on a pipe model and by Monte Carlo simulations for computing the probability of correct classification.

  2. 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.

  3. Neural Network Algorithm for Prediction of Secondary Protein Structure

    National Research Council Canada - National Science Library

    Zikrija Avdagic; Elvir Purisevic; Emir Buza; Zlatan Coralic

    2009-01-01

    .... In this paper we describe the method and results of using CB513 as a dataset suitable for development of artificial neural network algorithms for prediction of secondary protein structure with MATLAB...

  4. Social Network Characteristics Associated with Weight Loss among Black and Hispanic Adults with Overweight and Obesity

    Science.gov (United States)

    Winston, Ginger; Phillips, Erica G.; Wethington, Elaine; Devine, Carol; Wells, Martin; Peterson, Janey C.; Hippolyte, Jessica; Ramos, Rosio; Martinez, Guillerma; Eldridge, Johanna; Charlson, Mary

    2015-01-01

    Objective To examine social network member characteristics associated with weight loss. Methods Cross-sectional examination of egocentric network data from 245 Black and Hispanic adults with BMI ≥ 25 kg/m2 enrolled in a small change weight loss study. The relationship between weight loss at 12 months and characteristics of helpful and harmful network members (relationship, contact frequency, living proximity and body size) were examined. Results There were 2,571 network members identified. Mean weight loss was -4.8 (±11.3) lbs. among participants with network help and no harm with eating goals vs. +3.4 (±7.8) lbs. among participants with network harm alone. In a multivariable regression model, greater weight loss was associated with help from a child with eating goals (p=.0002) and coworker help with physical activity (p=.01). Weight gain was associated with having network members with obesity living in the home (p=.048) and increased network size (p=.002). Conclusions There was greater weight loss among participants with support from children and coworkers. Weight gain was associated with harmful network behaviors and having network members with obesity in the home. Incorporating child and co-worker support, and evaluating network harm and the body size of network members should be considered in future weight loss interventions. PMID:26179578

  5. Social network characteristics associated with weight loss among black and hispanic adults.

    Science.gov (United States)

    Winston, Ginger J; Phillips, Erica G; Wethington, Elaine; Devine, Carol; Wells, Martin; Peterson, Janey C; Hippolyte, Jessica; Ramos, Rosio; Martinez, Guillerma; Eldridge, Johanna; Charlson, Mary

    2015-08-01

    To examine social network member characteristics associated with weight loss. A cross-sectional examination included egocentric network data from 245 Black and Hispanic adults with BMI ≥ 25 kg/m(2) enrolled in a small change weight loss study. The relationships between weight loss at 12 months and characteristics of helpful and harmful network members (relationship, contact frequency, living proximity, and body size) were examined. There were 2,571 network members identified. Mean weight loss was -4.8 (±11.3) lbs. among participants with network help and no harm with eating goals vs. +3.4 (±7.8) lbs. among participants with network harm alone. In a multivariable regression model, greater weight loss was associated with help from a child with eating goals (P = 0.0002) and coworker help with physical activity (P = 0.01). Weight gain was associated with having network members with obesity living in the home (P = 0.048) and increased network size (P = 0.002). There was greater weight loss among participants with support from children and coworkers. Weight gain was associated with harmful network behaviors and having network members with obesity in the home. Incorporating child and coworker support and evaluating network harm and the body size of network members should be considered in future weight loss interventions. © 2015 The Obesity Society.

  6. Neural network definitions of highly predictable protein secondary structure classes

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A. [Los Alamos National Lab., NM (United States)]|[Santa Fe Inst., NM (United States); Steeg, E. [Toronto Univ., ON (Canada). Dept. of Computer Science; Farber, R. [Los Alamos National Lab., NM (United States)

    1994-02-01

    We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.

  7. 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 over...

  8. The effects of traffic structure on application and network performance

    CERN Document Server

    Aikat, Jay; Smith, F Donelson

    2012-01-01

    Over the past three decades, the Internet's rapid growth has spurred the development of new applications in mobile computing, digital music, online video, gaming and social networks. These applications rely heavily upon various underlying network protocols and mechanisms to enable, maintain and enhance their Internet functionalityThe Effects of Traffic Structure on Application and Network Performance provides the necessary tools for maximizing the network efficiency of any Internet application, and presents ground-breaking research that will influence how these applications are built in the fu

  9. Disrupted Structural and Functional Networks and Their Correlation with Alertness in Right Temporal Lobe Epilepsy: A Graph Theory Study.

    Science.gov (United States)

    Jiang, Wenyu; Li, Jianping; Chen, Xuemei; Ye, Wei; Zheng, Jinou

    2017-01-01

    Previous studies have shown that temporal lobe epilepsy (TLE) involves abnormal structural or functional connectivity in specific brain areas. However, limited comprehensive studies have been conducted on TLE associated changes in the topological organization of structural and functional networks. Additionally, epilepsy is associated with impairment in alertness, a fundamental component of attention. In this study, structural networks were constructed using diffusion tensor imaging tractography, and functional networks were obtained from resting-state functional MRI temporal series correlations in 20 right temporal lobe epilepsy (rTLE) patients and 19 healthy controls. Global network properties were computed by graph theoretical analysis, and correlations were assessed between global network properties and alertness. The results from these analyses showed that rTLE patients exhibit abnormal small-world attributes in structural and functional networks. Structural networks shifted toward more regular attributes, but functional networks trended toward more random attributes. After controlling for the influence of the disease duration, negative correlations were found between alertness, small-worldness, and the cluster coefficient. However, alertness did not correlate with either the characteristic path length or global efficiency in rTLE patients. Our findings show that disruptions of the topological construction of brain structural and functional networks as well as small-world property bias are associated with deficits in alertness in rTLE patients. These data suggest that reorganization of brain networks develops as a mechanism to compensate for altered structural and functional brain function during disease progression.

  10. Disrupted Structural and Functional Networks and Their Correlation with Alertness in Right Temporal Lobe Epilepsy: A Graph Theory Study

    Directory of Open Access Journals (Sweden)

    Wenyu Jiang

    2017-05-01

    Full Text Available Previous studies have shown that temporal lobe epilepsy (TLE involves abnormal structural or functional connectivity in specific brain areas. However, limited comprehensive studies have been conducted on TLE associated changes in the topological organization of structural and functional networks. Additionally, epilepsy is associated with impairment in alertness, a fundamental component of attention. In this study, structural networks were constructed using diffusion tensor imaging tractography, and functional networks were obtained from resting-state functional MRI temporal series correlations in 20 right temporal lobe epilepsy (rTLE patients and 19 healthy controls. Global network properties were computed by graph theoretical analysis, and correlations were assessed between global network properties and alertness. The results from these analyses showed that rTLE patients exhibit abnormal small-world attributes in structural and functional networks. Structural networks shifted toward more regular attributes, but functional networks trended toward more random attributes. After controlling for the influence of the disease duration, negative correlations were found between alertness, small-worldness, and the cluster coefficient. However, alertness did not correlate with either the characteristic path length or global efficiency in rTLE patients. Our findings show that disruptions of the topological construction of brain structural and functional networks as well as small-world property bias are associated with deficits in alertness in rTLE patients. These data suggest that reorganization of brain networks develops as a mechanism to compensate for altered structural and functional brain function during disease progression.

  11. Community structure from spectral properties in complex networks

    Science.gov (United States)

    Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

    2005-06-01

    We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

  12. Ranking influential nodes in complex networks with structural holes

    Science.gov (United States)

    Hu, Ping; Mei, Ting

    2018-01-01

    Ranking influential nodes in complex networks is of great theoretical and practical significance to ensure the safe operations of networks. In view of the important role structural hole nodes usually play in information spreading in complex networks, we propose a novel ranking method of influential nodes using structural holes called E-Burt method, which can be applied to weighted networks. This method fully takes into account the total connectivity strengths of the node in its local scope, the number of the connecting edges and the distributions of the total connectivity strengths on its connecting edges. The simulation results on the susceptible-infectious-recovered (SIR) dynamics suggest that the proposed E-Burt method can rank influential nodes more effectively and accurately in complex networks.

  13. Maps of random walks on complex networks reveal community structure.

    Science.gov (United States)

    Rosvall, Martin; Bergstrom, Carl T

    2008-01-29

    To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.

  14. 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

  15. Modeling Temporal Evolution and Multiscale Structure in Networks

    DEFF Research Database (Denmark)

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

    2013-01-01

    -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......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...

  16. Data center networks topologies, architectures and fault-tolerance characteristics

    CERN Document Server

    Liu, Yang; Veeraraghavan, Malathi; Lin, Dong; Hamdi, Mounir

    2013-01-01

    This SpringerBrief presents a survey of data center network designs and topologies and compares several properties in order to highlight their advantages and disadvantages. The brief also explores several routing protocols designed for these topologies and compares the basic algorithms to establish connections, the techniques used to gain better performance, and the mechanisms for fault-tolerance. Readers will be equipped to understand how current research on data center networks enables the design of future architectures that can improve performance and dependability of data centers. This con

  17. 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

  18. 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.

  19. Neural network structure for navigation using potential fields

    Science.gov (United States)

    Plumer, Edward S.

    1992-01-01

    A hybrid-network method for obstacle avoidance in the truck-backing system of D. Nguyen and B. Widrow (1989) is presented. A neural network technique for vehicle navigation and control in the presence of obstacles has been developed. A potential function which peaks at the surface of obstacles and has its minimum at the proper vehicle destination is computed using a network structure. The field is guaranteed not to have spurious local minima and does not have the property of flattening-out far from the goal. A feedforward neural network is used to control the steering of the vehicle using local field information. The network is trained in an obstacle-free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.

  20. 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. PMID:25409180

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

    Directory of Open Access Journals (Sweden)

    Marian-Gabriel Hâncean

    Full Text Available 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.

  2. 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.

  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. Structural integration and performance of inter-sectoral public health-related policy networks: An analysis across policy phases.

    Science.gov (United States)

    Peters, D T J M; Raab, J; Grêaux, K M; Stronks, K; Harting, J

    2017-12-01

    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 addressing environmental determinants of health. This study examines these relations in different phases of the policy process. A multiple-case study was performed on four public health-related policy networks. Using a snowball method among network actors, overall and sub-networks per policy phase were identified and the policy sector of each actor was assigned. To operationalise the outcome variable, interventions were classified by the proportion of environmental determinants they addressed. In the overall networks, no relation was found between structural network characteristics and network performance. In most effective cases, the policy development sub-networks were characterised by integration with less interrelations between actors (low cohesion), more equally distributed distances between the actors (low closeness centralisation), and horizontal integration in inter-sectoral cliques. The most effective case had non-public health central actors with less connections in all sub-networks. The results suggest that, to address environmental determinants of health, sub-networks should be inter-sectorally composed in the policy development rather than in the intervention development and implementation phases, and that policy development actors should have the opportunity to connect with other actors, without strong direction from a central actor. Copyright © 2017 Elsevier B.V. All rights reserved.

  5. Some Material Characteristics of Cold-Sprayed Structures

    Directory of Open Access Journals (Sweden)

    Victor K. Champagne

    2007-01-01

    Full Text Available The deposition and consolidation of metal powders by means of cold spray are methods whereby powder particles are accelerated to high velocity through entrainment in a gas undergoing expansion in a rocket nozzle and are subsequently impacted upon a surface. The impacted powder particles form a consolidated structure which can be several centimeters thick. The characteristics of this structure depend on the initial characteristics of the metal powder and upon impact velocity. The influence of impact velocity on strain hardening and porosity are examined. A materials model is proposed for these phenomena, and model calculation is compared with experiment for the cold spraying of aluminum.

  6. Conversation practices and network structure in Twitter

    DEFF Research Database (Denmark)

    Rossi, Luca; Magnani, Matteo

    2012-01-01

    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......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...

  7. Adolescent social networks: general and smoking-specific characteristics associated with smoking.

    Science.gov (United States)

    Roberts, Megan E; Nargiso, Jessica E; Gaitonde, Linda Brazil; Stanton, Cassandra A; Colby, Suzanne M

    2015-03-01

    Converging lines of research suggest that adolescents' smoking behaviors are strongly influenced by the characteristics of their social network and the social processes their network facilitates. The primary goal of this study was to conduct a detailed comparison of the social networks of adolescent smokers and nonsmokers to determine what aspects relate the most to smoking status. A secondary goal was to conduct within-group analyses to examine relationships between key measures of behavior-specific social support and (a) smoking susceptibility among nonsmokers, and (b) readiness to quit smoking among smokers. A matched sample of 190 adolescent smokers and nonsmokers (Mage = 16.8 years; 51% female) completed a questionnaire in which they nominated and reported on up to 10 important people in their lives. This measure allowed us to examine adolescents' overall networks (both peers and family) and to investigate numerous aspects, including general network characteristics (e.g., size of network, average contact with network members), social support (e.g., importance of people in the network), and the pervasiveness of smoking in the network (e.g., percentage of smoking peers). The pervasiveness of smoking in adolescents' social network was the strongest distinguisher of smokers versus nonsmokers. In addition, behavior-specific social support was strongly associated with susceptibility to initiate smoking among nonsmokers and readiness to quit among smokers. This research offers insight into potential targets for prevention and early intervention by demonstrating how social networks can both promote and attenuate risk for smoking.

  8. Adolescent Social Networks: General and Smoking-Specific Characteristics Associated With Smoking

    Science.gov (United States)

    Roberts, Megan E.; Nargiso, Jessica E.; Gaitonde, Linda Brazil; Stanton, Cassandra A.; Colby, Suzanne M.

    2015-01-01

    Objective: Converging lines of research suggest that adolescents’ smoking behaviors are strongly influenced by the characteristics of their social network and the social processes their network facilitates. The primary goal of this study was to conduct a detailed comparison of the social networks of adolescent smokers and nonsmokers to determine what aspects relate the most to smoking status. A secondary goal was to conduct within-group analyses to examine relationships between key measures of behavior-specific social support and (a) smoking susceptibility among nonsmokers, and (b) readiness to quit smoking among smokers. Method: A matched sample of 190 adolescent smokers and nonsmokers (Mage = 16.8 years; 51% female) completed a questionnaire in which they nominated and reported on up to 10 important people in their lives. This measure allowed us to examine adolescents’ overall networks (both peers and family) and to investigate numerous aspects, including general network characteristics (e.g., size of network, average contact with network members), social support (e.g., importance of people in the network), and the pervasiveness of smoking in the network (e.g., percentage of smoking peers). Results: The pervasiveness of smoking in adolescents’ social network was the strongest distinguisher of smokers versus nonsmokers. In addition, behavior-specific social support was strongly associated with susceptibility to initiate smoking among nonsmokers and readiness to quit among smokers. Conclusions: This research offers insight into potential targets for prevention and early intervention by demonstrating how social networks can both promote and attenuate risk for smoking. PMID:25785800

  9. Crystal networks in silk fibrous materials: from hierarchical structure to ultra performance.

    Science.gov (United States)

    Nguyen, Anh Tuan; Huang, Qiao-Ling; Yang, Zhen; Lin, Naibo; Xu, Gangqin; Liu, Xiang Yang

    2015-03-01

    This review provides a comprehensive survey of the structural characteristics of crystal networks of silk soft fibrous materials in correlation with the macroscopic properties/performance and the network formation mechanisms. The correlation between the hierarchical mesoscopic structures and the mechanical properties of silk soft fibrous materials including silk fibroin hydrogels and naturally spun silk fibers are addressed based on the hierarchical crystal network models. Namely, two types of hierarchical networks are identified: the weak nanofibril-nanofibril interaction case (i.e., silk fibroin hydrogels), and the strong nanofibril-nanofibril interaction case (i.e., silk fibers). The macroscopic properties, i.e., the rheological/mechanical properties, can be controlled in terms of tuning different levels of hierarchical network structures by ultrasonication-induced gelation, introducing the initial nucleation centers, etc. Such controls take effect by different mesoscale assembly pathways, which are found to occur via different routes of the nucleation and growth processes. Furthermore, the hierarchical network model of soft fibrous materials can be applied to explain the superior mechanical properties and the unique strain-hardening behaviors of spider silk fibers within the framework of hierarchical breaking mechanism. Obviously, a knowledge of crystal networks will allow the prediction of the performance and engineering strategy of silk fibrous materials in generals. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Predicting network structure using unlabeled interaction information

    OpenAIRE

    Nasim, Mehwish; Brandes, Ulrik

    2014-01-01

    We are interested in the question whether interactions in online social networks (OSNs) can serve as a proxy for more persistent social relation. With Facebook as the context of our analysis, we look at commenting on wall posts as a form of interaction, and friendship ties as social relations. Findings from a pretest suggest that others’ joint commenting patterns on someone’s status posts are indeed indicative of friendship ties between them, independent of the contents. This would have impli...

  11. Network structure impacts global commodity trade growth and resilience

    Science.gov (United States)

    Rovenskaya, Elena; Fath, Brian D.

    2017-01-01

    Global commodity trade networks are critical to our collective sustainable development. Their increasing interconnectedness pose two practical questions: (i) Do the current network configurations support their further growth? (ii) How resilient are these networks to economic shocks? We analyze the data of global commodity trade flows from 1996 to 2012 to evaluate the relationship between structural properties of the global commodity trade networks and (a) their dynamic growth, as well as (b) the resilience of their growth with respect to the 2009 global economic shock. Specifically, we explore the role of network efficiency and redundancy using the information theory-based network flow analysis. We find that, while network efficiency is positively correlated with growth, highly efficient systems appear to be less resilient, losing more and gaining less growth following an economic shock. While all examined networks are rather redundant, we find that network redundancy does not hinder their growth. Moreover, systems exhibiting higher levels of redundancy lose less and gain more growth following an economic shock. We suggest that a strategy to support making global trade networks more efficient via, e.g., preferential trade agreements and higher specialization, can promote their further growth; while a strategy to increase the global trade networks’ redundancy via e.g., more abundant free-trade agreements, can improve their resilience to global economic shocks. PMID:28207790

  12. Synthesis of Au nanowires with controlled morphological and structural characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Gurvinder, E-mail: gurvinder.singh@ntnu.no [Ugelstad Laboratory, Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim (Norway); Helvoort, Antonius T.J. van [Department of Physics, Norwegian University of Science and Technology, NO-7491 Trondheim (Norway); Bandyopadhyay, Sulalit; Volden, Sondre; Andreassen, Jens-Petter [Ugelstad Laboratory, Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim (Norway); Glomm, Wilhelm R., E-mail: Wilhelm.Glomm@sintef.no [Ugelstad Laboratory, Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), NO-7491 Trondheim (Norway); SINTEF Materials and Chemistry, Biotechnology and Nanomedicine Sector, NO-7491 Trondheim (Norway)

    2014-08-30

    Graphical abstract: - Highlights: • Synthesis of metal nanowires. • Control over structural and morphological characteristics via seed type nanoparticle. • Growth via surfactant or surface diffusion process. • Structural and surface characterization. - Abstract: A growth of one-dimensional noble metal nanostructure with controlled structural characteristic has been under intense investigation as the physical properties, for example, mechanical and electrical properties highly depend on the crystallinity of the nanostructure. Herein, we report a seed-mediated growth of gold nanowires with controlled structural and morphological characteristics, which can easily be varied by selecting appropriate seed nanoparticles, either spherical or rod type in aqueous solution at room temperature. The growth of nanowires was monitored by characterizing the samples at different time period during the reaction, and our observations suggest that growth occurs from seeds rapidly growing along one-dimension followed by surfactant induced fusion or welding and surface diffusion. The aspect ratio and morphology of these NWs can be tuned by CTAB concentration, pH and temperature of the growth solution. We show that the aspect ratio and morphology of these NWs can be tuned by the surfactant concentration, pH and temperature of the growth solution. Electron microscopy and X-ray Photoelectron spectroscopic techniques were employed for investigating structural and surface characteristics of nanowires. This approach can possibly help to synthesize nanowires of other metals with controlled crystalline behaviour which is highly essential for understanding their properties and practical applications in nanoelectronics, optical devices, catalysis, and sensors.

  13. Characteristics of Key Update Strategies for Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming

    2011-01-01

    Wireless sensor networks offer the advantages of simple and low-resource communication. Challenged by this simplicity and low-resources, security is of particular importance in many cases such as transmission of sensitive data or strict requirements of tamper-resistance. Updating the security keys...

  14. Characteristics of file sharing and peer to peer networking | Opara ...

    African Journals Online (AJOL)

    A peer-to-peer (p2p) network allows computer hardware and software to function without the need for special server devices. While file sharing is the practice of distributing or providing access to digitally stored information, such as computer programs, multi-media (audio, video) resources, documents, or electronic books.

  15. 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......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...... 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....

  16. The Characteristics of a Research Network for Radiation Oncology in Korea

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Jin Hyun; Park, Seo Hyun; Kang, Jin Oh [Kyung Hee University School of Medicine, Seoul (Korea, Republic of)

    2010-11-15

    To evaluate the structural characteristics of a scientific network of radiation oncology society A total of 1,512 articles published from 1986 to April 2010 with the terms 'radiation oncology' or 'therapeutic radiology' were obtained in the Korea Med database. The co-authors were analyzed according to their affiliation, and their relationship was used to build a matrix. With the matrix, centralization indices and the Key Player index were analyzed. We used UCINET 6.0 for the network analysis, Netdraw for determining a sociogram and Key Player 1.44 for the key player analysis. The centralization of the radiation oncology field decreased from 8.29% for the period from 1986-1990 to 1.84% from 2006-2010. However, when the Korean Journal of Medical Physics was excluded, centralization increased from 2.32% for the period from 2001-2005 to 3.80% from 2006-2010. This suggested that the communication in the clinical research field of radiation oncology is decreasing. In a node centralization analysis, Seoul National University was found to be the highest at 7.9%. Seoul National University showed the highest indices in the Outdegree (6.50%) and Indegree (8.54%), in addition to Betweenness (14.94%) and Eigenvector (135.234%). The Key Player analysis indicated that Inha University had the highest index at 0.491, but when the Korean Journal of Medical Physics was excluded, Yonsei University had the highest Key Player index at 0.584. The degree centrality in the network of radiation oncology decreased in the most recent period as more institutions are participating in network. However, the Betweenness centrality is still increasing, suggesting that the communications among research groups (clique) in radiation oncology is warranted.

  17. Modeling flood event characteristics using D-vine structures

    Science.gov (United States)

    Shafaei, Maryam; Fakheri-Fard, Ahmad; Dinpashoh, Yagob; Mirabbasi, Rasoul; De Michele, Carlo

    2017-11-01

    The authors investigate the use of drawable (D-)vine structures to model the dependences existing among the main characteristics of a flood event, i.e., flood volume, flood peak, duration, and peak time. Firstly, different three- and four-dimensional probability distributions were built considering all the permutations of the conditioning variables. The Frank copula was used to model the dependence of each pair of variables. Then, the appropriate D-vine structures were selected using information criteria and a goodness-of-fit test. The influence of varying the data length on the selected D-vine structure was also investigated. Finally, flood event characteristics were simulated using the four-dimensional D-vine structure.

  18. Seasonality, diaspore traits and the structure of plant-frugivore networks in Neotropical savanna forest

    Science.gov (United States)

    Darosci, Adriano Antonio Brito; Bruna, Emilio M.; Motta-Junior, José Carlos; Ferreira, Cristiane da Silva; Blake, John Gilman; Munhoz, Cássia Beatriz Rodrigues

    2017-10-01

    Complex frugivory networks are common in heterogeneous environments, but how the structure of those networks varies due to seasonality and other environmental factors remains unclear. For example, seasonal variation in rainfall can influence fruit production and diaspore characteristics, which could alter the quantity and quality of resources available to different animals in the network and, hence, network structure. We investigated how a frugivory network varied seasonally in Brazilian savanna (Cerrado), where there are well-defined dry and wet seasons and fructification mainly during the rainy season for most tree species. We recorded fruit consumption by animals during the dry and wet seasons in two different gallery forests and used these data to test the hypotheses that connectance, links per species and nestedness would be higher in the dry season than rainy season due to low available food in the former that would be consumed by various species of frugivores. Concomitantly, we also measured seed width and lipid content from diaspores of the fruiting trees to determine if these characteristics influenced interaction properties between fruiting trees and frugivores. Among the measured network parameters, connectance, links per species and specialization varied between seasons in one site but not in the other, indicating that seasonal variation in networks is not necessarily consistent over time or space. The number of tree species with small diaspores with high lipid content differed between seasons, and those characteristics were key factors increasing the interaction parameter of fruiting trees. We suggest that network stability between seasons may be related to local frugivore diversity, resource availability, and fruit quality.

  19. Neural network based semi-active control strategy for structural vibration mitigation with magnetorheological damper

    DEFF Research Database (Denmark)

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear...... frame structure. As demonstrated in the literature effective damping of flexible structures is obtained by a suitable combination of pure friction and negative damper stiffness. This damper model is rate-independent and fully described by the desired shape of the hysteresis loops or force...... mode of the structure. The neural network control is then developed to reproduce the desired force based on damper displacement and velocity as network input, and it is therefore referred to as an amplitude dependent model reference control method. An inverse model of the MR damper is needed...

  20. Urban Traffic Signal System Control Structural Optimization Based on Network Analysis

    Directory of Open Access Journals (Sweden)

    Li Wang

    2013-01-01

    Full Text Available Advanced urban traffic signal control systems such as SCOOT and SCATS normally coordinate traffic network using multilevel hierarchical control mechanism. In this mechanism, several key intersections will be selected from traffic signal network and the network will be divided into different control subareas. Traditionally, key intersection selection and control subareas division are executed according to dynamic traffic counts and link length between intersections, which largely rely on traffic engineers’ experience. However, it omits important inherent characteristics of traffic network topology. In this paper, we will apply network analysis approach into these two aspects for traffic system control structure optimization. Firstly, the modified C-means clustering algorithm will be proposed to assess the importance of intersections in traffic network and furthermore determine the key intersections based on three indexes instead of merely on traffic counts in traditional methods. Secondly, the improved network community discovery method will be used to give more reasonable evidence in traffic control subarea division. Finally, to test the effectiveness of network analysis approach, a hardware-in-loop simulation environment composed of regional traffic control system, microsimulation software and signal controller hardware, will be built. Both traditional method and proposed approach will be implemented on simulation test bed to evaluate traffic operation performance indexes, for example, travel time, stop times, delay and average vehicle speed. Simulation results show that the proposed network analysis approach can improve the traffic control system operation performance effectively.

  1. An exploratory analysis of network characteristics and quality of interactions among public health collaboratives

    Directory of Open Access Journals (Sweden)

    Danielle M. Varda

    2012-06-01

    Full Text Available While the benefits of collaboration have become widely accepted and the practice of collaboration is growing within the public health system, a paucity of research exists that examines factors and mechanisms related to effective collaboration between public health and their partner organizations. The purpose of this paper is to address this gap by exploring the structural and organizational characteristics of public health collaboratives. Design and Methods. Using both social network analysis and traditional statistical methods, we conduct an exploratory secondary data analysis of 11 public health collaboratives chosen from across the United States. All collaboratives are part of the PARTNER (www.partnertool.net database. We analyze data to identify relational patterns by exploring the structure (the way that organizations connect and exchange relationships, in relation to perceptions of value and trust, explanations for varying reports of success, and factors related to outcomes. We describe the characteristics of the collaboratives, types of resource contributions, outcomes of the collaboratives, perceptions of success, and reasons for success. We found high variation and significant differences within and between these collaboratives including perceptions of success. There were significant relationships among various factors such as resource contributions, reasons cited for success, and trust and value perceived by organizations. We find that although the unique structure of each collaborative makes it challenging to identify a specific set of factors to determine when a collaborative will be successful, the organizational characteristics and interorganizational dynamics do appear to impact outcomes. We recommend a quality improvement process that suggests matching assessment to goals and developing action steps for performance improvement.

  2. Structural Characteristics and Physical Properties of Tectonically Deformed Coals

    Directory of Open Access Journals (Sweden)

    Yiwen Ju

    2012-01-01

    Full Text Available Different mechanisms of deformation could make different influence on inner structure and physical properties of tectonically deformed coal (TDC reservoirs. This paper discusses the relationship between macromolecular structure and physical properties of the Huaibei-Huainan coal mine areas in southern North China. The macromolecular structure and pore characteristics are systematically investigated by using techniques such as X-ray diffraction (XRD, high-resolution transmission electron microscopy (HRTEM, electron paramagnetic resonance (EPR, nuclear magnetic resonance (NMR, and low-temperature nitrogen adsorption method. The results suggest that under the directional stress, basic structural units (BSU arrangement is closer, and the orientation becomes stronger from brittle deformed coal to ductile deformed coal. Structural deformation directly influences the macromolecular structure of coal, which results in changes of pore structure. The nanoscale pores of the cataclastic coal structure caused by the brittle deformation are mainly mesopores, and the proportion of mesopores volume in ductile deformed coal diminishes rapidly. So the exploration and development potential of coalbed gas are good in reservoirs such as schistose structure coal, mortar structure coal and cataclastic structure coal. It also holds promise for a certain degree of brittle deformation and wrinkle structure coal of low ductile deformation or later superimposed by brittle deformation.

  3. Some structural determinants of Pavlovian conditioning in artificial neural networks.

    Science.gov (United States)

    Sánchez, José M; Galeazzi, Juan M; Burgos, José E

    2010-05-01

    This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hippocampal and dopaminergic systems in conditioning. Neuroanatomical features were simulated as generic structural or architectural features of neural networks. We focused on the number of units per hidden layer and connectivity. The effect of the number of units per hidden layer was investigated through simulations of resistance to extinction in fully connected networks. Large networks were more resistant to extinction than small networks, a stochastic effect of the asynchronous random procedure used in the simulator to update activations and weights. These networks did not simulate second-order conditioning because weight competition prevented conditioning to a stimulus after conditioning to another. Partially connected networks simulated second-order conditioning and devaluation of the second-order stimulus after extinction of a similar first-order stimulus. Similar stimuli were simulated as nonorthogonal input-vectors. Copyright (c) 2009 Elsevier B.V. All rights reserved.

  4. A local immunization strategy for networks with overlapping community structure

    Science.gov (United States)

    Taghavian, Fatemeh; Salehi, Mostafa; Teimouri, Mehdi

    2017-02-01

    Since full coverage treatment is not feasible due to limited resources, we need to utilize an immunization strategy to effectively distribute the available vaccines. On the other hand, the structure of contact network among people has a significant impact on epidemics of infectious diseases (such as SARS and influenza) in a population. Therefore, network-based immunization strategies aim to reduce the spreading rate by removing the vaccinated nodes from contact network. Such strategies try to identify more important nodes in epidemics spreading over a network. In this paper, we address the effect of overlapping nodes among communities on epidemics spreading. The proposed strategy is an optimized random-walk based selection of these nodes. The whole process is local, i.e. it requires contact network information in the level of nodes. Thus, it is applicable to large-scale and unknown networks in which the global methods usually are unrealizable. Our simulation results on different synthetic and real networks show that the proposed method outperforms the existing local methods in most cases. In particular, for networks with strong community structures, high overlapping membership of nodes or small size communities, the proposed method shows better performance.

  5. 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.

  6. The influence of passenger flow on the topology characteristics of urban rail transit networks

    Science.gov (United States)

    Hu, Yingyue; Chen, Feng; Chen, Peiwen; Tan, Yurong

    2017-05-01

    Current researches on the network characteristics of metro networks are generally carried out on topology networks without passenger flows running on it, thus more complex features of the networks with ridership loaded on it cannot be captured. In this study, we incorporated the load of metro networks, passenger volume, into the exploration of network features. Thus, the network can be examined in the context of operation, which is the ultimate purpose of the existence of a metro network. To this end, section load was selected as an edge weight to demonstrate the influence of ridership on the network, and a weighted calculation method for complex network indicators and robustness were proposed to capture the unique behaviors of a metro network with passengers flowing in it. The proposed method was applied on Beijing Subway. Firstly, the passenger volume in terms of daily origin and destination matrix was extracted from exhausted transit smart card data. Using the established approach and the matrix as weighting, common indicators of complex network including clustering coefficient, betweenness and degree were calculated, and network robustness were evaluated under potential attacks. The results were further compared to that of unweighted networks, and it suggests indicators of the network with consideration of passenger volumes differ from that without ridership to some extent, and networks tend to be more vulnerable than that without load on it. The significance sequence for the stations can be changed. By introducing passenger flow weighting, actual operation status of the network can be reflected more accurately. It is beneficial to determine the crucial stations and make precautionary measures for the entire network’s operation security.

  7. 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.

  8. Understanding structure of urban traffic network based on spatial-temporal correlation analysis

    Science.gov (United States)

    Yang, Yanfang; Jia, Limin; Qin, Yong; Han, Shixiu; Dong, Honghui

    2017-08-01

    Understanding the structural characteristics of urban traffic network comprehensively can provide references for improving road utilization rate and alleviating traffic congestion. This paper focuses on the spatial-temporal correlations between different pairs of traffic series and proposes a complex network-based method of constructing the urban traffic network. In the network, the nodes represent road segments, and an edge between a pair of nodes is added depending on the result of significance test for the corresponding spatial-temporal correlation. Further, a modified PageRank algorithm, named the geographical weight-based PageRank algorithm (GWPA), is proposed to analyze the spatial distribution of important segments in the road network. Finally, experiments are conducted by using three kinds of traffic series collected from the urban road network in Beijing. Experimental results show that the urban traffic networks constructed by three traffic variables all indicate both small-world and scale-free characteristics. Compared with the results of PageRank algorithm, GWPA is proved to be valid in evaluating the importance of segments and identifying the important segments with small degree.

  9. Study on the complex network characteristics of urban road system based on GIS

    Science.gov (United States)

    Gao, Zhonghua; Chen, Zhenjie; Liu, Yongxue; Huang, Kang

    2007-06-01

    Urban road system is the basic bone of urban transportation and one of the most important factors that influent and controls the urban configuration. In this paper, an approach of modeling, analyzing and optimizing urban road system is described based on complex network theory and GIS technology. The urban road system is studied on three focuses: building the urban road network, modeling the computational procedures based on urban road networks and analyzing the urban road system of Changzhou City as the study case. The conclusion is that the urban road network is a scale-free network with small-world characteristic, and there is still space for development of the whole network as a small-world network, also the key road crosses should be kept expedite.

  10. The influence of phylogeny, social style, and sociodemographic factors on macaque social network structure.

    Science.gov (United States)

    Balasubramaniam, Krishna N; Beisner, Brianne A; Berman, Carol M; De Marco, Arianna; Duboscq, Julie; Koirala, Sabina; Majolo, Bonaventura; MacIntosh, Andrew J; McFarland, Richard; Molesti, Sandra; Ogawa, Hideshi; Petit, Odile; Schino, Gabriele; Sosa, Sebastian; Sueur, Cédric; Thierry, Bernard; de Waal, Frans B M; McCowan, Brenda

    2018-01-01

    Among nonhuman primates, the evolutionary underpinnings of variation in social structure remain debated, with both ancestral relationships and adaptation to current conditions hypothesized to play determining roles. Here we assess whether interspecific variation in higher-order aspects of female macaque (genus: Macaca) dominance and grooming social structure show phylogenetic signals, that is, greater similarity among more closely-related species. We use a social network approach to describe higher-order characteristics of social structure, based on both direct interactions and secondary pathways that connect group members. We also ask whether network traits covary with each other, with species-typical social style grades, and/or with sociodemographic characteristics, specifically group size, sex-ratio, and current living condition (captive vs. free-living). We assembled 34-38 datasets of female-female dyadic aggression and allogrooming among captive and free-living macaques representing 10 species. We calculated dominance (transitivity, certainty), and grooming (centrality coefficient, Newman's modularity, clustering coefficient) network traits as aspects of social structure. Computations of K statistics and randomization tests on multiple phylogenies revealed moderate-strong phylogenetic signals in dominance traits, but moderate-weak signals in grooming traits. GLMMs showed that grooming traits did not covary with dominance traits and/or social style grade. Rather, modularity and clustering coefficient, but not centrality coefficient, were strongly predicted by group size and current living condition. Specifically, larger groups showed more modular networks with sparsely-connected clusters than smaller groups. Further, this effect was independent of variation in living condition, and/or sampling effort. In summary, our results reveal that female dominance networks were more phylogenetically conserved across macaque species than grooming networks, which were more

  11. Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators

    Science.gov (United States)

    Papadopoulos, Lia; Kim, Jason Z.; Kurths, Jürgen; Bassett, Danielle S.

    2017-07-01

    Synchronization of non-identical oscillators coupled through complex networks is an important example of collective behavior, and it is interesting to ask how the structural organization of network interactions influences this process. Several studies have explored and uncovered optimal topologies for synchronization by making purposeful alterations to a network. On the other hand, the connectivity patterns of many natural systems are often not static, but are rather modulated over time according to their dynamics. However, this co-evolution and the extent to which the dynamics of the individual units can shape the organization of the network itself are less well understood. Here, we study initially randomly connected but locally adaptive networks of Kuramoto oscillators. In particular, the system employs a co-evolutionary rewiring strategy that depends only on the instantaneous, pairwise phase differences of neighboring oscillators, and that conserves the total number of edges, allowing the effects of local reorganization to be isolated. We find that a simple rule—which preserves connections between more out-of-phase oscillators while rewiring connections between more in-phase oscillators—can cause initially disordered networks to organize into more structured topologies that support enhanced synchronization dynamics. We examine how this process unfolds over time, finding a dependence on the intrinsic frequencies of the oscillators, the global coupling, and the network density, in terms of how the adaptive mechanism reorganizes the network and influences the dynamics. Importantly, for large enough coupling and after sufficient adaptation, the resulting networks exhibit interesting characteristics, including degree-frequency and frequency-neighbor frequency correlations. These properties have previously been associated with optimal synchronization or explosive transitions in which the networks were constructed using global information. On the contrary, by

  12. Structural analysis and functional characteristics of greenhouses in ...

    African Journals Online (AJOL)

    This study was carried out to determine the structural analysis and functional characteristics of the greenhouses in the Mediterranean region where 87% of the greenhouse production area in Turkey is concentrated. Information about types, material and construction properties, placement and arrangement of greenhouses in ...

  13. The Relationship between School Structural Characteristics and Job ...

    African Journals Online (AJOL)

    The objective was to determine if teachers' turnover is independent of the structural characteristics of the schools in which they teach. Using stratified random sampling, 365 (215 male and 150 female) teachers were drawn from twelve secondary schools in Cross River State, Nigeria to test the hypothesis that teachers' job ...

  14. A cost effective wireless structural health monitoring network for buildings in earthquake zones

    Science.gov (United States)

    Pentaris, F. P.; Stonham, J.; Makris, J. P.

    2014-10-01

    The design, programming and implementation of a cost effective wireless structural health monitoring system (wSHMs) is presented, able to monitor the seismic and/or man-made acceleration in buildings. This system actually operates as a sensor network exploiting internet connections that commonly exist, aiming to monitor the structural health of the buildings being installed. Key-feature of wSHMs is that it can be implemented in Wide Area Network mode to cover many remote structures and buildings, on metropolitan scale. Acceleration data is able to send, in real time, from dozens of buildings of a broad metropolitan area, to a central database, where they are analyzed in order to depict possible structural damages or nonlinear characteristics and alert for non-appropriateness of specific structures.

  15. Characteristics of Social Network Gamers: Results of an Online Survey.

    Science.gov (United States)

    Geisel, Olga; Panneck, Patricia; Stickel, Anna; Schneider, Michael; Müller, Christian A

    2015-01-01

    Current research on Internet addiction (IA) reported moderate to high prevalence rates of IA and comorbid psychiatric symptoms in users of social networking sites (SNS) and online role-playing games. The aim of this study was to characterize adult users of an Internet multiplayer strategy game within a SNS. Therefore, we conducted an exploratory study using an online survey to assess sociodemographic variables, psychopathology, and the rate of IA in a sample of adult social network gamers by Young's Internet Addiction Test (IAT), the Toronto Alexithymia Scale (TAS-26), the Beck Depression Inventory-II (BDI-II), the Symptom Checklist-90-R (SCL-90-R), and the WHO Quality of Life-BREF (WHOQOL-BREF). All participants were listed gamers of "Combat Zone" in the SNS "Facebook." In this sample, 16.2% of the participants were categorized as subjects with IA and 19.5% fulfilled the criteria for alexithymia. Comparing study participants with and without IA, the IA group had significantly more subjects with alexithymia, reported more depressive symptoms, and showed poorer quality of life. These findings suggest that social network gaming might also be associated with maladaptive patterns of Internet use. Furthermore, a relationship between IA, alexithymia, and depressive symptoms was found that needs to be elucidated by future studies.

  16. Comparing and Selecting Generalized Double Ring Network Structures

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup; Knudsen, Thomas Phillip; Madsen, Ole Brun

    2004-01-01

    N2R(p;q) network structures were introduced recently as a generalization of double rings, and they were shown to be superior compared to double rings in terms of average distance and diameter. For a given number of nodes, there is only one double ring, but often more different N2R(p;q) structures...

  17. Bayesian network structure learning using chaos hybrid genetic algorithm

    Science.gov (United States)

    Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC

    2012-06-01

    A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.

  18. Structural parameter identifiability analysis for dynamic reaction networks

    DEFF Research Database (Denmark)

    Davidescu, Florin Paul; Jørgensen, Sten Bay

    2008-01-01

    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...... 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...

  19. Social structures in Russia : cells and networks

    OpenAIRE

    Yefimov, Vladimir

    2001-01-01

    Russian companies heirs of Soviet enterprises are not Western-style companies, a significant difference is that they represent the basic structures of social life in the USSR : cells. The Soviet cellular system itself has deep roots in the history of Russia. The principal social structure of pre-revolutionary Russia was the rural community. In the late 1950s, Soviet society began to move away from the classic model. Cells gradually lose their exclusive role in the functioning of society. New ...

  20. The influence of network characteristics on costs in pharmaceutical new product development

    DEFF Research Database (Denmark)

    Buonansegna, Erika; Schultz, Carsten; Stargardt, Tom

    2015-01-01

    This paper develops a model relating prior experiences, network stability, exclusive partnership, geographical distance, and intermediation in inter-firm R&D networks to new product development (NPD) costs. The developed hypotheses are tested with unique multilevel R&D partnership data from 33...... becomes relevant for non-exclusive partnerships and dispersed networks. NPD costs also increase in more stable networks, reflecting the relevance of structural holes for control and information advantages. This study contributes to the network management literature by understanding the relation between...

  1. Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe

    NARCIS (Netherlands)

    Mazvimavi, D.; Meijerink, A.M.J.; Savenije, H.H.G.; Stein, A.

    2005-01-01

    The feasibility of predicting flow characteristics from basin descriptors using multiple regression and neural networks has been investigated on 52 basins in Zimbabwe. Flow characteristics considered were average annual runoff, base flow index, flow duration curve, and average monthly runoff . Mean

  2. 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.

  3. Structured learning via convolutional neural networks for vehicle detection

    Science.gov (United States)

    Maqueda, Ana I.; del Blanco, Carlos R.; Jaureguizar, Fernando; García, Narciso

    2017-05-01

    One of the main tasks in a vision-based traffic monitoring system is the detection of vehicles. Recently, deep neural networks have been successfully applied to this end, outperforming previous approaches. However, most of these works generally rely on complex and high-computational region proposal networks. Others employ deep neural networks as a segmentation strategy to achieve a semantic representation of the object of interest, which has to be up-sampled later. In this paper, a new design for a convolutional neural network is applied to vehicle detection in highways for traffic monitoring. This network generates a spatially structured output that encodes the vehicle locations. Promising results have been obtained in the GRAM-RTM dataset.

  4. 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.

  5. 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.

  6. 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.

  7. Finding community structure in networks using the eigenvectors of matrices.

    Science.gov (United States)

    Newman, M E J

    2006-09-01

    We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as "modularity" over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.

  8. Traffic characteristics analysis in optical burst switching networks with optical label processing

    Directory of Open Access Journals (Sweden)

    Edson Moschim

    2007-03-01

    Full Text Available An analysis is carried out with burst-switching optical networks which use label processing consisting of orthogonal optical codes (OOC, considering traffic characteristics such as length/duration and arrival rate of bursts. Main results show that the use of OOC label processing influences on the decrease of burst loss probability, especially for short-lived bursts. Therefore, short bursts that would be blocked in conventional electronic processing networks are transmitted when the OOC label processing is used. Thus, an increase in the network use occurs as well as a decrease in the burst transmission latency, reaching a granularity close to packets networks.

  9. 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.

  10. 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.

  11. Study on Aseismic Characteristics of Tibetan Ancient Timber Structure

    Directory of Open Access Journals (Sweden)

    Juan Wang

    2017-01-01

    Full Text Available Tibetan ancient timber structure has great historical, cultural, artistic, and scientific values. The structural configuration of members such as wall, roof, slab, and timber skeleton with semirigid joints is studied in this paper based on field survey results. The aseismic characteristics of Tibetan ancient timber structure have been summarized and introduced. One of the unique features of the Tibetan ancient timber structure is its special beam-column joint which has a semirigid behavior. Numerical simulations of a typical 3-storey structural unit in the Potala Palace are studied. The acceleration responses of the structure under the action of El-Centro seismic waves are studied. Results show that the arrangement of the joints is helpful for seismic resistance of the structure. Most Tibetan ancient timber structures are suffering from different types of damage to certain extent which is vulnerable to seismic actions. Typical damage problems of the structural components are summarized and the main causes of this damage are analyzed. Different rehabilitation methods that can be implemented are discussed, providing references for maintenance of the structures.

  12. 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.

  13. Dependency structure matrix modelling for stakeholder value networks

    OpenAIRE

    Feng, Wen; Crawley, Edward F.; de Weck, Olivier L.; Keller, Rene; Robinson, Bob

    2010-01-01

    This paper develops a qualitative/quantitative network approach, namely a “Stakeholder Value Network”, to understand the impacts of both direct and indirect relationships between stakeholders on the success of large engineering projects. Specifically, this paper explores the feasibility and benefit of applying the Dependency Structure Matrix (DSM) as the modelling platform for Stakeholder Value Networks. Further, an efficient algorithm is designed for computing indirect stakeholder influence ...

  14. MMPI-A structural summary characteristics of male juvenile delinquents.

    Science.gov (United States)

    Morton, Todd L; Farris, Kelly L

    2002-12-01

    The Minnesota Multiphasic Personal Inventory-Adolescent Structural Summary factor scores for 655 male juvenile delinquents were determined. These Structural Summary scores were compared to the Structural Summary scores in the normative sample of boys. Elevations on Factor 2 (Immaturity) were most characteristic, with half of delinquents' scores being elevated by at least one standard deviation. Discriminant analyses suggested that linear combinations of the Structural Summary scores are as capable of distinguishing between the normative and delinquent samples as were linear combinations of the clinical scales. Furthermore, use of Structural Summary scores provided incremental validity in distinguishing between the two samples, increasing positive predictive power by 20% to 40% as compared to use of only the clinical, content, and supplementary scales.

  15. Strain Concentration at Structural Discontinuities and Its Prediction Based on Characteristics of Compliance Change in Structures

    Science.gov (United States)

    Kasahara, Naoto

    Elevated temperature structural design codes pay attention to strain concentration at structural discontinuities due to creep and plasticity, since it causes an increase in creep-fatigue damage of materials. One of the difficulties in predicting strain concentration is its dependence on the magnitude of loading, the constitutive equations, and the duration of loading. In this study, the author investigated the fundamental mechanism of strain concentration and its main factors. The results revealed that strain concentration is caused by strain redistribution between elastic and inelastic regions, which can be quantified by the characteristics of structural compliance. The characteristics of structural compliance are controlled by elastic region in structures and are insensitive to constitutive equations. It means that inelastic analysis can be easily applied to obtain compliance characteristics. By utilizing this fact, a simplified inelastic analysis method was proposed based on the characteristics of compliance change for the prediction of strain concentration.

  16. Motif structure and cooperation in real-world complex networks

    Science.gov (United States)

    Salehi, Mostafa; Rabiee, Hamid R.; Jalili, Mahdi

    2010-12-01

    Networks of dynamical nodes serve as generic models for real-world systems in many branches of science ranging from mathematics to physics, technology, sociology and biology. Collective behavior of agents interacting over complex networks is important in many applications. The cooperation between selfish individuals is one of the most interesting collective phenomena. In this paper we address the interplay between the motifs’ cooperation properties and their abundance in a number of real-world networks including yeast protein-protein interaction, human brain, protein structure, email communication, dolphins’ social interaction, Zachary karate club and Net-science coauthorship networks. First, the amount of cooperativity for all possible undirected subgraphs with three to six nodes is calculated. To this end, the evolutionary dynamics of the Prisoner’s Dilemma game is considered and the cooperativity of each subgraph is calculated as the percentage of cooperating agents at the end of the simulation time. Then, the three- to six-node motifs are extracted for each network. The significance of the abundance of a motif, represented by a Z-value, is obtained by comparing them with some properly randomized versions of the original network. We found that there is always a group of motifs showing a significant inverse correlation between their cooperativity amount and Z-value, i.e. the more the Z-value the less the amount of cooperativity. This suggests that networks composed of well-structured units do not have good cooperativity properties.

  17. 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.

  18. 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.

  19. Acoustical properties of nonwoven fiber network structures

    Science.gov (United States)

    Tascan, Mevlut

    Sound insulation is one of the most important issues for the automotive and building industries. Because they are porous fibrous structures, textile materials can be used as sound insulating and sound absorbing materials. Very high-density materials such as steel can insulate sound very effectively but these rigid materials reflect most of the sound back to the environment, causing sound pollution. Additionally, because high-density, rigid materials are also heavy and high cost, they cannot be used for sound insulation for the automotive and building industries. Nonwoven materials are more suitable for these industries, and they can also absorb sound in order to decrease sound pollution in the environment. Therefore, nonwoven materials are one of the most important materials for sound insulation and absorption applications materials. Insulation and absorption properties of nonwoven fabrics depend on fiber geometry and fiber arrangement within the fabric structure. Because of their complex structure, it is very difficult to define the microstructure of nonwovens. The structure of nonwovens only has fibers and voids that are filled by air. Because of the complexity of fiber-void geometry, there is still not a very accurate theory or model that defines the structural arrangement. A considerable amount of modeling has been reported in literature [1--19], but most models are not accurate due to the assumptions made. Voids that are covered by fibers are called pores in nonwoven structures and their geometry is very important, especially for the absorption properties of nonwovens. In order to define the sound absorption properties of nonwoven fabrics, individual pore structure and the number of pores per unit thickness of the fabric should be determined. In this research, instead of trying to define pores, the properties of the fibers are investigated and the number of fibers per volume of fabric is taken as a parameter in the theory. Then the effect of the nonwoven

  20. Neural networks for harmonic structure in music perception and action

    OpenAIRE

    Bianco, R.; Novembre, G.; Keller, P. E.; Kim, S G; Scharf, F.; Friederici, A. D.; Villringer, A; Sammler, D.

    2016-01-01

    The ability to predict upcoming structured events based on long-term knowledge and contextual priors is a fundamental principle of human cognition. Tonal music triggers predictive processes based on structural properties of harmony, i.e., regularities defining the arrangement of chords into well-formed musical sequences. While the neural architecture of structure-based predictions during music perception is well described, little is known about the neural networks for analogous predictions in...

  1. Altered topological organization of white matter structural networks in patients with neuromyelitis optica.

    Directory of Open Access Journals (Sweden)

    Yaou Liu

    Full Text Available OBJECTIVE: To investigate the topological alterations of the whole-brain white-matter (WM structural networks in patients with neuromyelitis optica (NMO. METHODS: The present study involved 26 NMO patients and 26 age- and sex-matched healthy controls. WM structural connectivity in each participant was imaged with diffusion-weighted MRI and represented in terms of a connectivity matrix using deterministic tractography method. Graph theory-based analyses were then performed for the characterization of brain network properties. A multiple linear regression analysis was performed on each network metric between the NMO and control groups. RESULTS: The NMO patients exhibited abnormal small-world network properties, as indicated by increased normalized characteristic path length, increased normalized clustering and increased small-worldness. Furthermore, largely similar hub distributions of the WM structural networks were observed between NMO patients and healthy controls. However, regional efficiency in several brain areas of NMO patients was significantly reduced, which were mainly distributed in the default-mode, sensorimotor and visual systems. Furthermore, we have observed increased regional efficiency in a few brain regions such as the orbital parts of the superior and middle frontal and fusiform gyri. CONCLUSION: Although the NMO patients in this study had no discernible white matter T2 lesions in the brain, we hypothesize that the disrupted topological organization of WM networks provides additional evidence for subtle, widespread cerebral WM pathology in NMO.

  2. Abnormal structural connectivity in the brain networks of children with hydrocephalus.

    Science.gov (United States)

    Yuan, Weihong; Holland, Scott K; Shimony, Joshua S; Altaye, Mekibib; Mangano, Francesco T; Limbrick, David D; Jones, Blaise V; Nash, Tiffany; Rajagopal, Akila; Simpson, Sarah; Ragan, Dustin; McKinstry, Robert C

    2015-01-01

    Increased intracranial pressure and ventriculomegaly in children with hydrocephalus are known to have adverse effects on white matter structure. This study seeks to investigate the impact of hydrocephalus on topological features of brain networks in children. The goal was to investigate structural network connectivity, at both global and regional levels, in the brains in children with hydrocephalus using graph theory analysis and diffusion tensor tractography. Three groups of children were included in the study (29 normally developing controls, 9 preoperative hydrocephalus patients, and 17 postoperative hydrocephalus patients). Graph theory analysis was applied to calculate the global network measures including small-worldness, normalized clustering coefficients, normalized characteristic path length, global efficiency, and modularity. Abnormalities in regional network parameters, including nodal degree, local efficiency, clustering coefficient, and betweenness centrality, were also compared between the two patients groups (separately) and the controls using two tailed t-test at significance level of p path length and lower modularity. At regional level, significant group differences (or differences at trend level) in regional network measures were found between hydrocephalus patients and the controls in a series of brain regions including the medial occipital gyrus, medial frontal gyrus, thalamus, cingulate gyrus, lingual gyrus, rectal gyrus, caudate, cuneus, and insular. Our data showed that structural connectivity analysis using graph theory and diffusion tensor tractography is sensitive to detect abnormalities of brain network connectivity associated with hydrocephalus at both global and regional levels, thus providing a new avenue for potential diagnosis and prognosis tool for children with hydrocephalus.

  3. Influence and structural balance in social networks

    Science.gov (United States)

    Singh, P.; Sreenivasan, S.; Szymanski, B.; Korniss, G.

    2012-02-01

    Models on structural balance have been studied in the past with links being categorized as friendly or antagonistic [Ref- T. Antal et al., Phys. Rev. E 72, 036121 (2005)]. However no connection between the nature of the links and states of the nodes they connect has been made. We introduce a model which combines the dynamics of the structural balance with spread of social influence. In this model, every node is in one of the three possible states (e.g. leftist, centrist and rightist) [Ref- F. Vazquez, S. Redner, J. Phys A, 37 (2004) 8479-8494] where links between leftists and rightists are antagonistic while all other links are friendly. The evolution of the system is governed by the rules of structural balance and opinion spread takes place as a result of structural balance process. The dynamics can lead the system to a number of fixed points (absorbing states). We study how the initial density of centrists nc affects the dynamics and probabilities of ending up in different absorbing states. We also study the scaling behavior of the expected time to converge to one of the absorbing states as a function of the initial density of centrists and under some variations of our basic model.

  4. Electrical network method for the thermal or structural characterization of a conducting material sample or structure

    Science.gov (United States)

    Ortiz, Marco G.

    1993-01-01

    A method for modeling a conducting material sample or structure system, as an electrical network of resistances in which each resistance of the network is representative of a specific physical region of the system. The method encompasses measuring a resistance between two external leads and using this measurement in a series of equations describing the network to solve for the network resistances for a specified region and temperature. A calibration system is then developed using the calculated resistances at specified temperatures. This allows for the translation of the calculated resistances to a region temperature. The method can also be used to detect and quantify structural defects in the system.

  5. Network Structure and Dynamics, and Emergence of Robustness by Stabilizing Selection in an Artificial Genome

    CERN Document Server

    Rohlf, Thimo

    2008-01-01

    Genetic regulation is a key component in development, but a clear understanding of the structure and dynamics of genetic networks is not yet at hand. In this work we investigate these properties within an artificial genome model originally introduced by Reil. We analyze statistical properties of randomly generated genomes both on the sequence- and network level, and show that this model correctly predicts the frequency of genes in genomes as found in experimental data. Using an evolutionary algorithm based on stabilizing selection for a phenotype, we show that robustness against single base mutations, as well as against random changes in initial network states that mimic stochastic fluctuations in environmental conditions, can emerge in parallel. Evolved genomes exhibit characteristic patterns on both sequence and network level.

  6. Organizational structure and the periphery of the gene regulatory network in B-cell lymphoma.

    Science.gov (United States)

    de Matos Simoes, Ricardo; Tripathi, Shailesh; Emmert-Streib, Frank

    2012-05-14

    The physical periphery of a biological cell is mainly described by signaling pathways which are triggered by transmembrane proteins and receptors that are sentinels to control the whole gene regulatory network of a cell. However, our current knowledge about the gene regulatory mechanisms that are governed by extracellular signals is severely limited. The purpose of this paper is three fold. First, we infer a gene regulatory network from a large-scale B-cell lymphoma expression data set using the C3NET algorithm. Second, we provide a functional and structural analysis of the largest connected component of this network, revealing that this network component corresponds to the peripheral region of a cell. Third, we analyze the hierarchical organization of network components of the whole inferred B-cell gene regulatory network by introducing a new approach which exploits the variability within the data as well as the inferential characteristics of C3NET. As a result, we find a functional bisection of the network corresponding to different cellular components. Overall, our study allows to highlight the peripheral gene regulatory network of B-cells and shows that it is centered around hub transmembrane proteins located at the physical periphery of the cell. In addition, we identify a variety of novel pathological transmembrane proteins such as ion channel complexes and signaling receptors in B-cell lymphoma.

  7. Correlations between community structure and link formation in complex networks.

    Directory of Open Access Journals (Sweden)

    Zhen Liu

    Full Text Available BACKGROUND: Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. METHODOLOGY/PRINCIPAL FINDINGS: Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. CONCLUSIONS/SIGNIFICANCE: Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction.

  8. Correlations between community structure and link formation in complex networks.

    Science.gov (United States)

    Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

    2013-01-01

    Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction.

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

    CERN Document Server

    Galinski, Henning; Dong, Hao; Gongora, Juan S Totero; Favaro, Grégory; Döbeli, Max; Spolenak, Ralph; Fratalocchi, Andrea; Capasso, Federico

    2016-01-01

    Structural colours 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 realise robust colours with a scalable fabrication technique is still lacking, hampering the realisation of practical applications with this platform. Here we develop a new approach based on large scale network metamaterials, which combine dealloyed subwavelength structures at the nanoscale with loss-less, ultra-thin dielectrics coatings. By using theory and experiments, we show how sub-wavelength dielectric coatings control a mechanism of resonant light coupling with epsilon-near-zero (ENZ) regions generated in the metallic network, manifesting the formation of highly saturated structural colours that cover a wide portion of the spectrum. Ellipsometry measurements report the efficient observation of these colours even at angles of $70$ degrees. The network-like architecture of these nanoma...

  10. The interplay between microscopic and mesoscopic structures in complex networks.

    Directory of Open Access Journals (Sweden)

    Jörg Reichardt

    Full Text Available Understanding a complex network's structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database.

  11. Towards a proper assignment of systemic risk: the combined roles of network topology and shock characteristics.

    Directory of Open Access Journals (Sweden)

    Lasse Loepfe

    Full Text Available The 2007-2008 financial crisis solidified the consensus among policymakers that a macro-prudential approach to regulation and supervision should be adopted. The currently preferred policy option is the regulation of capital requirements, with the main focus on combating procyclicality and on identifying the banks that have a high systemic importance, those that are "too big to fail". Here we argue that the concept of systemic risk should include the analysis of the system as a whole and we explore systematically the most important properties for policy purposes of networks topology on resistance to shocks. In a thorough study going from analytical models to empirical data, we show two sharp transitions from safe to risky regimes: 1 diversification becomes harmful with just a small fraction (~2% of the shocks sampled from a fat tailed shock distributions and 2 when large shocks are present a critical link density exists where an effective giant cluster forms and most firms become vulnerable. This threshold depends on the network topology, especially on modularity. Firm size heterogeneity has important but diverse effects that are heavily dependent on shock characteristics. Similarly, degree heterogeneity increases vulnerability only when shocks are directed at the most connected firms. Furthermore, by studying the structure of the core of the transnational corporation network from real data, we show that its stability could be clearly increased by removing some of the links with highest centrality betweenness. Our results provide a novel insight and arguments for policy makers to focus surveillance on the connections between firms, in addition to capital requirements directed at the nodes.

  12. Towards a proper assignment of systemic risk: the combined roles of network topology and shock characteristics.

    Science.gov (United States)

    Loepfe, Lasse; Cabrales, Antonio; Sánchez, Angel

    2013-01-01

    The 2007-2008 financial crisis solidified the consensus among policymakers that a macro-prudential approach to regulation and supervision should be adopted. The currently preferred policy option is the regulation of capital requirements, with the main focus on combating procyclicality and on identifying the banks that have a high systemic importance, those that are "too big to fail". Here we argue that the concept of systemic risk should include the analysis of the system as a whole and we explore systematically the most important properties for policy purposes of networks topology on resistance to shocks. In a thorough study going from analytical models to empirical data, we show two sharp transitions from safe to risky regimes: 1) diversification becomes harmful with just a small fraction (~2%) of the shocks sampled from a fat tailed shock distributions and 2) when large shocks are present a critical link density exists where an effective giant cluster forms and most firms become vulnerable. This threshold depends on the network topology, especially on modularity. Firm size heterogeneity has important but diverse effects that are heavily dependent on shock characteristics. Similarly, degree heterogeneity increases vulnerability only when shocks are directed at the most connected firms. Furthermore, by studying the structure of the core of the transnational corporation network from real data, we show that its stability could be clearly increased by removing some of the links with highest centrality betweenness. Our results provide a novel insight and arguments for policy makers to focus surveillance on the connections between firms, in addition to capital requirements directed at the nodes.

  13. Data assimilation for identification of cardiovascular network characteristics.

    Science.gov (United States)

    Lal, Rajnesh; Mohammadi, Bijan; Nicoud, Franck

    2017-05-01

    A method to estimate the hemodynamics parameters of a network of vessels using an Ensemble Kalman filter is presented. The elastic moduli (Young's modulus) of blood vessels and the terminal boundary parameters are estimated as the solution of an inverse problem. Two synthetic test cases and a configuration where experimental data are available are presented. The sensitivity analysis confirms that the proposed method is quite robust even with a few numbers of observations. The simulations with the estimated parameters recovers target pressure or flow rate waveforms at given specific locations, improving the state-of-the-art predictions available in the literature. This shows the effectiveness and efficiency of both the parameter estimation algorithm and the blood flow model. Copyright © 2016 John Wiley & Sons, Ltd.

  14. Characteristics of Venture Capital Network and Its Correlation with Regional Economy: Evidence from China.

    Science.gov (United States)

    Jin, Yonghong; Zhang, Qi; Shan, Lifei; Li, Sai-Ping

    2015-01-01

    Financial networks have been extensively studied as examples of real world complex networks. In this paper, we establish and study the network of venture capital (VC) firms in China. We compute and analyze the statistical properties of the network, including parameters such as degrees, mean lengths of the shortest paths, clustering coefficient and robustness. We further study the topology of the network and find that it has small-world behavior. A multiple linear regression model is introduced to study the relation between network parameters and major regional economic indices in China. From the result of regression, we find that, economic aggregate (including the total GDP, investment, consumption and net export), upgrade of industrial structure, employment and remuneration of a region are all positively correlated with the degree and the clustering coefficient of the VC sub-network of the region, which suggests that the development of the VC industry has substantial effects on regional economy in China.

  15. Characteristics of Venture Capital Network and Its Correlation with Regional Economy: Evidence from China

    Science.gov (United States)

    Jin, Yonghong; Zhang, Qi; Shan, Lifei; Li, Sai-Ping

    2015-01-01

    Financial networks have been extensively studied as examples of real world complex networks. In this paper, we establish and study the network of venture capital (VC) firms in China. We compute and analyze the statistical properties of the network, including parameters such as degrees, mean lengths of the shortest paths, clustering coefficient and robustness. We further study the topology of the network and find that it has small-world behavior. A multiple linear regression model is introduced to study the relation between network parameters and major regional economic indices in China. From the result of regression, we find that, economic aggregate (including the total GDP, investment, consumption and net export), upgrade of industrial structure, employment and remuneration of a region are all positively correlated with the degree and the clustering coefficient of the VC sub-network of the region, which suggests that the development of the VC industry has substantial effects on regional economy in China. PMID:26340555

  16. EVOLUTIONARY MODELING PROBLEMS IN STRUCTURAL SYNTHESIS OF INFORMATION NETWORKS OF AUTOMATED CONTROL SYSTEMS

    Directory of Open Access Journals (Sweden)

    N.R.Yusupbekov

    2014-07-01

    Full Text Available This paper provides a new approach for solving a problem of modeling and structural syntheses of information networks of automated control systems by applying fuzzy sets theory, fuzzy logic and genetic algorithms. The procedure of formalizing structural syntheses of multi-level dispersed information networks of automated control systems is proposed. Also, the paper proposes a conceptual model of evolutionary syntheses based on genetic algorithms, which do not require additional information about the characteristics and features of target function. Modified genetic operators of crossover, mutation and algorithms of evolutionary syntheses of information networks systems are developed. Finally, the results of computational experiments on researching the influence of probability of the use of crossover and mutation operators, method of choosing parental pairs, and the size of initial population on the speed and precision of final results are provided.

  17. Asymmetry of Hemispheric Network Topology Reveals Dissociable Processes between Functional and Structural Brain Connectome in Community-Living Elders

    Directory of Open Access Journals (Sweden)

    Yu Sun

    2017-11-01

    Full Text Available Human brain is structurally and functionally asymmetrical and the asymmetries of brain phenotypes have been shown to change in normal aging. Recent advances in graph theoretical analysis have showed topological lateralization between hemispheric networks in the human brain throughout the lifespan. Nevertheless, apparent discrepancies of hemispheric asymmetry were reported between the structural and functional brain networks, indicating the potentially complex asymmetry patterns between structural and functional networks in aging population. In this study, using multimodal neuroimaging (resting-state fMRI and structural diffusion tensor imaging, we investigated the characteristics of hemispheric network topology in 76 (male/female = 15/61, age = 70.08 ± 5.30 years community-dwelling older adults. Hemispheric functional and structural brain networks were obtained for each participant. Graph theoretical approaches were then employed to estimate the hemispheric topological properties. We found that the optimal small-world properties were preserved in both structural and functional hemispheric networks in older adults. Moreover, a leftward asymmetry in both global and local levels were observed in structural brain networks in comparison with a symmetric pattern in functional brain network, suggesting a dissociable process of hemispheric asymmetry between structural and functional connectome in healthy older adults. Finally, the scores of hemispheric asymmetry in both structural and functional networks were associated with behavioral performance in various cognitive domains. Taken together, these findings provide new insights into the lateralized nature of multimodal brain connectivity, highlight the potentially complex relationship between structural and functional brain network alterations, and augment our understanding of asymmetric structural and functional specializations in normal aging.

  18. Validation of protein structure models using network similarity score.

    Science.gov (United States)

    Ghosh, Sambit; Gadiyaram, Vasundhara; Vishveshwara, Saraswathi

    2017-09-01

    Accurate structural validation of proteins is of extreme importance in studies like protein structure prediction, analysis of molecular dynamic simulation trajectories and finding subtle changes in very similar structures. The benchmarks for today's structure validation are scoring methods like global distance test-total structure (GDT-TS), TM-score and root mean square deviations (RMSD). However, there is a lack of methods that look at both the protein backbone and side-chain structures at the global connectivity level and provide information about the differences in connectivity. To address this gap, a graph spectral based method (NSS-network similarity score) which has been recently developed to rigorously compare networks in diverse fields, is adopted to compare protein structures both at the backbone and at the side-chain noncovalent connectivity levels. In this study, we validate the performance of NSS by investigating protein structures from X-ray structures, modeling (including CASP models), and molecular dynamics simulations. Further, we systematically identify the local and the global regions of the structures contributing to the difference in NSS, through the components of the score, a feature unique to this spectral based scoring scheme. It is demonstrated that the method can quantify subtle differences in connectivity compared to a reference protein structure and can form a robust basis for protein structure comparison. Additionally, we have also introduced a network-based method to analyze fluctuations in side chain interactions (edge-weights) in an ensemble of structures, which can be an useful tool for the analysis of MD trajectories. © 2017 Wiley Periodicals, Inc.

  19. Best Entry Points for Structured Document Retrieval - Part I: Characteristics

    DEFF Research Database (Denmark)

    Reid, Jane; Lalmas, Mounia; Finesilver, Karen

    2006-01-01

    Structured document retrieval makes use of document components as the basis of the retrieval process, rather than complete documents. The inherent relationships between these components make it vital to support users' natural browsing behaviour in order to offer effective and efficient access...... to structured documents. This paper examines the concept of best entry points, which are document components from which the user can browse to obtain optimal access to relevant document components. In particular this paper investigates the basic characteristics of best entry points....

  20. Uncommon characteristics of the structure and development of Trichosporon asahii.

    Science.gov (United States)

    Wang, Wen-ling; Yang, Rong-ya; Ao, Jun-hong

    2009-08-05

    Trichosporon asahii (T. asahii) is one of the most important pathogenic fungus in the genus of trichosporon. Although the species identification of T. asahii was based upon the complicated results of morphologic, biochemical and biologic examination, the morphology characteristic is still the first clue to the species. Some common structures of T. asahii had been described such as arthrofilaments and arthroconidia, but other important structures of T. asahii were unclear. Six strains of T. asahii were incubated on the slant and micro culture of Sabouraud's dextrose agar at 30 degrees C for 7 days. Samples were fixed using 2% paraformaldehyde and 2.5% glutaraldehyde. T. asahii was observed under scanning electron microscope and transmission electron microscope. The detailed characteristics of the diverse sites of germination, as well as some uncommon structures such as giant cell, sarcinate, and club-shaped macroconidia, were presented. The pseudohyphae of T. asahii were noted to produce true hyphae, either along the longitude axis or on the flank. T. asahii was noted to have blastic and thallic conidiation. Digitated branches, trichoid structures and septa inside the spores were detected. These results may add our knowledge to the structure and development of T. asahii.

  1. Structural and dynamic characteristics in monolayer square ice.

    Science.gov (United States)

    Zhu, YinBo; Wang, FengChao; Wu, HengAn

    2017-07-28

    When water is constrained between two sheets of graphene, it becomes an intriguing monolayer solid with a square pattern due to the ultrahigh van der Waals pressure. However, the square ice phase has become a matter of debate due to the insufficient experimental interpretation and the slightly rhomboidal feature in simulated monolayer square-like structures. Here, we performed classical molecular dynamics simulations to reveal monolayer square ice in graphene nanocapillaries from the perspective of structure and dynamic characteristics. Monolayer square-like ice (instantaneous snapshot), assembled square-rhombic units with stacking faults, is a long-range ordered structure, in which the square and rhombic units are assembled in an order of alternative distribution, and the other rhombic unit forms stacking faults (polarized water chains). Spontaneous flipping of water molecules in monolayer square-like ice is intrinsic and induces transformations among different elementary units, resulting in the structural evolution of monolayer square ice in dynamics. The existence of stacking faults should be attributed to the spontaneous flipping behavior of water molecules under ambient temperature. Statistical averaging results (thermal average positions) demonstrate the inherent square characteristic of monolayer square ice. The simulated data and insight obtained here might be significant for understanding the topological structure and dynamic behavior of monolayer square ice.

  2. Structure and Topology Dynamics of Hyper-Frequency Networks during Rest and Auditory Oddball Performance.

    Science.gov (United States)

    Müller, Viktor; Perdikis, Dionysios; von Oertzen, Timo; Sleimen-Malkoun, Rita; Jirsa, Viktor; Lindenberger, Ulman

    2016-01-01

    Resting-state and task-related recordings are characterized by oscillatory brain activity and widely distributed networks of synchronized oscillatory circuits. Electroencephalographic recordings (EEG) were used to assess network structure and network dynamics during resting state with eyes open and closed, and auditory oddball performance through phase synchronization between EEG channels. For this assessment, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that CFC generally differentiates between task conditions better than WFC. CFC was the highest during resting state with eyes open. Using a graph-theoretical approach (GTA), we found that HFNs possess small-world network (SWN) topology with a slight tendency to random network characteristics. Moreover, analysis of the temporal fluctuations of HFNs revealed specific network topology dynamics (NTD), i.e., temporal changes of different graph-theoretical measures such as strength, clustering coefficient, characteristic path length (CPL), local, and global efficiency determined for HFNs at different time windows. The different topology metrics showed significant differences between conditions in the mean and standard deviation of these metrics both across time and nodes. In addition, using an artificial neural network approach, we found stimulus-related dynamics that varied across the different network topology metrics. We conclude that functional connectivity dynamics (FCD), or NTD, which was found using the HFN approach during rest and stimulus processing, reflects temporal and topological changes in the functional organization and reorganization of neuronal cell assemblies.

  3. Integrating Temporal and Spatial Scales: Human Structural Network Motifs Across Age and Region of Interest Size

    Science.gov (United States)

    Echtermeyer, Christoph; Han, Cheol E.; Rotarska-Jagiela, Anna; Mohr, Harald; Uhlhaas, Peter J.; Kaiser, Marcus

    2011-01-01

    Human brain networks can be characterized at different temporal or spatial scales given by the age of the subject or the spatial resolution of the neuroimaging method. Integration of data across scales can only be successful if the combined networks show a similar architecture. One way to compare networks is to look at spatial features, based on fiber length, and topological features of individual nodes where outlier nodes form single node motifs whose frequency yields a fingerprint of the network. Here, we observe how characteristic single node motifs change over age (12–23 years) and network size (414, 813, and 1615 nodes) for diffusion tensor imaging structural connectivity in healthy human subjects. First, we find the number and diversity of motifs in a network to be strongly correlated. Second, comparing different scales, the number and diversity of motifs varied across the temporal (subject age) and spatial (network resolution) scale: certain motifs might only occur at one spatial scale or for a certain age range. Third, regions of interest which show one motif at a lower resolution may show a range of motifs at a higher resolution which may or may not include the original motif at the lower resolution. Therefore, both the type and localization of motifs differ for different spatial resolutions. Our results also indicate that spatial resolution has a higher effect on topological measures whereas spatial measures, based on fiber lengths, remain more comparable between resolutions. Therefore, spatial resolution is crucial when comparing characteristic node fingerprints given by topological and spatial network features. As node motifs are based on topological and spatial properties of brain connectivity networks, these conclusions are also relevant to other studies using connectome analysis. PMID:21811454

  4. The Applications for the Calculation and Optimization of Passive Optical Networks Attenuation Characteristics

    Directory of Open Access Journals (Sweden)

    Pavel Lafata

    2010-01-01

    Full Text Available The passive optical networks represent modern and promising solution to meet the increasing demands on the transmission speed in access networks. These optical networks consist only of passive optical components, essential role play mainly the passive optical splitters. Therefore, it is not possible to perform additional adjustments with the optical signal, such as its amplifying, dropping, routing, etc. It is necessary in early stage of planning the optical network to comply with its parameters for its proper operation, especially the values of total attenuation, optical return loss, delay, etc. It is therefore necessary to accomplish the detailed analysis and the calculation of total attenuation characteristics of proposed optical network. The program for the purposes of rapid calculation of the passive optical network attenuation was designed. It supports various network topologies and it enables to enter initial parameters of optical network. The outputs in the tabular form also include the assessment of the suitability of proposed optical network for the different variants of passive optical networks.

  5. Structure Learning for Deep Neural Networks Based on Multiobjective Optimization.

    Science.gov (United States)

    Liu, Jia; Gong, Maoguo; Miao, Qiguang; Wang, Xiaogang; Li, Hao

    2017-05-05

    This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible data are modeled with respect to structure based on the products of experts. In order to mitigate the difficulty of estimating the denominator in PoE, the denominator is simplified and taken as another objective, i.e., the connecting sparsity. Moreover, for the consideration of the contradictory nature between the representation ability and the network connecting sparsity, the multiobjective model is established. An improved multiobjective evolutionary algorithm is used to solve this model. Two tricks are designed to decrease the computational cost according to the properties of input data. The experiments on single-layer level, hierarchical level, and application level demonstrate the effectiveness of the proposed algorithm, and the learned structures can improve the performance of deep neural networks.

  6. SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

    Science.gov (United States)

    Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

    2015-02-01

    Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Structure and Controls of the Global Virtual Water Trade Network

    Science.gov (United States)

    Suweis, S. S.

    2011-12-01

    Recurrent or ephemeral water shortages are a crucial global challenge, in particular because of their impacts on food production. The global character of this challenge is reflected in the trade among nations of virtual water, i.e. the amount of water used to produce a given commodity. We build, analyze and model the network describing the transfer of virtual water between world nations for staple food products. We find that all the key features of the network are well described by a model, the fitness model, that reproduces both the topological and weighted properties of the global virtual water trade network, by assuming as sole controls each country's gross domestic product and yearly rainfall on agricultural areas. We capture and quantitatively describe the high degree of globalization of water trade and show that a small group of nations play a key role in the connectivity of the network and in the global redistribution of virtual water. Finally, we illustrate examples of prediction of the structure of the network under future political, economic and climatic scenarios, suggesting that the crucial importance of the countries that trade large volumes of water will be strengthened. Our results show the importance of incorporating a network framework in the study of virtual water trades and provide a model to study the structure and resilience of the GVWTN under future scenarios for social, economic and climate change.

  8. Synthesis of Au nanowires with controlled morphological and structural characteristics

    Science.gov (United States)

    Singh, Gurvinder; van Helvoort, Antonius T. J.; Bandyopadhyay, Sulalit; Volden, Sondre; Andreassen, Jens-Petter; Glomm, Wilhelm R.

    2014-08-01

    A growth of one-dimensional noble metal nanostructure with controlled structural characteristic has been under intense investigation as the physical properties, for example, mechanical and electrical properties highly depend on the crystallinity of the nanostructure. Herein, we report a seed-mediated growth of gold nanowires with controlled structural and morphological characteristics, which can easily be varied by selecting appropriate seed nanoparticles, either spherical or rod type in aqueous solution at room temperature. The growth of nanowires was monitored by characterizing the samples at different time period during the reaction, and our observations suggest that growth occurs from seeds rapidly growing along one-dimension followed by surfactant induced fusion or welding and surface diffusion. The aspect ratio and morphology of these NWs can be tuned by CTAB concentration, pH and temperature of the growth solution. We show that the aspect ratio and morphology of these NWs can be tuned by the surfactant concentration, pH and temperature of the growth solution. Electron microscopy and X-ray Photoelectron spectroscopic techniques were employed for investigating structural and surface characteristics of nanowires. This approach can possibly help to synthesize nanowires of other metals with controlled crystalline behaviour which is highly essential for understanding their properties and practical applications in nanoelectronics, optical devices, catalysis, and sensors.

  9. Emergence of Slow-Switching Assemblies in Structured Neuronal Networks.

    Directory of Open Access Journals (Sweden)

    Michael T Schaub

    2015-07-01

    Full Text Available Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics can impact on spatio-temporal neural activity and constrain the resulting dynamics.

  10. 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.

  11. Emergence of Slow-Switching Assemblies in Structured Neuronal Networks.

    Science.gov (United States)

    Schaub, Michael T; Billeh, Yazan N; Anastassiou, Costas A; Koch, Christof; Barahona, Mauricio

    2015-07-01

    Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics) can impact on spatio-temporal neural activity and constrain the resulting dynamics.

  12. Online social network size is reflected in human brain structure.

    Science.gov (United States)

    Kanai, R; Bahrami, B; Roylance, R; Rees, G

    2012-04-07

    The increasing ubiquity of web-based social networking services is a striking feature of modern human society. The degree to which individuals participate in these networks varies substantially for reasons that are unclear. Here, we show a biological basis for such variability by demonstrating that quantitative variation in the number of friends an individual declares on a web-based social networking service reliably predicted grey matter density in the right superior temporal sulcus, left middle temporal gyrus and entorhinal cortex. Such regions have been previously implicated in social perception and associative memory, respectively. We further show that variability in the size of such online friendship networks was significantly correlated with the size of more intimate real-world social groups. However, the brain regions we identified were specifically associated with online social network size, whereas the grey matter density of the amygdala was correlated both with online and real-world social network sizes. Taken together, our findings demonstrate that the size of an individual's online social network is closely linked to focal brain structure implicated in social cognition.

  13. Using a Bayesian network to predict barrier island geomorphologic characteristics

    Science.gov (United States)

    Gutierrez, Ben; Plant, Nathaniel G.; Thieler, E. Robert; Turecek, Aaron

    2015-01-01

    Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncertainty, which is intrinsic to future sea level rise, storminess, or anthropogenic processes (e.g., beach nourishment and other forms of coastal management). The BN was developed and tested at Assateague Island, Maryland/Virginia, USA, a barrier island with sufficient geomorphic and temporal variability to evaluate our approach. We tested the ability to predict dune height, beach width, and beach height variables using inputs that included longer-term, larger-scale, or external variables (historical shoreline change rates, distances to inlets, barrier width, mean barrier elevation, and anthropogenic modification). Data sets from three different years spanning nearly a decade sampled substantial temporal variability and serve as a proxy for analysis of future conditions. We show that distinct geomorphic conditions are associated with different long-term shoreline change rates and that the most skillful predictions of dune height, beach width, and beach height depend on including multiple input variables simultaneously. The predictive relationships are robust to variations in the amount of input data and to variations in model complexity. The resulting model can be used to evaluate scenarios related to coastal management plans and/or future scenarios where shoreline change rates may differ from those observed historically.

  14. 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.

  15. Modelling sequences and temporal networks with dynamic community structures.

    Science.gov (United States)

    Peixoto, Tiago P; Rosvall, Martin

    2017-09-19

    In evolving complex systems such as air traffic and social organisations, collective effects emerge from their many components' dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often necessary to use methods that extract the temporal networks' large-scale dynamic community structure. However, such methods are subject to overfitting or suffer from effects of arbitrary, a priori-imposed timescales, which should instead be extracted from data. Here we simultaneously address both problems and develop a principled data-driven method that determines relevant timescales and identifies patterns of dynamics that take place on networks, as well as shape the networks themselves. We base our method on an arbitrary-order Markov chain model with community structure, and develop a nonparametric Bayesian inference framework that identifies the simplest such model that can explain temporal interaction data.The description of temporal networks is usually simplified in terms of their dynamic community structures, whose identification however relies on a priori assumptions. Here the authors present a data-driven method that determines relevant timescales for the dynamics and uses it to identify communities.

  16. 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.

  17. 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.

  18. A Simplified Mobile Ad Hoc Network Structure for Helicopter Communication

    Directory of Open Access Journals (Sweden)

    Abdeldime Mohamed Salih Abdelgader

    2016-01-01

    Full Text Available There are a number of volunteer and statutory organizations who are capable of conducting an emergency response using helicopters. Rescue operations require a rapidly deployable high bandwidth network to coordinate necessary relief efforts between rescue teams on the ground and helicopters. Due to massive destruction and loss of services, ordinary communication infrastructures may collapse in these situations. Consequently, information exchange becomes one of the major challenges in these circumstances. Helicopters can be also employed for providing many services in rugged environments, military applications, and aerial photography. Ad hoc network can be used to provide alternative communication link between a set of helicopters, particularly in case of significant amount of data required to be shared. This paper addresses the ability of using ad hoc networks to support the communication between a set of helicopters. A simplified network structure model is presented and extensively discussed. Furthermore, a streamlined routing algorithm is proposed. Comprehensive simulations are conducted to evaluate the proposed routing algorithm.

  19. Sound radiation characteristics of a box-type structure

    Science.gov (United States)

    Ran Lin, Tian; Pan, Jie

    2009-09-01

    The finite element and boundary element methods are employed in this study to investigate the sound radiation characteristics of a box-type structure. It has been shown [T.R. Lin, J. Pan, Vibration characteristics of a box-type structure, Journal of Vibration and Acoustics, Transactions of ASME 131 (2009) 031004-1-031004-9] that modes of natural vibration of a box-type structure can be classified into six groups according to the symmetry properties of the three panel pairs forming the box. In this paper, we demonstrate that such properties also reveal information about sound radiation effectiveness of each group of modes. The changes of radiation efficiencies and directivity patterns with the wavenumber ratio (the ratio between the acoustic and the plate bending wavenumbers) are examined for typical modes from each group. Similar characteristics of modal radiation efficiencies between a box structure and a corresponding simply supported panel are observed. The change of sound radiation patterns as a function of the wavenumber ratio is also illustrated. It is found that the sound radiation directivity of each box mode can be correlated to that of elementary sound sources (monopole, dipole, etc.) at frequencies well below the critical frequency of the plates of the box. The sound radiation pattern on the box surface also closely related to the vibration amplitude distribution of the box structure at frequencies above the critical frequency. In the medium frequency range, the radiated sound field is dominated by the edge vibration pattern of the box. The radiation efficiency of all box modes reaches a peak at frequencies above the critical frequency, and gradually approaches unity at higher frequencies.

  20. A semi-automatic method for extracting thin line structures in images as rooted tree network

    Energy Technology Data Exchange (ETDEWEB)

    Brazzini, Jacopo [Los Alamos National Laboratory; Dillard, Scott [Los Alamos National Laboratory; Soille, Pierre [EC - JRC

    2010-01-01

    This paper addresses the problem of semi-automatic extraction of line networks in digital images - e.g., road or hydrographic networks in satellite images, blood vessels in medical images, robust. For that purpose, we improve a generic method derived from morphological and hydrological concepts and consisting in minimum cost path estimation and flow simulation. While this approach fully exploits the local contrast and shape of the network, as well as its arborescent nature, we further incorporate local directional information about the structures in the image. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the target network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given seed with this metric is combined with hydrological operators for overland flow simulation to extract the line network. The algorithm is demonstrated for the extraction of blood vessels in a retina image and of a river network in a satellite image.

  1. 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.

  2. The network structure of city-firm relations

    CERN Document Server

    Garas, Antonios; Schweitzer, Frank

    2015-01-01

    How are economic activities linked to geographic locations? To answer this question, we use a data-driven approach that builds on the information about location, ownership and economic activities of the world's 3,000 largest firms and their almost one million subsidiaries. From this information we generate a bipartite network of cities linked to economic activities. Analysing the structure of this network, we find striking similarities with nested networks observed in ecology, where links represent mutualistic interactions between species. This motivates us to apply ecological indicators to identify the unbalanced deployment of economic activities. Such deployment can lead to an over-representation of specific economic sectors in a given city, and poses a significant thread for the city's future especially in times when the over-represented activities face economic uncertainties. If we compare our analysis with external rankings about the quality of life in a city, we find that the nested structure of the cit...

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

    CERN Document Server

    Zañudo, Jorge G T; Albert, Réka

    2016-01-01

    Given the network of interactions underlying a complex system, what can we learn about controlling such a system solely from its structure? Over a century of research in control theory has given us tools to answer this question, which were widely applied in science and engineering. Yet the current tools do not always consider the inherently nonlinear dynamics of real systems and the naturally occurring system states in their definition of "control", a term whose interpretation varies across disciplines. Here we use a new mathematical framework for structure-based control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors and which are guaranteed to be effective regardless of the dynamic details and parameters of the underlying system. We use this framework on several real networks, compar...

  4. 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.

  5. Daily temporal structure in African savanna flower visitation networks and consequences for network sampling.

    Science.gov (United States)

    Baldock, Katherine C R; Memmott, Jane; Ruiz-Guajardo, Juan Carlos; Roze, Denis; Stone, Graham N

    2011-03-01

    Ecological interaction networks are a valuable approach to understanding plant-pollinator interactions at the community level. Highly structured daily activity patterns are a feature of the biology of many flower visitors, particularly provisioning female bees, which often visit different floral sources at different times. Such temporal structure implies that presence/absence and relative abundance of specific flower-visitor interactions (links) in interaction networks may be highly sensitive to the daily timing of data collection. Further, relative timing of interactions is central to their possible role in competition or facilitation of seed set among coflowering plants sharing pollinators. To date, however, no study has examined the network impacts of daily temporal variation in visitor activity at a community scale. Here we use temporally structured sampling to examine the consequences of daily activity patterns upon network properties using fully quantified flower-visitor interaction data for a Kenyan savanna habitat. Interactions were sampled at four sequential three-hour time intervals between 06:00 and 18:00, across multiple seasonal time points for two sampling sites. In all data sets the richness and relative abundance of links depended critically on when during the day visitation was observed. Permutation-based null modeling revealed significant temporal structure across daily time intervals at three of the four seasonal time points, driven primarily by patterns in bee activity. This sensitivity of network structure shows the need to consider daily time in network sampling design, both to maximize the probability of sampling links relevant to plant reproductive success and to facilitate appropriate interpretation of interspecific relationships. Our data also suggest that daily structuring at a community level could reduce indirect competitive interactions when coflowering plants share pollinators, as is commonly observed during flowering in highly

  6. Circular sensing networks for guided waves based structural health monitoring

    Science.gov (United States)

    Wandowski, T.; Malinowski, P. H.; Ostachowicz, W. M.

    2016-01-01

    In this paper, results of damage localization performed for four sensing network configurations are compared. Process of damage localization is based on guided waves propagation phenomenon. Guided waves are excited using piezoelectric transducer and received by scanning laser vibrometer. Different excitation frequencies are also investigated. In experimental investigations two types of piezoelectric transducers are used as guided waves exciters. Frequency-magnitude characteristics of symmetric and antisymmetric modes are created for both types of transducers. These characteristics allow a choice of an excitation frequency for efficient generation of selected wave mode. The amplitude of second mode in this case has negligibly small value. Finally, sensing networks in the form of circle with three different diameters are realized based on piezoelectric transducers. Damage localization algorithm is prepared in MATLAB® environment as well as in C++.

  7. Mechanisms through which drug, sex partner, and friendship network characteristics relate to risky needle use among high risk youth and young adults.

    Science.gov (United States)

    Lakon, Cynthia M; Ennett, Susan T; Norton, Edward C

    2006-11-01

    Drug injector social networks are a primary social space in which risky needle use behaviors associated with HIV transmission occur, but the mechanisms through which these social networks influence risky needle use are unclear. This study investigated two mechanisms, social support and social regulation, through which injection drug users' social networks might relate to risky needle use behaviors. We investigated how these mechanisms work in three types of social networks, namely, drug user, sex partner, and friendship networks. Data are from a study of HIV risk and protective behaviors of youth and young adults, ages 14-43, in the United States who were injection drug users and/or the sexual partners of users (N = 277). The three types of networks were constructed based on information respondents provided about their drug use partners, sexual partners, and friends. The networks were characterized by structural (i.e., size and density) and interactional (i.e., multiplexity and closeness) characteristics. We conducted tests for mediation using ordered probit models and multiple linear regression. In the drug networks, social regulation partially meditated the relationship between multiplexity and risky needle use (pfriendship networks. The findings demonstrate that network mechanisms explaining risky needle use differ for drug user and sex partner networks in ways consistent with the functional nature of each type of network tie.

  8. Vulnerability analysis for airport networks based on fuzzy soft sets: From the structural and functional perspective

    Directory of Open Access Journals (Sweden)

    Li Shanmei

    2015-06-01

    Full Text Available Recently, much attention has been paid to the reliability and vulnerability of critical infrastructure. In air traffic systems, the vulnerability analysis for airport networks can be used to guide air traffic administrations in their prioritization of the maintenance and repair of airports, as well as to avoid unnecessary disturbances in the planning of flight schedules. In this paper, the evaluation methods of airport importance and network efficiency are established. Firstly, the evaluation indices of airport importance are proposed from both the topological and functional perspectives. The topological characteristics come from the structure of airport network and the functional features stem from the traffic flow distribution taking place inside the network. Secondly, an integrated evaluation method based on fuzzy soft set theory is proposed to identify the key airports, which can fuse together importance indices over different time intervals. Thirdly, an airport network efficiency method is established for the purpose of assessing the accuracy of the evaluation method. Finally, empirical studies using real traffic data of US and China’s airport networks show that the evaluation method proposed in this paper is the most accurate. The vulnerability of US and China’s airport networks is compared. The similarities and differences between airport geography distribution and airport importance distribution are discussed here and the dynamics of airport importance is studied as well.

  9. Effects of the bipartite structure of a network on performance of recommenders

    Science.gov (United States)

    Wang, Qing-Xian; Li, Jian; Luo, Xin; Xu, Jian-Jun; Shang, Ming-Sheng

    2018-02-01

    Recommender systems aim to predict people's preferences for online items by analyzing their historical behaviors. A recommender can be modeled as a high-dimensional and sparse bipartite network, where the key issue is to understand the relation between the network structure and a recommender's performance. To address this issue, we choose three network characteristics, clustering coefficient, network density and user-item ratio, as the analyzing targets. For the cluster coefficient, we adopt the Degree-preserving rewiring algorithm to obtain a series of bipartite network with varying cluster coefficient, while the degree of user and item keep unchanged. Furthermore, five state-of-the-art recommenders are applied on two real datasets. The performances of recommenders are measured by both numerical and physical metrics. These results show that a recommender's performance is positively related to the clustering coefficient of a bipartite network. Meanwhile, higher density of a bipartite network can provide more accurate but less diverse or novel recommendations. Furthermore, the user-item ratio is positively correlated with the accuracy metrics but negatively correlated with the diverse and novel metrics.

  10. Reduction of Second-Order Network Systems with Structure Preservation

    NARCIS (Netherlands)

    Cheng, Xiaodong; Kawano, Yu; Scherpen, Jacquelien M.A.

    2017-01-01

    This paper proposes a general framework for structure-preserving model reduction of a second-order network system based on graph clustering. In this approach, vertex dynamics are captured by the transfer functions from inputs to individual states, and the dissimilarities of vertices are quantified

  11. Geometric and Network Model for Knowledge Structure and Mindspace

    OpenAIRE

    Chris Arney

    2012-01-01

    This paper describes an adaptive, complex network architecture for knowledge representation in virtual mindspace. Structures and processes for knowing, remembering, thinking, learning, deciding, and communicating describe a virtual geometric space (mathematical model) of a notional mind. This mindspace model can be visualized as a workspace and this paper provides a glimpse of a virtual model of the mind.

  12. Geometric and Network Model for Knowledge Structure and Mindspace

    Directory of Open Access Journals (Sweden)

    Chris Arney

    2012-02-01

    Full Text Available This paper describes an adaptive, complex network architecture for knowledge representation in virtual mindspace. Structures and processes for knowing, remembering, thinking, learning, deciding, and communicating describe a virtual geometric space (mathematical model of a notional mind. This mindspace model can be visualized as a workspace and this paper provides a glimpse of a virtual model of the mind.

  13. 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...

  14. A Hierarchy of Network Performance Characteristics for Grid Applications and Services

    Energy Technology Data Exchange (ETDEWEB)

    Lowekamp, B

    2004-07-06

    This document describes a standard set of network characteristics that are useful for Grid applications and services as well as a classification hierarchy for these characteristics. The goal of this work is to identify the various types of network measurements according to the network characteristic they measure and the network entity on which they are taken. This document defines standard terminology to describe those measurements, but it does not attempt to define new standard measurement methodologies or attempt to define the best measurement methodologies to use for grid applications. However, it does attempt to point out the advantages and disadvantages of different measurement methodologies. This document was motivated by the need for the interchange of measurements taken by various systems in the Grid and to develop a common dictionary to facilitate discussions about and specifications for measurement systems. The application of this naming system will facilitate the creation of common schemata for describing network monitoring data in Grid Monitoring and Discovery Services, and thus help to address portability issues between the wide variety of network measurements used between sites of a Grid.

  15. Predicting Successful Memes using Network and Community Structure

    OpenAIRE

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

    2014-01-01

    We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of...

  16. Anthropogenic impacts on tropical forest biodiversity: a network structure and ecosystem functioning perspective

    National Research Council Canada - National Science Library

    Rebecca J. Morris

    2010-01-01

    ... are organized in networks, and the function that those species perform. I discuss our current knowledge of network structure and ecosystem functioning, highlighting empirical examples of their response...

  17. Graph kernels, hierarchical clustering, and network community structure: experiments and comparative analysis

    Science.gov (United States)

    Zhang, S.; Ning, X.-M.; Zhang, X.-S.

    2007-05-01

    There has been a quickly growing interest in properties of complex networks, such as the small world property, power-law degree distribution, network transitivity, and community structure, which seem to be common to many real world networks. In this study, we consider the community property which is also found in many real networks. Based on the diffusion kernels of networks, a hierarchical clustering approach is proposed to uncover the community structure of different extent of complex networks. We test the method on some networks with known community structures and find that it can detect significant community structure in these networks. Comparison with related methods shows the effectiveness of the method.

  18. Abnormal structural connectivity in the brain networks of children with hydrocephalus

    Directory of Open Access Journals (Sweden)

    Weihong Yuan

    2015-01-01

    Full Text Available Increased intracranial pressure and ventriculomegaly in children with hydrocephalus are known to have adverse effects on white matter structure. This study seeks to investigate the impact of hydrocephalus on topological features of brain networks in children. The goal was to investigate structural network connectivity, at both global and regional levels, in the brains in children with hydrocephalus using graph theory analysis and diffusion tensor tractography. Three groups of children were included in the study (29 normally developing controls, 9 preoperative hydrocephalus patients, and 17 postoperative hydrocephalus patients. Graph theory analysis was applied to calculate the global network measures including small-worldness, normalized clustering coefficients, normalized characteristic path length, global efficiency, and modularity. Abnormalities in regional network parameters, including nodal degree, local efficiency, clustering coefficient, and betweenness centrality, were also compared between the two patients groups (separately and the controls using two tailed t-test at significance level of p < 0.05 (corrected for multiple comparison. Children with hydrocephalus in both the preoperative and postoperative groups were found to have significantly lower small-worldness and lower normalized clustering coefficient than controls. Children with hydrocephalus in the postoperative group were also found to have significantly lower normalized characteristic path length and lower modularity. At regional level, significant group differences (or differences at trend level in regional network measures were found between hydrocephalus patients and the controls in a series of brain regions including the medial occipital gyrus, medial frontal gyrus, thalamus, cingulate gyrus, lingual gyrus, rectal gyrus, caudate, cuneus, and insular. Our data showed that structural connectivity analysis using graph theory and diffusion tensor tractography is sensitive to

  19. Network Environments and Well-Being: An Examination of Personal Network Structure, Social Capital, and Perceived Social Support.

    Science.gov (United States)

    Lee, Seungyoon; Chung, Jae Eun; Park, Namkee

    2018-01-01

    Previous studies have demonstrated the role of social networks, social capital, and social support in individuals' well-being. However, the ways in which these related constructs simultaneously influence one's well-being outcomes and relate to one another have not been closely examined. This study pays particular attention to the structural characteristics of personal networks, distinction between offline and online social capital, and different indicators of well-being outcomes. Based on survey data collected from 574 college students, the study found that two dimensions of personal networks-density and gender homophily-and social capital in the form of offline bonding capital explained perceived social support. Further, perceived social support consistently predicted well-being outcomes and played a mediating role between personal network density and well-being, as well as between offline bonding capital and well-being. The results offer implications for a more nuanced understanding of the role of individuals' interpersonal and social environments in well-being outcomes.

  20. Software structure for broadband wireless sensor network system

    Science.gov (United States)

    Kwon, Hyeokjun; Oh, Sechang; Yoon, Hargsoon; Varadan, Vijay K.

    2010-04-01

    Zigbee Sensor Network system has been investigating for monitoring and analyzing the data measured from a lot of sensors because the Zigbee Sensor Network has several advantages of low power consumption, compact size, and multi-node connection. However, it has a disadvantage not to be able to monitor the data measured from sensors at the remote area such as other room that is located at other city. This paper describes the software structure to compensate the defect with combining the Zigbee Sensor Network and wireless LAN technology for remote monitoring of measured sensor data. The software structure has both benefits of Zigbee Sensor Network and the advantage of wireless LAN. The software structure has three main software structures. The first software structure consists of the function in order to acquire the data from sensors and the second software structure is to gather the sensor data through wireless Zigbee and to send the data to Monitoring system by using wireless LAN. The second part consists of Linux packages software based on 2440 CPU (Samsung corp.), which has ARM9 core. The Linux packages include bootloader, device drivers, kernel, and applications, and the applications are TCP/IP server program, the program interfacing with Zigbee RF module, and wireless LAN program. The last part of software structure is to receive the sensor data through TCP/IP client program from Wireless Gate Unit and to display graphically measured data by using MATLAB program; the sensor data is measured on 100Hz sampling rate and the measured data has 10bit data resolution. The wireless data transmission rate per each channel is 1.6kbps.

  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. Topological Analysis of Network Structures in Three Dimensions

    Science.gov (United States)

    Glicksman, Martin E.

    2004-03-01

    Kinetics, topology, integral and differential geometry are combined to impose space-filling requirements on network structures. The theory centers on representing network cells as "average N-hedra," with curvatures and dihedral angles that satisfy thermodynamic equilibria at contact faces, triple lines and quadrajunctions. Exact kinetic relations are derived for average N-hedra that accurately predict the volumetric and areal time-rates of change for irregular polyhedral cells that comprise constructible network microstructures. The new results extend to three dimensions the von Neumann-Mullins law, which provides the well-known topologically-based kinetic relation valid for tessellations in 2-d Kinetic laws derived for here for 3-d may prove useful for constructing more accurate models of grain growth and foam coarsening, and for clarifying several long-standing general issues on space-filling criteria required for three-dimensional network structures. Comparisons will be shown between foams and isolated N-hedra, modeled recently using Surface Evolver simulations, and predictions from the new analytical results and other theoretical estimates. The availability of an exact analytic kinetic law provides benchmarks for testing simulations and for guiding quantitative experiments on network dynamics in three-dimensional microstructures. * The Author gives his special thanks for the financial assistance obtained from the Alexander von Humboldt Stiftung, as a Forschungs Preissträger, and for scientific support so generously provided at the Institut für Metallkunde und Metallphysik, RWTH-Aachen.

  3. Changing the network structure: leaving the past behind

    Science.gov (United States)

    Herber, Ralf

    2010-01-01

    The infrastructure of the existing network is determined by the old copper access technology. Not only the copper access itself, also the number of central offices and their geographical distribution are results of the copper network and its physical limitations. Today, in Germany, several thousand active locations cater for the delivery of plain old telephony services, as well as for the delivery of new fiber-based broadband services. Due to the fact that the attenuation of optical fibers is relatively low, new concepts for the design of the network structure become possible and are discussed in this paper. A reach of 40 km, for example, on an optical transport system is no problem. Longer possible link lengths can result in a reduced number of central offices, leading to reduced expenditures for the building, power supply and air conditioning. In the case of Germany a number of some hundred central offices is envisaged. However, a significant drawback of today's existing optical access technologies is the very limited number of customers on a single fiber. For instance, GPON (Gigabit Passive Optical Network) provides typically a 32-way split and a distance of 20 km. This paper discusses some new ideas to introduce higher splitting ratios and longer access lengths into the network. With WDM (Wavelength Division Multiplexing) and/or coherent optical receivers new options for a future proof access network are available.

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

    Science.gov (United States)

    Li, Jianfu; Luo, Cheng; Peng, Yueheng; Xie, Qiankun; Gong, Jinnan; Dong, Li; Lai, Yongxiu; Li, Hong; Yao, Dezhong

    2014-01-01

    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. 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. 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. 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. 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.

  6. 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.

  7. Structure and dynamics in network-forming materials.

    Science.gov (United States)

    Wilson, Mark

    2016-12-21

    The study of the structure and dynamics of network-forming materials is reviewed. Experimental techniques used to extract key structural information are briefly considered. Strategies for building simulation models, based on both targeting key (experimentally-accessible) materials and on systematically controlling key model parameters, are discussed. As an example of the first class of materials, a key target system, SiO2, is used to highlight how the changing structure with applied pressure can be effectively modelled (in three dimensions) and used to link to both experimental results and simple structural models. As an example of the second class the topology of networks of tetrahedra in the MX2 stoichiometry are controlled using a single model parameter linked to the M-X-M bond angles. The evolution of ordering on multiple length-scales is observed as are the links between the static structure and key dynamical properties. The isomorphous relationship between the structures of amorphous Si and SiO2 is discussed as are the similarities and differences in the phase diagrams, the latter linked to potential polyamorphic and 'anomalous' (e.g. density maxima) behaviour. Links to both two-dimensional structures for C, Si and Ge and near-two-dimensional bilayers of SiO2 are discussed. Emerging low-dimensional structures in low temperature molten carbonates are also uncovered.

  8. Growing networks of overlapping communities with internal structure

    Science.gov (United States)

    Young, Jean-Gabriel; Hébert-Dufresne, Laurent; Allard, Antoine; Dubé, Louis J.

    2016-08-01

    We introduce an intuitive model that describes both the emergence of community structure and the evolution of the internal structure of communities in growing social networks. The model comprises two complementary mechanisms: One mechanism accounts for the evolution of the internal link structure of a single community, and the second mechanism coordinates the growth of multiple overlapping communities. The first mechanism is based on the assumption that each node establishes links with its neighbors and introduces new nodes to the community at different rates. We demonstrate that this simple mechanism gives rise to an effective maximal degree within communities. This observation is related to the anthropological theory known as Dunbar's number, i.e., the empirical observation of a maximal number of ties which an average individual can sustain within its social groups. The second mechanism is based on a recently proposed generalization of preferential attachment to community structure, appropriately called structural preferential attachment (SPA). The combination of these two mechanisms into a single model (SPA+) allows us to reproduce a number of the global statistics of real networks: The distribution of community sizes, of node memberships, and of degrees. The SPA+ model also predicts (a) three qualitative regimes for the degree distribution within overlapping communities and (b) strong correlations between the number of communities to which a node belongs and its number of connections within each community. We present empirical evidence that support our findings in real complex networks.

  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. Mapping Language Networks Using the Structural and Dynamic Brain Connectomes.

    Science.gov (United States)

    Del Gaizo, John; Fridriksson, Julius; Yourganov, Grigori; Hillis, Argye E; Hickok, Gregory; Misic, Bratislav; Rorden, Chris; Bonilha, Leonardo

    2017-01-01

    Lesion-symptom mapping is often employed to define brain structures that are crucial for human behavior. Even though poststroke deficits result from gray matter damage as well as secondary white matter loss, the impact of structural disconnection is overlooked by conventional lesion-symptom mapping because it does not measure loss of connectivity beyond the stroke lesion. This study describes how traditional lesion mapping can be combined with structural connectome lesion symptom mapping (CLSM) and connectome dynamics lesion symptom mapping (CDLSM) to relate residual white matter networks to behavior. Using data from a large cohort of stroke survivors with aphasia, we observed improved prediction of aphasia severity when traditional lesion symptom mapping was combined with CLSM and CDLSM. Moreover, only CLSM and CDLSM disclosed the importance of temporal-parietal junction connections in aphasia severity. In summary, connectome measures can uniquely reveal brain networks that are necessary for function, improving the traditional lesion symptom mapping approach.

  11. 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.

  12. Graph spectra and the detectability of community structure in networks.

    Science.gov (United States)

    Nadakuditi, Raj Rao; Newman, M E J

    2012-05-04

    We study networks that display community structure--groups of nodes within which connections are unusually dense. Using methods from random matrix theory, we calculate the spectra of such networks in the limit of large size, and hence demonstrate the presence of a phase transition in matrix methods for community detection, such as the popular modularity maximization method. The transition separates a regime in which such methods successfully detect the community structure from one in which the structure is present but is not detected. By comparing these results with recent analyses of maximum-likelihood methods, we are able to show that spectral modularity maximization is an optimal detection method in the sense that no other method will succeed in the regime where the modularity method fails.

  13. Prediction of Henry's law constants by a quantitative structure property relationship and neural networks.

    Science.gov (United States)

    English, N J; Carroll, D G

    2001-01-01

    Multiple linear regression analysis and neural networks were employed to develop predictive models for Henry's law constants (HLCs) for organic compounds of environmental concern in pure water at 25 degrees C, using a set of quantitative structure property relationship (QSPR)-based descriptors to encode various molecular structural features. Two estimation models were developed from a set of 303 compounds using 10 and 12 descriptors, one of these models using two descriptors to account for hydrogen-bonding characteristics explicitly; these were validated subsequently on an external set of 54 compounds. For each model, a linear regression and neural network version was prepared. The standard errors of the linear regression models for the training data set were 0.262 and 0.488 log(H(cc)) units, while those of the neural network analogues were lower at 0.202 and 0.224, respectively; the linear regression models explained 98.3% and 94.3% of the variance in the development data, respectively, the neural network models giving similar quality results of 99% and 98.3%, respectively. The various descriptors used describe connectivity, charge distribution, charged surface area, hydrogen-bonding characteristics, and group influences on HLC values.

  14. Structural brain network: What is the effect of LiFE optimization of whole brain tractography?

    Directory of Open Access Journals (Sweden)

    Shouliang eQi

    2016-02-01

    normalized clustering coefficient, the normalized characteristic path length and the small-worldness are higher in the optimized network weighted by the fiber number than in the non-optimized network. These observed differences suggest that LiFE optimization can be a crucial step for the construction of more reasonable and more accurate structural brain networks.

  15. 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.

  16. Bayesian blind source separation for data with network structure.

    Science.gov (United States)

    Illner, Katrin; Fuchs, Christiane; Theis, Fabian J

    2014-11-01

    In biology, more and more information about the interactions in regulatory systems becomes accessible, and this often leads to prior knowledge for recent data interpretations. In this work we focus on multivariate signaling data, where the structure of the data is induced by a known regulatory network. To extract signals of interest we assume a blind source separation (BSS) model, and we capture the structure of the source signals in terms of a Bayesian network. To keep the parameter space small, we consider stationary signals, and we introduce the new algorithm emGrade, where model parameters and source signals are estimated using expectation maximization. For network data, we find an improved estimation performance compared to other BSS algorithms, and the flexible Bayesian modeling enables us to deal with repeated and missing observation values. The main advantage of our method is the statistically interpretable likelihood, and we can use model selection criteria to determine the (in general unknown) number of source signals or decide between different given networks. In simulations we demonstrate the recovery of the source signals dependent on the graph structure and the dimensionality of the data.

  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. Network Representation of Multi-Cell Accelerating Structures

    CERN Document Server

    Raguin, J Y

    2001-01-01

    The analysis of the electrodynamic properties of a complete multi-cell accelerating structure using electromagnetic numerical simulation codes is presently at the edge of existing computer capabilities. To overcome this limitation, a network representation is proposed which derives the overall scattering transfer matrix of such multi-cell structures from single-cell data calculated using the commercial finite-element code HFSS. For a constant-impedance structure, computation of the eigenvalues of this matrix allows dispersion diagrams to be obtained. In the more general case, this formalism leads to a representation of the coupled-chain of cavities as a set of cascaded non identical multipoles.

  19. Vibration Response Characteristics of the Cross Tunnel Structure

    Directory of Open Access Journals (Sweden)

    Jinxing Lai

    2016-01-01

    Full Text Available It is well known that the tunnel structure will lose its function under the long-term repeated function of the vibration effect. A prime example is the Xi’an cross tunnel structure (CTS of Metro Line 2 and the Yongningmen tunnel, where the vibration response of the tunnel vehicle load and metro train load to the structure of shield tunnel was analyzed by applying the three-dimensional (3D dynamic finite element model. The effect of the train running was simulated by applying the time-history curves of vibration force of the track induced by wheel axles, using the fitted formulas for vehicle and train vibration load. The characteristics and the spreading rules of vibration response of metro tunnel structure were researched from the perspectives of acceleration, velocity, displacement, and stress. It was found that vehicle load only affects the metro tunnel within 14 m from the centre, and the influence decreases gradually from vault to spandrel, haunch, and springing. The high-speed driving effect of the train can be divided into the close period, the rising period, the stable period, the declining period, and the leaving period. The stress at haunch should be carefully considered. The research results presented for this case study provide theoretical support for the safety of vibration response of Metro Line 2 structure.

  20. Fire influences the structure of plant-bee networks.

    Science.gov (United States)

    Peralta, Guadalupe; Stevani, Erica L; Chacoff, Natacha P; Dorado, Jimena; Vázquez, Diego P

    2017-10-01

    Fire represents a frequent disturbance in many ecosystems, which can affect plant-pollinator assemblages and hence the services they provide. Furthermore, fire events could affect the architecture of plant-pollinator interaction networks, modifying the structure and function of communities. Some pollinators, such as wood-nesting bees, may be particularly affected by fire events due to damage to the nesting material and its long regeneration time. However, it remains unclear whether fire influences the structure of bee-plant interactions. Here, we used quantitative plant-wood-nesting bee interaction networks sampled across four different post-fire age categories (from freshly-burnt to unburnt sites) in an arid ecosystem to test whether the abundance of wood-nesting bees, the breadth of resource use and the plant-bee community structure change along a post-fire age gradient. We demonstrate that freshly-burnt sites present higher abundances of generalist than specialist wood-nesting bees and that this translates into lower network modularity than that of sites with greater post-fire ages. Bees do not seem to change their feeding behaviour across the post-fire age gradient despite changes in floral resource availability. Despite the effects of fire on plant-bee interaction network structure, these mutualistic networks seem to be able to recover a few years after the fire event. This result suggests that these interactions might be highly resilient to this type of disturbance. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

  1. China’s Capital Market: its Structure and Characteristics

    Directory of Open Access Journals (Sweden)

    Ionela Bălțătescu

    2010-10-01

    Full Text Available The purpose of the present article is to briefly overview the main characteristics and structure of China’s capital market. The first part of the paper presents the main stock exchanges, futures exchanges and commodity exchanges of China, securities products available in China, China’s level of domestic market capitalization and other key financial information and statistics regarding China capital market. The second part of the paper contains a short review on the liberalization and reforms of China’s financial market, especially the programs QFII (Qualified Foreign Institutional Investor, QDII (Qualified Domestic Institutional Investor and mini-QFII.

  2. Structural characteristics of the institutional environment for young children

    Directory of Open Access Journals (Sweden)

    Arintcina, Irina A.

    2016-09-01

    Full Text Available The research literature suggests that institutions for children left without parental care do not provide environments that adequately promote children’s development, and that characteristics of orphanages should be considered as an environmental factor influencing developmental difficulties in children living in institutions and later in post-institutional families. This study aimed to analyze the structural characteristics of the caregiving environment in two St. Petersburg (RF orphanages—baby homes for children from birth to 4–5 years of age (BH A and BH B, and the maintenance of the structural interventions that were implemented in BH A during 2000-05 (The St. Petersburg–USA Orphanage Research Team, 2008. Both institutions belong to the Ministry of Health and are managed under the same medical regulations, providing about the same quality of medical care and nutrition. The results of the study show that the number of children living in each ward (4 to 6 in BH A and 5 to 8 in BH B, and the child–caregiver ratio (2 to 3 for BH A and 2.5 to 4 for BH B in the two baby homes are about the same, while BH A have fewer staff members who are assigned to the ward (6–8 vs. 9–14 in BH B. The ward assistant teachers in BH A are assigned as the primary caregivers, working 5 days a week (39 hrs vs. about 25 hrs a week for assistant teachers in BH B. While living in the baby home, children in BH A are integrated by age and disability (vs. segregation by age and partial disability integration in BH B, and are assigned to one ward (meaning the same caregivers, peers, rooms, etc., while in BH B the children change their ward when they reach a certain age or developmental milestone (number of wards children experienced M(SD = 1.1 (0.2 in BH A and 2.7 (1.1 in BH B. Our results support the hypothesis that the structural characteristics of institutional environment in the two baby homes are different, and that in comparison with BH B, the structural

  3. Aging influence on grey matter structural associations within the default mode network utilizing Bayesian network modeling

    Directory of Open Access Journals (Sweden)

    Yan eWang

    2014-05-01

    Full Text Available Recent neuroimaging studies have revealed normal aging-related alterations in functional and structural brain networks such as the default mode network (DMN. However, less is understood about specific brain structural dependencies or interactions between brain regions within the DMN in the normal aging process. In this study, using Bayesian network (BN modeling, we analyzed grey matter volume data from 109 young and 82 old subjects to characterize the influence of aging on associations between core brain regions within the DMN. Furthermore, we investigated the discriminability of the aging-associated BN models for the young and old groups. Compared to their young counterparts, the old subjects showed significant reductions in connections from right inferior temporal cortex (ITC to medial prefrontal cortex (mPFC, right hippocampus (HP to right ITC, and mPFC to posterior cingulate cortex (PCC and increases in connections from left HP to mPFC and right inferior parietal cortex (IPC to right ITC. Moreover, the classification results showed that the aging-related BN models could predict group membership with 88.48% accuracy, 88.07% sensitivity and 89.02% specificity. Our findings suggest that structural associations within the DMN may be affected by normal aging and provide crucial information about aging effects on brain structural networks.

  4. Architecture of basic building blocks in protein and domain structural interaction networks.

    Science.gov (United States)

    Moon, Hyun S; Bhak, Jonghwa; Lee, Kwang H; Lee, Doheon

    2005-04-15

    The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. doheon@kaist.ac.kr.

  5. 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.

  6. Towards Multifunctional Characteristics of Embedded Structures With Carbon Nanotube Yarns

    Science.gov (United States)

    Hernandez, Corey D.; Gates, Thomas S.; Kahng, Seun K.

    2006-01-01

    This paper presents recent results on research of achieving multifunctional structures utilizing Carbon Nanotube (CNT) yarns. The investigation centers on creating composite structures with CNT yarns to simultaneously achieve increases in mechanical strength and the ability to sense strain. The CNT yarns used in our experiments are of the single-ply and two-ply variety with the single-ply yarns having diameters on the order of 10-20 m. The yarns are embedded in silicon rubber and polyurethane test specimens. Mechanical tests show an increase in modulus of elasticity, with an additional weight increase of far less than one-percent. Sensing characteristics of the yarns are investigated on stainless steel test beams in an electrical bridge configuration, and are observed to have a strain sensitivity of 0.7mV/V/1000 micro-strain. Also reported are measurements of the average strain distribution along the direction of the CNT yarns on square silicon rubber membranes.

  7. A unified method of detecting core-periphery structure and community structure in networks.

    Science.gov (United States)

    Xiang, Bing-Bing; Bao, Zhong-Kui; Ma, Chuang; Zhang, Xingyi; Chen, Han-Shuang; Zhang, Hai-Feng

    2018-01-01

    The core-periphery structure and the community structure are two typical meso-scale structures in complex networks. Although community detection has been extensively investigated from different perspectives, the definition and the detection of the core-periphery structure have not received much attention. Furthermore, the detection problems of the core-periphery and community structure were separately investigated. In this paper, we develop a unified framework to simultaneously detect the core-periphery structure and community structure in complex networks. Moreover, there are several extra advantages of our algorithm: our method can detect not only single but also multiple pairs of core-periphery structures; the overlapping nodes belonging to different communities can be identified; different scales of core-periphery structures can be detected by adjusting the size of the core. The good performance of the method has been validated on synthetic and real complex networks. So, we provide a basic framework to detect the two typical meso-scale structures: the core-periphery structure and the community structure.

  8. Product Cost Management Structures: a review and neural network modelling

    Directory of Open Access Journals (Sweden)

    P. Jha

    2003-11-01

    Full Text Available This paper reviews the growth of approaches in product costing and draws synergies with information management and resource planning systems, to investigate potential application of state of the art modelling techniques of neural networks. Increasing demands on costing systems to serve multiple decision-making objectives, have made it essential to use better techniques for analysis of available data. This need is highlighted in the paper. The approach of neural networks, which have several analogous facets to complement and aid the information demands of modern product costing, Enterprise Resource Planning (ERP structures and the dominant-computing environment (for information management in the object oriented paradigm form the domain for investigation. Simulated data is used in neural network applications across activities that consume resources and deliver products, to generate information for monitoring and control decisions. The results in application for feature extraction and variation detection and their implications are presented in the paper.

  9. Automated Modeling of Microwave Structures by Enhanced Neural Networks

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-12-01

    Full Text Available The paper describes the methodology of the automated creation of neural models of microwave structures. During the creation process, artificial neural networks are trained using the combination of the particle swarm optimization and the quasi-Newton method to avoid critical training problems of the conventional neural nets. In the paper, neural networks are used to approximate the behavior of a planar microwave filter (moment method, Zeland IE3D. In order to evaluate the efficiency of neural modeling, global optimizations are performed using numerical models and neural ones. Both approaches are compared from the viewpoint of CPU-time demands and the accuracy. Considering conclusions, methodological recommendations for including neural networks to the microwave design are formulated.

  10. The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments.

    Science.gov (United States)

    Leider, Jonathon P; Castrucci, Brian C; Harris, Jenine K; Hearne, Shelley

    2015-08-06

    The relationship between policy networks and policy development among local health departments (LHDs) is a growing area of interest to public health practitioners and researchers alike. In this study, we examine policy activity and ties between public health leadership across large urban health departments. This study uses data from a national profile of local health departments as well as responses from a survey sent to three staff members (local health official, chief of policy, chief science officer) in each of 16 urban health departments in the United States. Network questions related to frequency of contact with health department personnel in other cities. Using exponential random graph models, network density and centrality were examined, as were patterns of communication among those working on several policy areas using exponential random graph models. All 16 LHDs were active in communicating about chronic disease as well as about use of alcohol, tobacco, and other drugs (ATOD). Connectedness was highest among local health officials (density = .55), and slightly lower for chief science officers (d = .33) and chiefs of policy (d = .29). After accounting for organizational characteristics, policy homophily (i.e., when two network members match on a single characteristic) and tenure were the most significant predictors of formation of network ties. Networking across health departments has the potential for accelerating the adoption of public health policies. This study suggests similar policy interests and formation of connections among senior leadership can potentially drive greater connectedness among other staff.

  11. The Relationship of Policymaking and Networking Characteristics among Leaders of Large Urban Health Departments

    Directory of Open Access Journals (Sweden)

    Jonathon P. Leider

    2015-08-01

    Full Text Available Background: The relationship between policy networks and policy development among local health departments (LHDs is a growing area of interest to public health practitioners and researchers alike. In this study, we examine policy activity and ties between public health leadership across large urban health departments. Methods: This study uses data from a national profile of local health departments as well as responses from a survey sent to three staff members (local health official, chief of policy, chief science officer in each of 16 urban health departments in the United States. Network questions related to frequency of contact with health department personnel in other cities. Using exponential random graph models, network density and centrality were examined, as were patterns of communication among those working on several policy areas using exponential random graph models. Results: All 16 LHDs were active in communicating about chronic disease as well as about use of alcohol, tobacco, and other drugs (ATOD. Connectedness was highest among local health officials (density = .55, and slightly lower for chief science officers (d = .33 and chiefs of policy (d = .29. After accounting for organizational characteristics, policy homophily (i.e., when two network members match on a single characteristic and tenure were the most significant predictors of formation of network ties. Conclusion: Networking across health departments has the potential for accelerating the adoption of public health policies. This study suggests similar policy interests and formation of connections among senior leadership can potentially drive greater connectedness among other staff.

  12. A sparse sensor network topologized for cylindrical wave-based identification of damage in pipeline structures

    Science.gov (United States)

    Wang, Qiang; Hong, Ming; Su, Zhongqing

    2016-07-01

    A sparse sensor network, based on the concept of semi-decentralized and standardized sensing, is developed, to actively excite and acquire cylindrical waves for damage identification and health monitoring of pipe structures. Differentiating itself from conventional ‘ring-style’ transducer arrays which attempt to steer longitudinal axisymmetric cylindrical waves via transducer synchronism, this sparse sensor network shows advantages in some aspects, including the use of fewer sensors, simpler manipulation, quicker configuration, less mutual dependence among sensors, and an improved signal-to-noise ratio. The sparse network is expanded topologically, aimed at eliminating the presence of ‘blind zones’ and the challenges associated with multi-path propagation of cylindrical waves. Theoretical analysis is implemented to comprehend propagation characteristics of waves guided by a cylindrical structure. A probability-based diagnostic imaging algorithm is introduced to visualize damage in pixelated images in an intuitive, prompt, and automatic manner. A self-contained health monitoring system is configured for experimental validation, via which quantitative identification of mono- and multi-damage in a steel cylinder is demonstrated. The results highlight an expanded sensing coverage of the sparse sensor network and its enhanced capacity of acquiring rich information, avoiding the cost of augmenting the number of sensors in a sensor network.

  13. [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

    2017-07-17

    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.

  14. Characteristics of Social Network Gamers: in between Social Networking and Online Role-Playing Games

    Directory of Open Access Journals (Sweden)

    Olga eGeisel

    2015-07-01

    Full Text Available Current research on internet addiction (IA reported moderate to high prevalence rates of IA and comorbid psychiatric symptoms in users of social networking sites (SNS and online role-playing games. The aim of this study was to characterise adult users of an internet multiplayer strategy game within a SNS. Therefore, we conducted an exploratory study using an online survey to assess sociodemographic variables, psychopathology and the rate of IA in a sample of adult social network gamers by Young´s Internet Addiction Test (IAT, the Toronto Alexithymia Scale (TAS, the Beck Depression Inventory II (BDI-II, the Symptom Checklist-90-R (SCL-90-R and the WHO Quality of Life-BREF (WHOQOL-BREF. All participants were listed gamers of combat zone in the SNS Facebook. In the IAT analysis, 16.2 % of the participants (n = 60 were categorized as subjects with IA and 19.5 % (n = 72 fulfilled the criteria for alexithymia. Comparing study participants with and without IA, the IA group had significantly more subjects with alexithymia, reported more depressive symptoms, and showed poorer quality of life. These findings suggest that social network gaming might also be associated with maladaptive patterns of internet use. Furthermore, a relationship between IA, alexithymia and depressive symptoms was found that needs to be elucidated by future studies.

  15. Structure and sociodemographis characteristics of the family climate in Vojvodina

    Directory of Open Access Journals (Sweden)

    Mihić Ivana

    2006-01-01

    Full Text Available Research presented in this paper aimed to investigate psychometric characteristics and factor structure of Family Climate Inventory (FCI Kurdek et al., 1995, as well as the sociodemographic correlates of the quality of family environment operacionalized by this scale. The research sample consisted of 561 adolescents (average age 16. Four factores were extracted.Total variance explained by these four factors was 62. 29%. First factor has been defined as Acceptance, the second (Autonomy considers the estimated level of independence between family members. The third factor Conflicts implies frequency and influence of conflict situations and relations within the family. The fourth isolated factor- Supervision, was the dimension of original FCI scale, but has shown poor metric characteristics in our sample, and has been excluded from further analysis. The reduced Family Climate Inventory consists of 18 items, and has good psychometric characteristics (Cronbah a =.94. Family climate is assessed as better in dual earning families with egalitarian division of parental roles and household labor. Adolescents from rural families describe their family environment as better in quality.

  16. Networks and Models with Heterogeneous Population Structure in Epidemiology

    Science.gov (United States)

    Kao, R. R.

    Heterogeneous population structure can have a profound effect on infectious disease dynamics, and is particularly important when investigating “tactical” disease control questions. At times, the nature of the network involved in the transmission of the pathogen (bacteria, virus, macro-parasite, etc.) appears to be clear; however, the nature of the network involved is dependent on the scale (e.g. within-host, between-host, or between-population), the nature of the contact, which ranges from the highly specific (e.g. sexual acts or needle sharing at the person-to-person level) to almost completely non-specific (e.g. aerosol transmission, often over long distances as can occur with the highly infectious livestock pathogen foot-and-mouth disease virus—FMDv—at the farm-to-farm level, e.g. Schley et al. in J. R. Soc. Interface 6:455-462, 2008), and the timescale of interest (e.g. at the scale of the individual, the typical infectious period of the host). Theoretical approaches to examining the implications of particular network structures on disease transmission have provided critical insight; however, a greater challenge is the integration of network approaches with data on real population structures. In this chapter, some concepts in disease modelling will be introduced, the relevance of selected network phenomena discussed, and then results from real data and their relationship to network analyses summarised. These include examinations of the patterns of air traffic and its relation to the spread of SARS in 2003 (Colizza et al. in BMC Med., 2007; Hufnagel et al. in Proc. Natl. Acad. Sci. USA 101:15124-15129, 2004), the use of the extensively documented Great Britain livestock movements network (Green et al. in J. Theor. Biol. 239:289-297, 2008; Robinson et al. in J. R. Soc. Interface 4:669-674, 2007; Vernon and Keeling in Proc. R. Soc. Lond. B, Biol. Sci. 276:469-476, 2009) and the growing interest in combining contact structure data with phylogenetics to

  17. Structural-semantic characteristic of phraseologisms in modern German language

    Directory of Open Access Journals (Sweden)

    Abramova Natalya Viktorovna

    2015-03-01

    Full Text Available The article is devoted to the structural and semantic characteristics of phraseology of the modern German language. It reveals the essence of the concept of “idioms”, discusses various classification of phraseological units in German. Many linguists offer a variety of phraseological units classification. It is studied in detailed the classification by B. Fleischer, where the following types of phraseological units are distinguished: nominative collocations, communication idioms, phrasal templates. V.V. Vinogradov classified phraseological units according to their degree of semantic fusion. He identified three major types of phraseological units: phraseological seam, phraseological unity and phraseological (non-free combination. M.D. Stepanova and I.I. Chernyshev worked out structural and semantic classification of phraseological units, consisting of three groups: phraseological units, phraseological combinations, phraseological expressions. A special group of phraseological combinations is of E. Agricola - stable phrases. H. Burger classifies idioms according to their function in the communication process: reference idioms, structural phraseological units, communication idioms. Each classification is provided with vivid examples that characterize the structure and semantics of phraseological units of modern German language.

  18. Exploring the impact of network characteristics on substance use outcomes among homeless former foster youth.

    Science.gov (United States)

    Yoshioka-Maxwell, Amanda; Rice, Eric

    2017-04-01

    This study examined network structure differences between homeless youth with and without a history of foster care and the impact of network position and location on substance use outcomes among former foster youth specifically. Sociometric and individual-level data were collected from a cross-sectional community-based sample (n = 814) of homeless youth from drop-in centers in Los Angeles across three points in time. To determine the structure and position of foster youth in the larger homeless network, structural variables and regressions examining the impact of network structure on substance use were calculated. Results indicated that former foster youth were more likely to have low connectivity compared to homeless youth without a history of foster care. Furthermore, the impact of structure on substance use among former foster youth varied by panel; location in the core was significantly associated with recent and lifetime rates of substance use. Results highlight the need for a more in-depth understanding of the impact of network changes over time and their effect on substance use outcomes in this population.

  19. Revealing the hidden structure of dynamic ecological networks.

    Science.gov (United States)

    Miele, Vincent; Matias, Catherine

    2017-06-01

    In ecology, recent technological advances and long-term data studies now provide longitudinal interaction data (e.g. between individuals or species). Most often, time is the parameter along which interactions evolve but any other one-dimensional gradient (temperature, altitude, depth, humidity, etc.) can be considered. These data can be modelled through a sequence of different snapshots of an evolving ecological network, i.e. a dynamic network. Here, we present how the dynamic stochastic block model approach developed by Matias & Miele (Matias & Miele In press J. R. Stat. Soc. B (doi:10.1111/rssb.12200)) can capture the complexity and dynamics of these networks. First, we analyse a dynamic contact network of ants and we observe a clear high-level assembly with some variations in time at the individual level. Second, we explore the structure of a food web evolving during a year and we detect a stable predator-prey organization but also seasonal differences in the prey assemblage. Our approach, based on a rigorous statistical method implemented in the R package dynsbm, can pave the way for exploration of evolving ecological networks.

  20. Mesoscopic simulations of hydrophilic cross-linked polycarbonate polyurethane networks: structure and morphology.

    Science.gov (United States)

    Iype, E; Esteves, A C C; de With, G

    2016-06-14

    Polyurethane (PU) cross-linked networks are frequently used in biomedical and marine applications, e.g., as hydrophilic polymer coatings with antifouling or low-friction properties and have been reported to exhibit characteristic phase separation between soft and hard segments. Understanding this phase-separation behavior is critical to design novel hydrophilic polymer coatings. However, most of the studies on the structure and morphology of cross-linked coatings are experimental, which only assess the phase separation via indirect methods. Herein we present a mesoscopic simulation study of the network characteristics of model hydrophilic polymer networks, consisting of PU with and without methyl-polyethylene glycol (mPEG) dangling chains. The systems are analyzed using a number of tools, such as the radial distribution function, the cross-link point density distribution and the Voronoi volume distribution (of the cross-linking points). The combined results show that the cross-linked networks without dangling chains are rather homogeneous but contain a small amount of clustering of cross-linker molecules. A clear phase separation is observed when introducing the dangling chains. In spite of that, the amount of cross-linker molecules connected to dangling chains only, i.e., not connected to the main network, is relatively small, leading to about 3 wt% extractables. Thus, these cross-linked polymers consist of a phase-separated, yet highly connected network. This study provides valuable guidelines towards new self-healing hydrophilic coatings based on the molecular design of cross-linked networks in direct contact with water or aqueous fluids, e.g., as anti-fouling self-repairing coatings for marine applications.

  1. Multi-objective optimal power flow for active distribution network considering the stochastic characteristic of photovoltaic

    Science.gov (United States)

    Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming

    2017-05-01

    To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.

  2. Revealing and exploiting hierarchical material structure through complex atomic networks

    Science.gov (United States)

    Ahnert, Sebastian E.; Grant, William P.; Pickard, Chris J.

    2017-08-01

    One of the great challenges of modern science is to faithfully model, and understand, matter at a wide range of scales. Starting with atoms, the vastness of the space of possible configurations poses a formidable challenge to any simulation of complex atomic and molecular systems. We introduce a computational method to reduce the complexity of atomic configuration space by systematically recognising hierarchical levels of atomic structure, and identifying the individual components. Given a list of atomic coordinates, a network is generated based on the distances between the atoms. Using the technique of modularity optimisation, the network is decomposed into modules. This procedure can be performed at different resolution levels, leading to a decomposition of the system at different scales, from which hierarchical structure can be identified. By considering the amount of information required to represent a given modular decomposition we can furthermore find the most succinct descriptions of a given atomic ensemble. Our straightforward, automatic and general approach is applied to complex crystal structures. We show that modular decomposition of these structures considerably simplifies configuration space, which in turn can be used in discovery of novel crystal structures, and opens up a pathway towards accelerated molecular dynamics of complex atomic ensembles. The power of this approach is demonstrated by the identification of a possible allotrope of boron containing 56 atoms in the primitive unit cell, which we uncover using an accelerated structure search, based on a modular decomposition of a known dense phase of boron, γ-B28.

  3. Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

    KAUST Repository

    Zhang, Xuesong

    2011-11-01

    Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework (BNN-PIS) to incorporate the uncertainties associated with parameters, inputs, and structures into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform BNNs that only consider uncertainties associated with parameters and model structures. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters shows that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of and interactions among different uncertainty sources is expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting. © 2011 Elsevier B.V.

  4. CONCEPTUAL GENERALIZATION OF STRUCTURAL ORGANIZATION OF COMPUTER NETWORKS MEDICAL SCHOOL

    Directory of Open Access Journals (Sweden)

    O. P. Mintser

    2014-01-01

    Full Text Available The basic principles of the structural organization of computer networks in schools are presented. The questions of universities integration’s in the modern infrastructure of the information society are justified. Details the structural organizations of computer networks are presented. The effectiveness of implementing automated library information systems is shown. The big dynamical growths of technical and personal readiness of students to use virtual educational space are presented. In this regard, universities are required to provide advance information on filling the educational environment of modern virtual university, including multimedia resources for industry professional education programs. Based on information and educational environments virtual representations of universities should be formed distributed resource centers that will avoid duplication of effort on the development of innovative educational technologies, will provide a mutual exchange of results and further development of an open continuous professional education, providing accessibility, modularity and mobility training and retraining specialists.

  5. Positron annihilation lifetime spectroscopy (PAL) as a tool to study rubber (polyisoprene) vulcanizate network structures

    Energy Technology Data Exchange (ETDEWEB)

    Mallon, P.E.; Huang, C.M.; Chen, H.; Zhang, R.; Jean, Y.C. [Missouri Univ., Kansas City, MO (United States). Dept. of Chemistry; Gradwell, M.H.S. [Port Elizabeth Univ. (South Africa). Dept. of Chemistry

    2001-07-01

    We have applied the technique of positron annihilation lifetime (PAL) to study the nature and the characteristics of polyisoprene network structures produced by three distinctly different crosslinking systems. The crosslinking systems are: Dicumly peroxide (DCP, which leads to carbon-carbon crosslinks), tetramethylthiuram disulfide (TMTD) accelerated sulfur crosslinking and 2-bisbenzothiazole-2,2'-disulfide (MBTS) accelerated sulfur crosslinking. The positron annihilation lifetime measurements give information about the fractional free volume, the mean free volume hole size and the distribution of the free volume hole size for the vulcanizates. The inhibition effect of the curatives needs to be taken into account when interpreting the various positron lifetime parameters for the crosslinked samples. Each of the different crosslinking systems produces distinctly different trends. These differences are interpreted in terms of the network structures, which are in turn related to the differences in the mechanisms of the crosslinking reaction. (orig.)

  6. 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

  7. 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.

  8. 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.

  9. 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

  10. Structural and mixing characteristics in actively controlled transverse jets

    Science.gov (United States)

    Shoji, Takeshi; Besnard, Andrea; Harris, Elijah; M'closkey, Robert; Karagozian, Ann; Cortelezzi, Luca

    2016-11-01

    These experiments explore the effect of external excitation on gaseous transverse jet (TJ) structural and mixing characteristics, emphasizing axisymmetric jet forcing. Sinusoidal as well as single and multiple square wave pulses, the latter with variable amplitudes, are explored for a range of jet-to-crossflow momentum flux ratios J, spanning regimes of absolutely unstable upstream shear layers (J 10). The studies utilize acetone PLIF imaging of the jet, as done for unforced jets. Axisymmetric forcing, irrespective of the waveform, can enhance cross-sectional symmetry of the TJ for convectively unstable conditions, but generally disrupts the usually symmetric counter-rotating vortex pair (CVP) observed for the absolutely unstable TJ. Conditions producing deeply penetrating, periodic vortical structures, such as square wave forcing at critical stroke ratios, increase jet spread, but do not always optimize molecular mixing. Creating multiple vortex structures of different strengths via multiple square pulses leads to enhanced interactions and accelerated vortex breakdown, potentially increasing mixing. Supported by NSF (CBET-1437014) & AFOSR (FA9550-15-1-0261).

  11. Social Network Characteristics, Social Support, and Cigarette Smoking among Asian/Pacific Islander Young Adults.

    Science.gov (United States)

    Pokhrel, Pallav; Fagan, Pebbles; Cassel, Kevin; Trinidad, Dennis R; Kaholokula, Joseph Keawe'aimoku; Herzog, Thaddeus A

    2016-06-01

    Cigarette smoking may be one of the factors contributing to the high levels of cancer-related mortality experienced by certain Asian/Pacific Islander (A/PI) subgroups (e.g., Native Hawaiian). Given the collectivist cultural orientation attributed to A/PI groups, social strategies are recommended for substance abuse or smoking cessation treatment among A/PI. However, research examining how social network characteristics and social support relate to smoking across A/PI subgroups has been lacking. This study investigated the associations between social network characteristics (e.g., size, composition), perceived social support, and recent cigarette use across Native Hawaiian, Filipino, and East Asian (e.g., Japanese, Chinese) young adults (18-35 year old). Cross-sectional, self-report data were collected from N = 435 participants (M age = 25.6, SD = 8.3; 61% women). Ethnic differences were found in a number of pathways linking social network characteristics, perceived social support, and cigarette smoking. Larger network size was strongly associated with higher perceived social support and lower recent cigarette smoking among Native Hawaiians but not Filipinos or East Asians. Higher perceived social support was associated with lower recent smoking among East Asians and Filipinos but not Native Hawaiians. Implications are discussed with regard to smoking prevention and cessation among A/PI. © Society for Community Research and Action 2016.

  12. Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring

    Science.gov (United States)

    2016-02-02

    and (4) input sources (e.g., cracks, holes). This was achieved in terms of extensions to our previously developed techniques . These decentralized...SHM) deals with evaluating structures for changes in their characteristics, predicting useful lifetime without maintenance, and recommending...SHM transducers (e.g., ultrasound ) acting in both undamaged and damaged composite materials, and showed how to achieve simultaneous improved

  13. The geometrical effect of single walled carbon nanotube network transistors on low frequency noise characteristics.

    Science.gov (United States)

    Kim, Un Jeong; Kil, Joon Pyo; Park, Wanjun

    2011-07-01

    The noise characteristics of randomly networked single walled carbon nanotubes grown directly by plasma enhanced chemical vapor deposition with field effect transistor. Geometrical complexity due to the large number of tube-tube junctions in the nanotube network is expected to be one of the key factors for the noise power of 1/f dependence. We investigated low frequency noise as a function of channel length (2-10 microm) and found that increased with longer channel length. Percolational behaviors of nanotube network that differs from ordinary semiconducting and metallic materials can be characterized by a geometrical picture with electrical homo- and hetero-junctions. Fixed nanotube density provides a test conditions to evaluate the contributions of junctions as a noise center. Hooge's empirical law is applied to investigate the low frequency noise characteristics of single walled carbon nanotube random network transistors. The noise power shows the dependence of the transistor channel length. It is understood that nanotube/nanotube junctions act as a noise center. However, the differences induced by channel length in the noise power are observed as not so significant. We conclude that tolerance of low frequency noise is important property for SWNT networks as an electronic device application.

  14. Sexual Networking and Partner Characteristics Among Single, African, Caribbean, and Black Youth in Windsor, Ontario.

    Science.gov (United States)

    Kerr, Jelani; Maticka-Tyndale, Eleanor; Bynum, Shalanda; Mihan, Robert

    2017-10-01

    The disproportionate HIV burden shared by African, Caribbean, and Black (ACB) populations in Canada has not been explained by unique sexual behaviors in this population. This study investigates partner selection and sexual networking as potential contributors to HIV vulnerability. The study examines variations in the characteristics of sexual partners and sexual networking across groups based on differences in ethno-religious identity, gender, and length of Canadian residency among single, 16- to 27-year old, heterosexual-identified, ACB individuals living in Windsor, Ontario, Canada. Respondent-driven sampling maximized the representativeness of the sample of 250 (45 % male; 55 % female) youth with penile-vaginal intercourse experience who completed surveys. Logistic regression and analysis of variance compared groups with respect to number of lifetime partners, concurrency of sexual relationships, non-relational and age disparate partnering, and intra-ethnic sexual networking. For vulnerability associated with number of partners, concurrency and non-relational sex, women, newcomers to Canada, and African-Muslim participants were at lower vulnerability for HIV infection than their comparator groups. For vulnerability associated with sexual networking within a group with higher HIV prevalence, women and newcomers to Canada were at higher vulnerability to HIV infection than their comparator groups. There were insufficient data on age disparate partnering to support analysis. These results point to the importance of considering characteristics of partners and sexual networking both in further research and in developing policies and programs to curtail the spread of HIV and other sexually transmitted infections.

  15. A Hybrid Structure for Data Aggregation in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Hedieh Sajedi

    2014-01-01

    Full Text Available In recent years, wireless sensor networks have been used for various applications such as environmental monitoring, military and medical applications. A wireless sensor network uses a large number of sensor nodes that continuously collect and send data from a specific region to a base station. Data from sensors are collected from the study area in the common scenario of sensor networks. Afterward, sensed data is sent to the base station. However, neighboring sensors often lead to redundancy of data. Transmission of redundant data to the base station consumes energy and produces traffic, because process is run in a large network. Data aggregation was proposed in order to reduce redundancy in data transformation and traffic. The most popular communication protocol in this field is cluster based data aggregation. Clustering causes energy balance, but sometimes energy consumption is not efficient due to the long distance between cluster heads and base station. In another communication protocol, which is based on a tree construction, because of the short distance between the sensors, energy consumption is low. In this data aggregation approach, since each sensor node is considered as one of the vertices of a tree, the depth of tree is usually high. In this paper, an efficient hierarchical hybrid approach for data aggregation is presented. It reduces energy consumption based on clustering and minimum spanning tree. The benefit of combining clustering and tree structure is reducing the disadvantages of previous structures. The proposed method firstly employs clustering algorithm and then a minimum spanning tree is constructed based on cluster heads. Our proposed method was compared to LEACH which is a well-known data aggregation method in terms of energy consumption and the amount of energy remaining in each sensor network lifetime. Simulation results indicate that our proposed method is more efficient than LEACH algorithm considering energy

  16. Sexual networks: measuring sexual selection in structured, polyandrous populations.

    Science.gov (United States)

    McDonald, Grant C; James, Richard; Krause, Jens; Pizzari, Tommaso

    2013-03-05

    Sexual selection is traditionally measured at the population level, assuming that populations lack structure. However, increasing evidence undermines this approach, indicating that intrasexual competition in natural populations often displays complex patterns of spatial and temporal structure. This complexity is due in part to the degree and mechanisms of polyandry within a population, which can influence the intensity and scale of both pre- and post-copulatory sexual competition. Attempts to measure selection at the local and global scale have been made through multi-level selection approaches. However, definitions of local scale are often based on physical proximity, providing a rather coarse measure of local competition, particularly in polyandrous populations where the local scale of pre- and post-copulatory competition may differ drastically from each other. These limitations can be solved by social network analysis, which allows us to define a unique sexual environment for each member of a population: 'local scale' competition, therefore, becomes an emergent property of a sexual network. Here, we first propose a novel quantitative approach to measure pre- and post-copulatory sexual selection, which integrates multi-level selection with information on local scale competition derived as an emergent property of networks of sexual interactions. We then use simple simulations to illustrate the ways in which polyandry can impact estimates of sexual selection. We show that for intermediate levels of polyandry, the proposed network-based approach provides substantially more accurate measures of sexual selection than the more traditional population-level approach. We argue that the increasing availability of fine-grained behavioural datasets provides exciting new opportunities to develop network approaches to study sexual selection in complex societies.

  17. Workplace social networks and their relationship with job outcomes and other employment characteristics for people with severe mental illness.

    Science.gov (United States)

    Rollins, Angela L; Bond, Gary R; Jones, Amanda M; Kukla, Marina; Collins, Linda A

    2011-07-01

    Clients with severe mental illness (SMI) often struggle in their efforts to maintain