WorldWideScience

Sample records for network scaling effects

  1. Scaling Effect In Trade Network

    Science.gov (United States)

    Konar, M.; Lin, X.; Rushforth, R.; Ruddell, B. L.; Reimer, J.

    2015-12-01

    Scaling is an important issue in the physical sciences. Economic trade is increasingly of interest to the scientific community due to the natural resources (e.g. water, carbon, nutrients, etc.) embodied in traded commodities. Trade refers to the spatial and temporal redistribution of commodities, and is typically measured annually between countries. However, commodity exchange networks occur at many different scales, though data availability at finer temporal and spatial resolution is rare. Exchange networks may prove an important adaptation measure to cope with future climate and economic shocks. As such, it is essential to understand how commodity exchange networks scale, so that we can understand opportunities and roadblocks to the spatial and temporal redistribution of goods and services. To this end, we present an empirical analysis of trade systems across three spatial scales: global, sub-national in the United States, and county-scale in the United States. We compare and contrast the network properties, the self-sufficiency ratio, and performance of the gravity model of trade for these three exchange systems.

  2. Effects of degree correlation on scale-free gradient networks

    International Nuclear Information System (INIS)

    Pan Guijun; Yan Xiaoqing; Ma Weichuan; Luo Yihui; Huang Zhongbing

    2010-01-01

    We have studied the effects of degree correlation on congestion pressure in scale-free gradient networks. It is observed that the jamming coefficient J is insensitive to the degree correlation coefficient r for assortative and strongly disassortative scale-free networks, and J markedly decreases with an increase in r for weakly disassortative scale-free networks. We have also investigated the effects of degree correlation on the topology structure of scale-free gradient networks, and discussed the relation between the topology structure properties and transport efficiency of gradient networks.

  3. Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy

    Directory of Open Access Journals (Sweden)

    Chiang Wei

    2014-08-01

    Full Text Available Because of high variation in mountainous areas, rainfall data at different spatiotemporal scales may yield potential uncertainty for network design. However, few studies focus on the scaling effect on both the spatial and the temporal scale. By calculating the maximum joint entropy of hourly typhoon events, monthly, six dry and wet months and annual rainfall between 1992 and 2012 for 1-, 3-, and 5-km grids, the relocated candidate rain gauges in the National Taiwan University Experimental Forest of Central Taiwan are prioritized. The results show: (1 the network exhibits different locations for first prioritized candidate rain gauges for different spatiotemporal scales; (2 the effect of spatial scales is insignificant compared to temporal scales; and (3 a smaller number and a lower percentage of required stations (PRS reach stable joint entropy for a long duration at finer spatial scale. Prioritized candidate rain gauges provide key reference points for adjusting the network to capture more accurate information and minimize redundancy.

  4. Effects of maximum node degree on computer virus spreading in scale-free networks

    Science.gov (United States)

    Bamaarouf, O.; Ould Baba, A.; Lamzabi, S.; Rachadi, A.; Ez-Zahraouy, H.

    2017-10-01

    The increase of the use of the Internet networks favors the spread of viruses. In this paper, we studied the spread of viruses in the scale-free network with different topologies based on the Susceptible-Infected-External (SIE) model. It is found that the network structure influences the virus spreading. We have shown also that the nodes of high degree are more susceptible to infection than others. Furthermore, we have determined a critical maximum value of node degree (Kc), below which the network is more resistible and the computer virus cannot expand into the whole network. The influence of network size is also studied. We found that the network with low size is more effective to reduce the proportion of infected nodes.

  5. Network features of sector indexes spillover effects in China: A multi-scale view

    Science.gov (United States)

    Feng, Sida; Huang, Shupei; Qi, Yabin; Liu, Xueyong; Sun, Qingru; Wen, Shaobo

    2018-04-01

    The spillover effects among sectors are of concern for distinct market participants, who are in distinct investment horizons and concerned with the information in different time scales. In order to uncover the hidden spillover information in multi-time scales in the rapidly changing stock market and thereby offer guidance to different investors concerning distinct time scales from a system perspective, this paper constructed directional spillover effect networks for the economic sectors in distinct time scales. The results are as follows: (1) The "2-4 days" scale is the most risky scale, and the "8-16 days" scale is the least risky one. (2) The most influential and sensitive sectors are distinct in different time scales. (3) Although two sectors in the same community may not have direct spillover relations, the volatility of one sector will have a relatively strong influence on the other through indirect relations.

  6. The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks

    Directory of Open Access Journals (Sweden)

    Qiuming Liu

    2017-03-01

    Full Text Available In this paper, we jointly consider the inhomogeneity and spatial dimension in large scale wireless networks. We study the effects of topology on the throughput capacity. This problem is inherently difficult since it is complex to handle the interference caused by simultaneous transmission. To solve this problem, we, according to the inhomogeneity of topology, divide the transmission into intra-cluster transmission and inter-cluster transmission. For the intra-cluster transmission, a spheroidal percolation model is constructed. The spheroidal percolation model guarantees a constant rate when a power control strategy is adopted. We also propose a cube percolation mode for the inter-cluster transmission. Different from the spheroidal percolation model, a constant transmission rate can be achieved without power control. For both transmissions, we propose a routing scheme with five phases. By comparing the achievable rate of each phase, we get the rate bottleneck, which is the throughput capacity of the network.

  7. Gradient networks on uncorrelated random scale-free networks

    International Nuclear Information System (INIS)

    Pan Guijun; Yan Xiaoqing; Huang Zhongbing; Ma Weichuan

    2011-01-01

    Uncorrelated random scale-free (URSF) networks are useful null models for checking the effects of scale-free topology on network-based dynamical processes. Here, we present a comparative study of the jamming level of gradient networks based on URSF networks and Erdos-Renyi (ER) random networks. We find that the URSF networks are less congested than ER random networks for the average degree (k)>k c (k c ∼ 2 denotes a critical connectivity). In addition, by investigating the topological properties of the two kinds of gradient networks, we discuss the relations between the topological structure and the transport efficiency of the gradient networks. These findings show that the uncorrelated scale-free structure might allow more efficient transport than the random structure.

  8. Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function.

    Science.gov (United States)

    Lerman, Caryn; Gu, Hong; Loughead, James; Ruparel, Kosha; Yang, Yihong; Stein, Elliot A

    2014-05-01

    Interactions of large-scale brain networks may underlie cognitive dysfunctions in psychiatric and addictive disorders. To test the hypothesis that the strength of coupling among 3 large-scale brain networks--salience, executive control, and default mode--will reflect the state of nicotine withdrawal (vs smoking satiety) and will predict abstinence-induced craving and cognitive deficits and to develop a resource allocation index (RAI) that reflects the combined strength of interactions among the 3 large-scale networks. A within-subject functional magnetic resonance imaging study in an academic medical center compared resting-state functional connectivity coherence strength after 24 hours of abstinence and after smoking satiety. We examined the relationship of abstinence-induced changes in the RAI with alterations in subjective, behavioral, and neural functions. We included 37 healthy smoking volunteers, aged 19 to 61 years, for analyses. Twenty-four hours of abstinence vs smoking satiety. Inter-network connectivity strength (primary) and the relationship with subjective, behavioral, and neural measures of nicotine withdrawal during abstinence vs smoking satiety states (secondary). The RAI was significantly lower in the abstinent compared with the smoking satiety states (left RAI, P = .002; right RAI, P = .04), suggesting weaker inhibition between the default mode and salience networks. Weaker inter-network connectivity (reduced RAI) predicted abstinence-induced cravings to smoke (r = -0.59; P = .007) and less suppression of default mode activity during performance of a subsequent working memory task (ventromedial prefrontal cortex, r = -0.66, P = .003; posterior cingulate cortex, r = -0.65, P = .001). Alterations in coupling of the salience and default mode networks and the inability to disengage from the default mode network may be critical in cognitive/affective alterations that underlie nicotine dependence.

  9. Effect of morphology on water sorption in cellular solid foods. Part I: Pore scale network model

    NARCIS (Netherlands)

    Esveld, D.C.; Sman, van der R.G.M.; Dalen, van G.; Duynhoven, van J.P.M.; Meinders, M.B.J.

    2012-01-01

    A pore scale network model is developed to predict the dynamics of moisture diffusion into complex cellular solid foods like bread, crackers, and cereals. The morphological characteristics of the sample, including the characteristics of each cellular void and the open pore connections between them

  10. Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity.

    Science.gov (United States)

    Lei, Yu; Shao, Yongcong; Wang, Lubin; Zhai, Tianye; Zou, Feng; Ye, Enmao; Jin, Xiao; Li, Wuju; Qi, Jianlin; Yang, Zheng

    2015-01-01

    Interactions between large-scale brain networks have received most attention in the study of cognitive dysfunction of human brain. In this paper, we aimed to test the hypothesis that the coupling strength of large-scale brain networks will reflect the pressure for sleep and will predict cognitive performance, referred to as sleep pressure index (SPI). Fourteen healthy subjects underwent this within-subject functional magnetic resonance imaging (fMRI) study during rested wakefulness (RW) and after 36 h of total sleep deprivation (TSD). Self-reported scores of sleepiness were higher for TSD than for RW. A subsequent working memory (WM) task showed that WM performance was lower after 36 h of TSD. Moreover, SPI was developed based on the coupling strength of salience network (SN) and default mode network (DMN). Significant increase of SPI was observed after 36 h of TSD, suggesting stronger pressure for sleep. In addition, SPI was significantly correlated with both the visual analogue scale score of sleepiness and the WM performance. These results showed that alterations in SN-DMN coupling might be critical in cognitive alterations that underlie the lapse after TSD. Further studies may validate the SPI as a potential clinical biomarker to assess the impact of sleep deprivation.

  11. Large-Scale Brain Network Coupling Predicts Total Sleep Deprivation Effects on Cognitive Capacity.

    Directory of Open Access Journals (Sweden)

    Yu Lei

    Full Text Available Interactions between large-scale brain networks have received most attention in the study of cognitive dysfunction of human brain. In this paper, we aimed to test the hypothesis that the coupling strength of large-scale brain networks will reflect the pressure for sleep and will predict cognitive performance, referred to as sleep pressure index (SPI. Fourteen healthy subjects underwent this within-subject functional magnetic resonance imaging (fMRI study during rested wakefulness (RW and after 36 h of total sleep deprivation (TSD. Self-reported scores of sleepiness were higher for TSD than for RW. A subsequent working memory (WM task showed that WM performance was lower after 36 h of TSD. Moreover, SPI was developed based on the coupling strength of salience network (SN and default mode network (DMN. Significant increase of SPI was observed after 36 h of TSD, suggesting stronger pressure for sleep. In addition, SPI was significantly correlated with both the visual analogue scale score of sleepiness and the WM performance. These results showed that alterations in SN-DMN coupling might be critical in cognitive alterations that underlie the lapse after TSD. Further studies may validate the SPI as a potential clinical biomarker to assess the impact of sleep deprivation.

  12. Scaling in public transport networks

    Directory of Open Access Journals (Sweden)

    C. von Ferber

    2005-01-01

    Full Text Available We analyse the statistical properties of public transport networks. These networks are defined by a set of public transport routes (bus lines and the stations serviced by these. For larger networks these appear to possess a scale-free structure, as it is demonstrated e.g. by the Zipf law distribution of the number of routes servicing a given station or for the distribution of the number of stations which can be visited from a chosen one without changing the means of transport. Moreover, a rather particular feature of the public transport network is that many routes service common subsets of stations. We discuss the possibility of new scaling laws that govern intrinsic properties of such subsets.

  13. Growth Limits in Large Scale Networks

    DEFF Research Database (Denmark)

    Knudsen, Thomas Phillip

    limitations. The rising complexity of network management with the convergence of communications platforms is shown as problematic for both automatic management feasibility and for manpower resource management. In the fourth step the scope is extended to include the present society with the DDN project as its...... the fundamental technological resources in network technologies are analysed for scalability. Here several technological limits to continued growth are presented. The third step involves a survey of major problems in managing large scale networks given the growth of user requirements and the technological...... main focus. Here the general perception of the nature and role in society of large scale networks as a fundamental infrastructure is analysed. This analysis focuses on the effects of the technical DDN projects and on the perception of network infrastructure as expressed by key decision makers...

  14. Weighted Scale-Free Network Properties of Ecological Network

    International Nuclear Information System (INIS)

    Lee, Jae Woo; Maeng, Seong Eun

    2013-01-01

    We investigate the scale-free network properties of the bipartite ecological network, in particular, the plant-pollinator network. In plant-pollinator network, the pollinators visit the plant to get the nectars. In contrast to the other complex network, the plant-pollinator network has not only the trophic relationships among the interacting partners but also the complexities of the coevolutionary effects. The interactions between the plant and pollinators are beneficial relations. The plant-pollinator network is a bipartite and weighted network. The networks have two types of the nodes: plant and pollinator. We consider the visiting frequency of a pollinator to a plant as the weighting value of the link. We defined the strength of a node as the sum of the weighting value of the links. We reported the cumulative distribution function (CDF) of the degree and the strength of the plant-pollinator network. The CDF of the plants followed stretched exponential functions for both degree and strength, but the CDF of the pollinators showed the power law for both degree and strength. The average strength of the links showed the nonlinear dependence on the degree of the networks.

  15. Scale invariance in road networks.

    Science.gov (United States)

    Kalapala, Vamsi; Sanwalani, Vishal; Clauset, Aaron; Moore, Cristopher

    2006-02-01

    We study the topological and geographic structure of the national road networks of the United States, England, and Denmark. By transforming these networks into their dual representation, where roads are vertices and an edge connects two vertices if the corresponding roads ever intersect, we show that they exhibit both topological and geographic scale invariance. That is, we show that for sufficiently large geographic areas, the dual degree distribution follows a power law with exponent 2.2< or = alpha < or =2.4, and that journeys, regardless of their length, have a largely identical structure. To explain these properties, we introduce and analyze a simple fractal model of road placement that reproduces the observed structure, and suggests a testable connection between the scaling exponent and the fractal dimensions governing the placement of roads and intersections.

  16. On routing strategy with finite-capacity effect on scale-free networks

    International Nuclear Information System (INIS)

    Tang, S.; Jiang, X.; Ma, L.; Zhang, Z.; Zheng, Z.

    2010-01-01

    We propose a class of systems with finite-capacity effect to investigate routing-strategy optimization. The local topology and the variable capacity, two crucial elements for routing, are naturally coupled by considering the interactions among packets. We show how the combination of these two elements controls the normal and efficient functioning of routing in the frame of condensation and coverage, respectively. Specifically, it is shown that the dynamic behaviors of diffusing packets exhibit condensation, for which exact results of the stationary state and phase transition are given. Further, we explore the diffusion coverage of routed packets through simulation. Various alternatives for the strategy parameters are illustrated to apply standard techniques to alleviate condensation and accelerate coverage. Our results provide a practical way for the design of optimal routing strategies in complex networks by the manipulation of a few parameters. (author)

  17. Handbook of Large-Scale Random Networks

    CERN Document Server

    Bollobas, Bela; Miklos, Dezso

    2008-01-01

    Covers various aspects of large-scale networks, including mathematical foundations and rigorous results of random graph theory, modeling and computational aspects of large-scale networks, as well as areas in physics, biology, neuroscience, sociology and technical areas

  18. Energetic and Economic Assessment of Pipe Network Effects on Unused Energy Source System Performance in Large-Scale Horticulture Facilities

    Directory of Open Access Journals (Sweden)

    Jae Ho Lee

    2015-04-01

    Full Text Available As the use of fossil fuel has increased, not only in construction, but also in agriculture due to the drastic industrial development in recent times, the problems of heating costs and global warming are getting worse. Therefore, the introduction of more reliable and environmentally-friendly alternative energy sources has become urgent and the same trend is found in large-scale horticulture facilities. In this study, among many alternative energy sources, we investigated the reserves and the potential of various different unused energy sources which have infinite potential, but are nowadays wasted due to limitations in their utilization. This study investigated the effects of the distance between the greenhouse and the actual heat source by taking into account the heat transfer taking place inside the pipe network. This study considered CO2 emissions and economic aspects to determine the optimal heat source. Payback period analysis against initial investment cost shows that a heat pump based on a power plant’s waste heat has the shortest payback period of 7.69 years at a distance of 0 km. On the other hand, the payback period of a heat pump based on geothermal heat showed the shortest payback period of 10.17 year at the distance of 5 km, indicating that heat pumps utilizing geothermal heat were the most effective model if the heat transfer inside the pipe network between the greenhouse and the actual heat source is taken into account.

  19. Large scale network-centric distributed systems

    CERN Document Server

    Sarbazi-Azad, Hamid

    2014-01-01

    A highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systems Evolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems. Large Scale Network-Centric Distributed Systems provides in-depth coverage ranging from ground-level hardware issu

  20. Large scale road network generalization for vario-scale map

    NARCIS (Netherlands)

    Suba, R.; Meijers, B.M.; Van Oosterom, P.J.M.

    2015-01-01

    The classical approach for road network generalization consists of producing multiple maps, for a different scale or purpose, from a single detailed data source and quite often roads are represented by line objects. Our target is the generalization of a road network for the whole scale range from

  1. How Scale-Free Are Biological Networks

    NARCIS (Netherlands)

    Khanin, Raya; Wit, Ernst

    2006-01-01

    The concept of scale-free network has emerged as a powerful unifying paradigm in the study of complex systems in biology and in physical and social studies. Metabolic, protein, and gene interaction networks have been reported to exhibit scale-free behavior based on the analysis of the distribution

  2. Organization and scaling in water supply networks

    Science.gov (United States)

    Cheng, Likwan; Karney, Bryan W.

    2017-12-01

    Public water supply is one of the society's most vital resources and most costly infrastructures. Traditional concepts of these networks capture their engineering identity as isolated, deterministic hydraulic units, but overlook their physics identity as related entities in a probabilistic, geographic ensemble, characterized by size organization and property scaling. Although discoveries of allometric scaling in natural supply networks (organisms and rivers) raised the prospect for similar findings in anthropogenic supplies, so far such a finding has not been reported in public water or related civic resource supplies. Examining an empirical ensemble of large number and wide size range, we show that water supply networks possess self-organized size abundance and theory-explained allometric scaling in spatial, infrastructural, and resource- and emission-flow properties. These discoveries establish scaling physics for water supply networks and may lead to novel applications in resource- and jurisdiction-scale water governance.

  3. Self-similarity and scaling theory of complex networks

    Science.gov (United States)

    Song, Chaoming

    Scale-free networks have been studied extensively due to their relevance to many real systems as diverse as the World Wide Web (WWW), the Internet, biological and social networks. We present a novel approach to the analysis of scale-free networks, revealing that their structure is self-similar. This result is achieved by the application of a renormalization procedure which coarse-grains the system into boxes containing nodes within a given "size". Concurrently, we identify a power-law relation between the number of boxes needed to cover the network and the size of the box defining a self-similar exponent, which classifies fractal and non-fractal networks. By using the concept of renormalization as a mechanism for the growth of fractal and non-fractal modular networks, we show that the key principle that gives rise to the fractal architecture of networks is a strong effective "repulsion" between the most connected nodes (hubs) on all length scales, rendering them very dispersed. We show that a robust network comprised of functional modules, such as a cellular network, necessitates a fractal topology, suggestive of a evolutionary drive for their existence. These fundamental properties help to understand the emergence of the scale-free property in complex networks.

  4. Network effects in railways

    DEFF Research Database (Denmark)

    Landex, Alex

    2012-01-01

    . Therefore, sections 3 and 4 describe the network effects for passengers and how they can be measured using passenger delay models. Before the concluding remarks in section 6, section 5 discusses how the operation can be improved by examining network effects in the planning process. © 2012 WIT Press....... each other everywhere in the network. First this paper describes network effects in general (section 1). In section 2 the network effects for trains and how they can be measured by scheduled waiting time is described. When the trains are affected by network effects the passengers are also affected......Railway operation is often affected by network effects as a change in one part of the network can influence other parts of the network. Network effects occur because the train runs may be quite long and since the railway system has a high degree of interdependencies as trains cannot cross/overtake...

  5. Continuous Road Network Generalization throughout All Scales

    Directory of Open Access Journals (Sweden)

    Radan Šuba

    2016-08-01

    Full Text Available Until now, road network generalization has mainly been applied to the task of generalizing from one fixed source scale to another fixed target scale. These actions result in large differences in content and representation, e.g., a sudden change of the representation of road segments from areas to lines, which may confuse users. Therefore, we aim at the continuous generalization of a road network for the whole range, from the large scale, where roads are represented as areas, to mid- and small scales, where roads are represented progressively more frequently as lines. As a consequence of this process, there is an intermediate scale range where at the same time some roads will be represented as areas, while others will be represented as lines. We propose a new data model together with a specific data structure where for all map objects, a range of valid map scales is stored. This model is based on the integrated and explicit representation of: (1 a planar area partition; and (2 a linear road network. This enables the generalization process to include the knowledge and understanding of a linear network. This paper further discusses the actual generalization options and algorithms for populating this data structure with high quality vario-scale cartographic content.

  6. Search in spatial scale-free networks

    International Nuclear Information System (INIS)

    Thadakamalla, H P; Albert, R; Kumara, S R T

    2007-01-01

    We study the decentralized search problem in a family of parameterized spatial network models that are heterogeneous in node degree. We investigate several algorithms and illustrate that some of these algorithms exploit the heterogeneity in the network to find short paths by using only local information. In addition, we demonstrate that the spatial network model belongs to a classof searchable networks for a wide range of parameter space. Further, we test these algorithms on the US airline network which belongs to this class of networks and demonstrate that searchability is a generic property of the US airline network. These results provide insights on designing the structure of distributed networks that need effective decentralized search algorithms

  7. Evolution of vocabulary on scale-free and random networks

    Science.gov (United States)

    Kalampokis, Alkiviadis; Kosmidis, Kosmas; Argyrakis, Panos

    2007-06-01

    We examine the evolution of the vocabulary of a group of individuals (linguistic agents) on a scale-free network, using Monte Carlo simulations and assumptions from evolutionary game theory. It is known that when the agents are arranged in a two-dimensional lattice structure and interact by diffusion and encounter, then their final vocabulary size is the maximum possible. Knowing all available words is essential in order to increase the probability to “survive” by effective reproduction. On scale-free networks we find a different result. It is not necessary to learn the entire vocabulary available. Survival chances are increased by using the vocabulary of the “hubs” (nodes with high degree). The existence of the “hubs” in a scale-free network is the source of an additional important fitness generating mechanism.

  8. Scaling in small-world resistor networks

    International Nuclear Information System (INIS)

    Korniss, G.; Hastings, M.B.; Bassler, K.E.; Berryman, M.J.; Kozma, B.; Abbott, D.

    2006-01-01

    We study the effective resistance of small-world resistor networks. Utilizing recent analytic results for the propagator of the Edwards-Wilkinson process on small-world networks, we obtain the asymptotic behavior of the disorder-averaged two-point resistance in the large system-size limit. We find that the small-world structure suppresses large network resistances: both the average resistance and its standard deviation approaches a finite value in the large system-size limit for any non-zero density of random links. We also consider a scenario where the link conductance decays as a power of the length of the random links, l -α . In this case we find that the average effective system resistance diverges for any non-zero value of α

  9. Scaling and percolation in the small-world network model

    International Nuclear Information System (INIS)

    Newman, M. E. J.; Watts, D. J.

    1999-01-01

    In this paper we study the small-world network model of Watts and Strogatz, which mimics some aspects of the structure of networks of social interactions. We argue that there is one nontrivial length-scale in the model, analogous to the correlation length in other systems, which is well-defined in the limit of infinite system size and which diverges continuously as the randomness in the network tends to zero, giving a normal critical point in this limit. This length-scale governs the crossover from large- to small-world behavior in the model, as well as the number of vertices in a neighborhood of given radius on the network. We derive the value of the single critical exponent controlling behavior in the critical region and the finite size scaling form for the average vertex-vertex distance on the network, and, using series expansion and Pade approximants, find an approximate analytic form for the scaling function. We calculate the effective dimension of small-world graphs and show that this dimension varies as a function of the length-scale on which it is measured, in a manner reminiscent of multifractals. We also study the problem of site percolation on small-world networks as a simple model of disease propagation, and derive an approximate expression for the percolation probability at which a giant component of connected vertices first forms (in epidemiological terms, the point at which an epidemic occurs). The typical cluster radius satisfies the expected finite size scaling form with a cluster size exponent close to that for a random graph. All our analytic results are confirmed by extensive numerical simulations of the model. (c) 1999 The American Physical Society

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  11. Earthquake magnitude time series: scaling behavior of visibility networks

    Science.gov (United States)

    Aguilar-San Juan, B.; Guzmán-Vargas, L.

    2013-11-01

    We present a statistical analysis of earthquake magnitude sequences in terms of the visibility graph method. Magnitude time series from Italy, Southern California, and Mexico are transformed into networks and some organizational graph properties are discussed. Connectivities are characterized by a scale-free distribution with a noticeable effect for large scales due to either the presence or the lack of large events. Also, a scaling behavior is observed between different node measures like betweenness centrality, clustering coefficient, nearest neighbor connectivity, and earthquake magnitude. Moreover, parameters which quantify the difference between forward and backward links, are proposed to evaluate the asymmetry of visibility attachment mechanism. Our results show an alternating average behavior of these parameters as earthquake magnitude changes. Finally, we evaluate the effects of reducing temporal and spatial windows of observation upon visibility network properties for main-shocks.

  12. Living in a network of scaling cities and finite resources.

    Science.gov (United States)

    Qubbaj, Murad R; Shutters, Shade T; Muneepeerakul, Rachata

    2015-02-01

    Many urban phenomena exhibit remarkable regularity in the form of nonlinear scaling behaviors, but their implications on a system of networked cities has never been investigated. Such knowledge is crucial for our ability to harness the complexity of urban processes to further sustainability science. In this paper, we develop a dynamical modeling framework that embeds population-resource dynamics-a generalized Lotka-Volterra system with modifications to incorporate the urban scaling behaviors-in complex networks in which cities may be linked to the resources of other cities and people may migrate in pursuit of higher welfare. We find that isolated cities (i.e., no migration) are susceptible to collapse if they do not have access to adequate resources. Links to other cities may help cities that would otherwise collapse due to insufficient resources. The effects of inter-city links, however, can vary due to the interplay between the nonlinear scaling behaviors and network structure. The long-term population level of a city is, in many settings, largely a function of the city's access to resources over which the city has little or no competition. Nonetheless, careful investigation of dynamics is required to gain mechanistic understanding of a particular city-resource network because cities and resources may collapse and the scaling behaviors may influence the effects of inter-city links, thereby distorting what topological metrics really measure.

  13. Network-Based Effectiveness

    National Research Council Canada - National Science Library

    Friman, Henrik

    2006-01-01

    ... (extended from Leavitt, 1965). This text identifies aspects of network-based effectiveness that can benefit from a better understanding of leadership and management development of people, procedures, technology, and organizations...

  14. Network placement optimization for large-scale distributed system

    Science.gov (United States)

    Ren, Yu; Liu, Fangfang; Fu, Yunxia; Zhou, Zheng

    2018-01-01

    The network geometry strongly influences the performance of the distributed system, i.e., the coverage capability, measurement accuracy and overall cost. Therefore the network placement optimization represents an urgent issue in the distributed measurement, even in large-scale metrology. This paper presents an effective computer-assisted network placement optimization procedure for the large-scale distributed system and illustrates it with the example of the multi-tracker system. To get an optimal placement, the coverage capability and the coordinate uncertainty of the network are quantified. Then a placement optimization objective function is developed in terms of coverage capabilities, measurement accuracy and overall cost. And a novel grid-based encoding approach for Genetic algorithm is proposed. So the network placement is optimized by a global rough search and a local detailed search. Its obvious advantage is that there is no need for a specific initial placement. At last, a specific application illustrates this placement optimization procedure can simulate the measurement results of a specific network and design the optimal placement efficiently.

  15. Geometry of river networks. I. Scaling, fluctuations, and deviations

    International Nuclear Information System (INIS)

    Dodds, Peter Sheridan; Rothman, Daniel H.

    2001-01-01

    This paper is the first in a series of three papers investigating the detailed geometry of river networks. Branching networks are a universal structure employed in the distribution and collection of material. Large-scale river networks mark an important class of two-dimensional branching networks, being not only of intrinsic interest but also a pervasive natural phenomenon. In the description of river network structure, scaling laws are uniformly observed. Reported values of scaling exponents vary, suggesting that no unique set of scaling exponents exists. To improve this current understanding of scaling in river networks and to provide a fuller description of branching network structure, here we report a theoretical and empirical study of fluctuations about and deviations from scaling. We examine data for continent-scale river networks such as the Mississippi and the Amazon and draw inspiration from a simple model of directed, random networks. We center our investigations on the scaling of the length of a subbasin's dominant stream with its area, a characterization of basin shape known as Hack's law. We generalize this relationship to a joint probability density, and provide observations and explanations of deviations from scaling. We show that fluctuations about scaling are substantial, and grow with system size. We find strong deviations from scaling at small scales which can be explained by the existence of a linear network structure. At intermediate scales, we find slow drifts in exponent values, indicating that scaling is only approximately obeyed and that universality remains indeterminate. At large scales, we observe a breakdown in scaling due to decreasing sample space and correlations with overall basin shape. The extent of approximate scaling is significantly restricted by these deviations, and will not be improved by increases in network resolution

  16. Meeting the memory challenges of brain-scale network simulation

    Directory of Open Access Journals (Sweden)

    Susanne eKunkel

    2012-01-01

    Full Text Available The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10^5 neurons with up to 10^9 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are one or two orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been studied in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Bluegene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of a neuronal simulator as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place.

  17. Reorganizing Complex Network to Improve Large-Scale Multiagent Teamwork

    Directory of Open Access Journals (Sweden)

    Yang Xu

    2014-01-01

    Full Text Available Large-scale multiagent teamwork has been popular in various domains. Similar to human society infrastructure, agents only coordinate with some of the others, with a peer-to-peer complex network structure. Their organization has been proven as a key factor to influence their performance. To expedite team performance, we have analyzed that there are three key factors. First, complex network effects may be able to promote team performance. Second, coordination interactions coming from their sources are always trying to be routed to capable agents. Although they could be transferred across the network via different paths, their sources and sinks depend on the intrinsic nature of the team which is irrelevant to the network connections. In addition, the agents involved in the same plan often form a subteam and communicate with each other more frequently. Therefore, if the interactions between agents can be statistically recorded, we are able to set up an integrated network adjustment algorithm by combining the three key factors. Based on our abstracted teamwork simulations and the coordination statistics, we implemented the adaptive reorganization algorithm. The experimental results briefly support our design that the reorganized network is more capable of coordinating heterogeneous agents.

  18. The Scaling of Several Public Transport Networks in China

    Science.gov (United States)

    Guo, Long; Zhu, Yueying; Luo, Zhongjie; Li, Wei

    Public transport networks (PTNs) are often researched without reference to their geographical embedding. The question arises if there is any underlying structure or principle characterizing the observed behavior of geographically embedded transport routes. Here, we focus on the scaling properties of PTNs in Space L through fractal analysis and consider the effect of the real bus routes, which reflects the human movement between stations indirectly. We find that the PTN in Space L is the better basal one to mimic the human migration in city. Furthermore, we also research the scaling property of the correlation between stations and the distribution of node's weight, which shows the heterogeneous property of human activity between different stations. Our present work provides some new perspective and tools to realize the human migration on spatial networks.

  19. Chaotic Modes in Scale Free Opinion Networks

    Science.gov (United States)

    Kusmartsev, Feo V.; Kürten, Karl E.

    2010-12-01

    In this paper, we investigate processes associated with formation of public opinion in varies directed random, scale free and small-world social networks. The important factor of the opinion formation is the existence of contrarians which were discovered by Granovetter in various social psychology experiments1,2,3 long ago and later introduced in sociophysics by Galam.4 When the density of contrarians increases the system behavior drastically changes at some critical value. At high density of contrarians the system can never arrive to a consensus state and periodically oscillates with different periods depending on specific structure of the network. At small density of the contrarians the behavior is manifold. It depends primary on the initial state of the system. If initially the majority of the population agrees with each other a state of stable majority may be easily reached. However when originally the population is divided in nearly equal parts consensus can never be reached. We model the emergence of collective decision making by considering N interacting agents, whose opinions are described by two state Ising spin variable associated with YES and NO. We show that the dynamical behaviors are very sensitive not only to the density of the contrarians but also to the network topology. We find that a phase of social chaos may arise in various dynamical processes of opinion formation in many realistic models. We compare the prediction of the theory with data describing the dynamics of the average opinion of the USA population collected on a day-by-day basis by varies media sources during the last six month before the final Obama-McCain election. The qualitative ouctome is in reasonable agreement with the prediction of our theory. In fact, the analyses of these data made within the paradigm of our theory indicates that even in this campaign there were chaotic elements where the public opinion migrated in an unpredictable chaotic way. The existence of such a phase

  20. Exploring effective conservation networks based on multi-scale planning unit analysis. A case study of the Balsas sub-basin, Maranhao State, Brazil

    NARCIS (Netherlands)

    Barreto, L.; Ribeiro, M.C.; Veldkamp, A.; Eupen, van M.; Kok, K.; Pontes, E.

    2010-01-01

    Nature conservation and restoration activities require delineation of effective conservation networks. This paper presents a methodology which allows a quick evaluation of different planning options for extensive areas. We analyzed the spatial structure of remaining patches of the natural Cerrado

  1. Developing Large-Scale Bayesian Networks by Composition

    Data.gov (United States)

    National Aeronautics and Space Administration — In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale...

  2. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

    Science.gov (United States)

    Kaltenbacher, Barbara; Hasenauer, Jan

    2017-01-01

    Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

  3. Reconfigurable Network of Networks for Multi-Scale Computing

    National Research Council Canada - National Science Library

    Sutton, Jeff

    2002-01-01

    The Network of Networks (NoN) model, which is a neurobiologically motivated smart algorithm co-developed by the PI, has been applied for rapid and accurate image processing of forward and side scan sonar images in turbid environments...

  4. Reconfigurable Network of Networks for Multi-Scale Computing

    National Research Council Canada - National Science Library

    Sutton, Jeff

    2000-01-01

    The Network of Networks (NoN) model, which is a neurobiologically motivated smart algorithm co-developed by the PI, is being applied for rapid and accurate image processing of forward and side scan sonar images in turbid environments...

  5. Degree Landscapes in Scale-Free Networks

    OpenAIRE

    Axelsen, Jacob Bock; Bernhardsson, Sebastian; Rosvall, Martin; Sneppen, Kim; Trusina, Ala

    2005-01-01

    We generalize the degree-organizational view of real-world networks with broad degree-distributions in a landscape analogue with mountains (high-degree nodes) and valleys (low-degree nodes). For example, correlated degrees between adjacent nodes corresponds to smooth landscapes (social networks), hierarchical networks to one-mountain landscapes (the Internet), and degree-disassortative networks without hierarchical features to rough landscapes with several mountains. We also generate ridge la...

  6. Scaling in topological properties of brain networks

    NARCIS (Netherlands)

    Singh, S.S.; Khundrakpam, B.S.; Reid, A.T.; Lewis, J.D.; Evans, A.C.; Ishrat, R.; Sharma, B.I.; Singh, R.K.B.

    2016-01-01

    The organization in brain networks shows highly modular features with weak inter-modular interaction. The topology of the networks involves emergence of modules and sub-modules at different levels of constitution governed by fractal laws that are signatures of self-organization in complex networks.

  7. Adaptive local routing strategy on a scale-free network

    International Nuclear Information System (INIS)

    Feng, Liu; Han, Zhao; Ming, Li; Yan-Bo, Zhu; Feng-Yuan, Ren

    2010-01-01

    Due to the heterogeneity of the structure on a scale-free network, making the betweennesses of all nodes become homogeneous by reassigning the weights of nodes or edges is very difficult. In order to take advantage of the important effect of high degree nodes on the shortest path communication and preferentially deliver packets by them to increase the probability to destination, an adaptive local routing strategy on a scale-free network is proposed, in which the node adjusts the forwarding probability with the dynamical traffic load (packet queue length) and the degree distribution of neighbouring nodes. The critical queue length of a node is set to be proportional to its degree, and the node with high degree has a larger critical queue length to store and forward more packets. When the queue length of a high degree node is shorter than its critical queue length, it has a higher probability to forward packets. After higher degree nodes are saturated (whose queue lengths are longer than their critical queue lengths), more packets will be delivered by the lower degree nodes around them. The adaptive local routing strategy increases the probability of a packet finding its destination quickly, and improves the transmission capacity on the scale-free network by reducing routing hops. The simulation results show that the transmission capacity of the adaptive local routing strategy is larger than that of three previous local routing strategies. (general)

  8. Locating inefficient links in a large-scale transportation network

    Science.gov (United States)

    Sun, Li; Liu, Like; Xu, Zhongzhi; Jie, Yang; Wei, Dong; Wang, Pu

    2015-02-01

    Based on data from geographical information system (GIS) and daily commuting origin destination (OD) matrices, we estimated the distribution of traffic flow in the San Francisco road network and studied Braess's paradox in a large-scale transportation network with realistic travel demand. We measured the variation of total travel time Δ T when a road segment is closed, and found that | Δ T | follows a power-law distribution if Δ T 0. This implies that most roads have a negligible effect on the efficiency of the road network, while the failure of a few crucial links would result in severe travel delays, and closure of a few inefficient links would counter-intuitively reduce travel costs considerably. Generating three theoretical networks, we discovered that the heterogeneously distributed travel demand may be the origin of the observed power-law distributions of | Δ T | . Finally, a genetic algorithm was used to pinpoint inefficient link clusters in the road network. We found that closing specific road clusters would further improve the transportation efficiency.

  9. Optimal defense resource allocation in scale-free networks

    Science.gov (United States)

    Zhang, Xuejun; Xu, Guoqiang; Xia, Yongxiang

    2018-02-01

    The robustness research of networked systems has drawn widespread attention in the past decade, and one of the central topics is to protect the network from external attacks through allocating appropriate defense resource to different nodes. In this paper, we apply a specific particle swarm optimization (PSO) algorithm to optimize the defense resource allocation in scale-free networks. Results reveal that PSO based resource allocation shows a higher robustness than other resource allocation strategies such as uniform, degree-proportional, and betweenness-proportional allocation strategies. Furthermore, we find that assigning less resource to middle-degree nodes under small-scale attack while more resource to low-degree nodes under large-scale attack is conductive to improving the network robustness. Our work provides an insight into the optimal defense resource allocation pattern in scale-free networks and is helpful for designing a more robust network.

  10. Emergence, evolution and scaling of online social networks.

    Directory of Open Access Journals (Sweden)

    Le-Zhi Wang

    Full Text Available Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

  11. Signaling in large-scale neural networks

    DEFF Research Database (Denmark)

    Berg, Rune W; Hounsgaard, Jørn

    2009-01-01

    We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this m......We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages...... of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons....

  12. Projective synchronization of uncertain scale-free network based on modified sliding mode control technique

    Science.gov (United States)

    Li, Chengren; Lü, Ling; Zhao, Guannan; Li, Gang; Tian, Jing; Gu, Jiajia; Wang, Zhouyang

    2017-05-01

    We modify sliding mode control technique from the synchronization of a single dynamic system to the synchronization of complex network. Projective synchronization of uncertain scale-free network is investigated based on modified sliding mode control technique. Further, the sliding surfaces, the identification laws of uncertain parameters and the control inputs are designed, and the condition of realizing projective synchronization of uncertain scale-free network is obtained. Finally, the Logistic systems with chaotic behavior are taken as nodes to constitute the scale-free network and the synchronization target. The simulation results show that the synchronization mechanism is effective.

  13. Selective vulnerability related to aging in large-scale resting brain networks.

    Science.gov (United States)

    Zhang, Hong-Ying; Chen, Wen-Xin; Jiao, Yun; Xu, Yao; Zhang, Xiang-Rong; Wu, Jing-Tao

    2014-01-01

    Normal aging is associated with cognitive decline. Evidence indicates that large-scale brain networks are affected by aging; however, it has not been established whether aging has equivalent effects on specific large-scale networks. In the present study, 40 healthy subjects including 22 older (aged 60-80 years) and 18 younger (aged 22-33 years) adults underwent resting-state functional MRI scanning. Four canonical resting-state networks, including the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN) and salience network, were extracted, and the functional connectivities in these canonical networks were compared between the younger and older groups. We found distinct, disruptive alterations present in the large-scale aging-related resting brain networks: the ECN was affected the most, followed by the DAN. However, the DMN and salience networks showed limited functional connectivity disruption. The visual network served as a control and was similarly preserved in both groups. Our findings suggest that the aged brain is characterized by selective vulnerability in large-scale brain networks. These results could help improve our understanding of the mechanism of degeneration in the aging brain. Additional work is warranted to determine whether selective alterations in the intrinsic networks are related to impairments in behavioral performance.

  14. A scale-free neural network for modelling neurogenesis

    Science.gov (United States)

    Perotti, Juan I.; Tamarit, Francisco A.; Cannas, Sergio A.

    2006-11-01

    In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity.

  15. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network

    DEFF Research Database (Denmark)

    Förster, Jochen; Famili, I.; Fu, P.

    2003-01-01

    and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network...... with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions....

  16. Foundational perspectives on causality in large-scale brain networks

    Science.gov (United States)

    Mannino, Michael; Bressler, Steven L.

    2015-12-01

    A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical

  17. High Fidelity Simulations of Large-Scale Wireless Networks

    Energy Technology Data Exchange (ETDEWEB)

    Onunkwo, Uzoma [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Benz, Zachary [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-11-01

    The worldwide proliferation of wireless connected devices continues to accelerate. There are 10s of billions of wireless links across the planet with an additional explosion of new wireless usage anticipated as the Internet of Things develops. Wireless technologies do not only provide convenience for mobile applications, but are also extremely cost-effective to deploy. Thus, this trend towards wireless connectivity will only continue and Sandia must develop the necessary simulation technology to proactively analyze the associated emerging vulnerabilities. Wireless networks are marked by mobility and proximity-based connectivity. The de facto standard for exploratory studies of wireless networks is discrete event simulations (DES). However, the simulation of large-scale wireless networks is extremely difficult due to prohibitively large turnaround time. A path forward is to expedite simulations with parallel discrete event simulation (PDES) techniques. The mobility and distance-based connectivity associated with wireless simulations, however, typically doom PDES and fail to scale (e.g., OPNET and ns-3 simulators). We propose a PDES-based tool aimed at reducing the communication overhead between processors. The proposed solution will use light-weight processes to dynamically distribute computation workload while mitigating communication overhead associated with synchronizations. This work is vital to the analytics and validation capabilities of simulation and emulation at Sandia. We have years of experience in Sandia’s simulation and emulation projects (e.g., MINIMEGA and FIREWHEEL). Sandia’s current highly-regarded capabilities in large-scale emulations have focused on wired networks, where two assumptions prevent scalable wireless studies: (a) the connections between objects are mostly static and (b) the nodes have fixed locations.

  18. Multilevel method for modeling large-scale networks.

    Energy Technology Data Exchange (ETDEWEB)

    Safro, I. M. (Mathematics and Computer Science)

    2012-02-24

    Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

  19. Estimating Population Size Using the Network Scale Up Method.

    Science.gov (United States)

    Maltiel, Rachael; Raftery, Adrian E; McCormick, Tyler H; Baraff, Aaron J

    2015-09-01

    We develop methods for estimating the size of hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise by asking respondents how many people they know in a specific group (e.g. people named Michael, intravenous drug users). The Network Scale up Method (NSUM) is a tool for producing population size estimates using these indirect measures of respondents' networks. Killworth et al. (1998a,b) proposed maximum likelihood estimators of population size for a fixed effects model in which respondents' degrees or personal network sizes are treated as fixed. We extend this by treating personal network sizes as random effects, yielding principled statements of uncertainty. This allows us to generalize the model to account for variation in people's propensity to know people in particular subgroups (barrier effects), such as their tendency to know people like themselves, as well as their lack of awareness of or reluctance to acknowledge their contacts' group memberships (transmission bias). NSUM estimates also suffer from recall bias, in which respondents tend to underestimate the number of members of larger groups that they know, and conversely for smaller groups. We propose a data-driven adjustment method to deal with this. Our methods perform well in simulation studies, generating improved estimates and calibrated uncertainty intervals, as well as in back estimates of real sample data. We apply them to data from a study of HIV/AIDS prevalence in Curitiba, Brazil. Our results show that when transmission bias is present, external information about its likely extent can greatly improve the estimates. The methods are implemented in the NSUM R package.

  20. Emergence of cooperation in non-scale-free networks

    International Nuclear Information System (INIS)

    Zhang, Yichao; Aziz-Alaoui, M A; Bertelle, Cyrille; Zhou, Shi; Wang, Wenting

    2014-01-01

    Evolutionary game theory is one of the key paradigms behind many scientific disciplines from science to engineering. Previous studies proposed a strategy updating mechanism, which successfully demonstrated that the scale-free network can provide a framework for the emergence of cooperation. Instead, individuals in random graphs and small-world networks do not favor cooperation under this updating rule. However, a recent empirical result shows the heterogeneous networks do not promote cooperation when humans play a prisoner’s dilemma. In this paper, we propose a strategy updating rule with payoff memory. We observe that the random graphs and small-world networks can provide even better frameworks for cooperation than the scale-free networks in this scenario. Our observations suggest that the degree heterogeneity may be neither a sufficient condition nor a necessary condition for the widespread cooperation in complex networks. Also, the topological structures are not sufficed to determine the level of cooperation in complex networks. (paper)

  1. Triadic closure dynamics drives scaling laws in social multiplex networks

    International Nuclear Information System (INIS)

    Klimek, Peter; Thurner, Stefan

    2013-01-01

    Social networks exhibit scaling laws for several structural characteristics, such as degree distribution, scaling of the attachment kernel and clustering coefficients as a function of node degree. A detailed understanding if and how these scaling laws are inter-related is missing so far, let alone whether they can be understood through a common, dynamical principle. We propose a simple model for stationary network formation and show that the three mentioned scaling relations follow as natural consequences of triadic closure. The validity of the model is tested on multiplex data from a well-studied massive multiplayer online game. We find that the three scaling exponents observed in the multiplex data for the friendship, communication and trading networks can simultaneously be explained by the model. These results suggest that triadic closure could be identified as one of the fundamental dynamical principles in social multiplex network formation. (paper)

  2. Consensus of Multi-Agent Systems with Prestissimo Scale-Free Networks

    International Nuclear Information System (INIS)

    Yang Hongyong; Lu Lan; Cao Kecai; Zhang Siying

    2010-01-01

    In this paper, the relations of the network topology and the moving consensus of multi-agent systems are studied. A consensus-prestissimo scale-free network model with the static preferential-consensus attachment is presented on the rewired link of the regular network. The effects of the static preferential-consensus BA network on the algebraic connectivity of the topology graph are compared with the regular network. The robustness gain to delay is analyzed for variable network topology with the same scale. The time to reach the consensus is studied for the dynamic network with and without communication delays. By applying the computer simulations, it is validated that the speed of the convergence of multi-agent systems can be greatly improved in the preferential-consensus BA network model with different configuration. (interdisciplinary physics and related areas of science and technology)

  3. Video interpretability rating scale under network impairments

    Science.gov (United States)

    Kreitmair, Thomas; Coman, Cristian

    2014-01-01

    This paper presents the results of a study of the impact of network transmission channel parameters on the quality of streaming video data. A common practice for estimating the interpretability of video information is to use the Motion Imagery Quality Equation (MIQE). MIQE combines a few technical features of video images (such as: ground sampling distance, relative edge response, modulation transfer function, gain and signal-to-noise ratio) to estimate the interpretability level. One observation of this study is that the MIQE does not fully account for video-specific parameters such as spatial and temporal encoding, which are relevant to appreciating degradations caused by the streaming process. In streaming applications the main artifacts impacting the interpretability level are related to distortions in the image caused by lossy decompression of video data (due to loss of information and in some cases lossy re-encoding by the streaming server). One parameter in MIQE that is influenced by network transmission errors is the Relative Edge Response (RER). The automated calculation of RER includes the selection of the best edge in the frame, which in case of network errors may be incorrectly associated with a blocked region (e.g. low resolution areas caused by loss of information). A solution is discussed in this document to address this inconsistency by removing corrupted regions from the image analysis process. Furthermore, a recommendation is made on how to account for network impairments in the MIQE, such that a more realistic interpretability level is estimated in case of streaming applications.

  4. Multiple dynamical time-scales in networks with hierarchically ...

    Indian Academy of Sciences (India)

    Multiple dynamical time-scales in networks with hierarchically nested modular organization ... http://www.ias.ac.in/article/fulltext/pram/077/05/0833-0842 ... Many natural and engineered complex networks have intricate mesoscopic organization, e.g., the clustering of the constituent nodes into several communities or ...

  5. New Visions for Large Scale Networks: Research and Applications

    Data.gov (United States)

    Networking and Information Technology Research and Development, Executive Office of the President — This paper documents the findings of the March 12-14, 2001 Workshop on New Visions for Large-Scale Networks: Research and Applications. The workshops objectives were...

  6. Stochastic ecological network occupancy (SENO) models: a new tool for modeling ecological networks across spatial scales

    Science.gov (United States)

    Lafferty, Kevin D.; Dunne, Jennifer A.

    2010-01-01

    Stochastic ecological network occupancy (SENO) models predict the probability that species will occur in a sample of an ecological network. In this review, we introduce SENO models as a means to fill a gap in the theoretical toolkit of ecologists. As input, SENO models use a topological interaction network and rates of colonization and extinction (including consumer effects) for each species. A SENO model then simulates the ecological network over time, resulting in a series of sub-networks that can be used to identify commonly encountered community modules. The proportion of time a species is present in a patch gives its expected probability of occurrence, whose sum across species gives expected species richness. To illustrate their utility, we provide simple examples of how SENO models can be used to investigate how topological complexity, species interactions, species traits, and spatial scale affect communities in space and time. They can categorize species as biodiversity facilitators, contributors, or inhibitors, making this approach promising for ecosystem-based management of invasive, threatened, or exploited species.

  7. Fractal scale-free networks resistant to disease spread

    International Nuclear Information System (INIS)

    Zhang, Zhongzhi; Zhou, Shuigeng; Zou, Tao; Chen, Guisheng

    2008-01-01

    The conventional wisdom is that scale-free networks are prone to epidemic propagation; in the paper we demonstrate that, on the contrary, disease spreading is inhibited in fractal scale-free networks. We first propose a novel network model and show that it simultaneously has the following rich topological properties: scale-free degree distribution, tunable clustering coefficient, 'large-world' behavior, and fractal scaling. Existing network models do not display these characteristics. Then, we investigate the susceptible–infected–removed (SIR) model of the propagation of diseases in our fractal scale-free networks by mapping it to the bond percolation process. We establish the existence of non-zero tunable epidemic thresholds by making use of the renormalization group technique, which implies that power law degree distribution does not suffice to characterize the epidemic dynamics on top of scale-free networks. We argue that the epidemic dynamics are determined by the topological properties, especially the fractality and its accompanying 'large-world' behavior

  8. Tests of peak flow scaling in simulated self-similar river networks

    Science.gov (United States)

    Menabde, M.; Veitzer, S.; Gupta, V.; Sivapalan, M.

    2001-01-01

    The effect of linear flow routing incorporating attenuation and network topology on peak flow scaling exponent is investigated for an instantaneously applied uniform runoff on simulated deterministic and random self-similar channel networks. The flow routing is modelled by a linear mass conservation equation for a discrete set of channel links connected in parallel and series, and having the same topology as the channel network. A quasi-analytical solution for the unit hydrograph is obtained in terms of recursion relations. The analysis of this solution shows that the peak flow has an asymptotically scaling dependence on the drainage area for deterministic Mandelbrot-Vicsek (MV) and Peano networks, as well as for a subclass of random self-similar channel networks. However, the scaling exponent is shown to be different from that predicted by the scaling properties of the maxima of the width functions. ?? 2001 Elsevier Science Ltd. All rights reserved.

  9. Universality of the emergent scaling in finite random binary percolation networks.

    Science.gov (United States)

    Zhai, Chongpu; Hanaor, Dorian; Gan, Yixiang

    2017-01-01

    In this paper we apply lattice models of finite binary percolation networks to examine the effects of network configuration on macroscopic network responses. We consider both square and rectangular lattice structures in which bonds between nodes are randomly assigned to be either resistors or capacitors. Results show that for given network geometries, the overall normalised frequency-dependent electrical conductivities for different capacitor proportions are found to converge at a characteristic frequency. Networks with sufficiently large size tend to share the same convergence point uninfluenced by the boundary and electrode conditions, can be then regarded as homogeneous media. For these networks, the span of the emergent scaling region is found to be primarily determined by the smaller network dimension (width or length). This study identifies the applicability of power-law scaling in random two phase systems of different topological configurations. This understanding has implications in the design and testing of disordered systems in diverse applications.

  10. Large-scale networks in engineering and life sciences

    CERN Document Server

    Findeisen, Rolf; Flockerzi, Dietrich; Reichl, Udo; Sundmacher, Kai

    2014-01-01

    This edited volume provides insights into and tools for the modeling, analysis, optimization, and control of large-scale networks in the life sciences and in engineering. Large-scale systems are often the result of networked interactions between a large number of subsystems, and their analysis and control are becoming increasingly important. The chapters of this book present the basic concepts and theoretical foundations of network theory and discuss its applications in different scientific areas such as biochemical reactions, chemical production processes, systems biology, electrical circuits, and mobile agents. The aim is to identify common concepts, to understand the underlying mathematical ideas, and to inspire discussions across the borders of the various disciplines.  The book originates from the interdisciplinary summer school “Large Scale Networks in Engineering and Life Sciences” hosted by the International Max Planck Research School Magdeburg, September 26-30, 2011, and will therefore be of int...

  11. Fluid transfers in fractured media: scale effects

    International Nuclear Information System (INIS)

    Bour, Olivier

    1996-01-01

    As there has been a growing interest in the study of fluid circulations in fractured media for the last fifteen years, for example for projects of underground storage of different waste types, or to improve water resources, or for exploitation of underground oil products or geothermal resources, this research thesis first gives a large overview of the modelling and transport properties of fractured media. He presents the main notions related to fluid transfers in fractured media (structures of fracture networks, hydraulic properties of fractured media), and the various adopted approaches (the effective medium theory, the percolation theory, double porosity models, deterministic discrete fracture models, equivalent discontinuous model, fractal models), and outlines the originality of the approach developed in this research: scale change, conceptual hypotheses, methodology, tools). The second part addresses scale rules in fracture networks: presentation of fracture networks (mechanical aspects, statistical analysis), distribution of fracture lengths and of fracture networks, length-position relationship, modelling attempt, lessons learned and consequences in terms of hydraulic and mechanical properties, and of relationship between length distribution and fractal dimension. The third part proposes two articles published by the author and addressing the connectivity properties of fracture networks. The fifth chapter reports the application to natural media. It contains an article on the application of percolation theory to 2D natural fracture networks, and reports information collected on a site [fr

  12. A Network Contention Model for the Extreme-scale Simulator

    Energy Technology Data Exchange (ETDEWEB)

    Engelmann, Christian [ORNL; Naughton III, Thomas J [ORNL

    2015-01-01

    The Extreme-scale Simulator (xSim) is a performance investigation toolkit for high-performance computing (HPC) hardware/software co-design. It permits running a HPC application with millions of concurrent execution threads, while observing its performance in a simulated extreme-scale system. This paper details a newly developed network modeling feature for xSim, eliminating the shortcomings of the existing network modeling capabilities. The approach takes a different path for implementing network contention and bandwidth capacity modeling using a less synchronous and accurate enough model design. With the new network modeling feature, xSim is able to simulate on-chip and on-node networks with reasonable accuracy and overheads.

  13. Network Security: Policies and Guidelines for Effective Network Management

    Directory of Open Access Journals (Sweden)

    Jonathan Gana KOLO

    2008-12-01

    Full Text Available Network security and management in Information and Communication Technology (ICT is the ability to maintain the integrity of a system or network, its data and its immediate environment. The various innovations and uses to which networks are being put are growing by the day and hence are becoming complex and invariably more difficult to manage by the day. Computers are found in every business such as banking, insurance, hospital, education, manufacturing, etc. The widespread use of these systems implies crime and insecurity on a global scale. In addition, the tremendous benefits brought about by Internet have also widened the scope of crime and insecurity at an alarming rate. Also, ICT has fast become a primary differentiator for institution/organization leaders as it offers effective and convenient means of interaction with each other across the globe. This upsurge in the population of organizations depending on ICT for business transaction has brought with it a growing number of security threats and attacks on poorly managed and secured networks primarily to steal personal data, particularly financial information and password.This paper therefore proposes some policies and guidelines that should be followed by network administrators in organizations to help them ensure effective network management and security of ICT facilities and data.

  14. Degree correlations in directed scale-free networks.

    Science.gov (United States)

    Williams, Oliver; Del Genio, Charo I

    2014-01-01

    Scale-free networks, in which the distribution of the degrees obeys a power-law, are ubiquitous in the study of complex systems. One basic network property that relates to the structure of the links found is the degree assortativity, which is a measure of the correlation between the degrees of the nodes at the end of the links. Degree correlations are known to affect both the structure of a network and the dynamics of the processes supported thereon, including the resilience to damage, the spread of information and epidemics, and the efficiency of defence mechanisms. Nonetheless, while many studies focus on undirected scale-free networks, the interactions in real-world systems often have a directionality. Here, we investigate the dependence of the degree correlations on the power-law exponents in directed scale-free networks. To perform our study, we consider the problem of building directed networks with a prescribed degree distribution, providing a method for proper generation of power-law-distributed directed degree sequences. Applying this new method, we perform extensive numerical simulations, generating ensembles of directed scale-free networks with exponents between 2 and 3, and measuring ensemble averages of the Pearson correlation coefficients. Our results show that scale-free networks are on average uncorrelated across directed links for three of the four possible degree-degree correlations, namely in-degree to in-degree, in-degree to out-degree, and out-degree to out-degree. However, they exhibit anticorrelation between the number of outgoing connections and the number of incoming ones. The findings are consistent with an entropic origin for the observed disassortativity in biological and technological networks.

  15. Homeostatic scaling of excitability in recurrent neural networks.

    Directory of Open Access Journals (Sweden)

    Michiel W H Remme

    Full Text Available Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (pathophysiologically relevant types of plasticity, such as persistent changes in external drive, changes in connection strengths, or the loss of inhibitory cells from the network. However, HSE cannot prevent the unstable network dynamics that result when, due to such plasticity, recurrent excitation in the network becomes too strong compared to feedback inhibition. This suggests that keeping a neural network in a stable and functional state requires the coordination of distinct homeostatic mechanisms that operate not only by adjusting neural excitability, but also by controlling network connectivity.

  16. Superlinear and sublinear urban scaling in geographical networks modeling cities

    Science.gov (United States)

    Yakubo, K.; Saijo, Y.; Korošak, D.

    2014-08-01

    Using a geographical scale-free network to describe relations between people in a city, we explain both superlinear and sublinear allometric scaling of urban indicators that quantify activities or performances of the city. The urban indicator Y (N) of a city with the population size N is analytically calculated by summing up all individual activities produced by person-to-person relationships. Our results show that the urban indicator scales superlinearly with the population, namely, Y(N )∝Nβ with β >1, if Y (N) represents a creative productivity and the indicator scales sublinearly (β <1) if Y (N) is related to the degree of infrastructure development. These results coincide with allometric scaling observed in real-world urban indicators. We also show how the scaling exponent β depends on the strength of the geographical constraint in the network formation.

  17. Aggregated Representation of Distribution Networks for Large-Scale Transmission Network Simulations

    DEFF Research Database (Denmark)

    Göksu, Ömer; Altin, Müfit; Sørensen, Poul Ejnar

    2014-01-01

    As a common practice of large-scale transmission network analysis the distribution networks have been represented as aggregated loads. However, with increasing share of distributed generation, especially wind and solar power, in the distribution networks, it became necessary to include the distri......As a common practice of large-scale transmission network analysis the distribution networks have been represented as aggregated loads. However, with increasing share of distributed generation, especially wind and solar power, in the distribution networks, it became necessary to include...... the distributed generation within those analysis. In this paper a practical methodology to obtain aggregated behaviour of the distributed generation is proposed. The methodology, which is based on the use of the IEC standard wind turbine models, is applied on a benchmark distribution network via simulations....

  18. Weighted Scaling in Non-growth Random Networks

    International Nuclear Information System (INIS)

    Chen Guang; Yang Xuhua; Xu Xinli

    2012-01-01

    We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in non-growth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its total number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scale-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.

  19. Automatic Matching of Multi-scale Road Networks under the Constraints of Smaller Scale Road Meshes

    Directory of Open Access Journals (Sweden)

    PEI Hongxing

    2017-06-01

    Full Text Available A new method is proposed to achieve automatic matching for multi-scale roads under the constraints of the smaller scale data. Firstly, meshes should be extracted from the two different scales road data. Secondly, several basic meshes in the larger scale road network will be merged as a composite one, which will be matched with one mesh from the smaller scale road network, so that the meshes with many-to-one and one-to-one matching relationships will be matched. Thirdly, meshes from the two different scale road data with many-to-many matching relationships will be matched. Finally, road will be classified into two categories under the constraints of meshes: mesh border roads and mesh internal roads, and then matching will be done in their own categories according to the matching relationships between the two scales meshes. The results showed that roads from different scale will be more precisely matched.

  20. Effective professional networking.

    Science.gov (United States)

    Goolsby, Mary Jo; Knestrick, Joyce M

    2017-08-01

    The reasons for nurse practitioners to develop a professional network are boundless and are likely to change over time. Networking opens doors and creates relationships that support new opportunities, personal development, collaborative research, policy activism, evidence-based practice, and more. Successful professional networking involves shared, mutually beneficial interactions between individuals and/or individuals and groups, regardless of whether it occurs face to face or electronically. This article combines nuggets from the literature with guidance based on the authors' combined experience in networking activities at the local, national, and international levels. ©2017 American Association of Nurse Practitioners.

  1. Autonomous management of a recursive area hierarchy for large scale wireless sensor networks using multiple parents

    Energy Technology Data Exchange (ETDEWEB)

    Cree, Johnathan Vee [Washington State Univ., Pullman, WA (United States); Delgado-Frias, Jose [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2016-03-01

    Large scale wireless sensor networks have been proposed for applications ranging from anomaly detection in an environment to vehicle tracking. Many of these applications require the networks to be distributed across a large geographic area while supporting three to five year network lifetimes. In order to support these requirements large scale wireless sensor networks of duty-cycled devices need a method of efficient and effective autonomous configuration/maintenance. This method should gracefully handle the synchronization tasks duty-cycled networks. Further, an effective configuration solution needs to recognize that in-network data aggregation and analysis presents significant benefits to wireless sensor network and should configure the network in a way such that said higher level functions benefit from the logically imposed structure. NOA, the proposed configuration and maintenance protocol, provides a multi-parent hierarchical logical structure for the network that reduces the synchronization workload. It also provides higher level functions with significant inherent benefits such as but not limited to: removing network divisions that are created by single-parent hierarchies, guarantees for when data will be compared in the hierarchy, and redundancies for communication as well as in-network data aggregation/analysis/storage.

  2. Experimental performance evaluation of software defined networking (SDN) based data communication networks for large scale flexi-grid optical networks.

    Science.gov (United States)

    Zhao, Yongli; He, Ruiying; Chen, Haoran; Zhang, Jie; Ji, Yuefeng; Zheng, Haomian; Lin, Yi; Wang, Xinbo

    2014-04-21

    Software defined networking (SDN) has become the focus in the current information and communication technology area because of its flexibility and programmability. It has been introduced into various network scenarios, such as datacenter networks, carrier networks, and wireless networks. Optical transport network is also regarded as an important application scenario for SDN, which is adopted as the enabling technology of data communication networks (DCN) instead of general multi-protocol label switching (GMPLS). However, the practical performance of SDN based DCN for large scale optical networks, which is very important for the technology selection in the future optical network deployment, has not been evaluated up to now. In this paper we have built a large scale flexi-grid optical network testbed with 1000 virtual optical transport nodes to evaluate the performance of SDN based DCN, including network scalability, DCN bandwidth limitation, and restoration time. A series of network performance parameters including blocking probability, bandwidth utilization, average lightpath provisioning time, and failure restoration time have been demonstrated under various network environments, such as with different traffic loads and different DCN bandwidths. The demonstration in this work can be taken as a proof for the future network deployment.

  3. Episodic memory in aspects of large-scale brain networks

    Directory of Open Access Journals (Sweden)

    Woorim eJeong

    2015-08-01

    Full Text Available Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network. Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network. Altered patterns of functional connectivity among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment.

  4. Episodic memory in aspects of large-scale brain networks

    Science.gov (United States)

    Jeong, Woorim; Chung, Chun Kee; Kim, June Sic

    2015-01-01

    Understanding human episodic memory in aspects of large-scale brain networks has become one of the central themes in neuroscience over the last decade. Traditionally, episodic memory was regarded as mostly relying on medial temporal lobe (MTL) structures. However, recent studies have suggested involvement of more widely distributed cortical network and the importance of its interactive roles in the memory process. Both direct and indirect neuro-modulations of the memory network have been tried in experimental treatments of memory disorders. In this review, we focus on the functional organization of the MTL and other neocortical areas in episodic memory. Task-related neuroimaging studies together with lesion studies suggested that specific sub-regions of the MTL are responsible for specific components of memory. However, recent studies have emphasized that connectivity within MTL structures and even their network dynamics with other cortical areas are essential in the memory process. Resting-state functional network studies also have revealed that memory function is subserved by not only the MTL system but also a distributed network, particularly the default-mode network (DMN). Furthermore, researchers have begun to investigate memory networks throughout the entire brain not restricted to the specific resting-state network (RSN). Altered patterns of functional connectivity (FC) among distributed brain regions were observed in patients with memory impairments. Recently, studies have shown that brain stimulation may impact memory through modulating functional networks, carrying future implications of a novel interventional therapy for memory impairment. PMID:26321939

  5. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

    Directory of Open Access Journals (Sweden)

    Xiaolei Ma

    2017-04-01

    Full Text Available This paper proposes a convolutional neural network (CNN-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

  6. Synchronization control for large-scale network systems

    CERN Document Server

    Wu, Yuanqing; Su, Hongye; Shi, Peng; Wu, Zheng-Guang

    2017-01-01

    This book provides recent advances in analysis and synthesis of Large-scale network systems (LSNSs) with sampled-data communication and non-identical nodes. In its first chapter of the book presents an introduction to Synchronization of LSNSs and Algebraic Graph Theory as well as an overview of recent developments of LSNSs with sampled data control or output regulation control. The main text of the book is organized into two main parts - Part I: LSNSs with sampled-data communication and Part II: LSNSs with non-identical nodes. This monograph provides up-to-date advances and some recent developments in the analysis and synthesis issues for LSNSs with sampled-data communication and non-identical nodes. It describes the constructions of the adaptive reference generators in the first stage and the robust regulators in the second stage. Examples are presented to show the effectiveness of the proposed design techniques.

  7. The Network effects of Railway Investments

    DEFF Research Database (Denmark)

    Hansen, Sten; Landex, Alex; Kaas, Anders H.

    2006-01-01

    Network effects is when a change at one place in the railway network results in changes elsewhere in the network – maybe even far away from the original change. Railway investments have network effects, and therefore, this paper describes the network effects and how these network effects can...... be examined by queuing time. This paper gives examples of network effects and describes the importance of the size of the analysis area and the connections between trains in the railway network....

  8. The Network effects of Railway Investments

    DEFF Research Database (Denmark)

    Hansen, Sten; Landex, Alex; Kaas, Anders H.

    2006-01-01

    Network effects is when a change at one place in the railway network results in changes elsewhere in the network – maybe even far away from the original change. Railway investments have network effects, and therefore, this paper describes the network effects and how these network effects can...

  9. Power Laws, Scale-Free Networks and Genome Biology

    CERN Document Server

    Koonin, Eugene V; Karev, Georgy P

    2006-01-01

    Power Laws, Scale-free Networks and Genome Biology deals with crucial aspects of the theoretical foundations of systems biology, namely power law distributions and scale-free networks which have emerged as the hallmarks of biological organization in the post-genomic era. The chapters in the book not only describe the interesting mathematical properties of biological networks but moves beyond phenomenology, toward models of evolution capable of explaining the emergence of these features. The collection of chapters, contributed by both physicists and biologists, strives to address the problems in this field in a rigorous but not excessively mathematical manner and to represent different viewpoints, which is crucial in this emerging discipline. Each chapter includes, in addition to technical descriptions of properties of biological networks and evolutionary models, a more general and accessible introduction to the respective problems. Most chapters emphasize the potential of theoretical systems biology for disco...

  10. Pathogens, Social Networks, and the Paradox of Transmission Scaling

    Directory of Open Access Journals (Sweden)

    Matthew J. Ferrari

    2011-01-01

    Full Text Available Understanding the scaling of transmission is critical to predicting how infectious diseases will affect populations of different sizes and densities. The two classic “mean-field” epidemic models—either assuming density-dependent or frequency-dependent transmission—make predictions that are discordant with patterns seen in either within-population dynamics or across-population comparisons. In this paper, we propose that the source of this inconsistency lies in the greatly simplifying “mean-field” assumption of transmission within a fully-mixed population. Mixing in real populations is more accurately represented by a network of contacts, with interactions and infectious contacts confined to the local social neighborhood. We use network models to show that density-dependent transmission on heterogeneous networks often leads to apparent frequency dependency in the scaling of transmission across populations of different sizes. Network-methodology allows us to reconcile seemingly conflicting patterns of within- and across-population epidemiology.

  11. Designing a low-cost effective network for monitoring large scale regional seismicity in a soft-soil region (Alsace, France)

    Science.gov (United States)

    Bès de Berc, M.; Doubre, C.; Wodling, H.; Jund, H.; Hernandez, A.; Blumentritt, H.

    2015-12-01

    real-time transmission and a rsync daemon for delayed-time transmission.We plan to install and validate our first pilot station during the fall of 2015, and have an effective network by the summer of 2016.

  12. Network synchronization: optimal and pessimal scale-free topologies

    International Nuclear Information System (INIS)

    Donetti, Luca; Hurtado, Pablo I; Munoz, Miguel A

    2008-01-01

    By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability

  13. Network synchronization: optimal and pessimal scale-free topologies

    Energy Technology Data Exchange (ETDEWEB)

    Donetti, Luca [Departamento de Electronica y Tecnologia de Computadores and Instituto de Fisica Teorica y Computacional Carlos I, Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain); Hurtado, Pablo I; Munoz, Miguel A [Departamento de Electromagnetismo y Fisica de la Materia and Instituto Carlos I de Fisica Teorica y Computacional Facultad de Ciencias, Universidad de Granada, 18071 Granada (Spain)], E-mail: mamunoz@onsager.ugr.es

    2008-06-06

    By employing a recently introduced optimization algorithm we construct optimally synchronizable (unweighted) networks for any given scale-free degree distribution. We explore how the optimization process affects degree-degree correlations and observe a generic tendency toward disassortativity. Still, we show that there is not a one-to-one correspondence between synchronizability and disassortativity. On the other hand, we study the nature of optimally un-synchronizable networks, that is, networks whose topology minimizes the range of stability of the synchronous state. The resulting 'pessimal networks' turn out to have a highly assortative string-like structure. We also derive a rigorous lower bound for the Laplacian eigenvalue ratio controlling synchronizability, which helps understanding the impact of degree correlations on network synchronizability.

  14. Reliability Calculation of Large-scale Complex Initiation Network

    Science.gov (United States)

    Li, Xinjian; Yang, Jun; Yan, Bingqiang; Zheng, Xiao

    2018-02-01

    A method was proposed to calculate the reliability of bundle-series compound initiation network which was the widely used for large-scale demolition blasting in China. The network was defined reliable only when all the 2nd level Nonel detonator joints outside the blasting holes were initiated. Based on the definition a series of equations were inferred to calculate the reliability of the complex initiation network. A program is written by Matlab to solve the equations. The method showed good performance with much less computations compared to the traditional ones.

  15. Enabling parallel simulation of large-scale HPC network systems

    International Nuclear Information System (INIS)

    Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.; Carns, Philip

    2016-01-01

    Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks used in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations

  16. 77 FR 58416 - Large Scale Networking (LSN); Middleware and Grid Interagency Coordination (MAGIC) Team

    Science.gov (United States)

    2012-09-20

    ... NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN); Middleware and Grid Interagency Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD..., Grid, and cloud projects. The MAGIC Team reports to the Large Scale Networking (LSN) Coordinating Group...

  17. 78 FR 70076 - Large Scale Networking (LSN)-Middleware and Grid Interagency Coordination (MAGIC) Team

    Science.gov (United States)

    2013-11-22

    ... NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN)--Middleware and Grid Interagency Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD... projects. The MAGIC Team reports to the Large Scale Networking (LSN) Coordinating Group (CG). Public...

  18. LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.

  19. Partitioning Large Scale Deep Belief Networks Using Dropout

    OpenAIRE

    Huang, Yanping; Zhang, Sai

    2015-01-01

    Deep learning methods have shown great promise in many practical applications, ranging from speech recognition, visual object recognition, to text processing. However, most of the current deep learning methods suffer from scalability problems for large-scale applications, forcing researchers or users to focus on small-scale problems with fewer parameters. In this paper, we consider a well-known machine learning model, deep belief networks (DBNs) that have yielded impressive classification per...

  20. Climate, river network, and vegetation cover relationships across a climate gradient and their potential for predicting effects of decadal-scale climate change

    Science.gov (United States)

    Smith, Virginia B.; David, Cédric H.; Cardenas, M. Bayani; Yang, Zong-Liang

    2013-04-01

    We determined present-day (1981-2000) relationships between river network drainage density (Dd) and runoff (R), and between vegetation cover (V) and precipitation (P) across a contiguous 470,800 km2 area (the Texas Gulf Coast basin), where P varies from 438 to 1280 mm/yr. Dd(R) follows a saturation-growth model which is similar to process-based equilibrium landscape models. V(P) follows a linear relationship. The models for Dd(R) and V(P) were used to assess how Dd and V might respond to decadal-scale climate changes in R and P anomalies predicted by a regional climate model between 2041-2060 and 1981-2000. The regional climate model, CRCM, was forced following the SRES A2 emissions scenario. Our calculations indicate a tendency of 57,500 km of active river channels to drying up, representing 9.9% of the present-day total of 581,985 km, due to future decrease in R. This will be accompanied by a loss of 8150 km2 in V due to decrease in P. This study extends empirical studies of relationships between climate and landscape properties and explicitly links observations with process-based models. The results provide a simple framework for modeling potential trajectories of the landscape due to climate change.

  1. Activity patterns on random scale-free networks: global dynamics arising from local majority rules

    Science.gov (United States)

    Zhou, Haijun; Lipowsky, Reinhard

    2007-01-01

    Activity or spin patterns on a random scale-free network are studied using mean field analysis and computer simulations. These activity patterns evolve in time according to local majority rule dynamics which is implemented using (i) parallel or synchronous updating and (ii) random sequential or asynchronous updating. Our mean field calculations predict that the relaxation processes of disordered activity patterns become much more efficient as the scaling exponent γ of the scale-free degree distribution changes from γ > 5/2 to γ 5/2, the corresponding decay times increase as ln(N) with increasing network size N whereas they are independent of N for γ networks: (A) multi-networks as generated by the configuration method, which typically leads to many self-connections and multiple edges, and (B) simple networks without self-connections and multiple edges. We find that the mean field predictions are confirmed (i) for random sequential updating of multi-networks and (ii) for both parallel and random sequential updating of simple networks with γ = 2.25 and 2.6. For γ = 2.4, the data for the simple networks seem to be consistent with mean field theory as well, whereas we cannot draw a definite conclusion from the simulation data for the multi-networks. The latter difficulty can be understood in terms of an effective scaling exponent γeff = γeff(γ, N) for multi-networks. This effective exponent is determined by removing all self-connections and multiple edges; it satisfies γeff >= γ and decreases towards γ with increasing network size N. For γ = 2.4, we find γeff gtrsim 5/2 up to N = 217.

  2. 77 FR 58415 - Large Scale Networking (LSN); Joint Engineering Team (JET)

    Science.gov (United States)

    2012-09-20

    ... NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN); Joint Engineering Team (JET) AGENCY: The Networking and Information Technology Research and Development (NITRD) National Coordination Office (NCO... agencies and non-Federal participants with interest in high performance research networking and networking...

  3. 78 FR 7464 - Large Scale Networking (LSN) ; Joint Engineering Team (JET)

    Science.gov (United States)

    2013-02-01

    ... NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN) ; Joint Engineering Team (JET) AGENCY: The Networking and Information Technology Research and Development (NITRD) National Coordination... interest in high performance research networking and networking to support science applications. The JET...

  4. Innovation diffusion equations on correlated scale-free networks

    Energy Technology Data Exchange (ETDEWEB)

    Bertotti, M.L., E-mail: marialetizia.bertotti@unibz.it [Free University of Bozen–Bolzano, Faculty of Science and Technology, Bolzano (Italy); Brunner, J., E-mail: johannes.brunner@tis.bz.it [TIS Innovation Park, Bolzano (Italy); Modanese, G., E-mail: giovanni.modanese@unibz.it [Free University of Bozen–Bolzano, Faculty of Science and Technology, Bolzano (Italy)

    2016-07-29

    Highlights: • The Bass diffusion model can be formulated on scale-free networks. • In the trickle-down version, the hubs adopt earlier and act as monitors. • We improve the equations in order to describe trickle-up diffusion. • Innovation is generated at the network periphery, and hubs can act as stiflers. • We compare diffusion times, in dependence on the scale-free exponent. - Abstract: We introduce a heterogeneous network structure into the Bass diffusion model, in order to study the diffusion times of innovation or information in networks with a scale-free structure, typical of regions where diffusion is sensitive to geographic and logistic influences (like for instance Alpine regions). We consider both the diffusion peak times of the total population and of the link classes. In the familiar trickle-down processes the adoption curve of the hubs is found to anticipate the total adoption in a predictable way. In a major departure from the standard model, we model a trickle-up process by introducing heterogeneous publicity coefficients (which can also be negative for the hubs, thus turning them into stiflers) and a stochastic term which represents the erratic generation of innovation at the periphery of the network. The results confirm the robustness of the Bass model and expand considerably its range of applicability.

  5. Final Technical Report for Terabit-scale hybrid networking project.

    Energy Technology Data Exchange (ETDEWEB)

    Veeraraghavan, Malathi [Univ. of Virginia, Charlottesville, VA (United States)

    2015-12-12

    This report describes our accomplishments and activities for the project titled Terabit-Scale Hybrid Networking. The key accomplishment is that we developed, tested and deployed an Alpha Flow Characterization System (AFCS) in ESnet. It is being run in production mode since Sept. 2015. Also, a new QoS class was added to ESnet5 to support alpha flows.

  6. Quantifying the multi-scale performance of network inference algorithms.

    Science.gov (United States)

    Oates, Chris J; Amos, Richard; Spencer, Simon E F

    2014-10-01

    Graphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the "wisdom of crowds" network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales.

  7. THE BUILDUP OF A SCALE-FREE PHOTOSPHERIC MAGNETIC NETWORK

    International Nuclear Information System (INIS)

    Thibault, K.; Charbonneau, P.; Crouch, A. D.

    2012-01-01

    We use a global Monte Carlo simulation of the formation of the solar photospheric magnetic network to investigate the origin of the scale invariance characterizing magnetic flux concentrations visible on high-resolution magnetograms. The simulations include spatially and temporally homogeneous injection of small-scale magnetic elements over the whole photosphere, as well as localized episodic injection associated with the emergence and decay of active regions. Network elements form in response to cumulative pairwise aggregation or cancellation of magnetic elements, undergoing a random walk on the sphere and advected on large spatial scales by differential rotation and a poleward meridional flow. The resulting size distribution of simulated network elements is in very good agreement with observational inferences. We find that the fractal index and size distribution of network elements are determined primarily by these post-emergence surface mechanisms, and carry little or no memory of the scales at which magnetic flux is injected in the simulation. Implications for models of dynamo action in the Sun are briefly discussed.

  8. THE BUILDUP OF A SCALE-FREE PHOTOSPHERIC MAGNETIC NETWORK

    Energy Technology Data Exchange (ETDEWEB)

    Thibault, K.; Charbonneau, P. [Departement de Physique, Universite de Montreal, 2900 Edouard-Montpetit, Montreal, Quebec H3C 3J7 (Canada); Crouch, A. D., E-mail: kim@astro.umontreal.ca-a, E-mail: paulchar@astro.umontreal.ca-b, E-mail: ash@cora.nwra.com-c [CORA/NWRA, 3380 Mitchell Lane, Boulder, CO 80301 (United States)

    2012-10-01

    We use a global Monte Carlo simulation of the formation of the solar photospheric magnetic network to investigate the origin of the scale invariance characterizing magnetic flux concentrations visible on high-resolution magnetograms. The simulations include spatially and temporally homogeneous injection of small-scale magnetic elements over the whole photosphere, as well as localized episodic injection associated with the emergence and decay of active regions. Network elements form in response to cumulative pairwise aggregation or cancellation of magnetic elements, undergoing a random walk on the sphere and advected on large spatial scales by differential rotation and a poleward meridional flow. The resulting size distribution of simulated network elements is in very good agreement with observational inferences. We find that the fractal index and size distribution of network elements are determined primarily by these post-emergence surface mechanisms, and carry little or no memory of the scales at which magnetic flux is injected in the simulation. Implications for models of dynamo action in the Sun are briefly discussed.

  9. Multiple dynamical time-scales in networks with hierarchically ...

    Indian Academy of Sciences (India)

    Recent research, on the other hand, has revealed that networks which are indistinguishable at either of these two scales may nevertheless have radically different behaviour [7]. The origin of this difference lies in their mesoscopic organization which can be structurally manifested as patterns in the arrangement of links.

  10. Large-Scale Networked Virtual Environments: Architecture and Applications

    Science.gov (United States)

    Lamotte, Wim; Quax, Peter; Flerackers, Eddy

    2008-01-01

    Purpose: Scalability is an important research topic in the context of networked virtual environments (NVEs). This paper aims to describe the ALVIC (Architecture for Large-scale Virtual Interactive Communities) approach to NVE scalability. Design/methodology/approach: The setup and results from two case studies are shown: a 3-D learning environment…

  11. Energy scaling and reduction in controlling complex networks

    Science.gov (United States)

    Chen, Yu-Zhong; Wang, Le-Zhi; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-01-01

    Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks. PMID:27152220

  12. Formation control for a network of small-scale robots.

    Science.gov (United States)

    Kim, Yoonsoo

    2014-10-01

    In this paper, a network of small-scale robots (typically centimeter-scale robots) equipped with artificial actuators such as electric motors is considered. The purpose of this network is to have the robots keep a certain formation shape (or change to another formation shape) during maneuvers. The network has a fixed communication topology in the sense that robots have a fixed group of neighbors to communicate during maneuvers. Assuming that each robot and its actuator can be modeled as a linear system, a decentralized control law (such that each robot activates its actuator based on the information from its neighbors only) is introduced to achieve the purpose of formation keeping or change. A linear matrix inequality (LMI) for deriving the upper bound on the actuator's time constant is also presented. Simulation results are shown to demonstrate the merit of the introduced control law.

  13. Structural Quality of Service in Large-Scale Networks

    DEFF Research Database (Denmark)

    Pedersen, Jens Myrup

    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......, telephony and data. To meet the requirements of the different applications, and to handle the increased vulnerability to failures, the ability to design robust networks providing good Quality of Service is crucial. However, most planning of large-scale networks today is ad-hoc based, leading to highly...... complex networks lacking predictability and global structural properties. The thesis applies the concept of Structural Quality of Service to formulate desirable global properties, and it shows how regular graph structures can be used to obtain such properties....

  14. BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database

    Science.gov (United States)

    2013-01-01

    Background Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions. Method Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects. Result BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased. Conclusions The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates. Website: The web based application is developed and can be access through the following link http://compgenomics.utsa.edu/BRCAMoNet/ PMID:24564956

  15. Network Effects Versus Strategic Discounting

    DEFF Research Database (Denmark)

    Zucchini, Leon; Claussen, Jörg; Trüg, Moritiz

    . Alternatively, research on strategic discounting suggests small operators use on-net discounts to advertise with low on-net prices. We test the relative strength of these effects using data on tariff setting in German mobile telecommunications between 2001 and 2009. We find that large operators are more likely......Mobile telecommunication operators routinely charge subscribers lower prices for calls on their own network than for calls to other networks (on-net discounts). Studies on tariff-mediated network effects suggest this is due to large operators using on-net discounts to damage smaller rivals...

  16. Developing A Large-Scale, Collaborative, Productive Geoscience Education Network

    Science.gov (United States)

    Manduca, C. A.; Bralower, T. J.; Egger, A. E.; Fox, S.; Ledley, T. S.; Macdonald, H.; Mcconnell, D. A.; Mogk, D. W.; Tewksbury, B. J.

    2012-12-01

    Over the past 15 years, the geoscience education community has grown substantially and developed broad and deep capacity for collaboration and dissemination of ideas. While this community is best viewed as emergent from complex interactions among changing educational needs and opportunities, we highlight the role of several large projects in the development of a network within this community. In the 1990s, three NSF projects came together to build a robust web infrastructure to support the production and dissemination of on-line resources: On The Cutting Edge (OTCE), Earth Exploration Toolbook, and Starting Point: Teaching Introductory Geoscience. Along with the contemporaneous Digital Library for Earth System Education, these projects engaged geoscience educators nationwide in exploring professional development experiences that produced lasting on-line resources, collaborative authoring of resources, and models for web-based support for geoscience teaching. As a result, a culture developed in the 2000s in which geoscience educators anticipated that resources for geoscience teaching would be shared broadly and that collaborative authoring would be productive and engaging. By this time, a diverse set of examples demonstrated the power of the web infrastructure in supporting collaboration, dissemination and professional development . Building on this foundation, more recent work has expanded both the size of the network and the scope of its work. Many large research projects initiated collaborations to disseminate resources supporting educational use of their data. Research results from the rapidly expanding geoscience education research community were integrated into the Pedagogies in Action website and OTCE. Projects engaged faculty across the nation in large-scale data collection and educational research. The Climate Literacy and Energy Awareness Network and OTCE engaged community members in reviewing the expanding body of on-line resources. Building Strong

  17. The Multi-Scale Network Landscape of Collaboration.

    Directory of Open Access Journals (Sweden)

    Arram Bae

    Full Text Available Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena--which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists.

  18. The Multi-Scale Network Landscape of Collaboration.

    Science.gov (United States)

    Bae, Arram; Park, Doheum; Ahn, Yong-Yeol; Park, Juyong

    2016-01-01

    Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena--which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists.

  19. Development of large-scale functional brain networks in children.

    Directory of Open Access Journals (Sweden)

    Kaustubh Supekar

    2009-07-01

    Full Text Available The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y and 22 young-adults (ages 19-22 y. Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.

  20. A multi-scale network method for two-phase flow in porous media

    International Nuclear Information System (INIS)

    Khayrat, Karim; Jenny, Patrick

    2017-01-01

    Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.

  1. A multi-scale network method for two-phase flow in porous media

    Energy Technology Data Exchange (ETDEWEB)

    Khayrat, Karim, E-mail: khayratk@ifd.mavt.ethz.ch; Jenny, Patrick

    2017-08-01

    Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.

  2. Exact Solutions of a Generalized Weighted Scale Free Network

    Directory of Open Access Journals (Sweden)

    Li Tan

    2013-01-01

    Full Text Available We investigate a class of generalized weighted scale-free networks, where the new vertex connects to m pairs of vertices selected preferentially. The key contribution of this paper is that, from the standpoint of random processes, we provide rigorous analytic solutions for the steady state distributions, including the vertex degree distribution, the vertex strength distribution and the edge weight distribution. Numerical simulations indicate that this network model yields three power law distributions for the vertex degrees, vertex strengths and edge weights, respectively.

  3. Anomalous Metapopulation Dynamics on Scale-Free Networks

    Science.gov (United States)

    Fedotov, Sergei; Stage, Helena

    2017-03-01

    We model transport of individuals across a heterogeneous scale-free network where a few weakly connected nodes exhibit heavy-tailed residence times. Using the empirical law of the axiom of cumulative inertia and fractional analysis, we show that "anomalous cumulative inertia" overpowers highly connected nodes in attracting network individuals. This fundamentally challenges the classical result that individuals tend to accumulate in high-order nodes. The derived residence time distribution has a nontrivial U shape which we encounter empirically across human residence and employment times.

  4. Traffic dynamics in scale-free networks with limited buffers and decongestion strategy

    International Nuclear Information System (INIS)

    Wu Zhixi; Wang Wenxu; Yeung, K.-H.

    2008-01-01

    We studied the information traffic in Barabasi-Albert scale-free networks wherein each node has a finite queue length to store the packets. It is found that in the case of the shortest path routing strategy, the networks undergo a first-order phase transition, i.e. from a free flow state to a full congestion state, with increasing packet generation rate. We also incorporate the random effect (namely random selection of a neighbor to deliver packets) as well as a control method (namely the packet-dropping strategy of the congested nodes after some delay time T) into the routing protocol to test the traffic capacity of the heterogeneous networks. It is shown that there exists an optimal value of T for the networks to achieve the best handling ability, and the presence of an appropriate random effect also contributes to the performance of the networks

  5. Complex modular structure of large-scale brain networks

    Science.gov (United States)

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

    2009-06-01

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

  6. A characterization of scale invariant responses in enzymatic networks.

    Directory of Open Access Journals (Sweden)

    Maja Skataric

    Full Text Available An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO, whose validity we show is both necessary and sufficient for scale invariance of three-node enzymatic networks (and sufficient for any number of nodes. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions.

  7. Scale-integrated Network Hubs of the White Matter Structural Network.

    Science.gov (United States)

    Kwon, Hunki; Choi, Yong-Ho; Seo, Sang Won; Lee, Jong-Min

    2017-05-26

    The 'human connectome' concept has been proposed to significantly increase our understanding of how functional brain states emerge from their underlying structural substrates. Especially, the network hub has been considered one of the most important topological properties to interpret a network as a complex system. However, previous structural brain connectome studies have reported network hub regions based on various nodal resolutions. We hypothesized that brain network hubs should be determined considering various nodal scales in a certain range. We tested our hypothesis using the hub strength determined by the mean of the "hubness" values over a range of nodal scales. Some regions of the precuneus, superior occipital gyrus, and superior parietal gyrus in a bilaterally symmetric fashion had a relatively higher level of hub strength than other regions. These regions had a tendency of increasing contributions to local efficiency than other regions. We proposed a methodological framework to detect network hubs considering various nodal scales in a certain range. This framework might provide a benefit in the detection of important brain regions in the network.

  8. Validation of the Social Networking Activity Intensity Scale among Junior Middle School Students in China.

    Science.gov (United States)

    Li, Jibin; Lau, Joseph T F; Mo, Phoenix K H; Su, Xuefen; Wu, Anise M S; Tang, Jie; Qin, Zuguo

    2016-01-01

    Online social networking use has been integrated into adolescents' daily life and the intensity of online social networking use may have important consequences on adolescents' well-being. However, there are few validated instruments to measure social networking use intensity. The present study aims to develop the Social Networking Activity Intensity Scale (SNAIS) and validate it among junior middle school students in China. A total of 910 students who were social networking users were recruited from two junior middle schools in Guangzhou, and 114 students were retested after two weeks to examine the test-retest reliability. The psychometrics of the SNAIS were estimated using appropriate statistical methods. Two factors, Social Function Use Intensity (SFUI) and Entertainment Function Use Intensity (EFUI), were clearly identified by both exploratory and confirmatory factor analyses. No ceiling or floor effects were observed for the SNAIS and its two subscales. The SNAIS and its two subscales exhibited acceptable reliability (Cronbach's alpha = 0.89, 0.90 and 0.60, and test-retest Intra-class Correlation Coefficient = 0.85, 0.87 and 0.67 for Overall scale, SFUI and EFUI subscale, respectively, pnetworking, social networking addiction, Internet addiction, and characteristics related to social networking use. The SNAIS is an easily self-administered scale with good psychometric properties. It would facilitate more research in this field worldwide and specifically in the Chinese population.

  9. A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks

    Science.gov (United States)

    Zhou, Mingxing; Liu, Jing

    2014-09-01

    The robustness of the infrastructure of various real-life systems, which can be represented by networks and manifests the scale-free property, is of great importance. Thus, in this paper, a new memetic algorithm (MA), which is a type of effective optimization method combining both global and local searches, is proposed to enhance the robustness of scale-free (RSF) networks against malicious attacks (MA) without changing the degree distribution. The proposed algorithm is abbreviated as MA-RSF MA. Especially, with the intrinsic properties of the problem of optimizing network structure in mind, a crossover operator which can perform global search and a local search operator are designed. In the experiments, both synthetic scale-free networks and real-world networks, like the EU power grid network and the real Internet at the level of autonomous system (AS), are used. MA-RSFMA shows a strong ability in searching for the most robust network structure, and clearly outperforms existing local search methods.

  10. Global stability analysis and robust design of multi-time-scale biological networks under parametric uncertainties.

    Science.gov (United States)

    Meyer-Baese, Anke; Koshkouei, Ali J; Emmett, Mark R; Goodall, David P

    2009-01-01

    Biological networks are prone to internal parametric fluctuations and external noises. Robustness represents a crucial property of these networks, which militates the effects of internal fluctuations and external noises. In this paper biological networks are formulated as coupled nonlinear differential systems operating at different time-scales under vanishing perturbations. In contrast to previous work viewing biological parametric uncertain systems as perturbations to a known nominal linear system, the perturbed biological system is modeled as nonlinear perturbations to a known nonlinear idealized system and is represented by two time-scales (subsystems). In addition, conditions for the existence of a global uniform attractor of the perturbed biological system are presented. By using an appropriate Lyapunov function for the coupled system, a maximal upper bound for the fast time-scale associated with the fast state is derived. The proposed robust system design principles are potentially applicable to robust biosynthetic network design. Finally, two examples of two important biological networks, a neural network and a gene regulatory network, are presented to illustrate the applicability of the developed theoretical framework.

  11. Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

    OpenAIRE

    Mart?n-Jim?nez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; Gonz?lez, Janneth

    2017-01-01

    Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework t...

  12. The large-scale organization of the hadron decay network

    International Nuclear Information System (INIS)

    Xu, Xinping; Liu, Feng

    2008-01-01

    The standard model of particle physics predicts a complex structure of decay modes for hadrons, which opens up an avenue for observing the internal forces governing the decay dynamics. In this paper, we present the decay modes of hadrons as a network in which the nodes are particles and directed links are pointing from the mother particles to daughter particles. Using the database of decay modes collected from the Particle Data Group, we try to unveil the topological structure and possible intrinsic nature of hadron decays in the light of recent investigations of complex networks. We study distributions of the numbers of daughter and mother particles, and explore scaling laws that may govern the underlying decay structure of the system. We find that it is a small-world network with symmetrical structure. We also study the influence of constraints arising from conservation laws on the network structure, and our analysis suggests that the constraints of conservations of momentum–energy, charge, lepton number and baryon number play important roles in the topology of the decay network. Finally, we classify the hadrons into communities according to their quark component, and uncover the relationship between the particle roles and connection patterns in the communities

  13. The African Field Epidemiology Network - Networking for effective ...

    African Journals Online (AJOL)

    The African Field Epidemiology Network - Networking for effective field epidemiology capacity building and service delivery. Sheba Nakacubo Gitta, David Mukanga, Rebecca Babirye, Melissa Dahlke, Mufuta Tshimanga, Peter Nsubuga ...

  14. Multi-scale nonlinear constitutive models using artificial neural networks

    Science.gov (United States)

    Kim, Hoan-Kee

    This study presents a new approach for nonlinear multi-scale constitutive models using artificial neural networks (ANNs). Three ANN classes are proposed to characterize the nonlinear multi-axial stress-strain behavior of metallic, polymeric, and fiber reinforced polymeric (FRP) materials, respectively. Load-displacement responses from nanoindentation of metallic and polymeric materials are used to train new generation of dimensionless ANN models with different micro-structural properties as additional variables to the load-deflection. The proposed ANN models are effective in inverse-problems set to back-calculate in-situ material parameters from given overall nanoindentation test data with/without time-dependent material behavior. Towards that goal, nanoindentation tests have been performed for silicon (Si) substrate with/without a copper (Cu) film. Nanoindentation creep test data, available in the literature for Polycarbonate substrate, are used in these inverse problems. The predicted properties from the ANN models can also be used to calibrate classical constitutive parameters. The third class of ANN models is used to generate the effective multi-axial stress-strain behavior of FRP composites under plane-stress conditions. The training data are obtained from coupon tests performed in this study using off-axis tension/compression and pure shear tests for pultruded FRP E-glass/polyester composite systems. It is shown that the trained nonlinear ANN model can be directly coupled with finite-element (FE) formulation as a material model at the Gaussian integration points of each layered-shell element. This FE-ANN modeling approach is applied to simulate an FRP plate with an open-hole and compared with experimental results. Micromechanical nonlinear ANN models with damage formulation are also formulated and trained using simulated FE modeling of the periodic microstructure. These new multi-scale ANN constitutive models are effective and can be extended by including

  15. Research on Large-Scale Road Network Partition and Route Search Method Combined with Traveler Preferences

    Directory of Open Access Journals (Sweden)

    De-Xin Yu

    2013-01-01

    Full Text Available Combined with improved Pallottino parallel algorithm, this paper proposes a large-scale route search method, which considers travelers’ route choice preferences. And urban road network is decomposed into multilayers effectively. Utilizing generalized travel time as road impedance function, the method builds a new multilayer and multitasking road network data storage structure with object-oriented class definition. Then, the proposed path search algorithm is verified by using the real road network of Guangzhou city as an example. By the sensitive experiments, we make a comparative analysis of the proposed path search method with the current advanced optimal path algorithms. The results demonstrate that the proposed method can increase the road network search efficiency by more than 16% under different search proportion requests, node numbers, and computing process numbers, respectively. Therefore, this method is a great breakthrough in the guidance field of urban road network.

  16. On cost-effective communication network designing

    Science.gov (United States)

    Zhang, Guo-Qiang

    2010-02-01

    How to efficiently design a communication network is a paramount task for network designing and engineering. It is, however, not a single objective optimization process as perceived by most previous researches, i.e., to maximize its transmission capacity, but a multi-objective optimization process, with lowering its cost to be another important objective. These two objectives are often contradictive in that optimizing one objective may deteriorate the other. After a deep investigation of the impact that network topology, node capability scheme and routing algorithm as well as their interplays have on the two objectives, this letter presents a systematic approach to achieve a cost-effective design by carefully choosing the three designing aspects. Only when routing algorithm and node capability scheme are elegantly chosen can BA-like scale-free networks have the potential of achieving good tradeoff between the two objectives. Random networks, on the other hand, have the built-in character for a cost-effective design, especially when other aspects cannot be determined beforehand.

  17. Sparse cliques trump scale-free networks in coordination and competition

    Science.gov (United States)

    Gianetto, David A.; Heydari, Babak

    2016-02-01

    Cooperative behavior, a natural, pervasive and yet puzzling phenomenon, can be significantly enhanced by networks. Many studies have shown how global network characteristics affect cooperation; however, it is difficult to understand how this occurs based on global factors alone, low-level network building blocks, or motifs are necessary. In this work, we systematically alter the structure of scale-free and clique networks and show, through a stochastic evolutionary game theory model, that cooperation on cliques increases linearly with community motif count. We further show that, for reactive stochastic strategies, network modularity improves cooperation in the anti-coordination Snowdrift game and the Prisoner’s Dilemma game but not in the Stag Hunt coordination game. We also confirm the negative effect of the scale-free graph on cooperation when effective payoffs are used. On the flip side, clique graphs are highly cooperative across social environments. Adding cycles to the acyclic scale-free graph increases cooperation when multiple games are considered; however, cycles have the opposite effect on how forgiving agents are when playing the Prisoner’s Dilemma game.

  18. Multiple synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses

    International Nuclear Information System (INIS)

    Liu, Chen; Wang, Jiang; Wang, Lin; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

    2014-01-01

    Highlights: • Synchronization transitions in hybrid scale-free neuronal networks are investigated. • Multiple synchronization transitions can be induced by the time delay. • Effect of synchronization transitions depends on the ratio of the electrical and chemical synapses. • Coupling strength and the density of inter-neuronal links can enhance the synchronization. -- Abstract: The impacts of information transmission delay on the synchronization transitions in scale-free neuronal networks with electrical and chemical hybrid synapses are investigated. Numerical results show that multiple appearances of synchronization regions transitions can be induced by different information transmission delays. With the time delay increasing, the synchronization of neuronal activities can be enhanced or destroyed, irrespective of the probability of chemical synapses in the whole hybrid neuronal network. In particular, for larger probability of electrical synapses, the regions of synchronous activities appear broader with stronger synchronization ability of electrical synapses compared with chemical ones. Moreover, it can be found that increasing the coupling strength can promote synchronization monotonously, playing the similar role of the increasing the probability of the electrical synapses. Interestingly, the structures and parameters of the scale-free neuronal networks, especially the structural evolvement plays a more subtle role in the synchronization transitions. In the network formation process, it is found that every new vertex is attached to the more old vertices already present in the network, the more synchronous activities will be emerge

  19. Stochastic opinion formation in scale-free networks

    Science.gov (United States)

    Bartolozzi, M.; Leinweber, D. B.; Thomas, A. W.

    2005-10-01

    The dynamics of opinion formation in large groups of people is a complex nonlinear phenomenon whose investigation is just beginning. Both collective behavior and personal views play an important role in this mechanism. In the present work we mimic the dynamics of opinion formation of a group of agents, represented by two states ±1 , as a stochastic response of each agent to the opinion of his/her neighbors in the social network and to feedback from the average opinion of the whole. In the light of recent studies, a scale-free Barabási-Albert network has been selected to simulate the topology of the interactions. A turbulentlike dynamics, characterized by an intermittent behavior, is observed for a certain range of the model parameters. The problem of uncertainty in decision taking is also addressed both from a topological point of view, using random and targeted removal of agents from the network, and by implementing a three-state model, where the third state, zero, is related to the information available to each agent. Finally, the results of the model are tested against the best known network of social interactions: the stock market. A time series of daily closures of the Dow-Jones index has been used as an indicator of the possible applicability of our model in the financial context. Good qualitative agreement is found.

  20. Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities

    Directory of Open Access Journals (Sweden)

    Valerio Santangelo

    2018-02-01

    brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.

  1. Coordinated SLNR based Precoding in Large-Scale Heterogeneous Networks

    KAUST Repository

    Boukhedimi, Ikram

    2017-03-06

    This work focuses on the downlink of large-scale two-tier heterogeneous networks composed of a macro-cell overlaid by micro-cell networks. Our interest is on the design of coordinated beamforming techniques that allow to mitigate the inter-cell interference. Particularly, we consider the case in which the coordinating base stations (BSs) have imperfect knowledge of the channel state information. Under this setting, we propose a regularized SLNR based precoding design in which the regularization factor is used to allow better resilience with respect to the channel estimation errors. Based on tools from random matrix theory, we provide an analytical analysis of the SINR and SLNR performances. These results are then exploited to propose a proper setting of the regularization factor. Simulation results are finally provided in order to validate our findings and to confirm the performance of the proposed precoding scheme.

  2. Optical network scaling: roles of spectral and spatial aggregation.

    Science.gov (United States)

    Arık, Sercan Ö; Ho, Keang-Po; Kahn, Joseph M

    2014-12-01

    As the bit rates of routed data streams exceed the throughput of single wavelength-division multiplexing channels, spectral and spatial traffic aggregation become essential for optical network scaling. These aggregation techniques reduce network routing complexity by increasing spectral efficiency to decrease the number of fibers, and by increasing switching granularity to decrease the number of switching components. Spectral aggregation yields a modest decrease in the number of fibers but a substantial decrease in the number of switching components. Spatial aggregation yields a substantial decrease in both the number of fibers and the number of switching components. To quantify routing complexity reduction, we analyze the number of multi-cast and wavelength-selective switches required in a colorless, directionless and contentionless reconfigurable optical add-drop multiplexer architecture. Traffic aggregation has two potential drawbacks: reduced routing power and increased switching component size.

  3. Identifying large-scale brain networks in fragile X syndrome.

    Science.gov (United States)

    Hall, Scott S; Jiang, Heidi; Reiss, Allan L; Greicius, Michael D

    2013-11-01

    Fragile X syndrome (FXS) is an X-linked neurogenetic disorder characterized by a cognitive and behavioral phenotype resembling features of autism spectrum disorder. Until now, research has focused largely on identifying regional differences in brain structure and function between individuals with FXS and various control groups. Very little is known about the large-scale brain networks that may underlie the cognitive and behavioral symptoms of FXS. To identify large-scale, resting-state networks in FXS that differ from control individuals matched on age, IQ, and severity of behavioral and cognitive symptoms. Cross-sectional, in vivo neuroimaging study conducted in an academic medical center. Participants (aged 10-23 years) included 17 males and females with FXS and 16 males and females serving as controls. Univariate voxel-based morphometric analyses, fractional amplitude of low-frequency fluctuations (fALFF) analysis, and group-independent component analysis with dual regression. Patients with FXS showed decreased functional connectivity in the salience, precuneus, left executive control, language, and visuospatial networks compared with controls. Decreased fALFF in the bilateral insular, precuneus, and anterior cingulate cortices also was found in patients with FXS compared with control participants. Furthermore, fALFF in the left insular cortex was significantly positively correlated with IQ in patients with FXS. Decreased gray matter density, resting-state connectivity, and fALFF converged in the left insular cortex in patients with FXS. Fragile X syndrome results in widespread reductions in functional connectivity across multiple cognitive and affective brain networks. Converging structural and functional abnormalities in the left insular cortex, a region also implicated in individuals diagnosed with autism spectrum disorder, suggests that insula integrity and connectivity may be compromised in FXS. This method could prove useful in establishing an imaging

  4. Network-state modulation of power-law frequency-scaling in visual cortical neurons.

    Science.gov (United States)

    El Boustani, Sami; Marre, Olivier; Béhuret, Sébastien; Baudot, Pierre; Yger, Pierre; Bal, Thierry; Destexhe, Alain; Frégnac, Yves

    2009-09-01

    Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population signals measured

  5. Network-state modulation of power-law frequency-scaling in visual cortical neurons.

    Directory of Open Access Journals (Sweden)

    Sami El Boustani

    2009-09-01

    Full Text Available Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population

  6. Expanding Business With Effective Networking

    OpenAIRE

    Lehto, Tanja

    2012-01-01

    The aim of the thesis process was to investigate networking, personal selling and Russian markets in order to create a clear view of what is vital for effective networking in Russia. The objective of the study was to observe the case company´s readiness for expanding operations to Russia as well as analyzing the company´s existing values and how they could be developed further. Additionally the purpose of the thesis was to give recommendations of the factors needed to be developed and suggest...

  7. Generative models of rich clubs in Hebbian neuronal networks and large-scale human brain networks.

    Science.gov (United States)

    Vértes, Petra E; Alexander-Bloch, Aaron; Bullmore, Edward T

    2014-10-05

    Rich clubs arise when nodes that are 'rich' in connections also form an elite, densely connected 'club'. In brain networks, rich clubs incur high physical connection costs but also appear to be especially valuable to brain function. However, little is known about the selection pressures that drive their formation. Here, we take two complementary approaches to this question: firstly we show, using generative modelling, that the emergence of rich clubs in large-scale human brain networks can be driven by an economic trade-off between connection costs and a second, competing topological term. Secondly we show, using simulated neural networks, that Hebbian learning rules also drive the emergence of rich clubs at the microscopic level, and that the prominence of these features increases with learning time. These results suggest that Hebbian learning may provide a neuronal mechanism for the selection of complex features such as rich clubs. The neural networks that we investigate are explicitly Hebbian, and we argue that the topological term in our model of large-scale brain connectivity may represent an analogous connection rule. This putative link between learning and rich clubs is also consistent with predictions that integrative aspects of brain network organization are especially important for adaptive behaviour. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  8. Self-Organization in Coupled Map Scale-Free Networks

    Science.gov (United States)

    Liang, Xiao-Ming; Lü, Hua-Ping; Liu, Zong-Hua

    2008-02-01

    We study the self-organization of phase synchronization in coupled map scale-free networks with chaotic logistic map at each node and find that a variety of ordered spatiotemporal patterns emerge spontaneously in a regime of coupling strength. These ordered behaviours will change with the increase of the average links and are robust to both the system size and parameter mismatch. A heuristic theory is given to explain the mechanism of self-organization and to figure out the regime of coupling for the ordered spatiotemporal patterns.

  9. Epidemic mitigation via awareness propagation in communication networks: the role of time scales

    Science.gov (United States)

    Wang, Huijuan; Chen, Chuyi; Qu, Bo; Li, Daqing; Havlin, Shlomo

    2017-07-01

    The participation of individuals in multi-layer networks allows for feedback between network layers, opening new possibilities to mitigate epidemic spreading. For instance, the spread of a biological disease such as Ebola in a physical contact network may trigger the propagation of the information related to this disease in a communication network, e.g. an online social network. The information propagated in the communication network may increase the awareness of some individuals, resulting in them avoiding contact with their infected neighbors in the physical contact network, which might protect the population from the infection. In this work, we aim to understand how the time scale γ of the information propagation (speed that information is spread and forgotten) in the communication network relative to that of the epidemic spread (speed that an epidemic is spread and cured) in the physical contact network influences such mitigation using awareness information. We begin by proposing a model of the interaction between information propagation and epidemic spread, taking into account the relative time scale γ. We analytically derive the average fraction of infected nodes in the meta-stable state for this model (i) by developing an individual-based mean-field approximation (IBMFA) method and (ii) by extending the microscopic Markov chain approach (MMCA). We show that when the time scale γ of the information spread relative to the epidemic spread is large, our IBMFA approximation is better compared to MMCA near the epidemic threshold, whereas MMCA performs better when the prevalence of the epidemic is high. Furthermore, we find that an optimal mitigation exists that leads to a minimal fraction of infected nodes. The optimal mitigation is achieved at a non-trivial relative time scale γ, which depends on the rate at which an infected individual becomes aware. Contrary to our intuition, information spread too fast in the communication network could reduce the

  10. Modeling Peer-to-Peer Botnet on Scale-Free Network

    Directory of Open Access Journals (Sweden)

    Liping Feng

    2014-01-01

    Full Text Available Peer-to-peer (P2P botnets have emerged as one of the serious threats to Internet security. To prevent effectively P2P botnet, in this paper, a mathematical model which combines the scale-free trait of Internet with the formation of P2P botnet is presented. Explicit mathematical analysis demonstrates that the model has a globally stable endemic equilibrium when infection rate is greater than a critical value. Meanwhile, we find that, in scale-free network, the critical value is very little. Hence, it is unrealistic to completely dispel the P2P botnet. Numerical simulations show that one can take effective countermeasures to reduce the scale of P2P botnet or delay its outbreak. Our findings can provide meaningful instruction to network security management.

  11. Large scale network management. Condition indicators for network stations, high voltage power conductions and cables

    International Nuclear Information System (INIS)

    Eggen, Arnt Ove; Rolfseng, Lars; Langdal, Bjoern Inge

    2006-02-01

    In the Strategic Institute Programme (SIP) 'Electricity Business enters e-business (eBee)' SINTEF Energy research has developed competency that can help the energy business employ ICT systems and computer technology in an improved way. Large scale network management is now a reality, and it is characterized by large entities with increasing demands on efficiency and quality. These are goals that can only be reached by using ICT systems and computer technology in a more clever way than what is the case today. At the same time it is important that knowledge held by experienced co-workers is consulted when formal rules for evaluations and decisions in ICT systems are developed. In this project an analytical concept for evaluation of networks based information in different ICT systems has been developed. The method estimating the indicators to describe different conditions in a network is general, and indicators can be made to fit different levels of decision and network levels, for example network station, transformer circuit, distribution network and regional network. Moreover, the indicators can contain information about technical aspects, economy and HSE. An indicator consists of an indicator name, an indicator value, and an indicator colour based on a traffic-light analogy to indicate a condition or a quality for the indicator. Values on one or more indicators give an impression of important conditions in the network, and make up the basis for knowing where more detailed evaluations have to be conducted before a final decision on for example maintenance or renewal is made. A prototype has been developed for testing the new method. The prototype has been developed in Excel, and especially designed for analysing transformer circuits in a distribution network. However, the method is a general one, and well suited for implementation in a commercial computer system (ml)

  12. Green Supply Chain Network Design with Economies of Scale and Environmental Concerns

    Directory of Open Access Journals (Sweden)

    Dezhi Zhang

    2017-01-01

    Full Text Available This study considers a design problem in the supply chain network of an assembly manufacturing enterprise with economies of scale and environmental concerns. The study aims to obtain a rational tradeoff between environmental influence and total cost. A mixed-integer nonlinear programming model is developed to determine the optimal location and size of regional distribution centers (RDCs and the investment of environmental facilities considering the effects of economies of scale and CO2 emission taxes. Numerical examples are provided to illustrate the applications of the proposed model. Moreover, comparative analysis of the related key parameters is conducted (i.e., carbon emission tax, logistics demand of customers, and economies of scale of RDC, to explore the corresponding effects on the network design of a green supply chain. Moreover, the proposed model is applied in an actual case—network design of a supply chain of an electric meter company in China. Findings show that (i the optimal location of RDCs is affected by the demand of customers and the level of economies of scale and that (ii the introduction of CO2 emission taxes will change the structure of a supply chain network, which will decrease CO2 emissions per unit shipment.

  13. Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

    Directory of Open Access Journals (Sweden)

    Lorenzo L. Pesce

    2013-01-01

    Full Text Available Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons and processor pool sizes (1 to 256 processors. Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

  14. Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

    Science.gov (United States)

    Pesce, Lorenzo L; Lee, Hyong C; Hereld, Mark; Visser, Sid; Stevens, Rick L; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

  15. Neural Correlates of Unconsciousness in Large-Scale Brain Networks.

    Science.gov (United States)

    Mashour, George A; Hudetz, Anthony G

    2018-03-01

    The biological basis of consciousness is one of the most challenging and fundamental questions in 21st century science. A related pursuit aims to identify the neural correlates and causes of unconsciousness. We review current trends in the investigation of physiological, pharmacological, and pathological states of unconsciousness at the level of large-scale functional brain networks. We focus on the roles of brain connectivity, repertoire, graph-theoretical techniques, and neural dynamics in understanding the functional brain disconnections and reduced complexity that appear to characterize these states. Persistent questions in the field, such as distinguishing true correlates, linking neural scales, and understanding differential recovery patterns, are also addressed. Copyright © 2018 Elsevier Ltd. All rights reserved.

  16. Robustness of scale-free networks with various parameters against cascading failures

    Science.gov (United States)

    Yang, Zhirou; Liu, Jing

    2018-02-01

    Many crucial real-world networks could be modeled as scale-free networks, which play an important role in the human society. Once these functional network systems suffer from cascading failures, they may lead to the malfunction of the rest part of networks. In recent years, the researches on cascading failures of scale-free networks have drawn great attention, and many studies focused on modeling the cascading phenomena and studying how to improve the robustness of networks against failures. However, the scale-free networks used in most existing studies are with fixed network parameters including scaling exponent and assortativity, which is segmentary for depicting the functionality of networked systems comprehensively. Therefore, in this paper, a series of generated scale-free networks with a certain range of parameters is adopted to evaluate the robustness against cascading failures. In addition, to make an accurate description of the ability of scale-free networks against cascading failures, we propose a link-based robustness index. The results show that influenced by the network structure, the enlargement of assortativity makes the networks weaker to resist node-based cascading failures, yet the impact on promoting link-based robustness is not clear enough. With higher scaling exponents, the tolerance of scale-free networks against link-based cascading failures decreases, however, it does not show obvious relation to node-based robustness.

  17. Structure-preserving model reduction of large-scale logistics networks. Applications for supply chains

    Science.gov (United States)

    Scholz-Reiter, B.; Wirth, F.; Dashkovskiy, S.; Makuschewitz, T.; Schönlein, M.; Kosmykov, M.

    2011-12-01

    We investigate the problem of model reduction with a view to large-scale logistics networks, specifically supply chains. Such networks are modeled by means of graphs, which describe the structure of material flow. An aim of the proposed model reduction procedure is to preserve important features within the network. As a new methodology we introduce the LogRank as a measure for the importance of locations, which is based on the structure of the flows within the network. We argue that these properties reflect relative importance of locations. Based on the LogRank we identify subgraphs of the network that can be neglected or aggregated. The effect of this is discussed for a few motifs. Using this approach we present a meta algorithm for structure-preserving model reduction that can be adapted to different mathematical modeling frameworks. The capabilities of the approach are demonstrated with a test case, where a logistics network is modeled as a Jackson network, i.e., a particular type of queueing network.

  18. Validation of the Social Networking Activity Intensity Scale among Junior Middle School Students in China.

    Directory of Open Access Journals (Sweden)

    Jibin Li

    Full Text Available Online social networking use has been integrated into adolescents' daily life and the intensity of online social networking use may have important consequences on adolescents' well-being. However, there are few validated instruments to measure social networking use intensity. The present study aims to develop the Social Networking Activity Intensity Scale (SNAIS and validate it among junior middle school students in China.A total of 910 students who were social networking users were recruited from two junior middle schools in Guangzhou, and 114 students were retested after two weeks to examine the test-retest reliability. The psychometrics of the SNAIS were estimated using appropriate statistical methods.Two factors, Social Function Use Intensity (SFUI and Entertainment Function Use Intensity (EFUI, were clearly identified by both exploratory and confirmatory factor analyses. No ceiling or floor effects were observed for the SNAIS and its two subscales. The SNAIS and its two subscales exhibited acceptable reliability (Cronbach's alpha = 0.89, 0.90 and 0.60, and test-retest Intra-class Correlation Coefficient = 0.85, 0.87 and 0.67 for Overall scale, SFUI and EFUI subscale, respectively, p<0.001. As expected, the SNAIS and its subscale scores were correlated significantly with emotional connection to social networking, social networking addiction, Internet addiction, and characteristics related to social networking use.The SNAIS is an easily self-administered scale with good psychometric properties. It would facilitate more research in this field worldwide and specifically in the Chinese population.

  19. Marine Polysaccharide Networks and Diatoms at the Nanometric Scale

    Directory of Open Access Journals (Sweden)

    Tea Mišić Radić

    2013-10-01

    Full Text Available Despite many advances in research on photosynthetic carbon fixation in marine diatoms, the biophysical and biochemical mechanisms of extracellular polysaccharide production remain significant challenges to be resolved at the molecular scale in order to proceed toward an understanding of their functions at the cellular level, as well as their interactions and fate in the ocean. This review covers studies of diatom extracellular polysaccharides using atomic force microscopy (AFM imaging and the quantification of physical forces. Following a brief summary of the basic principle of the AFM experiment and the first AFM studies of diatom extracellular polymeric substance (EPS, we focus on the detection of supramolecular structures in polysaccharide systems produced by marine diatoms. Extracellular polysaccharide fibrils, attached to the diatom cell wall or released into the surrounding seawater, form distinct supramolecular assemblies best described as gel networks. AFM makes characterization of the diatom polysaccharide networks at the micro and nanometric scales and a clear distinction between the self-assembly and self-organization of these complex systems in marine environments possible.

  20. Directed partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.

    Directory of Open Access Journals (Sweden)

    Yinyin Yuan

    Full Text Available Inferring regulatory relationships among many genes based on their temporal variation in transcript abundance has been a popular research topic. Due to the nature of microarray experiments, classical tools for time series analysis lose power since the number of variables far exceeds the number of the samples. In this paper, we describe some of the existing multivariate inference techniques that are applicable to hundreds of variables and show the potential challenges for small-sample, large-scale data. We propose a directed partial correlation (DPC method as an efficient and effective solution to regulatory network inference using these data. Specifically for genomic data, the proposed method is designed to deal with large-scale datasets. It combines the efficiency of partial correlation for setting up network topology by testing conditional independence, and the concept of Granger causality to assess topology change with induced interruptions. The idea is that when a transcription factor is induced artificially within a gene network, the disruption of the network by the induction signifies a genes role in transcriptional regulation. The benchmarking results using GeneNetWeaver, the simulator for the DREAM challenges, provide strong evidence of the outstanding performance of the proposed DPC method. When applied to real biological data, the inferred starch metabolism network in Arabidopsis reveals many biologically meaningful network modules worthy of further investigation. These results collectively suggest DPC is a versatile tool for genomics research. The R package DPC is available for download (http://code.google.com/p/dpcnet/.

  1. The effect of aging on network structure

    OpenAIRE

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

    2003-01-01

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

  2. SOCIAL NETWORK EFFECTS ON ROMANTIC RELATIONSHIP

    Directory of Open Access Journals (Sweden)

    Fatma CAN

    2015-06-01

    Full Text Available The main objective of the study was to obtain information about social network variables in order to predict the relational commitment of married individuals and people having dating relationships. For this purpose, social network analysis has been carried out on 134 people having dating relationship and 154 married individuals and then Relationship Stability Scale, Subjective Norm Scale and Social Network Feature Survey prepared by the researcher were used. The results indicated that the approval of the closest social network member and the level of enjoyment of each other’s social network members had the best predictive value for relationship satisfaction and the investment to the relationship. The results also demonstrated that, approval of the social network had a negative impact on the level of the quality of alternatives and it showed that social networks were seen as a barrier function to have alternative relationships. Furthermore, by dividing social network members into two groups, for the dating group, the approval of the social network was the most significant variable for commitment but in the married group, the need for social network approval was not an important criteria because of having their relatioship already confirmed legally. When social network members were categorised and examined, the closest social network members did not differ by sex, but were varied in terms of relationship types. In the flirt group, one of their friends among his/her social network and their partners’ social network was specified as the closest social network member whereas in the married group, the closest social network member among his/her social network was their mother while it was their sibling among partner’s social network.

  3. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

    Science.gov (United States)

    Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

    2016-09-19

    Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

  4. A Networked Sensor System for the Analysis of Plot-Scale Hydrology

    Directory of Open Access Journals (Sweden)

    German Villalba

    2017-03-01

    Full Text Available This study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN. The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and soil water potential sensors and self-made sap flow sensors, across a heterogeneous deployment comprised of MICAz, IRIS and TelosB wireless motes. A low-cost sensor board and software driver was developed for communicating with the analog and digital sensors. Innovative techniques (e.g., balanced energy efficient routing and heterogeneous over-the-air mote reprogramming maintained high success rates (>96% and enabled effective software updating, throughout the large-scale heterogeneous WSN. The edaphic properties monitored by the network showed strong agreement with data logger measurements and were fitted to pedotransfer functions for estimating local soil hydraulic properties. Furthermore, sap flow measurements, scaled to tree stand transpiration, were found to be at or below potential evapotranspiration estimates. While outdoor WSNs still present numerous challenges, the ASWP testbed proves to be an effective and (relatively low-cost environmental monitoring solution and represents a step towards developing a platform for monitoring and quantifying statistically relevant environmental parameters from large-scale network deployments.

  5. A Networked Sensor System for the Analysis of Plot-Scale Hydrology

    Science.gov (United States)

    Villalba, German; Plaza, Fernando; Zhong, Xiaoyang; Davis, Tyler W.; Navarro, Miguel; Li, Yimei; Slater, Thomas A.; Liang, Yao; Liang, Xu

    2017-01-01

    This study presents the latest updates to the Audubon Society of Western Pennsylvania (ASWP) testbed, a $50,000 USD, 104-node outdoor multi-hop wireless sensor network (WSN). The network collects environmental data from over 240 sensors, including the EC-5, MPS-1 and MPS-2 soil moisture and soil water potential sensors and self-made sap flow sensors, across a heterogeneous deployment comprised of MICAz, IRIS and TelosB wireless motes. A low-cost sensor board and software driver was developed for communicating with the analog and digital sensors. Innovative techniques (e.g., balanced energy efficient routing and heterogeneous over-the-air mote reprogramming) maintained high success rates (>96%) and enabled effective software updating, throughout the large-scale heterogeneous WSN. The edaphic properties monitored by the network showed strong agreement with data logger measurements and were fitted to pedotransfer functions for estimating local soil hydraulic properties. Furthermore, sap flow measurements, scaled to tree stand transpiration, were found to be at or below potential evapotranspiration estimates. While outdoor WSNs still present numerous challenges, the ASWP testbed proves to be an effective and (relatively) low-cost environmental monitoring solution and represents a step towards developing a platform for monitoring and quantifying statistically relevant environmental parameters from large-scale network deployments. PMID:28335534

  6. A semi-analytical model for the flow behavior of naturally fractured formations with multi-scale fracture networks

    Science.gov (United States)

    Jia, Pin; Cheng, Linsong; Huang, Shijun; Wu, Yonghui

    2016-06-01

    This paper presents a semi-analytical model for the flow behavior of naturally fractured formations with multi-scale fracture networks. The model dynamically couples an analytical dual-porosity model with a numerical discrete fracture model. The small-scale fractures with the matrix are idealized as a dual-porosity continuum and an analytical flow solution is derived based on source functions in Laplace domain. The large-scale fractures are represented explicitly as the major fluid conduits and the flow is numerically modeled, also in Laplace domain. This approach allows us to include finer details of the fracture network characteristics while keeping the computational work manageable. For example, the large-scale fracture network may have complex geometry and varying conductivity, and the computations can be done at predetermined, discrete times, without any grids in the dual-porosity continuum. The validation of the semi-analytical model is demonstrated in comparison to the solution of ECLIPSE reservoir simulator. The simulation is fast, gridless and enables rapid model setup. On the basis of the model, we provide detailed analysis of the flow behavior of a horizontal production well in fractured reservoir with multi-scale fracture networks. The study has shown that the system may exhibit six flow regimes: large-scale fracture network linear flow, bilinear flow, small-scale fracture network linear flow, pseudosteady-state flow, interporosity flow and pseudoradial flow. During the first four flow periods, the large-scale fracture network behaves as if it only drains in the small-scale fracture network; that is, the effect of the matrix is negligibly small. The characteristics of the bilinear flow and the small-scale fracture network linear flow are predominantly determined by the dimensionless large-scale fracture conductivity. And low dimensionless fracture conductivity will generate large pressure drops in the large-scale fractures surrounding the wellbore. With

  7. THE EFFECT OF TOPOLOGY ON TEMPORAL NETWORK DYNAMICS

    Directory of Open Access Journals (Sweden)

    Valentina Yu. Guleva

    2016-11-01

    Full Text Available The effect of initial network topology on a temporal network dynamics is studied. An example of interbank exposures network is considered. It is modeled with a graph, where banks are represented by nodes and interbank lending is represented by edges. The dynamical processes in аtemporal network are defined by state changes of nodes and lie in edges and nodes addition and deletion in a graph, and modification of node states contribute to network evolution. The algorithm of network modification over the whole evolution period is fixed. We present parameters of random, scale free and small world generative models corresponding to different simulation results with fixed modification algorithms. The influence of initial graph topologies on temporal network dynamics is demonstrated. The results obtained give the possibility to assess time interval before the attainment of unstable topology state, and to estimate an optimal topology for the transition to a steady state under fixed modification algorithms.

  8. 78 FR 7464 - Large Scale Networking (LSN)-Middleware And Grid Interagency Coordination (MAGIC) Team

    Science.gov (United States)

    2013-02-01

    ... NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN)--Middleware And Grid Interagency Coordination (MAGIC) Team AGENCY: The Networking and Information Technology Research and Development (NITRD... middleware, Grid, and cloud projects. The MAGIC Team reports to the Large Scale Networking (LSN) Coordinating...

  9. An efficient method based on the uniformity principle for synthesis of large-scale heat exchanger networks

    International Nuclear Information System (INIS)

    Zhang, Chunwei; Cui, Guomin; Chen, Shang

    2016-01-01

    Highlights: • Two dimensionless uniformity factors are presented to heat exchange network. • The grouping of process streams reduces the computational complexity of large-scale HENS problems. • The optimal sub-network can be obtained by Powell particle swarm optimization algorithm. • The method is illustrated by a case study involving 39 process streams, with a better solution. - Abstract: The optimal design of large-scale heat exchanger networks is a difficult task due to the inherent non-linear characteristics and the combinatorial nature of heat exchangers. To solve large-scale heat exchanger network synthesis (HENS) problems, two dimensionless uniformity factors to describe the heat exchanger network (HEN) uniformity in terms of the temperature difference and the accuracy of process stream grouping are deduced. Additionally, a novel algorithm that combines deterministic and stochastic optimizations to obtain an optimal sub-network with a suitable heat load for a given group of streams is proposed, and is named the Powell particle swarm optimization (PPSO). As a result, the synthesis of large-scale heat exchanger networks is divided into two corresponding sub-parts, namely, the grouping of process streams and the optimization of sub-networks. This approach reduces the computational complexity and increases the efficiency of the proposed method. The robustness and effectiveness of the proposed method are demonstrated by solving a large-scale HENS problem involving 39 process streams, and the results obtained are better than those previously published in the literature.

  10. Scaling law of resistance fluctuations in stationary random resistor networks

    Science.gov (United States)

    Pennetta; Trefan; Reggiani

    2000-12-11

    In a random resistor network we consider the simultaneous evolution of two competing random processes consisting in breaking and recovering the elementary resistors with probabilities W(D) and W(R). The condition W(R)>W(D)/(1+W(D)) leads to a stationary state, while in the opposite case, the broken resistor fraction reaches the percolation threshold p(c). We study the resistance noise of this system under stationary conditions by Monte Carlo simulations. The variance of resistance fluctuations is found to follow a scaling law |p-p(c)|(-kappa(0)) with kappa(0) = 5.5. The proposed model relates quantitatively the defectiveness of a disordered media with its electrical and excess-noise characteristics.

  11. A survey on routing protocols for large-scale wireless sensor networks.

    Science.gov (United States)

    Li, Changle; Zhang, Hanxiao; Hao, Binbin; Li, Jiandong

    2011-01-01

    With the advances in micro-electronics, wireless sensor devices have been made much smaller and more integrated, and large-scale wireless sensor networks (WSNs) based the cooperation among the significant amount of nodes have become a hot topic. "Large-scale" means mainly large area or high density of a network. Accordingly the routing protocols must scale well to the network scope extension and node density increases. A sensor node is normally energy-limited and cannot be recharged, and thus its energy consumption has a quite significant effect on the scalability of the protocol. To the best of our knowledge, currently the mainstream methods to solve the energy problem in large-scale WSNs are the hierarchical routing protocols. In a hierarchical routing protocol, all the nodes are divided into several groups with different assignment levels. The nodes within the high level are responsible for data aggregation and management work, and the low level nodes for sensing their surroundings and collecting information. The hierarchical routing protocols are proved to be more energy-efficient than flat ones in which all the nodes play the same role, especially in terms of the data aggregation and the flooding of the control packets. With focus on the hierarchical structure, in this paper we provide an insight into routing protocols designed specifically for large-scale WSNs. According to the different objectives, the protocols are generally classified based on different criteria such as control overhead reduction, energy consumption mitigation and energy balance. In order to gain a comprehensive understanding of each protocol, we highlight their innovative ideas, describe the underlying principles in detail and analyze their advantages and disadvantages. Moreover a comparison of each routing protocol is conducted to demonstrate the differences between the protocols in terms of message complexity, memory requirements, localization, data aggregation, clustering manner and

  12. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

    Science.gov (United States)

    Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kuehn, Reimer; Sollich, Peter

    2015-01-01

    One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology. PMID:25919796

  13. Regional scale analysis of the altimetric stream network evolution

    Directory of Open Access Journals (Sweden)

    T. Ghizzoni

    2006-01-01

    Full Text Available Floods result from the limited carrying capacity of stream channels when compared to the discharge peak value. The transit of flood waves - with the associated erosion and sedimentation processes - often modifies local stream geometry. In some cases this results in a reduction of the stream carrying capacity, and consequently in an enhancement of the flooding risk. A mathematical model for the prediction of potential altimetric stream network evolution due to erosion and sedimentation processes is here formalized. It works at the regional scale, identifying the tendency of river segments to sedimentation, stability, or erosion. The model builds on geomorphologic concepts, and derives its parameters from extensive surveys. As a case study, tendencies of rivers pertaining to the Valle d'Aosta region are analyzed. Some validation is provided both at regional and local scales of analysis. Local validation is performed both through a mathematical model able to simulate the temporal evolution of the stream profile, and through comparison of the prediction with ante and post-event river surveys, where available. Overall results are strongly encouraging. Possible use of the information derived from the model in the context of flood and landslide hazard mitigation is briefly discussed.

  14. Equilibrium & Nonequilibrium Fluctuation Effects in Biopolymer Networks

    Science.gov (United States)

    Kachan, Devin Michael

    speculate that cells take advantage of this equilibrium effect by tuning near the transition point, where small changes in free cross-linker density will affect large structural rearrangements between free filament networks and networks of bundles. Cells are naturally found far from equilibrium, where the active influx of energy from ATP consumption controls the dynamics. Motor proteins actively generate forces within biopolymer networks, and one may ask how these differ from the random stresses characteristic of equilibrium fluctuations. Besides the trivial observation that the magnitude is independent of temperature, I find that the processive nature of the motors creates a temporally correlated, or colored, noise spectrum. I model the network with a nonlinear scalar elastic theory in the presence of active driving, and study the long distance and large scale properties of the system with renormalization group techniques. I find that there is a new critical point associated with diverging correlation time, and that the colored noise produces novel frequency dependence in the renormalized transport coefficients. Finally, I study marginally elastic solids which have vanishing shear modulus due to the presence of soft modes, modes with zero deformation cost. Although network coordination is a useful metric for determining the mechanical response of random spring networks in mechanical equilibrium, it is insufficient for describing networks under external stress. In particular, under-constrained networks which are fluid-like at zero load will dynamically stiffen at a critical strain, as observed in numerical simulations and experimentally in many biopolymer networks. Drawing upon analogies to the stress induced unjamming of emulsions, I develop a kinetic theory to explain the rigidity transition in spring and filament networks. Describing the dynamic evolution of non-affine deformation via a simple mechanistic picture, I recover the emergent nonlinear strain

  15. Computational Analysis of Topological Survivability of Large-Scale Engineering Networks with Heterogeneous Nodes

    Science.gov (United States)

    Poroseva, Svetlana

    2012-02-01

    The scale and complexity of modern networks, their integration, and the size of population and businesses they have impact on, make their massive damage catastrophic for the society and economy. Such damage is usually caused by adverse events and is not considered by traditional design practices. In the modern society, the likelihood of adverse events has substantially increased. Therefore, there is a need in evaluating the ability of a network to survive such damage. As the network topology is a key factor to consider, the goal of our research is to develop computational tools for quantifying its effect on the network survivability. ``Selfish'' algorithm will be presented that addresses exponential-time complexity associated with the problem of generation and analysis of all fault combinations possible in a given network. The reduction of computational complexity is achieved by mapping an initial network topology with multiple sources and sinks onto a set of simpler smaller topologies with multiple sources and a single sink. Application to the Texas power grid will be considered.

  16. A Topology Visualization Early Warning Distribution Algorithm for Large-Scale Network Security Incidents

    Directory of Open Access Journals (Sweden)

    Hui He

    2013-01-01

    Full Text Available It is of great significance to research the early warning system for large-scale network security incidents. It can improve the network system’s emergency response capabilities, alleviate the cyber attacks’ damage, and strengthen the system’s counterattack ability. A comprehensive early warning system is presented in this paper, which combines active measurement and anomaly detection. The key visualization algorithm and technology of the system are mainly discussed. The large-scale network system’s plane visualization is realized based on the divide and conquer thought. First, the topology of the large-scale network is divided into some small-scale networks by the MLkP/CR algorithm. Second, the sub graph plane visualization algorithm is applied to each small-scale network. Finally, the small-scale networks’ topologies are combined into a topology based on the automatic distribution algorithm of force analysis. As the algorithm transforms the large-scale network topology plane visualization problem into a series of small-scale network topology plane visualization and distribution problems, it has higher parallelism and is able to handle the display of ultra-large-scale network topology.

  17. Intranasal Oxytocin Selectively Modulates Large-Scale Brain Networks in Humans.

    Science.gov (United States)

    Brodmann, Katja; Gruber, Oliver; Goya-Maldonado, Roberto

    2017-09-01

    A growing body of evidence indicates that the neuropeptide oxytocin (OT) alters the neural correlates of socioemotional and salience processing. Yet the effects of OT over important large-scale networks involved in these processes, such as the default mode (DM), ventral attention (VA), and cingulo-opercular (CO) networks, remain unknown. Therefore, we conducted a placebo-controlled crossover study with intranasal 24 IU OT in 38 healthy male subjects using a resting-state functional magnetic resonance imaging paradigm to investigate its impact over these three networks candidates. To understand the underlying mechanisms of the neuropeptide, we compared the intranetwork connectivity for each network candidate and also the internetwork connectivity across all networks between both treatment conditions. Based on the relevance of interindividual factors for OT effects, we correlated individual network changes with behavioral performance in a decision-making task and with impulsivity scores. Our results show that OT mainly alters connectivity in the VA, on one side reducing the coupling to regions that typically form the nodes of DM, an introspective and self-referential network, and on the other side increasing the coupling to the edges of the CO, which is involved in salience processing. The results of the internetwork analyses confirmed the specificity of the OT effects. Indeed, we observed significant correlations with the erroneous performance during decision-making but not with the obtained impulsivity scores. Overall, our data support that the modulation of functional connectivity within the VA is a basic mechanism by which OT directs attentional resources from internal to external cues, preparing the brain for context-dependent salience processing.

  18. Broad-scale small-world network topology induces optimal synchronization of flexible oscillators

    International Nuclear Information System (INIS)

    Markovič, Rene; Gosak, Marko; Marhl, Marko

    2014-01-01

    The discovery of small-world and scale-free properties of many man-made and natural complex networks has attracted increasing attention. Of particular interest is how the structural properties of a network facilitate and constrain its dynamical behavior. In this paper we study the synchronization of weakly coupled limit-cycle oscillators in dependence on the network topology as well as the dynamical features of individual oscillators. We show that flexible oscillators, characterized by near zero values of divergence, express maximal correlation in broad-scale small-world networks, whereas the non-flexible (rigid) oscillators are best correlated in more heterogeneous scale-free networks. We found that the synchronization behavior is governed by the interplay between the networks global efficiency and the mutual frequency adaptation. The latter differs for flexible and rigid oscillators. The results are discussed in terms of evolutionary advantages of broad-scale small-world networks in biological systems

  19. Network Partitioning Domain Knowledge Multiobjective Application Mapping for Large-Scale Network-on-Chip

    Directory of Open Access Journals (Sweden)

    Yin Zhen Tei

    2014-01-01

    Full Text Available This paper proposes a multiobjective application mapping technique targeted for large-scale network-on-chip (NoC. As the number of intellectual property (IP cores in multiprocessor system-on-chip (MPSoC increases, NoC application mapping to find optimum core-to-topology mapping becomes more challenging. Besides, the conflicting cost and performance trade-off makes multiobjective application mapping techniques even more complex. This paper proposes an application mapping technique that incorporates domain knowledge into genetic algorithm (GA. The initial population of GA is initialized with network partitioning (NP while the crossover operator is guided with knowledge on communication demands. NP reduces the large-scale application mapping complexity and provides GA with a potential mapping search space. The proposed genetic operator is compared with state-of-the-art genetic operators in terms of solution quality. In this work, multiobjective optimization of energy and thermal-balance is considered. Through simulation, knowledge-based initial mapping shows significant improvement in Pareto front compared to random initial mapping that is widely used. The proposed knowledge-based crossover also shows better Pareto front compared to state-of-the-art knowledge-based crossover.

  20. Natural language acquisition in large scale neural semantic networks

    Science.gov (United States)

    Ealey, Douglas

    This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

  1. ASH : Tackling node mobility in large-scale networks

    NARCIS (Netherlands)

    Pruteanu, A.; Dulman, S.

    2012-01-01

    With the increased adoption of technologies likewireless sensor networks by real-world applications, dynamic network topologies are becoming the rule rather than the exception. Node mobility, however, introduces a range of problems (communication interference, path uncertainty, low quality of

  2. Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory

    Science.gov (United States)

    Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui

    2018-01-01

    Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are

  3. Full-Duplex Communications in Large-Scale Cellular Networks

    KAUST Repository

    AlAmmouri, Ahmad

    2016-04-01

    In-band full-duplex (FD) communications have been optimistically promoted to improve the spectrum utilization and efficiency. However, the penetration of FD communications to the cellular networks domain is challenging due to the imposed uplink/downlink interference. This thesis presents a tractable framework, based on stochastic geometry, to study FD communications in multi-tier cellular networks. Particularly, we assess the FD communications effect on the network performance and quantify the associated gains. The study proves the vulnerability of the uplink to the downlink interference and shows that the improved FD rate gains harvested in the downlink (up to 97%) comes at the expense of a significant degradation in the uplink rate (up to 94%). Therefore, we propose a novel fine-grained duplexing scheme, denoted as α-duplex scheme, which allows a partial overlap between the uplink and the downlink frequency bands. We derive the required conditions to harvest rate gains from the α-duplex scheme and show its superiority to both the FD and half-duplex (HD) schemes. In particular, we show that the α-duplex scheme provides a simultaneous improvement of 28% for the downlink rate and 56% for the uplink rate. We also show that the amount of the overlap can be optimized based on the network design objective. Moreover, backward compatibility is an essential ingredient for the success of new technologies. In the context of in-band FD communication, FD base stations (BSs) should support HD users\\' equipment (UEs) without sacrificing the foreseen FD gains. The results show that FD-UEs are not necessarily required to harvest rate gains from FD-BSs. In particular, the results show that adding FD-UEs to FD-BSs offers a maximum of 5% rate gain over FD-BSs and HD-UEs case, which is a marginal gain compared to the burden required to implement FD transceivers at the UEs\\' side. To this end, we shed light on practical scenarios where HD-UEs operation with FD-BSs outperforms the

  4. Multilayer network modeling creates opportunities for novel network statistics. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

    Science.gov (United States)

    Muldoon, Sarah Feldt

    2018-03-01

    As described in the review by Gosak et al., the field of network science has had enormous success in providing new insights into the structure and function of biological systems [1]. In the complex networks framework, system elements are network nodes, and connections between nodes represent some form of interaction between system elements [2]. The flexibility to define network nodes and edges to represent different aspects of biological systems has been employed to model numerous diverse systems at multiple scales.

  5. False Positive and False Negative Effects on Network Attacks

    Science.gov (United States)

    Shang, Yilun

    2018-01-01

    Robustness against attacks serves as evidence for complex network structures and failure mechanisms that lie behind them. Most often, due to detection capability limitation or good disguises, attacks on networks are subject to false positives and false negatives, meaning that functional nodes may be falsely regarded as compromised by the attacker and vice versa. In this work, we initiate a study of false positive/negative effects on network robustness against three fundamental types of attack strategies, namely, random attacks (RA), localized attacks (LA), and targeted attack (TA). By developing a general mathematical framework based upon the percolation model, we investigate analytically and by numerical simulations of attack robustness with false positive/negative rate (FPR/FNR) on three benchmark models including Erdős-Rényi (ER) networks, random regular (RR) networks, and scale-free (SF) networks. We show that ER networks are equivalently robust against RA and LA only when FPR equals zero or the initial network is intact. We find several interesting crossovers in RR and SF networks when FPR is taken into consideration. By defining the cost of attack, we observe diminishing marginal attack efficiency for RA, LA, and TA. Our finding highlights the potential risk of underestimating or ignoring FPR in understanding attack robustness. The results may provide insights into ways of enhancing robustness of network architecture and improve the level of protection of critical infrastructures.

  6. 78 FR 70076 - Large Scale Networking (LSN)-Joint Engineering Team (JET)

    Science.gov (United States)

    2013-11-22

    ... NATIONAL SCIENCE FOUNDATION Large Scale Networking (LSN)--Joint Engineering Team (JET) AGENCY: The Networking and Information Technology Research and Development (NITRD) National Coordination Office (NCO... Federal agencies and non-Federal participants with interest in high performance research networking and...

  7. Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

    Science.gov (United States)

    Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

    2016-01-01

    The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

  8. Network effects on scientific collaborations.

    Directory of Open Access Journals (Sweden)

    Shahadat Uddin

    Full Text Available BACKGROUND: The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly. METHODOLOGY/PRINCIPAL FINDINGS: Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality, we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count and formation (tie strength between authors of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of 'steel structure' for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s. Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations. CONCLUSIONS/SIGNIFICANCE: Authors' network positions in co

  9. Two-scale cost efficiency optimization of 5G wireless backhaul networks

    OpenAIRE

    Ge, Xiaohu; Tu, Song; Mao, Guoqiang; Lau, Vincent K. N.; Pan, Linghui

    2016-01-01

    To cater for the demands of future fifth generation (5G) ultra-dense small cell networks, the wireless backhaul network is an attractive solution for the urban deployment of 5G wireless networks. Optimization of 5G wireless backhaul networks is a key issue. In this paper we propose a two-scale optimization solution to maximize the cost efficiency of 5G wireless backhaul networks. Specifically, the number and positions of gateways are optimized in the long time scale of 5G wireless backhaul ne...

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

    Science.gov (United States)

    Carlson, Jamie; Kitchens, Brent; Kozary, Ben; Zaki, Mohamed

    2016-01-01

    Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizations from the U.S., U.K., and Australia, to visually map networks of 579 social media brand pages (represented by nodes), connected by 5,600 edges. This network data is analyzed using a collection of popular graph analysis techniques to assess the differences in the way each of the service organizations manage stakeholder networks. We also compare node meta-information against basic topology measures to emphasize the importance of effectively managing relationships with stakeholders who have large external audiences. Implications and future research directions are also discussed. PMID:27711236

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

    Science.gov (United States)

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

    2016-01-01

    Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizations from the U.S., U.K., and Australia, to visually map networks of 579 social media brand pages (represented by nodes), connected by 5,600 edges. This network data is analyzed using a collection of popular graph analysis techniques to assess the differences in the way each of the service organizations manage stakeholder networks. We also compare node meta-information against basic topology measures to emphasize the importance of effectively managing relationships with stakeholders who have large external audiences. Implications and future research directions are also discussed.

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

    Directory of Open Access Journals (Sweden)

    Jessica Wyllie

    Full Text Available Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizations from the U.S., U.K., and Australia, to visually map networks of 579 social media brand pages (represented by nodes, connected by 5,600 edges. This network data is analyzed using a collection of popular graph analysis techniques to assess the differences in the way each of the service organizations manage stakeholder networks. We also compare node meta-information against basic topology measures to emphasize the importance of effectively managing relationships with stakeholders who have large external audiences. Implications and future research directions are also discussed.

  13. Convergence speed of consensus problems over undirected scale-free networks

    International Nuclear Information System (INIS)

    Sun Wei; Dou Li-Hua

    2010-01-01

    Scale-free networks and consensus behaviour among multiple agents have both attracted much attention. To investigate the consensus speed over scale-free networks is the major topic of the present work. A novel method is developed to construct scale-free networks due to their remarkable power-law degree distributions, while preserving the diversity of network topologies. The time cost or iterations for networks to reach a certain level of consensus is discussed, considering the influence from power-law parameters. They are both demonstrated to be reversed power-law functions of the algebraic connectivity, which is viewed as a measurement on convergence speed of the consensus behaviour. The attempts of tuning power-law parameters may speed up the consensus procedure, but it could also make the network less robust over time delay at the same time. Large scale of simulations are supportive to the conclusions. (general)

  14. Evolutionary Prisoners' Dilemma Game on Scale-Free Networks with Degree-Degree Correlation

    Science.gov (United States)

    Cheng, Hong-Yan; Dai, Qiong-Lin; Li, Hai-Hong; Yang, Jun-Zhong

    2010-10-01

    Degree-degree correlation and heterogeneity in degree are important topological properties characterizing scale-free networks. We consider an evolutionary prisoners' dilemma game on scale-free networks and investigate how degree-degree correlation influences cooperation. It is found that the cooperator frequency displays resonance-like behavior with the variation of Pearson correlation coefficient. A measure on local heterogeneity in a network is proposed and it is realized that cooperation is proportional to the local heterogeneity.

  15. A Holistic Management Architecture for Large-Scale Adaptive Networks

    National Research Council Canada - National Science Library

    Clement, Michael R

    2007-01-01

    This thesis extends the traditional notion of network management as an indicator of resource availability and utilization into a systemic model of resource requirements, capabilities, and adaptable...

  16. Scale-space measures for graph topology link protein network architecture to function.

    Science.gov (United States)

    Hulsman, Marc; Dimitrakopoulos, Christos; de Ridder, Jeroen

    2014-06-15

    The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. Matlab(TM) code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA © The Author 2014. Published by Oxford University Press.

  17. Parameters affecting the resilience of scale-free networks to random failures.

    Energy Technology Data Exchange (ETDEWEB)

    Link, Hamilton E.; LaViolette, Randall A.; Lane, Terran (University of New Mexico, Albuquerque, NM); Saia, Jared (University of New Mexico, Albuquerque, NM)

    2005-09-01

    It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. in (1) study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, most of the remaining nodes would still be connected in a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions. In particular, we study scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.

  18. Scaling-Laws of Flow Entropy with Topological Metrics of Water Distribution Networks

    OpenAIRE

    Giovanni Francesco Santonastaso; Armando Di Nardo; Michele Di Natale; Carlo Giudicianni; Roberto Greco

    2018-01-01

    Robustness of water distribution networks is related to their connectivity and topological structure, which also affect their reliability. Flow entropy, based on Shannon’s informational entropy, has been proposed as a measure of network redundancy and adopted as a proxy of reliability in optimal network design procedures. In this paper, the scaling properties of flow entropy of water distribution networks with their size and other topological metrics are studied. To such aim, flow entropy, ma...

  19. Small-World and Scale-Free Network Models for IoT Systems

    Directory of Open Access Journals (Sweden)

    Insoo Sohn

    2017-01-01

    Full Text Available It is expected that Internet of Things (IoT revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.

  20. Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

    Science.gov (United States)

    Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

    2018-03-06

    Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

  1. Network effects on coordination in asymmetric games.

    Science.gov (United States)

    Broere, Joris; Buskens, Vincent; Weesie, Jeroen; Stoof, Henk

    2017-12-05

    Network structure can have an important effect on the behavior of players in an iterated 2 × 2 game. We study the effect of network structure on global and local behavior in asymmetric coordination games using best response dynamics. We find that global behavior is highly dependent on network topology. Random (Erdös-Rényi) networks mostly converge to homogeneous behavior, but the higher the clustering in the network the more heterogeneous the behavior becomes. Behavior within the communities of the network is almost exclusively homogeneous. The findings suggest that clustering of networks facilitates self-organization of uniform behavior within clusters, but heterogeneous behavior between clusters. At the local level we find that some nodes are more important in determining the equilibrium behavior than other nodes. Degree centrality is for most networks the main predictor for the behavior and nodes with an even degree have an advantage over nodes with an uneven degree in dictating the behavior. We conclude that the behavior is difficult to predict for (Erdös-Rényi) networks and that the network imposes the behavior as a function of clustering and degree heterogeneity in other networks.

  2. Network science of biological systems at different scales: A review

    Science.gov (United States)

    Gosak, Marko; Markovič, Rene; Dolenšek, Jurij; Slak Rupnik, Marjan; Marhl, Marko; Stožer, Andraž; Perc, Matjaž

    2018-03-01

    Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present

  3. Multiple dynamical time-scales in networks with hierarchically ...

    Indian Academy of Sciences (India)

    for simple modular networks, where fast intramodular and slow intermodular processes were clearly distinguished. ... ical world from the intracellular signalling system to neuronal networks to ecological food webs [3]. Analysis of ... connections, results in a sharp distinction between fast intramodular and slower intermod-.

  4. Indirect Network Effects in New Product Growth

    NARCIS (Netherlands)

    S. Stremersch (Stefan); G.J. Tellis (Gerard); Ph.H.B.F. Franses (Philip Hans); J.L.G. Binken (Jeroen)

    2007-01-01

    textabstractIndirect network effects are of prime interest to marketers because they affect the growth and takeoff of software availability for, and hardware sales of, a new product. While prior work on indirect network effects in the economics and marketing literature is valuable, these literatures

  5. A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

    Science.gov (United States)

    Xiao, Xiangyun; Zhang, Wei; Zou, Xiufen

    2015-01-01

    The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

  6. A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Xiangyun Xiao

    Full Text Available The reconstruction of gene regulatory networks (GRNs from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM, experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

  7. Electricity network limitations on large-scale deployment of wind energy

    Energy Technology Data Exchange (ETDEWEB)

    Fairbairn, R.J.

    1999-07-01

    This report sought to identify limitation on large scale deployment of wind energy in the UK. A description of the existing electricity supply system in England, Scotland and Wales is given, and operational aspects of the integrated electricity networks, licence conditions, types of wind turbine generators, and the scope for deployment of wind energy in the UK are addressed. A review of technical limitations and technical criteria stipulated by the Distribution and Grid Codes, the effects of system losses, and commercial issues are examined. Potential solutions to technical limitations are proposed, and recommendations are outlined.

  8. Default network modulation and large-scale network interactivity in healthy young and old adults.

    Science.gov (United States)

    Spreng, R Nathan; Schacter, Daniel L

    2012-11-01

    We investigated age-related changes in default, attention, and control network activity and their interactions in young and old adults. Brain activity during autobiographical and visuospatial planning was assessed using multivariate analysis and with intrinsic connectivity networks as regions of interest. In both groups, autobiographical planning engaged the default network while visuospatial planning engaged the attention network, consistent with a competition between the domains of internalized and externalized cognition. The control network was engaged for both planning tasks. In young subjects, the control network coupled with the default network during autobiographical planning and with the attention network during visuospatial planning. In old subjects, default-to-control network coupling was observed during both planning tasks, and old adults failed to deactivate the default network during visuospatial planning. This failure is not indicative of default network dysfunction per se, evidenced by default network engagement during autobiographical planning. Rather, a failure to modulate the default network in old adults is indicative of a lower degree of flexible network interactivity and reduced dynamic range of network modulation to changing task demands.

  9. Scaling an in situ network for high resolution modeling during SMAPVEX15

    Science.gov (United States)

    Among the greatest challenges within the field of soil moisture estimation is that of scaling sparse point measurements within a network to produce higher resolution map products. Large-scale field experiments present an ideal opportunity to develop methodologies for this scaling, by coupling in si...

  10. Managing Virtual Networks on Large-Scale Projects

    National Research Council Canada - National Science Library

    Noll, David

    2006-01-01

    The complexity of Boeing's 787 Program is too great for the formal planned information and communication network structure to fully meet the needs of companies, managers, and employees located throughout the world...

  11. Disease Modeling via Large-Scale Network Analysis

    Science.gov (United States)

    2015-05-20

    properties to important human diseases. Our methods, Katz on heterogeneous network and CATAPULT [1], for predicting gene-disease associations were...label noise in classification. Our CATAPULT system employs "biased" support vector machines (SVM), to cope with the lack of negative examples. Biased...important human diseases. Our methods, Katz on heterogeneous network and CATAPULT [1], for predicting gene-disease associations were published during the

  12. Cooperative HARQ Assisted NOMA Scheme in Large-scale D2D Networks

    KAUST Repository

    Shi, Zheng

    2017-07-13

    This paper develops an interference aware design for cooperative hybrid automatic repeat request (HARQ) assisted non-orthogonal multiple access (NOMA) scheme for large-scale device-to-device (D2D) networks. Specifically, interference aware rate selection and power allocation are considered to maximize long term average throughput (LTAT) and area spectral efficiency (ASE). The design framework is based on stochastic geometry that jointly accounts for the spatial interference correlation at the NOMA receivers as well as the temporal interference correlation across HARQ transmissions. It is found that ignoring the effect of the aggregate interference, or overlooking the spatial and temporal correlation in interference, highly overestimates the NOMA performance and produces misleading design insights. An interference oblivious selection for the power and/or transmission rates leads to violating the network outage constraints. To this end, the results demonstrate the effectiveness of NOMA transmission and manifest the importance of the cooperative HARQ to combat the negative effect of the network aggregate interference. For instance, comparing to the non-cooperative HARQ assisted NOMA, the proposed scheme can yield an outage probability reduction by $32$%. Furthermore, an interference aware optimal design that maximizes the LTAT given outage constraints leads to $47$% throughput improvement over HARQ-assisted orthogonal multiple access (OMA) scheme.

  13. Collaborative Catchment-Scale Water Quality Management using Integrated Wireless Sensor Networks

    Science.gov (United States)

    Zia, Huma; Harris, Nick; Merrett, Geoff

    2013-04-01

    Electronics and Computer Science, University of Southampton, United Kingdom Summary The challenge of improving water quality (WQ) is a growing global concern [1]. Poor WQ is mainly attributed to poor water management and outdated agricultural activities. We propose that collaborative sensor networks spread across an entire catchment can allow cooperation among individual activities for integrated WQ monitoring and management. We show that sharing information on critical parameters among networks of water bodies and farms can enable identification and quantification of the contaminant sources, enabling better decision making for agricultural practices and thereby reducing contaminants fluxes. Motivation and results Nutrient losses from land to water have accelerated due to agricultural and urban pursuits [2]. In many cases, the application of fertiliser can be reduced by 30-50% without any loss of yield [3]. Thus information about nutrient levels and trends around the farm can improve agricultural practices and thereby reduce water contamination. The use of sensor networks for monitoring WQ in a catchment is in its infancy, but more applications are being tested [4]. However, these are focussed on local requirements and are mostly limited to water bodies. They have yet to explore the use of this technology for catchment-scale monitoring and management decisions, in an autonomous and dynamic manner. For effective and integrated WQ management, we propose a system that utilises local monitoring networks across a catchment, with provision for collaborative information sharing. This system of networks shares information about critical events, such as rain or flooding. Higher-level applications make use of this information to inform decisions about nutrient management, improving the quality of monitoring through the provision of richer datasets of catchment information to local networks. In the full paper, we present example scenarios and analyse how the benefits of

  14. A general model for metabolic scaling in self-similar asymmetric networks.

    Directory of Open Access Journals (Sweden)

    Alexander Byers Brummer

    2017-03-01

    Full Text Available How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE model argues that these two principles (space-filling and energy minimization are (i general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.

  15. Aquatic Nitrate Retention at River Network Scales Across Flow Conditions Determined Using Nested In Situ Sensors

    Science.gov (United States)

    Wollheim, W. M.; Mulukutla, G. K.; Cook, C.; Carey, R. O.

    2017-11-01

    Nonpoint pollution sources are strongly influenced by hydrology and are therefore sensitive to climate variability. Some pollutants entering aquatic ecosystems, e.g., nitrate, can be mitigated by in-stream processes during transport through river networks. Whole river network nitrate retention is difficult to quantify with observations. High frequency, in situ nitrate sensors, deployed in nested locations within a single watershed, can improve estimates of both nonpoint inputs and aquatic retention at river network scales. We deployed a nested sensor network and associated sampling in the urbanizing Oyster River watershed in coastal New Hampshire, USA, to quantify storm event-scale loading and retention at network scales. An end member analysis used the relative behavior of reactive nitrate and conservative chloride to infer river network fate of nitrate. In the headwater catchments, nitrate and chloride concentrations are both increasingly diluted with increasing storm size. At the mouth of the watershed, chloride is also diluted, but nitrate tended to increase. The end member analysis suggests that this pattern is the result of high retention during small storms (51-78%) that declines to zero during large storms. Although high frequency nitrate sensors did not alter estimates of fluxes over seasonal time periods compared to less frequent grab sampling, they provide the ability to estimate nitrate flux versus storm size at event scales that is critical for such analyses. Nested sensor networks can improve understanding of the controls of both loading and network scale retention, and therefore also improve management of nonpoint source pollution.

  16. Effects of Social Networking on Adolescent Education

    Science.gov (United States)

    Miah, Muhammed; Omar, Adnan; Allison-Golding, Monique

    2013-01-01

    In recent years, the use of social networking sites has grown tremendously especially among the teens and high school students. However, very little is known about the scale of use, the purpose, how students use these sites and, more specifically, whether these sites help or hurt their academic progress. This study investigates how high school…

  17. Spatial dependencies between large-scale brain networks.

    Directory of Open Access Journals (Sweden)

    Robert Leech

    Full Text Available Functional neuroimaging reveals both increases (task-positive and decreases (task-negative in neural activation with many tasks. Many studies show a temporal relationship between task positive and task negative networks that is important for efficient cognitive functioning. Here we provide evidence for a spatial relationship between task positive and negative networks. There are strong spatial similarities between many reported task negative brain networks, termed the default mode network, which is typically assumed to be a spatially fixed network. However, this is not the case. The spatial structure of the DMN varies depending on what specific task is being performed. We test whether there is a fundamental spatial relationship between task positive and negative networks. Specifically, we hypothesize that the distance between task positive and negative voxels is consistent despite different spatial patterns of activation and deactivation evoked by different cognitive tasks. We show significantly reduced variability in the distance between within-condition task positive and task negative voxels than across-condition distances for four different sensory, motor and cognitive tasks--implying that deactivation patterns are spatially dependent on activation patterns (and vice versa, and that both are modulated by specific task demands. We also show a similar relationship between positively and negatively correlated networks from a third 'rest' dataset, in the absence of a specific task. We propose that this spatial relationship may be the macroscopic analogue of microscopic neuronal organization reported in sensory cortical systems, and that this organization may reflect homeostatic plasticity necessary for efficient brain function.

  18. Networking in a Large-Scale Distributed Agile Project

    OpenAIRE

    Moe, Nils Brede; Šmite, Darja; Šāblis, Aivars; Börjesson, Anne-Lie; Andréasson, Pia

    2014-01-01

    Context: In large-scale distributed software projects the expertise may be scattered across multiple locations. Goal: We describe and discuss a large-scale distributed agile project at Ericsson, a multinational telecommunications company headquartered in Sweden. The project is distributed across four development locations (one in Sweden, one in Korea and two in China) and employs 17 teams. In such a large scale environment the challenge is to have as few dependences between teams as possible,...

  19. Crowd counting via scale-adaptive convolutional neural network

    OpenAIRE

    Zhang, Lu; Shi, Miaojing; Chen, Qiaobo

    2017-01-01

    The task of crowd counting is to automatically estimate the pedestrian number in crowd images. To cope with the scale and perspective changes that commonly exist in crowd images, state-of-the-art approaches employ multi-column CNN architectures to regress density maps of crowd images. Multiple columns have different receptive fields corresponding to pedestrians (heads) of different scales. We instead propose a scale-adaptive CNN (SaCNN) architecture with a backbone of fixed small receptive fi...

  20. Benefits and Challenges of Scaling Up Expansion of Marine Protected Area Networks in the Verde Island Passage, Central Philippines.

    Science.gov (United States)

    Horigue, Vera; Pressey, Robert L; Mills, Morena; Brotánková, Jana; Cabral, Reniel; Andréfouët, Serge

    2015-01-01

    Locally-established marine protected areas (MPAs) have been proven to achieve local-scale fisheries and conservation objectives. However, since many of these MPAs were not designed to form ecologically-connected networks, their contributions to broader-scale goals such as complementarity and connectivity can be limited. In contrast, integrated networks of MPAs designed with systematic conservation planning are assumed to be more effective--ecologically, socially, and economically--than collections of locally-established MPAs. There is, however, little empirical evidence that clearly demonstrates the supposed advantages of systematic MPA networks. A key reason is the poor record of implementation of systematic plans attributable to lack of local buy-in. An intermediate scenario for the expansion of MPAs is scaling up of local decisions, whereby locally-driven MPA initiatives are coordinated through collaborative partnerships among local governments and their communities. Coordination has the potential to extend the benefits of individual MPAs and perhaps to approach the potential benefits offered by systematic MPA networks. We evaluated the benefits of scaling up local MPAs to form networks by simulating seven expansion scenarios for MPAs in the Verde Island Passage, central Philippines. The scenarios were: uncoordinated community-based establishment of MPAs; two scenarios reflecting different levels of coordinated MPA expansion through collaborative partnerships; and four scenarios guided by systematic conservation planning with different contexts for governance. For each scenario, we measured benefits through time in terms of achievement of objectives for representation of marine habitats. We found that: in any governance context, systematic networks were more efficient than non-systematic ones; systematic networks were more efficient in broader governance contexts; and, contrary to expectations but with caveats, the uncoordinated scenario was slightly more

  1. Benefits and Challenges of Scaling Up Expansion of Marine Protected Area Networks in the Verde Island Passage, Central Philippines.

    Directory of Open Access Journals (Sweden)

    Vera Horigue

    Full Text Available Locally-established marine protected areas (MPAs have been proven to achieve local-scale fisheries and conservation objectives. However, since many of these MPAs were not designed to form ecologically-connected networks, their contributions to broader-scale goals such as complementarity and connectivity can be limited. In contrast, integrated networks of MPAs designed with systematic conservation planning are assumed to be more effective--ecologically, socially, and economically--than collections of locally-established MPAs. There is, however, little empirical evidence that clearly demonstrates the supposed advantages of systematic MPA networks. A key reason is the poor record of implementation of systematic plans attributable to lack of local buy-in. An intermediate scenario for the expansion of MPAs is scaling up of local decisions, whereby locally-driven MPA initiatives are coordinated through collaborative partnerships among local governments and their communities. Coordination has the potential to extend the benefits of individual MPAs and perhaps to approach the potential benefits offered by systematic MPA networks. We evaluated the benefits of scaling up local MPAs to form networks by simulating seven expansion scenarios for MPAs in the Verde Island Passage, central Philippines. The scenarios were: uncoordinated community-based establishment of MPAs; two scenarios reflecting different levels of coordinated MPA expansion through collaborative partnerships; and four scenarios guided by systematic conservation planning with different contexts for governance. For each scenario, we measured benefits through time in terms of achievement of objectives for representation of marine habitats. We found that: in any governance context, systematic networks were more efficient than non-systematic ones; systematic networks were more efficient in broader governance contexts; and, contrary to expectations but with caveats, the uncoordinated scenario was

  2. Different behaviors of epidemic spreading in scale-free networks with identical degree sequence

    Energy Technology Data Exchange (ETDEWEB)

    Chu Xiangwei; Guan Jihong [School of Electronics and Information, Tongji University, 4800 Cao' an Road, Shanghai 201804 (China); Zhang Zhongzhi; Zhou Shuigeng [School of Computer Science, Fudan University, Shanghai 200433 (China); Li Mo, E-mail: zhangzz@fudan.edu.c, E-mail: jhguan@tongj.edu.c, E-mail: sgzhou@fudan.edu.c [Software School, Fudan University, Shanghai 200433 (China)

    2010-02-12

    Recently, the study of dynamical behaviors of the susceptible-infected (SI) disease model in complex networks, especially in Barabasi-Albert (BA) scale-free networks, has attracted much attention. Although some interesting phenomena have been observed, the formative reasons for those particular dynamical behaviors are still not well understood, despite the speculation that topological properties (for example the degree distribution) have a strong impact on epidemic spreading. In this paper, we study the evolution behaviors of epidemic spreading on a class of scale-free networks sharing identical degree sequence, and observe significantly different evolution behaviors in the whole family of networks. We show that the power-law degree distribution does not suffice to characterize the dynamical behaviors of disease diffusion on scale-free networks.

  3. Multidimensional scaling of ideological landscape on social network sites

    Science.gov (United States)

    Lee, Deokjae; Hahn, Kyu S.; Park, Juyong

    2012-02-01

    Social network sites (SNSs) are valuable source of information on various subjects in network science. Recently, political activity of SNSs users has increasing attention and is an interesting interdisciplinary subject of physical and social science. In this work, we measure ideological positions of the legislators of U.S. and South Korea (S.K.) evaluated by Twitter users, using the information employed in the bipartite network structure of the legislators and their Twitter followers. We compare the result with ideological positions constructed from roll call record of the legislators. This shows there is a discrepancy between the ideological positions evaluated by Twitter users and actual positions estimated from roll call votes in S.K. We also asses the ideological positions of the Twitter users themselves and analyze the distribution of the positions.

  4. Investigating the effects of virtual social networks on entrepreneurial marketing

    OpenAIRE

    Kambeiz Talebi; Mahnaz Behrad; Amir Ekhlassi

    2014-01-01

    This paper presents an empirical investigation to study the effects of virtual social networks on entrepreneurial marketing. The study designs a questionnaire in Likert scale based on a model originally developed by Morris et al. (2002) [Morris, M. H., Schindehutte, M., & LaForge, R. W. (2002). Entrepreneurial marketing: a construct for integrating emerging entrepreneurship and marketing perspectives. Journal of Marketing Theory and Practice, 10(4), 1-19.]. The study considers the effects of ...

  5. Large-Scale Functional Brain Network Reorganization During Taoist Meditation.

    Science.gov (United States)

    Jao, Tun; Li, Chia-Wei; Vértes, Petra E; Wu, Changwei Wesley; Achard, Sophie; Hsieh, Chao-Hsien; Liou, Chien-Hui; Chen, Jyh-Horng; Bullmore, Edward T

    2016-02-01

    Meditation induces a distinct and reversible mental state that provides insights into brain correlates of consciousness. We explored brain network changes related to meditation by graph theoretical analysis of resting-state functional magnetic resonance imaging data. Eighteen Taoist meditators with varying levels of expertise were scanned using a within-subjects counterbalanced design during resting and meditation states. State-related differences in network topology were measured globally and at the level of individual nodes and edges. Although measures of global network topology, such as small-worldness, were unchanged, meditation was characterized by an extensive and expertise-dependent reorganization of the hubs (highly connected nodes) and edges (functional connections). Areas of sensory cortex, especially the bilateral primary visual and auditory cortices, and the bilateral temporopolar areas, which had the highest degree (or connectivity) during the resting state, showed the biggest decrease during meditation. Conversely, bilateral thalamus and components of the default mode network, mainly the bilateral precuneus and posterior cingulate cortex, had low degree in the resting state but increased degree during meditation. Additionally, these changes in nodal degree were accompanied by reorganization of anatomical orientation of the edges. During meditation, long-distance longitudinal (antero-posterior) edges increased proportionally, whereas orthogonal long-distance transverse (right-left) edges connecting bilaterally homologous cortices decreased. Our findings suggest that transient changes in consciousness associated with meditation introduce convergent changes in the topological and spatial properties of brain functional networks, and the anatomical pattern of integration might be as important as the global level of integration when considering the network basis for human consciousness.

  6. Output Regulation of Large-Scale Hydraulic Networks

    DEFF Research Database (Denmark)

    De Persis, Claudio; Jensen, Tom Nørgaard; Ortega, Romeo

    2014-01-01

    The problem of output regulation for a class of hydraulic networks found in district heating systems is addressed in this brief. The results show that global asymptotic and semiglobal exponential output regulation is achievable using a set of decentralized proportional-integral controllers....... The fact that the result is global and independent of the number of end users has the consequence that structural changes such as end-user addition and removal can be made in the network while maintaining the stability properties of the system. Furthermore, the decentralized nature of the control...

  7. Heterogeneous Deployment Analysis for Cost-Effective Mobile Network Evolution

    DEFF Research Database (Denmark)

    Coletti, Claudio

    2013-01-01

    -powered base stations is a promising cost-effective solution to considerably enhance user experience. In such a network topology, which is denoted as heterogeneous deployment, the macro layer is expected to provide wider coverage but lower average data speeds whereas small cells are targeted at extending...... network coverage and boosting network capacity in traffic hot-spot areas. The thesis deals with the deployment of both outdoor small cells and indoor femto cells. Amongst the outdoor solution, particular emphasis is put on relay base stations as backhaul costs can be reduced by utilizing LTE spectrum...... statistical models of deployment areas, the performance analysis is carried out in the form of operator case studies for large-scale deployment scenarios, including realistic macro network layouts and inhomogeneous spatial traffic distributions. Deployment of small cells is performed by means of proposed...

  8. Impact of interaction style and degree on the evolution of cooperation on Barabási-Albert scale-free network.

    Directory of Open Access Journals (Sweden)

    Fengjie Xie

    Full Text Available In this work, we study an evolutionary prisoner's dilemma game (PDG on Barabási-Albert scale-free networks with limited player interactions, and explore the effect of interaction style and degree on cooperation. The results show that high-degree preference interaction, namely the most applicable interaction in the real world, is less beneficial for emergence of cooperation on scale-free networks than random interaction. Besides, cooperation on scale-free networks is enhanced with the increase of interaction degree regardless whether the interaction is high-degree preference or random. If the interaction degree is very low, the cooperation level on scale-free networks is much lower than that on regular ring networks, which is against the common belief that scale-free networks must be more beneficial for cooperation. Our analysis indicates that the interaction relations, the strategy and the game payoff of high-connectivity players play important roles in the evolution of cooperation on scale-free networks. A certain number of interactions are necessary for scale-free networks to exhibit strong capability of facilitating cooperation. Our work provides important insight for members on how to interact with others in a social organization.

  9. Super-transient scaling in time-delay autonomous Boolean network motifs

    Energy Technology Data Exchange (ETDEWEB)

    D' Huys, Otti, E-mail: otti.dhuys@phy.duke.edu; Haynes, Nicholas D. [Department of Physics, Duke University, Durham, North Carolina 27708 (United States); Lohmann, Johannes [Department of Physics, Duke University, Durham, North Carolina 27708 (United States); Institut für Theoretische Physik, Technische Universität Berlin, Hardenbergstraße 36, 10623 Berlin (Germany); Gauthier, Daniel J. [Department of Physics, Duke University, Durham, North Carolina 27708 (United States); Department of Physics, The Ohio State University, Columbus, Ohio 43210 (United States)

    2016-09-15

    Autonomous Boolean networks are commonly used to model the dynamics of gene regulatory networks and allow for the prediction of stable dynamical attractors. However, most models do not account for time delays along the network links and noise, which are crucial features of real biological systems. Concentrating on two paradigmatic motifs, the toggle switch and the repressilator, we develop an experimental testbed that explicitly includes both inter-node time delays and noise using digital logic elements on field-programmable gate arrays. We observe transients that last millions to billions of characteristic time scales and scale exponentially with the amount of time delays between nodes, a phenomenon known as super-transient scaling. We develop a hybrid model that includes time delays along network links and allows for stochastic variation in the delays. Using this model, we explain the observed super-transient scaling of both motifs and recreate the experimentally measured transient distributions.

  10. Evolution of cooperation on scale-free networks subject to error and attack

    International Nuclear Information System (INIS)

    Perc, Matjaz

    2009-01-01

    We study the evolution of cooperation in the prisoner's dilemma and the snowdrift game on scale-free networks that are subjected to intentional and random removal of vertices. We show that, irrespective of the game type, cooperation on scale-free networks is extremely robust against random deletion of vertices, but declines quickly if vertices with the maximal degree are targeted. In particular, attack tolerance is lowest if the temptation to defect is largest, whereby a small fraction of removed vertices suffices to decimate cooperators. The decline of cooperation can be directly linked to the decrease of heterogeneity of scale-free networks that sets in due to the removal of high degree vertices. We conclude that the evolution of cooperation is characterized by similar attack and error tolerance as was previously reported for information readiness and spread of viruses on scale-free networks.

  11. Spatial heterogeneity regulates plant-pollinator networks across multiple landscape scales.

    Directory of Open Access Journals (Sweden)

    Eduardo Freitas Moreira

    Full Text Available Mutualistic plant-pollinator interactions play a key role in biodiversity conservation and ecosystem functioning. In a community, the combination of these interactions can generate emergent properties, e.g., robustness and resilience to disturbances such as fluctuations in populations and extinctions. Given that these systems are hierarchical and complex, environmental changes must have multiple levels of influence. In addition, changes in habitat quality and in the landscape structure are important threats to plants, pollinators and their interactions. However, despite the importance of these phenomena for the understanding of biological systems, as well as for conservation and management strategies, few studies have empirically evaluated these effects at the network level. Therefore, the objective of this study was to investigate the influence of local conditions and landscape structure at multiple scales on the characteristics of plant-pollinator networks. This study was conducted in agri-natural lands in Chapada Diamantina, Bahia, Brazil. Pollinators were collected in 27 sampling units distributed orthogonally along a gradient of proportion of agriculture and landscape diversity. The Akaike information criterion was used to select models that best fit the metrics for network characteristics, comparing four hypotheses represented by a set of a priori candidate models with specific combinations of the proportion of agriculture, the average shape of the landscape elements, the diversity of the landscape and the structure of local vegetation. The results indicate that a reduction of habitat quality and landscape heterogeneity can cause species loss and decrease of networks nestedness. These structural changes can reduce robustness and resilience of plant-pollinator networks what compromises the reproductive success of plants, the maintenance of biodiversity and the pollination service stability. We also discuss the possible explanations for

  12. Spatial heterogeneity regulates plant-pollinator networks across multiple landscape scales.

    Science.gov (United States)

    Moreira, Eduardo Freitas; Boscolo, Danilo; Viana, Blandina Felipe

    2015-01-01

    Mutualistic plant-pollinator interactions play a key role in biodiversity conservation and ecosystem functioning. In a community, the combination of these interactions can generate emergent properties, e.g., robustness and resilience to disturbances such as fluctuations in populations and extinctions. Given that these systems are hierarchical and complex, environmental changes must have multiple levels of influence. In addition, changes in habitat quality and in the landscape structure are important threats to plants, pollinators and their interactions. However, despite the importance of these phenomena for the understanding of biological systems, as well as for conservation and management strategies, few studies have empirically evaluated these effects at the network level. Therefore, the objective of this study was to investigate the influence of local conditions and landscape structure at multiple scales on the characteristics of plant-pollinator networks. This study was conducted in agri-natural lands in Chapada Diamantina, Bahia, Brazil. Pollinators were collected in 27 sampling units distributed orthogonally along a gradient of proportion of agriculture and landscape diversity. The Akaike information criterion was used to select models that best fit the metrics for network characteristics, comparing four hypotheses represented by a set of a priori candidate models with specific combinations of the proportion of agriculture, the average shape of the landscape elements, the diversity of the landscape and the structure of local vegetation. The results indicate that a reduction of habitat quality and landscape heterogeneity can cause species loss and decrease of networks nestedness. These structural changes can reduce robustness and resilience of plant-pollinator networks what compromises the reproductive success of plants, the maintenance of biodiversity and the pollination service stability. We also discuss the possible explanations for these relationships and

  13. Spatial Heterogeneity Regulates Plant-Pollinator Networks across Multiple Landscape Scales

    Science.gov (United States)

    Moreira, Eduardo Freitas; Boscolo, Danilo; Viana, Blandina Felipe

    2015-01-01

    Mutualistic plant-pollinator interactions play a key role in biodiversity conservation and ecosystem functioning. In a community, the combination of these interactions can generate emergent properties, e.g., robustness and resilience to disturbances such as fluctuations in populations and extinctions. Given that these systems are hierarchical and complex, environmental changes must have multiple levels of influence. In addition, changes in habitat quality and in the landscape structure are important threats to plants, pollinators and their interactions. However, despite the importance of these phenomena for the understanding of biological systems, as well as for conservation and management strategies, few studies have empirically evaluated these effects at the network level. Therefore, the objective of this study was to investigate the influence of local conditions and landscape structure at multiple scales on the characteristics of plant-pollinator networks. This study was conducted in agri-natural lands in Chapada Diamantina, Bahia, Brazil. Pollinators were collected in 27 sampling units distributed orthogonally along a gradient of proportion of agriculture and landscape diversity. The Akaike information criterion was used to select models that best fit the metrics for network characteristics, comparing four hypotheses represented by a set of a priori candidate models with specific combinations of the proportion of agriculture, the average shape of the landscape elements, the diversity of the landscape and the structure of local vegetation. The results indicate that a reduction of habitat quality and landscape heterogeneity can cause species loss and decrease of networks nestedness. These structural changes can reduce robustness and resilience of plant-pollinator networks what compromises the reproductive success of plants, the maintenance of biodiversity and the pollination service stability. We also discuss the possible explanations for these relationships and

  14. Discretized kinetic theory on scale-free networks

    Science.gov (United States)

    Bertotti, Maria Letizia; Modanese, Giovanni

    2016-10-01

    The network of interpersonal connections is one of the possible heterogeneous factors which affect the income distribution emerging from micro-to-macro economic models. In this paper we equip our model discussed in [1, 2] with a network structure. The model is based on a system of n differential equations of the kinetic discretized-Boltzmann kind. The network structure is incorporated in a probabilistic way, through the introduction of a link density P(α) and of correlation coefficients P(β|α), which give the conditioned probability that an individual with α links is connected to one with β links. We study the properties of the equations and give analytical results concerning the existence, normalization and positivity of the solutions. For a fixed network with P(α) = c/α q , we investigate numerically the dependence of the detailed and marginal equilibrium distributions on the initial conditions and on the exponent q. Our results are compatible with those obtained from the Bouchaud-Mezard model and from agent-based simulations, and provide additional information about the dependence of the individual income on the level of connectivity.

  15. Reverse engineering large-scale genetic networks: synthetic versus ...

    Indian Academy of Sciences (India)

    The study illustrates that connections of gene networks can be significantly detected via SWNI with high confidence, when single gene perturbation experiments are performed complying with the algorithm requirements. In particular, our algorithm shows efficiency and outperforms the existing ones presented in this paper ...

  16. The Design of a Large Scale Airline Network

    NARCIS (Netherlands)

    Carmona Benitez, R.B.

    2012-01-01

    Airlines invest a lot of money before opening new pax transportation services, for this reason, airlines have to analyze if their profits will overcome the amount of money they have to invest to open new services. The design and analysis of the feasibility of airlines networks can be done by using

  17. Homeostatic scaling of excitability in recurrent neural networks.

    NARCIS (Netherlands)

    Remme, M.W.H.; Wadman, W.J.

    2012-01-01

    Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which

  18. Reverse engineering large-scale genetic networks: synthetic versus

    Indian Academy of Sciences (India)

    Development of microarray technology has resulted in an exponential rise in gene expression data. Linear computational methods are of great assistance in identifying molecular interactions, and elucidating the functional properties of gene networks. It overcomes the weaknesses of in vivo experiments including high cost, ...

  19. Energy Saving: Scaling Network Energy Efficiency Faster than Traffic Growth

    NARCIS (Netherlands)

    Chen, Y.; Blume, O.; Gati, A.; Capone, A.; Wu, C.-E.; Barth, U.; Marzetta, T.; Zhang, H.; Xu, S.

    2013-01-01

    As the mobile traffic is expected to continue its exponential growth in the near future, energy efficiency has gradually become a must criterion for wireless network design. Three fundamental questions need to be answered before the detailed design could be carried out, namely what energy efficiency

  20. Output Regulation of Large-Scale Hydraulic Networks

    NARCIS (Netherlands)

    De Persis, C.; Jensen, T.N.; Ortega, R.; Wisniewski, R.

    The problem of output regulation for a class of hydraulic networks found in district heating systems is addressed in this brief. The results show that global asymptotic and semiglobal exponential output regulation is achievable using a set of decentralized proportional-integral controllers. The fact

  1. Measuring Large-Scale Social Networks with High Resolution

    DEFF Research Database (Denmark)

    Stopczynski, Arkadiusz; Sekara, Vedran; Sapiezynski, Piotr

    2014-01-01

    , telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation...

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

    Science.gov (United States)

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

    2010-01-01

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

  3. Output regulation of large-scale hydraulic networks with minimal steady state power consumption

    NARCIS (Netherlands)

    Jensen, Tom Nørgaard; Wisniewski, Rafał; De Persis, Claudio; Kallesøe, Carsten Skovmose

    2014-01-01

    An industrial case study involving a large-scale hydraulic network is examined. The hydraulic network underlies a district heating system, with an arbitrary number of end-users. The problem of output regulation is addressed along with a optimization criterion for the control. The fact that the

  4. Students' Attitudes towards Edmodo, a Social Learning Network: A Scale Development Study

    Science.gov (United States)

    Yunkul, Eyup; Cankaya, Serkan

    2017-01-01

    Social Learning Networks (SLNs) are the developed forms of Social Network Sites (SNSs) adapted to educational environments, and they are used by quite a large population throughout the world. In addition, in related literature, there is no scale for the measurement of students' attitudes towards such sites. The purpose of this study was to develop…

  5. Implementation of Cyberinfrastructure and Data Management Workflow for a Large-Scale Sensor Network

    Science.gov (United States)

    Jones, A. S.; Horsburgh, J. S.

    2014-12-01

    Monitoring with in situ environmental sensors and other forms of field-based observation presents many challenges for data management, particularly for large-scale networks consisting of multiple sites, sensors, and personnel. The availability and utility of these data in addressing scientific questions relies on effective cyberinfrastructure that facilitates transformation of raw sensor data into functional data products. It also depends on the ability of researchers to share and access the data in useable formats. In addition to addressing the challenges presented by the quantity of data, monitoring networks need practices to ensure high data quality, including procedures and tools for post processing. Data quality is further enhanced if practitioners are able to track equipment, deployments, calibrations, and other events related to site maintenance and associate these details with observational data. In this presentation we will describe the overall workflow that we have developed for research groups and sites conducting long term monitoring using in situ sensors. Features of the workflow include: software tools to automate the transfer of data from field sites to databases, a Python-based program for data quality control post-processing, a web-based application for online discovery and visualization of data, and a data model and web interface for managing physical infrastructure. By automating the data management workflow, the time from collection to analysis is reduced and sharing and publication is facilitated. The incorporation of metadata standards and descriptions and the use of open-source tools enhances the sustainability and reusability of the data. We will describe the workflow and tools that we have developed in the context of the iUTAH (innovative Urban Transitions and Aridregion Hydrosustainability) monitoring network. The iUTAH network consists of aquatic and climate sensors deployed in three watersheds to monitor Gradients Along Mountain to Urban

  6. Unified Tractable Model for Large-Scale Networks Using Stochastic Geometry: Analysis and Design

    KAUST Repository

    Afify, Laila H.

    2016-12-01

    The ever-growing demands for wireless technologies necessitate the evolution of next generation wireless networks that fulfill the diverse wireless users requirements. However, upscaling existing wireless networks implies upscaling an intrinsic component in the wireless domain; the aggregate network interference. Being the main performance limiting factor, it becomes crucial to develop a rigorous analytical framework to accurately characterize the out-of-cell interference, to reap the benefits of emerging networks. Due to the different network setups and key performance indicators, it is essential to conduct a comprehensive study that unifies the various network configurations together with the different tangible performance metrics. In that regard, the focus of this thesis is to present a unified mathematical paradigm, based on Stochastic Geometry, for large-scale networks with different antenna/network configurations. By exploiting such a unified study, we propose an efficient automated network design strategy to satisfy the desired network objectives. First, this thesis studies the exact aggregate network interference characterization, by accounting for each of the interferers signals in the large-scale network. Second, we show that the information about the interferers symbols can be approximated via the Gaussian signaling approach. The developed mathematical model presents twofold analysis unification for uplink and downlink cellular networks literature. It aligns the tangible decoding error probability analysis with the abstract outage probability and ergodic rate analysis. Furthermore, it unifies the analysis for different antenna configurations, i.e., various multiple-input multiple-output (MIMO) systems. Accordingly, we propose a novel reliable network design strategy that is capable of appropriately adjusting the network parameters to meet desired design criteria. In addition, we discuss the diversity-multiplexing tradeoffs imposed by differently favored

  7. Differentiating unipolar and bipolar depression by alterations in large-scale brain networks.

    Science.gov (United States)

    Goya-Maldonado, Roberto; Brodmann, Katja; Keil, Maria; Trost, Sarah; Dechent, Peter; Gruber, Oliver

    2016-02-01

    Misdiagnosing bipolar depression can lead to very deleterious consequences of mistreatment. Although depressive symptoms may be similarly expressed in unipolar and bipolar disorder, changes in specific brain networks could be very distinct, being therefore informative markers for the differential diagnosis. We aimed to characterize specific alterations in candidate large-scale networks (frontoparietal, cingulo-opercular, and default mode) in symptomatic unipolar and bipolar patients using resting state fMRI, a cognitively low demanding paradigm ideal to investigate patients. Networks were selected after independent component analysis, compared across 40 patients acutely depressed (20 unipolar, 20 bipolar), and 20 controls well-matched for age, gender, and education levels, and alterations were correlated to clinical parameters. Despite comparable symptoms, patient groups were robustly differentiated by large-scale network alterations. Differences were driven in bipolar patients by increased functional connectivity in the frontoparietal network, a central executive and externally-oriented network. Conversely, unipolar patients presented increased functional connectivity in the default mode network, an introspective and self-referential network, as much as reduced connectivity of the cingulo-opercular network to default mode regions, a network involved in detecting the need to switch between internally and externally oriented demands. These findings were mostly unaffected by current medication, comorbidity, and structural changes. Moreover, network alterations in unipolar patients were significantly correlated to the number of depressive episodes. Unipolar and bipolar groups displaying similar symptomatology could be clearly distinguished by characteristic changes in large-scale networks, encouraging further investigation of network fingerprints for clinical use. Hum Brain Mapp 37:808-818, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  8. Genome scale metabolic network reconstruction of Spirochaeta cellobiosiphila

    Directory of Open Access Journals (Sweden)

    Bharat Manna

    2017-10-01

    Full Text Available Substantial rise in the global energy demand is one of the biggest challenges in this century. Environmental pollution due to rapid depletion of the fossil fuel resources and its alarming impact on the climate change and Global Warming have motivated researchers to look for non-petroleum-based sustainable, eco-friendly, renewable, low-cost energy alternatives, such as biofuel. Lignocellulosic biomass is one of the most promising bio-resources with huge potential to contribute to this worldwide energy demand. However, the complex organization of the Cellulose, Hemicellulose and Lignin in the Lignocellulosic biomass requires extensive pre-treatment and enzymatic hydrolysis followed by fermentation, raising overall production cost of biofuel. This encourages researchers to design cost-effective approaches for the production of second generation biofuels. The products from enzymatic hydrolysis of cellulose are mostly glucose monomer or cellobiose unit that are subjected to fermentation. Spirochaeta genus is a well-known group of obligate or facultative anaerobes, living primarily on carbohydrate metabolism. Spirochaeta cellobiosiphila sp. is a facultative anaerobe under this genus, which uses a variety of monosaccharides and disaccharides as energy sources. However, most rapid growth occurs on cellobiose and fermentation yields significant amount of ethanol, acetate, CO2, H2 and small amounts of formate. It is predicted to be promising microbial machinery for industrial fermentation processes for biofuel production. The metabolic pathways that govern cellobiose metabolism in Spirochaeta cellobiosiphila are yet to be explored. The function annotation of the genome sequence of Spirochaeta cellobiosiphila is in progress. In this work we aim to map all the metabolic activities for reconstruction of genome-scale metabolic model of Spirochaeta cellobiosiphila.

  9. A Study of Rank Defect and Network Effect in Processing the CMONOC Network on Bernese

    Directory of Open Access Journals (Sweden)

    Weiwei Wu

    2018-02-01

    Full Text Available High-precision GPS data processing on Bernese has been employed to routinely resolve daily position solutions of GPS stations in the Crustal Movement Observation Network of China (CMONOC. The rank-deficient problems of the normal equation (NEQ system and the network effect on the frame alignment of NEQs in the processing of CMONOC data on Bernese still present difficulties. In this study, we diagnose the rank-deficient problems of the original NEQ, review the efficiency of the controlled datum removal (CDR method in filtering out the three frame-origin-related datum contents, investigate the reliabilities of the inherited frame orientation and scale information from the fixation of the GPS satellite orbits and the Earth rotation parameters in establishing the NEQ of the CMONOC network on Bernese, and analyze the impact of the network effect on the position time series of GPS stations. Our results confirm the nonsingularity of the original NEQ and the efficiency of the CDR filtering in resolving the rank-deficient problems; show that the frame origin parameters are weakly defined and should be stripped off, while the frame orientation and scale parameters should be retained due to their insufficient redefinition from the minimal constraint (MC implementation through inhomogeneous and asymmetrical fiducial networks; and reveal the superiority of a globally distributed fiducial network for frame alignment of the reconstructed NEQs via No-Net-Translation (NNT MC conditions. Finally, we attribute the two apparent discontinuities in the position time series to the terrestrial reference frame (TRF conversions of the GPS satellite orbits, and identify it as the orbit TRF effect.

  10. Scaling-Laws of Flow Entropy with Topological Metrics of Water Distribution Networks

    Directory of Open Access Journals (Sweden)

    Giovanni Francesco Santonastaso

    2018-01-01

    Full Text Available Robustness of water distribution networks is related to their connectivity and topological structure, which also affect their reliability. Flow entropy, based on Shannon’s informational entropy, has been proposed as a measure of network redundancy and adopted as a proxy of reliability in optimal network design procedures. In this paper, the scaling properties of flow entropy of water distribution networks with their size and other topological metrics are studied. To such aim, flow entropy, maximum flow entropy, link density and average path length have been evaluated for a set of 22 networks, both real and synthetic, with different size and topology. The obtained results led to identify suitable scaling laws of flow entropy and maximum flow entropy with water distribution network size, in the form of power–laws. The obtained relationships allow comparing the flow entropy of water distribution networks with different size, and provide an easy tool to define the maximum achievable entropy of a specific water distribution network. An example of application of the obtained relationships to the design of a water distribution network is provided, showing how, with a constrained multi-objective optimization procedure, a tradeoff between network cost and robustness is easily identified.

  11. Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model.

    Directory of Open Access Journals (Sweden)

    Guido Gigante

    2015-11-01

    Full Text Available Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.

  12. Scaling-up camera traps: monitoring the planet's biodiversity with networks of remote sensors

    Science.gov (United States)

    Steenweg, Robin; Hebblewhite, Mark; Kays, Roland; Ahumada, Jorge A.; Fisher, Jason T.; Burton, Cole; Townsend, Susan E.; Carbone, Chris; Rowcliffe, J. Marcus; Whittington, Jesse; Brodie, Jedediah; Royle, Andy; Switalski, Adam; Clevenger, Anthony P.; Heim, Nicole; Rich, Lindsey N.

    2017-01-01

    Countries committed to implementing the Convention on Biological Diversity's 2011–2020 strategic plan need effective tools to monitor global trends in biodiversity. Remote cameras are a rapidly growing technology that has great potential to transform global monitoring for terrestrial biodiversity and can be an important contributor to the call for measuring Essential Biodiversity Variables. Recent advances in camera technology and methods enable researchers to estimate changes in abundance and distribution for entire communities of animals and to identify global drivers of biodiversity trends. We suggest that interconnected networks of remote cameras will soon monitor biodiversity at a global scale, help answer pressing ecological questions, and guide conservation policy. This global network will require greater collaboration among remote-camera studies and citizen scientists, including standardized metadata, shared protocols, and security measures to protect records about sensitive species. With modest investment in infrastructure, and continued innovation, synthesis, and collaboration, we envision a global network of remote cameras that not only provides real-time biodiversity data but also serves to connect people with nature.

  13. Working memory training mostly engages general-purpose large-scale networks for learning.

    Science.gov (United States)

    Salmi, Juha; Nyberg, Lars; Laine, Matti

    2018-03-21

    The present meta-analytic study examined brain activation changes following working memory (WM) training, a form of cognitive training that has attracted considerable interest. Comparisons with perceptual-motor (PM) learning revealed that WM training engages domain-general large-scale networks for learning encompassing the dorsal attention and salience networks, sensory areas, and striatum. Also the dynamics of the training-induced brain activation changes within these networks showed a high overlap between WM and PM training. The distinguishing feature for WM training was the consistent modulation of the dorso- and ventrolateral prefrontal cortex (DLPFC/VLPFC) activity. The strongest candidate for mediating transfer to similar untrained WM tasks was the frontostriatal system, showing higher striatal and VLPFC activations, and lower DLPFC activations after training. Modulation of transfer-related areas occurred mostly with longer training periods. Overall, our findings place WM training effects into a general perception-action cycle, where some modulations may depend on the specific cognitive demands of a training task. Copyright © 2018 Elsevier Ltd. All rights reserved.

  14. Social network analysis of multi-stakeholder platforms in agricultural research for development: Opportunities and constraints for innovation and scaling

    Science.gov (United States)

    Hermans, Frans; Sartas, Murat; van Schagen, Boudy; van Asten, Piet

    2017-01-01

    Multi-stakeholder platforms (MSPs) are seen as a promising vehicle to achieve agricultural development impacts. By increasing collaboration, exchange of knowledge and influence mediation among farmers, researchers and other stakeholders, MSPs supposedly enhance their ‘capacity to innovate’ and contribute to the ‘scaling of innovations’. The objective of this paper is to explore the capacity to innovate and scaling potential of three MSPs in Burundi, Rwanda and the South Kivu province located in the eastern part of Democratic Republic of Congo (DRC). In order to do this, we apply Social Network Analysis and Exponential Random Graph Modelling (ERGM) to investigate the structural properties of the collaborative, knowledge exchange and influence networks of these MSPs and compared them against value propositions derived from the innovation network literature. Results demonstrate a number of mismatches between collaboration, knowledge exchange and influence networks for effective innovation and scaling processes in all three countries: NGOs and private sector are respectively over- and under-represented in the MSP networks. Linkages between local and higher levels are weak, and influential organisations (e.g., high-level government actors) are often not part of the MSP or are not actively linked to by other organisations. Organisations with a central position in the knowledge network are more sought out for collaboration. The scaling of innovations is primarily between the same type of organisations across different administrative levels, but not between different types of organisations. The results illustrate the potential of Social Network Analysis and ERGMs to identify the strengths and limitations of MSPs in terms of achieving development impacts. PMID:28166226

  15. Social network analysis of multi-stakeholder platforms in agricultural research for development: Opportunities and constraints for innovation and scaling.

    Science.gov (United States)

    Hermans, Frans; Sartas, Murat; van Schagen, Boudy; van Asten, Piet; Schut, Marc

    2017-01-01

    Multi-stakeholder platforms (MSPs) are seen as a promising vehicle to achieve agricultural development impacts. By increasing collaboration, exchange of knowledge and influence mediation among farmers, researchers and other stakeholders, MSPs supposedly enhance their 'capacity to innovate' and contribute to the 'scaling of innovations'. The objective of this paper is to explore the capacity to innovate and scaling potential of three MSPs in Burundi, Rwanda and the South Kivu province located in the eastern part of Democratic Republic of Congo (DRC). In order to do this, we apply Social Network Analysis and Exponential Random Graph Modelling (ERGM) to investigate the structural properties of the collaborative, knowledge exchange and influence networks of these MSPs and compared them against value propositions derived from the innovation network literature. Results demonstrate a number of mismatches between collaboration, knowledge exchange and influence networks for effective innovation and scaling processes in all three countries: NGOs and private sector are respectively over- and under-represented in the MSP networks. Linkages between local and higher levels are weak, and influential organisations (e.g., high-level government actors) are often not part of the MSP or are not actively linked to by other organisations. Organisations with a central position in the knowledge network are more sought out for collaboration. The scaling of innovations is primarily between the same type of organisations across different administrative levels, but not between different types of organisations. The results illustrate the potential of Social Network Analysis and ERGMs to identify the strengths and limitations of MSPs in terms of achieving development impacts.

  16. Social network analysis of multi-stakeholder platforms in agricultural research for development: Opportunities and constraints for innovation and scaling.

    Directory of Open Access Journals (Sweden)

    Frans Hermans

    Full Text Available Multi-stakeholder platforms (MSPs are seen as a promising vehicle to achieve agricultural development impacts. By increasing collaboration, exchange of knowledge and influence mediation among farmers, researchers and other stakeholders, MSPs supposedly enhance their 'capacity to innovate' and contribute to the 'scaling of innovations'. The objective of this paper is to explore the capacity to innovate and scaling potential of three MSPs in Burundi, Rwanda and the South Kivu province located in the eastern part of Democratic Republic of Congo (DRC. In order to do this, we apply Social Network Analysis and Exponential Random Graph Modelling (ERGM to investigate the structural properties of the collaborative, knowledge exchange and influence networks of these MSPs and compared them against value propositions derived from the innovation network literature. Results demonstrate a number of mismatches between collaboration, knowledge exchange and influence networks for effective innovation and scaling processes in all three countries: NGOs and private sector are respectively over- and under-represented in the MSP networks. Linkages between local and higher levels are weak, and influential organisations (e.g., high-level government actors are often not part of the MSP or are not actively linked to by other organisations. Organisations with a central position in the knowledge network are more sought out for collaboration. The scaling of innovations is primarily between the same type of organisations across different administrative levels, but not between different types of organisations. The results illustrate the potential of Social Network Analysis and ERGMs to identify the strengths and limitations of MSPs in terms of achieving development impacts.

  17. Using Sunlight and Cell Networks to Bring Fleeting Tracking to Small Scale Fisheries

    Science.gov (United States)

    Garren, M.; Selbie, H.; Suchomel, D.; McDonald, W.; Solomon, D.

    2016-12-01

    Traditionally, the efforts of small scale fisheries have not been easily incorporated into the global picture of fishing effort and activity. That means that the activities of the vast majority ( 90%) of fishing vessels in the world have remained unquantified and largely opaque. With newly developed technology that harnesses solar power and cost-effective cellular networks to transmit data, it is becoming possible to provide vessel tracking systems on a large scale for vessels of all sizes. Furthermore, capitalizing on the relatively inexpensive cellular networks to transfer the data enables data of much higher granularity to be captured. By recording a vessel's position every few seconds, instead of minutes to hours as is typical of most satellite-based systems, we are able to resolve a diverse array of behaviors happening at sea including when and where fishing occurred and what type of fishing gear was used. This high granularity data is both incredibly useful and also a challenge to manage and mine. New approaches for handling and processing this continuous data stream of vessel positions are being developed to extract the most informative and actionable pieces of information for a variety of audiences including governing agencies, industry supply chains seeking transparency, non-profit organizations supporting conservation efforts, academic researchers and the fishers and boat owners.

  18. Scaling of differentiation in networks: nervous systems, organisms, ant colonies, ecosystems, businesses, universities, cities, electronic circuits, and Legos.

    Science.gov (United States)

    Changizi, M A; McDannald, M A; Widders, D

    2002-09-21

    Nodes in networks are often of different types, and in this sense networks are differentiated. Here we examine the relationship between network differentiation and network size in networks under economic or natural selective pressure, such as electronic circuits (networks of electronic components), Legos (networks of Lego pieces), businesses (networks of employees), universities (networks of faculty), organisms (networks of cells), ant colonies (networks of ants), and nervous systems (networks of neurons). For each of these we find that (i) differentiation increases with network size, and (ii) the relationship is consistent with a power law. These results are explained by a hypothesis that, because nodes are costly to build and maintain in such "selected networks", network size is optimized, and from this the power-law relationship may be derived. The scaling exponent depends on the particular kind of network, and is determined by the degree to which nodes are used in a combinatorial fashion to carry out network-level functions. We find that networks under natural selection (organisms, ant colonies, and nervous systems) have much higher combinatorial abilities than the networks for which human ingenuity is involved (electronic circuits, Legos, businesses, and universities). A distinct but related optimization hypothesis may be used to explain scaling of differentiation in competitive networks (networks where the nodes themselves, rather than the entire network, are under selective pressure) such as ecosystems (networks of organisms).

  19. Truncation of power law behavior in 'scale-free' network models due to information filtering

    International Nuclear Information System (INIS)

    Mossa, Stefano; Barthelemy, Marc; Eugene Stanley, H.; Nunes Amaral, Luis A.

    2002-01-01

    We formulate a general model for the growth of scale-free networks under filtering information conditions--that is, when the nodes can process information about only a subset of the existing nodes in the network. We find that the distribution of the number of incoming links to a node follows a universal scaling form, i.e., that it decays as a power law with an exponential truncation controlled not only by the system size but also by a feature not previously considered, the subset of the network 'accessible' to the node. We test our model with empirical data for the World Wide Web and find agreement

  20. Group Centric Networking: Large Scale Over the Air Testing of Group Centric Networking

    Science.gov (United States)

    2016-11-01

    standardization: A paradigm shift in wsn routing protocols,” Communications Surveys & Tutorials , IEEE , vol. 13, no. 4, pp. 688–707, 2011. [4] G. Kuperman, J. Sun...performance of Group Centric Networking (GCN), a networking protocol developed for robust and scalable communications in lossy networks where users are...automobiles, will connect to one another. In a given location (house, factory, etc.), we expect 10s to 100s of devices to communicate with one another

  1. Sampling network stratification by terrain classification in eroded agricultural landscapes at plot scale

    Science.gov (United States)

    Penížek, Vít; Zádorová, Tereza; Kodešová, Radka

    2015-04-01

    The description of soil properties variability is important aspect in land management and food production at plot scale. We describe novelty approach for design of sampling network on agricultural plots with high relief variability. The terrain properties were used for improved spatial prediction of soil properties including design of the sampling network. Regular sampling network, random sampling network, systematic unaligned sampling network and stratified sampling network schemes were compared to prove the advantages of relief based stratified sampling networks. The study was performed for humus horizon depth prediction on agriculture plot of 6.5 ha with dissected relief where originally homogenous soil cover was differentiated by erosion and sedimentation into mosaic of Chernozem, Regosol and colluvial soils. Moreover the comparison was done on three levels of sampling density (65, 40 and 24 sampling points). The stratification of sampling network was based on unsupervised relief classification. The performance of the soil properties prediction based on different sampling network was assesed by RMSE calculation based on predicted values versus validation dataset. According the RMSE, the stratified sampling network performed the best (7.4 cm) comparing the regular sampling network (10.8 cm), random sampling network (17.7 cm) and systematic unaligned sampling network (11.2 cm). The accuracy of the soil properties spatial prediction decreased with the decreasing number of sampling points, but the stratified network performed significantly better that other used methods. The study showed that, for soil properties spatial variability description at certain accuracy level, relief-based stratified network can contain 25 % less sampling points comparing to regular network. This leads to potential financial and person cost reduction for the soil survey. The study was supported by grant nr. 13-07516P of the Czech science foundation and by grant nr. QJ1230319 of the

  2. Synergistic effects in threshold models on networks

    Science.gov (United States)

    Juul, Jonas S.; Porter, Mason A.

    2018-01-01

    Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

  3. Scale dependence of effective media properties

    International Nuclear Information System (INIS)

    Tidwell, V.C.; VonDoemming, J.D.; Martinez, K.

    1992-01-01

    For problems where media properties are measured at one scale and applied at another, scaling laws or models must be used in order to define effective properties at the scale of interest. The accuracy of such models will play a critical role in predicting flow and transport through the Yucca Mountain Test Site given the sensitivity of these calculations to the input property fields. Therefore, a research programhas been established to gain a fundamental understanding of how properties scale with the aim of developing and testing models that describe scaling behavior in a quantitative-manner. Scaling of constitutive rock properties is investigated through physical experimentation involving the collection of suites of gas permeability data measured over a range of discrete scales. Also, various physical characteristics of property heterogeneity and the means by which the heterogeneity is measured and described are systematically investigated to evaluate their influence on scaling behavior. This paper summarizes the approach that isbeing taken toward this goal and presents the results of a scoping study that was conducted to evaluate the feasibility of the proposed research

  4. On the Effects of Frequency Scaling Over Capacity Scaling in Underwater Networks—Part I

    DEFF Research Database (Denmark)

    Shin, Won-Yong; Roetter, Daniel Enrique Lucani; Médard, Muriel

    2013-01-01

    In this two-part paper, information-theoretic capacity scaling laws are analyzed in an underwater acoustic network with n regularly located nodes on a square, in which both bandwidth and received signal power can be limited significantly. Parts I and II deal with an extended network of unit node...... propagation speed of acoustic channel, and show that it is order-optimal for all operating regimes of extended networks. Finally, these scaling results are extended to the case of random node deployments providing fundamental limits to more complex scenarios of extended underwater networks....

  5. Federated queries of clinical data repositories: Scaling to a national network.

    Science.gov (United States)

    Weber, Griffin M

    2015-06-01

    Federated networks of clinical research data repositories are rapidly growing in size from a handful of sites to true national networks with more than 100 hospitals. This study creates a conceptual framework for predicting how various properties of these systems will scale as they continue to expand. Starting with actual data from Harvard's four-site Shared Health Research Information Network (SHRINE), the framework is used to imagine a future 4000 site network, representing the majority of hospitals in the United States. From this it becomes clear that several common assumptions of small networks fail to scale to a national level, such as all sites being online at all times or containing data from the same date range. On the other hand, a large network enables researchers to select subsets of sites that are most appropriate for particular research questions. Developers of federated clinical data networks should be aware of how the properties of these networks change at different scales and design their software accordingly. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. The spread of computer viruses over a reduced scale-free network

    Science.gov (United States)

    Yang, Lu-Xing; Yang, Xiaofan

    2014-02-01

    Due to the high dimensionality of an epidemic model of computer viruses over a general scale-free network, it is difficult to make a close study of its dynamics. In particular, it is extremely difficult, if not impossible, to prove the global stability of its viral equilibrium, if any. To overcome this difficulty, we suggest to simplify a general scale-free network by partitioning all of its nodes into two classes: higher-degree nodes and lower-degree nodes, and then equating the degrees of all higher-degree nodes and all lower-degree nodes, respectively, yielding a reduced scale-free network. We then propose an epidemic model of computer viruses over a reduced scale-free network. A theoretical analysis reveals that the proposed model is bound to have a globally stable viral equilibrium, implying that any attempt to eradicate network viruses would prove unavailing. As a result, the next best thing we can do is to restrain virus prevalence. Based on an analysis of the impact of different model parameters on virus prevalence, some practicable measures are recommended to contain virus spreading. The work in this paper adequately justifies the idea of reduced scale-free networks.

  7. Scaling behaviours in the growth of networked systems and their geometric origins.

    Science.gov (United States)

    Zhang, Jiang; Li, Xintong; Wang, Xinran; Wang, Wen-Xu; Wu, Lingfei

    2015-04-29

    Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.

  8. Dynamic Dilution Effects in Polymeric Networks

    DEFF Research Database (Denmark)

    Skov, Anne Ladegaard; Sommer-Larsen, Peter; Hassager, Ole

    2006-01-01

    The relaxation processes occurring in slightly and well-entangled polydimetylsiloxane ( PDMS) networks are investigated. Swelling experiments are performed in order to determine the sol fractions. The low-frequency linear rheology of the two types of networks reveal two significant relaxation pro...... by the change in the amount of dangling arms and solubles with stoichiometry. The star arm relaxation is suppressed by washing out the sol fraction which is seen as a clear example of the dynamic dilution effect arising from the small amount of non-reactive PDMS....... processes, namely the reptation of linear species within the network and the arm withdrawal process of star arms in the sol fraction and of dangling single-chain ends attached to the network. The relaxation spectra are influenced by the stoichiometry to a large extent due to dynamic dilution effects caused...

  9. Corrections to scaling in random resistor networks and diluted continuous spin models near the percolation threshold.

    Science.gov (United States)

    Janssen, Hans-Karl; Stenull, Olaf

    2004-02-01

    We investigate corrections to scaling induced by irrelevant operators in randomly diluted systems near the percolation threshold. The specific systems that we consider are the random resistor network and a class of continuous spin systems, such as the x-y model. We focus on a family of least irrelevant operators and determine the corrections to scaling that originate from this family. Our field theoretic analysis carefully takes into account that irrelevant operators mix under renormalization. It turns out that long standing results on corrections to scaling are respectively incorrect (random resistor networks) or incomplete (continuous spin systems).

  10. Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales

    Directory of Open Access Journals (Sweden)

    Fei Yu

    2009-01-01

    Full Text Available Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory (BAM neural networks with distributed delays and impulses on time scales. This is the first time applying the time-scale calculus theory to unify the discrete-time and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework.

  11. Network modularity reveals critical scales for connectivity in ecology and evolution

    Science.gov (United States)

    Fletcher, Robert J.; Revell, Andre; Reichert, Brian E.; Kitchens, Wiley M.; Dixon, J.; Austin, James D.

    2013-01-01

    For nearly a century, biologists have emphasized the profound importance of spatial scale for ecology, evolution and conservation. Nonetheless, objectively identifying critical scales has proven incredibly challenging. Here we extend new techniques from physics and social sciences that estimate modularity on networks to identify critical scales for movement and gene flow in animals. Using four species that vary widely in dispersal ability and include both mark-recapture and population genetic data, we identify significant modularity in three species, two of which cannot be explained by geographic distance alone. Importantly, the inclusion of modularity in connectivity and population viability assessments alters conclusions regarding patch importance to connectivity and suggests higher metapopulation viability than when ignoring this hidden spatial scale. We argue that network modularity reveals critical meso-scales that are probably common in populations, providing a powerful means of identifying fundamental scales for biology and for conservation strategies aimed at recovering imperilled species.

  12. Cumulative effects assessment: Does scale matter?

    International Nuclear Information System (INIS)

    Therivel, Riki; Ross, Bill

    2007-01-01

    Cumulative effects assessment (CEA) is (or should be) an integral part of environmental assessment at both the project and the more strategic level. CEA helps to link the different scales of environmental assessment in that it focuses on how a given receptor is affected by the totality of plans, projects and activities, rather than on the effects of a particular plan or project. This article reviews how CEAs consider, and could consider, scale issues: spatial extent, level of detail, and temporal issues. It is based on an analysis of Canadian project-level CEAs and UK strategic-level CEAs. Based on a review of literature and, especially, case studies with which the authors are familiar, it concludes that scale issues are poorly considered at both levels, with particular problems being unclear or non-existing cumulative effects scoping methodologies; poor consideration of past or likely future human activities beyond the plan or project in question; attempts to apportion 'blame' for cumulative effects; and, at the plan level, limited management of cumulative effects caused particularly by the absence of consent regimes. Scale issues are important in most of these problems. However both strategic-level and project-level CEA have much potential for managing cumulative effects through better siting and phasing of development, demand reduction and other behavioural changes, and particularly through setting development consent rules for projects. The lack of strategic resource-based thresholds constrains the robust management of strategic-level cumulative effects

  13. Scalability analysis of large-scale LoRaWAN networks in ns-3

    OpenAIRE

    Abeele, Floris Van den; Haxhibeqiri, Jetmir; Moerman, Ingrid; Hoebeke, Jeroen

    2017-01-01

    As LoRaWAN networks are actively being deployed in the field, it is important to comprehend the limitations of this Low Power Wide Area Network technology. Previous work has raised questions in terms of the scalability and capacity of LoRaWAN networks as the number of end devices grows to hundreds or thousands per gateway. Some works have modeled LoRaWAN networks as pure ALOHA networks, which fails to capture important characteristics such as the capture effect and the effects of interference...

  14. Data Network Effects: Implications for Data Business

    OpenAIRE

    Mitomo, Hitoshi

    2017-01-01

    This paper aims to investigate the existence of “data network effects” in data platform services such as Big Data, Internet-of-Things (IoT) and Artificial Intelligence (AI) and their influence on the diffusion of the services. It intendsto present a preliminary formal analysis ofthe effects of data network externalities.Policy implications will be discussed in terms of the diffusion of services.

  15. Scaling up: human genetics as a Cold War network.

    Science.gov (United States)

    Lindee, Susan

    2014-09-01

    In this commentary I explore how the papers here illuminate the processes of collection that have been so central to the history of human genetics since 1945. The development of human population genetics in the Cold War period produced databases and biobanks that have endured into the present, and that continue to be used and debated. In the decades after the bomb, scientists collected and transferred human biological materials and information from populations of interest, and as they moved these biological resources or biosocial resources acquired new meanings and uses. The papers here collate these practices and map their desires and ironies. They explore how a large international network of geneticists, biological anthropologists, virologists and other physicians and scientists interacted with local informants, research subjects and public officials. They also track the networks and standards that mobilized the transfer of information, genealogies, tissue and blood samples. As Joanna Radin suggests here, the massive collections of human biological materials and data were often understood to be resources for an "as-yet-unknown" future. The stories told here contain elements of surveillance, extraction, salvage and eschatology. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

    OpenAIRE

    S Safinaz; A V Ravi Kumar

    2017-01-01

    In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames t...

  17. Multitrophic diversity effects of network degradation.

    Science.gov (United States)

    Nichols, Elizabeth; Peres, Carlos A; Hawes, Joseph E; Naeem, Shahid

    2016-07-01

    Predicting the functional consequences of biodiversity loss in realistic, multitrophic communities remains a challenge. No existing biodiversity-ecosystem function study to date has simultaneously incorporated information on species traits, network topology, and extinction across multiple trophic levels, while all three factors are independently understood as critical drivers of post-extinction network structure and function. We fill this gap by comparing the functional consequences of simulated species loss both within (monotrophic) and across (bitrophic) trophic levels, in an ecological interaction network estimated from spatially explicit field data on tropical fecal detritus producer and consumers (mammals and dung beetles). We simulated trait-ordered beetle and mammal extinction separately (monotrophic extinction) and the coextinction of beetles following mammal loss (bitrophic extinction), according to network structure. We also compared the diversity effects of bitrophic extinction models using a standard monotrophic function (the daily production or consumption of fecal detritus) and a unique bitrophic functional metric (the proportion of daily detritus production that is consumed). We found similar mono- and bitrophic diversity effects, regardless of which species traits were used to drive extinctions, yet divergent predictions when different measures of function were used. The inclusion of information on network structure had little apparent effect on the qualitative relationship between diversity and function. These results contribute to our growing understanding of the functional consequences of biodiversity from real systems and underscore the importance of species traits and realistic functional metrics to assessments of the ecosystem impacts of network degradation through species loss.

  18. Emergence of scale-free close-knit friendship structure in online social networks.

    Science.gov (United States)

    Cui, Ai-Xiang; Zhang, Zi-Ke; Tang, Ming; Hui, Pak Ming; Fu, Yan

    2012-01-01

    Although the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter, the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree. Structural properties of numerical simulated networks are analyzed and compared with each of the four real networks. This

  19. Investigation of scale effects in the TRF determined by VLBI

    Science.gov (United States)

    Wahl, Daniel; Heinkelmann, Robert; Schuh, Harald

    2017-04-01

    -linear motion or completely excluding affected stations is another important step for an improved scale determination. In addition to the investigation of single baseline repeatabilities also the spatial transformation, which is performed for determining parameters of the ITRF2008, are considered. Since the reliability of the resulting transformation parameters is higher the more identical points are used in the transformation, an approach where all possible stations are used as control points is comprehensible. Experiments that examine the scale-factor and its spatial behavior between control points in ITRF2008 and coordinates determined by VLBI only showed that the network geometry has a large influence on the outcome as well. Introducing an unequally distributed network for the datum configuration, the correlations between translation parameters and the scale-factor can become remarkably high. Only a homogeneous spatial distribution of participating stations yields a maximally uncorrelated scale-factor that can be interpreted independent from other parameters. In the current release of the ITRF, the ITRF2014, for the first time, non-linear effects in the time series of station coordinates are taken into account. The present work shows the importance and the right direction of the modification of the ITRF calculation. But also further improvements were found which lead to an enhanced scale determination.

  20. Population size estimation of men who have sex with men through the network scale-up method in Japan.

    Directory of Open Access Journals (Sweden)

    Satoshi Ezoe

    Full Text Available BACKGROUND: Men who have sex with men (MSM are one of the groups most at risk for HIV infection in Japan. However, size estimates of MSM populations have not been conducted with sufficient frequency and rigor because of the difficulty, high cost and stigma associated with reaching such populations. This study examined an innovative and simple method for estimating the size of the MSM population in Japan. We combined an internet survey with the network scale-up method, a social network method for estimating the size of hard-to-reach populations, for the first time in Japan. METHODS AND FINDINGS: An internet survey was conducted among 1,500 internet users who registered with a nationwide internet-research agency. The survey participants were asked how many members of particular groups with known population sizes (firepersons, police officers, and military personnel they knew as acquaintances. The participants were also asked to identify the number of their acquaintances whom they understood to be MSM. Using these survey results with the network scale-up method, the personal network size and MSM population size were estimated. The personal network size was estimated to be 363.5 regardless of the sex of the acquaintances and 174.0 for only male acquaintances. The estimated MSM prevalence among the total male population in Japan was 0.0402% without adjustment, and 2.87% after adjusting for the transmission error of MSM. CONCLUSIONS: The estimated personal network size and MSM prevalence seen in this study were comparable to those from previous survey results based on the direct-estimation method. Estimating population sizes through combining an internet survey with the network scale-up method appeared to be an effective method from the perspectives of rapidity, simplicity, and low cost as compared with more-conventional methods.

  1. Population size estimation of men who have sex with men through the network scale-up method in Japan.

    Science.gov (United States)

    Ezoe, Satoshi; Morooka, Takeo; Noda, Tatsuya; Sabin, Miriam Lewis; Koike, Soichi

    2012-01-01

    Men who have sex with men (MSM) are one of the groups most at risk for HIV infection in Japan. However, size estimates of MSM populations have not been conducted with sufficient frequency and rigor because of the difficulty, high cost and stigma associated with reaching such populations. This study examined an innovative and simple method for estimating the size of the MSM population in Japan. We combined an internet survey with the network scale-up method, a social network method for estimating the size of hard-to-reach populations, for the first time in Japan. An internet survey was conducted among 1,500 internet users who registered with a nationwide internet-research agency. The survey participants were asked how many members of particular groups with known population sizes (firepersons, police officers, and military personnel) they knew as acquaintances. The participants were also asked to identify the number of their acquaintances whom they understood to be MSM. Using these survey results with the network scale-up method, the personal network size and MSM population size were estimated. The personal network size was estimated to be 363.5 regardless of the sex of the acquaintances and 174.0 for only male acquaintances. The estimated MSM prevalence among the total male population in Japan was 0.0402% without adjustment, and 2.87% after adjusting for the transmission error of MSM. The estimated personal network size and MSM prevalence seen in this study were comparable to those from previous survey results based on the direct-estimation method. Estimating population sizes through combining an internet survey with the network scale-up method appeared to be an effective method from the perspectives of rapidity, simplicity, and low cost as compared with more-conventional methods.

  2. Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2017-04-01

    Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

  3. Temporal and spatial dynamics of scaling-specific features of a gene regulatory network in Drosophila.

    Science.gov (United States)

    Wu, Honggang; Manu; Jiao, Renjie; Ma, Jun

    2015-12-08

    A widely appreciated aspect of developmental robustness is pattern formation in proportion to size. But how such scaling features emerge dynamically remains poorly understood. Here we generate a data set of the expression profiles of six gap genes in Drosophila melanogaster embryos that differ significantly in size. Expression patterns exhibit size-dependent dynamics both spatially and temporally. We uncover a dynamic emergence of under-scaling in the posterior, accompanied by reduced expression levels of gap genes near the middle of large embryos. Simulation results show that a size-dependent Bicoid gradient input can lead to reduced Krüppel expression that can have long-range and dynamic effects on gap gene expression in the posterior. Thus, for emergence of scaled patterns, the entire embryo may be viewed as a single unified dynamic system where maternally derived size-dependent information interpreted locally can be propagated in space and time as governed by the dynamics of a gene regulatory network.

  4. System markets: Indirect network effects in action, or inaction?

    NARCIS (Netherlands)

    J.L.G. Binken (Jeroen)

    2010-01-01

    textabstractIn this dissertation, I empirically examine system markets up close. More specifically I examine indirect network effects, both demand-side and supply-side indirect network effects. Indirect network effects are the source of positive feedback in system markets, or so network effect

  5. Cascaded Subpatch Networks for Effective CNNs.

    Science.gov (United States)

    Jiang, Xiaoheng; Pang, Yanwei; Sun, Manli; Li, Xuelong

    2017-05-12

    Conventional convolutional neural networks use either a linear or a nonlinear filter to extract features from an image patch (region) of spatial size Hx W (typically, H is small and is equal to W, e.g., H is 5 or 7 ). Generally, the size of the filter is equal to the size Hx W of the input patch. We argue that the representational ability of equal-size strategy is not strong enough. To overcome the drawback, we propose to use subpatch filter whose spatial size hx w is smaller than Hx W . The proposed subpatch filter consists of two subsequent filters. The first one is a linear filter of spatial size hx w and is aimed at extracting features from spatial domain. The second one is of spatial size 1x 1 and is used for strengthening the connection between different input feature channels and for reducing the number of parameters. The subpatch filter convolves with the input patch and the resulting network is called a subpatch network. Taking the output of one subpatch network as input, we further repeat constructing subpatch networks until the output contains only one neuron in spatial domain. These subpatch networks form a new network called the cascaded subpatch network (CSNet). The feature layer generated by CSNet is called the csconv layer. For the whole input image, we construct a deep neural network by stacking a sequence of csconv layers. Experimental results on five benchmark data sets demonstrate the effectiveness and compactness of the proposed CSNet. For example, our CSNet reaches a test error of 5.68% on the CIFAR10 data set without model averaging. To the best of our knowledge, this is the best result ever obtained on the CIFAR10 data set.

  6. Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

    Science.gov (United States)

    Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

    2015-01-01

    Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system

  7. Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

    Directory of Open Access Journals (Sweden)

    Seungseob Lee

    Full Text Available Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets, in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms

  8. From local to central: a network analysis of who manages plant pest and disease outbreaks across scales

    Directory of Open Access Journals (Sweden)

    Ryan R. J. McAllister

    2015-03-01

    Full Text Available One of the key determinants of success in managing natural resources is "institutional fit," i.e., how well the suite of required actions collectively match the scale of the environmental problem. The effective management of pest and pathogen threats to plants is a natural resource problem of particular economic, social, and environmental importance. Responses to incursions are managed by a network of decision makers and managers acting at different spatial and temporal scales. We applied novel network theoretical methods to assess the propensity of growers, local industry, local state government, and state and national government head offices to foster either within- or across-scale coordination during the successful 2001 Australian response to the outbreak of the fungal pathogen black sigatoka (Mycosphaerella fijiensis. We also reconstructed the response network to proxy what that network would look like today under the Australian government's revised response system. We illustrate a structural move in the plant biosecurity response system from one that was locally driven to the current top-down system, in which the national government leads coordination of a highly partitioned engagement process. For biological incursions that spread widely across regions, nationally rather than locally managed responses may improve coordination of diverse tasks. However, in dealing with such challenges of institutional fit, local engagement will always be critical in deploying flexible and adaptive local responses based on a national system. The methods we propose detect where and how network structures foster cross-scale interactions, which will contribute to stronger empirical studies of cross-scale environmental governance.

  9. Scaling Effects on Materials Tribology: From Macro to Micro Scale.

    Science.gov (United States)

    Stoyanov, Pantcho; Chromik, Richard R

    2017-05-18

    The tribological study of materials inherently involves the interaction of surface asperities at the micro to nanoscopic length scales. This is the case for large scale engineering applications with sliding contacts, where the real area of contact is made up of small contacting asperities that make up only a fraction of the apparent area of contact. This is why researchers have sought to create idealized experiments of single asperity contacts in the field of nanotribology. At the same time, small scale engineering structures known as micro- and nano-electromechanical systems (MEMS and NEMS) have been developed, where the apparent area of contact approaches the length scale of the asperities, meaning the real area of contact for these devices may be only a few asperities. This is essentially the field of microtribology, where the contact size and/or forces involved have pushed the nature of the interaction between two surfaces towards the regime where the scale of the interaction approaches that of the natural length scale of the features on the surface. This paper provides a review of microtribology with the purpose to understand how tribological processes are different at the smaller length scales compared to macrotribology. Studies of the interfacial phenomena at the macroscopic length scales (e.g., using in situ tribometry) will be discussed and correlated with new findings and methodologies at the micro-length scale.

  10. A network inference method for large-scale unsupervised identification of novel drug-drug interactions.

    Directory of Open Access Journals (Sweden)

    Roger Guimerà

    Full Text Available Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others. Here we present a network inference algorithm to predict uncharacterized drug-drug interactions. Our algorithm takes, as its only input, sets of previously reported interactions, and does not require any pharmacological or biochemical information about the drugs, their targets or their mechanisms of action. Because the models we use are abstract, our approach can deal with adverse interactions, synergistic/antagonistic/suppressing interactions, or any other type of drug interaction. We show that our method is able to accurately predict interactions, both in exhaustive pairwise interaction data between small sets of drugs, and in large-scale databases. We also demonstrate that our algorithm can be used efficiently to discover interactions of new drugs as part of the drug discovery process.

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

    Science.gov (United States)

    Röhl, Annika; Bockmayr, Alexander

    2017-01-03

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

  12. Genome-scale reconstruction of the sigma factor network in Escherichia coli: topology and functional states

    DEFF Research Database (Denmark)

    Cho, Byung-Kwan; Kim, Donghyuk; Knight, Eric M.

    2014-01-01

    promoters, we do not yet have a genome-scale assessment of their function. Results: Using multiple genome-scale measurements, we elucidated the network of s-factor and promoter interactions in Escherichia coli. The reconstructed network includes 4,724 sigma-factor-specific promoters corresponding...... to transcription units (TUs), representing an increase of more than 300% over what has been previously reported. The reconstructed network was used to investigate competition between alternative sigma-factors (the sigma(70) and sigma(38) regulons), confirming the competition model of sigma substitution...... and negative regulation by alternative s-factors. Comparison with sigma-factor binding in Klebsiella pneumoniae showed that transcriptional regulation of conserved genes in closely related species is unexpectedly divergent. Conclusions: The reconstructed network reveals the regulatory complexity...

  13. Delayed feedback control of bursting synchronization in a scale-free neuronal network.

    Science.gov (United States)

    Batista, C A S; Lopes, S R; Viana, R L; Batista, A M

    2010-01-01

    Several neurological diseases (e.g. essential tremor and Parkinson's disease) are related to pathologically enhanced synchronization of bursting neurons. Suppression of these synchronized rhythms has potential implications in electrical deep-brain stimulation research. We consider a simplified model of a neuronal network where the local dynamics presents a bursting timescale, and the connection architecture displays the scale-free property (power-law distribution of connectivity). The networks exhibit collective oscillations in the form of synchronized bursting rhythms, without affecting the fast timescale dynamics. We investigate the suppression of these synchronized oscillations using a feedback control in the form of a time-delayed signal. We located domains of bursting synchronization suppression in terms of perturbation strength and time delay, and present computational evidence that synchronization suppression is easier in scale-free networks than in the more commonly studied global (mean-field) networks.

  14. Software-defined optical network for metro-scale geographically distributed data centers.

    Science.gov (United States)

    Samadi, Payman; Wen, Ke; Xu, Junjie; Bergman, Keren

    2016-05-30

    The emergence of cloud computing and big data has rapidly increased the deployment of small and mid-sized data centers. Enterprises and cloud providers require an agile network among these data centers to empower application reliability and flexible scalability. We present a software-defined inter data center network to enable on-demand scale out of data centers on a metro-scale optical network. The architecture consists of a combined space/wavelength switching platform and a Software-Defined Networking (SDN) control plane equipped with a wavelength and routing assignment module. It enables establishing transparent and bandwidth-selective connections from L2/L3 switches, on-demand. The architecture is evaluated in a testbed consisting of 3 data centers, 5-25 km apart. We successfully demonstrated end-to-end bulk data transfer and Virtual Machine (VM) migrations across data centers with less than 100 ms connection setup time and close to full link capacity utilization.

  15. Optimization-Based Artificial Bee Colony Algorithm for Data Collection in Large-Scale Mobile Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yinggao Yue

    2016-01-01

    Full Text Available Data collection is a fundamental operation in various mobile wireless sensor networks (MWSN applications. The energy of nodes around the Sink can be untimely depleted owing to the fact that sensor nodes must transmit vast amounts of data, readily forming a bottleneck in energy consumption; mobile wireless sensor networks have been designed to address this issue. In this study, we focused on a large-scale and intensive MWSN which allows a certain amount of data latency by investigating mobile Sink balance from three aspects: data collection maximization, mobile path length minimization, and network reliability optimization. We also derived a corresponding formula to represent the MWSN and proved that it represents an NP-hard problem. Traditional data collection methods only focus on increasing the amount data collection or reducing the overall network energy consumption, which is why we designed the proposed heuristic algorithm to jointly consider cluster head selection, the routing path from ordinary nodes to the cluster head node, and mobile Sink path planning optimization. The proposed data collection algorithm for mobile Sinks is, in effect, based on artificial bee colony. Simulation results show that, in comparison with other algorithms, the proposed algorithm can effectively reduce data transmission, save energy, improve network data collection efficiency and reliability, and extend the network lifetime.

  16. How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?

    Science.gov (United States)

    Proix, Timothée; Spiegler, Andreas; Schirner, Michael; Rothmeier, Simon; Ritter, Petra; Jirsa, Viktor K

    2016-11-15

    Recent efforts to model human brain activity on the scale of the whole brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale brain models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe brain activity on a large-scale such as recorded with magneto- or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale brain network dynamics by (i) systematically varying the number of brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connectome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40 Hz) are sensitive to the short-range connectivity if transmission delays are considered. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Indirect effects drive coevolution in mutualistic networks.

    Science.gov (United States)

    Guimarães, Paulo R; Pires, Mathias M; Jordano, Pedro; Bascompte, Jordi; Thompson, John N

    2017-10-26

    Ecological interactions have been acknowledged to play a key role in shaping biodiversity. Yet a major challenge for evolutionary biology is to understand the role of ecological interactions in shaping trait evolution when progressing from pairs of interacting species to multispecies interaction networks. Here we introduce an approach that integrates coevolutionary dynamics and network structure. Our results show that non-interacting species can be as important as directly interacting species in shaping coevolution within mutualistic assemblages. The contribution of indirect effects differs among types of mutualism. Indirect effects are more likely to predominate in nested, species-rich networks formed by multiple-partner mutualisms, such as pollination or seed dispersal by animals, than in small and modular networks formed by intimate mutualisms, such as those between host plants and their protective ants. Coevolutionary pathways of indirect effects favour ongoing trait evolution by promoting slow but continuous reorganization of the adaptive landscape of mutualistic partners under changing environments. Our results show that coevolution can be a major process shaping species traits throughout ecological networks. These findings expand our understanding of how evolution driven by interactions occurs through the interplay of selection pressures moving along multiple direct and indirect pathways.

  18. a Mean Field Approach for Ising Models on Scale-Free Networks

    Science.gov (United States)

    Iannone, G.; Luongo, Orlando

    Recently, the study of complex networks led to the analysis of the so-called scale-free models in statistical mechanics. This study has increased its importance, thanks to the wide range of applications in numerous physical contexts; for example, one important question is to understand the behavior of various models on such networks. We start first by investigating the Ising model in the mean field approximation and on scale-free networks, studying especially the Ising model with annealed dilution and Clock model, with particular attention devoted to focusing on similarities between the mean field approximations with or without scale-free statistics. A particular emphasis is given to the possible practical applications of these results in other disciplines such as medicine and social science.

  19. Using the reconstructed genome-scale human metabolic network to study physiology and pathology

    OpenAIRE

    Bordbar, Aarash; Palsson, Bernhard O.

    2012-01-01

    Metabolism plays a key role in many major human diseases. Generation of high-throughput omics data has ushered in a new era of systems biology. Genome-scale metabolic network reconstructions provide a platform to interpret omics data in a biochemically meaningful manner. The release of the global human metabolic network, Recon 1, in 2007 has enabled new systems biology approaches to study human physiology, pathology, and pharmacology. There are currently over 20 publications that utilize Reco...

  20. Self-organized and driven phase synchronization in coupled map scale free networks

    OpenAIRE

    Jalan, Sarika; Amritkar, R. E.

    2002-01-01

    As a model of evolving networks, we study coupled logistic maps on scale free networks. For small coupling strengths nodes show turbulent behavior but form phase synchronized clusters as coupling increases. We identify two different ways of cluster formation. For small coupling strengths we get {\\it self-organized clusters} which have mostly intra-cluster couplings and for large coupling strengths there is a crossover and reorganization to {\\it driven clusters} which have mostly inter-cluster...

  1. Scaling Dissolved Nutrient Removal in River Networks: A Comparative Modeling Investigation

    Science.gov (United States)

    Ye, Sheng; Reisinger, Alexander J.; Tank, Jennifer L.; Baker, Michelle A.; Hall, Robert O.; Rosi, Emma J.; Sivapalan, Murugesu

    2017-11-01

    Along the river network, water, sediment, and nutrients are transported, cycled, and altered by coupled hydrological and biogeochemical processes. Our current understanding of the rates and processes controlling the cycling and removal of dissolved inorganic nutrients in river networks is limited due to a lack of empirical measurements in large, (nonwadeable), rivers. The goal of this paper was to develop a coupled hydrological and biogeochemical process model to simulate nutrient uptake at the network scale during summer base flow conditions. The model was parameterized with literature values from headwater streams, and empirical measurements made in 15 rivers with varying hydrological, biological, and topographic characteristics, to simulate nutrient uptake at the network scale. We applied the coupled model to 15 catchments describing patterns in uptake for three different solutes to determine the role of rivers in network-scale nutrient cycling. Model simulation results, constrained by empirical data, suggested that rivers contributed proportionally more to nutrient removal than headwater streams given the fraction of their length represented in a network. In addition, variability of nutrient removal patterns among catchments was varied among solutes, and as expected, was influenced by nutrient concentration and discharge. Net ammonium uptake was not significantly correlated with any environmental descriptor. In contrast, net daily nitrate removal was linked to suspended chlorophyll a (an indicator of primary producers) and land use characteristics. Finally, suspended sediment characteristics and agricultural land use were correlated with net daily removal of soluble reactive phosphorus, likely reflecting abiotic sorption dynamics. Rivers are understudied relative to streams, and our model suggests that rivers can contribute more to network-scale nutrient removal than would be expected based upon their representative fraction of network channel length.

  2. An Energy Scaled and Expanded Vector-Based Forwarding Scheme for Industrial Underwater Acoustic Sensor Networks with Sink Mobility.

    Science.gov (United States)

    Wadud, Zahid; Hussain, Sajjad; Javaid, Nadeem; Bouk, Safdar Hussain; Alrajeh, Nabil; Alabed, Mohamad Souheil; Guizani, Nadra

    2017-09-30

    Industrial Underwater Acoustic Sensor Networks (IUASNs) come with intrinsic challenges like long propagation delay, small bandwidth, large energy consumption, three-dimensional deployment, and high deployment and battery replacement cost. Any routing strategy proposed for IUASN must take into account these constraints. The vector based forwarding schemes in literature forward data packets to sink using holding time and location information of the sender, forwarder, and sink nodes. Holding time suppresses data broadcasts; however, it fails to keep energy and delay fairness in the network. To achieve this, we propose an Energy Scaled and Expanded Vector-Based Forwarding (ESEVBF) scheme. ESEVBF uses the residual energy of the node to scale and vector pipeline distance ratio to expand the holding time. Resulting scaled and expanded holding time of all forwarding nodes has a significant difference to avoid multiple forwarding, which reduces energy consumption and energy balancing in the network. If a node has a minimum holding time among its neighbors, it shrinks the holding time and quickly forwards the data packets upstream. The performance of ESEVBF is analyzed through in network scenario with and without node mobility to ensure its effectiveness. Simulation results show that ESEVBF has low energy consumption, reduces forwarded data copies, and less end-to-end delay.

  3. How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.

    Science.gov (United States)

    Kim, Jinyoung

    2017-12-01

    As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n = 21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.

  4. Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.

    Science.gov (United States)

    Kaiser, Roselinde H; Andrews-Hanna, Jessica R; Wager, Tor D; Pizzagalli, Diego A

    2015-06-01

    Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. To investigate network dysfunction in MDD through a meta-analysis of rsFC studies. Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data. Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis. Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network. Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or

  5. Large scale silver nanowires network fabricated by MeV hydrogen (H+) ion beam irradiation

    International Nuclear Information System (INIS)

    S, Honey; S, Naseem; A, Ishaq; M, Maaza; M T, Bhatti; D, Wan

    2016-01-01

    A random two-dimensional large scale nano-network of silver nanowires (Ag-NWs) is fabricated by MeV hydrogen (H + ) ion beam irradiation. Ag-NWs are irradiated under H +  ion beam at different ion fluences at room temperature. The Ag-NW network is fabricated by H + ion beam-induced welding of Ag-NWs at intersecting positions. H +  ion beam induced welding is confirmed by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Moreover, the structure of Ag NWs remains stable under H +  ion beam, and networks are optically transparent. Morphology also remains stable under H +  ion beam irradiation. No slicings or cuttings of Ag-NWs are observed under MeV H +  ion beam irradiation. The results exhibit that the formation of Ag-NW network proceeds through three steps: ion beam induced thermal spikes lead to the local heating of Ag-NWs, the formation of simple junctions on small scale, and the formation of a large scale network. This observation is useful for using Ag-NWs based devices in upper space where protons are abandoned in an energy range from MeV to GeV. This high-quality Ag-NW network can also be used as a transparent electrode for optoelectronics devices. (paper)

  6. Generating clustered scale-free networks using Poisson based localization of edges

    Science.gov (United States)

    Türker, İlker

    2018-05-01

    We introduce a variety of network models using a Poisson-based edge localization strategy, which result in clustered scale-free topologies. We first verify the success of our localization strategy by realizing a variant of the well-known Watts-Strogatz model with an inverse approach, implying a small-world regime of rewiring from a random network through a regular one. We then apply the rewiring strategy to a pure Barabasi-Albert model and successfully achieve a small-world regime, with a limited capacity of scale-free property. To imitate the high clustering property of scale-free networks with higher accuracy, we adapted the Poisson-based wiring strategy to a growing network with the ingredients of both preferential attachment and local connectivity. To achieve the collocation of these properties, we used a routine of flattening the edges array, sorting it, and applying a mixing procedure to assemble both global connections with preferential attachment and local clusters. As a result, we achieved clustered scale-free networks with a computational fashion, diverging from the recent studies by following a simple but efficient approach.

  7. Management and services for large-scale virtual 3D urban model data based on network

    Science.gov (United States)

    He, Zhengwei; Chen, Jing; Wu, Huayi

    2008-10-01

    The buildings in modern city are complex and diverse, and the quantity is huge. These bring very big challenge for constructing 3D GIS under network circumstance and eventually realizing the Digital Earth. After analyzed the characteristic of network service about massive 3D urban building model data, this paper focuses on the organization and management of spatial data and the network services strategy, proposes a progressive network transmission schema based on the spatial resolution and the component elements of 3D building model data. Next, this paper put forward multistage-link three-dimensional spatial data organization model and encoding method of spatial index based on fully level quadtree structure. Then, a virtual earth platform, called GeoGlobe, was developed using above theory. Experimental results show that above 3D spatial data management model and service theory can availably provide network services for large-scale 3D urban model data. The application results and user experience good .

  8. Scalable and Fully Distributed Localization in Large-Scale Sensor Networks

    Directory of Open Access Journals (Sweden)

    Miao Jin

    2017-06-01

    Full Text Available This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and a non-uniform nodal distribution. In contrast to current state-of-the-art connectivity-based localization methods, the proposed algorithm is highly scalable with linear computation and communication costs with respect to the size of the network; and fully distributed where each node only needs the information of its neighbors without cumbersome partitioning and merging process. The algorithm is theoretically guaranteed and numerically stable. Moreover, the algorithm can be readily extended to the localization of networks with a one-hop transmission range distance measurement, and the propagation of the measurement error at one sensor node is limited within a small area of the network around the node. Extensive simulations and comparison with other methods under various representative network settings are carried out, showing the superior performance of the proposed algorithm.

  9. Networking for large-scale science: infrastructure, provisioning, transport and application mapping

    International Nuclear Information System (INIS)

    Rao, Nageswara S; Carter, Steven M; Wu Qishi; Wing, William R; Zhu Mengxia; Mezzacappa, Anthony; Veeraraghavan, Malathi; Blondin, John M

    2005-01-01

    Large-scale science computations and experiments require unprecedented network capabilities in the form of large bandwidth and dynamically stable connections to support data transfers, interactive visualizations, and monitoring and steering operations. A number of component technologies dealing with the infrastructure, provisioning, transport and application mappings must be developed and/or optimized to achieve these capabilities. We present a brief account of the following technologies that contribute toward achieving these network capabilities: (a) DOE UltraScienceNet and NSF CHEETAH network testbeds that provide on-demand and scheduled dedicated network connections; (b) experimental results on transport protocols that achieve close to 100% utilization on dedicated 1Gbps wide-area channels; (c) a scheme for optimally mapping a visualization pipeline onto a network to minimize the end-to-end delays; and (d) interconnect configuration and protocols that provides multiple Gbps flows from Cray X1 to external hosts

  10. Topology of the Italian airport network: A scale-free small-world network with a fractal structure?

    International Nuclear Information System (INIS)

    Guida, Michele; Maria, Funaro

    2007-01-01

    In this paper, for the first time we analyze the structure of the Italian Airport Network (IAN) looking at it as a mathematical graph and investigate its topological properties. We find that it has very remarkable features, being like a scale-free network, since both the degree and the 'betweenness centrality' distributions follow a typical power-law known in literature as a Double Pareto Law. From a careful analysis of the data, the Italian Airport Network turns out to have a self-similar structure. In short, it is characterized by a fractal nature, whose typical dimensions can be easily determined from the values of the power-law scaling exponents. Moreover, we show that, according to the period examined, these distributions exhibit a number of interesting features, such as the existence of some 'hubs', i.e. in the graph theory's jargon, nodes with a very large number of links, and others most probably associated with geographical constraints. Also, we find that the IAN can be classified as a small-world network because the average distance between reachable pairs of airports grows at most as the logarithm of the number of airports. The IAN does not show evidence of 'communities' and this result could be the underlying reason behind the smallness of the value of the clustering coefficient, which is related to the probability that two nearest neighbors of a randomly chosen airport are connected

  11. Fast sparsely synchronized brain rhythms in a scale-free neural network.

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2015-08-01

    We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For Dnetwork structure for the case of partial and sparse synchronization, which is in contrast to the case of

  12. Fast sparsely synchronized brain rhythms in a scale-free neural network

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2015-08-01

    We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of statistically homogeneous random graphs and small-world networks. Finally, we investigate the effect of network architecture on sparse synchronization for fixed values of J and D in the following three cases: (1) variation in the degree of symmetric attachment, (2) asymmetric preferential attachment of new nodes with different in- and out-degrees, and (3) preferential attachment between pre-existing nodes (without addition of new nodes). In these

  13. Investigating the effects of virtual social networks on entrepreneurial marketing

    Directory of Open Access Journals (Sweden)

    Kambeiz Talebi

    2014-10-01

    Full Text Available This paper presents an empirical investigation to study the effects of virtual social networks on entrepreneurial marketing. The study designs a questionnaire in Likert scale based on a model originally developed by Morris et al. (2002 [Morris, M. H., Schindehutte, M., & LaForge, R. W. (2002. Entrepreneurial marketing: a construct for integrating emerging entrepreneurship and marketing perspectives. Journal of Marketing Theory and Practice, 10(4, 1-19.]. The study considers the effects of three components of virtual social network (VSN; namely structural VSN, interaction VSN and functional VSN on entrepreneurial marketing. Using structural equation modeling, the study has determined positive and meaningful effects of all three VSN components on entrepreneurial marketing.

  14. Output Regulation of Large-Scale Hydraulic Networks with Minimal Steady State Power Consumption

    DEFF Research Database (Denmark)

    Jensen, Tom Nørgaard; Wisniewski, Rafal; De Persis, Claudio

    2014-01-01

    that the system is overactuated is exploited for minimizing the steady state electrical power consumption of the pumps in the system, while output regulation is maintained. The proposed control actions are decentralized in order to make changes in the structure of the hydraulic network easy to implement.......An industrial case study involving a large-scale hydraulic network is examined. The hydraulic network underlies a district heating system, with an arbitrary number of end-users. The problem of output regulation is addressed along with a optimization criterion for the control. The fact...

  15. Are there local-scale effects of altitude, slope and aspect on temporal trends in a spatially high-resolved plant phenological network in the Swiss Alps 1971-2000?

    Science.gov (United States)

    Jeanneret, François; Rutishauser, This; Kottmann, Silvan; Brügger, Robert

    2010-05-01

    Shifts in phenology of plants and animals have been widely observed as consequence of climate change impacts and temperature increase. Species-specific data are often assigned to limited and generalized site information on the precise location of the observation. However, as much meta-information as possible on the individual plant under observations is necessary to assess the impacts of changing weather patterns at the local scale that are related to changes in radiation, fog, frost and dominating circulation. Here we used plant phenological data of the BERNCLIM network that collects data in the Canton of Bern (Switzerland) and adjacent areas covering a total area of 7,000 km2 since 1970. The number of observation sites reached up to 600 observation sites with detailed meta-information of several locations within each site. The precision of coordinates for each location is generally less than one hectare. This information allows to differentiate several terrain-types, based on altitude, slope and aspect. We used original observations and two interpolated data sets based of the blooming of hazel (Corylus avellana L.) for early spring, dandelion (Taraxacum officinale aggr.) for mid spring, and apple trees (Malus domestica Borkh.) for late spring. In addition we used interpolated data by using averaged maximum differences between several locations of a site and an algorithm based on constant spatial patterns in the 1971-1974 period. Phenological maps were created using multiple linear regression models with longitude, latitude, altitude, slope and aspect as independent variables and phenological date of each phase as dependent variable models in a Geographical Information System (GIS). For this contribution we analysed the impact of local terrain differences on phenological trends of three plant species. Specifically, we addressed the question whether differences in altitude, slope and aspect lead to systematic differences in temporal trends for the 1971-2000 period

  16. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition

    Science.gov (United States)

    Hébert-Dufresne, Laurent; Grochow, Joshua A.; Allard, Antoine

    2016-08-01

    We introduce a network statistic that measures structural properties at the micro-, meso-, and macroscopic scales, while still being easy to compute and interpretable at a glance. Our statistic, the onion spectrum, is based on the onion decomposition, which refines the k-core decomposition, a standard network fingerprinting method. The onion spectrum is exactly as easy to compute as the k-cores: It is based on the stages at which each vertex gets removed from a graph in the standard algorithm for computing the k-cores. Yet, the onion spectrum reveals much more information about a network, and at multiple scales; for example, it can be used to quantify node heterogeneity, degree correlations, centrality, and tree- or lattice-likeness. Furthermore, unlike the k-core decomposition, the combined degree-onion spectrum immediately gives a clear local picture of the network around each node which allows the detection of interesting subgraphs whose topological structure differs from the global network organization. This local description can also be leveraged to easily generate samples from the ensemble of networks with a given joint degree-onion distribution. We demonstrate the utility of the onion spectrum for understanding both static and dynamic properties on several standard graph models and on many real-world networks.

  17. A probabilistic approach to quantifying spatial patterns of flow regimes and network-scale connectivity

    Science.gov (United States)

    Garbin, Silvia; Alessi Celegon, Elisa; Fanton, Pietro; Botter, Gianluca

    2017-04-01

    The temporal variability of river flow regime is a key feature structuring and controlling fluvial ecological communities and ecosystem processes. In particular, streamflow variability induced by climate/landscape heterogeneities or other anthropogenic factors significantly affects the connectivity between streams with notable implication for river fragmentation. Hydrologic connectivity is a fundamental property that guarantees species persistence and ecosystem integrity in riverine systems. In riverine landscapes, most ecological transitions are flow-dependent and the structure of flow regimes may affect ecological functions of endemic biota (i.e., fish spawning or grazing of invertebrate species). Therefore, minimum flow thresholds must be guaranteed to support specific ecosystem services, like fish migration, aquatic biodiversity and habitat suitability. In this contribution, we present a probabilistic approach aiming at a spatially-explicit, quantitative assessment of hydrologic connectivity at the network-scale as derived from river flow variability. Dynamics of daily streamflows are estimated based on catchment-scale climatic and morphological features, integrating a stochastic, physically based approach that accounts for the stochasticity of rainfall with a water balance model and a geomorphic recession flow model. The non-exceedance probability of ecologically meaningful flow thresholds is used to evaluate the fragmentation of individual stream reaches, and the ensuing network-scale connectivity metrics. A multi-dimensional Poisson Process for the stochastic generation of rainfall is used to evaluate the impact of climate signature on reach-scale and catchment-scale connectivity. The analysis shows that streamflow patterns and network-scale connectivity are influenced by the topology of the river network and the spatial variability of climatic properties (rainfall, evapotranspiration). The framework offers a robust basis for the prediction of the impact of

  18. The function of communities in protein interaction networks at multiple scales

    Directory of Open Access Journals (Sweden)

    Jones Nick S

    2010-07-01

    Full Text Available Abstract Background If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network. Results Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

  19. A mechanism to synchronize fluctuations in scale free networks using growth models

    Science.gov (United States)

    La Rocca, C. E.; Pastore y Piontti, A. L.; Braunstein, L. A.; Macri, P. A.

    2009-01-01

    In this paper, we study the steady state of the fluctuations of the surface for a model of surface growth with relaxation to any of its lower nearest neighbors (SRAM) [F. Family, J. Phys. A 19, (1986) L441] in scale free networks. It is known that for Euclidean lattices this model belongs to the same universality class as the model of surface relaxation to the minimum (SRM). For the SRM model, it was found that for scale free networks with broadness λ, the steady state of the fluctuations scales with the system size N as a constant for λ≥3 and has a logarithmic divergence for λRocca, L.A. Braunstein, P.A. Macri, Phys. Rev. E 77 (2008) 046120] that this logarithmic divergence is due to non-linear terms that arises from the topology of the network. In this paper, we show that the fluctuations for the SRAM model scale as in the SRM model. We also derive analytically the evolution equation for this model for any kind of complex graphs and find that, as in the SRM model, non-linear terms appear due to the heterogeneity and the lack of symmetry of the network. In spite of that, the two models have the same scaling, but the SRM model is more efficient to synchronize systems.

  20. Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors.

    Science.gov (United States)

    Hines, Michael L; Eichner, Hubert; Schürmann, Felix

    2008-08-01

    Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Splitting cells is useful in attaining load balance in neural network simulations, especially when there is a wide range of cell sizes and the number of cells is about the same as the number of processors. For compute-bound simulations load balance results in almost ideal runtime scaling. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing.

  1. Analysis of a large-scale weighted network of one-to-one human communication

    Energy Technology Data Exchange (ETDEWEB)

    Onnela, Jukka-Pekka [Laboratory of Computational Engineering, Helsinki University of Technology (Finland); Saramaeki, Jari [Laboratory of Computational Engineering, Helsinki University of Technology (Finland); Hyvoenen, Joerkki [Laboratory of Computational Engineering, Helsinki University of Technology (Finland); Szabo, Gabor [Department of Physdics and Center for Complex Networks Research, University of Notre Dame, IN (United States); Menezes, M Argollo de [Department of Physdics and Center for Complex Networks Research, University of Notre Dame, IN (United States); Kaski, Kimmo [Laboratory of Computational Engineering, Helsinki University of Technology (Finland); Barabasi, Albert-Laszlo [Department of Physdics and Center for Complex Networks Research, University of Notre Dame, IN (United States); Kertesz, Janos [Laboratory of Computational Engineering, Helsinki University of Technology (Finland)

    2007-06-15

    We construct a connected network of 3.9 million nodes from mobile phone call records, which can be regarded as a proxy for the underlying human communication network at the societal level. We assign two weights on each edge to reflect the strength of social interaction, which are the aggregate call duration and the cumulative number of calls placed between the individuals over a period of 18 weeks. We present a detailed analysis of this weighted network by examining its degree, strength, and weight distributions, as well as its topological assortativity and weighted assortativity, clustering and weighted clustering, together with correlations between these quantities. We give an account of motif intensity and coherence distributions and compare them to a randomized reference system. We also use the concept of link overlap to measure the number of common neighbours any two adjacent nodes have, which serves as a useful local measure for identifying the interconnectedness of communities. We report a positive correlation between the overlap and weight of a link, thus providing strong quantitative evidence for the weak ties hypothesis, a central concept in social network analysis. The percolation properties of the network are found to depend on the type and order of removed links, and they can help understand how the local structure of the network manifests itself at the global level. We hope that our results will contribute to modelling weighted large-scale social networks, and believe that the systematic approach followed here can be adopted to study other weighted networks.

  2. Analysis of a large-scale weighted network of one-to-one human communication

    International Nuclear Information System (INIS)

    Onnela, Jukka-Pekka; Saramaeki, Jari; Hyvoenen, Joerkki; Szabo, Gabor; Menezes, M Argollo de; Kaski, Kimmo; Barabasi, Albert-Laszlo; Kertesz, Janos

    2007-01-01

    We construct a connected network of 3.9 million nodes from mobile phone call records, which can be regarded as a proxy for the underlying human communication network at the societal level. We assign two weights on each edge to reflect the strength of social interaction, which are the aggregate call duration and the cumulative number of calls placed between the individuals over a period of 18 weeks. We present a detailed analysis of this weighted network by examining its degree, strength, and weight distributions, as well as its topological assortativity and weighted assortativity, clustering and weighted clustering, together with correlations between these quantities. We give an account of motif intensity and coherence distributions and compare them to a randomized reference system. We also use the concept of link overlap to measure the number of common neighbours any two adjacent nodes have, which serves as a useful local measure for identifying the interconnectedness of communities. We report a positive correlation between the overlap and weight of a link, thus providing strong quantitative evidence for the weak ties hypothesis, a central concept in social network analysis. The percolation properties of the network are found to depend on the type and order of removed links, and they can help understand how the local structure of the network manifests itself at the global level. We hope that our results will contribute to modelling weighted large-scale social networks, and believe that the systematic approach followed here can be adopted to study other weighted networks

  3. A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

    Science.gov (United States)

    Merlet, Benjamin; Paulhe, Nils; Vinson, Florence; Frainay, Clément; Chazalviel, Maxime; Poupin, Nathalie; Gloaguen, Yoann; Giacomoni, Franck; Jourdan, Fabien

    2016-01-01

    This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

  4. Alternative Path Communication in Wide-Scale Cluster-Tree Wireless Sensor Networks Using Inactive Periods.

    Science.gov (United States)

    Leão, Erico; Montez, Carlos; Moraes, Ricardo; Portugal, Paulo; Vasques, Francisco

    2017-05-06

    The IEEE 802.15.4/ZigBee cluster-tree topology is a suitable technology to deploy wide-scale Wireless Sensor Networks (WSNs). These networks are usually designed to support convergecast traffic, where all communication paths go through the PAN (Personal Area Network) coordinator. Nevertheless, peer-to-peer communication relationships may be also required for different types of WSN applications. That is the typical case of sensor and actuator networks, where local control loops must be closed using a reduced number of communication hops. The use of communication schemes optimised just for the support of convergecast traffic may result in higher network congestion and in a potentially higher number of communication hops. Within this context, this paper proposes an Alternative-Route Definition (ARounD) communication scheme for WSNs. The underlying idea of ARounD is to setup alternative communication paths between specific source and destination nodes, avoiding congested cluster-tree paths. These alternative paths consider shorter inter-cluster paths, using a set of intermediate nodes to relay messages during their inactive periods in the cluster-tree network. Simulation results show that the ARounD communication scheme can significantly decrease the end-to-end communication delay, when compared to the use of standard cluster-tree communication schemes. Moreover, the ARounD communication scheme is able to reduce the network congestion around the PAN coordinator, enabling the reduction of the number of message drops due to queue overflows in the cluster-tree network.

  5. Computational solution to automatically map metabolite libraries in the context of genome scale metabolic networks

    Directory of Open Access Journals (Sweden)

    Benjamin eMerlet

    2016-02-01

    Full Text Available This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc and flat file formats (SBML and Matlab files. We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics and Glasgow Polyomics on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks.In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks.In order to achieve this goal we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

  6. Large scale structure statistics: Finite volume effects

    Science.gov (United States)

    Colombi, S.; Bouchet, F. R.; Schaeffer, R.

    1994-01-01

    We study finite volume effects on the count probability distribution function PN(l) and the averaged Q-body correlations Xi-barQ (2 less than or = Q less than or equal 5). These statistics are computed for cubic cells, of size l. We use as an example the case of the matter distribution of a cold dark matter (CDM) universe involving approximately 3 x 105 particles. The main effect of the finiteness of the sampled volume is to induce an abrupt cut-off on the function PN(l) at large N. This clear signature makes an analysis of the consequences easy, and one can envisage a correction procedure. As a matter of fact, we demonstrate how an unfair sample can strongly affect the estimates of the functions Xi-barQ for Q greater than or = 3 (and decrease the measured zero of the two-body correlation function). We propose a method to correct for this are fact, or at least to evaluate the corresponding errors. We show that the correlations are systematically underestimated by direct measurements. We find that, once corrected, the statistical properties of the CDM universe appear compatible with the scaling relation SQ identically equals Xi-bar2 exp Q-1 = constant with respect to scale, in the non-linear regime; it was not the case with direct measurments. However, we note a deviation from scaling at scales close to the correlation length. It is probably due to the transition between the highly non-linear regime and the weakly correlated regime, where the functions SQ also seem to present a plateau. We apply the same procedure to simulations with hot dark matter (HDM) and white noise initial conditions, with similar results. Our method thus provides the first accurate measurement of the normalized skewness, S3, and the normalized kurtosis, S4, for three typical models of large scale structure formation in an expanding universe.

  7. Effects of traffic generation patterns on the robustness of complex networks

    Science.gov (United States)

    Wu, Jiajing; Zeng, Junwen; Chen, Zhenhao; Tse, Chi K.; Chen, Bokui

    2018-02-01

    Cascading failures in communication networks with heterogeneous node functions are studied in this paper. In such networks, the traffic dynamics are highly dependent on the traffic generation patterns which are in turn determined by the locations of the hosts. The data-packet traffic model is applied to Barabási-Albert scale-free networks to study the cascading failures in such networks and to explore the effects of traffic generation patterns on network robustness. It is found that placing the hosts at high-degree nodes in a network can make the network more robust against both intentional attacks and random failures. It is also shown that the traffic generation pattern plays an important role in network design.

  8. Scaling relationship for surface water transport in stream networks and sub-surface flow interaction

    Science.gov (United States)

    Worman, A.

    2005-12-01

    Ground surface topography is known to control the circulation pattern of groundwater and also reflects the surface hydrological pathways through the landscape. This means that similar geometrical distributions typical to the landscape can be related physically-mathematically to the overall circulation of water and solute elements on land. Such understanding is needed in the management of water resources, especially on the watershed scale or larger. This paper outlines a theory by which we represent landscape topography in terms of its Fourier spectrum of a typical wave-function, formally relate this spectrum to the sub-surface flow of water and solute elements. Further, the stream network characteristics is analysed both in terms of the fractal distribution of individual stream lengths and the distribution of total transport distances in the watershed. Empirical relationships between the three types of distributions are established for two example watersheds in the middle and southern Sweden. Because the flow of water and solute elements in the stream network can also be described by convoluting unit solutions over the stream network, this paper describes an approach that relate lanscape topography to hydrological and geochemical circulation. The study shows that surface topography, stream network characteristics and thickness of quaternary deposits controls the circulation pattern of the deep groundwater. The water exchange is controlled by topography on both the continental scale as well as regional scale. The residence of deep groundwater in the stream network - before entering the coastal zone - is, therefore also controlled by the landscape topography.

  9. Light scattering optimization of chitin random network in ultrawhite beetle scales

    Science.gov (United States)

    Utel, Francesco; Cortese, Lorenzo; Pattelli, Lorenzo; Burresi, Matteo; Vignolini, Silvia; Wiersma, Diederik

    2017-09-01

    Among the natural white colored photonics structures, a bio-system has become of great interest in the field of disordered optical media: the scale of the white beetle Chyphochilus. Despite its low thickness, on average 7 μm, and low refractive index, this beetle exhibits extreme high brightness and unique whiteness. These properties arise from the interaction of light with a complex network of chitin nano filaments embedded in the interior of the scales. As it's been recently claimed, this could be a consequence of the peculiar morphology of the filaments network that, by means of high filling fraction (0.61) and structural anisotropy, optimizes the multiple scattering of light. We therefore performed a numerical analysis on the structural properties of the chitin network in order to understand their role in the enhancement of the scale scattering intensity. Modeling the filaments as interconnected rod shaped scattering centers, we numerically generated the spatial coordinates of the network components. Controlling the quantities that are claimed to play a fundamental role in the brightness and whiteness properties of the investigated system (filling fraction and average rods orientation, i.e. the anisotropy of the ensemble of scattering centers), we obtained a set of customized random networks. FDTD simulations of light transport have been performed on these systems, observing high reflectance for all the visible frequencies and proving the implemented algorithm to numerically generate the structures is suitable to investigate the dependence of reflectance by anisotropy.

  10. Probing Rubber Cross-Linking Generation of Industrial Polymer Networks at Nanometer Scale.

    Science.gov (United States)

    Gabrielle, Brice; Gomez, Emmanuel; Korb, Jean-Pierre

    2016-06-23

    We present improved analyses of rheometric torque measurements as well as (1)H double-quantum (DQ) nuclear magnetic resonance (NMR) buildup data on polymer networks of industrial compounds. This latter DQ NMR analysis allows finding the distribution of an orientation order parameter (Dres) resulting from the noncomplete averaging of proton dipole-dipole couplings within the cross-linked polymer chains. We investigate the influence of the formulation (filler and vulcanization systems) as well as the process (curing temperature) ending to the final polymer network. We show that DQ NMR follows the generation of the polymer network during the vulcanization process from a heterogeneous network to a very homogeneous one. The time variations of microscopic Dres and macroscopic rheometric torques present power-law behaviors above a threshold time scale with characteristic exponents of the percolation theory. We observe also a very good linear correlation between the kinetics of Dres and rheometric data routinely performed in industry. All these observations confirm the description of the polymer network generation as a critical phenomenon. On the basis of all these results, we believe that DQ NMR could become a valuable tool for investigating in situ the cross-linking of industrial polymer networks at the nanometer scale.

  11. Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models.

    Science.gov (United States)

    Williamson, Ryan C; Cowley, Benjamin R; Litwin-Kumar, Ashok; Doiron, Brent; Kohn, Adam; Smith, Matthew A; Yu, Byron M

    2016-12-01

    Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction-shared dimensionality and percent shared variance-with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

  12. Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models.

    Directory of Open Access Journals (Sweden)

    Ryan C Williamson

    2016-12-01

    Full Text Available Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction-shared dimensionality and percent shared variance-with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure.

  13. Prediction of Full-Scale Propulsion Power using Artificial Neural Networks

    DEFF Research Database (Denmark)

    Pedersen, Benjamin Pjedsted; Larsen, Jan

    2009-01-01

    Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature from four different loading conditions, together with hind cast data of wind and sea properties; and noon report data has been used to train an Artificial Neural Network for prediction o...

  14. Local, distributed topology control for large-scale wireless ad-hoc networks

    NARCIS (Netherlands)

    Nieberg, T.; Hurink, Johann L.

    In this document, topology control of a large-scale, wireless network by a distributed algorithm that uses only locally available information is presented. Topology control algorithms adjust the transmission power of wireless nodes to create a desired topology. The algorithm, named local power

  15. The Mathematics of Networks Science: Scale-Free, Power-Law Graphs and Continuum Theoretical Analysis

    Science.gov (United States)

    Padula, Janice

    2012-01-01

    When hoping to initiate or sustain students' interest in mathematics teachers should always consider relevance, relevance to students' lives and in the middle and later years of instruction in high school and university, accessibility. A topic such as the mathematics behind networks science, more specifically scale-free graphs, is up-to-date,…

  16. The role of detachment of in-links in scale-free networks

    Czech Academy of Sciences Publication Activity Database

    Lánský, Petr; Polito, F.; Sacerdote, L.

    2014-01-01

    Roč. 47, č. 34 (2014), s. 345002 ISSN 1751-8113 R&D Projects: GA ČR(CZ) GBP304/12/G069 Institutional support: RVO:67985823 Keywords : network * detachment * scale-free Subject RIV: ED - Physiology Impact factor: 1.583, year: 2014

  17. A Minimum-Cost Network-Flow Solution to the Case v Thurstone Scaling Problem.

    Science.gov (United States)

    Lattin, James M.

    1990-01-01

    An approach is presented for determining unidimensional scale estimates that are relatively insensitive to limited inconsistencies in paired comparisons data. The solution procedure--a minimum-cost network-flow problem--is presented in conjunction with a sensitivity diagnostic that assesses the influence of a single pairwise comparison on…

  18. Global Stability of Complex-Valued Genetic Regulatory Networks with Delays on Time Scales

    Directory of Open Access Journals (Sweden)

    Wang Yajing

    2016-01-01

    Full Text Available In this paper, the global exponential stability of complex-valued genetic regulatory networks with delays is investigated. Besides presenting conditions guaranteeing the existence of a unique equilibrium pattern, its global exponential stability is discussed. Some numerical examples for different time scales.

  19. Emergence of Super Cooperation of Prisoner’s Dilemma Games on Scale-Free Networks

    Science.gov (United States)

    Li, Angsheng; Yong, Xi

    2015-01-01

    Recently, the authors proposed a quantum prisoner’s dilemma game based on the spatial game of Nowak and May, and showed that the game can be played classically. By using this idea, we proposed three generalized prisoner’s dilemma (GPD, for short) games based on the weak Prisoner’s dilemma game, the full prisoner’s dilemma game and the normalized Prisoner’s dilemma game, written by GPDW, GPDF and GPDN respectively. Our games consist of two players, each of which has three strategies: cooperator (C), defector (D) and super cooperator (denoted by Q), and have a parameter γ to measure the entangled relationship between the two players. We found that our generalised prisoner’s dilemma games have new Nash equilibrium principles, that entanglement is the principle of emergence and convergence (i.e., guaranteed emergence) of super cooperation in evolutions of our generalised prisoner’s dilemma games on scale-free networks, that entanglement provides a threshold for a phase transition of super cooperation in evolutions of our generalised prisoner’s dilemma games on scale-free networks, that the role of heterogeneity of the scale-free networks in cooperations and super cooperations is very limited, and that well-defined structures of scale-free networks allow coexistence of cooperators and super cooperators in the evolutions of the weak version of our generalised prisoner’s dilemma games. PMID:25643279

  20. Large-scale computer networks and the future of legal knowledge-based systems

    NARCIS (Netherlands)

    Leenes, R.E.; Svensson, Jorgen S.; Hage, J.C.; Bench-Capon, T.J.M.; Cohen, M.J.; van den Herik, H.J.

    1995-01-01

    In this paper we investigate the relation between legal knowledge-based systems and large-scale computer networks such as the Internet. On the one hand, researchers of legal knowledge-based systems have claimed huge possibilities, but despite the efforts over the last twenty years, the number of

  1. Received signal strength in large-scale wireless relay sensor network: a stochastic ray approach

    NARCIS (Netherlands)

    Hu, L.; Chen, Y.; Scanlon, W.G.

    2011-01-01

    The authors consider a point percolation lattice representation of a large-scale wireless relay sensor network (WRSN) deployed in a cluttered environment. Each relay sensor corresponds to a grid point in the random lattice and the signal sent by the source is modelled as an ensemble of photons that

  2. The Use of Online Social Networks by Polish Former Erasmus Students: A Large-Scale Survey

    Science.gov (United States)

    Bryla, Pawel

    2014-01-01

    There is an increasing role of online social networks in the life of young Poles. We conducted a large-scale survey among Polish former Erasmus students. We have received 2450 completed questionnaires from alumni of 115 higher education institutions all over Poland. 85.4% of our respondents reported they kept in touch with their former Erasmus…

  3. Correlations and clustering in a scale-free network in Euclidean space

    Indian Academy of Sciences (India)

    Empirical study shows that many real networks in nature and society share two generic properties: they are scale-free and they display a high degree of clustering. Quite often they are modular in nature also, implying occurrences of several small tightly linked groups which are connected in a hierarchical manner among ...

  4. Emergence of super cooperation of prisoner's dilemma games on scale-free networks.

    Directory of Open Access Journals (Sweden)

    Angsheng Li

    Full Text Available Recently, the authors proposed a quantum prisoner's dilemma game based on the spatial game of Nowak and May, and showed that the game can be played classically. By using this idea, we proposed three generalized prisoner's dilemma (GPD, for short games based on the weak Prisoner's dilemma game, the full prisoner's dilemma game and the normalized Prisoner's dilemma game, written by GPDW, GPDF and GPDN respectively. Our games consist of two players, each of which has three strategies: cooperator (C, defector (D and super cooperator (denoted by Q, and have a parameter γ to measure the entangled relationship between the two players. We found that our generalised prisoner's dilemma games have new Nash equilibrium principles, that entanglement is the principle of emergence and convergence (i.e., guaranteed emergence of super cooperation in evolutions of our generalised prisoner's dilemma games on scale-free networks, that entanglement provides a threshold for a phase transition of super cooperation in evolutions of our generalised prisoner's dilemma games on scale-free networks, that the role of heterogeneity of the scale-free networks in cooperations and super cooperations is very limited, and that well-defined structures of scale-free networks allow coexistence of cooperators and super cooperators in the evolutions of the weak version of our generalised prisoner's dilemma games.

  5. Dynamic scaling, data-collapse and self-similarity in Barabasi-Albert networks

    Energy Technology Data Exchange (ETDEWEB)

    Hassan, M Kamrul; Pavel, Neeaj I [Theoretical Physics Group, Department of Physics, University of Dhaka, Dhaka 1000 (Bangladesh); Hassan, M Zahedul, E-mail: khassan@univdhaka.edu [Institute of Computer Science, Bangladesh Atomic Energy Commission, Dhaka 1000 (Bangladesh)

    2011-04-29

    In this paper, we show that if each node of the Barabasi-Albert (BA) network is characterized by the generalized degree q, i.e. the product of their degree k and the square root of their respective birth time, then the distribution function F(q, t) exhibits dynamic scaling F(q, t {yields} {infinity}) {approx} t{sup -1/2}{phi}(q/t{sup 1/2}) where {phi}(x) is the scaling function. We verified it by showing that a series of distinct F(q, t) versus q curves for different network sizes N collapse onto a single universal curve if we plot t{sup 1/2}F(q, t) versus q/t{sup 1/2} instead. Finally, we show that the BA network falls into two universality classes depending on whether new nodes arrive with single edge (m = 1) or with multiple edges (m > 1).

  6. Road network selection for small-scale maps using an improved centrality-based algorithm

    Directory of Open Access Journals (Sweden)

    Roy Weiss

    2014-12-01

    Full Text Available The road network is one of the key feature classes in topographic maps and databases. In the task of deriving road networks for products at smaller scales, road network selection forms a prerequisite for all other generalization operators, and is thus a fundamental operation in the overall process of topographic map and database production. The objective of this work was to develop an algorithm for automated road network selection from a large-scale (1:10,000 to a small-scale database (1:200,000. The project was pursued in collaboration with swisstopo, the national mapping agency of Switzerland, with generic mapping requirements in mind. Preliminary experiments suggested that a selection algorithm based on betweenness centrality performed best for this purpose, yet also exposed problems. The main contribution of this paper thus consists of four extensions that address deficiencies of the basic centrality-based algorithm and lead to a significant improvement of the results. The first two extensions improve the formation of strokes concatenating the road segments, which is crucial since strokes provide the foundation upon which the network centrality measure is computed. Thus, the first extension ensures that roundabouts are detected and collapsed, thus avoiding interruptions of strokes by roundabouts, while the second introduces additional semantics in the process of stroke formation, allowing longer and more plausible strokes to built. The third extension detects areas of high road density (i.e., urban areas using density-based clustering and then locally increases the threshold of the centrality measure used to select road segments, such that more thinning takes place in those areas. Finally, since the basic algorithm tends to create dead-ends—which however are not tolerated in small-scale maps—the fourth extension reconnects these dead-ends to the main network, searching for the best path in the main heading of the dead-end.

  7. A mouse model for studying large-scale neuronal networks using EEG mapping techniques.

    Science.gov (United States)

    Mégevand, Pierre; Quairiaux, Charles; Lascano, Agustina M; Kiss, Jozsef Z; Michel, Christoph M

    2008-08-15

    Human functional imaging studies are increasingly focusing on the identification of large-scale neuronal networks, their temporal properties, their development, and their plasticity and recovery after brain lesions. A method targeting large-scale networks in rodents would open the possibility to investigate their neuronal and molecular basis in detail. We here present a method to study such networks in mice with minimal invasiveness, based on the simultaneous recording of epicranial EEG from 32 electrodes regularly distributed over the head surface. Spatiotemporal analysis of the electrical potential maps similar to human EEG imaging studies allows quantifying the dynamics of the global neuronal activation with sub-millisecond resolution. We tested the feasibility, stability and reproducibility of the method by recording the electrical activity evoked by mechanical stimulation of the mystacial vibrissae. We found a series of potential maps with different spatial configurations that suggested the activation of a large-scale network with generators in several somatosensory and motor areas of both hemispheres. The spatiotemporal activation pattern was stable both across mice and in the same mouse across time. We also performed 16-channel intracortical recordings of the local field potential across cortical layers in different brain areas and found tight spatiotemporal concordance with the generators estimated from the epicranial maps. Epicranial EEG mapping thus allows assessing sensory processing by large-scale neuronal networks in living mice with minimal invasiveness, complementing existing approaches to study the neurophysiological mechanisms of interaction within the network in detail and to characterize their developmental, experience-dependent and lesion-induced plasticity in normal and transgenic animals.

  8. Improved routing strategies for data traffic in scale-free networks

    International Nuclear Information System (INIS)

    Wu, Zhi-Xi; Peng, Gang; Wong, Wing-Ming; Yeung, Kai-Hau

    2008-01-01

    We study the information packet routing process in scale-free networks by mimicking Internet traffic delivery. We incorporate both the global shortest paths information and local degree information of the network in the dynamic process, via two tunable parameters, α and β, to guide the packet routing. We measure the performance of the routing method by both the average transit times of packets and the critical packet generation rate (above which packet aggregation occurs in the network). We found that the routing strategies which integrate ingredients of both global and local topological information of the underlying networks perform much better than the traditional shortest path routing protocol taking into account the global topological information only. Moreover, by doing comparative studies with some related works, we found that the performance of our proposed method shows universal efficiency characteristic against the amount of traffic

  9. On a digital wireless impact-monitoring network for large-scale composite structures

    International Nuclear Information System (INIS)

    Yuan, Shenfang; Mei, Hanfei; Qiu, Lei; Ren, Yuanqiang

    2014-01-01

    Impact, which may occur during manufacture, service or maintenance, is one of the major concerns to be monitored throughout the lifetime of aircraft composite structures. Aiming at monitoring impacts online while minimizing the weight added to the aircraft to meet the strict limitations of aerospace engineering, this paper puts forward a new digital wireless network based on miniaturized wireless digital impact-monitoring nodes developed for large-scale composite structures. In addition to investigations on the design methods of the network architecture, time synchronization and implementation method, a conflict resolution method based on the feature parameters of digital sequences is first presented to address impact localization conflicts when several nodes are arranged close together. To verify the feasibility and stability of the wireless network, experiments are performed on a complex aircraft composite wing box and an unmanned aerial vehicle (UAV) composite wing. Experimental results show the successful design of the presented network. (paper)

  10. Detecting and mitigating abnormal events in large scale networks: budget constrained placement on smart grids

    Energy Technology Data Exchange (ETDEWEB)

    Santhi, Nandakishore [Los Alamos National Laboratory; Pan, Feng [Los Alamos National Laboratory

    2010-10-19

    Several scenarios exist in the modern interconnected world which call for an efficient network interdiction algorithm. Applications are varied, including various monitoring and load shedding applications on large smart energy grids, computer network security, preventing the spread of Internet worms and malware, policing international smuggling networks, and controlling the spread of diseases. In this paper we consider some natural network optimization questions related to the budget constrained interdiction problem over general graphs, specifically focusing on the sensor/switch placement problem for large-scale energy grids. Many of these questions turn out to be computationally hard to tackle. We present a particular form of the interdiction question which is practically relevant and which we show as computationally tractable. A polynomial-time algorithm will be presented for solving this problem.

  11. Large-scale network dysfunction in Major Depressive Disorder: Meta-analysis of resting-state functional connectivity

    Science.gov (United States)

    Kaiser, Roselinde H.; Andrews-Hanna, Jessica R.; Wager, Tor D.; Pizzagalli, Diego A.

    2015-01-01

    IMPORTANCE Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive. OBJECTIVE To investigate network dysfunction in MDD through the first meta-analysis of rsFC studies. DATA SOURCES Seed-based voxel-wise rsFC studies comparing MDD with healthy individuals (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web-of-Science, EMBASE), and authors contacted for additional data. STUDY SELECTION Twenty-seven datasets from 25 publications (556 MDD adults/teens; 518 controls) were included in the meta-analysis. DATA EXTRACTION AND SYNTHESIS Coordinates of seed regions-of-interest and between-group effects were extracted. Seeds were categorized into “seed-networks” by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive, or reduced negative, connectivity) or hypoconnectivity (increased negative, or reduced positive, connectivity) with each seed-network. RESULTS MDD was characterized by hypoconnectivity within the frontoparietal network (FN), a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network (DAN) involved in attending to the external environment. MDD was also associated with hyperconnectivity within the default network (DN), a network believed to support internally-oriented and self-referential thought, and hyperconnectivity between FN control systems and regions of DN. Finally, MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline

  12. Modeling nutrient retention at the watershed scale: Does small stream research apply to the whole river network?

    Science.gov (United States)

    Aguilera, Rosana; Marcé, Rafael; Sabater, Sergi

    2013-06-01

    are conveyed from terrestrial and upstream sources through drainage networks. Streams and rivers contribute to regulate the material exported downstream by means of transformation, storage, and removal of nutrients. It has been recently suggested that the efficiency of process rates relative to available nutrient concentration in streams eventually declines, following an efficiency loss (EL) dynamics. However, most of these predictions are based at the reach scale in pristine streams, failing to describe the role of entire river networks. Models provide the means to study nutrient cycling from the stream network perspective via upscaling to the watershed the key mechanisms occurring at the reach scale. We applied a hybrid process-based and statistical model (SPARROW, Spatially Referenced Regression on Watershed Attributes) as a heuristic approach to describe in-stream nutrient processes in a highly impaired, high stream order watershed (the Llobregat River Basin, NE Spain). The in-stream decay specifications of the model were modified to include a partial saturation effect in uptake efficiency (expressed as a power law) and better capture biological nutrient retention in river systems under high anthropogenic stress. The stream decay coefficients were statistically significant in both nitrate and phosphate models, indicating the potential role of in-stream processing in limiting nutrient export. However, the EL concept did not reliably describe the patterns of nutrient uptake efficiency for the concentration gradient and streamflow values found in the Llobregat River basin, posing in doubt its complete applicability to explain nutrient retention processes in stream networks comprising highly impaired rivers.

  13. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming

    Energy Technology Data Exchange (ETDEWEB)

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-03-27

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.

  14. Integration of expression data in genome-scale metabolic network reconstructions

    Directory of Open Access Journals (Sweden)

    Anna S. Blazier

    2012-08-01

    Full Text Available With the advent of high-throughput technologies, the field of systems biology has amassed an abundance of omics data, quantifying thousands of cellular components across a variety of scales, ranging from mRNA transcript levels to metabolite quantities. Methods are needed to not only integrate this omics data but to also use this data to heighten the predictive capabilities of computational models. Several recent studies have successfully demonstrated how flux balance analysis (FBA, a constraint-based modeling approach, can be used to integrate transcriptomic data into genome-scale metabolic network reconstructions to generate predictive computational models. In this review, we summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations.

  15. Modelling of rate effects at multiple scales

    DEFF Research Database (Denmark)

    Pedersen, R.R.; Simone, A.; Sluys, L. J.

    2008-01-01

    , the length scale in the meso-model and the macro-model can be coupled. In this fashion, a bridging of length scales can be established. A computational analysis of  a Split Hopkinson bar test at medium and high impact load is carried out at macro-scale and meso-scale including information from  the micro-scale....

  16. Role of 3D force networks in linking grain scale to macroscale processes in sheared granular debris

    Science.gov (United States)

    Mair, K.; Jettestuen, E.; Abe, S.

    2013-12-01

    Active faults, landslides and subglacial tills contain accumulations of granular debris that evolve during sliding. The macroscopic motion in these environments is at least to some extent determined by processes operating in this sheared granular material. A valid question is how the local behavior at the individual granular contacts actually sums up to influence macroscopic sliding. Laboratory experiments and numerical modeling can potentially help elucidate this. Observations of jamming (stick) and unjamming (flow) as well as concentrated shear bands on the scale of 5-10 grains suggest that a simple continuum description may be insufficient to capture important elements of the behavior. We therefore seek a measure of the organization of the granular fabric and the 3D structure of the load bearing skeleton that effectively demonstrates how the individual grain interactions are manifested in the macroscopic sliding behavior we observe. Contact force networks are an expression of this. Here we investigate the structure and variability of the most connected system spanning force networks produced in 3D discrete element models of granular layers under shear. We use percolation measures to identify, characterize, compare and track the evolution of these strongly connected contact force networks. We show that specific topological measures used in describing the networks, such as number of contacts and coordination number, are sensitive to grain size distribution (and likely the grain shape) of the material as well as loading conditions. Hence, faults of different maturity would be expected to accommodate shear in different ways. Distinct changes in the topological characteristics i.e. the geometry of strong force networks with accumulated strain are directly correlated to fluctuations in macroscopic shearing resistance. This suggests that 3D force networks play an important bridging role between individual grain scale processes and macroscopic sliding behavior.

  17. PANET: a GPU-based tool for fast parallel analysis of robustness dynamics and feed-forward/feedback loop structures in large-scale biological networks.

    Directory of Open Access Journals (Sweden)

    Hung-Cuong Trinh

    Full Text Available It has been a challenge in systems biology to unravel relationships between structural properties and dynamic behaviors of biological networks. A Cytoscape plugin named NetDS was recently proposed to analyze the robustness-related dynamics and feed-forward/feedback loop structures of biological networks. Despite such a useful function, limitations on the network size that can be analyzed exist due to high computational costs. In addition, the plugin cannot verify an intrinsic property which can be induced by an observed result because it has no function to simulate the observation on a large number of random networks. To overcome these limitations, we have developed a novel software tool, PANET. First, the time-consuming parts of NetDS were redesigned to be processed in parallel using the OpenCL library. This approach utilizes the full computing power of multi-core central processing units and graphics processing units. Eventually, this made it possible to investigate a large-scale network such as a human signaling network with 1,609 nodes and 5,063 links. We also developed a new function to perform a batch-mode simulation where it generates a lot of random networks and conducts robustness calculations and feed-forward/feedback loop examinations of them. This helps us to determine if the findings in real biological networks are valid in arbitrary random networks or not. We tested our plugin in two case studies based on two large-scale signaling networks and found interesting results regarding relationships between coherently coupled feed-forward/feedback loops and robustness. In addition, we verified whether or not those findings are consistently conserved in random networks through batch-mode simulations. Taken together, our plugin is expected to effectively investigate various relationships between dynamics and structural properties in large-scale networks. Our software tool, user manual and example datasets are freely available at http://panet-csc.sourceforge.net/.

  18. Apprenticeship effect on a neural network

    International Nuclear Information System (INIS)

    Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

    1997-01-01

    Utilization of a neural network for determining the value of the impact parameter in heavy ion reactions is a two stage process: a apprenticeship stage followed by a utilization stage. During the first stage one determines the network parameters by means of a trial game for which the inputs and outputs are known. To realize the trial game a numerical simulation code was used. In order to estimate the biases of this procedure we generated trial games by resorting to two different models: a transport dynamical model (QMD) coupled to a deexcitation code (GEMINI) and a event generator based on a statistical approach (EUGENE). The procedure was applied to determine the impact parameter in 40 Ca + 40 Ca reactions. The effect of the model dependence of the apprenticeship procedure is studied thoroughly. In the two cases the outputs provided by the network are smaller then 7 fm which can be expected in case of complete events. The network trained with QMD gives outputs for input parameters within a larger range of values which is due to the fact that EUGENE does not contain 'Deep Inelastic'

  19. Coarse-Grain Bandwidth Estimation Scheme for Large-Scale Network

    Science.gov (United States)

    Cheung, Kar-Ming; Jennings, Esther H.; Sergui, John S.

    2013-01-01

    A large-scale network that supports a large number of users can have an aggregate data rate of hundreds of Mbps at any time. High-fidelity simulation of a large-scale network might be too complicated and memory-intensive for typical commercial-off-the-shelf (COTS) tools. Unlike a large commercial wide-area-network (WAN) that shares diverse network resources among diverse users and has a complex topology that requires routing mechanism and flow control, the ground communication links of a space network operate under the assumption of a guaranteed dedicated bandwidth allocation between specific sparse endpoints in a star-like topology. This work solved the network design problem of estimating the bandwidths of a ground network architecture option that offer different service classes to meet the latency requirements of different user data types. In this work, a top-down analysis and simulation approach was created to size the bandwidths of a store-and-forward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. These techniques were used to estimate the WAN bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network. A new analytical approach, called the "leveling scheme," was developed to model the store-and-forward mechanism of the network data flow. The term "leveling" refers to the spreading of data across a longer time horizon without violating the corresponding latency requirement of the data type. Two versions of the leveling scheme were developed: 1. A straightforward version that simply spreads the data of each data type across the time horizon and doesn't take into account the interactions among data types within a pass, or between data types across overlapping passes at a network node, and is inherently sub-optimal. 2. Two-state Markov leveling scheme that takes into account the second order behavior of

  20. Characterizing two-phase flow relative permeabilities in chemicalflooding using a pore-scale network model

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Qingjie; Shen, Pingping; Wu, Yu-Shu

    2004-03-15

    A dynamic pore-scale network model is presented for investigating the effects of interfacial tension and oil-water viscosity on relative permeability during chemical flooding. This model takes into account both viscous and capillary forces in analyzing the impact of chemical properties on flow behavior or displacement configuration, as opposed to the conventional or invasion percolation algorithm which incorporates capillary pressure only. The study results indicate that both water and oil relative-permeability curves are dependent strongly on interfacial tension as well as an oil-water viscosity ratio. In particular, water and oil relative-permeability curves are both found to shift upward as interfacial tension is reduced, and they both tend to become linear versus saturation once interfacial tension is at low values. In addition, the oil-water viscosity ratio appears to have only a small effect under conditions of high interfacial tension. When the interfacial tension is low, however, water relative permeability decreases more rapidly (with the increase in the aqueous-phase viscosity) than oil relative permeability. The breakthrough saturation of the aqueous phase during chemical flooding tends to decrease with the reduction of interfacial tension and may also be affected by the oil-water viscosity ratio.

  1. An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks

    Directory of Open Access Journals (Sweden)

    Grigory Evropeytsev

    2016-08-01

    Full Text Available In peer-to-peer (P2P overlay networks, a group of n (≥2 peer processes have to cooperate with each other. Each peer sends messages to every peer and receives messages from every peer in a group. In group communications, each message sent by a peer is required to be causally delivered to every peer. Most of the protocols designed to ensure causal message order are designed for networks with a plain architecture. These protocols can be adapted to use in free scale and hierarchical topologies; however, the amount of control information is O(n, where n is the number of peers in the system. Some protocols are designed for a free scale or hierarchical networks, but in general they force the whole system to accomplish the same order viewed by a super peer. In this paper, we present a protocol that is specifically designed to work with a free scale peer-to-peer network. By using the information about the network’s architecture and by representing message dependencies on a bit level, the proposed protocol ensures causal message ordering without enforcing super peers order. The designed protocol is simulated and compared with the Immediate Dependency Relation and the Dependency Sequences protocols to show its lower overhead.

  2. High Fidelity Simulations of Large-Scale Wireless Networks (Plus-Up)

    Energy Technology Data Exchange (ETDEWEB)

    Onunkwo, Uzoma [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-11-01

    Sandia has built a strong reputation in scalable network simulation and emulation for cyber security studies to protect our nation’s critical information infrastructures. Georgia Tech has preeminent reputation in academia for excellence in scalable discrete event simulations, with strong emphasis on simulating cyber networks. Many of the experts in this field, such as Dr. Richard Fujimoto, Dr. George Riley, and Dr. Chris Carothers, have strong affiliations with Georgia Tech. The collaborative relationship that we intend to immediately pursue is in high fidelity simulations of practical large-scale wireless networks using ns-3 simulator via Dr. George Riley. This project will have mutual benefits in bolstering both institutions’ expertise and reputation in the field of scalable simulation for cyber-security studies. This project promises to address high fidelity simulations of large-scale wireless networks. This proposed collaboration is directly in line with Georgia Tech’s goals for developing and expanding the Communications Systems Center, the Georgia Tech Broadband Institute, and Georgia Tech Information Security Center along with its yearly Emerging Cyber Threats Report. At Sandia, this work benefits the defense systems and assessment area with promise for large-scale assessment of cyber security needs and vulnerabilities of our nation’s critical cyber infrastructures exposed to wireless communications.

  3. Effective transmissivity of two-dimensional fracture networks

    International Nuclear Information System (INIS)

    Simmerman, R.W.; Bodvarsson, G.S.

    1995-04-01

    Many of the sites that have been proposed as potential locations of underground radioactive waste repositories contain fractured rocks. For example, both the saturated and unsaturated zone at Yucca Mountain, Nevada, contains many hydrogeologic units that are extensively fractured. When modeling the hydrological behavior of these sites, for either the purpose of site characterization of performance assessment, computational grid-blocks are often used that contain large number of fractures. In order to treat these as equivalent continua, it is necessary to develop a procedure for relating the hydraulic properties of the individual fractures and he topology of the fracture network to the overall scale permeability. One aspect of this problem is that of determining the in situ hydraulic properties of the individual fractures. Another aspect is to reconstruct the three-dimensional geometry of the fracture network based on borehole or outcrop measurements. The final stage in the problem is that of taking a network of known geometry and determining it effective scale conductivity. The purpose of this paper is to describe a simple procedure for solving this latter problem,a nd to demonstrate it use in cases of both saturated and unsaturated flow. The TOUGH simulator was used

  4. Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection

    KAUST Repository

    Bai, Yancheng

    2018-01-28

    Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMS-RC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales. On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales. Temporal contextual information is fused into activity classifiers for better recognition. More importantly, the entire model at all levels can be trained end-to-end. Our CMS-RC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network. We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet. Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.

  5. TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Jensen Paul A

    2011-09-01

    Full Text Available Abstract Background Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model. Results We present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae. Conclusion The TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.

  6. Global asymptotic stabilization of large-scale hydraulic networks using positive proportional controls

    DEFF Research Database (Denmark)

    Jensen, Tom Nørgaard; Wisniewski, Rafal

    2014-01-01

    An industrial case study involving a large-scale hydraulic network underlying a district heating system subject to structural changes is considered. The problem of controlling the pressure drop across the so-called end-user valves in the network to a designated vector of reference values under......-users. Furthermore, by a proper design of controller gains the closed-loop equilibrium point can be designed to belong to an arbitrarily small neighborhood of the desired equilibrium point. Since there exists a globally asymptotically stable equilibrium point independently on the number of end-users in the system...

  7. Identifying influential nodes in large-scale directed networks: the role of clustering.

    Science.gov (United States)

    Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

    2013-01-01

    Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

  8. Identifying influential nodes in large-scale directed networks: the role of clustering.

    Directory of Open Access Journals (Sweden)

    Duan-Bing Chen

    Full Text Available Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node's neighbors but do not directly make use of the interactions among it's neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors' influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about [Formula: see text] nodes, more than 15 times faster than PageRank.

  9. Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

    Science.gov (United States)

    Chen, Duan-Bing; Gao, Hui; Lü, Linyuan; Zhou, Tao

    2013-01-01

    Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank. PMID:24204833

  10. REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

    Directory of Open Access Journals (Sweden)

    S Safinaz

    2017-08-01

    Full Text Available In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.

  11. Extraversion is encoded by scale-free dynamics of default mode network.

    Science.gov (United States)

    Lei, Xu; Zhao, Zhiying; Chen, Hong

    2013-07-01

    Resting-state functional Magnetic Resonance Imaging (rsfMRI) is a powerful tool to investigate neurological and psychiatric diseases. Recently, the evidences linking the scaling properties of resting-state activity and the personality have been accumulated. However, it remains unknown whether the personality is associated with the scale-free dynamics of default mode network (DMN) - the most widely studied network in the rsfMRI literatures. To investigate this question, we estimated the Hurst exponent, quantifying long memory of a time-series, in DMN of rsfMRI in 20 healthy individuals. The Hurst exponent in DMN, whether extracted by independent component analysis (ICA) or region of interest (ROI), was significantly associated with the extraversion score of the revised Eysenck Personality Questionnaire. Specifically, longer memory in DMN corresponded to lower extraversion. We provide evidences for an association between individual differences in personality and scaling dynamics in DMN, whose alteration has been previously linked with introspective cognition. This association might arise from the efficiency in online information processing. Our results suggest that personality trait may be reflected by the scaling property of resting-state networks. Copyright © 2012 Elsevier Inc. All rights reserved.

  12. Regional scale air pollution - Sources and effects

    International Nuclear Information System (INIS)

    Husar, R.B.; Patterson, D.E.

    1980-01-01

    Several independent types of information relating to the sources and effects of regional-scale air pollution are reviewed. Much of the information was obtained by in situ observations of plumes and field studies of individual pollution episodes. Attention is given to anthropogenic pollution sources in the U.S. (primarily fossil fuel combustion), rates of SO/sub x/ and NO/sub x/ emission from such sources, ambient concentrations of sulfur compounds, the long-range transport of sulfate aerosol, and visibility trends in the eastern U.S. It is concluded that an episode of extreme haziness covering multistate regions of the eastern U.S. appeared to have been largely caused by secondary sulfate aerosol, that coal combustion and sulfate are only one factor in the production of optical effects, and that spatial and temporal aerosol trends and distributions of coal use, sulfate, light extinction, turbidity, and solar radiation exhibit reasonable internal consistency

  13. Time-Varying, Multi-Scale Adaptive System Reliability Analysis of Lifeline Infrastructure Networks

    Energy Technology Data Exchange (ETDEWEB)

    Gearhart, Jared Lee [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Kurtz, Nolan Scot [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2014-09-01

    The majority of current societal and economic needs world-wide are met by the existing networked, civil infrastructure. Because the cost of managing such infrastructure is high and increases with time, risk-informed decision making is essential for those with management responsibilities for these systems. To address such concerns, a methodology that accounts for new information, deterioration, component models, component importance, group importance, network reliability, hierarchical structure organization, and efficiency concerns has been developed. This methodology analyzes the use of new information through the lens of adaptive Importance Sampling for structural reliability problems. Deterioration, multi-scale bridge models, and time-variant component importance are investigated for a specific network. Furthermore, both bridge and pipeline networks are studied for group and component importance, as well as for hierarchical structures in the context of specific networks. Efficiency is the primary driver throughout this study. With this risk-informed approach, those responsible for management can address deteriorating infrastructure networks in an organized manner.

  14. Accelerating a Network Model of Care: Taking a Social Innovation to Scale

    Directory of Open Access Journals (Sweden)

    Kerry Byrne

    2012-07-01

    Full Text Available Government-funded systems of health and social care are facing enormous fiscal and human-resource challenges. The space for innovation in care is wide open and new disruptive patterns are emerging. These include self-management and personal budgets, participatory and integrated care, supported decision making and a renewed focus on prevention. Taking these disruptive patterns to scale can be accelerated by a technologically enabled shift to a network model of care to co-create the best outcomes for individuals, family caregivers, and health and social care organizations. The connections, relationships, and activities within an individual’s personal network lay the foundation for care that health and social care systems/policy must simultaneously support and draw on for positive outcomes. Practical tools, adequate information, and tangible resources are required to coordinate and sustain care. Tyze Personal Networks is a social venture that uses technology to engage and inform the individual, their personal networks, and their care providers to co-create the best outcomes. In this article, we demonstrate how Tyze contributes to a shift to a network model of care by strengthening our networks and enhancing partnerships between care providers, individuals, and family and friends.

  15. Interactions Between Large-Scale Functional Brain Networks are Captured by Sparse Coupled HMMs.

    Science.gov (United States)

    Bolton, Thomas A W; Tarun, Anjali; Sterpenich, Virginie; Schwartz, Sophie; Van De Ville, Dimitri

    2018-01-01

    Functional magnetic resonance imaging (fMRI) provides a window on the human brain at work. Spontaneous brain activity measured during resting-state has already provided many insights into brain function. In particular, recent interest in dynamic interactions between brain regions has increased the need for more advanced modeling tools. Here, we deploy a recent fMRI deconvolution technique to express resting-state temporal fluctuations as a combination of large-scale functional network activity profiles. Then, building upon a novel sparse coupled hidden Markov model (SCHMM) framework, we parameterised their temporal evolution as a mix between intrinsic dynamics, and a restricted set of cross-network modulatory couplings extracted in data-driven manner. We demonstrate and validate the method on simulated data, for which we observed that the SCHMM could accurately estimate network dynamics, revealing more precise insights about direct network-to-network modulatory influences than with conventional correlational methods. On experimental resting-state fMRI data, we unraveled a set of reproducible cross-network couplings across two independent datasets. Our framework opens new perspectives for capturing complex temporal dynamics and their changes in health and disease.

  16. Neural Networks For Electrohydrodynamic Effect Modelling

    Directory of Open Access Journals (Sweden)

    Wiesław Wajs

    2004-01-01

    Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamiceffect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.

  17. a Stochastic Approach to Multiobjective Optimization of Large-Scale Water Reservoir Networks

    Science.gov (United States)

    Bottacin-Busolin, A.; Worman, A. L.

    2013-12-01

    A main challenge for the planning and management of water resources is the development of multiobjective strategies for operation of large-scale water reservoir networks. The optimal sequence of water releases from multiple reservoirs depends on the stochastic variability of correlated hydrologic inflows and on various processes that affect water demand and energy prices. Although several methods have been suggested, large-scale optimization problems arising in water resources management are still plagued by the high dimensional state space and by the stochastic nature of the hydrologic inflows. In this work, the optimization of reservoir operation is approached using approximate dynamic programming (ADP) with policy iteration and function approximators. The method is based on an off-line learning process in which operating policies are evaluated for a number of stochastic inflow scenarios, and the resulting value functions are used to design new, improved policies until convergence is attained. A case study is presented of a multi-reservoir system in the Dalälven River, Sweden, which includes 13 interconnected reservoirs and 36 power stations. Depending on the late spring and summer peak discharges, the lowlands adjacent to Dalälven can often be flooded during the summer period, and the presence of stagnating floodwater during the hottest months of the year is the cause of a large proliferation of mosquitos, which is a major problem for the people living in the surroundings. Chemical pesticides are currently being used as a preventive countermeasure, which do not provide an effective solution to the problem and have adverse environmental impacts. In this study, ADP was used to analyze the feasibility of alternative operating policies for reducing the flood risk at a reasonable economic cost for the hydropower companies. To this end, mid-term operating policies were derived by combining flood risk reduction with hydropower production objectives. The performance

  18. Large-Scale Demand Driven Design of a Customized Bus Network: A Methodological Framework and Beijing Case Study

    Directory of Open Access Journals (Sweden)

    Jihui Ma

    2017-01-01

    Full Text Available In recent years, an innovative public transportation (PT mode known as the customized bus (CB has been proposed and implemented in many cities in China to efficiently and effectively shift private car users to PT to alleviate traffic congestion and traffic-related environmental pollution. The route network design activity plays an important role in the CB operation planning process because it serves as the basis for other operation planning activities, for example, timetable development, vehicle scheduling, and crew scheduling. In this paper, according to the demand characteristics and operational purpose, a methodological framework that includes the elements of large-scale travel demand data processing and analysis, hierarchical clustering-based route origin-destination (OD region division, route OD region pairing, and a route selection model is proposed for CB network design. Considering the operating cost and social benefits, a route selection model is proposed and a branch-and-bound-based solution method is developed. In addition, a computer-aided program is developed to analyze a real-world Beijing CB route network design problem. The results of the case study demonstrate that the current CB network of Beijing can be significantly improved, thus demonstrating the effectiveness of the proposed methodology.

  19. Harnessing diversity towards the reconstructing of large scale gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Takeshi Hase

    Full Text Available Elucidating gene regulatory network (GRN from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorithms to infer GRNs. Comprehensive performance benchmarking on a cloud computing framework demonstrated that (i a simple strategy to combine many algorithms does not always lead to performance improvement compared to the cost of consensus and (ii TopkNet integrating only high-performance algorithms provide significant performance improvement compared to the best individual algorithms and community prediction. These results suggest that a priori determination of high-performance algorithms is a key to reconstruct an unknown regulatory network. Similarity among gene-expression datasets can be useful to determine potential optimal algorithms for reconstruction of unknown regulatory networks, i.e., if expression-data associated with known regulatory network is similar to that with unknown regulatory network, optimal algorithms determined for the known regulatory network can be repurposed to infer the unknown regulatory network. Based on this observation, we developed a quantitative measure of similarity among gene-expression datasets and demonstrated that, if similarity between the two expression datasets is high, TopkNet integrating algorithms that are optimal for known dataset perform well on the unknown dataset. The consensus framework, TopkNet, together with the similarity measure proposed in this study provides a powerful strategy towards harnessing the wisdom of the crowds in reconstruction of unknown regulatory networks.

  20. Genome-scale metabolic network guided engineering of Streptomyces tsukubaensis for FK506 production improvement.

    Science.gov (United States)

    Huang, Di; Li, Shanshan; Xia, Menglei; Wen, Jianping; Jia, Xiaoqiang

    2013-05-24

    FK506 is an important immunosuppressant, which can be produced by Streptomyces tsukubaensis. However, the production capacity of the strain is very low. Hereby, a computational guided engineering approach was proposed in order to improve the intracellular precursor and cofactor availability of FK506 in S. tsukubaensis. First, a genome-scale metabolic model of S. tsukubaensis was constructed based on its annotated genome and biochemical information. Subsequently, several potential genetic targets (knockout or overexpression) that guaranteed an improved yield of FK506 were identified by the recently developed methodology. To validate the model predictions, each target gene was manipulated in the parent strain D852, respectively. All the engineered strains showed a higher FK506 production, compared with D852. Furthermore, the combined effect of the genetic modifications was evaluated. Results showed that the strain HT-ΔGDH-DAZ with gdhA-deletion and dahp-, accA2-, zwf2-overexpression enhanced FK506 concentration up to 398.9 mg/L, compared with 143.5 mg/L of the parent strain D852. Finally, fed-batch fermentations of HT-ΔGDH-DAZ were carried out, which led to the FK506 production of 435.9 mg/L, 1.47-fold higher than the parent strain D852 (158.7 mg/L). Results confirmed that the promising targets led to an increase in FK506 titer. The present work is the first attempt to engineer the primary precursor pathways to improve FK506 production in S. tsukubaensis with genome-scale metabolic network guided metabolic engineering. The relationship between model prediction and experimental results demonstrates the rationality and validity of this approach for target identification. This strategy can also be applied to the improvement of other important secondary metabolites.

  1. Effects of network node consolidation in optical access and aggregation networks on costs and power consumption

    Science.gov (United States)

    Lange, Christoph; Hülsermann, Ralf; Kosiankowski, Dirk; Geilhardt, Frank; Gladisch, Andreas

    2010-01-01

    The increasing demand for higher bit rates in access networks requires fiber deployment closer to the subscriber resulting in fiber-to-the-home (FTTH) access networks. Besides higher access bit rates optical access network infrastructure and related technologies enable the network operator to establish larger service areas resulting in a simplified network structure with a lower number of network nodes. By changing the network structure network operators want to benefit from a changed network cost structure by decreasing in short and mid term the upfront investments for network equipment due to concentration effects as well as by reducing the energy costs due to a higher energy efficiency of large network sites housing a high amount of network equipment. In long term also savings in operational expenditures (OpEx) due to the closing of central office (CO) sites are expected. In this paper different architectures for optical access networks basing on state-of-the-art technology are analyzed with respect to network installation costs and power consumption in the context of access node consolidation. Network planning and dimensioning results are calculated for a realistic network scenario of Germany. All node consolidation scenarios are compared against a gigabit capable passive optical network (GPON) based FTTH access network operated from the conventional CO sites. The results show that a moderate reduction of the number of access nodes may be beneficial since in that case the capital expenditures (CapEx) do not rise extraordinarily and savings in OpEx related to the access nodes are expected. The total power consumption does not change significantly with decreasing number of access nodes but clustering effects enable a more energyefficient network operation and optimized power purchase order quantities leading to benefits in energy costs.

  2. Micro-connectomics: probing the organization of neuronal networks at the cellular scale.

    Science.gov (United States)

    Schröter, Manuel; Paulsen, Ole; Bullmore, Edward T

    2017-03-01

    Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.

  3. Fabrication of Cu-induced networks of linear nanostructures on different length scales

    International Nuclear Information System (INIS)

    Adelung, R.; Hartung, W.; Ernst, F.

    2002-01-01

    Scanning electron microscopy and atomic force microscopy revealed that the deposition Cu onto VSe 2 substrates in ultra-high vacuum leads to the self-organized formation of linear nanostructures, nanowires and nanotunnels, on the substrate surface. The nanowires and nanotunnels are approximately equi-axed and form networks with a mesh width much larger than their diameter. Surprisingly, systematic increase of the Cu coverage studied here does not simply increase the thickness of the nanowires and nanotunnels, but induces the formation of further, distinct networks with increased feature size and increased mesh width. At very high Cu coverages, eventually, we obtained a hierarchy of apparently independent nanowire and nanotunnel networks on different length scales. A model is presented for the micromechanism that leads to this complex arrangement of nanostructures

  4. Optimal control strategy for a novel computer virus propagation model on scale-free networks

    Science.gov (United States)

    Zhang, Chunming; Huang, Haitao

    2016-06-01

    This paper aims to study the combined impact of reinstalling system and network topology on the spread of computer viruses over the Internet. Based on scale-free network, this paper proposes a novel computer viruses propagation model-SLBOSmodel. A systematic analysis of this new model shows that the virus-free equilibrium is globally asymptotically stable when its spreading threshold is less than one; nevertheless, it is proved that the viral equilibrium is permanent if the spreading threshold is greater than one. Then, the impacts of different model parameters on spreading threshold are analyzed. Next, an optimally controlled SLBOS epidemic model on complex networks is also studied. We prove that there is an optimal control existing for the control problem. Some numerical simulations are finally given to illustrate the main results.

  5. Weighted scale-free networks with variable power-law exponents

    Science.gov (United States)

    Tanaka, Takuma; Aoyagi, Toshio

    2008-06-01

    We present a weighted scale-free network model, in which the power-law exponents can be controlled by the model parameters. The network is generated through the weight-driven preferential attachment of new nodes to existing nodes and the growth of the weights of existing links. The simplicity of the model enables us to derive analytically the various statistical properties, such as the distributions of degree, strength, and weight, the degree-strength and degree-weight relationship, and the dependencies of these power-law exponents on the model parameters. Finally, we demonstrate that networks of words, coauthorship of researchers, and collaboration of actor/actresses are quantitatively well described by this model.

  6. Spreading dynamics of an e-commerce preferential information model on scale-free networks

    Science.gov (United States)

    Wan, Chen; Li, Tao; Guan, Zhi-Hong; Wang, Yuanmei; Liu, Xiongding

    2017-02-01

    In order to study the influence of the preferential degree and the heterogeneity of underlying networks on the spread of preferential e-commerce information, we propose a novel susceptible-infected-beneficial model based on scale-free networks. The spreading dynamics of the preferential information are analyzed in detail using the mean-field theory. We determine the basic reproductive number and equilibria. The theoretical analysis indicates that the basic reproductive number depends mainly on the preferential degree and the topology of the underlying networks. We prove the global stability of the information-elimination equilibrium. The permanence of preferential information and the global attractivity of the information-prevailing equilibrium are also studied in detail. Some numerical simulations are presented to verify the theoretical results.

  7. Ozone and atmospheric pollution at synoptic scale: the monitoring network Paes

    International Nuclear Information System (INIS)

    Gheusi, F.; Chevalier, A.; Delmas, R.; Athier, G.; Bouchou, P.; Cousin, J.M.; Meyerfeld, Y.; Laj, P.; Sellegri, K.; Ancellet, G.

    2007-01-01

    Ozone as an environmental concern extends beyond the questions usually covered by media - stratospheric ozone depletion and urban pollution peaks. Strong expositions to this pollutant are frequent even far from pollution sources, and the background tropospheric content of ozone has been growing fivefold over the last century. In response to this concern at the French national scale, formerly independent monitoring stations have been coordinated since 2004 in a structured network: Paes (French acronym for atmospheric pollution at synoptic scale). The data are put in free access online. (authors)

  8. APEnet+: high bandwidth 3D torus direct network for petaflops scale commodity clusters

    Science.gov (United States)

    Ammendola, R.; Biagioni, A.; Frezza, O.; Lo Cicero, F.; Lonardo, A.; Paolucci, P. S.; Rossetti, D.; Salamon, A.; Salina, G.; Simula, F.; Tosoratto, L.; Vicini, P.

    2011-12-01

    We describe herein the APElink+ board, a PCIe interconnect adapter featuring the latest advances in wire speed and interface technology plus hardware support for a RDMA programming model and experimental acceleration of GPU networking; this design allows us to build a low latency, high bandwidth PC cluster, the APEnet+ network, the new generation of our cost-effective, tens-of-thousands-scalable cluster network architecture. Some test results and characterization of data transmission of a complete testbench, based on a commercial development card mounting an Altera® FPGA, are provided.

  9. Macroecology of Australian Tall Eucalypt Forests: Baseline Data from a Continental-Scale Permanent Plot Network.

    Science.gov (United States)

    Wood, Sam W; Prior, Lynda D; Stephens, Helen C; Bowman, David M J S

    2015-01-01

    Tracking the response of forest ecosystems to climate change demands large (≥1 ha) monitoring plots that are repeatedly measured over long time frames and arranged across macro-ecological gradients. Continental scale networks of permanent forest plots have identified links between climate and carbon fluxes by monitoring trends in tree growth, mortality and recruitment. The relationship between tree growth and climate in Australia has been recently articulated through analysis of data from smaller forest plots, but conclusions were limited by (a) absence of data on recruitment and mortality, (b) exclusion of non-eucalypt species, and (c) lack of knowledge of stand age or disturbance histories. To remedy these gaps we established the Ausplots Forest Monitoring Network: a continental scale network of 48 1 ha permanent plots in highly productive tall eucalypt forests in the mature growth stage. These plots are distributed across cool temperate, Mediterranean, subtropical and tropical climates (mean annual precipitation 850 to 1900 mm per year; mean annual temperature 6 to 21°C). Aboveground carbon stocks (AGC) in these forests are dominated by eucalypts (90% of AGC) whilst non-eucalypts in the understorey dominated species diversity and tree abundance (84% of species; 60% of stems). Aboveground carbon stocks were negatively related to mean annual temperature, with forests at the warm end of the temperature range storing approximately half the amount of carbon as forests at the cool end of the temperature range. This may reflect thermal constraints on tree growth detected through other plot networks and physiological studies. Through common protocols and careful sampling design, the Ausplots Forest Monitoring Network will facilitate the integration of tall eucalypt forests into established global forest monitoring initiatives. In the context of projections of rapidly warming and drying climates in Australia, this plot network will enable detection of links between

  10. Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics

    Science.gov (United States)

    Maldonado, Elaina M.; Leoncikas, Vytautas; Fisher, Ciarán P.; Moore, J. Bernadette; Plant, Nick J.

    2017-01-01

    The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models. PMID:28782239

  11. In Silico Genome-Scale Reconstruction and Validation of the Corynebacterium glutamicum Metabolic Network

    DEFF Research Database (Denmark)

    Kjeldsen, Kjeld Raunkjær; Nielsen, J.

    2009-01-01

    A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolite, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model w...... and lactate. Comparable flux values between in silico model and experimental values were seen, although some differences in the phenotypic behavior between the model and the experimental data were observed,...

  12. Nature-Inspired Interconnects for Self-Assembled Large-Scale Network-on-Chip Designs

    OpenAIRE

    Teuscher, Christof

    2007-01-01

    Future nano-scale electronics built up from an Avogadro number of components needs efficient, highly scalable, and robust means of communication in order to be competitive with traditional silicon approaches. In recent years, the Networks-on-Chip (NoC) paradigm emerged as a promising solution to interconnect challenges in silicon-based electronics. Current NoC architectures are either highly regular or fully customized, both of which represent implausible assumptions for emerging bottom-up se...

  13. Network effects of deep brain stimulation.

    Science.gov (United States)

    Alhourani, Ahmad; McDowell, Michael M; Randazzo, Michael J; Wozny, Thomas A; Kondylis, Efstathios D; Lipski, Witold J; Beck, Sarah; Karp, Jordan F; Ghuman, Avniel S; Richardson, R Mark

    2015-10-01

    The ability to differentially alter specific brain functions via deep brain stimulation (DBS) represents a monumental advance in clinical neuroscience, as well as within medicine as a whole. Despite the efficacy of DBS in the treatment of movement disorders, for which it is often the gold-standard therapy when medical management becomes inadequate, the mechanisms through which DBS in various brain targets produces therapeutic effects is still not well understood. This limited knowledge is a barrier to improving efficacy and reducing side effects in clinical brain stimulation. A field of study related to assessing the network effects of DBS is gradually emerging that promises to reveal aspects of the underlying pathophysiology of various brain disorders and their response to DBS that will be critical to advancing the field. This review summarizes the nascent literature related to network effects of DBS measured by cerebral blood flow and metabolic imaging, functional imaging, and electrophysiology (scalp and intracranial electroencephalography and magnetoencephalography) in order to establish a framework for future studies. Copyright © 2015 the American Physiological Society.

  14. Network effects of deep brain stimulation

    Science.gov (United States)

    Alhourani, Ahmad; McDowell, Michael M.; Randazzo, Michael J.; Wozny, Thomas A.; Kondylis, Efstathios D.; Lipski, Witold J.; Beck, Sarah; Karp, Jordan F.; Ghuman, Avniel S.

    2015-01-01

    The ability to differentially alter specific brain functions via deep brain stimulation (DBS) represents a monumental advance in clinical neuroscience, as well as within medicine as a whole. Despite the efficacy of DBS in the treatment of movement disorders, for which it is often the gold-standard therapy when medical management becomes inadequate, the mechanisms through which DBS in various brain targets produces therapeutic effects is still not well understood. This limited knowledge is a barrier to improving efficacy and reducing side effects in clinical brain stimulation. A field of study related to assessing the network effects of DBS is gradually emerging that promises to reveal aspects of the underlying pathophysiology of various brain disorders and their response to DBS that will be critical to advancing the field. This review summarizes the nascent literature related to network effects of DBS measured by cerebral blood flow and metabolic imaging, functional imaging, and electrophysiology (scalp and intracranial electroencephalography and magnetoencephalography) in order to establish a framework for future studies. PMID:26269552

  15. Linking Supply Chain Network Complexity to Interdependence and Risk-Assessment: Scale Development and Empirical Investigation

    Directory of Open Access Journals (Sweden)

    Samyadip Chakraborty

    2015-12-01

    Full Text Available Concepts like supply chain network complexity, interdependence and risk assessment have been prominently discussed directly and indirectly in management literature over past decades and plenty of frameworks and conceptual prescriptive research works have been published contributing towards building the body of knowledge. However previous studies often lacked quantification of the findings. Consequently, the need for suitable scales becomes prominent for measuring those constructs to empirically support the conceptualized relationships. This paper expands the understanding of supply chain network complexity (SCNC and also highlights its implications on interdependence (ID between the actors and risk assessment (RAS in transaction relationships. In doing so, SCNC and RAS are operationalized to understand how SCNC affects interdependence and risk assessment between the actors in the supply chain network. The contribution of this study lies in developing and validating multi-item scales for these constructs and empirically establishing the hypothesized relationships in the Indian context based on firm data collected using survey–based questionnaire. The methodology followed included structural equation modeling. The study findings indicate that SCNC had significant relationship with interdependence, which in turn significantly affected risk assessment. This study carries both academic and managerial implications and provides an empirically supported framework linking network complexity with the two key variables (ID and RAS, playing crucial roles in managerial decision making. This study contributes to the body of knowledge and aims at guiding managers in better understanding transaction relationships.

  16. Efficiency of quarantine and self-protection processes in epidemic spreading control on scale-free networks

    Science.gov (United States)

    Esquivel-Gómez, Jose de Jesus; Barajas-Ramírez, Juan Gonzalo

    2018-01-01

    One of the most effective mechanisms to contain the spread of an infectious disease through a population is the implementation of quarantine policies. However, its efficiency is affected by different aspects, for example, the structure of the underlining social network where highly connected individuals are more likely to become infected; therefore, the speed of the transmission of the decease is directly determined by the degree distribution of the network. Another aspect that influences the effectiveness of the quarantine is the self-protection processes of the individuals in the population, that is, they try to avoid contact with potentially infected individuals. In this paper, we investigate the efficiency of quarantine and self-protection processes in preventing the spreading of infectious diseases over complex networks with a power-law degree distribution [ P ( k ) ˜ k - ν ] for different ν values. We propose two alternative scale-free models that result in power-law degree distributions above and below the exponent ν = 3 associated with the conventional Barabási-Albert model. Our results show that the exponent ν determines the effectiveness of these policies in controlling the spreading process. More precisely, we show that for the ν exponent below three, the quarantine mechanism loses effectiveness. However, the efficiency is improved if the quarantine is jointly implemented with a self-protection process driving the number of infected individuals significantly lower.

  17. Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding.

    Directory of Open Access Journals (Sweden)

    Daniele De Martino

    Full Text Available The uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales. In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metabolic networks of three models of growing size up to genomic scale. The first is based on principal component analysis (PCA, the second on linear programming (LP and finally we employ the Lovazs ellipsoid method (LEM. Our results show that a rounding procedure dramatically improves the performances of the HR in these inference problems and suggest that a combination of LEM or LP with a subsequent PCA perform the best. We finally compare the distributions of the HR with that of two heuristics based on the Artificially Centered hit-and-run (ACHR, gpSampler and optGpSampler. They show a good agreement with the results of the HR for the small network, while on genome scale models present inconsistencies.

  18. A Hybrid Testbed for Performance Evaluation of Large-Scale Datacenter Networks

    DEFF Research Database (Denmark)

    Pilimon, Artur; Ruepp, Sarah Renée

    2018-01-01

    Datacenters (DC) as well as their network interconnects are growing in scale and complexity. They are constantly being challenged in terms of energy and resource utilization efficiency, scalability, availability, reliability and performance requirements. Therefore, these resource-intensive enviro......Datacenters (DC) as well as their network interconnects are growing in scale and complexity. They are constantly being challenged in terms of energy and resource utilization efficiency, scalability, availability, reliability and performance requirements. Therefore, these resource......-intensive environments must be properly tested and analyzed in order to make timely upgrades and transformations. However, a limited number of academic institutions and Research and Development companies have access to production scale DC Network (DCN) testing facilities, and resource-limited studies can produce...... misleading or inaccurate results. To address this problem, we introduce an alternative solution, which forms a solid base for a more realistic and comprehensive performance evaluation of different aspects of DCNs. It is based on the System-in-the-loop (SITL) concept, where real commercial DCN equipment...

  19. Evolutionary prisoners dilemma game with voluntary participation on regular lattices and scale-free networks

    Science.gov (United States)

    Chen, Cai-Long; Cao, Xian-Bin; Du, Wen-Bo; Rong, Zhi-Hai

    2010-08-01

    The voluntary participation mechanism has been demonstrated as a natural extension to promote the cooperative behavior in evolutionary games. Apart from the cooperator strategy and defector strategy in the original Prisoner's Dilemma Game (PDG), players can choose the additional loner strategy to refuse to participate and get some small but fixed income Q. We allow for voluntary participation in the evolutionary PDG on regular lattices and scale-free networks, and focus on the strategy density and the evolution behavior of the system. Simulation shows that the system behavior is sensitive to the population structure. For lattices, the densities of cooperator, defector and loners exhibit non-trivial behaviors with the increment of Q and temptation parameter b except for the density of loners as a function of Q. For scale-free networks, the densities of cooperator and defectors monotonously decrease with the increment of Q, and the densities of defectors and loners monotonously increase with the increment of b. Moreover, this extension can lead to 'rock-scissors-paper'-like cyclic dominance of the three types of players on regular lattices. But for scale-free networks, the loners cannot coexist with cooperators or defectors.

  20. Scaling thermal effects in radial flow

    Science.gov (United States)

    Hudspeth, R. T.; Guenther, R. B.; Roley, K. L.; McDougal, W. G.

    To adequately evaluate the environmental impact of siting nuclear waste repositories in basalt aquicludes, it is essential to know the effects on parameter identification algorithms of thermal gradients that exist in these basaltic aquicludes. Temperatures of approximately 60°C and pressures of approximately 150 atm can be expected at potential repository sites located at depths of approximately 1000 m. The phenomenon of over-recovery has been observed in some pumping tests conducted at the Hanford Nuclear Reservation located in the Pasco Basin adjacent to the Columbia River in the state of Washington, USA. This over-recovery phenomenon may possibly be due to variations in the fluid density caused by thermal gradients. To assess the potential effects of these thermal gradients on indirect parameter identification algorithms, a systematic scaling of the governing field equations is required in order to obtain dimensionless equations based on the principle of similarity. The constitutive relationships for the specific weight of the fluid and for the porosity of the aquiclude are shown to be exponentially dependent on the pressure gradient. The dynamic pressure is converted to the piezometric head and the flow equation for the piezometric head is then scaled in radial coordinates. Order-of-magnitude estimates are made for all variables in unsteady flow for a typical well test in a basaltic aquiclude. Retaining all nonlinear terms, the parametric dependency of the flow equation on the classical dimensionless thermal and hydraulic parameters is demonstrated. These classical parameters include the Batchelor, Fourier, Froude, Grashof, and Reynolds Numbers associated with thermal flows. The flow equation is linearized from order-of-magnitude estimates based on these classical parameters for application in parameter identification algorithms.

  1. Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

    Science.gov (United States)

    Lee, Jongpil; Nam, Juhan

    2017-08-01

    Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

  2. Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks

    DEFF Research Database (Denmark)

    Hillmann, Robert; Trier, Matthias

    2012-01-01

    Communication in online social networks has been analyzed for some time regarding the expression of sentiments. So far, very little is known about the relationship between sentiments and network emergence, dissemination patterns and possible differences between positive and negative sentiments....... The dissemination patterns analyzed in this study consist of network motifs based on triples of actors and the ties among them. These motifs are associated with common social network effects to derive meaningful insights about dissemination activities. The data basis includes several thousand social networks...... with textual messages classified according to embedded positive and negative sentiments. Based on this data, sub-networks are extracted and analyzed with a dynamic network motif analysis to determine dissemination patterns and associated network effects. Results indicate that the emergence of digital social...

  3. Distributed and Cooperative Link Scheduling for Large-Scale Multihop Wireless Networks

    Directory of Open Access Journals (Sweden)

    Ananthram Swami

    2007-12-01

    Full Text Available A distributed and cooperative link-scheduling (DCLS algorithm is introduced for large-scale multihop wireless networks. With this algorithm, each and every active link in the network cooperatively calibrates its environment and converges to a desired link schedule for data transmissions within a time frame of multiple slots. This schedule is such that the entire network is partitioned into a set of interleaved subnetworks, where each subnetwork consists of concurrent cochannel links that are properly separated from each other. The desired spacing in each subnetwork can be controlled by a tuning parameter and the number of time slots specified for each frame. Following the DCLS algorithm, a distributed and cooperative power control (DCPC algorithm can be applied to each subnetwork to ensure a desired data rate for each link with minimum network transmission power. As shown consistently by simulations, the DCLS algorithm along with a DCPC algorithm yields significant power savings. The power savings also imply an increased feasible region of averaged link data rates for the entire network.

  4. Spontaneous Neuronal Activity in Developing Neocortical Networks: From Single Cells to Large-Scale Interactions.

    Science.gov (United States)

    Luhmann, Heiko J; Sinning, Anne; Yang, Jenq-Wei; Reyes-Puerta, Vicente; Stüttgen, Maik C; Kirischuk, Sergei; Kilb, Werner

    2016-01-01

    Neuronal activity has been shown to be essential for the proper formation of neuronal circuits, affecting developmental processes like neurogenesis, migration, programmed cell death, cellular differentiation, formation of local and long-range axonal connections, synaptic plasticity or myelination. Accordingly, neocortical areas reveal distinct spontaneous and sensory-driven neuronal activity patterns already at early phases of development. At embryonic stages, when immature neurons start to develop voltage-dependent channels, spontaneous activity is highly synchronized within small neuronal networks and governed by electrical synaptic transmission. Subsequently, spontaneous activity patterns become more complex, involve larger networks and propagate over several neocortical areas. The developmental shift from local to large-scale network activity is accompanied by a gradual shift from electrical to chemical synaptic transmission with an initial excitatory action of chloride-gated channels activated by GABA, glycine and taurine. Transient neuronal populations in the subplate (SP) support temporary circuits that play an important role in tuning early neocortical activity and the formation of mature neuronal networks. Thus, early spontaneous activity patterns control the formation of developing networks in sensory cortices, and disturbances of these activity patterns may lead to long-lasting neuronal deficits.

  5. Large Scale Environmental Monitoring through Integration of Sensor and Mesh Networks

    Directory of Open Access Journals (Sweden)

    Raja Jurdak

    2008-11-01

    Full Text Available Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper.

  6. Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

    Directory of Open Access Journals (Sweden)

    Min Peng

    2017-10-01

    Full Text Available Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.

  7. Neural Schematics as a unified formal graphical representation of large-scale Neural Network Structures

    Directory of Open Access Journals (Sweden)

    Matthias eEhrlich

    2013-10-01

    Full Text Available One of the major outcomes of neuroscientific research are models of Neural Network Structures. Descriptions of these models usually consist of a non-standardized mixture of text, figures, and other means of visual information communication in print media. However, as neuroscience is an interdisciplinary domain by nature, a standardized way of consistently representing models of Neural Network Structures is required. While generic descriptions of such models in textual form have recently been developed, a formalized way of schematically expressing them does not exist to date. Hence, in this paper we present Neural Schematics as a concept inspired by similar approaches from other disciplines for a generic two dimensional representation of said structures. After introducing Neural Network Structures in general, a set of current visualizations of models of Neural Network Structures is reviewed and analyzed for what information they convey and how their elements are rendered. This analysis then allows for the definition of general items and symbols to consistently represent these models as Neural Schematics on a two dimensional plane. We will illustrate the possibilities an agreed upon standard can yield on sampled diagrams transformed into Neural Schematics and an example application for the design and modeling of large-scale Neural Network Structures.

  8. Modeling Reservoir-River Networks in Support of Optimizing Seasonal-Scale Reservoir Operations

    Science.gov (United States)

    Villa, D. L.; Lowry, T. S.; Bier, A.; Barco, J.; Sun, A.

    2011-12-01

    HydroSCOPE (Hydropower Seasonal Concurrent Optimization of Power and the Environment) is a seasonal time-scale tool for scenario analysis and optimization of reservoir-river networks. Developed in MATLAB, HydroSCOPE is an object-oriented model that simulates basin-scale dynamics with an objective of optimizing reservoir operations to maximize revenue from power generation, reliability in the water supply, environmental performance, and flood control. HydroSCOPE is part of a larger toolset that is being developed through a Department of Energy multi-laboratory project. This project's goal is to provide conventional hydropower decision makers with better information to execute their day-ahead and seasonal operations and planning activities by integrating water balance and operational dynamics across a wide range of spatial and temporal scales. This presentation details the modeling approach and functionality of HydroSCOPE. HydroSCOPE consists of a river-reservoir network model and an optimization routine. The river-reservoir network model simulates the heat and water balance of river-reservoir networks for time-scales up to one year. The optimization routine software, DAKOTA (Design Analysis Kit for Optimization and Terascale Applications - dakota.sandia.gov), is seamlessly linked to the network model and is used to optimize daily volumetric releases from the reservoirs to best meet a set of user-defined constraints, such as maximizing revenue while minimizing environmental violations. The network model uses 1-D approximations for both the reservoirs and river reaches and is able to account for surface and sediment heat exchange as well as ice dynamics for both models. The reservoir model also accounts for inflow, density, and withdrawal zone mixing, and diffusive heat exchange. Routing for the river reaches is accomplished using a modified Muskingum-Cunge approach that automatically calculates the internal timestep and sub-reach lengths to match the conditions of

  9. Scaling of peak flows with constant flow velocity in random self-similar networks

    Science.gov (United States)

    Troutman, Brent M.; Mantilla, Ricardo; Gupta, Vijay K.

    2011-01-01

    A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs) with geometrically distributed interior and exterior generators having parameters pi and pe, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β(E) and φ(E) that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β(E) and φ(E) and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ(E) and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit hydrograph theory and flow dynamics. Our results provide a reference framework to study scaling exponents under more complex scenarios

  10. Scaling of peak flows with constant flow velocity in random self-similar networks

    Directory of Open Access Journals (Sweden)

    R. Mantilla

    2011-07-01

    Full Text Available A methodology is presented to understand the role of the statistical self-similar topology of real river networks on scaling, or power law, in peak flows for rainfall-runoff events. We created Monte Carlo generated sets of ensembles of 1000 random self-similar networks (RSNs with geometrically distributed interior and exterior generators having parameters pi and pe, respectively. The parameter values were chosen to replicate the observed topology of real river networks. We calculated flow hydrographs in each of these networks by numerically solving the link-based mass and momentum conservation equation under the assumption of constant flow velocity. From these simulated RSNs and hydrographs, the scaling exponents β and φ characterizing power laws with respect to drainage area, and corresponding to the width functions and flow hydrographs respectively, were estimated. We found that, in general, φ > β, which supports a similar finding first reported for simulations in the river network of the Walnut Gulch basin, Arizona. Theoretical estimation of β and φ in RSNs is a complex open problem. Therefore, using results for a simpler problem associated with the expected width function and expected hydrograph for an ensemble of RSNs, we give heuristic arguments for theoretical derivations of the scaling exponents β(E and φ(E that depend on the Horton ratios for stream lengths and areas. These ratios in turn have a known dependence on the parameters of the geometric distributions of RSN generators. Good agreement was found between the analytically conjectured values of β(E and φ(E and the values estimated by the simulated ensembles of RSNs and hydrographs. The independence of the scaling exponents φ(E and φ with respect to the value of flow velocity and runoff intensity implies an interesting connection between unit

  11. Atomic-scale friction : thermal effects and capillary condensation

    NARCIS (Netherlands)

    Jinesh, Kochupurackal Balakrishna Pillai

    2006-01-01

    This work entitled as "Atomic-scale friction: thermal effects and capillary condensation" is a study on the fundamental aspects of the origin of friction from the atomic-scale. We study two realistic aspects of atomic-scale friction, namely the effect of temperature and the effect of relative

  12. Networks And Effectiveness In Work Teams: The Impact Of Diversity

    OpenAIRE

    JUAN CARLOS PASTOR; MARGARITA MAYO

    2005-01-01

    This paper examine the role of social networks as mediating factors in the relationship between diversity and work team effectiveness. These effects were tested with a sample of 71 organizational teams. Results show that the degree of diversity in a team influences the density and centralization of the communication network. In turn, network density and centralization affect work team outcomes. Results are discussed in terms of their theoretical significance for network and diversity theory.

  13. Flexible sampling large-scale social networks by self-adjustable random walk

    Science.gov (United States)

    Xu, Xiao-Ke; Zhu, Jonathan J. H.

    2016-12-01

    Online social networks (OSNs) have become an increasingly attractive gold mine for academic and commercial researchers. However, research on OSNs faces a number of difficult challenges. One bottleneck lies in the massive quantity and often unavailability of OSN population data. Sampling perhaps becomes the only feasible solution to the problems. How to draw samples that can represent the underlying OSNs has remained a formidable task because of a number of conceptual and methodological reasons. Especially, most of the empirically-driven studies on network sampling are confined to simulated data or sub-graph data, which are fundamentally different from real and complete-graph OSNs. In the current study, we propose a flexible sampling method, called Self-Adjustable Random Walk (SARW), and test it against with the population data of a real large-scale OSN. We evaluate the strengths of the sampling method in comparison with four prevailing methods, including uniform, breadth-first search (BFS), random walk (RW), and revised RW (i.e., MHRW) sampling. We try to mix both induced-edge and external-edge information of sampled nodes together in the same sampling process. Our results show that the SARW sampling method has been able to generate unbiased samples of OSNs with maximal precision and minimal cost. The study is helpful for the practice of OSN research by providing a highly needed sampling tools, for the methodological development of large-scale network sampling by comparative evaluations of existing sampling methods, and for the theoretical understanding of human networks by highlighting discrepancies and contradictions between existing knowledge/assumptions of large-scale real OSN data.

  14. Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection.

    Science.gov (United States)

    Wang, Xiang; Ma, Huimin; Chen, Xiaozhi; You, Shaodi

    In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet ) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet ) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the

  15. Quantum phase transition of the transverse-field quantum Ising model on scale-free networks.

    Science.gov (United States)

    Yi, Hangmo

    2015-01-01

    I investigate the quantum phase transition of the transverse-field quantum Ising model in which nearest neighbors are defined according to the connectivity of scale-free networks. Using a continuous-time quantum Monte Carlo simulation method and the finite-size scaling analysis, I identify the quantum critical point and study its scaling characteristics. For the degree exponent λ=6, I obtain results that are consistent with the mean-field theory. For λ=4.5 and 4, however, the results suggest that the quantum critical point belongs to a non-mean-field universality class. Further simulations indicate that the quantum critical point remains mean-field-like if λ>5, but it continuously deviates from the mean-field theory as λ becomes smaller.

  16. Responding to the challenge of artisanal and small-scale mining. How can knowledge networks help?

    Energy Technology Data Exchange (ETDEWEB)

    Buxton, Abbi

    2013-02-15

    This paper reviews what is known about the problems and structural challenges facing the 20-30 million artisanal and small-scale miners and their communities worldwide. Better understanding of these structural challenges is needed to improve policies and policy implementation to further sustainable development opportunities for the sector. The paper explores the current gaps in knowledge to achieve policy change from researchers, practitioners and artisanal and small-scale miners themselves. It explores how a 'knowledge intermediary', which acts to link knowledge with policy, could address these gaps and includes case studies of IIED’s work on knowledge networks and programmes. The paper concludes by proposing a way forward for designing a knowledge programme to meet the particular needs of the artisanal and small-scale mining (ASM) sector, and by inviting ASM sector stakeholders to share their views on the options outlined.

  17. Pore morphologies of root induced biopores from single pore to network scale investigated by XRCT

    Science.gov (United States)

    Peth, Stephan; Wittig, Marlen C.; Uteau Puschmann, Daniel; Pagenkemper, Sebastian; Haas, Christoph; Holthusen, Dörthe; Horn, Rainer

    2015-04-01

    Biopores are assumed to be an important factor for nutrient acquisition by providing biologically highly active soil-root interfaces to re-colonizing roots and controlling oxygen and water flows at the pedon scale and within the rhizosphere through the formation of branching channel networks which potentially enhance microbial turnover processes. Characteristic differences in pore morphologies are to be expected depending on the genesis of biopores which, for example, can be earthworm-induced or root-induced or subsequently modified by one of the two. Our understanding of biophysical interactions between plants and soil can be significantly improved by quantifying 3D biopore architectures across scales ranging from single biopores to pedon scale pore networks and linking pore morphologies to microscale measurements of transport processes (e.g. oxygen diffusion). While a few studies in the past have investigated biopore networks on a larger scale yet little is known on the micro-morphology of root-induces biopores and their associated rhizosphere. Also little data is available on lateral transport of oxygen through the rhizosphere which will strongly influence microbial turnover processes and consequently control the release and uptake of nutrients. This paper highlights results gathered within a research unit on nutrient acquisition from the subsoil. Here we focus on X-ray microtomography (XRCT) studies ranging from large soil columns (70 cm length and 20 cm diameter) to individual biopores and its surrounding rhizosphere. Samples were collected from sites with different preceding crops (fescue, chicory, alfalfa) and various cropping durations (1-3 years). We will present an approach for quantitative image analysis combined with micro-sensor measurements of oxygen diffusion and spatial gradients of O2 partial pressures to relate pore structure with transport functions. Implications of various biopore architectures for the accessibility of nutrient resources in

  18. Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions.

    Science.gov (United States)

    Najafi, Mahshid; McMenamin, Brenton W; Simon, Jonathan Z; Pessoa, Luiz

    2016-07-15

    Large-scale analysis of functional MRI data has revealed that brain regions can be grouped into stable "networks" or communities. In many instances, the communities are characterized as relatively disjoint. Although recent work indicates that brain regions may participate in multiple communities (for example, hub regions), the extent of community overlap is poorly understood. To address these issues, here we investigated large-scale brain networks based on "rest" and task human functional MRI data by employing a mixed-membership Bayesian model that allows each brain region to belong to all communities simultaneously with varying membership strengths. The approach allowed us to 1) compare the structure of disjoint and overlapping communities; 2) determine the relationship between functional diversity (how diverse is a region's functional activation repertoire) and membership diversity (how diverse is a region's affiliation to communities); 3) characterize overlapping community structure; 4) characterize the degree of non-modularity in brain networks; 5) study the distribution of "bridges", including bottleneck and hub bridges. Our findings revealed the existence of dense community overlap that was not limited to "special" hubs. Furthermore, the findings revealed important differences between community organization during rest and during specific task states. Overall, we suggest that dense overlapping communities are well suited to capture the flexible and task dependent mapping between brain regions and their functions. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Functional models for large-scale gene regulation networks: realism and fiction.

    Science.gov (United States)

    Lagomarsino, Marco Cosentino; Bassetti, Bruno; Castellani, Gastone; Remondini, Daniel

    2009-04-01

    High-throughput experiments are shedding light on the topology of large regulatory networks and at the same time their functional states, namely the states of activation of the nodes (for example transcript or protein levels) in different conditions, times, environments. We now possess a certain amount of information about these two levels of description, stored in libraries, databases and ontologies. A current challenge is to bridge the gap between topology and function, i.e. developing quantitative models aimed at characterizing the expression patterns of large sets of genes. However, approaches that work well for small networks become impossible to master at large scales, mainly because parameters proliferate. In this review we discuss the state of the art of large-scale functional network models, addressing the issue of what can be considered as "realistic" and what the main limitations may be. We also show some directions for future work, trying to set the goals that future models should try to achieve. Finally, we will emphasize the possible benefits in the understanding of biological mechanisms underlying complex multifactorial diseases, and in the development of novel strategies for the description and the treatment of such pathologies.

  20. Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

    Science.gov (United States)

    Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.

    2014-01-01

    Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891

  1. Comprehensive Mapping of Pluripotent Stem Cell Metabolism Using Dynamic Genome-Scale Network Modeling

    Directory of Open Access Journals (Sweden)

    Sriram Chandrasekaran

    2017-12-01

    Full Text Available Summary: Metabolism is an emerging stem cell hallmark tied to cell fate, pluripotency, and self-renewal, yet systems-level understanding of stem cell metabolism has been limited by the lack of genome-scale network models. Here, we develop a systems approach to integrate time-course metabolomics data with a computational model of metabolism to analyze the metabolic state of naive and primed murine pluripotent stem cells. Using this approach, we find that one-carbon metabolism involving phosphoglycerate dehydrogenase, folate synthesis, and nucleotide synthesis is a key pathway that differs between the two states, resulting in differential sensitivity to anti-folates. The model also predicts that the pluripotency factor Lin28 regulates this one-carbon metabolic pathway, which we validate using metabolomics data from Lin28-deficient cells. Moreover, we identify and validate metabolic reactions related to S-adenosyl-methionine production that can differentially impact histone methylation in naive and primed cells. Our network-based approach provides a framework for characterizing metabolic changes influencing pluripotency and cell fate. : Chandrasekaran et al. use computational modeling, metabolomics, and metabolic inhibitors to discover metabolic differences between various pluripotent stem cell states and infer their impact on stem cell fate decisions. Keywords: systems biology, stem cell biology, metabolism, genome-scale modeling, pluripotency, histone methylation, naive (ground state, primed state, cell fate, metabolic network

  2. Sequential computation of elementary modes and minimal cut sets in genome-scale metabolic networks using alternate integer linear programming.

    Science.gov (United States)

    Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami

    2017-08-01

    Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2017. This work is written by US Government employees and are in the public domain in the US.

  3. Correlation of Social Network Attributes with Individuals’ Score on Bipolar Spectrum Diagnostic Scale

    Directory of Open Access Journals (Sweden)

    Amir Momeni Boroujeni

    2012-09-01

    Full Text Available Introduction: Bipolar Spectrum Disorders include a variety of mood disorders from bipolar II disorder to conditions characterized by hyperthymic mood states. It has been suggested that psychosocial factors also play an important role in bipolar disorders, in this study we have used social network analysis in order to better understand the social positions of those affected by bipolar spectrum disorders. Methods: In this cross sectional study 90 individuals within a bounded network were included and studied by using a standard questionnaire for bipolar spectrum disorder scale (BSDS and a sociometric questionnaire for analyzing the social network of those individuals.Results: This study showed that BSDS score is signi.cantly correlated with the Bonacich power of the participants (P= 0.009 as well as with their Outdegree Strength (P= 0.013.Discussion: The results of this study show that there is interplay between social attributes and Bipolar Spectrum Disorders. This emphasizes the need for understanding the role of social networks and performing further research into quantifying social aspects of psychiatric disorders.

  4. Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xing Li

    2015-07-01

    Full Text Available With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people’s lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs. Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs, this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme.

  5. Cardinality Estimation Algorithm in Large-Scale Anonymous Wireless Sensor Networks

    KAUST Repository

    Douik, Ahmed

    2017-08-30

    Consider a large-scale anonymous wireless sensor network with unknown cardinality. In such graphs, each node has no information about the network topology and only possesses a unique identifier. This paper introduces a novel distributed algorithm for cardinality estimation and topology discovery, i.e., estimating the number of node and structure of the graph, by querying a small number of nodes and performing statistical inference methods. While the cardinality estimation allows the design of more efficient coding schemes for the network, the topology discovery provides a reliable way for routing packets. The proposed algorithm is shown to produce a cardinality estimate proportional to the best linear unbiased estimator for dense graphs and specific running times. Simulation results attest the theoretical results and reveal that, for a reasonable running time, querying a small group of nodes is sufficient to perform an estimation of 95% of the whole network. Applications of this work include estimating the number of Internet of Things (IoT) sensor devices, online social users, active protein cells, etc.

  6. BFL: a node and edge betweenness based fast layout algorithm for large scale networks

    Science.gov (United States)

    Hashimoto, Tatsunori B; Nagasaki, Masao; Kojima, Kaname; Miyano, Satoru

    2009-01-01

    Background Network visualization would serve as a useful first step for analysis. However, current graph layout algorithms for biological pathways are insensitive to biologically important information, e.g. subcellular localization, biological node and graph attributes, or/and not available for large scale networks, e.g. more than 10000 elements. Results To overcome these problems, we propose the use of a biologically important graph metric, betweenness, a measure of network flow. This metric is highly correlated with many biological phenomena such as lethality and clusters. We devise a new fast parallel algorithm calculating betweenness to minimize the preprocessing cost. Using this metric, we also invent a node and edge betweenness based fast layout algorithm (BFL). BFL places the high-betweenness nodes to optimal positions and allows the low-betweenness nodes to reach suboptimal positions. Furthermore, BFL reduces the runtime by combining a sequential insertion algorim with betweenness. For a graph with n nodes, this approach reduces the expected runtime of the algorithm to O(n2) when considering edge crossings, and to O(n log n) when considering only density and edge lengths. Conclusion Our BFL algorithm is compared against fast graph layout algorithms and approaches requiring intensive optimizations. For gene networks, we show that our algorithm is faster than all layout algorithms tested while providing readability on par with intensive optimization algorithms. We achieve a 1.4 second runtime for a graph with 4000 nodes and 12000 edges on a standard desktop computer. PMID:19146673

  7. Dynamics of an epidemic model with quarantine on scale-free networks

    Science.gov (United States)

    Kang, Huiyan; Liu, Kaihui; Fu, Xinchu

    2017-12-01

    Quarantine strategies are frequently used to control or reduce the transmission risks of epidemic diseases such as SARS, tuberculosis and cholera. In this paper, we formulate a susceptible-exposed-infected-quarantined-recovered model on a scale-free network incorporating the births and deaths of individuals. Considering that the infectivity is related to the degrees of infectious nodes, we introduce quarantined rate as a function of degree into the model, and quantify the basic reproduction number, which is shown to be dependent on some parameters, such as quarantined rate, infectivity and network structures. A theoretical result further indicates the heterogeneity of networks and higher infectivity will raise the disease transmission risk while quarantine measure will contribute to the prevention of epidemic spreading. Meanwhile, the contact assumption between susceptibles and infectives may impact the disease transmission. Furthermore, we prove that the basic reproduction number serves as a threshold value for the global stability of the disease-free and endemic equilibria and the uniform persistence of the disease on the network by constructing appropriate Lyapunov functions. Finally, some numerical simulations are illustrated to perform and complement our analytical results.

  8. Correlation of Social Network Attributes with Individuals’ Score on Bipolar Spectrum Diagnostic Scale

    Directory of Open Access Journals (Sweden)

    Amir Momeni Boroujeni

    2012-12-01

    Full Text Available Bipolar Spectrum Disorders include a variety of mood disorders from bipolar II disorder to conditions characterized by hyperthymic mood states. It has been suggested that psychosocial factors also play an important role in bipolar disorders, in this study we have used social network analysis in order to better understand the social positions of those affected by bipolar spectrum disorders.Methods and Materials: In this cross sectional study 90 individuals within a bounded network were included and studied by using a standard questionnaire for bipolar spectrum disorder scale (BSDS and a sociometric questionnaire for analyzing the social network of those individuals.Results: This study showed that BSDS score is significantly correlated with the Bonacich power of the participants (P= 0.009 as well as with their Outdegree Strength (P= 0.013.Discussion:The results of this study show that there is interplay between social attributes and Bipolar Spectrum Disorders. This emphasizes the need for understanding the role of social networks and performing further research into quantifying social aspects of psychiatric disorders.

  9. Dendritic Connectivity, Heterogeneity, and Scaling in Urban Stormwater Networks: Implications for Socio-Hydrology

    Science.gov (United States)

    Mejia, A.; Jovanovic, T.; Hale, R. L.; Gironas, J. A.

    2017-12-01

    Urban stormwater networks (USNs) are unique dendritic (tree-like) structures that combine both artificial (e.g., swales and pipes) and natural (e.g., streams and wetlands) components. They are central to stream ecosystem structure and function in urban watersheds. The emphasis of conventional stormwater management, however, has been on localized, temporal impacts (e.g., changes to hydrographs at discrete locations), and the performance of individual stormwater control measures. This is the case even though control measures are implemented to prevent impacts on the USN. We develop a modeling approach to retrospectively study hydrological fluxes and states in USNs and apply the model to an urban watershed in Scottsdale, Arizona, USA. Using outputs from the model, we analyze over space and time the network properties of dendritic connectivity, heterogeneity, and scaling. Results show that as the network growth over time, due to increasing urbanization, it tends to become more homogenous in terms of topological features but increasingly heterogeneous in terms of dynamic features. We further use the modeling results to address socio-hydrological implications for USNs. We find that the adoption over time of evolving management strategies (e.g., widespread implementation of vegetated swales and retention ponds versus pipes) may be locally beneficial to the USN but benefits may not propagate systematically through the network. The latter can be reinforced by sudden, perhaps unintended, changes to the overall dendritic connectivity.

  10. Collective behavior of large-scale neural networks with GPU acceleration.

    Science.gov (United States)

    Qu, Jingyi; Wang, Rubin

    2017-12-01

    In this paper, the collective behaviors of a small-world neuronal network motivated by the anatomy of a mammalian cortex based on both Izhikevich model and Rulkov model are studied. The Izhikevich model can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Rulkov model is in the form of difference equations that generate a sequence of membrane potential samples in discrete moments of time to improve computational efficiency. These two models are suitable for the construction of large scale neural networks. By varying some key parameters, such as the connection probability and the number of nearest neighbor of each node, the coupled neurons will exhibit types of temporal and spatial characteristics. It is demonstrated that the implementation of GPU can achieve more and more acceleration than CPU with the increasing of neuron number and iterations. These two small-world network models and GPU acceleration give us a new opportunity to reproduce the real biological network containing a large number of neurons.

  11. Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks.

    Science.gov (United States)

    Li, Xing; Chen, Dexin; Li, Chunyan; Wang, Liangmin

    2015-07-03

    With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people's lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs). Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA) in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs), this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme.

  12. DYNAMIC FLARING NON-POTENTIAL FIELDS ON QUIET SUN NETWORK SCALES

    Energy Technology Data Exchange (ETDEWEB)

    Chesny, D. L.; Oluseyi, H. M. [Department of Physics and Space Sciences, Florida Institute of Technology, Melbourne, FL 32901 (United States); Orange, N. B. [OrangeWave Innovative Science, LLC, Moncks Corner, SC 29461 (United States)

    2016-05-10

    We report on the identification of dynamic flaring non-potential structures on quiet Sun (QS) supergranular network scales. Data from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory allow for the high spatial and temporal resolution of this diverse class of compact structures. The rapidly evolving non-potential events presented here, with lifetimes <10 minutes, are on the order of 10″ in length. Thus, they contrast significantly with well-known active region (AR) non-potential structures such as high-temperature X-ray and EUV sigmoids (>100″) and micro-sigmoids (>10″) with lifetimes on the order of hours to days. The photospheric magnetic field environment derived from the Helioseismic and Magnetic Imager shows a lack of evidence for these flaring non-potential fields being associated with significant concentrations of bipolar magnetic elements. Of much interest to our events is the possibility of establishing them as precursor signatures of eruptive dynamics, similar to notions for AR sigmoids and micro-sigmoids, but associated with uneventful magnetic network regions. We suggest that the mixed network flux of QS-like magnetic environments, though unresolved, can provide sufficient free magnetic energy for flaring non-potential plasma structuring. The appearance of non-potential magnetic fields could be a fundamental process leading to self-organized criticality in the QS-like supergranular network and contribute to coronal heating, as these events undergo rapid helicial and vortical relaxations.

  13. A Large Scale Code Resolution Service Network in the Internet of Things

    Directory of Open Access Journals (Sweden)

    Xiangzhan Yu

    2012-11-01

    Full Text Available In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT’s advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS.

  14. A multi-scale hybrid neural network retrieval model for dust storm detection, a study in Asia

    Science.gov (United States)

    Wong, Man Sing; Xiao, Fei; Nichol, Janet; Fung, Jimmy; Kim, Jhoon; Campbell, James; Chan, P. W.

    2015-05-01

    Dust storms are known to have adverse effects on human health and significant impact on weather, air quality, hydrological cycle, and ecosystem. Atmospheric dust loading is also one of the large uncertainties in global climate modeling, due to its significant impact on the radiation budget and atmospheric stability. Observations of dust storms in humid tropical south China (e.g. Hong Kong), are challenging due to high industrial pollution from the nearby Pearl River Delta region. This study develops a method for dust storm detection by combining ground station observations (PM10 concentration, AERONET data), geostationary satellite images (MTSAT), and numerical weather and climatic forecasting products (WRF/Chem). The method is based on a hybrid neural network (NN) retrieval model for two scales: (i) a NN model for near real-time detection of dust storms at broader regional scale; (ii) a NN model for detailed dust storm mapping for Hong Kong and Taiwan. A feed-forward multilayer perceptron (MLP) NN, trained using back propagation (BP) algorithm, was developed and validated by the k-fold cross validation approach. The accuracy of the near real-time detection MLP-BP network is 96.6%, and the accuracies for the detailed MLP-BP neural network for Hong Kong and Taiwan is 74.8%. This newly automated multi-scale hybrid method can be used to give advance near real-time mapping of dust storms for environmental authorities and the public. It is also beneficial for identifying spatial locations of adverse air quality conditions, and estimates of low visibility associated with dust events for port and airport authorities.

  15. Damage spreading on networks: Clustering effects

    Indian Academy of Sciences (India)

    Abstract. The damage spreading of the Ising model on three kinds of networks is studied with Glauber dynamics. One of the networks is generated by evolving the hexagonal lattice with the star-triangle transformation. Another kind of network is constructed by connecting the midpoints of the edges of the topological ...

  16. Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state.

    Science.gov (United States)

    Perendeci, Altinay; Arslan, Sever; Tanyolaç, Abdurrahman; Celebi, Serdar S

    2009-10-01

    A conceptual neural fuzzy model based on adaptive-network based fuzzy inference system, ANFIS, was proposed using available input on-line and off-line operational variables for a sugar factory anaerobic wastewater treatment plant operating under unsteady state to estimate the effluent chemical oxygen demand, COD. The predictive power of the developed model was improved as a new approach by adding the phase vector and the recent values of COD up to 5-10 days, longer than overall retention time of wastewater in the system. History of last 10 days for COD effluent with two-valued phase vector in the input variable matrix including all parameters had more predictive power. History of 7 days with two-valued phase vector in the matrix comprised of only on-line variables yielded fairly well estimations. The developed ANFIS model with phase vector and history extension has been able to adequately represent the behavior of the treatment system.

  17. A multi-time-scale analysis of chemical reaction networks: II. Stochastic systems.

    Science.gov (United States)

    Kan, Xingye; Lee, Chang Hyeong; Othmer, Hans G

    2016-11-01

    We consider stochastic descriptions of chemical reaction networks in which there are both fast and slow reactions, and for which the time scales are widely separated. We develop a computational algorithm that produces the generator of the full chemical master equation for arbitrary systems, and show how to obtain a reduced equation that governs the evolution on the slow time scale. This is done by applying a state space decomposition to the full equation that leads to the reduced dynamics in terms of certain projections and the invariant distributions of the fast system. The rates or propensities of the reduced system are shown to be the rates of the slow reactions conditioned on the expectations of fast steps. We also show that the generator of the reduced system is a Markov generator, and we present an efficient stochastic simulation algorithm for the slow time scale dynamics. We illustrate the numerical accuracy of the approximation by simulating several examples. Graph-theoretic techniques are used throughout to describe the structure of the reaction network and the state-space transitions accessible under the dynamics.

  18. Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets

    NARCIS (Netherlands)

    Levering, J.; Fiedler, T.; Sieg, A.; van Grinsven, K.W.A.; Hering, S.; Veith, N.; Olivier, B.G.; Klett, L.; Hugenholtz, J.; Teusink, B.; Kreikemeyer, B.; Kummer, U.

    2016-01-01

    Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes

  19. Dynamics of epidemic spreading model with drug-resistant variation on scale-free networks

    Science.gov (United States)

    Wan, Chen; Li, Tao; Zhang, Wu; Dong, Jing

    2018-03-01

    Considering the influence of the virus' drug-resistant variation, a novel SIVRS (susceptible-infected-variant-recovered-susceptible) epidemic spreading model with variation characteristic on scale-free networks is proposed in this paper. By using the mean-field theory, the spreading dynamics of the model is analyzed in detail. Then, the basic reproductive number R0 and equilibriums are derived. Studies show that the existence of disease-free equilibrium is determined by the basic reproductive number R0. The relationships between the basic reproductive number R0, the variation characteristic and the topology of the underlying networks are studied in detail. Furthermore, our studies prove the global stability of the disease-free equilibrium, the permanence of epidemic and the global attractivity of endemic equilibrium. Numerical simulations are performed to confirm the analytical results.

  20. Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

    DEFF Research Database (Denmark)

    Barah, Pankaj; Jayavelu, Naresh Doni; Rasmussen, Simon

    2013-01-01

    regulatory network contained 1,275 nodes and 7,720 connections, with 178 transcription factors and 1,331 target genes. CONCLUSIONS: A. thaliana ecotypes exhibit considerable variation in transcriptome level responses to non-freezing cold stress treatment. Ecotype specific transcripts and related gene...... ontology (GO) categories were identified to delineate natural variation of cold stress regulated differential gene expression in the model plant A. thaliana. The predicted regulatory network model was able to identify new ecotype specific transcription factors and their regulatory interactions, which might....... RESULTS: In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes...

  1. A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2016-02-01

    Full Text Available Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this issue, an efficient feature selection method for network traffic based on a new parallel computing framework called Spark is proposed in this paper. In our approach, the complete feature set is firstly preprocessed based on Fisher score, and a sequential forward search strategy is employed for subsets. The optimal feature subset is then selected using the continuous iterations of the Spark computing framework. The implementation demonstrates that, on the precondition of keeping the classification accuracy, our method reduces the time cost of modeling and classification, and improves the execution efficiency of feature selection significantly.

  2. The Medieval inquisition: scale-free networks and the suppression of heresy

    Science.gov (United States)

    Ormerod, Paul; Roach, Andrew P.

    2004-08-01

    Qualitative evidence suggests that heresy within the medieval Church had many of the characteristics of a scale-free network. From the perspective of the Church, heresy can be seen as an infectious disease. The disease persisted for long periods of time, breaking out again even when the Church believed it to have been eradicated. A principal mechanism of heresy was through a small number of individuals with very large numbers of social contacts. Initial attempts by the inquisition to suppress heresy by general persecution, or even mass slaughter, of populations thought to harbour the ‘disease’ failed. Gradually, however, inquisitors learned about the nature of the social networks by which heresy both spread and persisted. Eventually, a policy of targeting key individuals was implemented, which proved to be much more successful.

  3. NEON: High Frequency Monitoring Network for Watershed-Scale Processes and Aquatic Ecology

    Science.gov (United States)

    Vance, J. M.; Fitzgerald, M.; Parker, S. M.; Roehm, C. L.; Goodman, K. J.; Bohall, C.; Utz, R.

    2014-12-01

    Networked high frequency hydrologic and water quality measurements needed to investigate physical and biogeochemical processes at the watershed scale and create robust models are limited and lacking standardization. Determining the drivers and mechanisms of ecological changes in aquatic systems in response to natural and anthropogenic pressures is challenging due to the large amounts of terrestrial, aquatic, atmospheric, biological, chemical, and physical data it requires at varied spatiotemporal scales. The National Ecological Observatory Network (NEON) is a continental-scale infrastructure project designed to provide data to address the impacts of climate change, land-use, and invasive species on ecosystem structure and function. Using a combination of standardized continuous in situ measurements and observational sampling, the NEON Aquatic array will produce over 200 data products across its spatially-distributed field sites for 30 years to facilitate spatiotemporal analysis of the drivers of ecosystem change. Three NEON sites in Alabama were chosen to address linkages between watershed-scale processes and ecosystem changes along an eco-hydrological gradient within the Tombigbee River Basin. The NEON Aquatic design, once deployed, will include continuous measurements of surface water physical, chemical, and biological parameters, groundwater level, temperature and conductivity and local meteorology. Observational sampling will include bathymetry, water chemistry and isotopes, and a suite of organismal sampling from microbes to macroinvertebrates to vertebrates. NEON deployed a buoy to measure the temperature profile of the Black Warrior River from July - November, 2013 to determine the spatiotemporal variability across the water column from a daily to seasonal scale. In July 2014 a series of water quality profiles were performed to assess the contribution of physical and biogeochemical drivers over a diurnal cycle. Additional river transects were performed

  4. Environmental networks for large-scale monitoring of Earth and atmosphere

    Science.gov (United States)

    Maurodimou, Olga; Kolios, Stavros; Konstantaras, Antonios; Georgoulas, George; Stylios, Chrysostomos

    2013-04-01

    Installation and operation of instrument/sensor networks are proven fundamental in the monitoring of the physical environment from local to global scale. The advances in electronics, wireless communications and informatics has led to the development of a huge number of networks at different spatial scales that measure, collect and store a wide range of environmental parameters. These networks have been gradually evolved into integrated information systems that provide real time monitoring, forecasts and different products from the initial collected datasets. Instrument/sensor networks have nowadays become important solutions for environmental monitoring, comprising a basic component of fully automated systems developing worldwide that contribute in the efforts for a sustainable Earth's environment (e.g. Hart et al., 2006, Othman et al., 2012). They are also used as a source of data for models parameterization and as verification tools for accuracy assessment techniques of the satellite imagery. Environmental networks can be incorporated into decision support systems (e.g Rizzi et al., 2012) providing informational background along with data from satellites for decision making, manage problems, suggest solutions and best practices for a sustainable management of the environment. This is a comparative study aiming to examine and highlight the significant role of existing instrument/sensor networks for large-scale monitoring of environmental issues, especially atmospheric and marine environment as well as weather and climate. We provide characteristic examples of integrated systems based on large scale instrument/sensor networks along with other sources of data (like satellite datasets) as informational background to measure, identify, monitor, analyze and forecast a vast series of atmospheric parameters (like CO2, O3, particle matter and solar irradiance), weather, climate and their impacts (e.g., cloud systems, lightnings, rainfall, air and surface temperature

  5. Planck Scale Effects in Astrophysics and Cosmology

    Energy Technology Data Exchange (ETDEWEB)

    Padmanabhan, Thanu [Astrophysics (IUCAA), Poona University Campus, Post Bag No. 4, Ganeshkhind, Pune 411007 (India)

    2007-08-07

    reading through the articles in this book and it must have been an exciting conference. (The book under review is based on the lectures given at the 40th Karpacz Winter School.) This is a valuable addition to any library and will serve as a useful source of information for any graduate student or researcher who wants to enter or appreciate this field. (Book review of Planck Scale Effects in Astrophysics and Cosmology, G Amelino-Camelia and J Kowalski-Gilkman (eds), 2005 Berlin: Springer, ISBN: 978-3-540-25263-4)

  6. Topology effects on nonaffine behavior of semiflexible fiber networks

    Science.gov (United States)

    Hatami-Marbini, H.; Shriyan, V.

    2017-12-01

    Filamentous semiflexible networks define the mechanical and physical properties of many materials such as cytoskeleton. In the absence of a distinct unit cell, the Mikado fiber network model is commonly used algorithm for representing the microstructure of these networks in numerical models. Nevertheless, certain types of filamentous structures such as collagenous tissues, at early stages of their development, are assembled by growth of individual fibers from random nucleation sites. In this work, we develop a computational model to investigate the mechanical response of such networks by characterizing their nonaffine behavior. We show that the deformation of these networks is nonaffine at all length scales. Furthermore, similar to Mikado networks, the degree of nonaffinity in these structures decreases with increasing the probing length scale, the network fiber density, and/or the bending stiffness of constituting filaments. Nevertheless, despite the lower coordination number of these networks, their deformation field is more affine than that of the Mikado networks with the same fiber density and fiber mechanical properties.

  7. Full Scale Earth Fault Experiments on 10 kV laboratory network with comparative Measurements on Conventional CT's and VT's

    DEFF Research Database (Denmark)

    Sørensen, Stefan; Nielsen, Hans Ove; Bak-Jensen, Birgitte

    2002-01-01

    In this paper we present a result of a full scale earth fault carried out on the 10 kV research/laboratory distribution network at Kyndbyvaerket Denmark in May 2001. The network is compensated through a Petersen-Coil and current and voltage measurements were measured on conventional current...

  8. Upper Colorado River Basin Climate Effects Network

    Science.gov (United States)

    Belnap, Jayne; Campbell, Donald; Kershner, Jeff

    2011-01-01

    The Upper Colorado River Basin (UCRB) Climate Effects Network (CEN) is a science team established to provide information to assist land managers in future decision making processes by providing a better understanding of how future climate change, land use, invasive species, altered fire cycles, human systems, and the interactions among these factors will affect ecosystems and the services they provide to human communities. The goals of this group are to (1) identify science needs and provide tools to assist land managers in addressing these needs, (2) provide a Web site where users can access information pertinent to this region, and (3) provide managers technical assistance when needed. Answers to the team's working science questions are intended to address how interactions among climate change, land use, and management practices may affect key aspects of water availability, ecosystem changes, and societal needs within the UCRB.

  9. Multi-GNSS PPP-RTK: From Large- to Small-Scale Networks.

    Science.gov (United States)

    Nadarajah, Nandakumaran; Khodabandeh, Amir; Wang, Kan; Choudhury, Mazher; Teunissen, Peter J G

    2018-04-03

    Precise point positioning (PPP) and its integer ambiguity resolution-enabled variant, PPP-RTK (real-time kinematic), can benefit enormously from the integration of multiple global navigation satellite systems (GNSS). In such a multi-GNSS landscape, the positioning convergence time is expected to be reduced considerably as compared to the one obtained by a single-GNSS setup. It is therefore the goal of the present contribution to provide numerical insights into the role taken by the multi-GNSS integration in delivering fast and high-precision positioning solutions (sub-decimeter and centimeter levels) using PPP-RTK. To that end, we employ the Curtin PPP-RTK platform and process data-sets of GPS, BeiDou Navigation Satellite System (BDS) and Galileo in stand-alone and combined forms. The data-sets are collected by various receiver types, ranging from high-end multi-frequency geodetic receivers to low-cost single-frequency mass-market receivers. The corresponding stations form a large-scale (Australia-wide) network as well as a small-scale network with inter-station distances less than 30 km. In case of the Australia-wide GPS-only ambiguity-float setup, 90% of the horizontal positioning errors (kinematic mode) are shown to become less than five centimeters after 103 min. The stated required time is reduced to 66 min for the corresponding GPS + BDS + Galieo setup. The time is further reduced to 15 min by applying single-receiver ambiguity resolution. The outcomes are supported by the positioning results of the small-scale network.

  10. Effects of network resolution on topological properties of human neocortex

    DEFF Research Database (Denmark)

    Romero-Garcia, Rafael; Atienza, Mercedes; Clemmensen, Line Katrine Harder

    2012-01-01

    -world organization. The present study investigates how resolution of thickness-based cortical scales impacts on topological properties of human anatomical cortical networks. To this end, we designed a novel approach aimed at determining the best trade-off between small-world attributes of anatomical cortical...... regions). In summary, our study confirms that topological organization of anatomical cortical networks varies with both sparsity and resolution of cortical scale, and it further provides a novel methodological framework aimed at identifying cortical schemes that maximize small-worldness with the lowest......Graph theoretical analyses applied to neuroimaging datasets have provided valuable insights into the large-scale anatomical organization of the human neocortex. Most of these studies were performed with different cortical scales leading to cortical networks with different levels of small...

  11. Fitness effects of fluctuations in biochemical networks

    Science.gov (United States)

    Tanase-Nicola, Sorin

    2009-03-01

    The concentration of many cellular components fluctuates not only as a response to external and internal inputs but also due to random birth and death events of individual molecules. This biochemical noise affects the capacity of every individual cell in a population to respond and adapt to the environment. While the sources and effects of biochemical fluctuations on individual cells have been intensively studied, the effects of noise on the growth rate of a population of cells are much less understood. We present a model of the cell cycle in which the growth and division of individual cells are coupled with the noisy dynamics of their internal components. The model allows us to compute the contribution of the biochemical noise to the average growth rate of a population of cells as a function of the noise strength and the correlation time of the fluctuations. We show that, due to fluctuations, the growth rate of a population of cells is always larger than the average growth rate of a individual cell and can be larger even than a corresponding deterministic model. In most relevant cases it is assumed that the average concentration of a cellular component is close to a value that maximizes the population growth as given by the external, environmental, conditions and the internal cellular regulation. In such cases we show that contribution of fluctuations to the growth rate is negative and increases with the sensitivity of the biochemical networks to the noise sources and the noise correlation time. We also discuss how the selection pressure due to fluctuations affects the structure and parameters of genetic regulatory networks.

  12. Reorganization of large-scale cognitive networks during automation of imagination of a complex sequential movement.

    Science.gov (United States)

    Sauvage, C; De Greef, N; Manto, M; Jissendi, P; Nioche, C; Habas, C

    2015-04-01

    We investigated the functional reconfiguration of the cerebral networks involved in imagination of sequential movements of the left foot, both performed at regular and fast speed after mental imagery training. Thirty-five volunteers were scanned with a 3T MRI while they imagined a sequence of ankle movements (dorsiflexion, plantar flexion, varus and valgus) before and after mental practice. Subjects were distributed in two groups: the first group executed regular movements whereas the second group made fast movements. We applied the general linear model (GLM) and model-free, exploratory tensorial independent component analytic (TICA) approaches to identify plastic post-training effects on brain activation. GLM showed that post-training imagination of movement was accompanied by a dual effect: a specific recruitment of a medial prefronto-cingulo-parietal circuit reminiscent of the default-mode network, with the left putamen, and a decreased activity of a lateral fronto-parietal network. Training-related subcortical changes only consisted in an increased activity in the left striatum. Unexpectedly, no difference was observed in the cerebellum. TICA also revealed involvement of the left executive network, and of the dorsal control executive network but no significant differences were found between pre- and post-training phases. Therefore, repetitive motor mental imagery induced specific putamen (motor rehearsal) recruitment that one previously observed during learning of overt movements, and, simultaneously, a specific shift of activity from the dorsolateral prefrontal cortex (attention, working memory) to the medial posterior parietal and cingulate cortices (mental imagery and memory rehearsal). Our data complement and confirm the notion that differential and coupled recruitment of cognitive networks can constitute a neural marker of training effects. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

  13. Large-scale changes in network interactions as a physiological signature of spatial neglect

    Science.gov (United States)

    Baldassarre, Antonello; Ramsey, Lenny; Hacker, Carl L.; Callejas, Alicia; Astafiev, Serguei V.; Metcalf, Nicholas V.; Zinn, Kristi; Rengachary, Jennifer; Snyder, Abraham Z.; Carter, Alex R.; Shulman, Gordon L.

    2014-01-01

    The relationship between spontaneous brain activity and behaviour following focal injury is not well understood. Here, we report a large-scale study of resting state functional connectivity MRI and spatial neglect following stroke in a large (n = 84) heterogeneous sample of first-ever stroke patients (within 1–2 weeks). Spatial neglect, which is typically more severe after right than left hemisphere injury, includes deficits of spatial attention and motor actions contralateral to the lesion, and low general attention due to impaired vigilance/arousal. Patients underwent structural and resting state functional MRI scans, and spatial neglect was measured using the Posner spatial cueing task, and Mesulam and Behavioural Inattention Test cancellation tests. A principal component analysis of the behavioural tests revealed a main factor accounting for 34% of variance that captured three correlated behavioural deficits: visual neglect of the contralesional visual field, visuomotor neglect of the contralesional field, and low overall performance. In an independent sample (21 healthy subjects), we defined 10 resting state networks consisting of 169 brain regions: visual-fovea and visual-periphery, sensory-motor, auditory, dorsal attention, ventral attention, language, fronto-parietal control, cingulo-opercular control, and default mode. We correlated the neglect factor score with the strength of resting state functional connectivity within and across the 10 resting state networks. All damaged brain voxels were removed from the functional connectivity:behaviour correlational analysis. We found that the correlated behavioural deficits summarized by the factor score were associated with correlated multi-network patterns of abnormal functional connectivity involving large swaths of cortex. Specifically, dorsal attention and sensory-motor networks showed: (i) reduced interhemispheric functional connectivity; (ii) reduced anti-correlation with fronto-parietal and default mode

  14. Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli

    DEFF Research Database (Denmark)

    McCloskey, Douglas; Palsson, Bernhard; Feist, Adam

    2013-01-01

    of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective...... on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype-phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges...

  15. Toward Continental-scale Rainfall Monitoring Using Commercial Microwave Links From Cellular Communication Networks

    Science.gov (United States)

    Uijlenhoet, R.; Leijnse, H.; Overeem, A.

    2017-12-01

    Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. We have previously shown how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ˜35,500 km2), based on an unprecedented number of links (˜2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrated the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent. The presentation will focus on the potential for upscaling this technique to continental-scale rainfall monitoring in Europe. In addition, several examples of recent applications of this technique on other continents (South America, Africa, Asia and Australia) will be given.

  16. Restoring large-scale brain networks in PTSD and related disorders: a proposal for neuroscientifically-informed treatment interventions

    Directory of Open Access Journals (Sweden)

    Ruth A. Lanius

    2015-03-01

    Full Text Available Background: Three intrinsic connectivity networks in the brain, namely the central executive, salience, and default mode networks, have been identified as crucial to the understanding of higher cognitive functioning, and the functioning of these networks has been suggested to be impaired in psychopathology, including posttraumatic stress disorder (PTSD. Objective: 1 To describe three main large-scale networks of the human brain; 2 to discuss the functioning of these neural networks in PTSD and related symptoms; and 3 to offer hypotheses for neuroscientifically-informed interventions based on treating the abnormalities observed in these neural networks in PTSD and related disorders. Method: Literature relevant to this commentary was reviewed. Results: Increasing evidence for altered functioning of the central executive, salience, and default mode networks in PTSD has been demonstrated. We suggest that each network is associated with specific clinical symptoms observed in PTSD, including cognitive dysfunction (central executive network, increased and decreased arousal/interoception (salience network, and an altered sense of self (default mode network. Specific testable neuroscientifically-informed treatments aimed to restore each of these neural networks and related clinical dysfunction are proposed. Conclusions: Neuroscientifically-informed treatment interventions will be essential to future research agendas aimed at targeting specific PTSD and related symptoms.

  17. Multi-scale Fully Convolutional Network for Face Detection in the Wild

    KAUST Repository

    Bai, Yancheng

    2017-08-24

    Face detection is a classical problem in computer vision. It is still a difficult task due to many nuisances that naturally occur in the wild. In this paper, we propose a multi-scale fully convolutional network for face detection. To reduce computation, the intermediate convolutional feature maps (conv) are shared by every scale model. We up-sample and down-sample the final conv map to approximate K levels of a feature pyramid, leading to a wide range of face scales that can be detected. At each feature pyramid level, a FCN is trained end-to-end to deal with faces in a small range of scale change. Because of the up-sampling, our method can detect very small faces (10×10 pixels). We test our MS-FCN detector on four public face detection datasets, including FDDB, WIDER FACE, AFW and PASCAL FACE. Extensive experiments show that it outperforms state-of-the-art methods. Also, MS-FCN runs at 23 FPS on a GPU for images of size 640×480 with no assumption on the minimum detectable face size.

  18. Optogenetic stimulation effectively enhances intrinsically generated network synchrony

    Directory of Open Access Journals (Sweden)

    Ahmed eEl Hady

    2013-10-01

    Full Text Available Synchronized bursting is found in many brain areas and has also been implicated in the pathophysiology of neuropsychiatric disorders such as epilepsy, Parkinson’s disease and schizophrenia. Despite extensive studies of network burst synchronization, it is insufficiently understood how this type of network wide synchronization can be strengthened, reduced or even abolished. We combined electrical recording using multi-electrode array with optical stimulation of cultured channelrhodopsin-2 transducted hippocampal neurons to study and manipulate network burst synchronization. We found low frequency photo-stimulation protocols that are sufficient to induce potentiation of network bursting, modifying bursting dynamics and increasing interneuronal synchronization. Surprisingly, slowly fading-in light stimulation, which substantially delayed and reduced light driven spiking, was at least as effective in reorganizing network dynamics as much stronger pulsed light stimulation. Our study shows that mild stimulation protocols that do not enforce particular activity patterns onto the network can be highly effective inducers of network-level plasticity.

  19. Optogenetic stimulation effectively enhances intrinsically generated network synchrony

    Science.gov (United States)

    El Hady, Ahmed; Afshar, Ghazaleh; Bröking, Kai; Schlüter, Oliver M.; Geisel, Theo; Stühmer, Walter; Wolf, Fred

    2013-01-01

    Synchronized bursting is found in many brain areas and has also been implicated in the pathophysiology of neuropsychiatric disorders such as epilepsy, Parkinson’s disease, and schizophrenia. Despite extensive studies of network burst synchronization, it is insufficiently understood how this type of network wide synchronization can be strengthened, reduced, or even abolished. We combined electrical recording using multi-electrode array with optical stimulation of cultured channelrhodopsin-2 transducted hippocampal neurons to study and manipulate network burst synchronization. We found low frequency photo-stimulation protocols that are sufficient to induce potentiation of network bursting, modifying bursting dynamics, and increasing interneuronal synchronization. Surprisingly, slowly fading-in light stimulation, which substantially delayed and reduced light-driven spiking, was at least as effective in reorganizing network dynamics as much stronger pulsed light stimulation. Our study shows that mild stimulation protocols that do not enforce particular activity patterns onto the network can be highly effective inducers of network-level plasticity. PMID:24155695

  20. Social Capital, Network Effects and Savings in Rural Vietnam

    DEFF Research Database (Denmark)

    Newman, Carol; Tarp, Finn; Van Den Broeck, Katleen

    social networks in rural Vietnam can play a role in increasing formal savings where knowledge gaps exist. Networks are defined as active membership of women’s unions and the quality of networks is measured by the level of formal savings observed among group members. We find that membership of high...... quality networks leads to higher levels of saving in formal financial institutions and to higher levels of saving for productive investments as compared with other precautionary or lifecycle motives. Our results suggest that transmitting financial information through formal networks could be effective...... in increasing formal savings at grassroots level. We also conclude that ensuring information disseminated by networks is both accurate and desirable as well as important given that behavioural effects are also found in low-quality networks....

  1. Assembly effect of groups in online social networks

    Science.gov (United States)

    Fan, W.; Yeung, K. H.; Wong, K. Y.

    2013-03-01

    Due to the popularity and growth of online social networks, security in these networks becomes a critical problem. Previous works have proved that a virus can spread effectively in social networks. In this paper, groups in social networks are studied. We notice that groups on social network services sites can assemble people with similar characteristics, which may promote virus propagation in these networks. After our analysis, it is found that the use of groups can shorten the distance among users, and hence it would cause faster virus spread. We propose a virus propagation model and simulate it in a group network to show the assembly effect of groups. Our result shows that even with only one random attack, a virus can still spread rapidly, and the direct contact among group members is the reason for fast spreading.

  2. Analyzing Bullwhip Effect in Supply Networks under Exogenous Uncertainty

    Directory of Open Access Journals (Sweden)

    Mitra Darvish

    2014-05-01

    Full Text Available This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect.

  3. A large scale analysis of information-theoretic network complexity measures using chemical structures.

    Directory of Open Access Journals (Sweden)

    Matthias Dehmer

    Full Text Available This paper aims to investigate information-theoretic network complexity measures which have already been intensely used in mathematical- and medicinal chemistry including drug design. Numerous such measures have been developed so far but many of them lack a meaningful interpretation, e.g., we want to examine which kind of structural information they detect. Therefore, our main contribution is to shed light on the relatedness between some selected information measures for graphs by performing a large scale analysis using chemical networks. Starting from several sets containing real and synthetic chemical structures represented by graphs, we study the relatedness between a classical (partition-based complexity measure called the topological information content of a graph and some others inferred by a different paradigm leading to partition-independent measures. Moreover, we evaluate the uniqueness of network complexity measures numerically. Generally, a high uniqueness is an important and desirable property when designing novel topological descriptors having the potential to be applied to large chemical databases.

  4. Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501.

    Science.gov (United States)

    Babaei, Parizad; Marashi, Sayed-Amir; Asad, Sedigheh

    2015-11-01

    Pseudomonas stutzeri A1501 is an endophytic bacterium capable of nitrogen fixation. This strain has been isolated from the rice rhizosphere and provides the plant with fixed nitrogen and phytohormones. These interesting features encouraged us to study the metabolism of this microorganism at the systems-level. In this work, we present the first genome-scale metabolic model (iPB890) for P. stutzeri, involving 890 genes, 1135 reactions, and 813 metabolites. A combination of automatic and manual approaches was used in the reconstruction process. Briefly, using the metabolic networks of Pseudomonas aeruginosa and Pseudomonas putida as templates, a draft metabolic network of P. stutzeri was reconstructed. Then, the draft network was driven through an iterative and curative process of gap filling. In the next step, the model was evaluated using different experimental data such as specific growth rate, Biolog substrate utilization data and other experimental observations. In most of the evaluation cases, the model was successful in correctly predicting the cellular phenotypes. Thus, we posit that the iPB890 model serves as a suitable platform to explore the metabolism of P. stutzeri.

  5. SER Performance of Large Scale OFDM-SDMA Based Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Mostafa Hefnawi

    2014-01-01

    Full Text Available This paper proposes an adaptive large scale multiple input multiple output-beamforming scheme (LSMIMO-BF for uplink (UL access in broadband wireless cognitive networks with multiple primary users (PUs and secondary users (SUs sharing the same spectrum and employing orthogonal frequency-division multiplexing (OFDM. The proposed algorithm seeks the optimal transmit/receive weight vectors that maximize the UL MIMO channel capacity for each cognitive user while controlling the interference levels to PUs. Under the assumption of very large number of antennas at the base station, a closed-form expression for the symbol error rate (SER performance of the cognitive LSMIMO-OFDM system is derived and compared with the one based on conventional beamforming schemes such as MIMO-maximum ratio combining (MIMO-MRC. The analysis and simulation show that when SUs are transmitting with the proposed constrained capacity-aware (CCA scheme, the total interference level at the primary base station is reduced and the SER of PUs is improved compared to the case when the secondary network is using conventional MIMO-MRC. It was also shown that, as the number of base station antennas becomes larger, the constraints imposed by the primary network could be relaxed and the symbol error rate (SER of SUs is improved without harming the PUs performance.

  6. Walking Across Wikipedia: A Scale-Free Network Model of Semantic Memory Retrieval

    Directory of Open Access Journals (Sweden)

    Graham William Thompson

    2014-02-01

    Full Text Available Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like animals are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing.

  7. Construction and analysis of a genome-scale metabolic network for Bacillus licheniformis WX-02.

    Science.gov (United States)

    Guo, Jing; Zhang, Hong; Wang, Cheng; Chang, Ji-Wei; Chen, Ling-Ling

    2016-05-01

    We constructed the genome-scale metabolic network of Bacillus licheniformis (B. licheniformis) WX-02 by combining genomic annotation, high-throughput phenotype microarray (PM) experiments and literature-based metabolic information. The accuracy of the metabolic network was assessed by an OmniLog PM experiment. The final metabolic model iWX1009 contains 1009 genes, 1141 metabolites and 1762 reactions, and the predicted metabolic phenotypes showed an agreement rate of 76.8% with experimental PM data. In addition, key metabolic features such as growth yield, utilization of different substrates and essential genes were identified by flux balance analysis. A total of 195 essential genes were predicted from LB medium, among which 149 were verified with the experimental essential gene set of B. subtilis 168. With the removal of 5 reactions from the network, pathways for poly-γ-glutamic acid (γ-PGA) synthesis were optimized and the γ-PGA yield reached 83.8 mmol/h. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. The present study provides valuable clues for exploring the metabolisms and metabolic regulation of γ-PGA synthesis in B. licheniformis WX-02. Copyright © 2016 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  8. Experimental evidence of massive-scale emotional contagion through social networks.

    Science.gov (United States)

    Kramer, Adam D I; Guillory, Jamie E; Hancock, Jeffrey T

    2014-06-17

    Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others' positive experiences constitutes a positive experience for people.

  9. Long-period rhythmic synchronous firing in a scale-free network

    Science.gov (United States)

    Mi, Yuanyuan; Liao, Xuhong; Huang, Xuhui; Zhang, Lisheng; Gu, Weifeng; Hu, Gang; Wu, Si

    2013-01-01

    Stimulus information is encoded in the spatial-temporal structures of external inputs to the neural system. The ability to extract the temporal information of inputs is fundamental to brain function. It has been found that the neural system can memorize temporal intervals of visual inputs in the order of seconds. Here we investigate whether the intrinsic dynamics of a large-size neural circuit alone can achieve this goal. The network models we consider have scale-free topology and the property that hub neurons are difficult to be activated. The latter is implemented by either including abundant electrical synapses between neurons or considering chemical synapses whose efficacy decreases with the connectivity of the postsynaptic neuron. We find that hub neurons trigger synchronous firing across the network, loops formed by low-degree neurons determine the rhythm of synchronous firing, and the hardness of exciting hub neurons avoids epileptic firing of the network. Our model successfully reproduces the experimentally observed rhythmic synchronous firing with long periods and supports the notion that the neural system can process temporal information through the dynamics of local circuits in a distributed way. PMID:24277831

  10. Walking across Wikipedia: a scale-free network model of semantic memory retrieval.

    Science.gov (United States)

    Thompson, Graham W; Kello, Christopher T

    2014-01-01

    Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like "animals" are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing.

  11. Networks Depicting the Fine-Scale Co-Occurrences of Fungi in Soil Horizons.

    Directory of Open Access Journals (Sweden)

    Hirokazu Toju

    Full Text Available Fungi in soil play pivotal roles in nutrient cycling, pest controls, and plant community succession in terrestrial ecosystems. Despite the ecosystem functions provided by soil fungi, our knowledge of the assembly processes of belowground fungi has been limited. In particular, we still have limited knowledge of how diverse functional groups of fungi interact with each other in facilitative and competitive ways in soil. Based on the high-throughput sequencing data of fungi in a cool-temperate forest in northern Japan, we analyzed how taxonomically and functionally diverse fungi showed correlated fine-scale distributions in soil. By uncovering pairs of fungi that frequently co-occurred in the same soil samples, networks depicting fine-scale co-occurrences of fungi were inferred at the O (organic matter and A (surface soil horizons. The results then led to the working hypothesis that mycorrhizal, endophytic, saprotrophic, and pathogenic fungi could form compartmentalized (modular networks of facilitative, antagonistic, and/or competitive interactions in belowground ecosystems. Overall, this study provides a research basis for further understanding how interspecific interactions, along with sharing of niches among fungi, drive the dynamics of poorly explored biospheres in soil.

  12. Influence of different initial distributions on robust cooperation in scale-free networks: A comparative study

    International Nuclear Information System (INIS)

    Chen Xiaojie; Fu Feng; Wang Long

    2008-01-01

    We study the evolutionary Prisoner's dilemma game on scale-free networks, focusing on the influence of different initial distributions for cooperators and defectors on the evolution of cooperation. To address this issue, we consider three types of initial distributions for defectors: uniform distribution at random, occupying the most connected nodes, and occupying the lowest-degree nodes, respectively. It is shown that initial configurations for defectors can crucially influence the cooperation level and the evolution speed of cooperation. Interestingly, the situation where defectors initially occupy the lowest-degree vertices can exhibit the most robust cooperation, compared with two other distributions. That is, the cooperation level is least affected by the initial percentage of defectors. Moreover, in this situation, the whole system evolves fastest to the prevalent cooperation. Besides, we obtain the critical values of initial frequency of defectors above which the extinction of cooperators occurs for the respective initial distributions. Our results might be helpful in explaining the maintenance of high cooperation in scale-free networks

  13. Damage spreading on networks: Clustering effects

    Indian Academy of Sciences (India)

    Research Articles Volume 68 Issue 4 April 2007 pp 689-699 ... The damage spreading of the Ising model on three kinds of networks is studied with Glauber dynamics. ... The relationship between the transition temperature and the network measure-clustering coefficient is set up and it is shown that the increase of damage ...

  14. How Effective is Routing for Wireless Networking

    Science.gov (United States)

    2016-03-05

    generation (loadng),” draft-clausen-lln-loadng-12 (work in progress), 2014. [12] T. Winter, “Rpl: Ipv6 routing protocol for low-power and lossy networks...Neighbor discovery optimization for ipv6 over low-power wire- less personal area networks (6lowpans),” IETF RFC 6775, 2012. [18] J. Hui and R. Kelsey

  15. Enhancing Classroom Effectiveness through Social Networking Tools

    Science.gov (United States)

    Kurthakoti, Raghu; Boostrom, Robert E., Jr.; Summey, John H.; Campbell, David A.

    2013-01-01

    To determine the usefulness of social networking Web sites such as Ning.com as a communication tool in marketing courses, a study was designed with special concern for social network use in comparison to Blackboard. Students from multiple marketing courses were surveyed. Assessments of Ning.com and Blackboard were performed both to understand how…

  16. Large-scale brain networks underlying language acquisition in early infancy

    Directory of Open Access Journals (Sweden)

    Fumitaka eHomae

    2011-05-01

    Full Text Available A critical issue in human development is that of whether the language-related areas in the left frontal and temporal regions work as a functional network in preverbal infants. Here, we used 94-channel near-infrared spectroscopy (NIRS to reveal the functional networks in the brains of sleeping 3-month-old infants with and without presenting speech sounds. During the first 3 min, we measured spontaneous brain activation (period 1. After period 1, we provided stimuli by playing Japanese sentences for 3 min (period 2. Finally, we measured brain activation for 3 min without providing the stimulus (period 3, as in period 1. We found that not only the bilateral temporal and temporoparietal regions but also the prefrontal and occipital regions showed oxygenated hemoglobin (oxy-Hb signal increases and deoxygenated hemoglobin (deoxy-Hb signal decreases when speech sounds were presented to infants. By calculating time-lagged cross-correlations and coherences of oxy-Hb signals between channels, we tested the functional connectivity for the 3 periods. The oxy-Hb signals in neighboring channels, as well as their homologous channels in the contralateral hemisphere, showed high correlation coefficients in period 1. Similar correlations were observed in period 2; however, the number of channels showing high correlations was higher in the ipsilateral hemisphere, especially in the anterior-posterior direction. The functional connectivity in period 3 showed a close relationship between the frontal and temporal regions, which was less prominent in period 1, indicating that these regions form the functional networks and work as a hysteresis system that has memory of the previous inputs. We propose a hypothesis that the spatiotemporally large-scale brain networks, including the frontal and temporal regions, underlie speech processing in infants and they might play important roles in language acquisition during infancy.

  17. Flows, scaling, and the control of moment hierarchies for stochastic chemical reaction networks

    Science.gov (United States)

    Smith, Eric; Krishnamurthy, Supriya

    2017-12-01

    Stochastic chemical reaction networks (CRNs) are complex systems that combine the features of concurrent transformation of multiple variables in each elementary reaction event and nonlinear relations between states and their rates of change. Most general results concerning CRNs are limited to restricted cases where a topological characteristic known as deficiency takes a value 0 or 1, implying uniqueness and positivity of steady states and surprising, low-information forms for their associated probability distributions. Here we derive equations of motion for fluctuation moments at all orders for stochastic CRNs at general deficiency. We show, for the standard base case of proportional sampling without replacement (which underlies the mass-action rate law), that the generator of the stochastic process acts on the hierarchy of factorial moments with a finite representation. Whereas simulation of high-order moments for many-particle systems is costly, this representation reduces the solution of moment hierarchies to a complexity comparable to solving a heat equation. At steady states, moment hierarchies for finite CRNs interpolate between low-order and high-order scaling regimes, which may be approximated separately by distributions similar to those for deficiency-zero networks and connected through matched asymptotic expansions. In CRNs with multiple stable or metastable steady states, boundedness of high-order moments provides the starting condition for recursive solution downward to low-order moments, reversing the order usually used to solve moment hierarchies. A basis for a subset of network flows defined by having the same mean-regressing property as the flows in deficiency-zero networks gives the leading contribution to low-order moments in CRNs at general deficiency, in a 1 /n expansion in large particle numbers. Our results give a physical picture of the different informational roles of mean-regressing and non-mean-regressing flows and clarify the dynamical

  18. Model Selection and Hypothesis Testing for Large-Scale Network Models with Overlapping Groups

    Directory of Open Access Journals (Sweden)

    Tiago P. Peixoto

    2015-03-01

    Full Text Available The effort to understand network systems in increasing detail has resulted in a diversity of methods designed to extract their large-scale structure from data. Unfortunately, many of these methods yield diverging descriptions of the same network, making both the comparison and understanding of their results a difficult challenge. A possible solution to this outstanding issue is to shift the focus away from ad hoc methods and move towards more principled approaches based on statistical inference of generative models. As a result, we face instead the more well-defined task of selecting between competing generative processes, which can be done under a unified probabilistic framework. Here, we consider the comparison between a variety of generative models including features such as degree correction, where nodes with arbitrary degrees can belong to the same group, and community overlap, where nodes are allowed to belong to more than one group. Because such model variants possess an increasing number of parameters, they become prone to overfitting. In this work, we present a method of model selection based on the minimum description length criterion and posterior odds ratios that is capable of fully accounting for the increased degrees of freedom of the larger models and selects the best one according to the statistical evidence available in the data. In applying this method to many empirical unweighted networks from different fields, we observe that community overlap is very often not supported by statistical evidence and is selected as a better model only for a minority of them. On the other hand, we find that degree correction tends to be almost universally favored by the available data, implying that intrinsic node proprieties (as opposed to group properties are often an essential ingredient of network formation.

  19. Genome-wide scale-free network inference for Candida albicans

    Directory of Open Access Journals (Sweden)

    Robert eAltwasser

    2012-02-01

    Full Text Available Discovery of essential genes in pathogenic organisms is an important step in the development of new medication. Despite a growing number of genome data available, still little is known about C. albicans, the major fungal pathogen. Most of the human population carries C. albicans as commensal, but it can cause systemic infection that may lead to the death of the host if the immune system is deteriorated. In many organisms central nodes in the interaction network (hubs play a crucial role for information and energy transport. Indeed, knock-outs of such hubs often leads to lethal phenotypes making them interesting drug targets. To identify these central genes via topological analysis, we inferred gene regulatory networks that are of sparse and scale-free. We collected information from various sources of information as prior knowledge to complement the limited expression data available. We utilise a linear regression algorithm to infer genome-wide gene regulatory interaction networks. To evaluate the predictive power of our approach, we used an automated text-mining system that scanned full-text research papers for known interactions. With the help of the compendium of known interactions, we also optimise the influence of the prior knowledge we obtained and the sparseness of the model to achieve best results. We compare the results of our approach with those of other state-of-the-art network inference methods and show that we outperform these methods. Finally we identified a number of hubs in the genome of the fungus and investigate their biological relevance.

  20. Improving the coastal record of tsunamis in the ESI-07 scale: Tsunami Environmental Effects Scale (TEE-16 scale)

    Energy Technology Data Exchange (ETDEWEB)

    Lario, J.; Bardaji, T.; Silva, P.G.; Zazo, C.; Goy, J.L.

    2016-07-01

    This paper discusses possibilities to improve the Environmental Seismic Intensity Scale (ESI-07 scale), a scale based on the effects of earthquakes in the environment. This scale comprises twelve intensity degrees and considers primary and secondary effects, one of them the occurrence of tsunamis. Terminology and physical tsunami parameters corresponding to different intensity levels are often misleading and confusing. The present work proposes: i) a revised and updated catalogue of environmental and geological effects of tsunamis, gathering all the available information on Tsunami Environmental Effects (TEEs) produced by recent earthquake-tsunamis; ii) a specific intensity scale (TEE-16) for the effects of tsunamis in the natural environment at coastal areas. The proposed scale could be used in future tsunami events and, in historic and paleo-tsunami studies. The new TEE- 16 scale incorporates the size specific parameters already considered in the ESI-07 scale, such as wave height, run-up and inland extension of inundation, and a comprehensive and more accurate terminology that covers all the different intensity levels identifiable in the geological record (intensities VI-XII). The TEE-16 scale integrates the description and quantification of the potential sedimentary and erosional features (beach scours, transported boulders and classical tsunamites) derived from different tsunami events at diverse coastal environments (e.g. beaches, estuaries, rocky cliffs,). This new approach represents an innovative advance in relation to the tsunami descriptions provided by the ESI-07 scale, and allows the full application of the proposed scale in paleoseismological studies. The analysis of the revised and updated tsunami environmental damage suggests that local intensities recorded in coastal areas do not correlate well with the TEE-16 intensity (normally higher), but shows a good correlation with the earthquake magnitude (Mw). Tsunamis generated by earthquakes can then be

  1. Heterogeneity effects in power-grid network models

    OpenAIRE

    Ódor, Géza; Hartmann, Bálint

    2018-01-01

    We have compared the phase synchronization transition of the second order Kuramoto model on 2D lattices and on large, synthetic power-grid networks, generated from real data. The latter are weighted, hierarchical modular networks. Due to the inertia the synchronization transitions are of first order type, characterized by fast relaxation and hysteresis by varying the global coupling parameter $K$. Finite size scaling analysis shows that there is no real phase transition in the thermodynamic l...

  2. Revealing effective classifiers through network comparison

    Science.gov (United States)

    Gallos, Lazaros K.; Fefferman, Nina H.

    2014-11-01

    The ability to compare complex systems can provide new insight into the fundamental nature of the processes captured, in ways that are otherwise inaccessible to observation. Here, we introduce the n-tangle method to directly compare two networks for structural similarity, based on the distribution of edge density in network subgraphs. We demonstrate that this method can efficiently introduce comparative analysis into network science and opens the road for many new applications. For example, we show how the construction of a “phylogenetic tree” across animal taxa according to their social structure can reveal commonalities in the behavioral ecology of the populations, or how students create similar networks according to the University size. Our method can be expanded to study many additional properties, such as network classification, changes during time evolution, convergence of growth models, and detection of structural changes during damage.

  3. Multilayer network modeling of integrated biological systems. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

    Science.gov (United States)

    De Domenico, Manlio

    2018-03-01

    Biological systems, from a cell to the human brain, are inherently complex. A powerful representation of such systems, described by an intricate web of relationships across multiple scales, is provided by complex networks. Recently, several studies are highlighting how simple networks - obtained by aggregating or neglecting temporal or categorical description of biological data - are not able to account for the richness of information characterizing biological systems. More complex models, namely multilayer networks, are needed to account for interdependencies, often varying across time, of biological interacting units within a cell, a tissue or parts of an organism.

  4. Large-scale resting state network correlates of cognitive impairment in Parkinson's disease and related dopaminergic deficits.

    Science.gov (United States)

    Lebedev, Alexander V; Westman, Eric; Simmons, Andrew; Lebedeva, Aleksandra; Siepel, Françoise J; Pereira, Joana B; Aarsland, Dag

    2014-01-01

    Cognitive impairment is a common non-motor feature of Parkinson's disease (PD). Understanding the neural mechanisms of this deficit is crucial for the development of efficient methods for treatment monitoring and augmentation of cognitive functions in PD patients. The current study aimed to investigate resting state fMRI correlates of cognitive impairment in PD from a large-scale network perspective, and to assess the impact of dopamine deficiency on these networks. Thirty PD patients with resting state fMRI were included from the Parkinson's Progression Marker Initiative (PPMI) database. Eighteen patients from this sample were also scanned with (123)I-FP-CIT SPECT. A standardized neuropsychological battery was administered, evaluating verbal memory, visuospatial, and executive cognitive domains. Image preprocessing was performed using an SPM8-based workflow, obtaining time-series from 90 regions-of-interest (ROIs) defined from the AAL brain atlas. The Brain Connectivity Toolbox (BCT) was used to extract nodal strength from all ROIs, and modularity of the cognitive circuitry determined using the meta-analytical software Neurosynth. Brain-behavior covariance patterns between cognitive functions and nodal strength were estimated using Partial Least Squares. Extracted latent variable (LV) scores were matched with the performances in the three cognitive domains (memory, visuospatial, and executive) and striatal dopamine transporter binding ratios (SBR) using linear modeling. Finally, influence of nigrostriatal dopaminergic deficiency on the modularity of the "cognitive network" was analyzed. For the range of deficits studied, better executive performance was associated with increased dorsal fronto-parietal cortical processing and inhibited subcortical and primary sensory involvement. This profile was also characterized by a relative preservation of nigrostriatal dopaminergic function. The profile associated with better memory performance correlated with increased

  5. Disentangling the effects of reputation and network position on the evolution of alliance networks

    NARCIS (Netherlands)

    Ebbers, J.J.; Wijnberg, N.M.

    2010-01-01

    This study uses the panel data social network analysis program SIENA to estimate the effect of actor reputation derived from past performance on alliance formation, while controlling for other constant actor attributes and network position. The authors distinguish between individual reputation based

  6. Effects of multi-state links in network community detection

    International Nuclear Information System (INIS)

    Rocco, Claudio M.; Moronta, José; Ramirez-Marquez, José E.; Barker, Kash

    2017-01-01

    A community is defined as a group of nodes of a network that are densely interconnected with each other but only sparsely connected with the rest of the network. The set of communities (i.e., the network partition) and their inter-community links could be derived using special algorithms account for the topology of the network and, in certain cases, the possible weights associated to the links. In general, the set of weights represents some characteristic as capacity, flow and reliability, among others. The effects of considering weights could be translated to obtain a different partition. In many real situations, particularly when modeling infrastructure systems, networks must be modeled as multi-state networks (e.g., electric power networks). In such networks, each link is characterized by a vector of known random capacities (i.e., the weight on each link could vary according to a known probability distribution). In this paper a simple Monte Carlo approach is proposed to evaluate the effects of multi-state links on community detection as well as on the performance of the network. The approach is illustrated with the topology of an electric power system. - Highlights: • Identify network communities when considering multi-state links. • Identified how effects of considering weights translate to different partition. • Identified importance of Inter-Community Links and changes with respect to community. • Preamble to performing a resilience assessment able to mimic the evolution of the state of each community.

  7. Topological Effects and Performance Optimization in Transportation Continuous Network Design

    Directory of Open Access Journals (Sweden)

    Jianjun Wu

    2014-01-01

    Full Text Available Because of the limitation of budget, in the planning of road works, increased efforts should be made on links that are more critical to the whole traffic system. Therefore, it would be helpful to model and evaluate the vulnerability and reliability of the transportation network when the network design is processing. This paper proposes a bilevel transportation network design model, in which the upper level is to minimize the performance of the network under the given budgets, while the lower level is a typical user equilibrium assignment problem. A new solution approach based on particle swarm optimization (PSO method is presented. The topological effects on the performance of transportation networks are studied with the consideration of three typical networks, regular lattice, random graph, and small-world network. Numerical examples and simulations are presented to demonstrate the proposed model.

  8. Establishing and Scaling-Up Clinical Social Franchise Networks: Lessons Learned From Marie Stopes International and Population Services International

    Science.gov (United States)

    Thurston, Sarah; Chakraborty, Nirali M; Hayes, Brendan; Mackay, Anna; Moon, Pierre

    2015-01-01

    In many low- and middle-income countries, a majority of people seek health care from the private sector. However, fragmentation, poor economies of scale, inadequate financing, political opposition, a bias toward curative services, and weak regulatory and quality control systems pose serious challenges for the private sector. Social franchising addresses a number of these challenges by organizing small, independent health care businesses into quality-assured networks. Global franchisors Marie Stopes International (MSI) and Population Services International (PSI) have rapidly scaled their family planning social franchising programs in recent years, jointly delivering over 10.8 million couple-years of protection (CYPs) in 2014—up 26% from 8.6 million CYPs just 1 year prior. Drawing on experience across MSI’s 17 and PSI’s 25 social franchise networks across Africa, Asia, and Latin America and the Caribbean, this article documents the organizations’ operational approaches, challenges faced, and solutions implemented. The organizations provide intensive capacity building and support for private-sector providers, including clinical training, branding, monitoring quality of franchised services, and commodity support. In addition, franchising programs engage providers and clients through behavior change communication (BCC) and demand generation activities to raise awareness and to attract clients, and they implement initiatives to ensure services are affordable for the lowest-income clients. Social franchise programs offer the private sector a collective platform to better engage government in health policy advocacy and for integrating into new public health care financing and procurement mechanisms. The future of social franchising will require developing approaches to scale-up and sustain the model cost-effectively, selectively integrating other health services into the franchise package, and being responsive to evolving health care financing approaches with the

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

    Science.gov (United States)

    Rich, Scott; Zochowski, Michal; Booth, Victoria

    2018-01-01

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

  10. ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

    Science.gov (United States)

    Huan, Tianxiao; Sivachenko, Andrey Y; Harrison, Scott H; Chen, Jake Y

    2008-08-12

    New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges

  11. Atmospheric conditions measured by a wireless sensor network on the local scale

    Science.gov (United States)

    Lengfeld, K.; Ament, F.

    2010-09-01

    Atmospheric conditions close to the surface, like temperature, wind speed and humidity, vary on small scales because of surface heterogeneities. Therefore, the traditional measuring approach of using a single, highly accurate station is of limited representativeness for a larger domain, because it is not able to determine these small scale variabilities. However, both the variability and the domain averages are important information for the development and validation of atmospheric models and soil-vegetation-atmosphere-transfer (SVAT) schemes. Due to progress in microelectronics it is possible to construct networks of comparably cheap meteorological stations with moderate accuracy. Such a