NASA Astrophysics Data System (ADS)
Strogatz, Steven
Everyone is familiar with the small-world phenomenon: soon after meeting a stranger, we are often suprised to discover that we have a mutual friend, or that we are somehow linked by a short chain of friends. In this talk, I'll present evidence that the small-world phenomenon is more than a curiosity of social networks — it is actually a general property of large, sparse networks whose topology is neither completely regular nor completely random. To check this idea, Duncan Watts and I have analyzed three networks of scientific interest: the neural network of the nematode worm C. elegans, the electrical power grid of the western United States, and the collaboration graph of actors in feature films. All three are small worlds, in the sense that the average number of "handshakes" separating any two members is extremely small (close to the theoretical lower limit set by a random graph). Yet at the same time, all three networks exhibit much more local clustering than a random net, demonstrating that they are not random. I'll also discuss a class of model networks that interpolate between regular lattices and random graphs. Previous theoretical research on complex systems in a wide range of disciplines has focused almost exclusively on networks that are either regular or random. Real networks often lie somewhere in between. Our mathematical model shows that networks in this middle ground tend to exhibit the small-world phenomenon, thanks to the presence of a few long-range edges that link parts of the graph that would otherwise be far apart. Furthermore, we find that when various dynamical systems are coupled in a small-world fashion, they exhibit much greater propagation speed, computational power, and synchronizability than their locally connected, regular counterparts. We explore the implications of these results for simple models of disease spreading, global computation in cellular automata, and collective locking of biological oscillators.
Unravelling small world networks
NASA Astrophysics Data System (ADS)
Higham, Desmond J.
2003-09-01
New classes of random graphs have recently been shown to exhibit the small world phenomenon--they are clustered like regular lattices and yet have small average pathlengths like traditional random graphs. Small world behaviour has been observed in a number of real life networks, and hence these random graphs represent a useful modelling tool. In particular, Grindrod [Phys. Rev. E 66 (2002) 066702-1] has proposed a class of range dependent random graphs for modelling proteome networks in bioinformatics. A property of these graphs is that, when suitably ordered, most edges in the graph are short-range, in the sense that they connect near-neighbours, and relatively few are long-range. Grindrod also looked at an inverse problem--given a graph that is known to be an instance of a range dependent random graph, but with vertices in arbitrary order, can we reorder the vertices so that the short-range/long-range connectivity structure is apparent? When the graph is viewed in terms of its adjacency matrix, this becomes a problem in sparse matrix theory: find a symmetric row/column reordering that places most nonzeros close to the diagonal. Algorithms of this general nature have been proposed for other purposes, most notably for reordering to reduce fill-in and for clustering large data sets. Here, we investigate their use in the small world reordering problem. Our numerical results suggest that a spectral reordering algorithm is extremely promising, and we give some theoretical justification for this observation via the maximum likelihood principle.
Bassett, Danielle Smith; Bullmore, Ed
2006-12-01
Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems.
Epidemics in small world networks
NASA Astrophysics Data System (ADS)
Telo da Gama, M. M.; Nunes, A.
2006-03-01
For many infectious diseases, a small-world network on an underlying regular lattice is a suitable simplified model for the contact structure of the host population. It is well known that the contact network, described in this setting by a single parameter, the small-world parameter p, plays an important role both in the short term and in the long term dynamics of epidemic spread. We have studied the effect of the network structure on models of immune for life diseases and found that in addition to the reduction of the effective transmission rate, through the screening of infectives, spatial correlations may strongly enhance the stochastic fluctuations. As a consequence, time series of unforced Susceptible-Exposed-Infected-Recovered (SEIR) models provide patterns of recurrent epidemics with realistic amplitudes, suggesting that these models together with complex networks of contacts are the key ingredients to describe the prevaccination dynamical patterns of diseases such as measles and pertussis. We have also studied the role of the host contact strucuture in pathogen antigenic variation, through its effect on the final outcome of an invasion by a viral strain of a population where a very similar virus is endemic. Similar viral strains are modelled by the same infection and reinfection parameters, and by a given degree of cross immunity that represents the antigenic distance between the competing strains. We have found, somewhat surprisingly, that clustering on the network decreases the potential to sustain pathogen diversity.
Epidemics in Interconnected Small-World Networks
Liu, Meng; Li, Daqing; Qin, Pengju; Liu, Chaoran; Wang, Huijuan; Wang, Feilong
2015-01-01
Networks can be used to describe the interconnections among individuals, which play an important role in the spread of disease. Although the small-world effect has been found to have a significant impact on epidemics in single networks, the small-world effect on epidemics in interconnected networks has rarely been considered. Here, we study the susceptible-infected-susceptible (SIS) model of epidemic spreading in a system comprising two interconnected small-world networks. We find that the epidemic threshold in such networks decreases when the rewiring probability of the component small-world networks increases. When the infection rate is low, the rewiring probability affects the global steady-state infection density, whereas when the infection rate is high, the infection density is insensitive to the rewiring probability. Moreover, epidemics in interconnected small-world networks are found to spread at different velocities that depend on the rewiring probability. PMID:25799143
The ubiquity of small-world networks.
Telesford, Qawi K; Joyce, Karen E; Hayasaka, Satoru; Burdette, Jonathan H; Laurienti, Paul J
2011-01-01
Small-world networks, according to Watts and Strogatz, are a class of networks that are "highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs." These characteristics result in networks with unique properties of regional specialization with efficient information transfer. Social networks are intuitive examples of this organization, in which cliques or clusters of friends being interconnected but each person is really only five or six people away from anyone else. Although this qualitative definition has prevailed in network science theory, in application, the standard quantitative application is to compare path length (a surrogate measure of distributed processing) and clustering (a surrogate measure of regional specialization) to an equivalent random network. It is demonstrated here that comparing network clustering to that of a random network can result in aberrant findings and that networks once thought to exhibit small-world properties may not. We propose a new small-world metric, ω (omega), which compares network clustering to an equivalent lattice network and path length to a random network, as Watts and Strogatz originally described. Example networks are presented that would be interpreted as small-world when clustering is compared to a random network but are not small-world according to ω. These findings have important implications in network science because small-world networks have unique topological properties, and it is critical to accurately distinguish them from networks without simultaneous high clustering and short path length.
The Ubiquity of Small-World Networks
Joyce, Karen E.; Hayasaka, Satoru; Burdette, Jonathan H.; Laurienti, Paul J.
2011-01-01
Abstract Small-world networks, according to Watts and Strogatz, are a class of networks that are “highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.” These characteristics result in networks with unique properties of regional specialization with efficient information transfer. Social networks are intuitive examples of this organization, in which cliques or clusters of friends being interconnected but each person is really only five or six people away from anyone else. Although this qualitative definition has prevailed in network science theory, in application, the standard quantitative application is to compare path length (a surrogate measure of distributed processing) and clustering (a surrogate measure of regional specialization) to an equivalent random network. It is demonstrated here that comparing network clustering to that of a random network can result in aberrant findings and that networks once thought to exhibit small-world properties may not. We propose a new small-world metric, ω (omega), which compares network clustering to an equivalent lattice network and path length to a random network, as Watts and Strogatz originally described. Example networks are presented that would be interpreted as small-world when clustering is compared to a random network but are not small-world according to ω. These findings have important implications in network science because small-world networks have unique topological properties, and it is critical to accurately distinguish them from networks without simultaneous high clustering and short path length. PMID:22432451
Population synchrony in small-world networks.
Ranta, Esa; Fowler, Mike S; Kaitala, Veijo
2008-02-22
Network topography ranges from regular graphs (linkage between nearest neighbours only) via small-world graphs (some random connections between nodes) to completely random graphs. Small-world linkage is seen as a revolutionary architecture for a wide range of social, physical and biological networks, and has been shown to increase synchrony between oscillating subunits. We study small-world topographies in a novel context: dispersal linkage between spatially structured populations across a range of population models. Regular dispersal between population patches interacting with density-dependent renewal provides one ecological explanation for the large-scale synchrony seen in the temporal fluctuations of many species, for example, lynx populations in North America, voles in Fennoscandia and grouse in the UK. Introducing a small-world dispersal kernel leads to a clear reduction in synchrony with both increasing dispersal rate and small-world dispersal probability across a variety of biological scenarios. Synchrony is also reduced when populations are affected by globally correlated noise. We discuss ecological implications of small-world dispersal in the frame of spatial synchrony in population fluctuations.
Small-world networks on a sphere
NASA Astrophysics Data System (ADS)
Corso, Gilberto; Torres Cruz, Claudia P.
2017-01-01
The Small-World Network on a Sphere SWNS is a non-crossing network that has no hubs and presents the small-world property diam log N with diam being the maximal distance between any two vertices and N being the number of vertices. The SWNS is constructed using a partition of the sphere and the parallels are regular sections of the sphere with constant latitude. The number of cells on the parallels, however, increases exponentially from the pole to the equator of the sphere. We analytically compute the distribution of connectivity, the clustering coefficient and the SWNS distances. The resilience of the model against selective attacks is also discussed.
Disrupted Small-World Networks in Schizophrenia
ERIC Educational Resources Information Center
Liu, Yong; Liang, Meng; Zhou, Yuan; He, Yong; Hao, Yihui; Song, Ming; Yu, Chunshui; Liu, Haihong; Liu, Zhening; Jiang, Tianzi
2008-01-01
The human brain has been described as a large, sparse, complex network characterized by efficient small-world properties, which assure that the brain generates and integrates information with high efficiency. Many previous neuroimaging studies have provided consistent evidence of "dysfunctional connectivity" among the brain regions in…
Disrupted Small-World Networks in Schizophrenia
ERIC Educational Resources Information Center
Liu, Yong; Liang, Meng; Zhou, Yuan; He, Yong; Hao, Yihui; Song, Ming; Yu, Chunshui; Liu, Haihong; Liu, Zhening; Jiang, Tianzi
2008-01-01
The human brain has been described as a large, sparse, complex network characterized by efficient small-world properties, which assure that the brain generates and integrates information with high efficiency. Many previous neuroimaging studies have provided consistent evidence of "dysfunctional connectivity" among the brain regions in…
Small-World Brain Networks Revisited
Bassett, Danielle S.; Bullmore, Edward T.
2016-01-01
It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex. PMID:27655008
Small-World Brain Networks Revisited.
Bassett, Danielle S; Bullmore, Edward T
2016-09-21
It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.
Searching in small-world networks
NASA Astrophysics Data System (ADS)
de Moura, Alessandro P.; Motter, Adilson E.; Grebogi, Celso
2003-09-01
We study the average time it takes to find a desired node in the Watts-Strogatz family of networks. We consider the case when the look-up time can be neglected and when it is important, where the look-up time is the time needed to choose one among all the neighboring nodes of a node at each step in the search. We show that in both cases, the search time is minimum in the small-world regime, when an appropriate distance between the nodes is defined. Through an analytical model, we show that the search time scales as N1/D(D+1) for small-world networks, where N is the number of nodes and D is the dimension of the underlying lattice. This model is shown to be in agreement with numerical simulations.
Enhanced Flow in Small-World Networks
NASA Astrophysics Data System (ADS)
Oliveira, Cláudio L. N.; Morais, Pablo A.; Moreira, André A.; Andrade, José S.
2014-04-01
The proper addition of shortcuts to a regular substrate can lead to the formation of a complex network with a highly efficient structure for navigation [J. M. Kleinberg, Nature 406, 845 (2000)]. Here we show that enhanced flow properties can also be observed in these small-world topologies. Precisely, our model is a network built from an underlying regular lattice over which long-range connections are randomly added according to the probability, Pij˜rij-α, where rij is the Manhattan distance between nodes i and j, and the exponent α is a controlling parameter. The mean two-point global conductance of the system is computed by considering that each link has a local conductance given by gij∝rij-C, where C determines the extent of the geographical limitations (costs) on the long-range connections. Our results show that the best flow conditions are obtained for C =0 with α=0, while for C≫1 the overall conductance always increases with α. For C≈1, α=d becomes the optimal exponent, where d is the topological dimension of the substrate. Interestingly, this exponent is identical to the one obtained for optimal navigation in small-world networks using decentralized algorithms.
Laing, Carlo R.
2016-01-01
We consider a network of coupled excitatory and inhibitory theta neurons which is capable of supporting stable spatially-localized “bump” solutions. We randomly add long-range and simultaneously remove short-range connections within the network to form a small-world network and investigate the effects of this rewiring on the existence and stability of the bump solution. We consider two limits in which continuum equations can be derived; bump solutions are fixed points of these equations. We can thus use standard numerical bifurcation analysis to determine the stability of these bumps and to follow them as parameters (such as rewiring probabilities) are varied. We find that under some rewiring schemes bumps are quite robust, whereas in other schemes they can become unstable via Hopf bifurcation or even be destroyed in saddle-node bifurcations. PMID:27378897
Spatially embedded growing small-world networks
Zitin, Ari; Gorowara, Alexander; Squires, Shane; Herrera, Mark; Antonsen, Thomas M.; Girvan, Michelle; Ott, Edward
2014-01-01
Networks in nature are often formed within a spatial domain in a dynamical manner, gaining links and nodes as they develop over time. Motivated by the growth and development of neuronal networks, we propose a class of spatially-based growing network models and investigate the resulting statistical network properties as a function of the dimension and topology of the space in which the networks are embedded. In particular, we consider two models in which nodes are placed one by one in random locations in space, with each such placement followed by configuration relaxation toward uniform node density, and connection of the new node with spatially nearby nodes. We find that such growth processes naturally result in networks with small-world features, including a short characteristic path length and nonzero clustering. We find no qualitative differences in these properties for two different topologies, and we suggest that results for these properties may not depend strongly on the topology of the embedding space. The results do depend strongly on dimension, and higher-dimensional spaces result in shorter path lengths but less clustering. PMID:25395180
Small-world networks in neuronal populations: a computational perspective.
Zippo, Antonio G; Gelsomino, Giuliana; Van Duin, Pieter; Nencini, Sara; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E M
2013-08-01
The analysis of the brain in terms of integrated neural networks may offer insights on the reciprocal relation between structure and information processing. Even with inherent technical limits, many studies acknowledge neuron spatial arrangements and communication modes as key factors. In this perspective, we investigated the functional organization of neuronal networks by explicitly assuming a specific functional topology, the small-world network. We developed two different computational approaches. Firstly, we asked whether neuronal populations actually express small-world properties during a definite task, such as a learning task. For this purpose we developed the Inductive Conceptual Network (ICN), which is a hierarchical bio-inspired spiking network, capable of learning invariant patterns by using variable-order Markov models implemented in its nodes. As a result, we actually observed small-world topologies during learning in the ICN. Speculating that the expression of small-world networks is not solely related to learning tasks, we then built a de facto network assuming that the information processing in the brain may occur through functional small-world topologies. In this de facto network, synchronous spikes reflected functional small-world network dependencies. In order to verify the consistency of the assumption, we tested the null-hypothesis by replacing the small-world networks with random networks. As a result, only small world networks exhibited functional biomimetic characteristics such as timing and rate codes, conventional coding strategies and neuronal avalanches, which are cascades of bursting activities with a power-law distribution. Our results suggest that small-world functional configurations are liable to underpin brain information processing at neuronal level.
Growing networks with geographical attachment preference: emergence of small worlds.
Ozik, Jonathan; Hunt, Brian R; Ott, Edward
2004-02-01
We introduce a simple mechanism for the evolution of small world networks. Our model is a growing network in which all connections are made locally to geographically nearby sites. Although connections are made purely locally, network growth leads to stretching of old connections and to high clustering. Our results suggest that the abundance of small world networks in geographically constrained systems is a natural consequence of system growth and local interactions.
Small-World Propensity and Weighted Brain Networks
Muldoon, Sarah Feldt; Bridgeford, Eric W.; Bassett, Danielle S.
2016-01-01
Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks. PMID:26912196
Small-World Propensity and Weighted Brain Networks
NASA Astrophysics Data System (ADS)
Muldoon, Sarah Feldt; Bridgeford, Eric W.; Bassett, Danielle S.
2016-02-01
Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks.
Small-World Propensity and Weighted Brain Networks.
Muldoon, Sarah Feldt; Bridgeford, Eric W; Bassett, Danielle S
2016-02-25
Quantitative descriptions of network structure can provide fundamental insights into the function of interconnected complex systems. Small-world structure, diagnosed by high local clustering yet short average path length between any two nodes, promotes information flow in coupled systems, a key function that can differ across conditions or between groups. However, current techniques to quantify small-worldness are density dependent and neglect important features such as the strength of network connections, limiting their application in real-world systems. Here, we address both limitations with a novel metric called the Small-World Propensity (SWP). In its binary instantiation, the SWP provides an unbiased assessment of small-world structure in networks of varying densities. We extend this concept to the case of weighted brain networks by developing (i) a standardized procedure for generating weighted small-world networks, (ii) a weighted extension of the SWP, and (iii) a method for mapping observed brain network data onto the theoretical model. In applying these techniques to compare real-world brain networks, we uncover the surprising fact that the canonical biological small-world network, the C. elegans neuronal network, has strikingly low SWP. These metrics, models, and maps form a coherent toolbox for the assessment and comparison of architectural properties in brain networks.
Small-world network spectra in mean-field theory.
Grabow, Carsten; Grosskinsky, Stefan; Timme, Marc
2012-05-25
Collective dynamics on small-world networks emerge in a broad range of systems with their spectra characterizing fundamental asymptotic features. Here we derive analytic mean-field predictions for the spectra of small-world models that systematically interpolate between regular and random topologies by varying their randomness. These theoretical predictions agree well with the actual spectra (obtained by numerical diagonalization) for undirected and directed networks and from fully regular to strongly random topologies. These results may provide analytical insights to empirically found features of dynamics on small-world networks from various research fields, including biology, physics, engineering, and social science.
Information dynamics in small-world Boolean networks.
Lizier, Joseph T; Pritam, Siddharth; Prokopenko, Mikhail
2011-01-01
Small-world networks have been one of the most influential concepts in complex systems science, partly due to their prevalence in naturally occurring networks. It is often suggested that this prevalence is due to an inherent capability to store and transfer information efficiently. We perform an ensemble investigation of the computational capabilities of small-world networks as compared to ordered and random topologies. To generate dynamic behavior for this experiment, we imbue the nodes in these networks with random Boolean functions. We find that the ordered phase of the dynamics (low activity in dynamics) and topologies with low randomness are dominated by information storage, while the chaotic phase (high activity in dynamics) and topologies with high randomness are dominated by information transfer. Information storage and information transfer are somewhat balanced (crossed over) near the small-world regime, providing quantitative evidence that small-world networks do indeed have a propensity to combine comparably large information storage and transfer capacity.
Geographical effect on small-world network synchronization.
Yin, Chuan-Yang; Wang, Bing-Hong; Wang, Wen-Xu; Chen, Guan-Rong
2008-02-01
We investigate the geographical effect on the synchronization of small-world oscillator networks. We construct small-world geographical networks by randomly adding links to one- and two-dimensional regular lattices, and we find that the synchronizability is a nonmonotonic function of both the coupling strength and the geographical distance of randomly added shortcuts. Our findings demonstrate that the geographical effect plays an important role in network synchronization, which may shed some light on the study of collective dynamics of complex networks.
Hodge Decomposition of Information Flow on Small-World Networks.
Haruna, Taichi; Fujiki, Yuuya
2016-01-01
We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow.
Hodge Decomposition of Information Flow on Small-World Networks
Haruna, Taichi; Fujiki, Yuuya
2016-01-01
We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow. PMID:27733817
Interface motion and pinning in small-world networks.
Boyer, Denis; Miramontes, Octavio
2003-03-01
We show that the nonequilibrium dynamics of systems with many interacting elements located on a small-world network can be much slower than on regular networks. As an example, we study the phase ordering dynamics of the Ising model on a Watts-Strogatz network, after a quench in the ferromagnetic phase at zero temperature. In one and two dimensions, small-world features produce dynamically frozen configurations, disordered at large length scales, analogous to random field models. This picture differs from the common knowledge (supported by equilibrium results) that ferromagnetic shortcut connections favor order and uniformity. We briefly discuss some implications of these results regarding the dynamics of social changes.
Cascading failures of interdependent modular small-world networks
NASA Astrophysics Data System (ADS)
Zhu, Guowei; Wang, Xianpei; Tian, Meng; Dai, Dangdang; Long, Jiachuan; Zhang, Qilin
2016-07-01
Much empirical evidence shows that many real-world networks fall into the broad class of small-world networks and have a modular structure. The modularity has been revealed to have an important effect on cascading failure in isolated networks. However, the corresponding results for interdependent modular small-world networks remain missing. In this paper, we investigate the relationship between cascading failures and the intra-modular rewiring probabilities and inter-modular connections under different coupling preferences, i.e. random coupling with modules (RCWM), assortative coupling in modules (ACIM) and assortative coupling with modules (ACWM). The size of the largest connected component is used to evaluate the robustness from global and local perspectives. Numerical results indicate that increasing intra-modular rewiring probabilities and inter-modular connections can improve the robustness of interdependent modular small-world networks under intra-attacks and inter-attacks. Meanwhile, experiments on three coupling strategies demonstrate that ACIM has a better effect on preventing the cascading failures compared with RCWM and ACWM. These results can be helpful to allocate and optimize the topological structure of interdependent modular small-world networks to improve the robustness of such networks.
Distance Preferences Small-World Communication Topology for Agent Network
NASA Astrophysics Data System (ADS)
Wu, Zhengping; Wang, Renming
In multi-agent system (MAS), the communication topology of agent network plays a very important role in its collaboration. Small-world networks are the networks with high local clustering and small average path length, and the communication networks of MAS can be analyzed within the frame of small-world topology. Yet the real multiagent communication networks are abundant and the classical WS small-world model is not suitable for all cases. In this paper, two new small-world network models are presented. One is based on random graph substrate and local nodes preference reconnection and the other is based on regular graph substrate and long-range nodes preference reconnection. The characteristic of the network parameter such as the clustering coefficients, average path length, and eigenvalue λ2 and λn of the Laplacian matrix for these two models and WS model is studied. The consensus problem that based on these three models is also studied. An example is given and the conclusions are made in the end.
Small-world properties of the Indian railway network.
Sen, Parongama; Dasgupta, Subinay; Chatterjee, Arnab; Sreeram, P A; Mukherjee, G; Manna, S S
2003-03-01
Structural properties of the Indian railway network is studied in the light of recent investigations of the scaling properties of different complex networks. Stations are considered as "nodes" and an arbitrary pair of stations is said to be connected by a "link" when at least one train stops at both stations. Rigorous analysis of the existing data shows that the Indian railway network displays small-world properties. We define and estimate several other quantities associated with this network.
A small-world network model of facial emotion recognition.
Takehara, Takuma; Ochiai, Fumio; Suzuki, Naoto
2016-01-01
Various models have been proposed to increase understanding of the cognitive basis of facial emotions. Despite those efforts, interactions between facial emotions have received minimal attention. If collective behaviours relating to each facial emotion in the comprehensive cognitive system could be assumed, specific facial emotion relationship patterns might emerge. In this study, we demonstrate that the frameworks of complex networks can effectively capture those patterns. We generate 81 facial emotion images (6 prototypes and 75 morphs) and then ask participants to rate degrees of similarity in 3240 facial emotion pairs in a paired comparison task. A facial emotion network constructed on the basis of similarity clearly forms a small-world network, which features an extremely short average network distance and close connectivity. Further, even if two facial emotions have opposing valences, they are connected within only two steps. In addition, we show that intermediary morphs are crucial for maintaining full network integration, whereas prototypes are not at all important. These results suggest the existence of collective behaviours in the cognitive systems of facial emotions and also describe why people can efficiently recognize facial emotions in terms of information transmission and propagation. For comparison, we construct three simulated networks--one based on the categorical model, one based on the dimensional model, and one random network. The results reveal that small-world connectivity in facial emotion networks is apparently different from those networks, suggesting that a small-world network is the most suitable model for capturing the cognitive basis of facial emotions.
Collective relaxation dynamics of small-world networks.
Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc
2015-05-01
Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N, average degree k, and topological randomness q. We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q, including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.
Small world in a seismic network: the California case
NASA Astrophysics Data System (ADS)
Jiménez, A.; Tiampo, K. F.; Posadas, A. M.
2008-05-01
Recent work has shown that disparate systems can be described as complex networks i.e. assemblies of nodes and links with nontrivial topological properties. Examples include technological, biological and social systems. Among them, earthquakes have been studied from this perspective. In the present work, we divide the Southern California region into cells of 0.1°, and calculate the correlation of activity between them to create functional networks for that seismic area, in the same way that the brain activity is studied from the complex network perspective. We found that the network shows small world features.
Scaling properties of random walks on small-world networks.
Almaas, E; Kulkarni, R V; Stroud, D
2003-11-01
Using both numerical simulations and scaling arguments, we study the behavior of a random walker on a one-dimensional small-world network. For the properties we study, we find that the random walk obeys a characteristic scaling form. These properties include the average number of distinct sites visited by the random walker, the mean-square displacement of the walker, and the distribution of first-return times. The scaling form has three characteristic time regimes. At short times, the walker does not see the small-world shortcuts and effectively probes an ordinary Euclidean network in d dimensions. At intermediate times, the properties of the walker shows scaling behavior characteristic of an infinite small-world network. Finally, at long times, the finite size of the network becomes important, and many of the properties of the walker saturate. We propose general analytical forms for the scaling properties in all three regimes, and show that these analytical forms are consistent with our numerical simulations.
Small-world human brain networks: Perspectives and challenges.
Liao, Xuhong; Vasilakos, Athanasios V; He, Yong
2017-04-05
Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.
Epidemiology Model on Shortcut and Small World Networks
NASA Astrophysics Data System (ADS)
Shanker, O.; Hogg, Tad
We show that the behavior of an epidemiology model depends sensitively on the shortcut density in the shortcut network. This is consistent with an earlier work on other processes on the shortcut network. We analytically study the reason for the sensitivity. The shortcut network is similar to the small world network, and it has the advantage that the model dependence on the shortcut density can be analytically studied. The model would be relevant to the spread of diseases in human, animal, plant or other populations, to the spread of viruses in computer networks, or to the spread of social contagion in social networks. It would also be relevant in understanding the variations in the load on routers connecting different computer networks, as the network topology gets extended by the addition of new links, and in analyzing the placement of certain special sensors in a sensor network laid out in a non-random way with some shortcut links.
Coherence Resonance of Small World Networks with Adaptive Coupling
NASA Astrophysics Data System (ADS)
Miyakawa, Kenji
2015-06-01
The phenomenon of coherence resonance (CR) in small world networks with adaptive coupling is investigated by modeling a real experimental situation with a photosensitive Belousov-Zhabotinsky reaction. We show that both spatial synchronization and temporal coherence of noise-induced firings can be considerably improved by adjusting control parameters, such as the degree of connectivity and the coupling strength. A small fraction of possible long-range connections is enough to obtain a great enhancement in CR.
Phase multistability in a dynamical small world network
Shabunin, A. V.
2015-01-15
The effect of phase multistability is explored in a small world network of periodic oscillators with diffusive couplings. The structure of the network represents a ring with additional non-local links, which spontaneously arise and vanish between arbitrary nodes. The dynamics of random couplings is modeled by “birth” and “death” stochastic processes by means of the cellular automate approach. The evolution of the network under gradual increasing of the number of random couplings goes through stages of phases fluctuations and spatial cluster formation. Finally, in the presence of non-local couplings the phase multistability “dies” and only the in-phase regime survives.
Hawks and Doves on small-world networks
NASA Astrophysics Data System (ADS)
Tomassini, Marco; Luthi, Leslie; Giacobini, Mario
2006-01-01
We explore the Hawk-Dove game on networks with topologies ranging from regular lattices to random graphs with small-world networks in between. This is done by means of computer simulations using several update rules for the population evolutionary dynamics. We find the overall result that cooperation is sometimes inhibited and sometimes enhanced in those network structures, with respect to the mixing population case. The differences are due to different update rules and depend on the gain-to-cost ratio. We analyze and qualitatively explain this behavior by using local topological arguments.
Mandala Networks: ultra-small-world and highly sparse graphs
NASA Astrophysics Data System (ADS)
Sampaio Filho, Cesar I. N.; Moreira, André A.; Andrade, Roberto F. S.; Herrmann, Hans J.; Andrade, José S.
2015-03-01
The increasing demands in security and reliability of infrastructures call for the optimal design of their embedded complex networks topologies. The following question then arises: what is the optimal layout to fulfill best all the demands? Here we present a general solution for this problem with scale-free networks, like the Internet and airline networks. Precisely, we disclose a way to systematically construct networks which are robust against random failures. Furthermore, as the size of the network increases, its shortest path becomes asymptotically invariant and the density of links goes to zero, making it ultra-small world and highly sparse, respectively. The first property is ideal for communication and navigation purposes, while the second is interesting economically. Finally, we show that some simple changes on the original network formulation can lead to an improved topology against malicious attacks.
Effects of Route Guidance Systems on Small-World Networks
NASA Astrophysics Data System (ADS)
Wu, Jian-Jun; Sun, Hui-Jun; Gao, Zi-You; Li, Shu-Bin
The route guidance systems (RGS) are efficient in alleviating traffic congestion and reducing transit time of transportation networks. This paper studies the effects of RGS on performance of variably weighted small-world networks. The properties of the average shortest path length, the maximum degree, and the largest betweenness, as important indices for characterizing the network structure in complex networks, are simulated. Results show that there is an optimal guided rate of RGS to minimize the total system cost and the average shortest path length, and proper RGS can reduce the load of the node with maximum degree or largest betweenness. In addition, we found that the load distribution of nodes guided by RGS decay as the power laws which is very important for us to understand and control traffic congestion feasible.
Epidemics with pathogen mutation on small-world networks
NASA Astrophysics Data System (ADS)
Shao, Zhi-Gang; Tan, Zhi-Jie; Zou, Xian-Wu; Jin, Zhun-Zhi
2006-05-01
We study the dynamical behavior of the epidemiological model with pathogen mutation on small-world networks, and discuss the influence of the immunity duration τR, the cross-immunity threshold hthr, and system size N on epidemic dynamics. A decaying oscillation occurs because of the interplay between the immune response and the pathogen mutation. These results have implications for the interpretation of longitudinal epidemiological data on strain abundance, and they will be helpful to assess the threat of highly pathogenic and mutative viruses, such as avian influenza.
Critical behavior of epidemic spreading in dynamic small world networks
NASA Astrophysics Data System (ADS)
Stone, Thomas; McKay, Susan
2010-03-01
Dynamic small-world (DSW) contact networks model populations that have fixed short range links but time varying stochastic long range links between individuals, such as in mobile populations. The measure of mobility is given by a parameter p that is directly analogous to the rewiring parameter in standard small-world networks. This study investigates the relative effects of vaccinations and avoidance of infected individuals in a susceptible-infected-recovered (SIR) epidemic model on a DSW network. We derive (1) the critical mobility required for an outbreak to occur as a function of the disease's infectivity, recovery rate, avoidance rate, and vaccination rate and (2) an expression to calculate the amount of vaccination and/or avoidance necessary to prevent the disease-free to endemic transition. Agreement between these calculated points and numerical simulation is excellent. We then show via finite size scaling that the transition is indeed a continuous phase transition and find the associated critical exponent. From this and other scaling relations at the critical point we can comment on the model's potential universality.
Applications of small-world network theory in alcohol epidemiology.
Braun, Richard J; Wilson, Robert A; Pelesko, John A; Buchanan, J Robert; Gleeson, James P
2006-07-01
This study develops a mathematical model of alcohol abuse in structured populations, such as communities and college campuses. The study employs a network model that has the capacity to incorporate a variety of forms of connectivity membership besides personal acquaintance, such as geographic proximity and common organizations. The model also incorporates a resilience dimension that indicates the susceptibility of each individual in a network to alcohol abuse. The model has the capacity to simulate the effect of moving alcohol abusers into networks of nonabusers, either as the result of treatment or membership in self-help organizations. The study employs a small-world model. A cubic equation for each person (vertex on a graph) governs the evolution of an individual's state between 0 and 1 with regard to alcohol dependence, with 1 indicating absolute certainty of alcohol dependence. The simulations are dependent on initial conditions, the structure of the network, and the resilience distribution of the network. The simulations incorporate multiple realizations of social networks, showing the effect of different network structures. The model suggests that the prevalence of alcohol abuse can be minimized by treating a relatively small percentage of the study population. In the small populations that we studied, the critical point was 10% or less of the study population, but we emphasize that this is within the limitations and assumptions of this model. The use of a simple model that incorporates the influence of the social network neighbors in structured populations shows promise for helping to inform treatment and prevention policy.
Growing Homophilic Networks Are Natural Navigable Small Worlds
Malkov, Yury A.; Ponomarenko, Alexander
2016-01-01
Navigability, an ability to find a logarithmically short path between elements using only local information, is one of the most fascinating properties of real-life networks. However, the exact mechanism responsible for the formation of navigation properties remained unknown. We show that navigability can be achieved by using only two ingredients present in the majority of networks: network growth and local homophily, giving a persuasive answer how the navigation appears in real-life networks. A very simple algorithm produces hierarchical self-similar optimally wired navigable small world networks with exponential degree distribution by using only local information. Adding preferential attachment produces a scale-free network which has shorter greedy paths, but worse (power law) scaling of the information extraction locality (algorithmic complexity of a search). Introducing saturation of the preferential attachment leads to truncated scale-free degree distribution that offers a good tradeoff between these parameters and can be useful for practical applications. Several features of the model are observed in real-life networks, in particular in the brain neural networks, supporting the earlier suggestions that they are navigable. PMID:27348120
Optimal transport in time-varying small-world networks
NASA Astrophysics Data System (ADS)
Chen, Qu; Qian, Jiang-Hai; Zhu, Liang; Han, Ding-Ding
2016-03-01
The time-order of interactions, which is regulated by some intrinsic activity, surely plays a crucial role regarding the transport efficiency of transportation systems. Here we study the optimal transport structure by measure of the length of time-respecting paths. Our network is built from a two-dimensional regular lattice, and long-range connections are allocated with probability Pi j˜rij -α , where ri j is the Manhattan distance. By assigning each shortcut an activity rate subjected to its geometric distance τi j˜rij -C , long-range links become active intermittently, leading to the time-varying dynamics. We show that for 0
Functional brain networks: random, "small world" or deterministic?
Blinowska, Katarzyna J; Kaminski, Maciej
2013-01-01
Lately the problem of connectivity in brain networks is being approached frequently by graph theoretical analysis. In several publications based on bivariate estimators of relations between EEG channels authors reported random or "small world" structure of networks. The results of these works often have no relation to other evidence based on imaging, inverse solutions methods, physiological and anatomical data. Herein we try to find reasons for this discrepancy. We point out that EEG signals are very much interdependent, thus bivariate measures applied to them may produce many spurious connections. In fact, they may outnumber the true connections. Giving all connections equal weights, as it is usual in the framework of graph theoretical analysis, further enhances these spurious links. In effect, close to random and disorganized patterns of connections emerge. On the other hand, multivariate connectivity estimators, which are free of the artificial links, show specific, well determined patterns, which are in a very good agreement with other evidence. The modular structure of brain networks may be identified by multivariate estimators based on Granger causality and formalism of assortative mixing. In this way, the strength of coupling may be evaluated quantitatively. During working memory task, by means of multivariate Directed Transfer Function, it was demonstrated that the modules characterized by strong internal bonds exchange the information by weaker connections.
A weighted small world network measure for assessing functional connectivity.
Bolaños, Marcos; Bernat, Edward M; He, Bin; Aviyente, Selin
2013-01-15
There is a growing need to develop measures that can characterize complex patterns of functional connectivity among brain regions. Graph theoretic measures have emerged as an important way to characterize the multivariate connectivity between nodes in a network, which have been successfully applied to neurophysiologic activity. In this paper, we propose a new small-world measure based on advances in both the bivariate measures underlying the graph theoretic approach, as well as in the definition of the measure for weighted graphs. Specifically, we recently proposed a new bivariate time-frequency phase-synchrony (TFPS) measure, which quantifies the dynamic nature of the interactions between neuronal oscillations with a higher time-frequency resolution than previous approaches and is better at isolating relevant activity. The proposed graph theoretic measures, weighted clustering coefficient and path length, represent a new approach to the calculation of weighted graph measures based on this improved bivariate TFPS measure. The new graph theoretic measures are applied to two datasets. The first is a well-known social network, Zachary's Karate Club. The second application contains event-related potential (ERP) indexing the well-known error-related negativity (ERN) component related to cognitive control. Results indicate that the new measures outperform the previously published weighted graph measures, and produces expectable results for both applications. Copyright © 2012 Elsevier B.V. All rights reserved.
Algebraic approach to small-world network models
NASA Astrophysics Data System (ADS)
Rudolph-Lilith, Michelle; Muller, Lyle E.
2014-01-01
We introduce an analytic model for directed Watts-Strogatz small-world graphs and deduce an algebraic expression of its defining adjacency matrix. The latter is then used to calculate the small-world digraph's asymmetry index and clustering coefficient in an analytically exact fashion, valid nonasymptotically for all graph sizes. The proposed approach is general and can be applied to all algebraically well-defined graph-theoretical measures, thus allowing for an analytical investigation of finite-size small-world graphs.
Emergence of Small-World and Limitations to Its Maximization in a Macaque Cerebral Cortical Network
NASA Astrophysics Data System (ADS)
Zhao, Qing-Bai; Liao, Meng-Jie; Chen, Qi-Cai
2011-06-01
We study both the emergence of small-world topology in a macaque cerebral cortical network and the limitations to maximization of small-worldness. The results show that the maximization of neural complexity leads to a small-world topology, but it also limits the maximization of small-worldness. It is suggested that the modular organization that corresponds to different functions may be a limitation. Additionally, the need for strong resilience against attacks may be another limitation.
Influence of choice of null network on small-world parameters of structural correlation networks.
Hosseini, S M Hadi; Kesler, Shelli R
2013-01-01
In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.
Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks
Hosseini, S. M. Hadi; Kesler, Shelli R.
2013-01-01
In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672
Small-World Propensity: A novel statistic to quantify weighted networks
NASA Astrophysics Data System (ADS)
Bassett, Danielle; Muldoon, Sarah; Bridgeford, Eric
2015-03-01
Many real-world networks have been shown to display a small-world structure with high local clustering yet short average path length between any two nodes. However, characterization of small-world properties has generally relied on a binarized representation of such graphs, neglecting the important fact that, in reality, many real-world networks are actually composed of weighted connections spanning a wide range of strengths. Here, we present a generalization of the Watts-Strogtaz formalism for weighted networks along with a novel statistic called the Small-World Propensity that quantifies both binary and weighted small-world structure. We apply this measure to real-world brain networks and show that by retaining network weights, we are able to better understand the small-world structure of these systems.
A multi-community homogeneous small-world network and its fundamental characteristics
NASA Astrophysics Data System (ADS)
Tanimoto, Jun
2016-10-01
We introduce a new small-world network-which we call the multi-community homogeneous-small-world network-that is divided into multiple communities that are relatively isolated, similar to sparsely connected islands. A generating algorithm is presented and its network parameters are explored. To elucidate the fundamental characteristics of the proposed topology, we adopt spatial prisoner's dilemma games as a template for discussion. Comparing with a conventional homogeneous small-world network, more enhanced network reciprocity is observed in games where a stag hunt-type dilemma is large. With intensive analysis, we find how this enhancement is brought about.
Low-rank network decomposition reveals structural characteristics of small-world networks
NASA Astrophysics Data System (ADS)
Barranca, Victor J.; Zhou, Douglas; Cai, David
2015-12-01
Small-world networks occur naturally throughout biological, technological, and social systems. With their prevalence, it is particularly important to prudently identify small-world networks and further characterize their unique connection structure with respect to network function. In this work we develop a formalism for classifying networks and identifying small-world structure using a decomposition of network connectivity matrices into low-rank and sparse components, corresponding to connections within clusters of highly connected nodes and sparse interconnections between clusters, respectively. We show that the network decomposition is independent of node indexing and define associated bounded measures of connectivity structure, which provide insight into the clustering and regularity of network connections. While many existing network characterizations rely on constructing benchmark networks for comparison or fail to describe the structural properties of relatively densely connected networks, our classification relies only on the intrinsic network structure and is quite robust with respect to changes in connection density, producing stable results across network realizations. Using this framework, we analyze several real-world networks and reveal new structural properties, which are often indiscernible by previously established characterizations of network connectivity.
The small world phenomenon and assortative mixing in Polish corporate board and director networks
NASA Astrophysics Data System (ADS)
Sankowska, Anna; Siudak, Dariusz
2016-02-01
This paper investigates the corporate board and director networks in the Polish capital market in 2014. We examined real board and director networks in comparison with networks that were randomly constructed. Through empirical analyses, we demonstrated that the real networks have the characteristics of small-world networks. In addition, the networks are assortative and highly clustered, which imposes certain behaviors on them.
From brain to earth and climate systems: Small-world interaction networks or not?
NASA Astrophysics Data System (ADS)
Bialonski, Stephan; Horstmann, Marie-Therese; Lehnertz, Klaus
2010-03-01
We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations, we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with the commonly used time series analysis based approaches to network characterization.
From brain to earth and climate systems: small-world interaction networks or not?
Bialonski, Stephan; Horstmann, Marie-Therese; Lehnertz, Klaus
2010-03-01
We consider recent reports on small-world topologies of interaction networks derived from the dynamics of spatially extended systems that are investigated in diverse scientific fields such as neurosciences, geophysics, or meteorology. With numerical simulations that mimic typical experimental situations, we have identified an important constraint when characterizing such networks: indications of a small-world topology can be expected solely due to the spatial sampling of the system along with the commonly used time series analysis based approaches to network characterization.
Small Pure Carbon Molecules with Small-World Networks Using Density Functional Theory Simulations
NASA Astrophysics Data System (ADS)
Yancey, Jeremy A.; Novotny, M. A.; Gwaltney, Steven R.
The possible existence of small, pure carbon molecules based on small-world networks is addressed using density functional theory simulations. A ring of atoms with one or more small-world connections between pairs of non-nearest-neighbor sites was chosen for the network topology. The small-world connections are made with and without additional carbon atoms placed along the link. The energy per atom of these small-world carbon systems is compared with benchmark molecules such as the C20 ring, bowl, and cage isomers, the C60 Buckyball, monocyclic pure carbon rings ranging from C4 to C60, bare linear carbon chains ranging from C2 to C36, and various graphitic fragments without hydrogens. The results of the energy per atom for some of these small-world clusters provide an indication that such pure carbon molecules are reasonable for real world synthesis.
Scale-free networks which are highly assortative but not small world
NASA Astrophysics Data System (ADS)
Small, Michael; Xu, Xiaoke; Zhou, Jin; Zhang, Jie; Sun, Junfeng; Lu, Jun-An
2008-06-01
Uncorrelated scale-free networks are necessarily small world (and, in fact, smaller than small world). Nonetheless, for scale-free networks with correlated degree distribution this may not be the case. We describe a mechanism to generate highly assortative scale-free networks which are not small world. We show that it is possible to generate scale-free networks, with arbitrary degree exponent γ>1 , such that the average distance between nodes in the network is large. To achieve this, nodes are not added to the network with preferential attachment. Instead, we greedily optimize the assortativity of the network. The network generation scheme is physically motivated, and we show that the recently observed global network of Avian Influenza outbreaks arises through a mechanism similar to what we present here. Simulations show that this network exhibits very similar physical characteristics (very high assortativity, clustering, and path length).
Homeostatic structural plasticity increases the efficiency of small-world networks.
Butz, Markus; Steenbuck, Ines D; van Ooyen, Arjen
2014-01-01
In networks with small-world topology, which are characterized by a high clustering coefficient and a short characteristic path length, information can be transmitted efficiently and at relatively low costs. The brain is composed of small-world networks, and evolution may have optimized brain connectivity for efficient information processing. Despite many studies on the impact of topology on information processing in neuronal networks, little is known about the development of network topology and the emergence of efficient small-world networks. We investigated how a simple growth process that favors short-range connections over long-range connections in combination with a synapse formation rule that generates homeostasis in post-synaptic firing rates shapes neuronal network topology. Interestingly, we found that small-world networks benefited from homeostasis by an increase in efficiency, defined as the averaged inverse of the shortest paths through the network. Efficiency particularly increased as small-world networks approached the desired level of electrical activity. Ultimately, homeostatic small-world networks became almost as efficient as random networks. The increase in efficiency was caused by the emergent property of the homeostatic growth process that neurons started forming more long-range connections, albeit at a low rate, when their electrical activity was close to the homeostatic set-point. Although global network topology continued to change when neuronal activities were around the homeostatic equilibrium, the small-world property of the network was maintained over the entire course of development. Our results may help understand how complex systems such as the brain could set up an efficient network topology in a self-organizing manner. Insights from our work may also lead to novel techniques for constructing large-scale neuronal networks by self-organization.
Homeostatic structural plasticity increases the efficiency of small-world networks
Butz, Markus; Steenbuck, Ines D.; van Ooyen, Arjen
2014-01-01
In networks with small-world topology, which are characterized by a high clustering coefficient and a short characteristic path length, information can be transmitted efficiently and at relatively low costs. The brain is composed of small-world networks, and evolution may have optimized brain connectivity for efficient information processing. Despite many studies on the impact of topology on information processing in neuronal networks, little is known about the development of network topology and the emergence of efficient small-world networks. We investigated how a simple growth process that favors short-range connections over long-range connections in combination with a synapse formation rule that generates homeostasis in post-synaptic firing rates shapes neuronal network topology. Interestingly, we found that small-world networks benefited from homeostasis by an increase in efficiency, defined as the averaged inverse of the shortest paths through the network. Efficiency particularly increased as small-world networks approached the desired level of electrical activity. Ultimately, homeostatic small-world networks became almost as efficient as random networks. The increase in efficiency was caused by the emergent property of the homeostatic growth process that neurons started forming more long-range connections, albeit at a low rate, when their electrical activity was close to the homeostatic set-point. Although global network topology continued to change when neuronal activities were around the homeostatic equilibrium, the small-world property of the network was maintained over the entire course of development. Our results may help understand how complex systems such as the brain could set up an efficient network topology in a self-organizing manner. Insights from our work may also lead to novel techniques for constructing large-scale neuronal networks by self-organization. PMID:24744727
Fractal and Small-World Networks Formed by Self-Organized Critical Dynamics
NASA Astrophysics Data System (ADS)
Watanabe, Akitomo; Mizutaka, Shogo; Yakubo, Kousuke
2015-11-01
We propose a dynamical model in which a network structure evolves in a self-organized critical (SOC) manner and explain a possible origin of the emergence of fractal and small-world networks. Our model combines a network growth and its decay by failures of nodes. The decay mechanism reflects the instability of large functional networks against cascading overload failures. It is demonstrated that the dynamical system surely exhibits SOC characteristics, such as power-law forms of the avalanche size distribution, the cluster size distribution, and the distribution of the time interval between intermittent avalanches. During the network evolution, fractal networks are spontaneously generated when networks experience critical cascades of failures that lead to a percolation transition. In contrast, networks far from criticality have small-world structures. We also observe the crossover behavior from fractal to small-world structure in the network evolution.
Trade-offs between robustness and small-world effect in complex networks
Peng, Guan-Sheng; Tan, Suo-Yi; Wu, Jun; Holme, Petter
2016-01-01
Robustness and small-world effect are two crucial structural features of complex networks and have attracted increasing attention. However, little is known about the relation between them. Here we demonstrate that, there is a conflicting relation between robustness and small-world effect for a given degree sequence. We suggest that the robustness-oriented optimization will weaken the small-world effect and vice versa. Then, we propose a multi-objective trade-off optimization model and develop a heuristic algorithm to obtain the optimal trade-off topology for robustness and small-world effect. We show that the optimal network topology exhibits a pronounced core-periphery structure and investigate the structural properties of the optimized networks in detail. PMID:27853301
Trade-offs between robustness and small-world effect in complex networks
NASA Astrophysics Data System (ADS)
Peng, Guan-Sheng; Tan, Suo-Yi; Wu, Jun; Holme, Petter
2016-11-01
Robustness and small-world effect are two crucial structural features of complex networks and have attracted increasing attention. However, little is known about the relation between them. Here we demonstrate that, there is a conflicting relation between robustness and small-world effect for a given degree sequence. We suggest that the robustness-oriented optimization will weaken the small-world effect and vice versa. Then, we propose a multi-objective trade-off optimization model and develop a heuristic algorithm to obtain the optimal trade-off topology for robustness and small-world effect. We show that the optimal network topology exhibits a pronounced core-periphery structure and investigate the structural properties of the optimized networks in detail.
Trade-offs between robustness and small-world effect in complex networks.
Peng, Guan-Sheng; Tan, Suo-Yi; Wu, Jun; Holme, Petter
2016-11-17
Robustness and small-world effect are two crucial structural features of complex networks and have attracted increasing attention. However, little is known about the relation between them. Here we demonstrate that, there is a conflicting relation between robustness and small-world effect for a given degree sequence. We suggest that the robustness-oriented optimization will weaken the small-world effect and vice versa. Then, we propose a multi-objective trade-off optimization model and develop a heuristic algorithm to obtain the optimal trade-off topology for robustness and small-world effect. We show that the optimal network topology exhibits a pronounced core-periphery structure and investigate the structural properties of the optimized networks in detail.
Graph analysis of structural brain networks in Alzheimer's disease: beyond small world properties.
John, Majnu; Ikuta, Toshikazu; Ferbinteanu, Janina
2017-03-01
Changes in brain connectivity in patients with early Alzheimer's disease (AD) have been investigated using graph analysis. However, these studies were based on small data sets, explored a limited range of network parameters, and did not focus on more restricted sub-networks, where neurodegenerative processes may introduce more prominent alterations. In this study, we constructed structural brain networks out of 87 regions using data from 135 healthy elders and 100 early AD patients selected from the Open Access Series of Imaging Studies (OASIS) database. We evaluated the graph properties of these networks by investigating metrics of network efficiency, small world properties, segregation, product measures of complexity, and entropy. Because degenerative processes take place at different rates in different brain areas, analysis restricted to sub-networks may reveal changes otherwise undetected. Therefore, we first analyzed the graph properties of a network encompassing all brain areas considered together, and then repeated the analysis after dividing the brain areas into two sub-networks constructed by applying a clustering algorithm. At the level of large scale network, the analysis did not reveal differences between AD patients and controls. In contrast, the same analysis performed on the two sub-networks revealed that small worldness diminished with AD only in the sub-network containing the areas of medial temporal lobe known to be heaviest and earliest affected. The second sub-network, which did not present significant AD-induced modifications of 'classical' small world parameters, nonetheless showed a trend towards an increase in small world propensity, a novel metric that unbiasedly quantifies small world structure. Beyond small world properties, complexity and entropy measures indicated that the intricacy of connection patterns and structural diversity decreased in both sub-networks. These results show that neurodegenerative processes impact volumetric
Current redistribution in resistor networks: Fat-tail statistics in regular and small-world networks
NASA Astrophysics Data System (ADS)
Lehmann, Jörg; Bernasconi, Jakob
2017-03-01
The redistribution of electrical currents in resistor networks after single-bond failures is analyzed in terms of current-redistribution factors that are shown to depend only on the topology of the network and on the values of the bond resistances. We investigate the properties of these current-redistribution factors for regular network topologies (e.g., d -dimensional hypercubic lattices) as well as for small-world networks. In particular, we find that the statistics of the current redistribution factors exhibits a fat-tail behavior, which reflects the long-range nature of the current redistribution as determined by Kirchhoff's circuit laws.
Damage spreading in spatial and small-world random Boolean networks.
Lu, Qiming; Teuscher, Christof
2014-02-01
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities (K ≪ 1) and that the critical connectivity of stability K(s) changes compared to random networks. At higher K, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size N increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Damage spreading in spatial and small-world random Boolean networks
NASA Astrophysics Data System (ADS)
Lu, Qiming; Teuscher, Christof
2014-02-01
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean networks (RBNs) are commonly used as a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other nonrandom connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the Hamming distance at very low connectivities (K¯≪1) and that the critical connectivity of stability Ks changes compared to random networks. At higher K¯, this scaling remains unchanged. We also show that the Hamming distance of spatially local networks scales with a power law as the system size N increases, but with a different exponent for local and small-world networks. The scaling arguments for small-world networks are obtained with respect to the system sizes and strength of spatially local connections. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Dynamics of helping behavior and networks in a small world
NASA Astrophysics Data System (ADS)
Jo, Hang-Hyun; Jung, Woo-Sung; Moon, Hie-Tae
2006-08-01
To investigate an effect of social interaction on the bystanders’ intervention in emergency situations a rescue model was introduced which includes the effects of the victim’s acquaintance with bystanders and those among bystanders from a network perspective. This model reproduces the experimental result that the helping rate (success rate in our model) tends to decrease although the number of bystanders k increases. And the interaction among homogeneous bystanders results in the emergence of hubs in a helping network. For more realistic consideration it is assumed that the agents are located on a one-dimensional lattice (ring), then the randomness pɛ[0,1] is introduced: the kp random bystanders are randomly chosen from a whole population and the k-kp near bystanders are chosen in the nearest order to the victim. We find that there appears another peak of the network density in the vicinity of k=9 and p=0.3 due to the cooperative and competitive interaction between the near and random bystanders.
Temporal efficiency evaluation and small-worldness characterization in temporal networks
NASA Astrophysics Data System (ADS)
Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu
2016-09-01
Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.
Temporal efficiency evaluation and small-worldness characterization in temporal networks
Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu
2016-01-01
Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks. PMID:27682314
Small-world to fractal transition in complex networks: a renormalization group approach.
Rozenfeld, Hernán D; Song, Chaoming; Makse, Hernán A
2010-01-15
We show that renormalization group (RG) theory applied to complex networks is useful to classify network topologies into universality classes in the space of configurations. The RG flow readily identifies a small-world-fractal transition by finding (i) a trivial stable fixed point of a complete graph, (ii) a nontrivial point of a pure fractal topology that is stable or unstable according to the amount of long-range links in the network, and (iii) another stable point of a fractal with shortcuts that exist exactly at the small-world-fractal transition. As a collateral, the RG technique explains the coexistence of the seemingly contradicting fractal and small-world phases and allows us to extract information on the distribution of shortcuts in real-world networks, a problem of importance for information flow in the system.
Comparative effects of avoidance and vaccination in disease spread on a dynamic small-world network
NASA Astrophysics Data System (ADS)
Stone, Thomas E.; Jones, Matthew M.; McKay, Susan R.
2010-12-01
Dynamic small-world contact networks have fixed short range links and time-varying stochastic long range links. They are used to model mobile populations or as minimal models for traditional small-world networks. Here we study the relative effects of vaccinations and avoidance of infected individuals in a susceptible-infected-recovered (SIR) epidemic model on a dynamic small-world network. We derive the critical mobility required for an outbreak to occur as a function of the disease’s infectivity, recovery rate, avoidance rate, and vaccination rate. We also derive an expression that allows us to calculate the amount of vaccination and/or avoidance necessary to prevent an epidemic. Calculated quantities show excellent agreement with simulations.
Damage spreading in spatial and small-world random boolean networks
Lu, Qiming; Teuscher, Christof
2008-01-01
Random Boolean Networks (RBNs) are often used as generic models for certain dynamics of complex systems, ranging from social networks, neural networks, to gene or protein interaction networks. Traditionally, RBNs are interconnected randomly and without considering any spatial arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ({bar K} << 1) and that the critical connectivity of stability K{sub s} changes compared to random networks. At higher {bar K}, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key trade-offs between damage spreading (robustness), the network wiring cost, and the network's communication characteristics.
Investigation of the forest-fire model on a small-world network
NASA Astrophysics Data System (ADS)
Graham, I.; Matthai, C. C.
2003-09-01
It is shown that the forest-fire model of Bak et al. run on a square lattice network with additional long-range interactions in the spirit of a small-world network results in a scale-free system reminiscent of self-organized criticality without recourse to fine tuning. As the number of these long-range interactions is increased, the cluster size distribution exponent is found to decrease in magnitude as the small-world regime is entered, indicating a change in its universality class. It is suggested that such a model could have applicability in the study of disease spreading in human populations.
Basin stability for burst synchronization in small-world networks of chaotic slow-fast oscillators
NASA Astrophysics Data System (ADS)
Maslennikov, Oleg V.; Nekorkin, Vladimir I.; Kurths, Jürgen
2015-10-01
The impact of connectivity and individual dynamics on the basin stability of the burst synchronization regime in small-world networks consisting of chaotic slow-fast oscillators is studied. It is shown that there are rewiring probabilities corresponding to the largest basin stabilities, which uncovers a reason for finding small-world topologies in real neuronal networks. The impact of coupling density and strength as well as the nodal parameters of relaxation or excitability are studied. Dynamic mechanisms are uncovered that most strongly influence basin stability of the burst synchronization regime.
Renormalization and small-world model of fractal quantum repeater networks
Wei, Zong-Wen; Wang, Bing-Hong; Han, Xiao-Pu
2013-01-01
Quantum networks provide access to exchange of quantum information. The primary task of quantum networks is to distribute entanglement between remote nodes. Although quantum repeater protocol enables long distance entanglement distribution, it has been restricted to one-dimensional linear network. Here we develop a general framework that allows application of quantum repeater protocol to arbitrary quantum repeater networks with fractal structure. Entanglement distribution across such networks is mapped to renormalization. Furthermore, we demonstrate that logarithmical times of recursive such renormalization transformations can trigger fractal to small-world transition, where a scalable quantum small-world network is achieved. Our result provides new insight into quantum repeater theory towards realistic construction of large-scale quantum networks. PMID:23386977
Renormalization and small-world model of fractal quantum repeater networks.
Wei, Zong-Wen; Wang, Bing-Hong; Han, Xiao-Pu
2013-01-01
Quantum networks provide access to exchange of quantum information. The primary task of quantum networks is to distribute entanglement between remote nodes. Although quantum repeater protocol enables long distance entanglement distribution, it has been restricted to one-dimensional linear network. Here we develop a general framework that allows application of quantum repeater protocol to arbitrary quantum repeater networks with fractal structure. Entanglement distribution across such networks is mapped to renormalization. Furthermore, we demonstrate that logarithmical times of recursive such renormalization transformations can trigger fractal to small-world transition, where a scalable quantum small-world network is achieved. Our result provides new insight into quantum repeater theory towards realistic construction of large-scale quantum networks.
Damage Spreading in Spatial and Small-world Random Boolean Networks
Lu, Qiming; Teuscher, Christof
2014-02-18
The study of the response of complex dynamical social, biological, or technological networks to external perturbations has numerous applications. Random Boolean Networks (RBNs) are commonly used a simple generic model for certain dynamics of complex systems. Traditionally, RBNs are interconnected randomly and without considering any spatial extension and arrangement of the links and nodes. However, most real-world networks are spatially extended and arranged with regular, power-law, small-world, or other non-random connections. Here we explore the RBN network topology between extreme local connections, random small-world, and pure random networks, and study the damage spreading with small perturbations. We find that spatially local connections change the scaling of the relevant component at very low connectivities ($\\bar{K} \\ll 1$) and that the critical connectivity of stability $K_s$ changes compared to random networks. At higher $\\bar{K}$, this scaling remains unchanged. We also show that the relevant component of spatially local networks scales with a power-law as the system size N increases, but with a different exponent for local and small-world networks. The scaling behaviors are obtained by finite-size scaling. We further investigate the wiring cost of the networks. From an engineering perspective, our new findings provide the key design trade-offs between damage spreading (robustness), the network's wiring cost, and the network's communication characteristics.
Fast and robust image segmentation by small-world neural oscillator networks.
Li, Chunguang; Li, Yuke
2011-06-01
Inspired by the temporal correlation theory of brain functions, researchers have presented a number of neural oscillator networks to implement visual scene segmentation problems. Recently, it is shown that many biological neural networks are typical small-world networks. In this paper, we propose and investigate two small-world models derived from the well-known LEGION (locally excitatory and globally inhibitory oscillator network) model. To form a small-world network, we add a proper proportion of unidirectional shortcuts (random long-range connections) to the original LEGION model. With local connections and shortcuts, the neural oscillators can not only communicate with neighbors but also exchange phase information with remote partners. Model 1 introduces excitatory shortcuts to enhance the synchronization within an oscillator group representing the same object. Model 2 goes further to replace the global inhibitor with a sparse set of inhibitory shortcuts. Simulation results indicate that the proposed small-world models could achieve synchronization faster than the original LEGION model and are more likely to bind disconnected image regions belonging together. In addition, we argue that these two models are more biologically plausible.
Structural connectivity within neural ganglia: A default small-world network.
Ould Ismail, Abdol Aziz O; Amouzandeh, Ghoncheh; Grant, Samuel C
2016-11-19
Diffusion tensor imaging (DTI) provides a unique contrast based on the restricted directionality of water movement in an anisotropic environment. As such, DTI-based tractography can be used to characterize and quantify the structural connectivity within neural tissue. Here, DTI-based connectivity within isolated abdominal ganglia of Aplysia californica (ABG) is analyzed using network theory. In addition to quantifying the regional physical proprieties of the fractional anisotropy and apparent diffusion coefficient, DTI tractography was used to probe inner-connections of local communities, yielding unweighted, undirected graphs that represent community structures. Local and global efficiency, characteristic path lengths and clustering analysis are performed on both experimental and simulated data. The relevant intensity by which these specific nodes communicate is probed through weighted clustering coefficient measurements. Both small-worldness and novel small-world metrics were used as tools to verify the small-world properties for the experimental results. The aim of this manuscript is to categorize the properties exhibited by structural networks in a model neural tissue to derive unique mean field information that quantitatively describe macroscopic connectivity. For ABG, findings demonstrate a default structural network with preferential specific small-world properties when compared to simulated lattice and random networks that are equivalent in order and degree. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.
Monomer-dimer model on a scale-free small-world network
NASA Astrophysics Data System (ADS)
Zhang, Zhongzhi; Sheng, Yibin; Jiang, Qiang
2012-02-01
The explicit determination of the number of monomer-dimer arrangements on a network is a theoretical challenge, and exact solutions to monomer-dimer problem are available only for few limiting graphs with a single monomer on the boundary, e.g., rectangular lattice and quartic lattice; however, analytical research (even numerical result) for monomer-dimer problem on scale-free small-world networks is still missing despite the fact that a vast variety of real systems display simultaneously scale-free and small-world structures. In this paper, we address the monomer-dimer problem defined on a scale-free small-world network and obtain the exact formula for the number of all possible monomer-dimer arrangements on the network, based on which we also determine the asymptotic growth constant of the number of monomer-dimer arrangements in the network. We show that the obtained asymptotic growth constant is much less than its counterparts corresponding to two-dimensional lattice and Sierpinski fractal having the same average degree as the studied network, which indicates from another aspect that scale-free networks have a fundamentally distinct architecture as opposed to regular lattices and fractals without power-law behavior.
NASA Astrophysics Data System (ADS)
Wang, Qingyun; Duan, Zhisheng; Perc, Matjaž; Chen, Guanrong
2008-09-01
Synchronization transitions are investigated in small-world neuronal networks that are locally modeled by the Rulkov map with additive spatiotemporal noise. In particular, we investigate the impact of different information transmission delays and rewiring probability. We show that short delays induce zigzag fronts of excitations, whereas intermediate delays can further detriment synchrony in the network due to a dynamic clustering anti-phase synchronization transition. Detailed investigations reveal, however, that for longer delay lengths the synchrony of excitations in the network can again be enhanced due to the emergence of in-phase synchronization. In addition, we show that an appropriate small-world topology can restore synchronized behavior provided information transmission delays are either short or long. On the other hand, within the intermediate delay region, which is characterized by anti-phase synchronization and clustering, differences in the network topology do not notably affect the synchrony of neuronal activity.
The Phase Transitions of Self-similar Small-world Networks
NASA Astrophysics Data System (ADS)
Brunson, Trent; Boettcher, Stefan
2010-03-01
A novel set of self-similar networks called Hanoi networksfootnotetextS. Boettcher, B Goncalves, Europhysics Letters 84, 30002 (2008). (HN) have been developed to study the critical phenomena of small-world networks using the renormalization group (RG). Physically, HNs contain a more desirable geometry than random small-world networks. Their structure consists of a one-dimensional backbone with a hierarchy of long-range bonds, which allows the flexibility of studying planar and non-planar networks with either a regular or exponential degree distribution. The RG and Ising model simulation results for HNs reveal unique phase transitions and non-universal behavior, which can be attributed to their hierarchical structure.footnotetextSee also http://www.physics.emory.edu/faculty/boettcher/.
IMDB Network Revisited: Unveiling Fractal and Modular Properties from a Typical Small-World Network
Gallos, Lazaros K.; Potiguar, Fabricio Q.; Andrade, José S.; Makse, Hernan A.
2013-01-01
We study a subset of the movie collaboration network, http://www.imdb.com, where only adult movies are included. We show that there are many benefits in using such a network, which can serve as a prototype for studying social interactions. We find that the strength of links, i.e., how many times two actors have collaborated with each other, is an important factor that can significantly influence the network topology. We see that when we link all actors in the same movie with each other, the network becomes small-world, lacking a proper modular structure. On the other hand, by imposing a threshold on the minimum number of links two actors should have to be in our studied subset, the network topology becomes naturally fractal. This occurs due to a large number of meaningless links, namely, links connecting actors that did not actually interact. We focus our analysis on the fractal and modular properties of this resulting network, and show that the renormalization group analysis can characterize the self-similar structure of these networks. PMID:23826098
a Small-World and Scale-Free Network Generated by Sierpinski Tetrahedron
NASA Astrophysics Data System (ADS)
Chen, Jin; Gao, Fei; Le, Anbo; Xi, Lifeng; Yin, Shuhua
2016-12-01
The Sierpinski tetrahedron is used to construct evolving networks, whose vertexes are all solid regular tetrahedra in the construction of the Sierpinski tetrahedron up to the stage t and any two vertexes are neighbors if and only if the corresponding tetrahedra are in contact with each other on boundary. We show that such networks have the small-world and scale-free effects, but are not fractal scaling.
NASA Astrophysics Data System (ADS)
Yu, Haitao; Guo, Xinmeng; Wang, Jiang; Deng, Bin; Wei, Xile
2015-10-01
The phenomenon of vibrational resonance is investigated in adaptive Newman-Watts small-world neuronal networks, where the strength of synaptic connections between neurons is modulated based on spike-timing-dependent plasticity. Numerical results demonstrate that there exists appropriate amplitude of high-frequency driving which is able to optimize the neural ensemble response to the weak low-frequency periodic signal. The effect of networked vibrational resonance can be significantly affected by spike-timing-dependent plasticity. It is shown that spike-timing-dependent plasticity with dominant depression can always improve the efficiency of vibrational resonance, and a small adjusting rate can promote the transmission of weak external signal in small-world neuronal networks. In addition, the network topology plays an important role in the vibrational resonance in spike-timing-dependent plasticity-induced neural systems, where the system response to the subthreshold signal is maximized by an optimal network structure. Furthermore, it is demonstrated that the introduction of inhibitory synapses can considerably weaken the phenomenon of vibrational resonance in the hybrid small-world neuronal networks with spike-timing-dependent plasticity.
Small-world network effects on innovation: evidences from nanotechnology patenting
NASA Astrophysics Data System (ADS)
Shi, Yuan; Guan, JianCheng
2016-11-01
This paper explores the effects of collaboration network on innovation in nanotechnology. We extend the idea of small-world to the heterogeneous network positions of actors by capturing the variation of how closely a given actor is connected to others in the same network and how clustered its neighbors are. We test the effects of small-world network in the context of nanotechnology patenting in China. Empirical results reveal that small-worldness, or the co-existence of high clustering and low path length in the network, displays inverse U-shape relationships with future patent output of the individual inventors and the system. Interestingly, the inflection point of the nonlinear relationship is significantly higher at the individual level. Based on these findings, we suggest that researchers of nanotechnology maintain a balance between friends in close-knit inner circles and colleagues in distant areas in their collaboration decisions and that policymakers interested in furthering the field offer collaboration opportunities for researchers in distant locations and areas.
Health, 'small-worlds', fractals and complex networks: an emerging field.
Mutch, W Alan; Lefevre, Gerald R
2003-05-01
The importance of 'small-worlds', fractals and complex networks to medicine are discussed. The interrelationship between the concepts is highlighted. 'Small-worlds'--where large populations are linked at the level of the individual have considerable importance for understanding disease transmission. Complex networks where linkages are based on the concept 'the rich get richer' are fundamental in the medical sciences--from enzymatic interactions at the subcellular level to social interactions such as sexual liaisons. Mathematically 'the rich get richer' can be modeled as a power law. Fractal architecture and time sequences can also be modeled by power laws and are ubiquitous in nature with many important examples in medicine. The potential of fractal life support--the return of physiological time sequences to devices such as mechanical ventilators and cardiopulmonary bypass pumps--is presented in the context of a failing complex network. Experimental work suggests that using fractal time sequences improves support of failing organs. Medicine, as a science has much to gain by embracing the interrelated concepts of 'small-worlds', fractals and complex networks. By so doing, medicine will move from the historical reductionist approach toward a more holistic one.
Bhaumik, Himangsu; Santra, S B
2016-12-01
A dissipative stochastic sandpile model is constructed and studied on small-world networks in one and two dimensions with different shortcut densities ϕ, where ϕ=0 represents regular lattice and ϕ=1 represents random network. The effect of dimension, network topology, and specific dissipation mode (bulk or boundary) on the the steady-state critical properties of nondissipative and dissipative avalanches along with all avalanches are analyzed. Though the distributions of all avalanches and nondissipative avalanches display stochastic scaling at ϕ=0 and mean-field scaling at ϕ=1, the dissipative avalanches display nontrivial critical properties at ϕ=0 and 1 in both one and two dimensions. In the small-world regime (2^{-12}≤ϕ≤0.1), the size distributions of different types of avalanches are found to exhibit more than one power-law scaling with different scaling exponents around a crossover toppling size s_{c}. Stochastic scaling is found to occur for s
NASA Astrophysics Data System (ADS)
Bhaumik, Himangsu; Santra, S. B.
2016-12-01
A dissipative stochastic sandpile model is constructed and studied on small-world networks in one and two dimensions with different shortcut densities ϕ , where ϕ =0 represents regular lattice and ϕ =1 represents random network. The effect of dimension, network topology, and specific dissipation mode (bulk or boundary) on the the steady-state critical properties of nondissipative and dissipative avalanches along with all avalanches are analyzed. Though the distributions of all avalanches and nondissipative avalanches display stochastic scaling at ϕ =0 and mean-field scaling at ϕ =1 , the dissipative avalanches display nontrivial critical properties at ϕ =0 and 1 in both one and two dimensions. In the small-world regime (2-12≤ϕ ≤0.1 ) , the size distributions of different types of avalanches are found to exhibit more than one power-law scaling with different scaling exponents around a crossover toppling size sc. Stochastic scaling is found to occur for s
A small-world network derived from the deterministic uniform recursive tree by line graph operation
NASA Astrophysics Data System (ADS)
Hou, Pengfeng; Zhao, Haixing; Mao, Yaping; Wang, Zhao
2016-03-01
The deterministic uniform recursive tree ({DURT}) is one of the deterministic versions of the uniform recursive tree ({URT}). Zhang et al (2008 Eur. Phys. J. B 63 507-13) studied the properties of DURT, including its topological characteristics and spectral properties. Although DURT shows a logarithmic scaling with the size of the network, DURT is not a small-world network since its clustering coefficient is zero. Lu et al (2012 Physica A 391 87-92) proposed a deterministic small-world network by adding some edges with a simple rule in each DURT iteration. In this paper, we intoduce a method for constructing a new deterministic small-world network by the line graph operation in each DURT iteration. The line graph operation brings about cliques at each node of the previous given graph, and the resulting line graph possesses larger clustering coefficients. On the other hand, this operation can decrease the diameter at almost one, then giving the analytic solutions to several topological characteristics of the model proposed. Supported by The Ministry of Science and Technology 973 project (No. 2010C B334708); National Science Foundation of China (Nos. 61164005, 11161037, 11101232, 11461054, 11551001); The Ministry of education scholars and innovation team support plan of Yangtze River (No. IRT1068); Qinghai Province Nature Science Foundation Project (Nos. 2012-Z-943, 2014-ZJ-907).
Altered small-world brain networks in schizophrenia patients during working memory performance.
He, Hao; Sui, Jing; Yu, Qingbao; Turner, Jessica A; Ho, Beng-Choon; Sponheim, Scott R; Manoach, Dara S; Clark, Vincent P; Calhoun, Vince D
2012-01-01
Impairment of working memory (WM) performance in schizophrenia patients (SZ) is well-established. Compared to healthy controls (HC), SZ patients show aberrant blood oxygen level dependent (BOLD) activations and disrupted functional connectivity during WM performance. In this study, we examined the small-world network metrics computed from functional magnetic resonance imaging (fMRI) data collected as 35 HC and 35 SZ performed a Sternberg Item Recognition Paradigm (SIRP) at three WM load levels. Functional connectivity networks were built by calculating the partial correlation on preprocessed time courses of BOLD signal between task-related brain regions of interest (ROIs) defined by group independent component analysis (ICA). The networks were then thresholded within the small-world regime, resulting in undirected binarized small-world networks at different working memory loads. Our results showed: 1) at the medium WM load level, the networks in SZ showed a lower clustering coefficient and less local efficiency compared with HC; 2) in SZ, most network measures altered significantly as the WM load level increased from low to medium and from medium to high, while the network metrics were relatively stable in HC at different WM loads; and 3) the altered structure at medium WM load in SZ was related to their performance during the task, with longer reaction time related to lower clustering coefficient and lower local efficiency. These findings suggest brain connectivity in patients with SZ was more diffuse and less strongly linked locally in functional network at intermediate level of WM when compared to HC. SZ show distinctly inefficient and variable network structures in response to WM load increase, comparing to stable highly clustered network topologies in HC.
Spatial prisoner's dilemma game with volunteering in Newman-Watts small-world networks
NASA Astrophysics Data System (ADS)
Wu, Zhi-Xi; Xu, Xin-Jian; Chen, Yong; Wang, Ying-Hai
2005-03-01
A modified spatial prisoner’s dilemma game with voluntary participation in Newman-Watts small-world networks is studied. Some reasonable ingredients are introduced to the game evolutionary dynamics: each agent in the network is a pure strategist and can only take one of three strategies (cooperator, defector, and loner); its strategical transformation is associated with both the number of strategical states and the magnitude of average profits, which are adopted and acquired by its coplayers in the previous round of play; a stochastic strategy mutation is applied when it gets into the trouble of local commons that the agent and its neighbors are in the same state and get the same average payoffs. In the case of very low temptation to defect, it is found that agents are willing to participate in the game in typical small-world region and intensive collective oscillations arise in more random region.
Emergence of a Small-World Functional Network in Cultured Neurons
Downes, Julia H.; Hammond, Mark W.; Xydas, Dimitris; Spencer, Matthew C.; Becerra, Victor M.; Warwick, Kevin; Whalley, Ben J.; Nasuto, Slawomir J.
2012-01-01
The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks. PMID:22615555
Scale-free and small-world properties of Sierpinski networks
NASA Astrophysics Data System (ADS)
Wang, Songjing; Xi, Lifeng; Xu, Hui; Wang, Lihong
2017-01-01
In this paper, we construct the evolving networks from Sierpinski carpet, using the encoding approach in fractal geometry. We consider the small similar copies of unit square as nodes of network, where two nodes are neighbors if and only if their corresponding copies have common surface. For our networks, we check their scale-free and small-world effect by the self-similar structures, the exponent of power-law on degree distribution is log3 8 which is the Hausdorff dimension of the carpet.
Effects of time delay on the stochastic resonance in small-world neuronal networks.
Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile; Liu, Chen
2013-03-01
The effects of time delay on stochastic resonance in small-world neuronal networks are investigated. Without delay, an intermediate intensity of additive noise is able to optimize the temporal response of the neural system to the subthreshold periodic signal imposed on all neurons constituting the network. The time delay in the coupling process can either enhance or destroy stochastic resonance of neuronal activity in the small-world network. In particular, appropriately tuned delays can induce multiple stochastic resonances, which appear intermittently at integer multiples of the oscillation period of weak external forcing. It is found that the delay-induced multiple stochastic resonances are most efficient when the forcing frequency is close to the global-resonance frequency of each individual neuron. Furthermore, the impact of time delay on stochastic resonance is largely independent of the small-world topology, except for resonance peaks. Considering that information transmission delays are inevitable in intra- and inter-neuronal communication, the presented results could have important implications for the weak signal detection and information propagation in neural systems.
Adaptive reconfiguration of fractal small-world human brain functional networks.
Bassett, Danielle S; Meyer-Lindenberg, Andreas; Achard, Sophie; Duke, Thomas; Bullmore, Edward
2006-12-19
Brain function depends on adaptive self-organization of large-scale neural assemblies, but little is known about quantitative network parameters governing these processes in humans. Here, we describe the topology and synchronizability of frequency-specific brain functional networks using wavelet decomposition of magnetoencephalographic time series, followed by construction and analysis of undirected graphs. Magnetoencephalographic data were acquired from 22 subjects, half of whom performed a finger-tapping task, whereas the other half were studied at rest. We found that brain functional networks were characterized by small-world properties at all six wavelet scales considered, corresponding approximately to classical delta (low and high), , alpha, beta, and gamma frequency bands. Global topological parameters (path length, clustering) were conserved across scales, most consistently in the frequency range 2-37 Hz, implying a scale-invariant or fractal small-world organization. Dynamical analysis showed that networks were located close to the threshold of order/disorder transition in all frequency bands. The highest-frequency gamma network had greater synchronizability, greater clustering of connections, and shorter path length than networks in the scaling regime of (lower) frequencies. Behavioral state did not strongly influence global topology or synchronizability; however, motor task performance was associated with emergence of long-range connections in both beta and gamma networks. Long-range connectivity, e.g., between frontal and parietal cortex, at high frequencies during a motor task may facilitate sensorimotor binding. Human brain functional networks demonstrate a fractal small-world architecture that supports critical dynamics and task-related spatial reconfiguration while preserving global topological parameters.
Stochastic resonance enhancement of small-world neural networks by hybrid synapses and time delay
NASA Astrophysics Data System (ADS)
Yu, Haitao; Guo, Xinmeng; Wang, Jiang
2017-01-01
The synergistic effect of hybrid electrical-chemical synapses and information transmission delay on the stochastic response behavior in small-world neuronal networks is investigated. Numerical results show that, the stochastic response behavior can be regulated by moderate noise intensity to track the rhythm of subthreshold pacemaker, indicating the occurrence of stochastic resonance (SR) in the considered neural system. Inheriting the characteristics of two types of synapses-electrical and chemical ones, neural networks with hybrid electrical-chemical synapses are of great improvement in neuron communication. Particularly, chemical synapses are conducive to increase the network detectability by lowering the resonance noise intensity, while the information is better transmitted through the networks via electrical coupling. Moreover, time delay is able to enhance or destroy the periodic stochastic response behavior intermittently. In the time-delayed small-world neuronal networks, the introduction of electrical synapses can significantly improve the signal detection capability by widening the range of optimal noise intensity for the subthreshold signal, and the efficiency of SR is largely amplified in the case of pure chemical couplings. In addition, the stochastic response behavior is also profoundly influenced by the network topology. Increasing the rewiring probability in pure chemically coupled networks can always enhance the effect of SR, which is slightly influenced by information transmission delay. On the other hand, the capacity of information communication is robust to the network topology within the time-delayed neuronal systems including electrical couplings.
Small-world anatomical networks in the human brain revealed by cortical thickness from MRI.
He, Yong; Chen, Zhang J; Evans, Alan C
2007-10-01
An important issue in neuroscience is the characterization for the underlying architectures of complex brain networks. However, little is known about the network of anatomical connections in the human brain. Here, we investigated large-scale anatomical connection patterns of the human cerebral cortex using cortical thickness measurements from magnetic resonance images. Two areas were considered anatomically connected if they showed statistically significant correlations in cortical thickness and we constructed the network of such connections using 124 brains from the International Consortium for Brain Mapping database. Significant short- and long-range connections were found in both intra- and interhemispheric regions, many of which were consistent with known neuroanatomical pathways measured by human diffusion imaging. More importantly, we showed that the human brain anatomical network had robust small-world properties with cohesive neighborhoods and short mean distances between regions that were insensitive to the selection of correlation thresholds. Additionally, we also found that this network and the probability of finding a connection between 2 regions for a given anatomical distance had both exponentially truncated power-law distributions. Our results demonstrated the basic organizational principles for the anatomical network in the human brain compatible with previous functional networks studies, which provides important implications of how functional brain states originate from their structural underpinnings. To our knowledge, this study provides the first report of small-world properties and degree distribution of anatomical networks in the human brain using cortical thickness measurements.
Neural progenitors organize in small-world networks to promote cell proliferation.
Malmersjö, Seth; Rebellato, Paola; Smedler, Erik; Planert, Henrike; Kanatani, Shigeaki; Liste, Isabel; Nanou, Evanthia; Sunner, Hampus; Abdelhady, Shaimaa; Zhang, Songbai; Andäng, Michael; El Manira, Abdeljabbar; Silberberg, Gilad; Arenas, Ernest; Uhlén, Per
2013-04-16
Coherent network activity among assemblies of interconnected cells is essential for diverse functions in the adult brain. However, cellular networks before formations of chemical synapses are poorly understood. Here, embryonic stem cell-derived neural progenitors were found to form networks exhibiting synchronous calcium ion (Ca(2+)) activity that stimulated cell proliferation. Immature neural cells established circuits that propagated electrical signals between neighboring cells, thereby activating voltage-gated Ca(2+) channels that triggered Ca(2+) oscillations. These network circuits were dependent on gap junctions, because blocking prevented electrotonic transmission both in vitro and in vivo. Inhibiting connexin 43 gap junctions abolished network activity, suppressed proliferation, and affected embryonic cortical layer formation. Cross-correlation analysis revealed highly correlated Ca(2+) activities in small-world networks that followed a scale-free topology. Graph theory predicts that such network designs are effective for biological systems. Taken together, these results demonstrate that immature cells in the developing brain organize in small-world networks that critically regulate neural progenitor proliferation.
Studies on the signal amplification in weighted and unweighted small-world networks
NASA Astrophysics Data System (ADS)
Gao, Yang; Wang, Jianjun; Ma, Fuqiu
2017-02-01
Weighted and unweighted networks composed of coupled bistable oscillators with small-world topology are investigated under the co-presence of a weak signal and multiplicative Gaussian white noise. As the noise intensity is adjusted to one or two optimal values, the temporal periodicity of the output of the system reaches the maximum, indicating the occurrence of stochastic resonance (SR) or stochastic bi-resonance (SBR). The resonance behavior is strongly-dependent on the coupling strength in both networks. At a weak coupling, SR more likely takes place; whereas at a strong coupling, SBR is prone to occur. Compared with unweighted networks, the span of coupling strength for SBR is narrower in weighted networks. In addition, the weak signal cannot be amplified so effectively in the weighted networks as in the unweighted networks, attributing to the weakening effect of the link weight on the coupling between oscillators and the heterogeneity of the whole network connectivity caused by the weight distribution.
Phase transitions in small-world systems: application to functional brain networks
NASA Astrophysics Data System (ADS)
Gadjiev, B. R.; Progulova, T. B.
2015-04-01
In the present paper the problem of symmetry breaking in the systems with a small- world property is considered. The obtained results are applied to the description of the functional brain networks. Origin of the entropy of fractal and multifractal small-world systems is discussed. Applying the maximum entropy principle the topology of these networks has been determined. The symmetry of the regular subgroup of a small-world system is described by a discrete subgroup of the Galilean group. The algorithm of determination of this group and transformation properties of the order parameter have been proposed. The integer basis of the irreducible representation is constructed and a free energy functional is introduced. It has been shown that accounting the presence of random connections leads to an integro- differential equation for the order parameter. For q-exponential distributions an equation of motion for the order parameter takes the form of a fractional differential equation. We consider the system that is described by a two-component order parameter and discuss the features of the spatial distribution of solutions.
Griffiths phase on hierarchical modular networks with small-world edges
NASA Astrophysics Data System (ADS)
Li, Shanshan
2017-03-01
The Griffiths phase has been proposed to induce a stretched critical regime that facilitates self-organizing of brain networks for optimal function. This phase stems from the intrinsic structural heterogeneity of brain networks, i.e., the hierarchical modular structure. In this work, the Griffiths phase is studied in modified hierarchical networks with small-world connections based on the 3-regular Hanoi network. Through extensive simulations, the hierarchical level-dependent inter-module wiring probabilities are identified to determine the emergence of the Griffiths phase. Numerical results and the complementary spectral analysis of the relevant networks can be helpful for a deeper understanding of the essential structural characteristics of finite-dimensional networks to support the Griffiths phase.
Scale-Free and Small-World Properties of Vaf Fractal Networks
NASA Astrophysics Data System (ADS)
Li, Hao; Huang, Jian; Le, Anbo; Wang, Qin; Xi, Lifeng
2016-06-01
In this paper, we investigate the vertical-affiliation-free (VAF) evolving networks whose node set is the basic squares in the process of generating the Sierpinski carpet and edge exists between any two nodes if and only if the corresponding basic squares intersect just on their boundary. Although the VAF networks gets rid of the hierarchial organizations produced naturally by the self-similar structures of fractals, we still prove that they are scale-free and have the small-world effect.
Routing strategy on a two-dimensional small-world network model.
Li, Ming; Liu, Feng; Ren, Feng-Yuan
2007-06-01
Based on a two-dimensional small-world network model, we propose an efficient routing strategy that enhances the network capacity while keeping the average packet travel time low. We deterministically increase the weight of the links attached to the "congestible nodes" and compute the effective distance of a path by summing up the weight of the links belong to that path. The routing cost of a node is a linear combination of the minimum effective distance from the node to the target and its queue length. The weight assignment reduces the maximum load of the network, while the incorporation of dynamic information further balances the traffic on the network. Simulation results show that the network capacity is much improved compared with the reference strategies, while the average packet travel time is relatively small.
Emergence of the small-world architecture in neural networks by activity dependent growth
NASA Astrophysics Data System (ADS)
Gafarov, F. M.
2016-11-01
In this paper, we propose a model describing the growth and development of neural networks based on the latest achievements of experimental neuroscience. The model is based on two evolutionary equations. The first equation is for the evolution of the neurons state and the second is for the growth of axon tips. By using the model, we demonstrated the neuronal growth process from disconnected neurons to fully connected three-dimensional networks. For the analysis of the network's connections structure, we used the random graphs theory methods. It is shown that the growth in neural networks results in the formation of a well-known ;small-world; network model. The analysis of the connectivity distribution shows the presence of a strictly non-Gaussian but no scale-free degree distribution for the in-degree node distribution. In terms of the graphs theory, this study developed a new model of dynamic graph.
Synchronization and stochastic resonance of the small-world neural network based on the CPG.
Lu, Qiang; Tian, Juan
2014-06-01
According to biological knowledge, the central nervous system controls the central pattern generator (CPG) to drive the locomotion. The brain is a complex system consisting of different functions and different interconnections. The topological properties of the brain display features of small-world network. The synchronization and stochastic resonance have important roles in neural information transmission and processing. In order to study the synchronization and stochastic resonance of the brain based on the CPG, we establish the model which shows the relationship between the small-world neural network (SWNN) and the CPG. We analyze the synchronization of the SWNN when the amplitude and frequency of the CPG are changed and the effects on the CPG when the SWNN's parameters are changed. And we also study the stochastic resonance on the SWNN. The main findings include: (1) When the CPG is added into the SWNN, there exists parameters space of the CPG and the SWNN, which can make the synchronization of the SWNN optimum. (2) There exists an optimal noise level at which the resonance factor Q gets its peak value. And the correlation between the pacemaker frequency and the dynamical response of the network is resonantly dependent on the noise intensity. The results could have important implications for biological processes which are about interaction between the neural network and the CPG.
Jarman, Nicholas; Trengove, Chris; Steur, Erik; Tyukin, Ivan; van Leeuwen, Cees
2014-12-01
A modular small-world topology in functional and anatomical networks of the cortex is eminently suitable as an information processing architecture. This structure was shown in model studies to arise adaptively; it emerges through rewiring of network connections according to patterns of synchrony in ongoing oscillatory neural activity. However, in order to improve the applicability of such models to the cortex, spatial characteristics of cortical connectivity need to be respected, which were previously neglected. For this purpose we consider networks endowed with a metric by embedding them into a physical space. We provide an adaptive rewiring model with a spatial distance function and a corresponding spatially local rewiring bias. The spatially constrained adaptive rewiring principle is able to steer the evolving network topology to small world status, even more consistently so than without spatial constraints. Locally biased adaptive rewiring results in a spatial layout of the connectivity structure, in which topologically segregated modules correspond to spatially segregated regions, and these regions are linked by long-range connections. The principle of locally biased adaptive rewiring, thus, may explain both the topological connectivity structure and spatial distribution of connections between neuronal units in a large-scale cortical architecture.
Lei, Hui; Cui, Yan; Fan, Jie; Zhang, Xiaocui; Zhong, Mingtian; Yi, Jinyao; Cai, Lin; Yao, Dezhong; Zhu, Xiongzhao
2017-09-01
There are limited data on neurobiological correlates of poor insight in obsessive-compulsive disorder (OCD). This study explored whether specific changes occur in small-world network (SWN) properties in the brain functional network of OCD patients with poor insight. Resting-state electroencephalograms (EEGs) were recorded for 12 medication-free OCD patients with poor insight, 50 medication-free OCD patients with good insight, and 36 healthy controls. Both of the OCD groups exhibited topological alterations in the brain functional network characterized by abnormal small-world parameters at the beta band. However, the alterations at the theta band only existed in the OCD patients with poor insight. A relatively small sample size. Subjects were naïve to medications and those with Axis I comorbidity were excluded, perhaps limiting generalizability. Disrupted functional integrity at the beta bands of the brain functional network may be related to OCD, while disrupted functional integrity at the theta band may be associated with poor insight in OCD patients, thus this study might provide novel insight into our understanding of the pathophysiology of OCD. Copyright © 2017 Elsevier B.V. All rights reserved.
Impact of mobility structure on optimization of small-world networks of mobile agents
NASA Astrophysics Data System (ADS)
Lee, Eun; Holme, Petter
2016-06-01
In ad hoc wireless networking, units are connected to each other rather than to a central, fixed, infrastructure. Constructing and maintaining such networks create several trade-off problems between robustness, communication speed, power consumption, etc., that bridges engineering, computer science and the physics of complex systems. In this work, we address the role of mobility patterns of the agents on the optimal tuning of a small-world type network construction method. By this method, the network is updated periodically and held static between the updates. We investigate the optimal updating times for different scenarios of the movement of agents (modeling, for example, the fat-tailed trip distances, and periodicities, of human travel). We find that these mobility patterns affect the power consumption in non-trivial ways and discuss how these effects can best be handled.
Integration of neuroblasts into a two-dimensional small world neuronal network
NASA Astrophysics Data System (ADS)
Schneider-Mizell, Casey; Zochowski, Michal; Sander, Leonard
2009-03-01
Neurogenesis in the adult brain has been suggested to be important for learning and functional robustness to the neuronal death. New neurons integrate themselves into existing neuronal networks by moving into a target destination, extending axonal and dendritic processes, and inducing synaptogenesis to connect to active neurons. We hypothesize that increased plasticity of the network to novel stimuli can arise from activity-dependent cell and process motility rules. In complement to a similar in vitro model, we investigate a computational model of a two-dimensional small world network of integrate and fire neurons. After steady-state activity is reached in the extant network, we introduce new neurons which move, stop, and connect themselves through rules governed by position and firing rate.
Dynamic range in small-world networks of Hodgkin-Huxley neurons with chemical synapses
NASA Astrophysics Data System (ADS)
Batista, C. A. S.; Viana, R. L.; Lopes, S. R.; Batista, A. M.
2014-09-01
According to Stevens' law the relationship between stimulus and response is a power-law within an interval called the dynamic range. The dynamic range of sensory organs is found to be larger than that of a single neuron, suggesting that the network structure plays a key role in the behavior of both the scaling exponent and the dynamic range of neuron assemblies. In order to verify computationally the relationships between stimulus and response for spiking neurons, we investigate small-world networks of neurons described by the Hodgkin-Huxley equations connected by chemical synapses. We found that the dynamic range increases with the network size, suggesting that the enhancement of the dynamic range observed in sensory organs, with respect to single neurons, is an emergent property of complex network dynamics.
Global and local synchrony of coupled neurons in small-world networks.
Masuda, Naoki; Aihara, Kazuyuki
2004-04-01
Synchronous firing of neurons is thought to play important functional roles such as feature binding and switching of cognitive states. Although synchronization has mainly been investigated so far using model neurons with simple connection topology, real neural networks have more complex structures. Here we examine the behavior of pulse-coupled leaky integrate-and-fire neurons with various network structures. We first show that the dispersion of the number of connections for neurons influences dynamical behavior even if other major topological statistics are kept fixed. The rewiring probability parameter representing the randomness of networks bridges two spatially opposite frameworks: precise local synchrony and rough global synchrony. Finally, cooperation of the global connections and the local clustering property, which is prominent in small-world networks, forces synchrony of distant neuronal groups receiving coherent inputs.
Abe, Jun; Bomze, David; Cremasco, Viviana; Scandella, Elke; Stein, Jens V.; Turley, Shannon J.; Ludewig, Burkhard
2016-01-01
Fibroblastic reticular cells (FRCs) form the cellular scaffold of lymph nodes (LNs) and establish distinct microenvironmental niches to provide key molecules that drive innate and adaptive immune responses and control immune regulatory processes. Here, we have used a graph theory-based systems biology approach to determine topological properties and robustness of the LN FRC network in mice. We found that the FRC network exhibits an imprinted small-world topology that is fully regenerated within 4 wk after complete FRC ablation. Moreover, in silico perturbation analysis and in vivo validation revealed that LNs can tolerate a loss of approximately 50% of their FRCs without substantial impairment of immune cell recruitment, intranodal T cell migration, and dendritic cell-mediated activation of antiviral CD8+ T cells. Overall, our study reveals the high topological robustness of the FRC network and the critical role of the network integrity for the activation of adaptive immune responses. PMID:27415420
Effects of channel noise on firing coherence of small-world Hodgkin-Huxley neuronal networks
NASA Astrophysics Data System (ADS)
Sun, X. J.; Lei, J. Z.; Perc, M.; Lu, Q. S.; Lv, S. J.
2011-01-01
We investigate the effects of channel noise on firing coherence of Watts-Strogatz small-world networks consisting of biophysically realistic HH neurons having a fraction of blocked voltage-gated sodium and potassium ion channels embedded in their neuronal membranes. The intensity of channel noise is determined by the number of non-blocked ion channels, which depends on the fraction of working ion channels and the membrane patch size with the assumption of homogeneous ion channel density. We find that firing coherence of the neuronal network can be either enhanced or reduced depending on the source of channel noise. As shown in this paper, sodium channel noise reduces firing coherence of neuronal networks; in contrast, potassium channel noise enhances it. Furthermore, compared with potassium channel noise, sodium channel noise plays a dominant role in affecting firing coherence of the neuronal network. Moreover, we declare that the observed phenomena are independent of the rewiring probability.
Novkovic, Mario; Onder, Lucas; Cupovic, Jovana; Abe, Jun; Bomze, David; Cremasco, Viviana; Scandella, Elke; Stein, Jens V; Bocharov, Gennady; Turley, Shannon J; Ludewig, Burkhard
2016-07-01
Fibroblastic reticular cells (FRCs) form the cellular scaffold of lymph nodes (LNs) and establish distinct microenvironmental niches to provide key molecules that drive innate and adaptive immune responses and control immune regulatory processes. Here, we have used a graph theory-based systems biology approach to determine topological properties and robustness of the LN FRC network in mice. We found that the FRC network exhibits an imprinted small-world topology that is fully regenerated within 4 wk after complete FRC ablation. Moreover, in silico perturbation analysis and in vivo validation revealed that LNs can tolerate a loss of approximately 50% of their FRCs without substantial impairment of immune cell recruitment, intranodal T cell migration, and dendritic cell-mediated activation of antiviral CD8+ T cells. Overall, our study reveals the high topological robustness of the FRC network and the critical role of the network integrity for the activation of adaptive immune responses.
Effects of Different Connectivity Topologies in Small World Networks on EEG-Like Activities
NASA Astrophysics Data System (ADS)
Lin, Min; Zhang, Gui-Qing; Chen, Tian-Lun
2006-02-01
Based on our previously pulse-coupled integrate-and-fire neuron model in small world networks, we investigate the effects of different connectivity topologies on complex behavior of electroencephalographic-like signals produced by this model. We show that several times series analysis methods that are often used for analyzing complex behavior of electroencephalographic-like signals, such as reconstruction of the phase space, correlation dimension, fractal dimension, and the Hurst exponent within the rescaled range analysis (R/S). We find that the different connectivity topologies lead to different dynamical behaviors in models of integrate-and-fire neurons.
Quantification of degeneracy in Hodgkin-Huxley neurons on Newman-Watts small world network.
Man, Menghua; Zhang, Ya; Ma, Guilei; Friston, Karl; Liu, Shanghe
2016-08-07
Degeneracy is a fundamental source of biological robustness, complexity and evolvability in many biological systems. However, degeneracy is often confused with redundancy. Furthermore, the quantification of degeneracy has not been addressed for realistic neuronal networks. The objective of this paper is to characterize degeneracy in neuronal network models via quantitative mathematic measures. Firstly, we establish Hodgkin-Huxley neuronal networks with Newman-Watts small world network architectures. Secondly, in order to calculate the degeneracy, redundancy and complexity in the ensuing networks, we use information entropy to quantify the information a neuronal response carries about the stimulus - and mutual information to measure the contribution of each subset of the neuronal network. Finally, we analyze the interdependency of degeneracy, redundancy and complexity - and how these three measures depend upon network architectures. Our results suggest that degeneracy can be applied to any neuronal network as a formal measure, and degeneracy is distinct from redundancy. Qualitatively degeneracy and complexity are more highly correlated over different network architectures, in comparison to redundancy. Quantitatively, the relationship between both degeneracy and redundancy depends on network coupling strength: both degeneracy and redundancy increase with complexity for small coupling strengths; however, as coupling strength increases, redundancy decreases with complexity (in contrast to degeneracy, which is relatively invariant). These results suggest that the degeneracy is a general topologic characteristic of neuronal networks, which could be applied quantitatively in neuroscience and connectomics.
Effects of hybrid synapses on the vibrational resonance in small-world neuronal networks
NASA Astrophysics Data System (ADS)
Yu, Haitao; Wang, Jiang; Sun, Jianbing; Yu, Haifeng
2012-09-01
We investigate the effect of vibrational resonance in small-world neuronal networks with hybrid chemical and electrical synapses. It is shown that, irrespective of the probability of chemical synapses, an optimal amplitude of high-frequency component of the signal can optimize the dynamical response of neuron populations to the low-frequency component, which encodes the information. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, which determine the ability of neuronal networks to enhance the outreach of localized subthreshold low-frequency signal. In particular, chemical synaptic coupling is more efficient than the electrical coupling for the transmission of local input signal due to its selective coupling. Moreover, there exists an optimal small-world topology characterized by an optimal value of rewiring probability, warranting the largest peak value of the system response. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for signal processing in neuronal systems.
A study on small-world brain functional networks altered by postherpetic neuralgia.
Zhang, Yue; Liu, Jing; Li, Longchuan; Du, Minyi; Fang, Wenxue; Wang, Dongxin; Jiang, Xuexiang; Hu, Xiaoping; Zhang, Jue; Wang, Xiaoying; Fang, Jing
2014-05-01
Understanding the effect of postherpetic neuralgia (PHN) pain on brain activity is important for clinical strategies. This is the first study, to our knowledge, to relate PHN pain to small-world properties of brain functional networks. Functional magnetic resonance imaging (fMRI) was used to construct functional brain networks of the subjects during the resting state. Sixteen patients with PHN pain and 16 (8 males, 8 females for both groups) age-matched controls were studied. The PHN patients exhibited decreased local efficiency along with non-significant changes of global efficiency in comparison with the healthy controls. Moreover, regional nodal efficiency was found to be significantly affected by PHN pain in the areas related to sense (postcentral gyrus, inferior parietal gyrus and thalamus), memory/affective processes (parahippocampal gyrus) and emotional activities (putamen). Significant correlation (p<0.05) was also found between the nodal efficiency of putamen and pain intensity in PHN patients. Our results suggest that PHN modulates the local efficiency, and the small-world properties of brain networks may have potentials to objectively evaluate pain information in clinic.
Effects of hybrid synapses on the vibrational resonance in small-world neuronal networks.
Yu, Haitao; Wang, Jiang; Sun, Jianbing; Yu, Haifeng
2012-09-01
We investigate the effect of vibrational resonance in small-world neuronal networks with hybrid chemical and electrical synapses. It is shown that, irrespective of the probability of chemical synapses, an optimal amplitude of high-frequency component of the signal can optimize the dynamical response of neuron populations to the low-frequency component, which encodes the information. This effect of vibrational resonance of neuronal systems depends extensively on the network structure and parameters, which determine the ability of neuronal networks to enhance the outreach of localized subthreshold low-frequency signal. In particular, chemical synaptic coupling is more efficient than the electrical coupling for the transmission of local input signal due to its selective coupling. Moreover, there exists an optimal small-world topology characterized by an optimal value of rewiring probability, warranting the largest peak value of the system response. Considering that two-frequency signals are ubiquity in brain dynamics, we expect the presented results could have important implications for signal processing in neuronal systems.
Disease transmission in territorial populations: the small-world network of Serengeti lions.
Craft, Meggan E; Volz, Erik; Packer, Craig; Meyers, Lauren Ancel
2011-06-06
Territoriality in animal populations creates spatial structure that is thought to naturally buffer disease invasion. Often, however, territorial populations also include highly mobile, non-residential individuals that potentially serve as disease superspreaders. Using long-term data from the Serengeti Lion Project, we characterize the contact network structure of a territorial wildlife population and address the epidemiological impact of nomadic individuals. As expected, pride contacts are dominated by interactions with neighbouring prides and interspersed by encounters with nomads as they wander throughout the ecosystem. Yet the pride-pride network also includes occasional long-range contacts between prides, making it surprisingly small world and vulnerable to epidemics, even without nomads. While nomads increase both the local and global connectivity of the network, their epidemiological impact is marginal, particularly for diseases with short infectious periods like canine distemper virus. Thus, territoriality in Serengeti lions may be less protective and non-residents less important for disease transmission than previously considered.
Disease transmission in territorial populations: the small-world network of Serengeti lions
Craft, Meggan E.; Volz, Erik; Packer, Craig; Meyers, Lauren Ancel
2011-01-01
Territoriality in animal populations creates spatial structure that is thought to naturally buffer disease invasion. Often, however, territorial populations also include highly mobile, non-residential individuals that potentially serve as disease superspreaders. Using long-term data from the Serengeti Lion Project, we characterize the contact network structure of a territorial wildlife population and address the epidemiological impact of nomadic individuals. As expected, pride contacts are dominated by interactions with neighbouring prides and interspersed by encounters with nomads as they wander throughout the ecosystem. Yet the pride–pride network also includes occasional long-range contacts between prides, making it surprisingly small world and vulnerable to epidemics, even without nomads. While nomads increase both the local and global connectivity of the network, their epidemiological impact is marginal, particularly for diseases with short infectious periods like canine distemper virus. Thus, territoriality in Serengeti lions may be less protective and non-residents less important for disease transmission than previously considered. PMID:21030428
Relaxation dynamics of small-world degree-distributed treelike polymer networks
NASA Astrophysics Data System (ADS)
Galiceanu, Mircea; Oliveira, Edieliton S.; Dolgushev, Maxim
2016-11-01
Hyperbranched polymers are typically treelike macromolecules with a very disordered structure. Here we construct hyperbranched polymers based on the degree distribution of the small-world networks. This algorithm allows us to study a transition from monodisperse linear chains to structurally-disordered dendritic polymers by varying the parameter p (0 ≤ p ≤ 1), which measures the randomness and the degree of branching of the network. Employing the framework of generalized Gaussian structures, we determine for the obtained structures the relaxation spectra, which are exemplified on the mechanical relaxation moduli (storage and loss moduli). We monitor these physical quantities for networks of different sizes and for various values of the parameter p. In the intermediate frequency domain, we encounter macroscopically distinguishable behaviours.
Latching chains in K-nearest-neighbor and modular small-world networks.
Song, Sanming; Yao, Hongxun; Simonov, Alexander Yurievich
2015-01-01
Latching dynamics retrieve pattern sequences successively by neural adaption and pattern correlation. We have previously proposed a modular latching chain model in Song et al. (2014) to better accommodate the structured transitions in the brain. Different cortical areas have different network structures. To explore how structural parameters like rewiring probability, threshold, noise and feedback connections affect the latching dynamics, two different connection schemes, K-nearest-neighbor network and modular network both having modular structure are considered. Latching chains are measured using two proposed measures characterizing length of intra-modular latching chains and sequential inter-modular association transitions. Our main findings include: (1) With decreasing threshold coefficient and rewiring probability, both the K-nearest-neighbor network and the modular network experience quantitatively similar phase change processes. (2) The modular network exhibits selectively enhanced latching in the small-world range of connectivity. (3) The K-nearest-neighbor network is more robust to changes in rewiring probability, while the modular network is more robust to the presence of noise pattern pairs and to changes in the strength of feedback connections. According to our findings, the relationships between latching chains in K-nearest-neighbor and modular networks and different forms of cognition and information processing emerging in the brain are discussed.
NASA Astrophysics Data System (ADS)
Liu, Hongxiao; Zhang, Zhongzhi
2013-03-01
A central issue in the study of polymer physics is to understand the relation between the geometrical properties of macromolecules and various dynamics, most of which are encoded in the Laplacian spectra of a related graph describing the macrostructural structure. In this paper, we introduce a family of treelike polymer networks with a parameter, which has the same size as the Vicsek fractals modeling regular hyperbranched polymers. We study some relevant properties of the networks and show that they have an exponentially decaying degree distribution and exhibit the small-world behavior. We then study the Laplacian eigenvalues and their corresponding eigenvectors of the networks under consideration, with both quantities being determined through the recursive relations deduced from the network structure. Using the obtained recursive relations we can find all the eigenvalues and eigenvectors for the networks with any size. Finally, as some applications, we use the eigenvalues to study analytically or semi-analytically three dynamical processes occurring in the networks, including random walks, relaxation dynamics in the framework of generalized Gaussian structure, as well as the fluorescence depolarization under quasiresonant energy transfer. Moreover, we compare the results with those corresponding to Vicsek fractals, and show that the dynamics differ greatly for the two network families, which thus enables us to distinguish between them.
A unified model for Sierpinski networks with scale-free scaling and small-world effect
NASA Astrophysics Data System (ADS)
Guan, Jihong; Wu, Yuewen; Zhang, Zhongzhi; Zhou, Shuigeng; Wu, Yonghui
2009-06-01
In this paper, we propose an evolving Sierpinski gasket, based on which we establish a model of evolutionary Sierpinski networks (ESNs) that unifies deterministic Sierpinski network [Z.Z. Zhang, S.G. Zhou, T. Zou, L.C. Chen, J.H. Guan, Eur. Phys. J. B 60 (2007) 259] and random Sierpinski network [Z.Z. Zhang, S.G. Zhou, Z. Su, T. Zou, J.H. Guan, Eur. Phys. J. B 65 (2008) 141] to the same framework. We suggest an iterative algorithm generating the ESNs. On the basis of the algorithm, some relevant properties of presented networks are calculated or predicted analytically. Analytical solution shows that the networks under consideration follow a power-law degree distribution, with the distribution exponent continuously tuned in a wide range. The obtained accurate expression of clustering coefficient, together with the prediction of average path length reveals that the ESNs possess small-world effect. All our theoretical results are successfully contrasted by numerical simulations. Moreover, the evolutionary prisoner’s dilemma game is also studied on some limitations of the ESNs, i.e., deterministic Sierpinski network and random Sierpinski network.
Sparsely-synchronized brain rhythm in a small-world neural network
NASA Astrophysics Data System (ADS)
Kim, Sang-Yoon; Lim, Woochang
2013-07-01
Sparsely-synchronized cortical rhythms, associated with diverse cognitive functions, have been observed in electric recordings of brain activity. At the population level, cortical rhythms exhibit small-amplitude fast oscillations while at the cellular level, individual neurons show stochastic firings sparsely at a much lower rate than the population rate. We study the effect of network architecture on sparse synchronization in an inhibitory population of subthreshold Morris-Lecar neurons (which cannot fire spontaneously without noise). Previously, sparse synchronization was found to occur for cases of both global coupling ( i.e., regular all-to-all coupling) and random coupling. However, a real neural network is known to be non-regular and non-random. Here, we consider sparse Watts-Strogatz small-world networks which interpolate between a regular lattice and a random graph via rewiring. We start from a regular lattice with only short-range connections and then investigate the emergence of sparse synchronization by increasing the rewiring probability p for the short-range connections. For p = 0, the average synaptic path length between pairs of neurons becomes long; hence, only an unsynchronized population state exists because the global efficiency of information transfer is low. However, as p is increased, long-range connections begin to appear, and global effective communication between distant neurons may be available via shorter synaptic paths. Consequently, as p passes a threshold p th (}~ 0.044), sparsely-synchronized population rhythms emerge. However, with increasing p, longer axon wirings become expensive because of their material and energy costs. At an optimal value p* DE (}~ 0.24) of the rewiring probability, the ratio of the synchrony degree to the wiring cost is found to become maximal. In this way, an optimal sparse synchronization is found to occur at a minimal wiring cost in an economic small-world network through trade-off between synchrony and
Effects of distance-dependent delay on small-world neuronal networks.
Zhu, Jinjie; Chen, Zhen; Liu, Xianbin
2016-04-01
We study firing behaviors and the transitions among them in small-world noisy neuronal networks with electrical synapses and information transmission delay. Each neuron is modeled by a two-dimensional Rulkov map neuron. The distance between neurons, which is a main source of the time delay, is taken into consideration. Through spatiotemporal patterns and interspike intervals as well as the interburst intervals, the collective behaviors are revealed. It is found that the networks switch from resting state into intermittent firing state under Gaussian noise excitation. Initially, noise-induced firing behaviors are disturbed by small time delays. Periodic firing behaviors with irregular zigzag patterns emerge with an increase of the delay and become progressively regular after a critical value is exceeded. More interestingly, in accordance with regular patterns, the spiking frequency doubles compared with the former stage for the spiking neuronal network. A growth of frequency persists for a larger delay and a transition to antiphase synchronization is observed. Furthermore, it is proved that these transitions are generic also for the bursting neuronal network and the FitzHugh-Nagumo neuronal network. We show these transitions due to the increase of time delay are robust to the noise strength, coupling strength, network size, and rewiring probability.
Tekin, Ramazan; Tagluk, Mehmet Emin
2017-03-01
Physiological rhythms play a critical role in the functional development of living beings. Many biological functions are executed with an interaction of rhythms produced by internal characteristics of scores of cells. While synchronized oscillations may be associated with normal brain functions, anomalies in these oscillations may cause or relate the emergence of some neurological or neuropsychological pathologies. This study was designed to investigate the effects of topological structure and synaptic conductivity noise on the spatial synchronization and temporal rhythmicity of the waves generated by cells in the network. Because of holding the ability of clustering and randomizing with change of parameters, small-world (SW) network topology was chosen. The oscillatory activity of network was tried out by manipulating an insulated SW, cortical network model whose morphology is very close to real world. According to the obtained results, it was observed that at the optimal probabilistic rates of conductivity noise and rewiring of SW, powerful synchronized oscillatory small waves are generated in relation to the internal dynamics of cells, which are in line with the network's input. These two parameters were observed to be quite effective on the excitation-inhibition balance of the network. Accordingly, it may be suggested that the topological dynamics of SW and noisy synaptic conductivity may be associated with the normal and abnormal development of neurobiological structure.
Dong, Zhekang; Duan, Shukai; Hu, Xiaofang; Wang, Lidan
2014-01-01
In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme. PMID:25202723
Generalized mean-field theory for Ising spins in small world networks.
Meilikhov, E Z; Farzetdinova, R M
2005-04-01
A generalization of mean-field theory for random systems is described. The results of that analytic model could be reconciled with the results of numerical calculations of the Curie temperature for a system of Ising spins in small world (SW) networks by introducing the effective interaction energy associated with long-range links which exceeds the real energy of spin interaction. Such a model describes qualitatively well the increasing Curie temperature T(C) with the growth of the long-range links fraction p in the two-dimensional SW system with fixed coordination number. On the basis of simple physical considerations, concentration dependences T(C)(p) are found for SW systems of different dimensions.
NASA Astrophysics Data System (ADS)
She, Qi; Chen, Guanrong; Chan, Rosa H. M.
2016-02-01
The amount of publicly accessible experimental data has gradually increased in recent years, which makes it possible to reconsider many longstanding questions in neuroscience. In this paper, an efficient framework is presented for reconstructing functional connectivity using experimental spike-train data. A modified generalized linear model (GLM) with L1-norm penalty was used to investigate 10 datasets. These datasets contain spike-train data collected from the entorhinal-hippocampal region in the brains of rats performing different tasks. The analysis shows that entorhinal-hippocampal network of well-trained rats demonstrated significant small-world features. It is found that the connectivity structure generated by distance-dependent models is responsible for the observed small-world features of the reconstructed networks. The models are utilized to simulate a subset of units recorded from a large biological neural network using multiple electrodes. Two metrics for quantifying the small-world-ness both suggest that the reconstructed network from the sampled nodes estimates a more prominent small-world-ness feature than that of the original unknown network when the number of recorded neurons is small. Finally, this study shows that it is feasible to adjust the estimated small-world-ness results based on the number of neurons recorded to provide a more accurate reference of the network property.
She, Qi; Chen, Guanrong; Chan, Rosa H. M.
2016-01-01
The amount of publicly accessible experimental data has gradually increased in recent years, which makes it possible to reconsider many longstanding questions in neuroscience. In this paper, an efficient framework is presented for reconstructing functional connectivity using experimental spike-train data. A modified generalized linear model (GLM) with L1-norm penalty was used to investigate 10 datasets. These datasets contain spike-train data collected from the entorhinal-hippocampal region in the brains of rats performing different tasks. The analysis shows that entorhinal-hippocampal network of well-trained rats demonstrated significant small-world features. It is found that the connectivity structure generated by distance-dependent models is responsible for the observed small-world features of the reconstructed networks. The models are utilized to simulate a subset of units recorded from a large biological neural network using multiple electrodes. Two metrics for quantifying the small-world-ness both suggest that the reconstructed network from the sampled nodes estimates a more prominent small-world-ness feature than that of the original unknown network when the number of recorded neurons is small. Finally, this study shows that it is feasible to adjust the estimated small-world-ness results based on the number of neurons recorded to provide a more accurate reference of the network property. PMID:26902707
Small-world network model of propagation of the AIDS epidemic
NASA Astrophysics Data System (ADS)
Shi, Pengliang; Small, Michael
2004-03-01
Sexual contact and intravenus drug-use are the most common modes of transmission of HIV-AIDS. In this paper, homogenerous and heterogeneous models are proposed to model the dynamics in a system contains Small-World clusters. Four high risk groups: intravenus drug-users (D); homosexuals (H); individuals with multiple-sexual partners (M) and prostitutes (P), are classified using two models. Both networks are embedded among a background (low-risk) population using rich-get-richer preferential attachment. When a network is established, an epidemic is simulated in it by seeding randomly. We compare the two epidemic networks in detail and consider the effect of different levels of control policies in both. This study highlights two main conclusions: (i) set high protection coefficient for a massive-linkage-vertex (i.e. protect the highly connected individuals); and, (ii) a quick removal for the infected massive-linkage-veterx from the network is essential (rapidly quarantine infected individuals). While these conclusions may be intuitive, they indicate a necessary change of public policy toward prostitution in some developing countries such as China and India. An active effort to prevent possible infection from super-spreader is recommended.
Yu, Yongqiang; Zhou, Xia; Wang, Haibao; Hu, Xiaopeng; Zhu, Xiaoqun; Xu, Liyan; Zhang, Chao; Sun, Zhongwu
2015-01-01
To investigate the topological properties of the functional connectivity and their relationships with cognition impairment in subcortical vascular cognitive impairment (SVCI) patients, resting-state fMRI and graph theory approaches were employed in 23 SVCI patients and 20 healthy controls. Functional connectivity between 90 brain regions was estimated using bivariate correlation analysis and thresholded to construct a set of undirected graphs. Moreover, all of them were subjected to a battery of cognitive assessment, and the correlations between graph metrics and cognitive performance were further analyzed. Our results are as follows: functional brain networks of both SVCI patients and controls showed small-world attributes over a range of thresholds(0.15≤sparsity≤0.40). However, global topological organization of the functional brain networks in SVCI was significantly disrupted, as indicated by reduced global and local efficiency, clustering coefficients and increased characteristic path lengths relative to normal subjects. The decreased activity areas in SVCI predominantly targeted in the frontal-temporal lobes, while subcortical regions showed increased topological properties, which are suspected to compensate for the inefficiency of the functional network. We also demonstrated that altered brain network properties in SVCI are closely correlated with general cognitive and praxis dysfunction. The disruption of whole-brain topological organization of the functional connectome provides insight into the functional changes in the human brain in SVCI. PMID:26132397
Emergence of small-world anatomical networks in self-organizing clustered neuronal cultures.
de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A; Anava, Sarit; Ayali, Amir; Papo, David; Boccaletti, Stefano
2014-01-01
In vitro primary cultures of dissociated invertebrate neurons from locust ganglia are used to experimentally investigate the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. At all the different stages of the culture's development, identification of neurons' and neurites' location by means of a dedicated software allows to ultimately extract an adjacency matrix from each image of the culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main network's characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graph's micro- and meso-scale properties emerge. Finally, we identify the main physical processes ruling the culture's morphological transformations, and embed them into a simplified growth model qualitatively reproducing the overall set of experimental observations.
Emergence of Small-World Anatomical Networks in Self-Organizing Clustered Neuronal Cultures
de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A.; Anava, Sarit; Ayali, Amir; Papo, David; Boccaletti, Stefano
2014-01-01
In vitro primary cultures of dissociated invertebrate neurons from locust ganglia are used to experimentally investigate the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. At all the different stages of the culture's development, identification of neurons' and neurites' location by means of a dedicated software allows to ultimately extract an adjacency matrix from each image of the culture. In turn, a systematic statistical analysis of a group of topological observables grants us the possibility of quantifying and tracking the progression of the main network's characteristics during the self-organization process of the culture. Our results point to the existence of a particular state corresponding to a small-world network configuration, in which several relevant graph's micro- and meso-scale properties emerge. Finally, we identify the main physical processes ruling the culture's morphological transformations, and embed them into a simplified growth model qualitatively reproducing the overall set of experimental observations. PMID:24489675
Transmission of severe acute respiratory syndrome in dynamical small-world networks
NASA Astrophysics Data System (ADS)
Masuda, Naoki; Konno, Norio; Aihara, Kazuyuki
2004-03-01
The outbreak of severe acute respiratory syndrome (SARS) is still threatening the world because of a possible resurgence. In the current situation that effective medical treatments such as antiviral drugs are not discovered yet, dynamical features of the epidemics should be clarified for establishing strategies for tracing, quarantine, isolation, and regulating social behavior of the public at appropriate costs. Here we propose a network model for SARS epidemics and discuss why superspreaders emerged and why SARS spread especially in hospitals, which were key factors of the recent outbreak. We suggest that superspreaders are biologically contagious patients, and they may amplify the spreads by going to potentially contagious places such as hospitals. To avoid mass transmission in hospitals, it may be a good measure to treat suspected cases without hospitalizing them. Finally, we indicate that SARS probably propagates in small-world networks associated with human contacts and that the biological nature of individuals and social group properties are factors more important than the heterogeneous rates of social contacts among individuals. This is in marked contrast with epidemics of sexually transmitted diseases or computer viruses to which scale-free network models often apply.
Networks of neuroblastoma cells on porous silicon substrates reveal a small world topology.
Marinaro, Giovanni; La Rocca, Rosanna; Toma, Andrea; Barberio, Marianna; Cancedda, Laura; Di Fabrizio, Enzo; Decuzzi, Paolo; Gentile, Francesco
2015-02-01
The human brain is a tightly interweaving network of neural cells where the complexity of the network is given by the large number of its constituents and its architecture. The topological structure of neurons in the brain translates into its increased computational capabilities, low energy consumption, and nondeterministic functions, which differentiate human behavior from artificial computational schemes. In this manuscript, we fabricated porous silicon chips with a small pore size ranging from 8 to 75 nm and large fractal dimensions up to Df ∼ 2.8. In culturing neuroblastoma N2A cells on the described substrates, we found that those cells adhere more firmly to and proliferate on the porous surfaces compared to the conventional nominally flat silicon substrates, which were used as controls. More importantly, we observed that N2A cells on the porous substrates create highly clustered, small world topology patterns. We conjecture that neurons with a similar architecture may elaborate information more efficiently than in random or regular grids. Moreover, we hypothesize that systems of neurons on nano-scale geometry evolve in time to form networks in which the propagation of information is maximized.
Abnormal small-world architecture of top–down control networks in obsessive–compulsive disorder
Zhang, Tijiang; Wang, Jinhui; Yang, Yanchun; Wu, Qizhu; Li, Bin; Chen, Long; Yue, Qiang; Tang, Hehan; Yan, Chaogan; Lui, Su; Huang, Xiaoqi; Chan, Raymond C.K.; Zang, Yufeng; He, Yong; Gong, Qiyong
2011-01-01
Background Obsessive–compulsive disorder (OCD) is a common neuropsychiatric disorder that is characterized by recurrent intrusive thoughts, ideas or images and repetitive ritualistic behaviours. Although focal structural and functional abnormalities in specific brain regions have been widely studied in populations with OCD, changes in the functional relations among them remain poorly understood. This study examined OCD–related alterations in functional connectivity patterns in the brain’s top–down control network. Methods We applied resting-state functional magnetic resonance imaging to investigate the correlation patterns of intrinsic or spontaneous blood oxygen level–dependent signal fluctuations in 18 patients with OCD and 16 healthy controls. The brain control networks were first constructed by thresholding temporal correlation matrices of 39 brain regions associated with top–down control and then analyzed using graph theory-based approaches. Results Compared with healthy controls, the patients with OCD showed decreased functional connectivity in the posterior temporal regions and increased connectivity in various control regions such as the cingulate, precuneus, thalamus and cerebellum. Furthermore, the brain’s control networks in the healthy controls showed small-world architecture (high clustering coefficients and short path lengths), suggesting an optimal balance between modularized and distributed information processing. In contrast, the patients with OCD showed significantly higher local clustering, implying abnormal functional organization in the control network. Further analysis revealed that the changes in network properties occurred in regions of increased functional connectivity strength in patients with OCD. Limitations The patient group in the present study was heterogeneous in terms of symptom clusters, and most of the patients with OCD were medicated. Conclusion Our preliminary results suggest that the organizational patterns of
Abnormal small-world architecture of top-down control networks in obsessive-compulsive disorder.
Zhang, Tijiang; Wang, Jinhui; Yang, Yanchun; Wu, Qizhu; Li, Bin; Chen, Long; Yue, Qiang; Tang, Hehan; Yan, Chaogan; Lui, Su; Huang, Xiaoqi; Chan, Raymond C K; Zang, Yufeng; He, Yong; Gong, Qiyong
2011-01-01
Obsessive-compulsive disorder (OCD) is a common neuropsychiatric disorder that is characterized by recurrent intrusive thoughts, ideas or images and repetitive ritualistic behaviours. Although focal structural and functional abnormalities in specific brain regions have been widely studied in populations with OCD, changes in the functional relations among them remain poorly understood. This study examined OCD-related alterations in functional connectivity patterns in the brain's top-down control network. We applied resting-state functional magnetic resonance imaging to investigate the correlation patterns of intrinsic or spontaneous blood oxygen level-dependent signal fluctuations in 18 patients with OCD and 16 healthy controls. The brain control networks were first constructed by thresholding temporal correlation matrices of 39 brain regions associated with top-down control and then analyzed using graph theory-based approaches. Compared with healthy controls, the patients with OCD showed decreased functional connectivity in the posterior temporal regions and increased connectivity in various control regions such as the cingulate, precuneus, thalamus and cerebellum. Furthermore, the brain's control networks in the healthy controls showed small-world architecture (high clustering coefficients and short path lengths), suggesting an optimal balance between modularized and distributed information processing. In contrast, the patients with OCD showed significantly higher local clustering, implying abnormal functional organization in the control network. Further analysis revealed that the changes in network properties occurred in regions of increased functional connectivity strength in patients with OCD. The patient group in the present study was heterogeneous in terms of symptom clusters, and most of the patients with OCD were medicated. Our preliminary results suggest that the organizational patterns of intrinsic brain activity in the control networks are altered in
Zhang, Yue; Jiang, Yin; Glielmi, Christopher B; Li, Longchuan; Hu, Xiaoping; Wang, Xiaoying; Han, Jisheng; Zhang, Jue; Cui, Cailian; Fang, Jing
2013-09-01
Acupuncture, which is recognized as an alternative and complementary treatment in Western medicine, has long shown efficiencies in chronic pain relief, drug addiction treatment, stroke rehabilitation and other clinical practices. The neural mechanism underlying acupuncture, however, is still unclear. Many studies have focused on the sustained effects of acupuncture on healthy subjects, yet there are very few on the topological organization of functional networks in the whole brain in response to long-duration acupuncture (longer than 20 min). This paper presents a novel study on the effects of long-duration transcutaneous electric acupoint stimulation (TEAS) on the small-world properties of brain functional networks. Functional magnetic resonance imaging was used to construct brain functional networks of 18 healthy subjects (9 males and 9 females) during the resting state. All subjects received both TEAS and minimal TEAS (MTEAS) and were scanned before and after each stimulation. An altered functional network was found with lower local efficiency and no significant change in global efficiency for healthy subjects after TEAS, while no significant difference was observed after MTEAS. The experiments also showed that the nodal efficiencies in several paralimbic/limbic regions were altered by TEAS, and those in middle frontal gyrus and other regions by MTEAS. To remove the psychological effects and the baseline, we compared the difference between diffTEAS (difference between after and before TEAS) and diffMTEAS (difference between after and before MTEAS). The results showed that the local efficiency was decreased and that the nodal efficiencies in frontal gyrus, orbitofrontal cortex, anterior cingulate gyrus and hippocampus gyrus were changed. Based on those observations, we conclude that long-duration TEAS may modulate the short-range connections of brain functional networks and also the limbic system. Copyright © 2013 Elsevier Inc. All rights reserved.
Isles within islets: The lattice origin of small-world networks in pancreatic tissues
NASA Astrophysics Data System (ADS)
Barua, Amlan K.; Goel, Pranay
2016-02-01
The traditional computational model of the pancreatic islets of Langerhans is a lattice of β-cells connected with gap junctions. Numerous studies have investigated the behavior of networks of coupled β-cells and have shown that gap junctions synchronize bursting strongly. This simplistic architecture of islets, however, seems increasingly untenable at the face of recent experimental advances. In a microfluidics experiment on isolated islets, Rocheleau et al. (2004) showed a failure of penetration of excitation when one end received high glucose and other end was not excited sufficiently; this suggested that gap junctions may not be efficient at inducing synchrony throughout the islet. Recently, Stozer et al. (2013) have argued that the functional networks of β-cells in an islet are small world. Their results implicate the existence of a few long-range connections among cells in the network. The physiological reason underlying this claim is not well understood. These studies cast doubt on the original lattice model that largely predict an all-or-none synchrony among the cells. Here we have attempted to reconcile these observations in a unified framework. We assume that cells in the islet are coupled randomly to their nearest neighbors with some probability, p. We simulated detailed β-cell bursting in such islets. By varying p systematically we were led to network parameters similar to those obtained by Stozer et al. (2013). We find that the networks within islets break up into components giving rise to smaller isles within the super structure-isles-within-islets, as it were. This structure can also account for the partial excitation seen by Rocheleau et al. (2004). Our updated view of islet architecture thus explains the paradox how islets can have strongly synchronizing gap junctions, and be weakly coordinated at the same time.
The dynamic consequences of cooperation and competition in small-world networks.
Fernández-Rosales, Iván Y; Liebovitch, Larry S; Guzmán-Vargas, Lev
2015-01-01
We present a study of the social dynamics among cooperative and competitive actors interacting on a complex network that has a small-world topology. In this model, the state of each actor depends on its previous state in time, its inertia to change, and the influence of its neighboring actors. Using numerical simulations, we determine how the distribution of final states of the actors and measures of the distances between the values of the actors at local and global levels, depend on the number of cooperative to competitive actors and the connectivity of the actors in the network. We find that similar numbers of cooperative and competitive actors yield the lowest values for the local and global measures of the distances between the values of the actors. On the other hand, when the number of either cooperative or competitive actors dominate the system, then the divergence is largest between the values of the actors. Our findings make new testable predictions on how the dynamics of a conflict depends on the strategies chosen by groups of actors and also have implications for the evolution of behaviors.
The Dynamic Consequences of Cooperation and Competition in Small-World Networks
Fernández-Rosales, Iván Y.; Liebovitch, Larry S.; Guzmán-Vargas, Lev
2015-01-01
We present a study of the social dynamics among cooperative and competitive actors interacting on a complex network that has a small-world topology. In this model, the state of each actor depends on its previous state in time, its inertia to change, and the influence of its neighboring actors. Using numerical simulations, we determine how the distribution of final states of the actors and measures of the distances between the values of the actors at local and global levels, depend on the number of cooperative to competitive actors and the connectivity of the actors in the network. We find that similar numbers of cooperative and competitive actors yield the lowest values for the local and global measures of the distances between the values of the actors. On the other hand, when the number of either cooperative or competitive actors dominate the system, then the divergence is largest between the values of the actors. Our findings make new testable predictions on how the dynamics of a conflict depends on the strategies chosen by groups of actors and also have implications for the evolution of behaviors. PMID:25927995
NASA Astrophysics Data System (ADS)
Yilmaz, Ergin; Baysal, Veli; Ozer, Mahmut; Perc, Matjaž
2016-02-01
We study the effects of an autapse, which is mathematically described as a self-feedback loop, on the propagation of weak, localized pacemaker activity across a Newman-Watts small-world network consisting of stochastic Hodgkin-Huxley neurons. We consider that only the pacemaker neuron, which is stimulated by a subthreshold periodic signal, has an electrical autapse that is characterized by a coupling strength and a delay time. We focus on the impact of the coupling strength, the network structure, the properties of the weak periodic stimulus, and the properties of the autapse on the transmission of localized pacemaker activity. Obtained results indicate the existence of optimal channel noise intensity for the propagation of the localized rhythm. Under optimal conditions, the autapse can significantly improve the propagation of pacemaker activity, but only for a specific range of the autaptic coupling strength. Moreover, the autaptic delay time has to be equal to the intrinsic oscillation period of the Hodgkin-Huxley neuron or its integer multiples. We analyze the inter-spike interval histogram and show that the autapse enhances or suppresses the propagation of the localized rhythm by increasing or decreasing the phase locking between the spiking of the pacemaker neuron and the weak periodic signal. In particular, when the autaptic delay time is equal to the intrinsic period of oscillations an optimal phase locking takes place, resulting in a dominant time scale of the spiking activity. We also investigate the effects of the network structure and the coupling strength on the propagation of pacemaker activity. We find that there exist an optimal coupling strength and an optimal network structure that together warrant an optimal propagation of the localized rhythm.
NASA Astrophysics Data System (ADS)
Tachimori, Yutaka; Iwanaga, Hiroaki; Tahara, Takashi
2013-12-01
Here, we constructed and analyzed a network (henceforth, “medical knowledge network”) derived from a commonly used medical text. We show that this medical knowledge network has small-world, scale-free, and hierarchical features. We then constructed a network from data from a hospital information system that reflected actual clinical practice and found that this network also had small-world, scale-free, and hierarchical features. Moreover, we found that both the diagnosis frequency distribution of the hospital network and the diagnosis degree distribution of the medical knowledge network obeyed a similar power law. These findings suggest that the structure of clinical practice may emerge from the mutual influence of medical knowledge and clinical practice, and that the analysis of a medical knowledge network may facilitate the investigation of the characteristics of medical practice.
Anatomic Insights into Disrupted Small-World Networks in Pediatric Posttraumatic Stress Disorder.
Suo, Xueling; Lei, Du; Chen, Fuqin; Wu, Min; Li, Lei; Sun, Ling; Wei, Xiaoli; Zhu, Hongyan; Li, Lingjiang; Kemp, Graham J; Gong, Qiyong
2017-03-01
Purpose To use diffusion-tensor (DT) imaging and graph theory approaches to explore the brain structural connectome in pediatric posttraumatic stress disorder (PTSD). Materials and Methods This study was approved by the relevant research ethics committee, and all participants' parents or guardians provided informed consent. Twenty-four pediatric patients with PTSD and 23 control subjects exposed to trauma but without PTSD were recruited after the 2008 Sichuan earthquake. The structural connectome was constructed by using DT imaging tractography and thresholding the mean fractional anisotropy of 90 brain regions to yield 90 × 90 partial correlation matrixes. Graph theory analysis was used to examine the group-specific topologic properties, and nonparametric permutation tests were used for group comparisons of topologic metrics. Results Both groups exhibited small-world topology. However, patients with PTSD showed an increase in the characteristic path length (P = .0248) and decreases in local efficiency (P = .0498) and global efficiency (P = .0274). Furthermore, patients with PTSD showed reduced nodal centralities, mainly in the default mode, salience, central executive, and visual regions (P < .05, corrected for false-discovery rate). The Clinician-Administered PTSD Scale score was negatively correlated with the nodal efficiency of the left superior parietal gyrus (r = -0.446, P = .043). Conclusion The structural connectome showed a shift toward "regularization," providing a structural basis for functional alterations of pediatric PTSD. These abnormalities suggest that PTSD can be understood by examining the dysfunction of large-scale spatially distributed neural networks. (©) RSNA, 2016.
NASA Astrophysics Data System (ADS)
Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile; Liu, Chen
2013-05-01
The effects of time delay and rewiring probability on stochastic resonance and spatiotemporal order in small-world neuronal networks are studied in this paper. Numerical results show that, irrespective of the pacemaker introduced to one single neuron or all neurons of the network, the phenomenon of stochastic resonance occurs. The time delay in the coupling process can either enhance or destroy stochastic resonance on small-world neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances, which appear intermittently at integer multiples of the oscillation period of the pacemaker. More importantly, it is found that the small-world topology can significantly affect the stochastic resonance on excitable neuronal networks. For small time delays, increasing the rewiring probability can largely enhance the efficiency of pacemaker-driven stochastic resonance. We argue that the time delay and the rewiring probability both play a key role in determining the ability of the small-world neuronal network to improve the noise-induced outreach of the localized subthreshold pacemaker.
Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile; Liu, Chen
2013-05-01
The effects of time delay and rewiring probability on stochastic resonance and spatiotemporal order in small-world neuronal networks are studied in this paper. Numerical results show that, irrespective of the pacemaker introduced to one single neuron or all neurons of the network, the phenomenon of stochastic resonance occurs. The time delay in the coupling process can either enhance or destroy stochastic resonance on small-world neuronal networks. In particular, appropriately tuned delays can induce multiple stochastic resonances, which appear intermittently at integer multiples of the oscillation period of the pacemaker. More importantly, it is found that the small-world topology can significantly affect the stochastic resonance on excitable neuronal networks. For small time delays, increasing the rewiring probability can largely enhance the efficiency of pacemaker-driven stochastic resonance. We argue that the time delay and the rewiring probability both play a key role in determining the ability of the small-world neuronal network to improve the noise-induced outreach of the localized subthreshold pacemaker.
Zhang, Zhongzhi; Lin, Yuan; Guo, Xiaoye
2015-06-01
The eigenvalues of the transition matrix for random walks on a network play a significant role in the structural and dynamical aspects of the network. Nevertheless, it is still not well understood how the eigenvalues behave in small-world and scale-free networks, which describe a large variety of real systems. In this paper, we study the eigenvalues for the transition matrix of a network that is simultaneously scale-free, small-world, and clustered. We derive explicit simple expressions for all eigenvalues and their multiplicities, with the spectral density exhibiting a power-law form. We then apply the obtained eigenvalues to determine the mixing time and random target access time for random walks, both of which exhibit unusual behaviors compared with those for other networks, signaling discernible effects of topologies on spectral features. Finally, we use the eigenvalues to count spanning trees in the network.
Effects of inspections in small world social networks with different contagion rules
NASA Astrophysics Data System (ADS)
Muñoz, Francisco; Nuño, Juan Carlos; Primicerio, Mario
2015-08-01
We study the way the structure of social links determines the effects of random inspections on a population formed by two types of individuals, e.g. tax-payers and tax-evaders (free riders). It is assumed that inspections occur in a larger scale than the population relaxation time and, therefore, a unique initial inspection is performed on a population that is completely formed by tax-evaders. Besides, the inspected tax-evaders become tax-payers forever. The social network is modeled as a Watts-Strogatz Small World whose topology can be tuned in terms of a parameter p ∈ [ 0 , 1 ] from regular (p = 0) to random (p = 1). Two local contagion rules are considered: (i) a continuous one that takes the proportion of neighbors to determine the next status of an individual (node) and (ii) a discontinuous (threshold rule) that assumes a minimum number of neighbors to modify the current state. In the former case, irrespective of the inspection intensity ν, the equilibrium population is always formed by tax-payers. In the mean field approach, we obtain the characteristic time of convergence as a function of ν and p. For the threshold contagion rule, we show that the response of the population to the intensity of inspections ν is a function of the structure of the social network p and the willingness of the individuals to change their state, r. It is shown that sharp transitions occur at critical values of ν that depends on p and r. We discuss these results within the context of tax evasion and fraud where the strategies of inspection could be of major relevance.
Luongo, Francisco J.; Zimmerman, Chris A.; Horn, Meryl E.
2016-01-01
Sequential patterns of prefrontal activity are believed to mediate important behaviors, e.g., working memory, but it remains unclear exactly how they are generated. In accordance with previous studies of cortical circuits, we found that prefrontal microcircuits in young adult mice spontaneously generate many more stereotyped sequences of activity than expected by chance. However, the key question of whether these sequences depend on a specific functional organization within the cortical microcircuit, or emerge simply as a by-product of random interactions between neurons, remains unanswered. We observed that correlations between prefrontal neurons do follow a specific functional organization—they have a small-world topology. However, until now it has not been possible to directly link small-world topologies to specific circuit functions, e.g., sequence generation. Therefore, we developed a novel analysis to address this issue. Specifically, we constructed surrogate data sets that have identical levels of network activity at every point in time but nevertheless represent various network topologies. We call this method shuffling activity to rearrange correlations (SHARC). We found that only surrogate data sets based on the actual small-world functional organization of prefrontal microcircuits were able to reproduce the levels of sequences observed in actual data. As expected, small-world data sets contained many more sequences than surrogate data sets with randomly arranged correlations. Surprisingly, small-world data sets also outperformed data sets in which correlations were maximally clustered. Thus the small-world functional organization of cortical microcircuits, which effectively balances the random and maximally clustered regimes, is optimal for producing stereotyped sequential patterns of activity. PMID:26888108
Luongo, Francisco J; Zimmerman, Chris A; Horn, Meryl E; Sohal, Vikaas S
2016-05-01
Sequential patterns of prefrontal activity are believed to mediate important behaviors, e.g., working memory, but it remains unclear exactly how they are generated. In accordance with previous studies of cortical circuits, we found that prefrontal microcircuits in young adult mice spontaneously generate many more stereotyped sequences of activity than expected by chance. However, the key question of whether these sequences depend on a specific functional organization within the cortical microcircuit, or emerge simply as a by-product of random interactions between neurons, remains unanswered. We observed that correlations between prefrontal neurons do follow a specific functional organization-they have a small-world topology. However, until now it has not been possible to directly link small-world topologies to specific circuit functions, e.g., sequence generation. Therefore, we developed a novel analysis to address this issue. Specifically, we constructed surrogate data sets that have identical levels of network activity at every point in time but nevertheless represent various network topologies. We call this method shuffling activity to rearrange correlations (SHARC). We found that only surrogate data sets based on the actual small-world functional organization of prefrontal microcircuits were able to reproduce the levels of sequences observed in actual data. As expected, small-world data sets contained many more sequences than surrogate data sets with randomly arranged correlations. Surprisingly, small-world data sets also outperformed data sets in which correlations were maximally clustered. Thus the small-world functional organization of cortical microcircuits, which effectively balances the random and maximally clustered regimes, is optimal for producing stereotyped sequential patterns of activity.
Shim, Miseon; Kim, Do-Won; Lee, Seung-Hwan; Im, Chang-Hwan
2014-07-01
P300 deficits in patients with schizophrenia have previously been investigated using EEGs recorded during auditory oddball tasks. However, small-world cortical functional networks during auditory oddball tasks and their relationships with symptom severity scores in schizophrenia have not yet been investigated. In this study, the small-world characteristics of source-level functional connectivity networks of EEG responses elicited by an auditory oddball paradigm were evaluated using two representative graph-theoretical measures, clustering coefficient and path length. EEG signals from 34 patients with schizophrenia and 34 healthy controls were recorded while each subject was asked to attend to oddball tones. The results showed reduced clustering coefficients and increased path lengths in patients with schizophrenia, suggesting that the small-world functional network is disrupted in patients with schizophrenia. In addition, the negative and cognitive symptom components of positive and negative symptom scales were negatively correlated with the clustering coefficient and positively correlated with path length, demonstrating that both indices are indicators of symptom severity in patients with schizophrenia. Our study results suggest that disrupted small-world characteristics are potential biomarkers for patients with schizophrenia.
Li, Meiling; Chen, Heng; Wang, Junping; Liu, Feng; Long, Zhiliang; Wang, Yifeng; Iturria-Medina, Yasser; Zhang, Jiang
2014-01-01
Abstract Previous behavioral and scanning studies have suggested that handedness is associated with differences in brain morphology as well as in anatomical and functional lateralization. However, little is known about the topological organization of the white matter (WM) structural networks related to handedness. We employed diffusion tensor imaging tractography to investigate handedness- and hemisphere-related differences in the topological organization of the human cortical anatomical network. After constructing left hemispheric/right hemispheric weighted structural networks in 32 right-handed and 24 left-handed healthy individuals, we analyzed the networks by graph theoretic analysis. We found that both the right and left hemispheric WM structural networks in the two groups possessed small-world attributes (high local clustering and short paths between nodes), findings which are consistent with recent results from whole-brain structural networks. In addition, the right hemisphere tended to be more efficient than the left hemisphere, suggesting a high efficiency of general information processing in the right hemisphere. Finally, we found that the right-handed subjects had significant asymmetries in small-world properties (normalized clustering coefficient γ, normalized path length λ, and small-worldness σ), while left-handed subjects had fewer asymmetries. Our findings from large-scale brain networks aid in understanding the structural substrates underlying handedness-related and hemisphere-related differences in cognition and behavior. PMID:24564422
Li, Meiling; Chen, Heng; Wang, Junping; Liu, Feng; Long, Zhiliang; Wang, Yifeng; Iturria-Medina, Yasser; Zhang, Jiang; Yu, Chunshui; Chen, Huafu
2014-03-01
Previous behavioral and scanning studies have suggested that handedness is associated with differences in brain morphology as well as in anatomical and functional lateralization. However, little is known about the topological organization of the white matter (WM) structural networks related to handedness. We employed diffusion tensor imaging tractography to investigate handedness- and hemisphere-related differences in the topological organization of the human cortical anatomical network. After constructing left hemispheric/right hemispheric weighted structural networks in 32 right-handed and 24 left-handed healthy individuals, we analyzed the networks by graph theoretic analysis. We found that both the right and left hemispheric WM structural networks in the two groups possessed small-world attributes (high local clustering and short paths between nodes), findings which are consistent with recent results from whole-brain structural networks. In addition, the right hemisphere tended to be more efficient than the left hemisphere, suggesting a high efficiency of general information processing in the right hemisphere. Finally, we found that the right-handed subjects had significant asymmetries in small-world properties (normalized clustering coefficient γ, normalized path length λ, and small-worldness σ), while left-handed subjects had fewer asymmetries. Our findings from large-scale brain networks aid in understanding the structural substrates underlying handedness-related and hemisphere-related differences in cognition and behavior.
Zhang, Jiang; Lin, Xiaohong; Fu, Genyu; Sai, Liyang; Chen, Huafu; Yang, Jianbo; Wang, Mingwen; Liu, Qi; Yang, Gang; Zhang, Junran; Yuan, Zhen
2016-01-01
Deception is not a rare occurrence among human behaviors; however, the present brain mapping techniques are insufficient to reveal the neural mechanism of deception under spontaneous or controlled conditions. Interestingly, functional near-infrared spectroscopy (fNIRS) has emerged as a highly promising neuroimaging technique that enables continuous and noninvasive monitoring of changes in blood oxygenation and blood volume in the human brain. In this study, fNIRS was used in combination with complex network theory to extract the attribute features of the functional brain networks underling deception in subjects exhibiting spontaneous or controlled behaviors. Our findings revealed that the small-world networks of the subjects engaged in spontaneous behaviors exhibited greater clustering coefficients, shorter average path lengths, greater average node degrees, and stronger randomness compared with those of subjects engaged in control behaviors. Consequently, we suggest that small-world network topology is capable of distinguishing well between spontaneous and controlled deceptions. PMID:27126145
NASA Astrophysics Data System (ADS)
Kim, Sang-Yoon; Lim, Woochang
2015-11-01
We consider a clustered network with small-world subnetworks of inhibitory fast spiking interneurons and investigate the effect of intermodular connection on the emergence of fast sparsely synchronized rhythms by varying both the intermodular coupling strength Jinter and the average number of intermodular links per interneuron Msyn(inter ). In contrast to the case of nonclustered networks, two kinds of sparsely synchronized states such as modular and global synchronization are found. For the case of modular sparse synchronization, the population behavior reveals the modular structure, because the intramodular dynamics of subnetworks make some mismatching. On the other hand, in the case of global sparse synchronization, the population behavior is globally identical, independently of the cluster structure, because the intramodular dynamics of subnetworks make perfect matching. We introduce a realistic cross-correlation modularity measure, representing the matching degree between the instantaneous subpopulation spike rates of the subnetworks, and examine whether the sparse synchronization is global or modular. Depending on its magnitude, the intermodular coupling strength Jinter seems to play "dual" roles for the pacing between spikes in each subnetwork. For large Jinter, due to strong inhibition it plays a destructive role to "spoil" the pacing between spikes, while for small Jinter it plays a constructive role to "favor" the pacing between spikes. Through competition between the constructive and the destructive roles of Jinter, there exists an intermediate optimal Jinter at which the pacing degree between spikes becomes maximal. In contrast, the average number of intermodular links per interneuron Msyn(inter ) seems to play a role just to favor the pacing between spikes. With increasing Msyn(inter ), the pacing degree between spikes increases monotonically thanks to the increase in the degree of effectiveness of global communication between spikes. Furthermore, we
Zhang, Jiang; Li, Yuyao; Chen, Huafu; Ding, Jurong; Yuan, Zhen
2016-01-01
In this study, small-world network analysis was performed to identify the similarities and differences between functional brain networks for right- and left-hand motor imageries (MIs). First, Pearson correlation coefficients among the nodes within the functional brain networks from healthy subjects were calculated. Then, small-world network indicators, including the clustering coefficient, the average path length, the global efficiency, the local efficiency, the average node degree, and the small-world index, were generated for the functional brain networks during both right- and left-hand MIs. We identified large differences in the small-world network indicators between the functional networks during MI and in the random networks. More importantly, the functional brain networks underlying the right- and left-hand MIs exhibited similar small-world properties in terms of the clustering coefficient, the average path length, the global efficiency, and the local efficiency. By contrast, the right- and left-hand MI brain networks showed differences in small-world characteristics, including indicators such as the average node degree and the small-world index. Interestingly, our findings also suggested that the differences in the activity intensity and range, the average node degree, and the small-world index of brain networks between the right- and left-hand MIs were associated with the asymmetry of brain functions. PMID:27811962
Zhang, Jiang; Li, Yuyao; Chen, Huafu; Ding, Jurong; Yuan, Zhen
2016-11-04
In this study, small-world network analysis was performed to identify the similarities and differences between functional brain networks for right- and left-hand motor imageries (MIs). First, Pearson correlation coefficients among the nodes within the functional brain networks from healthy subjects were calculated. Then, small-world network indicators, including the clustering coefficient, the average path length, the global efficiency, the local efficiency, the average node degree, and the small-world index, were generated for the functional brain networks during both right- and left-hand MIs. We identified large differences in the small-world network indicators between the functional networks during MI and in the random networks. More importantly, the functional brain networks underlying the right- and left-hand MIs exhibited similar small-world properties in terms of the clustering coefficient, the average path length, the global efficiency, and the local efficiency. By contrast, the right- and left-hand MI brain networks showed differences in small-world characteristics, including indicators such as the average node degree and the small-world index. Interestingly, our findings also suggested that the differences in the activity intensity and range, the average node degree, and the small-world index of brain networks between the right- and left-hand MIs were associated with the asymmetry of brain functions.
Zhang, Jianbao; Ma, Zhongjun; Chen, Guanrong
2014-06-15
All edges in the classical Watts and Strogatz's small-world network model are unweighted and cooperative (positive). By introducing competitive (negative) inter-cluster edges and assigning edge weights to mimic more realistic networks, this paper develops a modified model which possesses co-competitive weighted couplings and cluster structures while maintaining the common small-world network properties of small average shortest path lengths and large clustering coefficients. Based on theoretical analysis, it is proved that the new model with inter-cluster co-competition balance has an important dynamical property of robust cluster synchronous pattern formation. More precisely, clusters will neither merge nor split regardless of adding or deleting nodes and edges, under the condition of inter-cluster co-competition balance. Numerical simulations demonstrate the robustness of the model against the increase of the coupling strength and several topological variations.
Yu, Haitao; Guo, Xinmeng; Wang, Jiang Deng, Bin; Wei, Xile
2014-09-01
The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.
Yu, Haitao; Guo, Xinmeng; Wang, Jiang; Deng, Bin; Wei, Xile
2014-09-01
The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.
Fekete, Tomer; Beacher, Felix D C C; Cha, Jiook; Rubin, Denis; Mujica-Parodi, Lilianne R
2014-01-15
Near infrared spectroscopy (NIRS) is an emerging imaging technique that is relatively inexpensive, portable, and particularly well suited for collecting data in ecological settings. Therefore, it holds promise as a potential neurodiagnostic for young children. We set out to explore whether NIRS could be utilized in assessing the risk of developmental psychopathology in young children. A growing body of work indicates that temperament at young age is associated with vulnerability to psychopathology later on in life. In particular, it has been shown that low effortful control (EC), which includes the focusing and shifting of attention, inhibitory control, perceptual sensitivity, and a low threshold for pleasure, is linked to conditions such as anxiety, depression and attention deficit hyperactivity disorder (ADHD). Physiologically, EC has been linked to a control network spanning among other sites the prefrontal cortex. Several psychopathologies, such as depression and ADHD, have been shown to result in compromised small-world network properties. Therefore we set out to explore the relationship between EC and the small-world properties of PFC using NIRS. NIRS data were collected from 44 toddlers, ages 3-5, while watching naturalistic stimuli (movie clips). Derived complex network measures were then correlated to EC as derived from the Children's Behavior Questionnaire (CBQ). We found that reduced levels of EC were associated with compromised small-world properties of the prefrontal network. Our results suggest that the longitudinal NIRS studies of complex network properties in young children hold promise in furthering our understanding of developmental psychopathology. © 2013.
Stanton, Neville A; Walker, Guy H; Sorensen, Linda J
2012-01-01
This article presents the rationale behind an important enhancement to a socio-technical model of organisations and teams derived from military research. It combines this with empirical results which take advantage of these enhancements. In Part 1, a new theoretical legacy for the model is developed based on Ergonomics theories and insights. This allows team communications data to be plotted into the model and for it to demonstrate discriminate validity between alternative team structures. Part 2 presents multinational data from the Experimental Laboratory for Investigating Collaboration, Information-sharing, and Trust (ELICIT) community. It was surprising to see that teams in both traditional hierarchical command and control and networked 'peer-to-peer' organisations operate in broadly the same area of the model, a region occupied by networks of communication exhibiting 'small world' properties. Small world networks may be of considerable importance for the Ergonomics analysis of team organisation and performance. This article is themed around macro and systems Ergonomics, and examines the effects of command and control structures. Despite some differences in behaviour and measures of agility, when given the freedom to do so, participants organised themselves into a small world network. This network type has important and interesting implications for the Ergonomics design of teams and organisations.
NASA Astrophysics Data System (ADS)
Chen, Yong; Qin, Shao-Meng; Yu, Lianchun; Zhang, Shengli
2008-03-01
We studied synchronization between prisoner’s dilemma games with voluntary participation in two Newman-Watts small-world networks. It was found that there are three kinds of synchronization: partial phase synchronization, total phase synchronization, and complete synchronization, for varied coupling factors. Besides, two games can reach complete synchronization for the large enough coupling factor. We also discussed the effect of the coupling factor on the amplitude of oscillation of cooperator density.
Small-world bias of correlation networks: From brain to climate
NASA Astrophysics Data System (ADS)
Hlinka, Jaroslav; Hartman, David; Jajcay, Nikola; Tomeček, David; Tintěra, Jaroslav; Paluš, Milan
2017-03-01
Complex systems are commonly characterized by the properties of their graph representation. Dynamical complex systems are then typically represented by a graph of temporal dependencies between time series of state variables of their subunits. It has been shown recently that graphs constructed in this way tend to have relatively clustered structure, potentially leading to spurious detection of small-world properties even in the case of systems with no or randomly distributed true interactions. However, the strength of this bias depends heavily on a range of parameters and its relevance for real-world data has not yet been established. In this work, we assess the relevance of the bias using two examples of multivariate time series recorded in natural complex systems. The first is the time series of local brain activity as measured by functional magnetic resonance imaging in resting healthy human subjects, and the second is the time series of average monthly surface air temperature coming from a large reanalysis of climatological data over the period 1948-2012. In both cases, the clustering in the thresholded correlation graph is substantially higher compared with a realization of a density-matched random graph, while the shortest paths are relatively short, showing thus distinguishing features of small-world structure. However, comparable or even stronger small-world properties were reproduced in correlation graphs of model processes with randomly scrambled interconnections. This suggests that the small-world properties of the correlation matrices of these real-world systems indeed do not reflect genuinely the properties of the underlying interaction structure, but rather result from the inherent properties of correlation matrix.
Yu, Haitao; Wang, Jiang; Du, Jiwei; Deng, Bin; Wei, Xile
2015-02-01
Effects of time delay on the local and global synchronization in small-world neuronal networks with chemical synapses are investigated in this paper. Numerical results show that, for both excitatory and inhibitory coupling types, the information transmission delay can always induce synchronization transitions of spiking neurons in small-world networks. In particular, regions of in-phase and out-of-phase synchronization of connected neurons emerge intermittently as the synaptic delay increases. For excitatory coupling, all transitions to spiking synchronization occur approximately at integer multiples of the firing period of individual neurons; while for inhibitory coupling, these transitions appear at the odd multiples of the half of the firing period of neurons. More importantly, the local synchronization transition is more profound than the global synchronization transition, depending on the type of coupling synapse. For excitatory synapses, the local in-phase synchronization observed for some values of the delay also occur at a global scale; while for inhibitory ones, this synchronization, observed at the local scale, disappears at a global scale. Furthermore, the small-world structure can also affect the phase synchronization of neuronal networks. It is demonstrated that increasing the rewiring probability can always improve the global synchronization of neuronal activity, but has little effect on the local synchronization of neighboring neurons.
Age-Related Differences in the Modulation of Small-World Brain Networks during a Go/NoGo Task
Hong, Xiangfei; Liu, Yuelu; Sun, Junfeng; Tong, Shanbao
2016-01-01
Although inter-regional phase synchrony of neural oscillations has been proposed as a plausible mechanism for response control, little is known about the possible effects due to normal aging. We recorded multi-channel electroencephalography (EEG) from healthy younger and older adults in a Go/NoGo task, and examined the aging effects on synchronous brain networks with graph theoretical analysis. We found that in both age groups, brain networks in theta, alpha or beta band for either response execution (Go) or response inhibition (NoGo) condition showed prominent small-world property. Furthermore, small-world property of brain networks showed significant differences between different task conditions. Further analyses of node degree suggested that frontal-central theta band phase synchrony was enhanced during response inhibition, whereas during response execution, increased phase synchrony was observed in beta band over central-parietal regions. More interestingly, these task-related modulations on brain networks were well preserved and even more robust in older adults compared with younger adults. Taken together, our findings not only suggest that response control involves synchronous brain networks in functionally-distinct frequency bands, but also indicate an increase in the recruitment of brain network resources due to normal aging. PMID:27242512
Impact of Partial Time Delay on Temporal Dynamics of Watts-Strogatz Small-World Neuronal Networks
NASA Astrophysics Data System (ADS)
Yan, Hao; Sun, Xiaojuan
2017-06-01
In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts-Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay τ and the other is the probability of partial time delay pdelay. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay τ, the probability pdelay could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay τ, temporal coherence and mean firing rate do not have great changes with respect to pdelay. Time delay τ always has great influence on both temporal coherence and mean firing rate no matter what is the value of pdelay. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay τ. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.
NASA Astrophysics Data System (ADS)
Yuan, Wu-Jie; Luo, Xiao-Shu; Jiang, Pin-Qun
2007-02-01
In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current (AC) stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.
Massobrio, Paolo; Pasquale, Valentina; Martinoia, Sergio
2015-01-01
The spontaneous activity of cortical networks is characterized by the emergence of different dynamic states. Although several attempts were accomplished to understand the origin of these dynamics, the underlying factors continue to be elusive. In this work, we specifically investigated the interplay between network topology and spontaneous dynamics within the framework of self-organized criticality (SOC). The obtained results support the hypothesis that the emergence of critical states occurs in specific complex network topologies. By combining multi-electrode recordings of spontaneous activity of in vitro cortical assemblies with theoretical models, we demonstrate that different ‘connectivity rules’ drive the network towards different dynamic states. In particular, scale-free architectures with different degree of small-worldness account better for the variability observed in experimental data, giving rise to different dynamic states. Moreover, in relationship with the balance between excitation and inhibition and percentage of inhibitory hubs, the simulated cortical networks fall in a critical regime. PMID:26030608
Massobrio, Paolo; Pasquale, Valentina; Martinoia, Sergio
2015-06-01
The spontaneous activity of cortical networks is characterized by the emergence of different dynamic states. Although several attempts were accomplished to understand the origin of these dynamics, the underlying factors continue to be elusive. In this work, we specifically investigated the interplay between network topology and spontaneous dynamics within the framework of self-organized criticality (SOC). The obtained results support the hypothesis that the emergence of critical states occurs in specific complex network topologies. By combining multi-electrode recordings of spontaneous activity of in vitro cortical assemblies with theoretical models, we demonstrate that different 'connectivity rules' drive the network towards different dynamic states. In particular, scale-free architectures with different degree of small-worldness account better for the variability observed in experimental data, giving rise to different dynamic states. Moreover, in relationship with the balance between excitation and inhibition and percentage of inhibitory hubs, the simulated cortical networks fall in a critical regime.
Varoquaux, G; Gramfort, A; Poline, J B; Thirion, B
2012-01-01
Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems. Copyright © 2012 Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Liu, Yan; Liu, Li-Guang; Wang, Hang
2012-06-01
The small-world network model represented by a set of evolution equations with time delay is used to investigate the nonlinear dynamics of networks, and the nature of instability phenomena in traffic, namely, congestion and bursting in the networks, are studied and explained from bifurcation analysis. Then, the governing equation in the vector field is further reduced into a map, and the ensuing period-doubling bifurcation, sequence of period-doubling bifurcation and period-3 are studied intuitively. The existence of chaos is verified numerically. In particular, the influences of time delay on the nonlinear dynamics are presented. The results show that there are a rich variety of nonlinear dynamics related to the intermittency of the traffic flows in the system, and the results can gain a fundamental understanding of the instability in the networks, and the time delay can be used as a key parameter in the control of the systems.
Liu, Tian; Chen, Yanni; Lin, Pan; Wang, Jue
2015-07-01
We investigated the topologic properties of human brain attention-related functional networks associated with Multi-Source Interference Task (MSIT) performance using electroencephalography (EEG). Data were obtained from 13 children diagnosed with attention-deficit/hyperactivity disorder (ADHD) and 13 normal control children. Functional connectivity between all pairwise combinations of EEG channels was established by calculating synchronization likelihood (SL). The cluster coefficients and path lengths were computed as a function of degree K. The results showed that brain attention functional networks of normal control subjects had efficient small-world topologic properties, whereas these topologic properties were altered in ADHD. In particular, increased local characteristics combined with decreased global characteristics in ADHD led to a disorder-related shift of the network topologic structure toward ordered networks. These findings are consistent with a hypothesis of dysfunctional segregation and integration of the brain in ADHD, and enhance our understanding of the underlying pathophysiologic mechanism of this illness.
Lubeiro, Alba; Gomez-Pilar, Javier; Martín, Oscar; Palomino, Aitor; Fernández, Myriam; González-Pinto, Ana; Poza, Jesús; Hornero, Roberto; Molina, Vicente
2017-02-01
Functional brain networks possess significant small-world (SW) properties. Genetic variation relevant to both inhibitory and excitatory transmission may contribute to modulate these properties. In healthy controls, genotypic variation in Neuregulin 1 (NRG1) related to the risk of psychosis (risk alleles) would contribute to functional SW modulation of the cortical network. Electroencephalographic activity during an odd-ball task was recorded in 144 healthy controls. Then, small-worldness (SWn) was calculated in five frequency bands (i.e., theta, alpha, beta1, beta2 and gamma) for baseline (from -300 to the stimulus onset) and response (150-450 ms post-target stimulus) windows. The SWn modulation was defined as the difference in SWn between both windows. Association between SWn modulation and carrying the risk allele for three single nucleotide polymorphisms (SNP) of NRG1 (i.e., rs6468119, rs6994992 and rs7005606) was assessed. A significant association between three SNPs of NRG1 and the SWn modulation was found, specifically: NRG1 rs6468119 in alpha and beta1 bands; NRG1 rs6994992 in theta band; and NRG1 rs7005606 in theta and beta1 bands. Genetic variation at NRG1 may influence functional brain connectivity through the modulation of SWn properties of the cortical network.
Yan, Yan; Song, Jian; Xu, Guozheng; Yao, Shun; Cao, Chenglong; Li, Chang; Peng, Guibao; Du, Hao
2017-10-01
This study investigated the characteristics of the small-world brain network architecture of patients with mild traumatic brain injury (MTBI), and a correlation between brain functional connectivity network properties in the resting-state fMRI and Standardized Assessment of Concussion (SAC) parameters. The neurological conditions of 22 MTBI patients and 17 normal control individuals were evaluated according to the SAC. Resting-state fMRI was performed in all subjects 3 and 7days after injury respectively. After preprocessing the fMRI data, cortex functional regions were marked using AAL90 and Dosenbach160 templates. The small-world network parameters and areas under the integral curves were computed in the range of sparsity from 0.01 to 0.5. Independent-sample t-tests were used to compare these parameters between the MTBI and control group. Significantly different parameters were investigated for correlations with SAC scores; those that correlated were chosen for further curve fitting. The clustering coefficient, the communication efficiency across in local networks, and the strength of connectivity were all higher in MTBI patients relative to control individuals. Parameters in 160 brain regions of the MTBI group significantly correlated with total SAC score and score for attention; the network parameters may be a quadratic function of attention scores of SAC and a cubic function of SAC scores. MTBI patients were characterized by elevated communication efficiency across global brain regions, and in local networks, and strength of mean connectivity. These features may be associated with brain function compensation. The network parameters significantly correlated with SAC total and attention scores. Copyright © 2017 Elsevier Ltd. All rights reserved.
NASA Astrophysics Data System (ADS)
Sun, Xiaojuan; Perc, Matjaž; Kurths, Jürgen
2017-05-01
In this paper, we study effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks. Our focus is on the impact of two parameters, namely the time delay τ and the probability of partial time delay pdelay, whereby the latter determines the probability with which a connection between two neurons is delayed. Our research reveals that partial time delays significantly affect phase synchronization in this system. In particular, partial time delays can either enhance or decrease phase synchronization and induce synchronization transitions with changes in the mean firing rate of neurons, as well as induce switching between synchronized neurons with period-1 firing to synchronized neurons with period-2 firing. Moreover, in comparison to a neuronal network where all connections are delayed, we show that small partial time delay probabilities have especially different influences on phase synchronization of neuronal networks.
Uehara, Taira; Yamasaki, Takao; Okamoto, Tsuyoshi; Koike, Takahiko; Kan, Shigeyuki; Miyauchi, Satoru; Kira, Jun-Ichi; Tobimatsu, Shozo
2014-06-01
It has been revealed that spontaneous coherent brain activity during rest, measured by functional magnetic resonance imaging (fMRI), self-organizes a "small-world" network by which the human brain could sustain higher communication efficiency across global brain regions with lower energy consumption. However, the state-dependent dynamics of the network, especially the dependency on the conscious state, remain poorly understood. In this study, we conducted simultaneous electroencephalographic recording with resting-state fMRI to explore whether functional network organization reflects differences in the conscious state between an awake state and stage 1 sleep. We then evaluated whole-brain functional network properties with fine spatial resolution (3781 regions of interest) using graph theoretical analysis. We found that the efficiency of the functional network evaluated by path length decreased not only at the global level, but also in several specific regions depending on the conscious state. Furthermore, almost two-thirds of nodes that showed a significant decrease in nodal efficiency during stage 1 sleep were categorized as the default-mode network. These results suggest that brain functional network organizations are dynamically optimized for a higher level of information integration in the fully conscious awake state, and that the default-mode network plays a pivotal role in information integration for maintaining conscious awareness.
Noise influence on spike activation in a Hindmarsh-Rose small-world neural network
NASA Astrophysics Data System (ADS)
Zhe, Sun; Micheletto, Ruggero
2016-07-01
We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh-Rose (H-R) neural networks.
The small world of osteocytes: connectomics of the lacuno-canalicular network in bone
NASA Astrophysics Data System (ADS)
Kollmannsberger, Philip; Kerschnitzki, Michael; Repp, Felix; Wagermaier, Wolfgang; Weinkamer, Richard; Fratzl, Peter
2017-07-01
Osteocytes and their cell processes reside in a large, interconnected network of voids pervading the mineralized bone matrix of most vertebrates. This osteocyte lacuno-canalicular network (OLCN) is believed to play important roles in mechanosensing, mineral homeostasis, and for the mechanical properties of bone. While the extracellular matrix structure of bone is extensively studied on ultrastructural and macroscopic scales, there is a lack of quantitative knowledge on how the cellular network is organized. Using a recently introduced imaging and quantification approach, we analyze the OLCN in different bone types from mouse and sheep that exhibit different degrees of structural organization not only of the cell network but also of the fibrous matrix deposited by the cells. We define a number of robust, quantitative measures that are derived from the theory of complex networks. These measures enable us to gain insights into how efficient the network is organized with regard to intercellular transport and communication. Our analysis shows that the cell network in regularly organized, slow-growing bone tissue from sheep is less connected, but more efficiently organized compared to irregular and fast-growing bone tissue from mice. On the level of statistical topological properties (edges per node, edge length and degree distribution), both network types are indistinguishable, highlighting that despite pronounced differences at the tissue level, the topological architecture of the osteocyte canalicular network at the subcellular level may be independent of species and bone type. Our results suggest a universal mechanism underlying the self-organization of individual cells into a large, interconnected network during bone formation and mineralization.
Tian, Lixia; Wang, Jinhui; Yan, Chaogan; He, Yong
2011-01-01
We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world attributes (high clustering and short paths) that are compatible with previous results in the whole-brain functional networks. Furthermore, we found that compared with females, males have a higher normalized clustering coefficient in the right hemispheric network but a lower clustering coefficient in the left hemispheric network, suggesting a gender-hemisphere interaction. Moreover, we observed significant hemisphere-related differences in the regional nodal characteristics in various brain regions, such as the frontal and occipital regions (leftward asymmetry) and the temporal regions (rightward asymmetry), findings that are consistent with previous studies of brain structural and functional asymmetries. Together, our results suggest that the topological organization of human brain functional networks is associated with gender and hemispheres, and they provide insights into the understanding of functional substrates underlying individual differences in behaviors and cognition.
A Small World of Citations? The Influence of Collaboration Networks on Citation Practices
Wallace, Matthew L.; Larivière, Vincent; Gingras, Yves
2012-01-01
This paper examines the proximity of authors to those they cite using degrees of separation in a co-author network, essentially using collaboration networks to expand on the notion of self-citations. While the proportion of direct self-citations (including co-authors of both citing and cited papers) is relatively constant in time and across specialties in the natural sciences (10% of references) and the social sciences (20%), the same cannot be said for citations to authors who are members of the co-author network. Differences between fields and trends over time lie not only in the degree of co-authorship which defines the large-scale topology of the collaboration network, but also in the referencing practices within a given discipline, computed by defining a propensity to cite at a given distance within the collaboration network. Overall, there is little tendency to cite those nearby in the collaboration network, excluding direct self-citations. These results are interpreted in terms of small-scale structure, field-specific citation practices, and the value of local co-author networks for the production of knowledge and for the accumulation of symbolic capital. Given the various levels of integration between co-authors, our findings shed light on the question of the availability of ‘arm's length’ expert reviewers of grant applications and manuscripts. PMID:22413016
Arzouan, Yossi; Solomon, Sorin; Faust, Miriam; Goldstein, Abraham
2011-01-01
Language comprehension is a complex task that involves a wide network of brain regions. We used topological measures to qualify and quantify the functional connectivity of the networks used under various comprehension conditions. To that aim we developed a technique to represent functional networks based on EEG recordings, taking advantage of their excellent time resolution in order to capture the fast processes that occur during language comprehension. Networks were created by searching for a specific causal relation between areas, the negative feedback loop, which is ubiquitous in many systems. This method is a simple way to construct directed graphs using event-related activity, which can then be analyzed topologically. Brain activity was recorded while subjects read expressions of various types and indicated whether they found them meaningful. Slightly different functional networks were obtained for event-related activity evoked by each expression type. The differences reflect the special contribution of specific regions in each condition and the balance of hemispheric activity involved in comprehending different types of expressions and are consistent with the literature in the field. Our results indicate that representing event-related brain activity as a network using a simple temporal relation, such as the negative feedback loop, to indicate directional connectivity is a viable option for investigation which also derives new information about aspects not reflected in the classical methods for investigating brain activity. PMID:21556324
NASA Astrophysics Data System (ADS)
Huang, Xu-Hui; Hu, Gang
2014-10-01
Phase transitions widely exist in nature and occur when some control parameters are changed. In neural systems, their macroscopic states are represented by the activity states of neuron populations, and phase transitions between different activity states are closely related to corresponding functions in the brain. In particular, phase transitions to some rhythmic synchronous firing states play significant roles on diverse brain functions and disfunctions, such as encoding rhythmical external stimuli, epileptic seizure, etc. However, in previous studies, phase transitions in neuronal networks are almost driven by network parameters (e.g., external stimuli), and there has been no investigation about the transitions between typical activity states of neuronal networks in a self-organized way by applying plastic connection weights. In this paper, we discuss phase transitions in electrically coupled and lattice-based small-world neuronal networks (LBSW networks) under spike-timing-dependent plasticity (STDP). By applying STDP on all electrical synapses, various known and novel phase transitions could emerge in LBSW networks, particularly, the phenomenon of self-organized phase transitions (SOPTs): repeated transitions between synchronous and asynchronous firing states. We further explore the mechanics generating SOPTs on the basis of synaptic weight dynamics.
Ribeiro, Tiago L; Ribeiro, Sidarta; Belchior, Hindiael; Caixeta, Fábio; Copelli, Mauro
2014-01-01
The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent [Formula: see text]. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.
Ribeiro, Tiago L.; Ribeiro, Sidarta; Belchior, Hindiael; Caixeta, Fábio; Copelli, Mauro
2014-01-01
The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent . Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches. PMID:24751599
Kleczkowski, Adam; Oleś, Katarzyna; Gudowska-Nowak, Ewa; Gilligan, Christopher A.
2012-01-01
We present a combined epidemiological and economic model for control of diseases spreading on local and small-world networks. The disease is characterized by a pre-symptomatic infectious stage that makes detection and control of cases more difficult. The effectiveness of local (ring-vaccination or culling) and global control strategies is analysed by comparing the net present values of the combined cost of preventive treatment and illness. The optimal strategy is then selected by minimizing the total cost of the epidemic. We show that three main strategies emerge, with treating a large number of individuals (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. If the disease spreads within the well-defined neighbourhood, the local strategy is optimal unless the cost of a single vaccine is much higher than the cost associated with hospitalization. In the latter case, it is most cost-effective to refrain from prevention. Destruction of local correlations, either by long-range (small-world) links or by inclusion of many initial foci, expands the range of costs for which the NS is most cost-effective. The GS emerges for the case when the cost of prevention is much lower than the cost of treatment and there is a substantial non-local component in the disease spread. We also show that local treatment is only desirable if the disease spreads on a small-world network with sufficiently few long-range links; otherwise it is optimal to treat globally. In the mean-field case, there are only two optimal solutions, to treat all if the cost of the vaccine is low and to treat nobody if it is high. The basic reproduction ratio, R0, does not depend on the rate of responsive treatment in this case and the disease always invades (but
Kleczkowski, Adam; Oleś, Katarzyna; Gudowska-Nowak, Ewa; Gilligan, Christopher A
2012-01-07
We present a combined epidemiological and economic model for control of diseases spreading on local and small-world networks. The disease is characterized by a pre-symptomatic infectious stage that makes detection and control of cases more difficult. The effectiveness of local (ring-vaccination or culling) and global control strategies is analysed by comparing the net present values of the combined cost of preventive treatment and illness. The optimal strategy is then selected by minimizing the total cost of the epidemic. We show that three main strategies emerge, with treating a large number of individuals (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. If the disease spreads within the well-defined neighbourhood, the local strategy is optimal unless the cost of a single vaccine is much higher than the cost associated with hospitalization. In the latter case, it is most cost-effective to refrain from prevention. Destruction of local correlations, either by long-range (small-world) links or by inclusion of many initial foci, expands the range of costs for which the NS is most cost-effective. The GS emerges for the case when the cost of prevention is much lower than the cost of treatment and there is a substantial non-local component in the disease spread. We also show that local treatment is only desirable if the disease spreads on a small-world network with sufficiently few long-range links; otherwise it is optimal to treat globally. In the mean-field case, there are only two optimal solutions, to treat all if the cost of the vaccine is low and to treat nobody if it is high. The basic reproduction ratio, R(0), does not depend on the rate of responsive treatment in this case and the disease always invades
Yu, Dongyuan; Xu, Xu; Zhou, Jing; Li, Eric
2017-03-02
This study considers a delayed neural network with excitatory and inhibitory shortcuts. The global stability of the trivial equilibrium is investigated based on Lyapunov's direct method and the delay-dependent criteria are obtained. It is shown that both the excitatory and inhibitory shortcuts decrease the stability interval, but a time delay can be employed as a global stabilizer. In addition, we analyze the bounds of the eigenvalues of the adjacent matrix using matrix perturbation theory and then obtain the generalized sufficient conditions for local stability. The possibility of small inhibitory shortcuts is helpful for maintaining stability. The mechanisms of instability, bifurcation modes, and chaos are also investigated. Compared with methods based on mean-field theory, the proposed method can guarantee the stability of the system in most cases with random events. The proposed method is effective for cases where excitatory and inhibitory shortcuts exist simultaneously in the network.
Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study
Wang, Xiangbin; Liu, Bing; Xi, Qian; Guo, Qihao; Jiang, Hong; Jiang, Tianzi; Wang, Peijun
2012-01-01
The small-world organization has been hypothesized to reflect a balance between local processing and global integration in the human brain. Previous multimodal imaging studies have consistently demonstrated that the topological architecture of the brain network is disrupted in Alzheimer's disease (AD). However, these studies have reported inconsistent results regarding the topological properties of brain alterations in AD. One potential explanation for these inconsistent results lies with the diverse homogeneity and distinct progressive stages of the AD involved in these studies, which are thought to be critical factors that might affect the results. We investigated the topological properties of brain functional networks derived from resting functional magnetic resonance imaging (fMRI) of carefully selected moderate AD patients and normal controls (NCs). Our results showed that the topological properties were found to be disrupted in AD patients, which showing increased local efficiency but decreased global efficiency. We found that the altered brain regions are mainly located in the default mode network, the temporal lobe and certain subcortical regions that are closely associated with the neuropathological changes in AD. Of note, our exploratory study revealed that the ApoE genotype modulates brain network properties, especially in AD patients. PMID:22457774
Qian, Yu
2014-01-01
The synchronization transitions in Newman-Watts small-world neuronal networks (SWNNs) induced by time delay and long-range connection (LRC) probability have been investigated by synchronization parameter and space-time plots. Four distinct parameter regions, that is, asynchronous region, transition region, synchronous region, and oscillatory region have been discovered at certain LRC probability as time delay is increased. Interestingly, desynchronization is observed in oscillatory region. More importantly, we consider the spatiotemporal patterns obtained in delayed Newman-Watts SWNNs are the competition results between long-range drivings (LRDs) and neighboring interactions. In addition, for moderate time delay, the synchronization of neuronal network can be enhanced remarkably by increasing LRC probability. Furthermore, lag synchronization has been found between weak synchronization and complete synchronization as LRC probability is a little less than 1.0. Finally, the two necessary conditions, moderate time delay and large numbers of LRCs, are exposed explicitly for synchronization in delayed Newman-Watts SWNNs. PMID:24810595
Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng
2014-03-15
The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. Copyright © 2013 Elsevier B.V. All rights reserved.
NASA Astrophysics Data System (ADS)
Aldrich, Preston R.; El-Zabet, Jermeen; Hassan, Seerat; Briguglio, Joseph; Aliaj, Enela; Radcliffe, Maria; Mirza, Taha; Comar, Timothy; Nadolski, Jeremy; Huebner, Cynthia D.
2015-11-01
Several studies have shown that human transportation networks exhibit small-world structure, meaning they have high local clustering and are easily traversed. However, some have concluded this without statistical evaluations, and others have compared observed structure to globally random rather than planar models. Here, we use Monte Carlo randomizations to test US transportation infrastructure data for small-worldness. Coarse-grained network models were generated from GIS data wherein nodes represent the 3105 contiguous US counties and weighted edges represent the number of highway or railroad links between counties; thus, we focus on linkage topologies and not geodesic distances. We compared railroad and highway transportation networks with a simple planar network based on county edge-sharing, and with networks that were globally randomized and those that were randomized while preserving their planarity. We conclude that terrestrial transportation networks have small-world architecture, as it is classically defined relative to global randomizations. However, this topological structure is sufficiently explained by the planarity of the graphs, and in fact the topological patterns established by the transportation links actually serve to reduce the amount of small-world structure.
ERIC Educational Resources Information Center
Sousa, Fernando Cardoso; Monteiro, Ileana Pardal; Pellissier, René
2014-01-01
This article presents the development of a small-world network using an adapted version of the large-group problem-solving method "Future Search." Two management classes in a higher education setting were selected and required to plan a project. The students completed a survey focused on the frequency of communications before and after…
Anderson, Ariana; Cohen, Mark S.
2013-01-01
Functional network connectivity (FNC) is a method of analyzing the temporal relationship of anatomical brain components, comparing the synchronicity between patient groups or conditions. We use functional-connectivity measures between independent components to classify between Schizophrenia patients and healthy controls during resting-state. Connectivity is measured using a variety of graph-theoretic connectivity measures such as graph density, average path length, and small-worldness. The Schizophrenia patients showed significantly less clustering (transitivity) among components than healthy controls (p < 0.05, corrected) with networks less likely to be connected, and also showed lower small-world connectivity than healthy controls. Using only these connectivity measures, an SVM classifier (without parameter tuning) could discriminate between Schizophrenia patients and healthy controls with 65% accuracy, compared to 51% chance. This implies that the global functional connectivity between resting-state networks is altered in Schizophrenia, with networks more likely to be disconnected and behave dissimilarly for diseased patients. We present this research finding as a tutorial using the publicly available COBRE dataset of 146 Schizophrenia patients and healthy controls, provided as part of the 1000 Functional Connectomes Project. We demonstrate preprocessing, using independent component analysis (ICA) to nominate networks, computing graph-theoretic connectivity measures, and finally using these connectivity measures to either classify between patient groups or assess between-group differences using formal hypothesis testing. All necessary code is provided for both running command-line FSL preprocessing, and for computing all statistical measures and SVM classification within R. Collectively, this work presents not just findings of diminished FNC among resting-state networks in Schizophrenia, but also a practical connectivity tutorial. PMID:24032010
Li, Yan-Long; Chen, Zhao-Yang; Ma, Jun; Chen, Yu-Hong
2008-02-01
Adopting small-world neural networks of the Hodgkin-Huxley (HH) model, the stimulation parameters in desynchronisation and its possible implications for vagus nerve stimulation (VNS) are numerically investigated. With the synchronisation status of networks to represent epilepsy, then, adding pulse to stimulations to 10% of neurons to simulate the VNS, we obtain the desynchronisation status of networks (representing antiepileptic effects). The simulations show that synchronisation evolves into desynchronisation in the HH neural networks when a part (10%) of neurons are stimulated with a pulse current signal. The network desynchronisation appears to be sensitive to the stimulation parameters. For the case of the same stimulation intensity, weakly coupled networks reach desynchronisation more easily than strongly coupled networks. The network desynchronisation reduced by short-stimulation interval is more distinct than that of induced by long stimulation interval. We find that there exist the optimal stimulation interval and optimal stimulation intensity when the other stimulation parameters remain certain.
Exact Solution of Ising Model in 2d Shortcut Network
NASA Astrophysics Data System (ADS)
Shanker, O.
We give the exact solution to the Ising model in the shortcut network in the 2D limit. The solution is found by mapping the model to the square lattice model with Brascamp and Kunz boundary conditions.
NASA Astrophysics Data System (ADS)
Nunes Amaral, Luis A.
2002-03-01
We study the statistical properties of a variety of diverse real-world networks including the neural network of C. Elegans, food webs for seven distinct environments, transportation and technological networks, and a number of distinct social networks [1-5]. We present evidence of the occurrence of three classes of small-world networks [2]: (a) scale-free networks, characterized by a vertex connectivity distribution that decays as a power law; (b) broad-scale networks, characterized by a connectivity distribution that has a power-law regime followed by a sharp cut-off; (c) single-scale networks, characterized by a connectivity distribution with a fast decaying tail. Moreover, we note for the classes of broad-scale and single-scale networks that there are constraints limiting the addition of new links. Our results suggest that the nature of such constraints may be the controlling factor for the emergence of different classes of networks. [See http://polymer.bu.edu/ amaral/Networks.html for details and htpp://polymer.bu.edu/ amaral/Professional.html for access to PDF files of articles.] 1. M. Barthélémy, L. A. N. Amaral, Phys. Rev. Lett. 82, 3180-3183 (1999). 2. L. A. N. Amaral, A. Scala, M. Barthélémy, H. E. Stanley, Proc. Nat. Acad. Sci. USA 97, 11149-11152 (2000). 3. F. Liljeros, C. R. Edling, L. A. N. Amaral, H. E. Stanley, and Y. Åberg, Nature 411, 907-908 (2001). 4. J. Camacho, R. Guimera, L.A.N. Amaral, Phys. Rev. E RC (to appear). 5. S. Mossa, M. Barthelemy, H.E. Stanley, L.A.N. Amaral (submitted).
NASA Astrophysics Data System (ADS)
Ma, Fei; Yao, Bing
2017-10-01
It is always an open, demanding and difficult task for generating available model to simulate dynamical functions and reveal inner principles from complex systems and networks. In this article, due to lots of real-life and artificial networks are built from series of simple and small groups (components), we discuss some interesting and helpful network-operation to generate more realistic network models. In view of community structure (modular topology), we present a class of sparse network models N(t , m) . At the moment, we capture the fact the N(t , 4) has not only scale-free feature, which means that the probability that a randomly selected vertex with degree k decays as a power-law, following P(k) ∼k-γ, where γ is the degree exponent, but also small-world property, which indicates that the typical distance between two uniform randomly chosen vertices grows proportionally to logarithm of the order of N(t , 4) , namely, relatively shorter diameter and lower average path length, simultaneously displays higher clustering coefficient. Next, as a new topological parameter correlating to reliability, synchronization capability and diffusion properties of networks, the number of spanning trees over a network is studied in more detail, an exact analytical solution for the number of spanning trees of the N(t , 4) is obtained. Based on the network-operation, part hub-vertex linking with each other will be helpful for structuring various network models and investigating the rules related with real-life networks.
Rothkegel, Alexander; Lehnertz, Klaus
2009-03-01
We investigate numerically the collective dynamical behavior of pulse-coupled nonleaky integrate-and-fire neurons that are arranged on a two-dimensional small-world network. To ensure ongoing activity, we impose a probability for spontaneous firing for each neuron. We study network dynamics evolving from different sets of initial conditions in dependence on coupling strength and rewiring probability. Besides a homogeneous equilibrium state for low coupling strength, we observe different local patterns including cyclic waves, spiral waves, and turbulentlike patterns, which-depending on network parameters-interfere with the global collective firing of the neurons. We attribute the various network dynamics to distinct regimes in the parameter space. For the same network parameters different network dynamics can be observed depending on the set of initial conditions only. Such a multistable behavior and the interplay between local pattern formation and global collective firing may be attributable to the spatiotemporal dynamics of biological networks.
NASA Astrophysics Data System (ADS)
Rothkegel, Alexander; Lehnertz, Klaus
2009-03-01
We investigate numerically the collective dynamical behavior of pulse-coupled nonleaky integrate-and-fire neurons that are arranged on a two-dimensional small-world network. To ensure ongoing activity, we impose a probability for spontaneous firing for each neuron. We study network dynamics evolving from different sets of initial conditions in dependence on coupling strength and rewiring probability. Besides a homogeneous equilibrium state for low coupling strength, we observe different local patterns including cyclic waves, spiral waves, and turbulentlike patterns, which—depending on network parameters—interfere with the global collective firing of the neurons. We attribute the various network dynamics to distinct regimes in the parameter space. For the same network parameters different network dynamics can be observed depending on the set of initial conditions only. Such a multistable behavior and the interplay between local pattern formation and global collective firing may be attributable to the spatiotemporal dynamics of biological networks.
Koelsch, Stefan; Skouras, Stavros
2014-07-01
Current knowledge about small-world networks underlying emotions is sparse, and confined to functional magnetic resonance imaging (fMRI) studies using resting-state paradigms. This fMRI study applied Eigenvector Centrality Mapping (ECM) and functional connectivity analysis to reveal neural small-world networks underlying joy and fear. Joy and fear were evoked using music, presented in 4-min blocks. Results show that the superficial amygdala (SF), laterobasal amygdala (LB), striatum, and hypothalamus function as computational hubs during joy. Out of these computational hubs, the amygdala nuclei showed the highest centrality values. The SF showed functional connectivity during joy with the mediodorsal thalamus (MD) and nucleus accumbens (Nac), suggesting that SF, MD, and Nac modulate approach behavior in response to positive social signals such as joyful music. The striatum was functionally connected during joy with the LB, as well as with premotor cortex, areas 1 and 7a, hippocampus, insula and cingulate cortex, showing that sensorimotor, attentional, and emotional processes converge in the striatum during music perception. The hypothalamus showed functional connectivity during joy with hippocampus and MD, suggesting that hypothalamic endocrine activity is modulated by hippocampal and thalamic activity during sustained periods of music-evoked emotion. Our study indicates high centrality of the amygdala nuclei groups within a functional network underlying joy, suggesting that these nuclei play a central role for the modulation of emotion-specific activity within this network.
NASA Astrophysics Data System (ADS)
Ma, Jun; Yang, Li-Jian; Wu, Ying; Zhang, Cai-Rong
2010-09-01
The effect of small-world connection and noise on the formation and transition of spiral wave in the networks of Hodgkin-Huxley neurons are investigated in detail. Some interesting results are found in our numerical studies. i) The quiescent neurons are activated to propagate electric signal to others by generating and developing spiral wave from spiral seed in small area. ii) A statistical factor is defined to describe the collective properties and phase transition induced by the topology of networks and noise. iii) Stable rotating spiral wave can be generated and keeps robust when the rewiring probability is below certain threshold, otherwise, spiral wave can not be developed from the spiral seed and spiral wave breakup occurs for a stable rotating spiral wave. iv) Gaussian white noise is introduced on the membrane of neurons to study the noise-induced phase transition on spiral wave in small-world networks of neurons. It is confirmed that Gaussian white noise plays active role in supporting and developing spiral wave in the networks of neurons, and appearance of smaller factor of synchronization indicates high possibility to induce spiral wave.
NASA Astrophysics Data System (ADS)
Kleinberg, Jon M.
2000-08-01
The small-world phenomenon - the principle that most of us are linked by short chains of acquaintances - was first investigated as a question in sociology and is a feature of a range of networks arising in nature and technology. Experimental study of the phenomenon revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed, and here I investigate the second by modelling how individuals can find short chains in a large social network.
NASA Astrophysics Data System (ADS)
Qin, Ying-Hua; Luo, Xiao-Shu
2009-07-01
We investigate how the firing activity and the subsequent phase synchronization of neural networks with small-world topological connections depend on the probability p of adding-links. Network elements are described by two-dimensional map neurons (2DMNs) in a quiescent original state. Neurons burst for a given coupling strength when the topological randomness p increases, which is absent in a regular-lattice neural network. The bursting activity becomes frequent and synchronization of neurons emerges as topological randomness further increases. The maximal firing frequency and phase synchronization appear at a particular value of p. However, if the randomness p further increases, the firing frequency decreases and synchronization is apparently destroyed.
Cluster-size entropy in the Axelrod model of social influence: Small-world networks and mass media
NASA Astrophysics Data System (ADS)
Gandica, Y.; Charmell, A.; Villegas-Febres, J.; Bonalde, I.
2011-10-01
We study the Axelrod's cultural adaptation model using the concept of cluster-size entropy Sc, which gives information on the variability of the cultural cluster size present in the system. Using networks of different topologies, from regular to random, we find that the critical point of the well-known nonequilibrium monocultural-multicultural (order-disorder) transition of the Axelrod model is given by the maximum of the Sc(q) distributions. The width of the cluster entropy distributions can be used to qualitatively determine whether the transition is first or second order. By scaling the cluster entropy distributions we were able to obtain a relationship between the critical cultural trait qc and the number F of cultural features in two-dimensional regular networks. We also analyze the effect of the mass media (external field) on social systems within the Axelrod model in a square network. We find a partially ordered phase whose largest cultural cluster is not aligned with the external field, in contrast with a recent suggestion that this type of phase cannot be formed in regular networks. We draw a q-B phase diagram for the Axelrod model in regular networks.
Cluster-size entropy in the Axelrod model of social influence: small-world networks and mass media.
Gandica, Y; Charmell, A; Villegas-Febres, J; Bonalde, I
2011-10-01
We study the Axelrod's cultural adaptation model using the concept of cluster-size entropy S(c), which gives information on the variability of the cultural cluster size present in the system. Using networks of different topologies, from regular to random, we find that the critical point of the well-known nonequilibrium monocultural-multicultural (order-disorder) transition of the Axelrod model is given by the maximum of the S(c)(q) distributions. The width of the cluster entropy distributions can be used to qualitatively determine whether the transition is first or second order. By scaling the cluster entropy distributions we were able to obtain a relationship between the critical cultural trait q(c) and the number F of cultural features in two-dimensional regular networks. We also analyze the effect of the mass media (external field) on social systems within the Axelrod model in a square network. We find a partially ordered phase whose largest cultural cluster is not aligned with the external field, in contrast with a recent suggestion that this type of phase cannot be formed in regular networks. We draw a q-B phase diagram for the Axelrod model in regular networks.
Toppi, J.; De Vico Fallani, F.; Vecchiato, G.; Maglione, A. G.; Cincotti, F.; Mattia, D.; Salinari, S.; Babiloni, F.; Astolfi, L.
2012-01-01
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes. PMID:22919427
Toppi, J; De Vico Fallani, F; Vecchiato, G; Maglione, A G; Cincotti, F; Mattia, D; Salinari, S; Babiloni, F; Astolfi, L
2012-01-01
The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.
NASA Astrophysics Data System (ADS)
Ausloos, Marcel
2015-06-01
Diffusion of knowledge is expected to be huge when agents are open minded. The report concerns a more difficult diffusion case when communities are made of stubborn agents. Communities having markedly different opinions are for example the Neocreationist and Intelligent Design Proponents (IDP), on one hand, and the Darwinian Evolution Defenders (DED), on the other hand. The case of knowledge diffusion within such communities is studied here on a network based on an adjacency matrix built from time ordered selected quotations of agents, whence for inter- and intra-communities. The network is intrinsically directed and not necessarily reciprocal. Thus, the adjacency matrices have complex eigenvalues; the eigenvectors present complex components. A quantification of the slow-down or speed-up effects of information diffusion in such temporal networks, with non-Markovian contact sequences, can be made by comparing the real time dependent (directed) network to its counterpart, the time aggregated (undirected) network, - which has real eigenvalues. In order to do so, small world networks which both contain an odd number of nodes are studied and compared to similar networks with an even number of nodes. It is found that (i) the diffusion of knowledge is more difficult on the largest networks; (ii) the network size influences the slowing-down or speeding-up diffusion process. Interestingly, it is observed that (iii) the diffusion of knowledge is slower in IDP and faster in DED communities. It is suggested that the finding can be "rationalized", if some "scientific quality" and "publication habit" is attributed to the agents, as common sense would guess. This finding offers some opening discussion toward tying scientific knowledge to belief.
Li, Wenjun; Ward, B. Douglas; Liu, Xiaolin; Chen, Gang; Jones, Jennifer L; Antuono, Piero G.; Li, Shi-Jiang; Goveas, Joseph S.
2015-01-01
Background The topological architecture of the whole-brain functional networks in those with and without late-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are unknown. Aims To investigate the differences in the small-world measures and the modular community structure of the functional networks between patients with LLD and aMCI when occurring alone or in combination and cognitively healthy nondepressed controls. Methods Seventy-nine elderly participants [LLD (n = 23), aMCI (n = 18), comorbid LLD and aMCI (n = 13), and controls (n = 25)] completed neuropsychiatric assessments. Graph theoretical methods were employed on resting-state functional connectivity magnetic resonance imaging data. Results LLD and aMCI comorbidity was associated with the greatest disruptions in functional integration measures (decreased global efficiency and increased path length); both LLD groups showed abnormal functional segregation (reduced local efficiency). The modular network organization was most variable in the comorbid group, followed by LLD-only patients. Decreased mean global, local and nodal efficiency metrics were associated with greater depressive symptom severity but not memory performance. Conclusions Consider the whole brain as a complex network may provide unique insights on the neurobiological underpinnings of LLD with and without cognitive impairment. PMID:25433036
Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong; Yu, Chunshui; Chen, Huafu
2015-05-01
The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size.
Li, Meiling; Wang, Junping; Liu, Feng; Chen, Heng; Lu, Fengmei; Wu, Guorong
2015-01-01
Abstract The human brain has been described as a complex network, which integrates information with high efficiency. However, the relationships between the efficiency of human brain functional networks and handedness and brain size remain unclear. Twenty-one left-handed and 32 right-handed healthy subjects underwent a resting-state functional magnetic resonance imaging scan. The whole brain functional networks were constructed by thresholding Pearson correlation matrices of 90 cortical and subcortical regions. Graph theory-based methods were employed to further analyze their topological properties. As expected, all participants demonstrated small-world topology, suggesting a highly efficient topological structure. Furthermore, we found that smaller brains showed higher local efficiency, whereas larger brains showed higher global efficiency, reflecting a suitable efficiency balance between local specialization and global integration of brain functional activity. Compared with right-handers, significant alterations in nodal efficiency were revealed in left-handers, involving the anterior and median cingulate gyrus, middle temporal gyrus, angular gyrus, and amygdala. Our findings indicated that the functional network organization in the human brain was associated with handedness and brain size. PMID:25535788
Frantzidis, Christos A.; Vivas, Ana B.; Tsolaki, Anthoula; Klados, Manousos A.; Tsolaki, Magda; Bamidis, Panagiotis D.
2014-01-01
Previous neuroscientific findings have linked Alzheimer's Disease (AD) with less efficient information processing and brain network disorganization. However, pathological alterations of the brain networks during the preclinical phase of amnestic Mild Cognitive Impairment (aMCI) remain largely unknown. The present study aimed at comparing patterns of the detection of functional disorganization in MCI relative to Mild Dementia (MD). Participants consisted of 23 cognitively healthy adults, 17 aMCI and 24 mild AD patients who underwent electroencephalographic (EEG) data acquisition during a resting-state condition. Synchronization analysis through the Orthogonal Discrete Wavelet Transform (ODWT), and directional brain network analysis were applied on the EEG data. This computational model was performed for networks that have the same number of edges (N = 500, 600, 700, 800 edges) across all participants and groups (fixed density values). All groups exhibited a small-world (SW) brain architecture. However, we found a significant reduction in the SW brain architecture in both aMCI and MD patients relative to the group of Healthy controls. This functional disorganization was also correlated with the participant's generic cognitive status. The deterioration of the network's organization was caused mainly by deficient local information processing as quantified by the mean cluster coefficient value. Functional hubs were identified through the normalized betweenness centrality metric. Analysis of the local characteristics showed relative hub preservation even with statistically significant reduced strength. Compensatory phenomena were also evident through the formation of additional hubs on left frontal and parietal regions. Our results indicate a declined functional network organization even during the prodromal phase. Degeneration is evident even in the preclinical phase and coexists with transient network reorganization due to compensation. PMID:25206333
Connectivity, formation factor and permeability of 2D fracture network
NASA Astrophysics Data System (ADS)
Tang, Y. B.; Li, M.; Li, X. F.
2017-10-01
The purpose of this paper is to investigate the effects of fracture connectivity and length distributions on the electrical formation factor, F, of random fracture network using percolation theory. We assumed that the matrix was homogeneous and low-permeable, but the connectivity and length distributions of fracture system were randomly variable. F of fracture network is analyzed via finite element method. The main result is that: different from the classical percolation ;universal; power law for porous-type rocks, F of fracture network obeys a normalized ;universal; scaling relation using the length-scale < l > / L (< l > is fracture mean length, and L is the domain size). Our proposed formation factor model, derived from the normalized ;universal; scaling relationship, is valid in fracture network with constant fracture length and length distributions, showing that the normalized ;universal; scaling law is independent of fracture patterns. The normalized scaling relation is also successfully used to derive the permeability model of 2D random fracture network using the previously published dataset, which obtained better fitting results than before.
Yao, Yuangen; Deng, Haiyou; Ma, Chengzhang; Yi, Ming; Ma, Jun
2017-01-01
Spiral waves are observed in the chemical, physical and biological systems, and the emergence of spiral waves in cardiac tissue is linked to some diseases such as heart ventricular fibrillation and epilepsy; thus it has importance in theoretical studies and potential medical applications. Noise is inevitable in neuronal systems and can change the electrical activities of neuron in different ways. Many previous theoretical studies about the impacts of noise on spiral waves focus an unbounded Gaussian noise and even colored noise. In this paper, the impacts of bounded noise and rewiring of network on the formation and instability of spiral waves are discussed in small-world (SW) network of Hodgkin-Huxley (HH) neurons through numerical simulations, and possible statistical analysis will be carried out. Firstly, we present SW network of HH neurons subjected to bounded noise. Then, it is numerically demonstrated that bounded noise with proper intensity σ, amplitude A, or frequency f can facilitate the formation of spiral waves when rewiring probability p is below certain thresholds. In other words, bounded noise-induced resonant behavior can occur in the SW network of neurons. In addition, rewiring probability p always impairs spiral waves, while spiral waves are confirmed to be robust for small p, thus shortcut-induced phase transition of spiral wave with the increase of p is induced. Furthermore, statistical factors of synchronization are calculated to discern the phase transition of spatial pattern, and it is confirmed that larger factor of synchronization is approached with increasing of rewiring probability p, and the stability of spiral wave is destroyed.
Yao, Yuangen; Deng, Haiyou; Ma, Chengzhang; Yi, Ming
2017-01-01
Spiral waves are observed in the chemical, physical and biological systems, and the emergence of spiral waves in cardiac tissue is linked to some diseases such as heart ventricular fibrillation and epilepsy; thus it has importance in theoretical studies and potential medical applications. Noise is inevitable in neuronal systems and can change the electrical activities of neuron in different ways. Many previous theoretical studies about the impacts of noise on spiral waves focus an unbounded Gaussian noise and even colored noise. In this paper, the impacts of bounded noise and rewiring of network on the formation and instability of spiral waves are discussed in small-world (SW) network of Hodgkin-Huxley (HH) neurons through numerical simulations, and possible statistical analysis will be carried out. Firstly, we present SW network of HH neurons subjected to bounded noise. Then, it is numerically demonstrated that bounded noise with proper intensity σ, amplitude A, or frequency f can facilitate the formation of spiral waves when rewiring probability p is below certain thresholds. In other words, bounded noise-induced resonant behavior can occur in the SW network of neurons. In addition, rewiring probability p always impairs spiral waves, while spiral waves are confirmed to be robust for small p, thus shortcut-induced phase transition of spiral wave with the increase of p is induced. Furthermore, statistical factors of synchronization are calculated to discern the phase transition of spatial pattern, and it is confirmed that larger factor of synchronization is approached with increasing of rewiring probability p, and the stability of spiral wave is destroyed. PMID:28129401
NASA Astrophysics Data System (ADS)
Peng, Junhao
2016-03-01
In this paper, we study random walks on a small-world scale-free network, also called as pseudofractal scale-free web (PSFW), and analyze the volatilities of first passage time (FPT) and first return time (FRT) by using the variance and reduced moment as measures. Note that the FRT and FPT are deeply affected by the starting or target site. We do not intend to enumerate all the possible cases and analyze them. We only study the volatilities of FRT for a given hub (i.e. node with highest degree) and the volatilities of the global FPT (GFPT) to a given hub, which is the average of the FPTs for arriving at a given hub from any possible starting site selected randomly according to the equilibrium distribution of the Markov chain. Firstly, we calculate exactly the probability generating function of the GFPT and FRT based on the self-similar structure of the PSFW. Then, we calculate the probability distribution, mean, variance and reduced moment of the GFPT and FRT by using the generating functions as a tool. Results show that: the reduced moment of FRT grows with increasing network order N and tends to infinity while N\\to ∞ ; but for the reduced moments of GFPT, it is almost a constant(≈ 1.1605) for large N. Therefore, on the PSFW of large size, the FRT has huge fluctuations and the estimate provided by MFRT is unreliable, whereas the fluctuations of the GFPT are much smaller and the estimate provided by its mean is more reliable. The method we propose can also be used to analyze the volatilities of FPT and FRT on other networks with self-similar structure, such as (u, v) flowers and recursive scale-free trees.
NASA Astrophysics Data System (ADS)
Dong, Lin-Rong
2010-09-01
This paper investigates the dynamic evolution with limited learning information on a small-world network. In the system, the information among the interaction players is not very lucid, and the players are not allowed to inspect the profit collected by its neighbors, thus the focal player cannot choose randomly a neighbor or the wealthiest one and compare its payoff to copy its strategy. It is assumed that the information acquainted by the player declines in the form of the exponential with the geographical distance between the players, and a parameter V is introduced to denote the inspect-ability about the players. It is found that under the hospitable conditions, cooperation increases with the randomness and is inhibited by the large connectivity for the prisoner's dilemma; however, cooperation is maximal at the moderate rewiring probability and is chaos with the connectivity for the snowdrift game. For the two games, the acuminous sight is in favor of the cooperation under the hospitable conditions; whereas, the myopic eyes are advantageous to cooperation and cooperation increases with the randomness under the hostile condition.
Kim, Sang-Yoon; Lim, Woochang
2017-09-01
We consider an inhomogeneous small-world network (SWN) composed of inhibitory short-range (SR) and long-range (LR) interneurons, and investigate the effect of network architecture on emergence of synchronized brain rhythms by varying the fraction of LR interneurons plong. The betweenness centralities of the LR and SR interneurons (characterizing the potentiality in controlling communication between other interneurons) are distinctly different. Hence, in view of the betweenness, SWNs we consider are inhomogeneous, unlike the "canonical" Watts-Strogatz SWN with nearly the same betweenness centralities. For small plong, the load of communication traffic is much concentrated on a few LR interneurons. However, as plong is increased, the number of LR connections (coming from LR interneurons) increases, and then the load of communication traffic is less concentrated on LR interneurons, which leads to better efficiency of global communication between interneurons. Sparsely synchronized rhythms are thus found to emerge when passing a small critical value plong((c))(≃0.16). The population frequency of the sparsely synchronized rhythm is ultrafast (higher than 100 Hz), while the mean firing rate of individual interneurons is much lower (∼30 Hz) due to stochastic and intermittent neural discharges. These dynamical behaviors in the inhomogeneous SWN are also compared with those in the homogeneous Watts-Strogatz SWN, in connection with their network topologies. Particularly, we note that the main difference between the two types of SWNs lies in the distribution of betweenness centralities. Unlike the case of the Watts-Strogatz SWN, dynamical responses to external stimuli vary depending on the type of stimulated interneurons in the inhomogeneous SWN. We consider two cases of external time-periodic stimuli applied to sub-populations of the LR and SR interneurons, respectively. Dynamical responses (such as synchronization suppression and enhancement) to these two cases of
Adaptation algorithms for 2-D feedforward neural networks.
Kaczorek, T
1995-01-01
The generalized weight adaptation algorithms presented by J.G. Kuschewski et al. (1993) and by S.H. Zak and H.J. Sira-Ramirez (1990) are extended for 2-D madaline and 2-D two-layer feedforward neural nets (FNNs).
NASA Astrophysics Data System (ADS)
Zekri, Nouredine; Clerc, Jean Pierre
We study numerically in this work the statistical and dynamical properties of the clusters in a one dimensional small world model. The parameters chosen correspond to a realistic network of children of school age where a disease like measles can propagate. Extensive results on the statistical behavior of the clusters around the percolation threshold, as well as the evoltion with time, are discussed. To cite this article: N. Zekri, J.P. Clerc, C. R. Physique 3 (2002) 741-747.
2D Carbon Nanotube Network: A New material for Electronics
NASA Astrophysics Data System (ADS)
Gruner, George
2006-03-01
This talk will focus on the electronic properties of two dimensional carbon nanotube networks, and on their application potential. Percolation issues, together with the frequency, and temperature dependent activity will be discussed. The network can be tuned from having semiconducting to metallic like behavior, and doping with electron withdrawing and donating species leads to networks with tailor-made electronic properties. The network is also highly transparent in the visible spectral range, this attribute -- together with simple room temperature fab processes -- opens up application opportunities in the area of electronics, opto-electronics, photovoltaics and sensors. Recent results on solar cells, OLEDs and smart windows will be reviewed. Field effect transistors that incorporate nanotube network conducting channels, together with complex functional devices that incorporate networks and functional molecules will also be discussed. Finally a comparison will be made with conventional and emerging materials that compete area of disposable, flexible and printable electronics.
NASA Astrophysics Data System (ADS)
Li, Yan-Long; Ma, Jun; Zhang, Wei; Liu, Yan-Jun
2009-10-01
This paper numerically investigates the order parameter and synchronisation in the small world connected FitzHugh-Nagumo excitable systems. The simulations show that the order parameter continuously decreases with increasing D, the quality of the synchronisation worsens for large noise intensity. As the coupling intensity goes up, the quality of the synchronisation worsens, and it finds that the larger rewiring probability becomes the larger order parameter. It obtains the complete phase diagram for a wide range of values of noise intensity D and control parameter g.
2D pattern evolution constrained by complex network dynamics
NASA Astrophysics Data System (ADS)
da Rocha, L. E. C.; Costa, L. da F.
2007-03-01
Complex networks have established themselves in recent years as being particularly suitable and flexible for representing and modelling several complex natural and artificial systems. In the same time in which the structural intricacies of such networks are being revealed and understood, efforts have also been directed at investigating how such connectivity properties define and constrain the dynamics of systems unfolding on such structures. However, less attention has been focused on hybrid systems, i.e. involving more than one type of network and/or dynamics. Several real systems present such an organization, e.g. the dynamics of a disease coexisting with the dynamics of the immune system. The current paper investigates a specific system involving diffusive (linear and nonlinear) dynamics taking place in a regular network while interacting with a complex network of defensive agents following Erdös Rényi (ER) and Barabási Albert (BA) graph models with moveable nodes. More specifically, the complex network is expected to control, and if possible, to extinguish the diffusion of some given unwanted process (e.g. fire, oil spilling, pest dissemination, and virus or bacteria reproduction during an infection). Two types of pattern evolution are considered: Fick and Gray Scott. The nodes of the defensive network then interact with the diffusing patterns and communicate between themselves in order to control the diffusion. The main findings include the identification of higher efficiency for the BA control networks and the presence of relapses in the case of the ER model.
Magnetic anisotropy of metal functionalized phthalocyanine 2D networks
NASA Astrophysics Data System (ADS)
Zhu, Guojun; Zhang, Yun; Xiao, Huaping; Cao, Juexian
2016-06-01
The magnetic anisotropy of metal including Cr, Mn, Fe, Co, Mo, Tc, Ru, Rh, W, Re, Os, Ir atoms functionalized phthalocyanine networks have been investigated with first-principles calculations. The magnetic moments can be expressed as 8-n μB with n the electronic number of outmost d shell in the transition metals. The huge magnetocrystalline anisotropy energy (MAE) is obtained by torque method. Especially, the MAE of Re functionalized phthalocyanine network is about 20 meV with an easy axis perpendicular to the plane of phthalocyanine network. The MAE is further manipulated by applying the external biaxial strain. It is found that the MAE is linear increasing with the external strain in the range of -2% to 2%. Our results indicate an effective approach to modulate the MAE for practical application.
Magnetic anisotropy of metal functionalized phthalocyanine 2D networks
Zhu, Guojun; Zhang, Yun; Xiao, Huaping; Cao, Juexian
2016-06-15
The magnetic anisotropy of metal including Cr, Mn, Fe, Co, Mo, Tc, Ru, Rh, W, Re, Os, Ir atoms functionalized phthalocyanine networks have been investigated with first-principles calculations. The magnetic moments can be expressed as 8-n μ{sub B} with n the electronic number of outmost d shell in the transition metals. The huge magnetocrystalline anisotropy energy (MAE) is obtained by torque method. Especially, the MAE of Re functionalized phthalocyanine network is about 20 meV with an easy axis perpendicular to the plane of phthalocyanine network. The MAE is further manipulated by applying the external biaxial strain. It is found that the MAE is linear increasing with the external strain in the range of −2% to 2%. Our results indicate an effective approach to modulate the MAE for practical application. - Graphical abstract: The charge density redistribution (ρ{sub MPc}-ρ{sub M}-ρ{sub Pc}) and spin density of the CoPc molecule, from top- and side-views. Purple and green isosurfaces indicate charge depletion and accumulation, respectively. Display Omitted.
Construction and repair of highly ordered 2D covalent networks by chemical equilibrium regulation.
Guan, Cui-Zhong; Wang, Dong; Wan, Li-Jun
2012-03-21
The construction of well-ordered 2D covalent networks via the dehydration of di-borate aromatic molecules was successfully realized through introducing a small amount of water into a closed reaction system to regulate the chemical equilibrium.
Small Worldness in Dense and Weighted Connectomes
NASA Astrophysics Data System (ADS)
Colon-Perez, Luis; Couret, Michelle; Triplett, William; Price, Catherine; Mareci, Thomas
2016-05-01
The human brain is a heterogeneous network of connected functional regions; however, most brain network studies assume that all brain connections can be described in a framework of binary connections. The brain is a complex structure of white matter tracts connected by a wide range of tract sizes, which suggests a broad range of connection strengths. Therefore, the assumption that the connections are binary yields an incomplete picture of the brain. Various thresholding methods have been used to remove spurious connections and reduce the graph density in binary networks. But these thresholds are arbitrary and make problematic the comparison of networks created at different thresholds. The heterogeneity of connection strengths can be represented in graph theory by applying weights to the network edges. Using our recently introduced edge weight parameter, we estimated the topological brain network organization using a complimentary weighted connectivity framework to the traditional framework of a binary network. To examine the reproducibility of brain networks in a controlled condition, we studied the topological network organization of a single healthy individual by acquiring 10 repeated diffusion-weighted magnetic resonance image datasets, over a one-month period on the same scanner, and analyzing these networks with deterministic tractography. We applied a threshold to both the binary and weighted networks and determined that the extra degree of freedom that comes with the framework of weighting network connectivity provides a robust result as any threshold level. The proposed weighted connectivity framework provides a stable result and is able to demonstrate the small world property of brain networks in situations where the binary framework is inadequate and unable to demonstrate this network property.
Small Worldness in Dense and Weighted Connectomes.
Colon-Perez, Luis M; Couret, Michelle; Triplett, William; Price, Catherine C; Mareci, Thomas H
2016-05-01
The human brain is a heterogeneous network of connected functional regions; however, most brain network studies assume that all brain connections can be described in a framework of binary connections. The brain is a complex structure of white matter tracts connected by a wide range of tract sizes, which suggests a broad range of connection strengths. Therefore, the assumption that the connections are binary yields an incomplete picture of the brain. Various thresholding methods have been used to remove spurious connections and reduce the graph density in binary networks. But these thresholds are arbitrary and make problematic the comparison of networks created at different thresholds. The heterogeneity of connection strengths can be represented in graph theory by applying weights to the network edges. Using our recently introduced edge weight parameter, we estimated the topological brain network organization using a complimentary weighted connectivity framework to the traditional framework of a binary network. To examine the reproducibility of brain networks in a controlled condition, we studied the topological network organization of a single healthy individual by acquiring 10 repeated diffusion-weighted magnetic resonance image datasets, over a 1-month period on the same scanner, and analyzing these networks with deterministic tractography. We applied a threshold to both the binary and weighted networks and determined that the extra degree of freedom that comes with the framework of weighting network connectivity provides a robust result as any threshold level. The proposed weighted connectivity framework provides a stable result and is able to demonstrate the small world property of brain networks in situations where the binary framework is inadequate and unable to demonstrate this network property.
Batalle, Dafnis; Eixarch, Elisenda; Figueras, Francesc; Muñoz-Moreno, Emma; Bargallo, Nuria; Illa, Miriam; Acosta-Rojas, Ruthy; Amat-Roldan, Ivan; Gratacos, Eduard
2012-04-02
Intrauterine growth restriction (IUGR) due to placental insufficiency affects 5-10% of all pregnancies and it is associated with a wide range of short- and long-term neurodevelopmental disorders. Prediction of neurodevelopmental outcomes in IUGR is among the clinical challenges of modern fetal medicine and pediatrics. In recent years several studies have used magnetic resonance imaging (MRI) to demonstrate differences in brain structure in IUGR subjects, but the ability to use MRI for individual predictive purposes in IUGR is limited. Recent research suggests that MRI in vivo access to brain connectivity might have the potential to help understanding cognitive and neurodevelopment processes. Specifically, MRI based connectomics is an emerging approach to extract information from MRI data that exhaustively maps inter-regional connectivity within the brain to build a graph model of its neural circuitry known as brain network. In the present study we used diffusion MRI based connectomics to obtain structural brain networks of a prospective cohort of one year old infants (32 controls and 24 IUGR) and analyze the existence of quantifiable brain reorganization of white matter circuitry in IUGR group by means of global and regional graph theory features of brain networks. Based on global and regional analyses of the brain network topology we demonstrated brain reorganization in IUGR infants at one year of age. Specifically, IUGR infants presented decreased global and local weighted efficiency, and a pattern of altered regional graph theory features. By means of binomial logistic regression, we also demonstrated that connectivity measures were associated with abnormal performance in later neurodevelopmental outcome as measured by Bayley Scale for Infant and Toddler Development, Third edition (BSID-III) at two years of age. These findings show the potential of diffusion MRI based connectomics and graph theory based network characteristics for estimating differences in the
Small worlds in space: Synchronization, spatial and relational modularity
NASA Astrophysics Data System (ADS)
Brede, M.
2010-06-01
In this letter we investigate networks that have been optimized to realize a trade-off between enhanced synchronization and cost of wire to connect the nodes in space. Analyzing the evolved arrangement of nodes in space and their corresponding network topology, a class of small-world networks characterized by spatial and network modularity is found. More precisely, for low cost of wire optimal configurations are characterized by a division of nodes into two spatial groups with maximum distance from each other, whereas network modularity is low. For high cost of wire, the nodes organize into several distinct groups in space that correspond to network modules connected on a ring. In between, spatially and relationally modular small-world networks are found.
NASA Astrophysics Data System (ADS)
Du, Wen-Bo; Cao, Xian-Bin; Zhao, Lin; Zhou, Hong
2009-05-01
We investigate the evolutionary prisoner's dilemma game (PDG) on weighted Newman-Watts (NW) networks. In weighted NW networks, the link weight wij is assigned to the link between the nodes i and j as: wij = (κi · κj)β, where κi(κj) is the degree of node i(j) and β represents the strength of the correlations. Obviously, the link weight can be tuned by only one parameter β. We focus on the cooperative behavior and wealth distribution in the system. Simulation results show that the cooperator frequency is promoted by a large range of β and there is a minimal cooperation frequency around β = - 1. Moreover, we also employ the Gini coefficient to study the wealth distribution in the population. Numerical results show that the Gini coefficient reaches its minimum when β approx - 1. Our work may be helpful in understanding the emergence of cooperation and unequal wealth distribution in society.
Higher-Order Neural Networks Applied to 2D and 3D Object Recognition
NASA Technical Reports Server (NTRS)
Spirkovska, Lilly; Reid, Max B.
1994-01-01
A Higher-Order Neural Network (HONN) can be designed to be invariant to geometric transformations such as scale, translation, and in-plane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Thus, for 2D object recognition, the network needs to be trained on just one view of each object class, not numerous scaled, translated, and rotated views. Because the 2D object recognition task is a component of the 3D object recognition task, built-in 2D invariance also decreases the size of the training set required for 3D object recognition. We present results for 2D object recognition both in simulation and within a robotic vision experiment and for 3D object recognition in simulation. We also compare our method to other approaches and show that HONNs have distinct advantages for position, scale, and rotation-invariant object recognition. The major drawback of HONNs is that the size of the input field is limited due to the memory required for the large number of interconnections in a fully connected network. We present partial connectivity strategies and a coarse-coding technique for overcoming this limitation and increasing the input field to that required by practical object recognition problems.
Wilson punctured network defects in 2D q-deformed Yang-Mills theory
NASA Astrophysics Data System (ADS)
Watanabe, Noriaki
2016-12-01
In the context of class S theories and 4D/2D duality relations there, we discuss the skein relations of general topological defects on the 2D side which are expected to be counterparts of composite surface-line operators in 4D class S theory. Such defects are geometrically interpreted as networks in a three dimensional space. We also propose a conjectural computational procedure for such defects in two dimensional SU( N ) topological q-deformed Yang-Mills theory by interpreting it as a statistical mechanical system associated with ideal triangulations.
Automatic angle measurement of a 2D object using optical correlator-neural networks hybrid system
NASA Astrophysics Data System (ADS)
Manivannan, N.; Neil, M. A. A.
2011-04-01
In this paper a novel method is proposed and demonstrated for automatic rotation angle measurement of a 2D object using a hybrid architecture, consisting of a 4f optical correlator with a binary phase only multiplexed matched filter and a single layer neural network. The hybrid set-up can be considered as a two-layer perceptron-like neural network; an optical correlator is the first layer and the standard single layer neural network is the second layer. The training scheme used to train the hybrid architecture is a combination of a Direct Binary Search algorithm, to train the optical correlator, and an Error Back Propagation algorithm, to train the neural network. The aim is to perform the major information processing by the optical correlator with a small additional processing by the neural network stage. This allows the system to be used for real-time applications as optics has the inherent ability to process information in a parallel manner at high speed. The neural network stage gives an extra dimension of freedom so that complicated tasks like automatic rotation angle measurement can be achieved. Results of both computer simulation and experimental set-up are presented for rotation angle measurement of an English alphabetic character as a 2D object. The experimental set-up consists of a real optical correlator using two spatial light modulators for both input and frequency plane representations and a PC based model of a single layer network.
Nikolaisen, Julie; Nilsson, Linn I. H.; Pettersen, Ina K. N.; Willems, Peter H. G. M.; Lorens, James B.; Koopman, Werner J. H.; Tronstad, Karl J.
2014-01-01
Mitochondrial morphology and function are coupled in healthy cells, during pathological conditions and (adaptation to) endogenous and exogenous stress. In this sense mitochondrial shape can range from small globular compartments to complex filamentous networks, even within the same cell. Understanding how mitochondrial morphological changes (i.e. “mitochondrial dynamics”) are linked to cellular (patho) physiology is currently the subject of intense study and requires detailed quantitative information. During the last decade, various computational approaches have been developed for automated 2-dimensional (2D) analysis of mitochondrial morphology and number in microscopy images. Although these strategies are well suited for analysis of adhering cells with a flat morphology they are not applicable for thicker cells, which require a three-dimensional (3D) image acquisition and analysis procedure. Here we developed and validated an automated image analysis algorithm allowing simultaneous 3D quantification of mitochondrial morphology and network properties in human endothelial cells (HUVECs). Cells expressing a mitochondria-targeted green fluorescence protein (mitoGFP) were visualized by 3D confocal microscopy and mitochondrial morphology was quantified using both the established 2D method and the new 3D strategy. We demonstrate that both analyses can be used to characterize and discriminate between various mitochondrial morphologies and network properties. However, the results from 2D and 3D analysis were not equivalent when filamentous mitochondria in normal HUVECs were compared with circular/spherical mitochondria in metabolically stressed HUVECs treated with rotenone (ROT). 2D quantification suggested that metabolic stress induced mitochondrial fragmentation and loss of biomass. In contrast, 3D analysis revealed that the mitochondrial network structure was dissolved without affecting the amount and size of the organelles. Thus, our results demonstrate that 3D
The small-world of economy: a speculative proposal
NASA Astrophysics Data System (ADS)
Corso, G.; Lucena, L. S.; Thomé, Z. D.
2003-06-01
Using the small-world approach we suggest a network model for the economy. Our basic assumption is that the economic agents prefer to make business with the big business. This assumption makes the preferential attachment the main mechanism for the evolution of the economic network. We hypothesize that the connectivity of the economic network should reflect the wealth distribution of the society which is considered to be an exponential truncated power law. The objective of this paper is to model qualitatively the wealth distribution of a society using concepts based on evolving network. Several alternatives of evolving networks are discussed in an economic context.
Adaptive reorganization of 2D molecular nanoporous network induced by coadsorbed guest molecule.
Zheng, Qing-Na; Wang, Lei; Zhong, Yu-Wu; Liu, Xuan-He; Chen, Ting; Yan, Hui-Juan; Wang, Dong; Yao, Jian-Nian; Wan, Li-Jun
2014-03-25
The ordered array of nanovoids in nanoporous networks, such as honeycomb, Kagome, and square, provides a molecular template for the accommodation of "guest molecules". Compared with the commonly studied guest molecules featuring high symmetry evenly incorporated into the template, guest molecules featuring lower symmetry are rare to report. Herein, we report the formation of a distinct patterned superlattice of guest molecules by selective trapping of guest molecules into the honeycomb network of trimesic acid (TMA). Two distinct surface patterns have been achieved by the guest inclusion induced adaptive reconstruction of a 2D molecular nanoporous network. The honeycomb networks can synergetically tune the arrangement upon inclusion of the guest molecules with different core size but similar peripherals groups, resulting in a trihexagonal Kagome or triangular patterns.
Kim, Byoung Soo; Lee, Kangsuk; Kang, Seulki; Lee, Soyeon; Pyo, Jun Beom; Choi, In Suk; Char, Kookheon; Park, Jong Hyuk; Lee, Sang-Soo; Lee, Jonghwi; Son, Jeong Gon
2017-09-14
Stretchable energy storage systems are essential for the realization of implantable and epidermal electronics. However, high-performance stretchable supercapacitors have received less attention because currently available processing techniques and material structures are too limited to overcome the trade-off relationship among electrical conductivity, ion-accessible surface area, and stretchability of electrodes. Herein, we introduce novel 2D reentrant cellular structures of porous graphene/CNT networks for omnidirectionally stretchable supercapacitor electrodes. Reentrant structures, with inwardly protruded frameworks in porous networks, were fabricated by the radial compression of vertically aligned honeycomb-like rGO/CNT networks, which were prepared by a directional crystallization method. Unlike typical porous graphene structures, the reentrant structure provided structure-assisted stretchability, such as accordion and origami structures, to otherwise unstretchable materials. The 2D reentrant structures of graphene/CNT networks maintained excellent electrical conductivities under biaxial stretching conditions and showed a slightly negative or near-zero Poisson's ratio over a wide strain range because of their structural uniqueness. For practical applications, we fabricated all-solid-state supercapacitors based on 2D auxetic structures. A radial compression process up to 1/10(th) densified the electrode, significantly increasing the areal and volumetric capacitances of the electrodes. Additionally, vertically aligned graphene/CNT networks provided a plentiful surface area and induced sufficient ion transport pathways for the electrodes. Therefore, they exhibited high gravimetric and areal capacitance values of 152.4 F g(-1) and 2.9 F cm(-2), respectively, and had an excellent retention ratio of 88% under a biaxial strain of 100%. Auxetic cellular and vertically aligned structures provide a new strategy for the preparation of robust platforms for stretchable
Bosi, Susanna; Rauti, Rossana; Laishram, Jummi; Turco, Antonio; Lonardoni, Davide; Nieus, Thierry; Prato, Maurizio; Scaini, Denis; Ballerini, Laura
2015-01-01
To recreate in vitro 3D neuronal circuits will ultimately increase the relevance of results from cultured to whole-brain networks and will promote enabling technologies for neuro-engineering applications. Here we fabricate novel elastomeric scaffolds able to instruct 3D growth of living primary neurons. Such systems allow investigating the emerging activity, in terms of calcium signals, of small clusters of neurons as a function of the interplay between the 2D or 3D architectures and network dynamics. We report the ability of 3D geometry to improve functional organization and synchronization in small neuronal assemblies. We propose a mathematical modelling of network dynamics that supports such a result. Entrapping carbon nanotubes in the scaffolds remarkably boosted synaptic activity, thus allowing for the first time to exploit nanomaterial/cell interfacing in 3D growth support. Our 3D system represents a simple and reliable construct, able to improve the complexity of current tissue culture models. PMID:25910072
Simulation and analysis of solute transport in 2D fracture/pipe networks: The SOLFRAC program
NASA Astrophysics Data System (ADS)
Bodin, Jacques; Porel, Gilles; Delay, Fred; Ubertosi, Fabrice; Bernard, Stéphane; de Dreuzy, Jean-Raynald
2007-01-01
The Time Domain Random Walk (TDRW) method has been recently developed by Delay and Bodin [Delay, F. and Bodin, J., 2001. Time domain random walk method to simulate transport by advection-dispersion and matrix diffusion in fracture networks. Geophys. Res. Lett., 28(21): 4051-4054.] and Bodin et al. [Bodin, J., Porel, G. and Delay, F., 2003c. Simulation of solute transport in discrete fracture networks using the time domain random walk method. Earth Planet. Sci. Lett., 6566: 1-8.] for simulating solute transport in discrete fracture networks. It is assumed that the fracture network can reasonably be represented by a network of interconnected one-dimensional pipes (i.e. flow channels). Processes accounted for are: (1) advection and hydrodynamic dispersion in the channels, (2) matrix diffusion, (3) diffusion into stagnant zones within the fracture planes, (4) sorption reactions onto the fracture walls and in the matrix, (5) linear decay, and (6) mass sharing at fracture intersections. The TDRW method is handy and very efficient in terms of computation costs since it allows for the one-step calculation of the particle residence time in each bond of the network. This method has been programmed in C++, and efforts have been made to develop an efficient and user-friendly software, called SOLFRAC. This program is freely downloadable at the URL http://labo.univ-poitiers.fr/hydrasa/intranet/telechargement.htm. It calculates solute transport into 2D pipe networks, while considering different types of injections and different concepts of local dispersion within each flow channel. Post-simulation analyses are also available, such as the mean velocity or the macroscopic dispersion at the scale of the entire network. The program may be used to evaluate how a given transport mechanism influences the macroscopic transport behaviour of fracture networks. It may also be used, as is the case, e.g., with analytical solutions, to interpret laboratory or field tracer test experiments
Simulation and analysis of solute transport in 2D fracture/pipe networks: the SOLFRAC program.
Bodin, Jacques; Porel, Gilles; Delay, Fred; Ubertosi, Fabrice; Bernard, Stéphane; de Dreuzy, Jean-Raynald
2007-01-05
The Time Domain Random Walk (TDRW) method has been recently developed by Delay and Bodin [Delay, F. and Bodin, J., 2001. Time domain random walk method to simulate transport by advection-dispersion and matrix diffusion in fracture networks. Geophys. Res. Lett., 28(21): 4051-4054.] and Bodin et al. [Bodin, J., Porel, G. and Delay, F., 2003c. Simulation of solute transport in discrete fracture networks using the time domain random walk method. Earth Planet. Sci. Lett., 6566: 1-8.] for simulating solute transport in discrete fracture networks. It is assumed that the fracture network can reasonably be represented by a network of interconnected one-dimensional pipes (i.e. flow channels). Processes accounted for are: (1) advection and hydrodynamic dispersion in the channels, (2) matrix diffusion, (3) diffusion into stagnant zones within the fracture planes, (4) sorption reactions onto the fracture walls and in the matrix, (5) linear decay, and (6) mass sharing at fracture intersections. The TDRW method is handy and very efficient in terms of computation costs since it allows for the one-step calculation of the particle residence time in each bond of the network. This method has been programmed in C++, and efforts have been made to develop an efficient and user-friendly software, called SOLFRAC. This program is freely downloadable at the URL (labo.univ-poitiers.fr/hydrasa/intranet/telechargement.htm). It calculates solute transport into 2D pipe networks, while considering different types of injections and different concepts of local dispersion within each flow channel. Post-simulation analyses are also available, such as the mean velocity or the macroscopic dispersion at the scale of the entire network. The program may be used to evaluate how a given transport mechanism influences the macroscopic transport behaviour of fracture networks. It may also be used, as is the case, e.g., with analytical solutions, to interpret laboratory or field tracer test experiments performed
On the relation between the small world structure and scientific activities.
Ebadi, Ashkan; Schiffauerova, Andrea
2015-01-01
The modern science has become more complex and interdisciplinary in its nature which might encourage researchers to be more collaborative and get engaged in larger collaboration networks. Various aspects of collaboration networks have been examined so far to detect the most determinant factors in knowledge creation and scientific production. One of the network structures that recently attracted much theoretical attention is called small world. It has been suggested that small world can improve the information transmission among the network actors. In this paper, using the data on 12 periods of journal publications of Canadian researchers in natural sciences and engineering, the co-authorship networks of the researchers are created. Through measuring small world indicators, the small worldiness of the mentioned network and its relation with researchers' productivity, quality of their publications, and scientific team size are assessed. Our results show that the examined co-authorship network strictly exhibits the small world properties. In addition, it is suggested that in a small world network researchers expand their team size through getting connected to other experts of the field. This team size expansion may result in higher productivity of the whole team as a result of getting access to new resources, benefitting from the internal referring, and exchanging ideas among the team members. Moreover, although small world network is positively correlated with the quality of the articles in terms of both citation count and journal impact factor, it is negatively related with the average productivity of researchers in terms of the number of their publications.
Confinement properties of 2D porous molecular networks on metal surfaces.
Müller, Kathrin; Enache, Mihaela; Stöhr, Meike
2016-04-20
Quantum effects that arise from confinement of electronic states have been extensively studied for the surface states of noble metals. Utilizing small artificial structures for confinement allows tailoring of the surface properties and offers unique opportunities for applications. So far, examples of surface state confinement include thin films, artificial nanoscale structures, vacancy and adatom islands, self-assembled 1D chains, vicinal surfaces, quantum dots and quantum corrals. In this review we summarize recent achievements in changing the electronic structure of surfaces by adsorption of nanoporous networks whose design principles are based on the concepts of supramolecular chemistry. Already in 1993, it was shown that quantum corrals made from Fe atoms on a Cu(1 1 1) surface using single atom manipulation with a scanning tunnelling microscope confine the Shockley surface state. However, since the atom manipulation technique for the construction of corral structures is a relatively time consuming process, the fabrication of periodic two-dimensional (2D) corral structures is practically impossible. On the other side, by using molecular self-assembly extended 2D porous structures can be achieved in a parallel process, i.e. all pores are formed at the same time. The molecular building blocks are usually held together by non-covalent interactions like hydrogen bonding, metal coordination or dipolar coupling. Due to the reversibility of the bond formation defect-free and long-range ordered networks can be achieved. However, recently also examples of porous networks formed by covalent coupling on the surface have been reported. By the choice of the molecular building blocks, the dimensions of the network (pore size and pore to pore distance) can be controlled. In this way, the confinement properties of the individual pores can be tuned. In addition, the effect of the confined state on the hosting properties of the pores will be discussed in this review article.
Confinement properties of 2D porous molecular networks on metal surfaces
NASA Astrophysics Data System (ADS)
Müller, Kathrin; Enache, Mihaela; Stöhr, Meike
2016-04-01
Quantum effects that arise from confinement of electronic states have been extensively studied for the surface states of noble metals. Utilizing small artificial structures for confinement allows tailoring of the surface properties and offers unique opportunities for applications. So far, examples of surface state confinement include thin films, artificial nanoscale structures, vacancy and adatom islands, self-assembled 1D chains, vicinal surfaces, quantum dots and quantum corrals. In this review we summarize recent achievements in changing the electronic structure of surfaces by adsorption of nanoporous networks whose design principles are based on the concepts of supramolecular chemistry. Already in 1993, it was shown that quantum corrals made from Fe atoms on a Cu(1 1 1) surface using single atom manipulation with a scanning tunnelling microscope confine the Shockley surface state. However, since the atom manipulation technique for the construction of corral structures is a relatively time consuming process, the fabrication of periodic two-dimensional (2D) corral structures is practically impossible. On the other side, by using molecular self-assembly extended 2D porous structures can be achieved in a parallel process, i.e. all pores are formed at the same time. The molecular building blocks are usually held together by non-covalent interactions like hydrogen bonding, metal coordination or dipolar coupling. Due to the reversibility of the bond formation defect-free and long-range ordered networks can be achieved. However, recently also examples of porous networks formed by covalent coupling on the surface have been reported. By the choice of the molecular building blocks, the dimensions of the network (pore size and pore to pore distance) can be controlled. In this way, the confinement properties of the individual pores can be tuned. In addition, the effect of the confined state on the hosting properties of the pores will be discussed in this review article.
Smith, Brian J; Overholts, Anna C; Hwang, Nicky; Dichtel, William R
2016-03-04
We explore the crystallization of a high surface area imine-linked two-dimensional covalent organic framework (2D COF). The growth process reveals rapid initial formation of an amorphous network that subsequently crystallizes into the layered 2D network. The metastable amorphous polymer may be isolated and resubjected to growth conditions to form the COF. These experiments provide the first mechanistic insight into the mechanism of imine-linked 2D COF formation, which is distinct from that of boronate-ester linked COFs.
Statistical complexity is maximized in a small-world brain.
Tan, Teck Liang; Cheong, Siew Ann
2017-01-01
In this paper, we study a network of Izhikevich neurons to explore what it means for a brain to be at the edge of chaos. To do so, we first constructed the phase diagram of a single Izhikevich excitatory neuron, and identified a small region of the parameter space where we find a large number of phase boundaries to serve as our edge of chaos. We then couple the outputs of these neurons directly to the parameters of other neurons, so that the neuron dynamics can drive transitions from one phase to another on an artificial energy landscape. Finally, we measure the statistical complexity of the parameter time series, while the network is tuned from a regular network to a random network using the Watts-Strogatz rewiring algorithm. We find that the statistical complexity of the parameter dynamics is maximized when the neuron network is most small-world-like. Our results suggest that the small-world architecture of neuron connections in brains is not accidental, but may be related to the information processing that they do.
NASA Astrophysics Data System (ADS)
Mendoza-Torres, F.; Diaz-Viera, M. A.
2015-12-01
In many natural fractured porous media, such as aquifers, soils, oil and geothermal reservoirs, fractures play a crucial role in their flow and transport properties. An approach that has recently gained popularity for modeling fracture systems is the Discrete Fracture Network (DFN) model. This approach consists in applying a stochastic boolean simulation method, also known as object simulation method, where fractures are represented as simplified geometric objects (line segments in 2D and polygons in 3D). One of the shortcomings of this approach is that it usually does not consider the dependency relationships that may exist between the geometric properties of fractures (direction, length, aperture, etc), that is, each property is simulated independently. In this work a method for modeling such dependencies by copula theory is introduced. In particular, a nonparametric model using Bernstein copulas for direction-length fracture dependency in 2D is presented. The application of this method is illustrated in a case study for a fractured rock sample from a carbonate reservoir outcrop.
2D image classification for 3D anatomy localization: employing deep convolutional neural networks
NASA Astrophysics Data System (ADS)
de Vos, Bob D.; Wolterink, Jelmer M.; de Jong, Pim A.; Viergever, Max A.; Išgum, Ivana
2016-03-01
Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs -- heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was >=0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.
Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study
Kalantzis, Georgios; Vasquez-Quino, Luis A.; Zalman, Travis; Pratx, Guillem; Lei, Yu
2011-10-15
Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE{sup 3} v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the {gamma}-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average {gamma}-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average {gamma}-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations
GB(2D)2 PCA-based convolutional network for face recognition
NASA Astrophysics Data System (ADS)
Jiang, Min; Lu, Ruru; Kong, Jun; Wu, Xiao-Jun; Huo, Hongtao; Wang, Xiaofeng
2017-03-01
Face recognition is a challenging task in computer vision. Numerous efforts have been made to design low-level hand-crafted features for face recognition. Low-level hand-crafted features highly depend on prior knowledge, which is difficult to obtain without learning new domain knowledge. Recently, ConvNets have generated great attention for their ability of feature learning and achieved state-of-the-art results on many computer vision tasks. However, typical ConvNets are trained by a gradient descent method in supervised mode, which results in high computational complexity. To solve this problem, an efficient unsupervised deep learning network is proposed for face recognition in this paper, which combines both 2-D Gabor filters and 2 PCA to learn the multistage convolutional filters. To speed up the calculation, the learned high-dimensional features are further encoded using short binary hashes. Finally, the obtained output features are trained using LinearSVM. Extensive experimental results on several facial benchmark databases show that the proposed network can obtain competitive performance and robust distortion-tolerance for face recognition.
Güell, Aleix G.; Ebejer, Neil; Snowden, Michael E.; McKelvey, Kim; Macpherson, Julie V.; Unwin, Patrick R.
2012-01-01
Carbon nanotubes have attracted considerable interest for electrochemical, electrocatalytic, and sensing applications, yet there remains uncertainty concerning the intrinsic electrochemical (EC) activity. In this study, we use scanning electrochemical cell microscopy (SECCM) to determine local heterogeneous electron transfer (HET) kinetics in a random 2D network of single-walled carbon nanotubes (SWNTs) on an Si/SiO2 substrate. The high spatial resolution of SECCM, which employs a mobile nanoscale EC cell as a probe for imaging, enables us to sample the responses of individual portions of a wide range of SWNTs within this complex arrangement. Using two redox processes, the oxidation of ferrocenylmethyl trimethylammonium and the reduction of ruthenium (III) hexaamine, we have obtained conclusive evidence for the high intrinsic EC activity of the sidewalls of the large majority of SWNTs in networks. Moreover, we show that the ends of SWNTs and the points where two SWNTs cross do not show appreciably different HET kinetics relative to the sidewall. Using finite element method modeling, we deduce standard rate constants for the two redox couples and demonstrate that HET based solely on characteristic defects in the SWNT side wall is highly unlikely. This is further confirmed by the analysis of individual line profiles taken as the SECCM probe scans over an SWNT. More generally, the studies herein demonstrate SECCM to be a powerful and versatile method for activity mapping of complex electrode materials under conditions of high mass transport, where kinetic assignments can be made with confidence. PMID:22635266
Wealth redistribution in our small world
NASA Astrophysics Data System (ADS)
Iglesias, J. R.; Gonçalves, S.; Pianegonda, S.; Vega, J. L.; Abramson, G.
2003-09-01
We present a simplified model for the exploitation of resources by interacting agents, in an economy with small-world properties. It is shown that Gaussian distributions of wealth, with some cutoff at a poverty line are present for all values of the parameters, while the frequency of maxima and minima strongly depends on the connectivity and the disorder of the lattice. Finally, we compare a system where the commercial links are frozen with an economy where agents can choose their commercial partners at each time step.
Enhanced brain small-worldness after sleep deprivation: a compensatory effect.
Liu, Huan; Li, Hong; Wang, Yulin; Lei, Xu
2014-10-01
Sleep deprivation has a variable impact on extrinsic activities during multiple cognitive tasks, especially on mood and emotion processing. There is also a trait-like individual vulnerability or compensatory effect in cognition. Previous studies have elucidated the altered functional connectivity after sleep deprivation. However, it remains unclear whether the small-world properties of resting-state network are sensitive to sleep deprivation. A small-world network is a type of graph that combines a high local connectivity as well as a few long-range connections, which ensures a higher information-processing efficiency at a low cost. The complex network of the brain can be described as a small-world network, in which a node is a brain region and an edge is present when there is a functional correlation between two nodes. Here, we investigated the topological properties of the human brain networks of 22 healthy subjects under sufficient sleep and sleep-deprived conditions. Specifically, small-worldness is utilized to quantify the small-world property, by comparing the clustering coefficient and path length of a given network to an equivalent random network with same degree distribution. After sufficient sleep, the brain networks showed the property of small-worldness. Compared with the resting state under sufficient sleep, the small-world property was significantly enhanced in the sleep deprivation condition, suggesting a possible compensatory adaptation of the human brain. Specifically, the altered measurements were correlated with the neuroticism of subjects, indicating that individuals with low-levels of neuroticism are more resilient to sleep deprivation.
Caballero, Julio; Garriga, Miguel; Fernández, Michael
2006-05-15
Negative inotropic potency of 60 benzothiazepine-like calcium entry blockers (CEBs), Diltiazem analogs, was successfully modeled using Bayesian-regularized genetic neural networks (BRGNNs) and 2D autocorrelation vectors. This approach yielded reliable and robust models whilst by means of a linear genetic algorithm (GA) search routine no multilinear regression model was found describing more than 50% of the training set. On the contrary, the optimum neural network predictor with five inputs described about 84% and 65% variances of 50 randomly selected training and test sets. Autocorrelation vectors in the nonlinear model contained information regarding 2D spatial distributions on the CEB structure of van der Waals volumes, electronegativities, and polarizabilities. However, a sensitivity analysis of the network inputs pointed out to the electronegativity and polarizability 2D topological distributions at substructural fragments of sizes 3 and 4 as the most relevant features governing the nonlinear modeling of the negative inotropic potency.
Facial Sketch Synthesis Using 2D Direct Combined Model-Based Face-Specific Markov Network.
Tu, Ching-Ting; Chan, Yu-Hsien; Chen, Yi-Chung
2016-08-01
A facial sketch synthesis system is proposed, featuring a 2D direct combined model (2DDCM)-based face-specific Markov network. In contrast to the existing facial sketch synthesis systems, the proposed scheme aims to synthesize sketches, which reproduce the unique drawing style of a particular artist, where this drawing style is learned from a data set consisting of a large number of image/sketch pairwise training samples. The synthesis system comprises three modules, namely, a global module, a local module, and an enhancement module. The global module applies a 2DDCM approach to synthesize the global facial geometry and texture of the input image. The detailed texture is then added to the synthesized sketch in a local patch-based manner using a parametric 2DDCM model and a non-parametric Markov random field (MRF) network. Notably, the MRF approach gives the synthesized results an appearance more consistent with the drawing style of the training samples, while the 2DDCM approach enables the synthesis of outcomes with a more derivative style. As a result, the similarity between the synthesized sketches and the input images is greatly improved. Finally, a post-processing operation is performed to enhance the shadowed regions of the synthesized image by adding strong lines or curves to emphasize the lighting conditions. The experimental results confirm that the synthesized facial images are in good qualitative and quantitative agreement with the input images as well as the ground-truth sketches provided by the same artist. The representing power of the proposed framework is demonstrated by synthesizing facial sketches from input images with a wide variety of facial poses, lighting conditions, and races even when such images are not included in the training data set. Moreover, the practical applicability of the proposed framework is demonstrated by means of automatic facial recognition tests.
Oh, Sejong; Choe, Yoonsuck
2007-08-01
Texture boundary detection (or segmentation) is an important capability in human vision. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct (i.e., occluded) surfaces. Hence, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this paper, we conducted computational experiments with artificial neural networks to investigate the relative difficulty of learning to segment textures defined on flat 2D surfaces vs. those in 3D configurations where the boundaries are defined by occluding surfaces and their change over time due to the observer's motion. It turns out that learning is faster and more accurate in 3D, very much in line with our expectation. Furthermore, our results showed that the neural network's learned ability to segment texture in 3D transfers well into 2D texture segmentation, bolstering our initial hypothesis, and providing insights on the possible developmental origin of 2D texture segmentation function in human vision.
NASA Astrophysics Data System (ADS)
Wang, Ping; Le-Ngoc, Tho
2005-10-01
This paper presents a two-dimensional optical code division multiple access (2-D-OCDMA) scheme using multiwavelength pulse modulation (MWPM), double optical hard limiters (DHL), and modified carrier-hopping prime sequences (MCHP) to increase the achievable system capacity. Design criteria to reduce multiaccess interference (MAI) are established and indicate that suitable signature sequences for 2-D-OCDMA/MWPM must have good cross-correlation property in terms of both time shift and wavelength shift. Performance analysis of 2-D-OCDMA/MWPM/DHL systems in the presence of MAI and photo detector shot noise is developed. Simulation and analytical results are in very good agreement and indicate that the proposed 2-D-OCDMA/MWPM/DHL systems using MCHP sequences can offer a much larger capacity than others, suitable for applications in broadband fiber-optic access networks.
Possible Origin of Efficient Navigation in Small Worlds
NASA Astrophysics Data System (ADS)
Hu, Yanqing; Wang, Yougui; Li, Daqing; Havlin, Shlomo; di, Zengru
2011-03-01
The small-world phenomenon is one of the most important properties found in social networks. It includes both short path lengths and efficient navigation between two individuals. It is found by Kleinberg that navigation is efficient only if the probability density distribution of an individual to have a friend at distance r scales as P(r)˜r-1. Although this spatial scaling is found in many empirical studies, the origin of how this scaling emerges is still missing. In this Letter, we propose the origin of this scaling law using the concept of entropy from statistical physics and show that this scaling is the result of optimization of collecting information in social networks.
On the Relation between the Small World Structure and Scientific Activities
Ebadi, Ashkan; Schiffauerova, Andrea
2015-01-01
The modern science has become more complex and interdisciplinary in its nature which might encourage researchers to be more collaborative and get engaged in larger collaboration networks. Various aspects of collaboration networks have been examined so far to detect the most determinant factors in knowledge creation and scientific production. One of the network structures that recently attracted much theoretical attention is called small world. It has been suggested that small world can improve the information transmission among the network actors. In this paper, using the data on 12 periods of journal publications of Canadian researchers in natural sciences and engineering, the co-authorship networks of the researchers are created. Through measuring small world indicators, the small worldiness of the mentioned network and its relation with researchers’ productivity, quality of their publications, and scientific team size are assessed. Our results show that the examined co-authorship network strictly exhibits the small world properties. In addition, it is suggested that in a small world network researchers expand their team size through getting connected to other experts of the field. This team size expansion may result in higher productivity of the whole team as a result of getting access to new resources, benefitting from the internal referring, and exchanging ideas among the team members. Moreover, although small world network is positively correlated with the quality of the articles in terms of both citation count and journal impact factor, it is negatively related with the average productivity of researchers in terms of the number of their publications. PMID:25780922
Formats and Network Protocols for Browser Access to 2D Raster Data
NASA Astrophysics Data System (ADS)
Plesea, L.
2015-12-01
Tiled web maps in browsers are a major success story, forming the foundation of many current web applications. Enabling tiled data access is the next logical step, and is likely to meet with similar success. Many ad-hoc approaches have already started to appear, and something similar is explored within the Open Geospatial Consortium. One of the main obstacles in making browser data access a reality is the lack of a well-known data format. This obstacle also represents an opportunity to analyze the requirements and possible candidates, applying lessons learned from web tiled image services and protocols. Similar to the image counterpart, a web tile raster data format needs to have good intrinsic compression and be able to handle high byte count data types including floating point. An overview of a possible solution to the format problem, a 2D data raster compression algorithm called Limited Error Raster Compression (LERC) will be presented. In addition to the format, best practices for high request rate HTTP services also need to be followed. In particular, content delivery network (CDN) caching suitability needs to be part of any design, not an after-thought. Last but not least, HTML 5 browsers will certainly be part of any solution since they provide improved access to binary data, as well as more powerful ways to view and interact with the data in the browser. In a simple but relevant application, digital elevation model (DEM) raster data is served as LERC compressed data tiles which are used to generate terrain by a HTML5 scene viewer.
Carpanese, Cristina; Ferlay, Sylvie; Kyritsakas, Nathalie; Henry, Marc; Hosseini, Mir Wais
2009-11-28
Using a combination of charge-assisted H- and coordination-bonds, a tetra component system composed of a dicationic and a dianionic organic tecton, Ag(+) cation and XF(6)(-) (X= P, As, Sb) anion behaves as planned and leads to the formation of 2-D isostructural networks for which the energetic contributions of the two recognition events dominate the construction process.
Space missions orbits around small worlds
NASA Astrophysics Data System (ADS)
Cardoso dos Santos, Josué; dos Santos Carvalho, Jean Paulo; Vilhena de Moraes, Rodolpho; Bertachini de Almeida Prado, Antônio Fernando
2015-08-01
Space missions under study to visit icy moons and small worlds in our solar system will requires orbits with low-altitude and high inclinations. These orbits provides a better coverage to map the surface and to analyse the gravitational and magnetic fields. In this context, obtain these orbits has become important in planning of these missions. Celestial bodies like Haumea, Europa, Ganymede, Callisto, Enceladus, Titan and Triton are among the objects under study study to receive missions in a near future. In order to obtain low-altitude and high inclined orbits for future exploration of these bodies, this work aims to present an analytical study to describe and evaluate gravitational disturbances over a spacecraft's orbit around a minor body. An analytical model for the third-body perturbation is presented. Perturbations due to the non-sphericity of the minor body are considered. The effects on spacecraft's orbital elements are analyzed to provide the the more useful and desired orbits. The dynamic of these orbits is explored by numerical simulations. The results present good accordance with the literature.
Kim, Byoung Soo; Pyo, Jun Beom; Son, Jeong Gon; Zi, Goangseup; Lee, Sang-Soo; Park, Jong Hyuk; Lee, Jonghwi
2017-03-29
Networks of silver nanowires (Ag NWs) have been considered as promising materials for stretchable and transparent conductors. Despite various improvements of their optoelectronic and electromechanical properties over the past few years, Ag NW networks with a sufficient stretchability in multiple directions that is essential for the accommodation of the multidirectional strains of human movement have seldom been reported. For this paper, biaxially stretchable, transparent conductors were developed based on 2D mass-spring networks of wavy Ag NWs. Inspired by the traditional papermaking process, the 2D wavy networks were produced by floating Ag NW networks on the surface of water and subsequently applying biaxial compression to them. It was demonstrated that this floating-compression process can reduce the friction between the Ag NW-water interfaces, providing a uniform and isotropic in-plane waviness for the networks without buckling or cracking. The resulting Ag NW networks that were transferred onto elastomeric substrates successfully acted as conductors with an excellent transparency, conductivity, and electromechanical stability under a biaxial strain of 30%. The strain sensors that are based on the prepared conductors demonstrated a great potential for the enhanced performances of future wearable devices.
Identifying critical regions in small-world marine metapopulations.
Watson, James R; Siegel, David A; Kendall, Bruce E; Mitarai, Satoshi; Rassweiller, Andrew; Gaines, Steven D
2011-10-25
The precarious state of many nearshore marine ecosystems has prompted the use of marine protected areas as a tool for management and conservation. However, there remains substantial debate over their design and, in particular, how to best account for the spatial dynamics of nearshore marine species. Many commercially important nearshore marine species are sedentary as adults, with limited home ranges. It is as larvae that they disperse greater distances, traveling with ocean currents sometimes hundreds of kilometers. As a result, these species exist in spatially complex systems of connected subpopulations. Here, we explicitly account for the mutual dependence of subpopulations and approach protected area design in terms of network robustness. Our goal is to characterize the topology of nearshore metapopulation networks and their response to perturbation, and to identify critical subpopulations whose protection would reduce the risk for stock collapse. We define metapopulation networks using realistic estimates of larval dispersal generated from ocean circulation simulations and spatially explicit metapopulation models, and we then explore their robustness using node-removal simulation experiments. Nearshore metapopulations show small-world network properties, and we identify a set of highly connected hub subpopulations whose removal maximally disrupts the metapopulation network. Protecting these subpopulations reduces the risk for systemic failure and stock collapse. Our focus on catastrophe avoidance provides a unique perspective for spatial marine planning and the design of marine protected areas.
Shi, Li; Niu, Xiaoke; Wan, Hong
2015-05-01
The biological networks have been widely reported to present small-world properties. However, the effects of small-world network structure on population's encoding performance remain poorly understood. To address this issue, we applied a small world-based framework to quantify and analyze the response dynamics of cell assemblies recorded from rat primary visual cortex, and further established a population encoding model based on small world-based generalized linear model (SW-GLM). The electrophysiological experimental results show that the small world-based population responses to different topological shapes present significant variation (t test, p < 0.01; effect size: Hedge's g > 0.8), while no significant variation was found for control networks without considering their spatial connectivity (t test, p > 0.05; effect size: Hedge's g < 0.5). Furthermore, the numerical experimental results show that the predicted response under SW-GLM is more accurate and reliable compared to the control model without small-world structure, and the decoding performance is also improved about 10 % by taking the small-world structure into account. The above results suggest the important role of the small-world neural structure in encoding visual information for the neural population by providing electrophysiological and theoretical evidence, respectively. The study helps greatly to well understand the population encoding mechanisms of visual cortex.
NASA Astrophysics Data System (ADS)
Rabbani, Arash; Ayatollahi, Shahab; Kharrat, Riyaz; Dashti, Nader
2016-08-01
In this study, we have utilized 3-D micro-tomography images of real and synthetic rocks to introduce two mathematical correlations which estimate the distribution parameters of 3-D coordination number using a single 2-D cross-sectional image. By applying a watershed segmentation algorithm, it is found that the distribution of 3-D coordination number is acceptably predictable by statistical analysis of the network extracted from 2-D images. In this study, we have utilized 25 volumetric images of rocks in order to propose two mathematical formulas. These formulas aim to approximate the average and standard deviation of coordination number in 3-D pore networks. Then, the formulas are applied for five independent test samples to evaluate the reliability. Finally, pore network flow modeling is used to find the error of absolute permeability prediction using estimated and measured coordination numbers. Results show that the 2-D images are considerably informative about the 3-D network of the rocks and can be utilized to approximate the 3-D connectivity of the porous spaces with determination coefficient of about 0.85 that seems to be acceptable considering the variety of the studied samples.
NASA Astrophysics Data System (ADS)
Neyamadpour, Ahmad; Taib, Samsudin; Wan Abdullah, W. A. T.
2009-11-01
MATLAB is a high-level matrix/array language with control flow statements and functions. MATLAB has several useful toolboxes to solve complex problems in various fields of science, such as geophysics. In geophysics, the inversion of 2D DC resistivity imaging data is complex due to its non-linearity, especially for high resistivity contrast regions. In this paper, we investigate the applicability of MATLAB to design, train and test a newly developed artificial neural network in inverting 2D DC resistivity imaging data. We used resilient propagation to train the network. The model used to produce synthetic data is a homogeneous medium of 100 Ω m resistivity with an embedded anomalous body of 1000 Ω m. The location of the anomalous body was moved to different positions within the homogeneous model mesh elements. The synthetic data were generated using a finite element forward modeling code by means of the RES2DMOD. The network was trained using 21 datasets and tested on another 16 synthetic datasets, as well as on real field data. In field data acquisition, the cable covers 120 m between the first and the last take-out, with a 3 m x-spacing. Three different electrode spacings were measured, which gave a dataset of 330 data points. The interpreted result shows that the trained network was able to invert 2D electrical resistivity imaging data obtained by a Wenner-Schlumberger configuration rapidly and accurately.
2D warp-and-woof interwoven networks constructed by helical chains with different chirality.
Feng, Yuhua; Guo, Yang; OuYang, Yan; Liu, Zhanquan; Liao, Daizheng; Cheng, Peng; Yan, Shiping; Jiang, Zonghui
2007-09-21
Two unprecedented 2D entangled layers of warp-and-woof threads interwoven by left- and right-handed helical chains, {[Mn(salen)Au(CN)2]4(H2O)}n (salen = N,N'-ethylenebis(salicylideneaminato)) and {Mn(acacen)Ag(CN)2}n (acacen = N,N'-ethylenebis(acetylacetonylideneiminate)) 2, have been synthesized and characterized.
NASA Astrophysics Data System (ADS)
Shurong, Sun; Yin, Hongxi; Wang, Ziyu; Xu, Anshi
2006-04-01
A new family of two-dimensional optical orthogonal code (2-D OOC), one-coincidence frequency hop code (OCFHC)/OOC, which employs OCFHC and OOC as wavelengthhopping and time-spreading patterns, respectively, is proposed in this paper. In contrary to previously constructed 2-D OOCs, OCFHC/OOC provides more choices on the number of available wavelengths and its cardinality achieves the upper bound in theory without sacrificing good auto-and-cross correlation properties, i.e., the correlation properties of the code is still ideal. Meanwhile, we utilize a new method, called effective normalized throughput, to compare the performance of diverse codes applicable to optical code division multiple access (OCDMA) systems besides conventional measure bit error rate, and the results indicate that our code performs better than obtained OCDMA codes and is truly applicable to OCDMA networks as multiaccess codes and will greatly facilitate the implementation of OCDMA access networks.
NASA Astrophysics Data System (ADS)
Verbovšek, T.
2009-04-01
Fractal dimensions of fracture networks (D) are usually determined from 2-D objects, like the digitized fracture traces in outcrops. Sometimes, extrapolations to higher dimensions are required if the measurements (for example fracture traces in the boreholes or in the scanlines) are performed in 1-D environment (D1-D) and are later upscaled to higher dimensions (D2-D). For isotropic fractals this relation should be straight-forward according to the theory: D2-D = D1-D +1, as the intersection of a 2-D fractal with a plane results in a fractal with D1-D equal to D2-D minus one. Some authors have questioned this relation and proposed different empirical relationships. Still, there exist very few field studies of natural fracture networks to support or test such a relationship. The study was therefore focused on the analysis of 23 natural fracture networks in Triassic dolomites in Slovenia. The traces of these fractures were analyzed separately in both 1-D and 2-D environments, and relationships between the obtained fractal dimensions were determined. For 2-D data, the digitized images of fracture traces in 2048x2048 pixel resolution were analyzed by the box-counting method, considering truncation and censoring effects (the 'cut-off' method, using only the valid data right of the cut-off points) and also by considering the complete data range interval (the 'full' method). These values were consequently compared to 1-D values. Those were obtained by dissecting images in both x- and y-directions into 2048 smaller linear images of 1-pixel width, simulating the intersection with a plane. Such line images were then examined by the fracture line-counting method, a 1-D equivalent of the box-counting technique. Results show that the values of all fractal dimensions, regardless of the different fracture networks or the method used, lie in a very narrow data range, and the standard deviations are very small (up to 0.03). The small range can be attributed to a similar fracturing
NASA Astrophysics Data System (ADS)
Michie, Craig; Andonovic, Ivan; Atkinson, R.; Deng, Yanhua; Szefer, Jakub; Bres, Camille-Sophie; Huang, Yue Kai; Glesk, Ivan; Prucnal, Paul; Sasaki, Kensuke; Gupta, Gyaneshwar
2007-06-01
The results of a range of experimental characterization exercises of interferometric noise for the case of a representative 2-D time-spreading wavelength-hopping optical code family are presented. Interferometric noise is evaluated at a data rate of 2.5 Gbits/s within an OCDMA network emulation test bed established utilizing fiber Bragg grating encoders/decoders. The results demonstrate that this form of noise introduces significant system power penalties and must be taken into consideration in any OCDMA network designs and implementations.
Design of multi-functional 2D open-shell organic networks with mechanically controllable properties.
Alcón, Isaac; Reta, Daniel; Moreira, Iberio de P R; Bromley, Stefan T
2017-02-01
Triarylmethyls (TAMs) are prominent highly attractive open shell organic molecular building blocks for materials science, having been used in breakthrough syntheses of organic magnetic polymers and metal organic frameworks. With their radical π-conjugated nature and a proven capacity to possess high stability via suitable chemical design, TAMs display a variety of desirable characteristics which can be exploited for a wide range of applications. Due to their particular molecular and electronic structure, the spin localization in TAMs almost entirely depends on the dihedral angles of their three aryl rings with respect to the central methyl carbon atom plane, which opens up the possibility of controlling their fundamental properties by twisting the three aryl rings. Aryl ring twist angles can be tuned to a single value by specific chemical functionalisation but controlling them by external means in organic materials or devices represents a challenging task which has not yet been experimentally achieved. Herein, through rational chemical design we propose two 2D covalent organic frameworks (2D-COFs) based on specific TAM building blocks. By employing ab initio computational modeling we demonstrate that it is possible to externally manipulate the aryl ring twist angles in these 2D-linked TAM frameworks by external mechanical means. Furthermore, we show this structural manipulation allows for finely tuning the most important characteristics of these materials such as spin localization, optical electronic transitions and magnetic interactions. Due to the enormous technological potential offered by this new class of material and the fact that our work is guided by real advances in organic materials synthesis, we believe that our predictions will inspire the experimental realization of radical-2D-COFs with externally controllable characteristics.
Small-World Characteristics of Cortical Connectivity Changes in Acute Stroke.
Caliandro, Pietro; Vecchio, Fabrizio; Miraglia, Francesca; Reale, Giuseppe; Della Marca, Giacomo; La Torre, Giuseppe; Lacidogna, Giordano; Iacovelli, Chiara; Padua, Luca; Bramanti, Placido; Rossini, Paolo Maria
2017-01-01
Background After cerebral ischemia, disruption and subsequent reorganization of functional connections occur both locally and remote to the lesion. Recently, complexity of brain connectivity has been described using graph theory, a mathematical approach that depicts important properties of complex systems by quantifying topologies of network representations. Functional and dynamic changes of brain connectivity can be reliably analyzed via electroencephalography (EEG) recordings even when they are not yet reflected in structural changes of connections. Objective We tested whether and how ischemic stroke in the acute stage may determine changes in small-worldness of cortical networks as measured by cortical sources of EEG. Methods Graph characteristics of EEG of 30 consecutive stroke patients in acute stage (no more than 5 days after the event) were examined. Connectivity analysis was performed using eLORETA in both hemispheres. Results Network rearrangements were mainly detected in delta, theta, and alpha bands when patients were compared with healthy subjects. In delta and alpha bands similar findings were observed in both hemispheres regardless of the side of ischemic lesion: bilaterally decreased small-worldness in the delta band and bilaterally increased small-worldness in the alpha2 band. In the theta band, bilaterally decreased small-worldness was observed only in patients with stroke in the left hemisphere. Conclusions After an acute stroke, brain cortex rearranges its network connections diffusely, in a frequency-dependent modality probably in order to face the new anatomical and functional frame.
Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain
2016-10-01
This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.
Tuning the resonance properties of 2D carbon nanotube networks towards a mechanical resonator
NASA Astrophysics Data System (ADS)
Zhan, Haifei; Zhang, Guiyong; Zhang, Baocheng; Bell, John M.; Gu, Yuantong
2015-08-01
The capabilities of the mechanical resonator-based nanosensors in detecting ultra-small mass or force shifts have driven a continuing exploration of the palette of nanomaterials for such application purposes. Based on large-scale molecular dynamics simulations, we have assessed the applicability of a new class of carbon nanomaterials for nanoresonator usage, i.e. the single-wall carbon nanotube (SWNT) network. It is found that SWNT networks inherit excellent mechanical properties from the constituent SWNTs, possessing a high natural frequency. However, although a high quality factor is suggested from the simulation results, it is hard to obtain an unambiguous Q-factor due to the existence of vibration modes in addition to the dominant mode. The nonlinearities resulting from these extra vibration modes are found to exist uniformly under various testing conditions including different initial actuations and temperatures. Further testing shows that these modes can be effectively suppressed through the introduction of axial strain, leading to an extremely high quality factor in the order of 109 estimated from the SWNT network with 2% tensile strain. Additional studies indicate that the carbon rings connecting the SWNTs can also be used to alter the vibrational properties of the resulting network. This study suggests that the SWNT network can be a good candidate for applications as nanoresonators.
A 2D zinc-organic network being easily exfoliated into isolated sheets
NASA Astrophysics Data System (ADS)
Yu, Guihong; Li, Ruiqing; Leng, Zhihua; Gan, Shucai
2016-08-01
A metal-organic aggregate, namely {Zn2Cl2(BBC)}n (BBC = 4,4‧,4‧‧-(benzene-1,3,5-triyl-tris(benzene-4,1-diyl))tribenzoate) was obtained by solvothermal synthesis. Its structure is featured with the Zn2(COO)3 paddle-wheels with two chloride anions on axial positions and hexagonal pores in the layers. The exclusion of water in the precursor and the solvent plays a crucial role in the formation of target compound. This compound can be easily dissolved in alkaline solution and exfoliated into isolated sheets, which shows a novel way for the preparation of 2D materials.
2D wireless sensor network deployment based on Centroidal Voronoi Tessellation
NASA Astrophysics Data System (ADS)
Iliodromitis, Athanasios; Pantazis, George; Vescoukis, Vasileios
2017-06-01
In recent years, Wireless Sensor Networks (WSNs) have rapidly evolved and now comprise a powerful tool in monitoring and observation of the natural environment, among other fields. The use of WSNs is critical in early warning systems, which are of high importance today. In fact, WSNs are adopted more and more in various applications, e.g. for fire or deformation detection. The optimum deployment of sensors is a multi-dimensional problem, which has two main components; network and positioning approach. Although lots of work has dealt with the issue, most of it emphasizes on mere network approach (communication, energy consumption) and not on the topography (positioning) of the sensors in achieving ideal geometry. In some cases, it is hard or even impossible to achieve perfect geometry in nodes' deployment. The ideal and desirable scenario of nodes arranged in square or hexagonal grid would raise extremely the cost of the network, especially in unfriendly or hostile environments. In such environments the positions of the sensors have to be chosen among a list of possible points, which in most cases are randomly distributed. This constraint has to be taken under consideration during the WSN planning. Full geographical coverage is in some applications of the same, if not of greater, importance than the network coverage. Cost is a crucial factor at network planning and given that resources are often limited, what matters, is to cover the whole area with the minimum number of sensors. This paper suggests a deployment method for nodes, in large scale and high density WSNs, based on Centroidal Voronoi Tessellation (CVT). It approximates the solution through the geometry of the random points and proposes a deployment plan, for the given characteristics of the study area, in order to achieve a deployment as near as possible to the ideal one.
NASA Astrophysics Data System (ADS)
Zhang, Z.-Z.; Zhou, S.-G.; Zou, T.
2007-04-01
In this paper, firstly, we study analytically the topological features of a family of hierarchical lattices (HLs) from the view point of complex networks. We derive some basic properties of HLs controlled by a parameter q: scale-free degree distribution with exponent γ=2+ln 2/(ln q), null clustering coefficient, power-law behavior of grid coefficient, exponential growth of average path length (non-small-world), fractal scaling with dimension dB=ln (2q)/(ln 2), and disassortativity. Our results show that scale-free networks are not always small-world, and support the conjecture that self-similar scale-free networks are not assortative. Secondly, we define a deterministic family of graphs called small-world hierarchical lattices (SWHLs). Our construction preserves the structure of hierarchical lattices, including its degree distribution, fractal architecture, clustering coefficient, while the small-world phenomenon arises. Finally, the dynamical processes of intentional attacks and collective synchronization are studied and the comparisons between HLs and Barabási-Albert (BA) networks as well as SWHLs are shown. We find that the self-similar property of HLs and SWHLs significantly increases the robustness of such networks against targeted damage on hubs, as compared to the very vulnerable non fractal BA networks, and that HLs have poorer synchronizability than their counterparts SWHLs and BA networks. We show that degree distribution of scale-free networks does not suffice to characterize their synchronizability, and that networks with smaller average path length are not always easier to synchronize.
NASA Astrophysics Data System (ADS)
Hamerly, Ryan; Inaba, Kensuke; Inagaki, Takahiro; Takesue, Hiroki; Yamamoto, Yoshihisa; Mabuchi, Hideo
2016-09-01
A network of optical parametric oscillators (OPOs) is used to simulate classical Ising and XY spin chains. The collective nonlinear dynamics of this network, driven by quantum noise rather than thermal fluctuations, seeks out the Ising/XY ground state as the system transitions from below to above the lasing threshold. We study the behavior of this “Ising machine” for three canonical problems: a 1D ferromagnetic spin chain, a 2D square lattice and problems where next-nearest-neighbor couplings give rise to frustration. If the pump turn-on time is finite, topological defects form (domain walls for the Ising model, winding number and vortices for XY) and their density can be predicted from a numerical model involving a linear “growth stage” and a nonlinear “saturation stage”. These predictions are compared against recent data for a 10,000-spin 1D Ising machine.
Ferrimagnetism in 2D networks of porphyrin-X and -XO (X=Sc,...,Zn) with acetylene bridges
NASA Astrophysics Data System (ADS)
Wierzbowska, Małgorzata; Sobolewski, Andrzej L.
2016-03-01
Magnetism in 2D networks of the acetylene-bridged transition metal porphyrins M(P)-2(C-C)-2 (denoted P-TM), and oxo-TM-porphyrins OM(P)-2(C-C)-2 (denoted P-TMO), is studied with the density functional theory (DFT) and the self-interaction corrected pseudopotential scheme (pSIC). Addition of oxygen lowers magnetism of P-TMO with respect to the corresponding P-TM for most of the first-half 3d-row TMs. In contrast, binding O with the second-half 3d-row TMs or Sc increases the magnetic moments. Ferrimagnetism is found for the porphyrin networks with the TMs from V to Co and also for these cases with oxygen. This is a long-range effect of the delocalized spin-polarization, extended even to the acetylene bridges.
Artificial neural networks and model-based recognition of 3-D objects from 2-D images
NASA Astrophysics Data System (ADS)
Chao, Chih-Ho; Dhawan, Atam P.
1992-09-01
A computer vision system is developed for 3-D object recognition using artificial neural networks and a knowledge-based top-down feedback analysis system. This computer vision system can adequately analyze an incomplete edge map provided by a low-level processor for 3-D representation and recognition using key features. The key features are selected using a priority assignment and then used in an artificial neural network for matching with model key features. The result of such matching is utilized in generating the model-driven top-down feedback analysis. From the incomplete edge map we try to pick a candidate pattern utilizing the key feature priority assignment. The highest priority is given for the most connected node and associated features. The features are space invariant structures and sets of orientation for edge primitives. These features are now mapped into real numbers. A Hopfield network is then applied with two levels of matching to reduce the search time. The first match is to choose the class of possible model, the second match is then to find the model closest to the data patterns. This model is then rotated in 3-D to find the best match with the incomplete edge patterns and to provide the additional features in 3-D. In the case of multiple objects, a dynamically interconnected search strategy is designed to recognize objects using one pattern at a time. This strategy is also useful in recognizing occluded objects. The experimental results presented show the capability and effectiveness of this system.
Probing Magnetism in 2D Molecular Networks after in Situ Metalation by Transition Metal Atoms.
Schouteden, K; Ivanova, Ts; Li, Z; Iancu, V; Janssens, E; Van Haesendonck, C
2015-03-19
Metalated molecules are the ideal building blocks for the bottom-up fabrication of, e.g., two-dimensional arrays of magnetic particles for spintronics applications. Compared to chemical synthesis, metalation after network formation by an atom beam can yield a higher degree of control and flexibility and allows for mixing of different types of magnetic atoms. We report on successful metalation of tetrapyridyl-porphyrins (TPyP) by Co and Cr atoms, as demonstrated by scanning tunneling microscopy experiments. For the metalation, large periodic networks formed by the TPyP molecules on a Ag(111) substrate are exposed in situ to an atom beam. Voltage-induced dehydrogenation experiments support the conclusion that the porphyrin macrocycle of the TPyP molecule incorporates one transition metal atom. The newly synthesized Co-TPyP and Cr-TPyP complexes exhibit striking differences in their electronic behavior, leading to a magnetic character for Cr-TPyP only as evidenced by Kondo resonance measurements.
Fracture network evaluation program (FraNEP): A software for analyzing 2D fracture trace-line maps
NASA Astrophysics Data System (ADS)
Zeeb, Conny; Gomez-Rivas, Enrique; Bons, Paul D.; Virgo, Simon; Blum, Philipp
2013-10-01
Fractures, such as joints, faults and veins, strongly influence the transport of fluids through rocks by either enhancing or inhibiting flow. Techniques used for the automatic detection of lineaments from satellite images and aerial photographs, LIDAR technologies and borehole televiewers significantly enhanced data acquisition. The analysis of such data is often performed manually or with different analysis software. Here we present a novel program for the analysis of 2D fracture networks called FraNEP (Fracture Network Evaluation Program). The program was developed using Visual Basic for Applications in Microsoft Excel™ and combines features from different existing software and characterization techniques. The main novelty of FraNEP is the possibility to analyse trace-line maps of fracture networks applying the (1) scanline sampling, (2) window sampling or (3) circular scanline and window method, without the need of switching programs. Additionally, binning problems are avoided by using cumulative distributions, rather than probability density functions. FraNEP is a time-efficient tool for the characterisation of fracture network parameters, such as density, intensity and mean length. Furthermore, fracture strikes can be visualized using rose diagrams and a fitting routine evaluates the distribution of fracture lengths. As an example of its application, we use FraNEP to analyse a case study of lineament data from a satellite image of the Oman Mountains.
Temperature dependence of the partially localized state in a 2D molecular nanoporous network
NASA Astrophysics Data System (ADS)
Piquero-Zulaica, Ignacio; Nowakowska, Sylwia; Ortega, J. Enrique; Stöhr, Meike; Gade, Lutz H.; Jung, Thomas A.; Lobo-Checa, Jorge
2017-01-01
Two-dimensional organic and metal-organic nanoporous networks can scatter surface electrons, leading to their partial localization. Such quantum states are related to intrinsic surface states of the substrate material. We further corroborate this relation by studying the thermally induced energy shifts of the electronic band stemming from coupled quantum states hosted in a metal-organic array formed by a perylene derivative on Cu(111). We observe by angle-resolved photoemission spectroscopy (ARPES), that both, the Shockley and the partially localized states, shift by the same amount to higher binding energies upon decreasing the sample temperature, providing evidence of their common origin. Our experimental approach and results further support the use of surface states for modelling these systems, which are expected to provide new insight into the physics concerning partially confined electronic states: scattering processes, potential barrier strengths, excited state lifetimes or the influence of guest molecules.
Gállego, Isaac; Oncins, Gerard; Sisquella, Xavier; Fernàndez-Busquets, Xavier; Daban, Joan-Ramon
2010-01-01
In a previous study, we found that metaphase chromosomes are formed by thin plates, and here we have applied atomic force microscopy (AFM) and friction force measurements at the nanoscale (nanotribology) to analyze the properties of these planar structures in aqueous media at room temperature. Our results show that high concentrations of NaCl and EDTA and extensive digestion with protease and nuclease enzymes cause plate denaturation. Nanotribology studies show that native plates under structuring conditions (5 mM Mg2+) have a relatively high friction coefficient (μ ≈ 0.3), which is markedly reduced when high concentrations of NaCl or EDTA are added (μ ≈ 0.1). This lubricant effect can be interpreted considering the electrostatic repulsion between DNA phosphate groups and the AFM tip. Protease digestion increases the friction coefficient (μ ≈ 0.5), but the highest friction is observed when DNA is cleaved by micrococcal nuclease (μ ≈ 0.9), indicating that DNA is the main structural element of plates. Whereas nuclease-digested plates are irreversibly damaged after the friction measurement, native plates can absorb kinetic energy from the AFM tip without suffering any damage. These results suggest that plates are formed by a flexible and mechanically resistant two-dimensional network which allows the safe storage of DNA during mitosis. PMID:21156137
Leader neurons in population bursts of 2D living neural networks
NASA Astrophysics Data System (ADS)
Eckmann, J.-P.; Jacobi, Shimshon; Marom, Shimon; Moses, Elisha; Zbinden, Cyrille
2008-01-01
Eytan and Marom (2006 J. Neurosci. 26 8465-76) recently showed that the spontaneous bursting activity of rat neuron cultures includes 'first-to-fire' cells that consistently fire earlier than others. Here, we analyze the behavior of these neurons in long-term recordings of spontaneous activity of rat hippocampal and rat cortical neuron cultures from three different laboratories. We identify precursor events that may either subside ('aborted bursts') or can lead to a full-blown burst ('pre-bursts'). We find that the activation in the pre-burst typically has a first neuron ('leader'), followed by a localized response in its neighborhood. Locality is diminished in the bursts themselves. The long-term dynamics of the leaders is relatively robust, evolving with a half-life of 23-34 h. Stimulation of the culture alters the leader distribution, but the distribution stabilizes within about 1 h. We show that the leaders carry information about the identity of the burst, as measured by the signature of the number of spikes per neuron in a burst. The number of spikes from leaders in the first few spikes of a precursor event is furthermore shown to be predictive with regard to the transition into a burst (pre-burst versus aborted burst). We conclude that the leaders play a role in the development of the bursts and conjecture that they are part of an underlying sub-network that is excited first and then acts as a nucleation center for the burst.
Annexin-A5 organized in 2D-network at the plasmalemma eases human trophoblast fusion
Degrelle, Severine A.; Gerbaud, Pascale; Leconte, Ludovic; Ferreira, Fatima; Pidoux, Guillaume
2017-01-01
Only a limited number of human cells can fuse to form a multinucleated syncytium. Cell fusion occurs as part of the differentiation of some cell types, including myotubes in muscle and osteoclasts in remodeling bone. In the differentiation of the human placenta, mononuclear cytotrophoblasts aggregate and fuse to form endocrinologically active, non-proliferative, multinucleated syncytia. These syncytia allow the exchange of nutrients and gases between the maternal and fetal circulation. Alteration of syncytial formation during pregnancy affects fetal growth and the outcome of the pregnancy. Here, we demonstrate the role of annexin A5 (AnxA5) in syncytial formation by cellular delivery of recombinant AnxA5 and RNA interference. By a variety of co-immunoprecipitation, immunolocalization and proximity experiments, we show that a pool of AnxA5 organizes at the inner-leaflet of the plasma membrane in the vicinity of a molecular complex that includes E-Cadherin, α-Catenin and β-Catenin, three proteins previously shown to form adherens junctions implicated in cell fusion. A combination of knockdown and reconstitution experiments with AnxA5, with or without the ability to self-assemble in 2D-arrays, demonstrate that this AnxA5 2D-network mediates E-Cadherin mobility in the plasmalemma that triggers human trophoblasts aggregation and thereby cell fusion. PMID:28176826
NASA Astrophysics Data System (ADS)
Li, Zhaoyang; Yang, Xiangbo; Timon Liu, Chengyi
2014-09-01
In this paper, we investigate the properties of optical transmission and photonic localization of two-dimensional (2D) defect two-segment-connected quadrangular waveguide networks (DTSCQWNs) and find that many groups of extreme narrow photonic bands are created in the middle of the transmission spectra. The electromagnetic (EM) waves in DTSCQWNs with the frequencies of extreme narrow photonic bands can produce strong photonic localizations by adjusting defect broken degree. On the other hand, we obtain the formula of extreme narrow photonic bands' frequencies dependent on defect broken degree and the formula of the largest intensity of photonic localization dependent on defect broken degree, respectively. It may possess potential application for designing all-optical devices based on strong photonic localizations. Additionally, we propose a so-called defecton mode to study the splitting rules of extreme narrow photonic bands, where decomposition-decimation method is expanded from the field of electronic energy spectra to that of optical transmission spectra.
A small-world-based population encoding model of the primary visual cortex.
Shi, Li; Niu, Xiaoke; Wan, Hong; Shang, Zhigang; Wang, Zhizhong
2015-06-01
A wide range of evidence has shown that information encoding performed by the visual cortex involves complex activities of neuronal populations. However, the effects of the neuronal connectivity structure on the population's encoding performance remain poorly understood. In this paper, a small-world-based population encoding model of the primary visual cortex (V1) is established on the basis of the generalized linear model (GLM) to describe the computation of the neuronal population. The model mainly consists of three sets of filters, including a spatiotemporal stimulus filter, a post-spike history filter, and a set of coupled filters with the coupling neurons organizing as a small-world network. The parameters of the model were fitted with neuronal data of the rat V1 recorded with a micro-electrode array. Compared to the traditional GLM, without considering the small-world structure of the neuronal population, the proposed model was proved to produce more accurate spiking response to grating stimuli and enhance the capability of the neuronal population to carry information. The comparison results proved the validity of the proposed model and further suggest the role of small-world structure in the encoding performance of local populations in V1, which provides new insights for understanding encoding mechanisms of a small scale population in visual system.
A multifractality analysis of Ising financial markets with small world topology
NASA Astrophysics Data System (ADS)
Zhang, Yi; Li, Xue
2015-03-01
Following our preceding study [H. Zhao et al., Europhys. Lett. 101, 18001 (2013)], in which a self-organizing Ising-like model of artificial financial markets with underlying small world (SW) network topology was investigated, we continue to proceed a multifractal analysis of the price dynamics of the model in current paper. We find that the price return exhibits multifractal property. This suggests that our Ising-like model reproduces the major stylized facts of real world financial markets.
Unusual percolation in simple small-world networks
NASA Astrophysics Data System (ADS)
Cohen, Reuven; Dawid, Daryush Jonathan; Kardar, Mehran; Bar-Yam, Yaneer
2009-06-01
We present an exact solution of percolation in a generalized class of Watts-Strogatz graphs defined on a one-dimensional underlying lattice. We find a nonclassical critical point in the limit of the number of long-range bonds in the system going to zero, with a discontinuity in the percolation probability and a divergence in the mean finite-cluster size. We show that the critical behavior falls into one of three regimes depending on the proportion of occupied long-range to unoccupied nearest-neighbor bonds, with each regime being characterized by different critical exponents. The three regimes can be united by a single scaling function around the critical point. These results can be used to identify the number of long-range links necessary to secure connectivity in a communication or transportation chain. As an example, we can resolve the communication problem in a game of “telephone.”
NASA Astrophysics Data System (ADS)
Khotanzad, Alireza R.; Liou, James H.
1992-09-01
In this paper, a robust, and fast system for recognition as well as pose estimation of a 3-D object from a single 2-D perspective of it taken from an arbitrary viewpoint is developed. The approach is invariant to location, orientation, and scale of the object in the perspective. The silhouette of the object in the 2-D perspective is first normalized with respect to location and scale. A set of rotation invariant features derived from complex and orthogonal pseudo- Zernike moments of the image are then extracted. The next stage includes a bank of multilayer feed-forward neural networks (NN) each of which classifies the extracted features. The training set for these nets consists of perspective views of each object taken from several different viewing angles. The NNs in the bank differ in the size of their hidden layer nodes as well as their initial conditions but receive the same input. The classification decisions of all the nets are combined through a majority voting scheme. It is shown that this collective decision making yields better results compared to a single NN operating alone. After the object is classified, two of its pose parameters, namely elevation and aspect angles, are estimated by another module of NNs in a two-stage process. The first stage identifies the likely region of the space that the object is being viewed from. In the second stage, an NN estimator for the identified region is used to compute the pose angles. Extensive experimental studies involving clean and noisy images of seven military ground vehicles are carried out. The performance is compared to two other traditional methods, namely a nearest neighbor rule and a binary decision tree classifier and it is shown that our approach has major advantages over them.
Coupled 1-D sewer and street networks and 2-D flooding model to rapidly evaluate surface inundation
NASA Astrophysics Data System (ADS)
Kao, Hong-Ming; Hsu, Hao-Ming
2017-04-01
Flash floods have occurred frequently in the urban areas around the world and cause the infrastructure and people living to expose continuously in the high risk level of pluvial flooding. According to historical surveys, the major reasons of severe surface inundations in the urban areas can be attributed to heavy rainfall in the short time and/or drainage system failure. In order to obtain real-time flood forecasting with high accuracy and less uncertainty, an appropriate system for predicting floods is necessary. For the reason, this study coupled 1-D sewer and street networks and 2-D flooding model as an operational modelling system for rapidly evaluating surface inundation. The proposed system is constructed by three significant components: (1) all the rainfall-runoff of a sub-catchment collected via gullies is simulated by the RUNOFF module of the Storm Water Management Model (SWMM); (2) and directly drained to the 1-D sewer and street networks via manholes as inflow discharges to conduct flow routing by using the EXTRAN module of SWMM; (3) after the 1-D simulations, the surcharges from manholes are considered as point sources in 2-D overland flow simulations that are executed by the WASH123D model. It can thus be used for urban flood modelling that reflects the rainfall-runoff processes, and the dynamic flow interactions between the storm sewer system and the ground surface in urban areas. In the present study, we adopted the Huwei Science and Technology Park, located in the south-western part of Taiwan, as the demonstration area because of its high industrial values. The region has an area about 1 km2 with approximately 1 km in both length and width. It is as isolated urban drainage area in which there is a complete sewer system that collects the runoff and drains to the detention pond. Based on the simulated results, the proposed modelling system was found that the simulated floods fit to the survey records because the physical rainfall-runoff phenomena in
Tutte polynomial of a small-world Farey graph
NASA Astrophysics Data System (ADS)
Liao, Yunhua; Hou, Yaoping; Shen, Xiaoling
2013-11-01
In this paper, we find recursive formulas for the Tutte polynomials of a family of small-world Farey graphs, which are modular, and has an exponential degree hierarchy. As applications of the recursive formula, the exact expressions for the chromatic polynomial and the reliability polynomial of Fare graphs are derived and the number of connected spanning subgraphs is also obtained.
Walking and Talking Geography: A Small-World Approach
ERIC Educational Resources Information Center
Fertig, Gary; Silverman, Rick
2007-01-01
When teaching geography to students in the primary grades, teachers should provide firsthand experiences that young children need to make meaningful sense of their world. David Sobel, author of "Mapmaking with Children: Sense of Place Education for the Elementary Years," suggests that teachers in the early grades adopt a small-world approach to…
Allain, Ariane; Chauvot de Beauchêne, Isaure; Langenfeld, Florent; Guarracino, Yann; Laine, Elodie; Tchertanov, Luba
2014-01-01
Allostery is a universal phenomenon that couples the information induced by a local perturbation (effector) in a protein to spatially distant regulated sites. Such an event can be described in terms of a large scale transmission of information (communication) through a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. To elaborate a rational description of allosteric coupling, we propose an original approach - MOdular NETwork Analysis (MONETA) - based on the analysis of inter-residue dynamical correlations to localize the propagation of both structural and dynamical effects of a perturbation throughout a protein structure. MONETA uses inter-residue cross-correlations and commute times computed from molecular dynamics simulations and a topological description of a protein to build a modular network representation composed of clusters of residues (dynamic segments) linked together by chains of residues (communication pathways). MONETA provides a brand new direct and simple visualization of protein allosteric communication. A GEPHI module implemented in the MONETA package allows the generation of 2D graphs of the communication network. An interactive PyMOL plugin permits drawing of the communication pathways between chosen protein fragments or residues on a 3D representation. MONETA is a powerful tool for on-the-fly display of communication networks in proteins. We applied MONETA for the analysis of communication pathways (i) between the main regulatory fragments of receptors tyrosine kinases (RTKs), KIT and CSF-1R, in the native and mutated states and (ii) in proteins STAT5 (STAT5a and STAT5b) in the phosphorylated and the unphosphorylated forms. The description of the physical support for allosteric coupling by MONETA allowed a comparison of the mechanisms of (a) constitutive activation induced by equivalent mutations in two RTKs and (b) allosteric regulation in the activated and non
Small-world topology of functional connectivity in randomly connected dynamical systems.
Hlinka, J; Hartman, D; Paluš, M
2012-09-01
Characterization of real-world complex systems increasingly involves the study of their topological structure using graph theory. Among global network properties, small-world property, consisting in existence of relatively short paths together with high clustering of the network, is one of the most discussed and studied. When dealing with coupled dynamical systems, links among units of the system are commonly quantified by a measure of pairwise statistical dependence of observed time series (functional connectivity). We argue that the functional connectivity approach leads to upwardly biased estimates of small-world characteristics (with respect to commonly used random graph models) due to partial transitivity of the accepted functional connectivity measures such as the correlation coefficient. In particular, this may lead to observation of small-world characteristics in connectivity graphs estimated from generic randomly connected dynamical systems. The ubiquity and robustness of the phenomenon are documented by an extensive parameter study of its manifestation in a multivariate linear autoregressive process, with discussion of the potential relevance for nonlinear processes and measures.
Small Worlds Week: Raising Curiosity and Contributing to STEM
NASA Astrophysics Data System (ADS)
Ng, C.; Mayo, L.; Stephenson, B. E.; Keck, A.; Cline, T. D.; Lewis, E. M.
2015-12-01
Dwarf planets, comets, asteroids, and icy moons took center stage in the years 2014-2015 as multiple spacecraft (New Horizons, Dawn, Rosetta, Cassini) and ground-based observing campaigns observed these small and yet amazing celestial bodies. Just prior to the historic New Horizons encounter with the Pluto system, NASA celebrated Small Worlds Week (July 6-10) as a fully online program to highlight small worlds mission discoveries. Small Worlds Week leveraged the infrastructure of Sun-Earth Days that included a robust web design, exemplary education materials, hands-on fun activities, multimedia resources, science and career highlights, and a culminating event. Each day from July 6-9, a new class of solar system small worlds was featured on the website: Monday-comets, Tuesday-asteroids, Wednesday-icy moons, and Thursday-dwarf planets. Then on Friday, July 10, nine scientists from Goddard Space Flight Center, Jet Propulsion Laboratory, Naval Research Laboratory, and Lunar and Planetary Institute gathered online for four hours to answer questions from the public via Facebook and Twitter. Throughout the afternoon the scientists worked closely with a social media expert and several summer interns to reply to inquirers and to archive their chats. By all accounts, Small Worlds Week was a huge success. The group plans to improve and replicate the program during the school year with a more classroom focus, and then to build and extend the program to be held every year. For more information, visit http:// sunearthday.nasa.gov or catch us on Twitter, #nasasww.
NASA Astrophysics Data System (ADS)
Mirabella, S.; Oliveri, I. P.; Ruffino, F.; Maccarrone, G.; Di Bella, S.
2016-10-01
A marked chemiresistive behavior is revealed in a nanostructured material obtained by spin-coating a solution of a bis(salycilaldiminato)Zn(II) Schiff-base (ZnSB) complex. The resulting submicron 2D network exhibits reversible changes in absorbance and resistance under the cycles of absorption and desorption of a volatile amine. These results are explained in terms of a Lewis donor-acceptor interaction between the ZnSB (acceptor) and the chemisorbed amine (donor). The 2D network of ZnSB was employed as a sensing element to fabricate a low-cost device for the volatile amines detection, showing promising results for food spoilage detection.
Piot, Luc; Silly, Fabien; Tortech, Ludovic; Nicolas, Yohann; Blanchard, Philippe; Roncali, Jean; Fichou, Denis
2009-09-16
We show by means of STM that C(60) molecules can be trapped into specific sites of a 2D double-cavity open network, thus forming long-range alignments of single molecules. Since only one of the two cavities has the right size to host C(60), the smallest cavity remains empty and is thus available to trap additional species of smaller size. This novel 2D supramolecular network opens new perspectives in the design of multicomponent guest-host architectures with electronic functionalities.
Spectral and structural properties of 2D network complex [Ni(4,4'-bipyridine) 2(NCS) 2] n
NASA Astrophysics Data System (ADS)
Zhang, Y.; Jianmin, L.; Nishiura, M.; Imamoto, T.
2000-02-01
The complex [Ni(4,4'-bipyridine) 2(NCS) 2] n, in which nickel atoms are linked by two different Ni-4,4'-bpy-Ni assemblies to form two-dimensional distorted square net structure and the most effective packing of layers, has been isolated and structurally characterized. It represents the first example of Ni(II)-4,4'-bpy complex possesses 2D network. Crystal data for I: Fw=487.23, a=12.156(3), b=11.38(2), c=16.646(8) Å, β=100.43(3), V=2265(1) Å3, Z=4, space group=C2/c, T=298 K, λ((Mo-K α)=0.71070 Å, ρ calc=1.429 g cm -3, μ=10.62 cm-1, F(000)=1000, R=0.054, Rw=0.086, GOF=3.98. The UV-VIS absorption spectrum of the title complex is also reported and explained perfectly by the scaling radial theory which proposed by us. The strong and broad absorption bands occurred at 10433, 16830, 26556 cm -1, and they are assigned as d-d transitions of Ni(II) ion in octahedral field: 3A2g→ 3T2ga,b+ 3T2gc; 3A2g→ 3T1gz+ 3T1gy,x; 3A2g→ 3T1gz+ 3T1gy,x. The calculated results of the d-d transition energy levels agree well with the experimental values.
Frary, R.; Louie, J.; Pullammanappallil, S.; Eisses, A.
2016-08-01
Roxanna Frary, John N. Louie, Sathish Pullammanappallil, Amy Eisses, 2011, Preliminary 3d depth migration of a network of 2d seismic lines for fault imaging at a Pyramid Lake, Nevada geothermal prospect: presented at American Geophysical Union Fall Meeting, San Francisco, Dec. 5-9, abstract T13G-07.
Bazeley, Peter S; Prithivi, Sridevi; Struble, Craig A; Povinelli, Richard J; Sem, Daniel S
2006-01-01
Cytochrome P450 2D6 (CYP2D6) is used to develop an approach for predicting affinity and relevant binding conformation(s) for highly flexible binding sites. The approach combines the use of docking scores and compound properties as attributes in building a neural network (NN) model. It begins by identifying segments of CYP2D6 that are important for binding specificity, based on structural variability among diverse CYP enzymes. A family of distinct, low-energy conformations of CYP2D6 are generated using simulated annealing (SA) and a collection of 82 compounds with known CYP2D6 affinities are docked. Interestingly, docking poses are observed on the backside of the heme as well as in the known active site. Docking scores for the active site binders, along with compound-specific attributes, are used to train a neural network model to properly bin compounds as strong binders, moderate binders, or nonbinders. Attribute selection is used to preselect the most important scores and compound-specific attributes for the model. A prediction accuracy of 85+/-6% is achieved. Dominant attributes include docking scores for three of the 20 conformations in the ensemble as well as the compound's formal charge, number of aromatic rings, and AlogP. Although compound properties were highly predictive attributes (12% improvement over baseline) in the NN-based prediction of CYP2D6 binders, their combined use with docking score attributes is synergistic (net increase of 23% above baseline). Beyond prediction of affinity, attribute selection provides a way to identify the most relevant protein conformation(s), in terms of binding competence. In the case of CYP2D6, three out of the ensemble of 20 SA-generated structures are found to be the most predictive for binding.
Oh, Sejong; Choe, Yoonsuck
2009-01-01
Texture boundary detection (or segmentation) is an important capability in human vision. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A possible answer to this question is that textures naturally define physically distinct (i.e., occluded) surfaces. Hence, we can hypothesize that 2D texture segmentation may be an outgrowth of the ability to discriminate surfaces in 3D. In this paper, we conducted computational experiments with artificial neural networks to investigate the relative difficulty of learning to segment textures defined on flat 2D surfaces vs. those in 3D configurations where the boundaries are defined by occluding surfaces and their change over time due to the observer’s motion. It turns out that learning is faster and more accurate in 3D, very much in line with our expectation. Furthermore, our results showed that the neural network’s learned ability to segment texture in 3D transfers well into 2D texture segmentation, bolstering our initial hypothesis, and providing insights on the possible developmental origin of 2D texture segmentation function in human vision. PMID:19562098
NASA Astrophysics Data System (ADS)
Gvozdik, L.; Polak, M.; Zaruba, J.; Vanecek, M.
2010-12-01
A geological environment labeled as a Granite massif represents in terms of groundwater flow and transport a distinct hydrogeological environment from that of sedimentary basins, the characterisation of which is generally more complex and uncertain. Massifs are composed of hard crystalline rocks with the very low effective porosity. Due to their rheological properties such rocks are predisposed to brittle deformation resulting from changes in stress conditions. Our specific research project (Research on the influence of intergrangular porosity on deep geological disposal: geological formations, methodology and the development of measurement apparatus) is focussed on the problem of permeable zones within apparently undisturbed granitic rock matrix. The project including the both laboratory and in-situ tracer tests study migration along and through mineral grains in fresh and altered granite. The objective of the project is to assess whether intergranular porosity is a general characteristic of the granitic rock matrix or subject to significant evolution resulting from geochemical and/or hydrogeochemical processes, geotechnical and/or mechanical processes. Moreover, the research is focussed on evaluating methods quantifying intergranular porosity by both physical testing and mathematical modelling using verified standard hydrological software tools. Groundwater flow in microfractures and intergranular pores in granite rock matrix were simulated in three standard hydrogeological modeling programs with completely different conceptual approaches: MODFLOW (Equivalent Continuum concept), FEFLOW (Discrete Fracture and Equivalent Continuum concepts) and NAPSAC (Discrete Fracture Network concept). Specialized random fracture generators were used for creation of several 2D and 3D models in each of the chosen program. Percolation characteristics of these models were tested and analyzed. Several scenarios of laboratory tests of the rock samples permeability made in triaxial
NASA Astrophysics Data System (ADS)
Mei, Hong-Xin; Zhang, Ting; Huang, Hua-Qi; Huang, Rong-Bin; Zheng, Lan-Sun
2016-03-01
Three mix-ligand Ag(I) coordination compounds, namely, {[Ag10(tpyz) 5(L1) 5(H2 O)2].(H2 O)4}n (1, tpyz = 2,3,4,5-tetramethylpyrazine, H2 L1 = phthalic acid), [Ag4(tpyz) 2(L2) 2(H2 O)].(H2 O)5}n (2, H2 L2 = isophthalic acid) {[Ag2(tpyz) 2(L3) (H2 O)4].(H2 O)8}n (3, H2 L3 = terephthalic acid), have been synthesized and characterized by elemental analysis, IR, PXRD and X-ray single-crystal diffraction. 1 exhibits a 2D layer which can be simplified as a (4,4) net. 2 is a 3D network which can be simplified as a (3,3)-connected 2-nodal net with a point symbol of {102.12}{102}. 3 consists of linear [Ag(tpyz) (H2 O)2]n chain. Of particular interest, discrete hexamer water clusters were observed in 1 and 2, while a 2D L10(6) water layer exists in 3. The results suggest that the benzene dicarboxylates play pivotal roles in the formation of the different host architectures as well as different water aggregations. Moreover, thermogravimetric analysis (TGA) and emissive behaviors of these compounds were investigated.
Electron Transport through Models for Small-World Nanomaterials
NASA Astrophysics Data System (ADS)
Solomon, Lazarus; Novotny, Mark
2008-03-01
We investigate the quantum transport of (spinless) electrons through simplified models related to small-world nanomaterials. We employ a tight-binding Hamiltonian, and obtain the transmission coefficient from a matrix solution of the associated time-independent Schrödinger Equation. The system studied corresponds to d=1 semi-infinite input and output leads, connected to a `blob' of N atoms. We first present exact results for N inter-connected atoms, a fully-connected graph. The exact solution, for any N, is given both for symmetric and non-symmetric connections between the `blob' and the input/output. We then present numerical results obtained by removing some of the connections within the N-site `blob', thereby approaching transport through a small-world nanomaterial [1-4]. [1] S. Caliskan, M.A. Novotny, and J.I. Cerd'a, J. Appl. Phys., 102, 013707 (2007). [2] M.A. Novotny et al., J. Appl. Phys., 97, 10B309 (2005). [3] M.A. Novotny and S.M. Wheeler, Braz. J. Physics 34, 395 (2004). [4] J. Yancey, M.A. Novotny, and S.R. Gwaltney, 2008 March Meeting presentation.
Predictive protocol of flocks with small-world connection pattern
NASA Astrophysics Data System (ADS)
Zhang, Hai-Tao; Chen, Michael Z. Q.; Zhou, Tao
2009-01-01
By introducing a predictive mechanism with small-world connections, we propose a new motion protocol for self-driven flocks. The small-world connections are implemented by randomly adding long-range interactions from the leader to a few distant agents, namely, pseudoleaders. The leader can directly affect the pseudoleaders, thereby influencing all the other agents through them efficiently. Moreover, these pseudoleaders are able to predict the leader’s motion several steps ahead and use this information in decision making towards coherent flocking with more stable formation. It is shown that drastic improvement can be achieved in terms of both the consensus performance and the communication cost. From the engineering point of view, the current protocol allows for a significant improvement in the cohesion and rigidity of the formation at a fairly low cost of adding a few long-range links embedded with predictive capabilities. Significantly, this work uncovers an important feature of flocks that predictive capability and long-range links can compensate for the insufficiency of each other. These conclusions are valid for both the attractive and repulsive swarm model and the Vicsek model.
Ehrhart, Jérôme; Planeix, Jean-Marc; Kyritsakas-Gruber, Nathalie; Hosseini, Mir Wais
2010-02-28
The combination of tectons based on the [1111]metacyclophane backbone blocked the 1,3-alternate conformation bearing two imidazoly or pyrazolyl groups located on the same side with metal halide complexes leads to the formation of either discrete metallmacrobicycles or infinite 1-D coordination networks. The same backbone bearing two sets of two different coordinating poles composed of two pyridyl and two pyrazolyl units, owing to its non-centrosymmetric nature, forms a directional 2-D network packed in an anti-parallel fashion.
Reduced small world brain connectivity in probands with a family history of epilepsy.
Bharath, R D; Chaitanya, G; Panda, R; Raghavendra, K; Sinha, S; Sahoo, A; Gohel, S; Biswal, B B; Satishchandra, P
2016-12-01
The role of inheritance in ascertaining susceptibility to epilepsy is well established, although the pathogenetic mechanisms are still not very clear. Interviewing for a positive family history is a popular epidemiological tool in the understanding of this susceptibility. Our aim was to visualize and localize network abnormalities that could be associated with a positive family history in a group of patients with hot water epilepsy (HWE) using resting-state functional magnetic resonance imaging (rsfMRI). Graph theory analysis of rsfMRI (clustering coefficient γ; path length λ; small worldness σ) in probands with a positive family history of epilepsy (FHE+, 25) were compared with probands without FHE (FHE-, 33). Whether a closer biological relationship was associated with a higher likelihood of network abnormalities was also ascertained. A positive family history of epilepsy had decreased γ, increased λ and decreased σ in bilateral temporofrontal regions compared to FHE- (false discovery rate corrected P ≤ 0.0062). These changes were more pronounced in probands having first degree relatives and siblings with epilepsy. Probands with multiple types of epilepsy in the family showed decreased σ in comparison to only HWE in the family. Graph theory analysis of the rsfMRI can be used to understand the neurobiology of diseases like genetic susceptibility in HWE. Reduced small worldness, proportional to the degree of relationship, is consistent with the current understanding that disease severity is higher in closer biological relations. © 2016 EAN.
Geographical threshold graphs with small-world and scale-free properties.
Masuda, Naoki; Miwa, Hiroyoshi; Konno, Norio
2005-03-01
Many real networks are equipped with short diameters, high clustering, and power-law degree distributions. With preferential attachment and network growth, the model by Barabási and Albert simultaneously reproduces these properties, and geographical versions of growing networks have also been analyzed. However, nongrowing networks with intrinsic vertex weights often explain these features more plausibly, since not all networks are really growing. We propose a geographical nongrowing network model with vertex weights. Edges are assumed to form when a pair of vertices are spatially close and/or have large summed weights. Our model generalizes a variety of models as well as the original nongeographical counterpart, such as the unit disk graph, the Boolean model, and the gravity model, which appear in the contexts of percolation, wire communication, mechanical and solid physics, sociology, economy, and marketing. In appropriate configurations, our model produces small-world networks with power-law degree distributions. We also discuss the relation between geography, power laws in networks, and power laws in general quantities serving as vertex weights.
Geographical threshold graphs with small-world and scale-free properties
NASA Astrophysics Data System (ADS)
Masuda, Naoki; Miwa, Hiroyoshi; Konno, Norio
2005-03-01
Many real networks are equipped with short diameters, high clustering, and power-law degree distributions. With preferential attachment and network growth, the model by Barabási and Albert simultaneously reproduces these properties, and geographical versions of growing networks have also been analyzed. However, nongrowing networks with intrinsic vertex weights often explain these features more plausibly, since not all networks are really growing. We propose a geographical nongrowing network model with vertex weights. Edges are assumed to form when a pair of vertices are spatially close and/or have large summed weights. Our model generalizes a variety of models as well as the original nongeographical counterpart, such as the unit disk graph, the Boolean model, and the gravity model, which appear in the contexts of percolation, wire communication, mechanical and solid physics, sociology, economy, and marketing. In appropriate configurations, our model produces small-world networks with power-law degree distributions. We also discuss the relation between geography, power laws in networks, and power laws in general quantities serving as vertex weights.
Range-dependent random graphs and their application to modeling large small-world Proteome datasets
NASA Astrophysics Data System (ADS)
Grindrod, Peter
2002-12-01
In this paper we consider the problem of characterizing and modeling large-scale networks using classes of range-dependent graphs which possess appropriate small-world properties. The application we have in mind is to bioinformatics, where methods of rapid protein identification mean that such proteome datasets, listing various observed protein-protein associations, will become more and more prevalent. We introduce a class of range-dependent graphs, governed by a power law relating intervertex range to edge probability, which are amenable to analysis, and for which macroscopic graph parameters are given by explicit forms. We show how these may be employed in representing a given network using a maximum likelihood approach. This in turn annotates every given edge with its range, representing the tendency for such an association to be transitive. We apply this technique to published proteome data, and demonstrate that known protein associations are thus identified.
Ovsyannikov, A; Ferlay, S; Solovieva, S E; Antipin, I S; Konovalov, A I; Kyritsakas, N; Hosseini, M W
2013-07-21
Three p-H-thiacalix[4]arene pyridyl appended coordinating tectons (2-4) in a 1,3-alternate conformation have been prepared and structurally characterised in the solid state. These compounds are positional isomers differing only by the position of the nitrogen atom on the pyridyl ring. Their combinations with HgCl2 lead to the formation of 1- and 2-D neutral mercury coordination networks. Whereas for tecton 2 (ortho isomer) a 2D architecture resulting from the bridging of consecutive tectons by the mononuclear HgCl2 unit is obtained, for tecton 3 (meta isomer) again a 2D network is formed. However, in that case, the interconnection of consecutive organic tectons 3 takes place through a binuclear Hg2Cl4 species. Finally, in the case of tecton 4 (para position), a 1D ribbon type double chain arrangement resulting from the bridging of consecutive tectons by trinuclear Hg3Cl6 units followed by the interconnection of two chains through the fusion of the trinuclear centres into a hexanuclear node is observed.
Ciompi, Francesco; de Hoop, Bartjan; van Riel, Sarah J; Chung, Kaman; Scholten, Ernst Th; Oudkerk, Matthijs; de Jong, Pim A; Prokop, Mathias; van Ginneken, Bram
2015-12-01
In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.
A 2-D Pore-Network Model of the Drying of Single-Component Liquids in Porous Media
Yortsos, Yanic C.; Yiotis, A.G.; Stubos, A.K.; Boundovis, A.G.
2000-01-20
The drying of liquid-saturated porous media is typically approaching using macroscopic continuum models involving phenomenological coefficients. Insight on these coefficients can be obtained by a more fundamental study at the pore- and pore-network levels. In this report, a model based on pore-network representation of porous media that accounts for various process at the pore-scale is presented. These include mass transfer by advection and diffusion in the gas phase, viscous flow in liquid and gas phases and capillary effects at the gas-liquid menisci in the pore throats.
Collective behavior of a small-world recurrent neural system with scale-free distribution.
Deng, Zhidong; Zhang, Yi
2007-09-01
This paper proposes a scale-free highly clustered echo state network (SHESN). We designed the SHESN to include a naturally evolving state reservoir according to incremental growth rules that account for the following features: (1) short characteristic path length, (2) high clustering coefficient, (3) scale-free distribution, and (4) hierarchical and distributed architecture. This new state reservoir contains a large number of internal neurons that are sparsely interconnected in the form of domains. Each domain comprises one backbone neuron and a number of local neurons around this backbone. Such a natural and efficient recurrent neural system essentially interpolates between the completely regular Elman network and the completely random echo state network (ESN) proposed by Jaeger et al. We investigated the collective characteristics of the proposed complex network model. We also successfully applied it to challenging problems such as the Mackey-Glass (MG) dynamic system and the laser time-series prediction. Compared to the ESN, our experimental results show that the SHESN model has a significantly enhanced echo state property and better performance in approximating highly complex nonlinear dynamics. In a word, this large scale dynamic complex network reflects some natural characteristics of biological neural systems in many aspects such as power law, small-world property, and hierarchical architecture. It should have strong computing power, fast signal propagation speed, and coherent synchronization.
Wu, Jian; Su, Zhong; Li, Zuofeng
2016-01-01
Our purpose was to develop a neural network-based registration quality evaluator (RQE) that can improve the 2D/3D image registration robustness for pediatric patient setup in external beam radiotherapy. Orthogonal daily setup X-ray images of six pediatric patients with brain tumors receiving proton therapy treatments were retrospectively registered with their treatment planning computed tomography (CT) images. A neural network-based pattern classifier was used to determine whether a registration solution was successful based on geometric features of the similarity measure values near the point-of-solution. Supervised training and test datasets were generated by rigidly registering a pair of orthogonal daily setup X-ray images to the treatment planning CT. The best solution for each registration task was selected from 50 optimizing attempts that differed only by the randomly generated initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error tolerance to determine whether that solution was acceptable. A supervised training was then used to train the RQE. Performance of the RQE was evaluated using test dataset consisting of registration results that were not used in training. The RQE was integrated with our in-house 2D/3D registration system and its performance was evaluated using the same patient dataset. With an optimized sampling step size (i.e., 5 mm) in the feature space, the RQE has the sensitivity and the specificity in the ranges of 0.865-0.964 and 0.797-0.990, respectively, when used to detect registration error with mean voxel displacement (MVD) greater than 1 mm. The trial-to-acceptance ratio of the integrated 2D/3D registration system, for all patients, is equal to 1.48. The final acceptance ratio is 92.4%. The proposed RQE can potentially be used in a 2D/3D rigid image registration system to improve the overall robustness by rejecting
Wu, Jian; Su, Zhong; Li, Zuofeng
2016-01-08
Our purpose was to develop a neural network-based registration quality evaluator (RQE) that can improve the 2D/3D image registration robustness for pediatric patient setup in external beam radiotherapy. Orthogonal daily setup X-ray images of six pediatric patients with brain tumors receiving proton therapy treatments were retrospectively registered with their treatment planning computed tomography (CT) images. A neural network-based pattern classifier was used to determine whether a registration solution was successful based on geometric features of the similarity measure values near the point-of-solution. Supervised training and test datasets were generated by rigidly registering a pair of orthogonal daily setup X-ray images to the treatment planning CT. The best solution for each registration task was selected from 50 optimizing attempts that differed only by the randomly generated initial transformation parameters. The distance from each individual solution to the best solution in the normalized parametrical space was compared to a user-defined error tolerance to determine whether that solution was acceptable. A supervised training was then used to train the RQE. Performance of the RQE was evaluated using test dataset consisting of registration results that were not used in training. The RQE was integrated with our in-house 2D/3D registration system and its performance was evaluated using the same patient dataset. With an optimized sampling step size (i.e., 5 mm) in the feature space, the RQE has the sensitivity and the specificity in the ranges of 0.865-0.964 and 0.797-0.990, respectively, when used to detect registration error with mean voxel displacement (MVD) greater than 1 mm. The trial-to-acceptance ratio of the integrated 2D/3D registration system, for all patients, is equal to 1.48. The final acceptance ratio is 92.4%. The proposed RQE can potentially be used in a 2D/3D rigid image registration system to improve the overall robustness by rejecting
Koneru, Suvarna Vani; Bhavani, Durga S
2015-01-01
A novel approach to Contact Map Overlap (CMO) problem is proposed using the two dimensional clusters present in the contact maps. Each protein is represented as a set of the non-trivial clusters of contacts extracted from its contact map. The approach involves finding matching regions between the two contact maps using approximate 2D-pattern matching algorithm and dynamic programming technique. These matched pairs of small contact maps are submitted in parallel to a fast heuristic CMO algorithm. The approach facilitates parallelization at this level since all the pairs of contact maps can be submitted to the algorithm in parallel. Then, a merge algorithm is used in order to obtain the overall alignment. As a proof of concept, MSVNS, a heuristic CMO algorithm is used for global as well as local alignment. The divide and conquer approach is evaluated for two benchmark data sets that of Skolnick and Ding et al. It is interesting to note that along with achieving saving of time, better overlap is also obtained for certain protein folds.
NASA Astrophysics Data System (ADS)
Krasilenko, Vladimir G.; Nikolsky, Alexander I.; Lazarev, Alexander A.; Michalnichenko, Nikolay N.
2004-04-01
The article deals with a conception of building arithmetic-logic devices (ALD) with a 2D-structure and optical 2D-array inputs-outputs as advanced high-productivity parallel basic operational training modules for realization of basic operation of continuous, neuro-fuzzy, multilevel, threshold and others logics and vector-matrix, vector-tensor procedures in neural networks, that consists in use of time-pulse coding (TPC) architecture and 2D-array smart optoelectronic pulse-width (or pulse-phase) modulators (PWM or PPM) for transformation of input pictures. The input grayscale image is transformed into a group of corresponding short optical pulses or time positions of optical two-level signal swing. We consider optoelectronic implementations of universal (quasi-universal) picture element of two-valued ALD, multi-valued ALD, analog-to-digital converters, multilevel threshold discriminators and we show that 2D-array time-pulse photoconverters are the base elements for these devices. We show simulation results of the time-pulse photoconverters as base components. Considered devices have technical parameters: input optical signals power is 200nW_200μW (if photodiode responsivity is 0.5A/W), conversion time is from tens of microseconds to a millisecond, supply voltage is 1.5_15V, consumption power is from tens of microwatts to a milliwatt, conversion nonlinearity is less than 1%. One cell consists of 2-3 photodiodes and about ten CMOS transistors. This simplicity of the cells allows to carry out their integration in arrays of 32x32, 64x64 elements and more.
Vecchio, Fabrizio; Miraglia, Francesca; Piludu, Francesca; Granata, Giuseppe; Romanello, Roberto; Caulo, Massimo; Onofrj, Valeria; Bramanti, Placido; Colosimo, Cesare; Rossini, Paolo Maria
2017-04-01
Brain imaging plays an important role in the study of Alzheimer's disease (AD), where atrophy has been found to occur in the hippocampal formation during the very early disease stages and to progress in parallel with the disease's evolution. The aim of the present study was to evaluate a possible correlation between "Small World" characteristics of the brain connectivity architecture-as extracted from EEG recordings-and hippocampal volume in AD patients. A dataset of 144 subjects, including 110 AD (MMSE 21.3) and 34 healthy Nold (MMSE 29.8) individuals, was evaluated. Weighted and undirected networks were built by the eLORETA solutions of the cortical sources' activities moving from EEG recordings. The evaluation of the hippocampal volume was carried out on a subgroup of 60 AD patients who received a high-resolution T1-weighted sequence and underwent processing for surface-based cortex reconstruction and volumetric segmentation using the Freesurfer image analysis software. Results showed that, quantitatively, more correlation was observed in the right hemisphere, but the same trend was seen in both hemispheres. Alpha band connectivity was negatively correlated, while slow (delta) and fast-frequency (beta, gamma) bands positively correlated with hippocampal volume. Namely, the larger the hippocampal volume, the lower the alpha and the higher the delta, beta, and gamma Small World characteristics of connectivity. Accordingly, the Small World connectivity pattern could represent a functional counterpart of structural hippocampal atrophying and related-network disconnection.
NASA Astrophysics Data System (ADS)
Morasca, Paola; Massa, Marco; Laprocina, Enrica; Mayeda, Kevin; Phillips, Scott; Malagnini, Luca; Spallarossa, Daniele; Costa, Giovanni; Augliera, Paolo
2010-10-01
A merged, high-quality waveform dataset from different seismic networks has been used to improve our understanding of lateral seismic attenuation for Northern Italy. In a previous study on the same region, Morasca et al. (Bull Seismol Soc Am 98:1936-1946, 2008) were able to resolve only a small area due to limited data coverage. For this reason, the interpretation of the attenuation anomalies was difficult given the complexity of the region and the poor resolution of the available data. In order to better understand the lateral changes in the crustal structure and thickness of this region, we selected 770 earthquakes recorded by 54 stations for a total of almost 16,000 waveforms derived from seismic networks operating totally or partially in Northern Italy. Direct S-wave and coda attenuation images were obtained using an amplitude ratio technique that eliminates source terms from the formulation. Both direct and early-coda amplitudes are used as input for the inversions, and the results are compared. Results were obtained for various frequency bands ranging between 0.3 and 25.0 Hz and in all cases show significant improvement with respect to the previous study since the resolved area has been extended and more crossing paths have been used to image smaller scale anomalies. Quality-factor estimates are consistent with the regional tectonic structure exhibiting a general trend of low attenuation under the Po Plain basin and higher values for the Western Alps and Northern Apennines. The interpretation of the results for the Eastern Alps is not simple, possibly because our resolution for this area is still not adequate to resolve small-scale structures.
NASA Astrophysics Data System (ADS)
Ahn, Chong Hyun
The most effective method for stimulating shale gas reservoirs is a massive hydraulic fracture treatment. Recent analysis using microseismic technology have shown that complex fracture networks are commonly created in the field as a result of the stimulation of shale wells. The interaction between pre-existing natural fractures and the propagating hydraulic fracture is a critical factor affecting the created complex fracture network; however, many existing numerical models simulate only planar hydraulic fractures without considering the pre-existing fractures in the formation. The shale formations already contain a large number of natural fractures, so an accurate fracture propagation model needs to be developed to optimize the fracturing process. In this research, we first characterized the mechanics of hydraulic fracturing and fluid flow in the shale gas reservoir. Then, a 2D, single-phase numerical model and a 3D, 2-phase coupled model were developed, which integrate dynamic fracture propagation, interactions between hydraulic fractures and pre-existing natural fractures, fracture fluid leakoff, and fluid flow in a petroleum reservoir. By using the developed model, we conducted parametric studies to quantify the effects of treatment rate, treatment size, fracture fluid viscosity, differential horizontal stress, natural fracture spacing, fracture toughness, matrix permeability, and proppant size on the geometry of the hydraulic fracture network. The findings elucidate important trends in hydraulic fracturing of shale reservoirs that are useful in improving the design of treatments for specific reservoir settings.
NASA Astrophysics Data System (ADS)
Wu, Tong; Gao, Jingqun; Wang, Jun; Wang, Shujun; Bai, Yuan; Li, Ying; Li, Kai; Zhang, Xiangdong
2012-02-01
A novel two-dimensional (2D) mononuclear complex, namely, (enH 2)[Tb III(egta)(H 2O)] 2·6H 2O (H 4egta = ethyleneglycol-bis-(2-aminoethylether)- N, N, N', N'-tetraacetic acid and en = ethylenediamine), was successfully synthesized and characterized by infrared spectrum, UV-vis spectrum, fluorescence spectrum, thermal analysis and single-crystal X-ray diffraction techniques. Single-crystal X-ray diffraction analysis reveals that the central Tb(III) ion is nine-coordinate in geometry of pseudo-monocapped square antiprismatic polyhedron. Furthermore, the hydrogen bonds play an important role in the fabrication of layer structure of the complex. Through hydrogen bonds between ethylenediamine cation (enH 22+) and [Tb III(egta)(H 2O)] - complex anion, the title complex forms a 2D layer network along [1 1 1] crystallographic direction. Particularly, the fluorescent property is also fully investigated, which indicates that the title complex would be a potential candidate as fluorescent materials.
Wu, Tong; Gao, Jingqun; Wang, Jun; Wang, Shujun; Bai, Yuan; Li, Ying; Li, Kai; Zhang, Xiangdong
2012-02-01
A novel two-dimensional (2D) mononuclear complex, namely, (enH(2))[Tb(III)(egta)(H(2)O)](2)·6H(2)O (H(4)egta=ethyleneglycol-bis-(2-aminoethylether)-N,N,N',N'-tetraacetic acid and en=ethylenediamine), was successfully synthesized and characterized by infrared spectrum, UV-vis spectrum, fluorescence spectrum, thermal analysis and single-crystal X-ray diffraction techniques. Single-crystal X-ray diffraction analysis reveals that the central Tb(III) ion is nine-coordinate in geometry of pseudo-monocapped square antiprismatic polyhedron. Furthermore, the hydrogen bonds play an important role in the fabrication of layer structure of the complex. Through hydrogen bonds between ethylenediamine cation (enH(2)(2+)) and [Tb(III)(egta)(H(2)O)](-) complex anion, the title complex forms a 2D layer network along [111] crystallographic direction. Particularly, the fluorescent property is also fully investigated, which indicates that the title complex would be a potential candidate as fluorescent materials. Copyright © 2011 Elsevier B.V. All rights reserved.
NASA Astrophysics Data System (ADS)
Gravalos, I.; Moshou, D.; Loutridis, S.; Gialamas, Th.; Kateris, D.; Bompolas, E.; Tsiropoulos, Z.; Xyradakis, P.; Fountas, S.
2013-08-01
In this study a prototype sensor-based platform moving inside a subsurface network of pipes with the task of monitoring the soil moisture content is presented. It comprises of a mobile platform, a modified commercial soil moisture sensor (Diviner 2000), a network of subsurface polyvinylchloride (PVC) access pipes, driving hardware and image processing software. The software allows the composition of two-dimensional (2D) or three-dimensional (3D) images with high accuracy and at a large scale. The 3D soil moisture images are created by using 2D slices for better illustration of the soil moisture variability. Three case studies of varying soil moisture content using an experimental soil tank were examined. In the first case study, the irrigation water was applied uniformly on the entire tank surface. In second and third case studies, the irrigation water was applied uniformly only on the surface of the intermediate and last part of the soil tank respectively. The processed images give a detailed description of the soil moisture distribution of a layer at 15 cm depth under the soil surface in the tank. In all case studies that have been investigated, the distribution of soil moisture was characterized by a significant variability (difference between poorly and well-drained regions) of the soil tank. A very poorly-drained region was located in the middle of the soil tank, while well-drained soil areas were located southwest and northeast. The knowledge of the spatial and temporal distribution of soil moisture is a valuable tool for proper management of crop irrigation.
2016-05-31
information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. West Virginia University...Integration of Neural Networks with Bayesian Networks for Data Fusion and Predictive Modeling Dr. Suzanne Bell, West Virginia University 1. Basis of the
Six Degrees of Information Seeking: Stanley Milgram and the Small World of the Library
ERIC Educational Resources Information Center
James, Kathryn
2006-01-01
Stanley Milgram's 1967 "small world" social connectivity study is used to analyze information connectivity, or patron information-seeking behavior. The "small world" study, upon examination, offers a clear example of the failure of social connectivity. This failure is used to highlight the importance of the subjectivities of patron experience of…
Ko, Jung Woo; Min, Kil Sik; Suh, Myunghyun Paik
2002-04-22
A 2-D metal-organic open framework having 1-D channels, [Cu(C(10)H(26)N(6))](3)[C(6)H(3)(COO)(3)](2).18H(2)O (1), was constructed by the self-assembly of the Cu(II) complex of hexaazamacrocycle A (A = C(10)H(26)N(6)) with sodium 1,3,5-benzenetricarboxylate (BTC(3)(-)) in DMSO-H(2)O solution. 1 crystallizes in the trigonal space group P with a = b = 17.705(1) A, c = 6.940(1) A, alpha = beta = 90 degrees, gamma = 120 degrees, V = 1884.0(3) A(3), Z = 1, and rho(calcd) = 1.428 g cm(-3). The X-ray crystal structure of 1 indicates that each Cu(II) macrocyclic unit binds two BTC(3-) ions in a trans position and each BTC(3-) ion coordinates three Cu(II) macrocyclic complexes to form 2-D coordination polymer layers with honeycomb cavities (effective size 8.1 A), and the layers are packed to generate 1-D channels perpendicularly to the 2-D layers. Solid 1 binds guest molecules such as MeOH, EtOH, and PhOH with different binding constant and capacity. By the treatment of 1 with aqueous solution of phenol, a hybrid solid [Cu(C(10)H(26)N(6))](3)[C(6)H(3)(COO)(3)](2).9PhOH.6H(2)O (2) was assembled. 2 crystallizes in the trigonal R3 space group with a = b = 20.461(1) A, c = 24.159(1) A, alpha = beta = 90 degrees, gamma = 120 degrees, V = 8759.2(7) A(3), Z = 3, and rho(calcd) = 1.280 g cm(-3). In 2, highly ordered 2-D noncovalent phenol layers are formed by the edge-to-face pi-pi interactions between the phenol molecules and are alternately packed with the coordination polymer layers in the crystal lattice.
NASA Astrophysics Data System (ADS)
Thurai, Merhala; Bringi, Viswanathan; Galvez, Miguel; Vijay Mishra, Kumar; Krajewski, Witold; Goska, Radoslaw; Petersen, Walt
2015-04-01
As part of the GPM ground validation campaign, the Iowa Flood Studies (IFloodS) was conducted in eastern Iowa from May to June 2013. This was the first GPM campaign focused on hydrology studies and featured four units of Iowa XPOL radars and several ground-based instruments for in situ observations. In this paper, we analyze radar observations from the S-band NPOL radar and the Iowa XPOLs at locations that hosted a network of 2D video disdrometers. Three events during May 2013 have been analyzed using NPOL radar data and the measurements from the 2DVDs, in terms of drop size distribution parameters and rainfall rates. Based on these results, we derive rain rate estimators for both NPOL and XPOL radars. The estimators were then applied to radar observations for another event (12 June 2013) and compared with rain gauge measurements. Reasonable agreement is found for both NPOL and for XPOL radars, especially after taking into account the time taken for drops to fall from the radar pulse volume over the gauge network to ground level.
Leandro, Jorge; Martins, Ricardo
2016-01-01
Pluvial flooding in urban areas is characterized by a gradually varying inundation process caused by surcharge of the sewer manholes. Therefore urban flood models need to simulate the interaction between the sewer network and the overland flow in order to accurately predict the flood inundation extents. In this work we present a methodology for linking 2D overland flow models with the storm sewer model SWMM 5. SWMM 5 is a well-known free open-source code originally developed in 1971. The latest major release saw its structure re-written in C ++ allowing it to be compiled as a command line executable or through a series of calls made to function inside a dynamic link library (DLL). The methodology developed herein is written inside the same DLL in C + +, and is able to simulate the bi-directional interaction between both models during simulation. Validation is done in a real case study with an existing urban flood coupled model. The novelty herein is that the new methodology can be added to SWMM without the need for editing SWMM's original code. Furthermore, it is directly applicable to other coupled overland flow models aiming to use SWMM 5 as the sewer network model.
NASA Astrophysics Data System (ADS)
Li, S. H.; Xia, X. H.; Wang, Y. D.; Wang, X. L.; Tu, J. P.
2017-02-01
It is a core task to find solutions to suppress the ;shuttle effect; of polysulfides and improve high rate capability at the sulfur cathode of lithium sulfur batteries. Herein we first time propose a concept of multileveled blocking ;dams; to suppress the diffusion of polysulfides. We report a facile and effective strategy to construct multidimensional conductive carbon hosts for accommodation of active sulfur. Multidimensional ternary carbon networks (MTCNs) with 0D nanospheres, 1D nanotubes and 2D nanoflakes are organically combined together to provide multileveled conductive channels to reserve active sulfur and promote stable sustained reactions. In the light of enhanced conductivity and multileveled blocking ;dams; for polysulfides, the designed MTCNs/S cathode has been demonstrated with noticeable improvement in discharge capacity (1472 mAh g-1 at 0.l C) and long-term cycling stability (65% retention at 5.0 C after 500 cycles). Our research may provide a new insight in the gradient blocking of polysulfides with the help of multidimensional carbon networks.
Excitation waves on a minimal small-world model
NASA Astrophysics Data System (ADS)
Isele, Thomas; Hartung, Benedikt; Hövel, Philipp; Schöll, Eckehard
2015-04-01
We examine traveling-wave solutions on a regular ring network with one additional long-range link that spans a distance d. The nodes obey the FitzHugh-Nagumo kinetics in the excitable regime. The additional shortcut induces a plethora of spatio-temporal behavior that is not present without it. We describe the underlying mechanisms for different types of patterns: propagation failure, period decreasing, bistability, shortcut blocking and period multiplication. For this purpose, we investigate the dependence on d, the network size, the coupling range in the original ring and the global coupling strength and present a phase diagram summarizing the different scenarios. Furthermore, we discuss the scaling behavior of the critical distance by analytical means and address the connection to spatially continuous excitable media.
Koziol, Leonard F; Barker, Lauren A; Joyce, Arthur W; Hrin, Skip
2014-01-01
Brain structure and function is characterized by large-scale brain systems. However, each system has its own "small-world" organization, with sub-regions, or "hubs," that have varying degrees of specialization for certain cognitive and behavioral processes. This article describes this small-world organization, and the concepts of functional specialization and functional integration are defined and explained through practical examples. We also describe the development of large-scale brain systems and this small-world organization as a sensitive, protracted process, vulnerable to a variety of influences that generate neurodevelopmental disorders.
'Small worlds' and the evolution of virulence: infection occurs locally and at a distance.
Boots, M; Sasaki, A
1999-01-01
Why are some discases more virulent than others? Vector-borne diseases such as malaria and water-borne diseases such as cholera are generally more virulent than diseases spread by direct contagion. One factor that characterizes both vector- and water-borne diseases is their ability to spread over long distances, thus causing infection of susceptible individuals distant from the infected individual. Here we show that this ability of the pathogen to infect distant individuals in a spatially structured host population leads to the evolution of a more virulent pathogen. We use a lattice model in which reproduction is local but infection can vary between completely local to completely global. With completely global infection the evolutionarily stable strategy (ESS) is the same as in mean-field models while a lower virulence is predicted as infection becomes more local. There is characteristically a period of relatively moderate increase in virulence followed by a more rapid rise with increasing proportions of global infection as we move beyond a 'critical connectivity'. In the light of recent work emphasizing the existence of 'small world' networks in human populations, our results suggests that if the world is getting 'smaller'--as populations become more connected--diseases may evolve higher virulence. PMID:10584335
van Winden, Wouter A; van Gulik, Walter M; Schipper, Dick; Verheijen, Peter J T; Krabben, Preben; Vinke, Jacobus L; Heijnen, Joseph J
2003-07-05
At present two alternative methods are available for analyzing the fluxes in a metabolic network: (1) combining measurements of net conversion rates with a set of metabolite balances including the cofactor balances, or (2) leaving out the cofactor balances and fitting the resulting free fluxes to measured (13)C-labeling data. In this study these two approaches are applied to the fluxes in the glycolysis and pentose phosphate pathway of Penicillium chrysogenum growing on either ammonia or nitrate as the nitrogen source, which is expected to give different pentose phosphate pathway fluxes. The presented flux analyses are based on extensive sets of 2D [(13)C, (1)H] COSY data. A new concept is applied for simulation of this type of (13)C-labeling data: cumulative bondomer modeling. The outcomes of the (13)C-labeling based flux analysis substantially differ from those of the pure metabolite balancing approach. The fluxes that are determined using (13)C-labeling data are shown to be highly dependent on the chosen metabolic network. Extending the traditional nonoxidative pentose phosphate pathway with additional transketolase and transaldolase reactions, extending the glycolysis with a fructose 6-phosphate aldolase/dihydroxyacetone kinase reaction sequence or adding a phosphoenolpyruvate carboxykinase reaction to the model considerably improves the fit of the measured and the simulated NMR data. The results obtained using the extended version of the nonoxidative pentose phosphate pathway model show that the transketolase and transaldolase reactions need not be assumed reversible to get a good fit of the (13)C-labeling data. Strict statistical testing of the outcomes of (13)C-labeling based flux analysis using realistic measurement errors is demonstrated to be of prime importance for verifying the assumed metabolic model. Copyright 2003 Wiley Periodicals, Inc. Biotechnol Bioeng 83: 75-92, 2003.
Ehrhart, Jérôme; Planeix, Jean-Marc; Kyritsakas-Gruber, Nathalie; Hosseini, Mir Wais
2009-08-28
The combination of a [1111] metacyclophane blocked in 1,3-alternate conformation and bearing four pyrazolyl coordinating units with MX(2) (M = Co, Zn and X = Cl or Br) leads to the formation of crystals formed by packing of 2D coordination networks. In the case of CuBr(2), the formation of a 1D network was observed. Structural studies by X-ray diffraction methods on single crystals were performed on all cases reported.
NASA Astrophysics Data System (ADS)
Al-karawi, Dhurgham; Sayasneh, A.; Al-Assam, Hisham; Jassim, Sabah; Page, N.; Timmerman, D.; Bourne, T.; Du, Hongbo
2017-05-01
Ovarian cysts are a common pathology in women of all age groups. It is estimated that 5-10% of women have a surgical intervention to remove an ovarian cyst in their lifetime. Given this frequency rate, characterization of ovarian masses is essential for optimal management of patients. Patients with benign ovarian masses can be managed conservatively if they are asymptomatic. Mature teratomas are common benign ovarian cysts that occur, in most cases, in premenopausal women. These ovarian cysts can contain different types of human tissue including bone, cartilage, fat, hair, or other tissue. If they are causing no symptoms, they can be harmless and may not require surgery. Subjective assessment by ultrasound examiners has a high diagnostic accuracy when characterising mature teratomas from other types of tumours. The aim of this study is to develop a computerised technique with the potential to characterise mature teratomas and distinguish them from other types of benign ovarian tumours. Local Binary Pattern (LBP) was applied to extract texture features that are specific in distinguishing teratomas. Neural Networks (NN) was then used as a classifier for recognising mature teratomas. A pilot sample set of 130 B-mode static ovarian ultrasound images (41 mature teratomas tumours and 89 other types of benign tumours) was used to test the effectiveness of the proposed technique. Test results show an average accuracy rate of 99.4% with a sensitivity of 100%, specificity of 98.8% and positive predictive value of 98.9%. This study demonstrates that the NN and LBP techniques can accurately classify static 2D B-mode ultrasound images of benign ovarian masses into mature teratomas and other types of benign tumours.
NASA Astrophysics Data System (ADS)
Krawiecki, A.
A multi-agent spin model for changes of prices in the stock market based on the Ising-like cellular automaton with interactions between traders randomly varying in time is investigated by means of Monte Carlo simulations. The structure of interactions has topology of a small-world network obtained from regular two-dimensional square lattices with various coordination numbers by randomly cutting and rewiring edges. Simulations of the model on regular lattices do not yield time series of logarithmic price returns with statistical properties comparable with the empirical ones. In contrast, in the case of networks with a certain degree of randomness for a wide range of parameters the time series of the logarithmic price returns exhibit intermittent bursting typical of volatility clustering. Also the tails of distributions of returns obey a power scaling law with exponents comparable to those obtained from the empirical data.
Bharath, Rose D; Panda, Rajanikant; Reddam, Venkateswara Reddy; Bhaskar, M V; Gohel, Suril; Bhardwaj, Sujas; Prajapati, Arvind; Pal, Pramod Kumar
2017-01-01
Background and Purpose: Repetitive transcranial magnetic stimulation (rTMS) induces widespread changes in brain connectivity. As the network topology differences induced by a single session of rTMS are less known we undertook this study to ascertain whether the network alterations had a small-world morphology using multi-modal graph theory analysis of simultaneous EEG-fMRI. Method: Simultaneous EEG-fMRI was acquired in duplicate before (R1) and after (R2) a single session of rTMS in 14 patients with Writer's Cramp (WC). Whole brain neuronal and hemodynamic network connectivity were explored using the graph theory measures and clustering coefficient, path length and small-world index were calculated for EEG and resting state fMRI (rsfMRI). Multi-modal graph theory analysis was used to evaluate the correlation of EEG and fMRI clustering coefficients. Result: A single session of rTMS was found to increase the clustering coefficient and small-worldness significantly in both EEG and fMRI (p < 0.05). Multi-modal graph theory analysis revealed significant modulations in the fronto-parietal regions immediately after rTMS. The rsfMRI revealed additional modulations in several deep brain regions including cerebellum, insula and medial frontal lobe. Conclusion: Multi-modal graph theory analysis of simultaneous EEG-fMRI can supplement motor physiology methods in understanding the neurobiology of rTMS in vivo. Coinciding evidence from EEG and rsfMRI reports small-world morphology for the acute phase network hyper-connectivity indicating changes ensuing low-frequency rTMS is probably not "noise".
NASA Astrophysics Data System (ADS)
Wang, Xiaonan; Lu, Ying; Jiang, Minxi; Ouyang, Qi
2004-05-01
Trapping and untrapping of spiral tips in a two-dimensional homogeneous excitable medium with local small-world connections are studied by numerical simulation. In a homogeneous medium which can be simulated with a lattice of regular neighborhood connections, the spiral wave is in the meandering regime. When changing the topology of a small region from regular connections to small-world connections, the tip of the spiral waves is attracted by the small-world region, where the average path length declines with the introduction of long distant connections. The “trapped” phenomenon also occurs in regular lattices where the diffusion coefficient of the small region is increased. The above results can be explained by the eikonal equation, the Luther equation, and the relation between the core radius and the diffusion coefficient.
Potts model on directed small-world Voronoi-Delaunay lattices
NASA Astrophysics Data System (ADS)
Marques, R. M.; Lima, F. W. S.; Costa Filho, Raimundo N.
2016-06-01
The critical properties of the Potts model with q = 3 and 4 states in two-dimensions on directed small-world Voronoi-Delaunay random lattices with quenched connectivity disorder are investigated. This disordered system is simulated by applying the Monte Carlo update heat bath algorithm. The Potts model on these directed small-world random lattices presents in fact a second-order phase transition with new critical exponents for q = 3 and value of the rewiring probability p = 0.01, but for q = 4 the system exhibits only a first-order phase transition independent of p (0 < p < 1).
NASA Astrophysics Data System (ADS)
Zeng, Hong-Li; Zhu, Chen-Ping; Guo, Yan-Dong; Teng, Ao; Jia, Jing; Kong, Hui; Zhou, Rui; Yang, Juan-Ping; Li, Su-Quan
2015-04-01
A co-evolutionary neuronal network model based on previous ones is proposed, and both functional and structural properties are numerically calculated. Recent experiments have revealed power-law behavior in electrocorticogram (ECoG) spectrum related with synaptic plasticity and reorganization. In the present neuronal network model, the network starts its evolution from the initial configuration of random network which is the least biased and without special structure, and the interaction rules among neurons are modified from both models by Bornholdt's and Arcangelis' groups to simulate the process of synaptic development and maturation. The system exhibits dynamic small-world structure which is the result of evolution instead of the assumption beforehand. Meanwhile, the power spectrum of electrical signals reproduces the power-law behavior with the exponent 2.0 just as what is experimentally measured in ECoG spectrum. Moreover, the power spectrum of the average degree per neuron over time also exhibits power-law behavior, with the exponent 2.0 again over more than 5 orders of magnitude. Different from previous results, our network exhibits assortative degree-degree correlation which is expected to be checked by experiments.
Sassen, D. S.; Peterson, J. E.
2010-03-15
.g. Bautu et al., 2006). In the technique of algebraic reconstruction tomography (ART), which is used herein for the travel time inversion (Peterson et al., 1985), a small relaxation parameter will smooth imaging artifacts caused by data errors at the expense of resolution and contrast (Figure 2). However, large data errors such as unaccounted well deviations cannot be adequately suppressed through inversion weighting schemes. Previously, problems with tomograms were treated manually. However, in large data sets and/or networks of data sets, trial and error changes to well geometries become increasingly difficult and ineffective. Mislocation of the transmitter and receiver stations of GPR cross-well tomography data sets can lead to serious imaging artifacts if not accounted for prior to inversion. Previously, problems with tomograms have been treated manually prior to inversion. In large data sets and/or networks of tomographic data sets, trial and error changes to well geometries become increasingly difficult and ineffective. Our approach is to use cross-well data quality checks and a simplified model of borehole deviation with particle swarm optimization (PSO) to automatically correct for source and receiver locations prior to tomographic inversion. We present a simple model of well deviation, which is designed to minimize potential corruption of actual data trends. We also provide quantitative quality control measures based on minimizing correlations between take-off angle and apparent velocity, and a quality check on the continuity of velocity between adjacent wells. This methodology is shown to be accurate and robust for simple 2-D synthetic test cases. Plus, we demonstrate the method on actual field data where it is compared to deviation logs. This study shows the promise for automatic correction of well deviations in GPR tomographic data. Analysis of synthetic data shows that very precise estimates of well deviation can be made for small deviations, even in the
Avoiding the "It's a Small World" Effect: A Lesson Plan to Explore Diversity
ERIC Educational Resources Information Center
Endacott, Jason L.; Bowles, Freddie A.
2013-01-01
Classroom instruction about other cultures all too often resembles the Disney version of "It's a Small World" with Fantasyland-like cultural stereotypes, ceremonial activities, and traditional dress that can lead to serious misunderstandings about the depth and complexity of global societies. Social studies instruction presents the…
Big Policies and a Small World: An Analysis of Policy Problems and Solutions in Physical Education
ERIC Educational Resources Information Center
Penney, Dawn
2017-01-01
This paper uses Ball's [1998. Big policies/small world: An introduction to international perspectives in education policy. "Comparative Education," 34(2), 119-130] policy analysis and Bernstein's [1990. "The structuring of pedagogic discourse. Volume IV class, codes and control". London: Routledge; 2000, "Pedagogy,…
NASA Astrophysics Data System (ADS)
Lotsch, Bettina V.
2015-07-01
Graphene's legacy has become an integral part of today's condensed matter science and has equipped a whole generation of scientists with an armory of concepts and techniques that open up new perspectives for the postgraphene area. In particular, the judicious combination of 2D building blocks into vertical heterostructures has recently been identified as a promising route to rationally engineer complex multilayer systems and artificial solids with intriguing properties. The present review highlights recent developments in the rapidly emerging field of 2D nanoarchitectonics from a materials chemistry perspective, with a focus on the types of heterostructures available, their assembly strategies, and their emerging properties. This overview is intended to bridge the gap between two major—yet largely disjunct—developments in 2D heterostructures, which are firmly rooted in solid-state chemistry or physics. Although the underlying types of heterostructures differ with respect to their dimensions, layer alignment, and interfacial quality, there is common ground, and future synergies between the various assembly strategies are to be expected.
Small Worlds Week: An online celebration of planetary science using social media to reach millions
NASA Astrophysics Data System (ADS)
Mayo, Louis
2015-11-01
In celebration of the many recent discoveries from New Horizons, Dawn, Rosetta, and Cassini, NASA launched Small Worlds Week, an online, social media driven outreach program leveraging the infrastructure of Sun-Earth Days that included a robust web design, exemplary education materials, hands-on fun activities, multimedia resources, science and career highlights, and a culminating social media event. Each day from July 6-9, a new class of solar system small worlds was featured on the website: Monday-comets, Tuesday-asteroids, Wednesday-icy moons, and Thursday-dwarf planets. Then on Friday, July 10, nine scientists from Goddard Space Flight Center, Jet Propulsion Laboratory, Naval Research Laboratory, and Lunar and Planetary Institute gathered online for four hours to answer questions from the public via Facebook and Twitter. Throughout the afternoon the scientists worked closely with a social media expert and several summer interns to reply to inquirers and to archive their chats. By all accounts, Small Worlds Week was a huge success with 37 million potential views of the social media Q&A posts. The group plans to improve and replicate the program during the school year with a more classroom focus, and then to build and extend the program to be held every year. For more information, visit http:// sunearthday.nasa.gov or catch us on Twitter, #nasasww.
NASA Astrophysics Data System (ADS)
Amayuelas, Eder; Fidalgo-Marijuan, Arkaitz; Bazán, Begoña; Urtiaga, Miren Karmele; Barandika, Gotzone; Lezama, Luis; Arriortua, María Isabel
2017-03-01
Metalloporphyrins exhibit outstanding chemical, physical and biological properties in dissolution, however, it is a challenge to synthesize them as stable solid frameworks. Long-time stability is crucial for future applications of these materials, and we have detected a slow, solid-state transformation of a 2D MnII-porphyrin at RT. The remarkable point is that this transformation showed up as a result of Electronic Paramagnetic Resonance measurements. Otherwise, the evolution of the system could have remained undetected. Thus, 2D [Mn3(TCPP)(H2O)4]·nD (1) (where TCPP is meso-tetra(4-carboxyphenyl)porphyrin and D is the solvent) has been synthesized hydrothermally, and characterised by means of X-ray diffraction (XRD), Thermogravimetry and X-ray thermodiffractometry (XRTD). This compound slowly transforms into [Mn(H4TCPP)(H2O)2]·nD (2) according to the equilibrium [Mn3(TCPP)]+4H+ ↔ [Mn(H4TCPP)]+2Mn2+. The evolution of the system has been studied through analysis of the distortion (both of the coordination sphere and the tetrapyrrolic macrocycle) and Density Functional Theory (DFT) quantum mechanical calculations.
2D semiconductor optoelectronics
NASA Astrophysics Data System (ADS)
Novoselov, Kostya
The advent of graphene and related 2D materials has recently led to a new technology: heterostructures based on these atomically thin crystals. The paradigm proved itself extremely versatile and led to rapid demonstration of tunnelling diodes with negative differential resistance, tunnelling transistors, photovoltaic devices, etc. By taking the complexity and functionality of such van der Waals heterostructures to the next level we introduce quantum wells engineered with one atomic plane precision. Light emission from such quantum wells, quantum dots and polaritonic effects will be discussed.
The role of dimensionality in neuronal network dynamics.
Ulloa Severino, Francesco Paolo; Ban, Jelena; Song, Qin; Tang, Mingliang; Bianconi, Ginestra; Cheng, Guosheng; Torre, Vincent
2016-07-11
Recent results from network theory show that complexity affects several dynamical properties of networks that favor synchronization. Here we show that synchronization in 2D and 3D neuronal networks is significantly different. Using dissociated hippocampal neurons we compared properties of cultures grown on a flat 2D substrates with those formed on 3D graphene foam scaffolds. Both 2D and 3D cultures had comparable glia to neuron ratio and the percentage of GABAergic inhibitory neurons. 3D cultures because of their dimension have many connections among distant neurons leading to small-world networks and their characteristic dynamics. After one week, calcium imaging revealed moderately synchronous activity in 2D networks, but the degree of synchrony of 3D networks was higher and had two regimes: a highly synchronized (HS) and a moderately synchronized (MS) regime. The HS regime was never observed in 2D networks. During the MS regime, neuronal assemblies in synchrony changed with time as observed in mammalian brains. After two weeks, the degree of synchrony in 3D networks decreased, as observed in vivo. These results show that dimensionality determines properties of neuronal networks and that several features of brain dynamics are a consequence of its 3D topology.
The role of dimensionality in neuronal network dynamics
Ulloa Severino, Francesco Paolo; Ban, Jelena; Song, Qin; Tang, Mingliang; Bianconi, Ginestra; Cheng, Guosheng; Torre, Vincent
2016-01-01
Recent results from network theory show that complexity affects several dynamical properties of networks that favor synchronization. Here we show that synchronization in 2D and 3D neuronal networks is significantly different. Using dissociated hippocampal neurons we compared properties of cultures grown on a flat 2D substrates with those formed on 3D graphene foam scaffolds. Both 2D and 3D cultures had comparable glia to neuron ratio and the percentage of GABAergic inhibitory neurons. 3D cultures because of their dimension have many connections among distant neurons leading to small-world networks and their characteristic dynamics. After one week, calcium imaging revealed moderately synchronous activity in 2D networks, but the degree of synchrony of 3D networks was higher and had two regimes: a highly synchronized (HS) and a moderately synchronized (MS) regime. The HS regime was never observed in 2D networks. During the MS regime, neuronal assemblies in synchrony changed with time as observed in mammalian brains. After two weeks, the degree of synchrony in 3D networks decreased, as observed in vivo. These results show that dimensionality determines properties of neuronal networks and that several features of brain dynamics are a consequence of its 3D topology. PMID:27404281
Liu Guocheng; Chen Yongqiang; Wang Xiuli Chen Baokuan; Lin Hongyan
2009-03-15
Three novel Cd(II) coordination polymers, namely, [Cd(Dpq)(1,8-NDC)(H{sub 2}O){sub 2}][Cd(Dpq)(1,8-NDC)].2H{sub 2}O (1), [Cd(Dpq)(1,4-NDC)(H{sub 2}O)] (2), and [Cd(Dpq)(2,6-NDC)] (3) have been obtained from hydrothermal reactions of cadmium(II) nitrate with the mixed ligands dipyrido [3,2-d:2',3'-f]quinoxaline (Dpq) and three structurally related naphthalene-dicarboxylate ligands [1,8-naphthalene-dicarboxylic acid (1,8-H{sub 2}NDC), 1,4-naphthalene-dicarboxylic acid (1,4-H{sub 2}NDC), and 2,6-naphthalene-dicarboxylic acid (2,6-H{sub 2}NDC)]. Single-crystal X-ray diffraction analysis reveals that the three polymers exhibit novel structures due to different naphthalene-dicarboxylic acid. Compound 1 is a novel cocrystal of left- and right-handed helical chains and binuclear complexes and ultimately packed into a 3D supramolecular structure through hydrogen bonds and {pi}-{pi} stacking interactions. Compound 2 shows a 2D rectangular network (4,4) bridged by 1,4-NDC with two kinds of coordination modes and ultimately packed into a 3D supramolecular structure through inter-layer {pi}-{pi} stacking interactions. Compound 3 is a new 3D coordination polymer with distorted PtS-type network. In addition, the title compounds exhibit blue/green emission in solid state at room temperature. - Graphical abstract: Three novel Cd(II) compounds have been synthesized under hydrothermal conditions exhibiting a systematic variation of architecture by the employment of three structurally related naphthalene-dicarboxylate ligands.
Lee, Eunjoo; Sung, Jungwoo; An, Taechang; Shin, Heungjoo; Nam, Hong Gil; Lim, Geunbae
2015-05-07
The application of nanomaterials for biosensors and fuel cells is becoming more common, but it requires an understanding of the relationship between the structure and electrochemical characteristics of the materials at the nanoscale. Herein, we report the development of scanning electrochemical microscopy-atomic force microscopy (SECM-AFM) nanoprobes for collecting spatially resolved data regarding the electrochemical activity of nanomaterials such as carbon nanotube (CNT) networks. The fabrication of the nanoprobe begins with the integration of a CNT-bundle wire into a conventional AFM probe followed by the deposition of an insulating layer and cutting of the probe end. In addition, a protrusive insulating tip is integrated at the end of the insulated CNT-bundle wire to maintain a constant distance between the nanoelectrode and the substrate; this yields an L-shaped nanoprobe. The resulting nanoprobes produced well-fitted maps of faradaic current data with less than 300 nm spatial resolution and topographical images of CNT networks owing to the small effective distance (of the order of tens of nanometers) between the electrode and the substrate. Electrochemical imaging using the L-shaped nanoprobe revealed that the electrochemical activity of the CNT network is not homogeneous and provided further understanding of the relationship between the topography and electrochemical characteristics of CNT networks.
van den Heuvel, M P; Stam, C J; Boersma, M; Hulshoff Pol, H E
2008-11-15
The brain is a complex dynamic system of functionally connected regions. Graph theory has been successfully used to describe the organization of such dynamic systems. Recent resting-state fMRI studies have suggested that inter-regional functional connectivity shows a small-world topology, indicating an organization of the brain in highly clustered sub-networks, combined with a high level of global connectivity. In addition, a few studies have investigated a possible scale-free topology of the human brain, but the results of these studies have been inconclusive. These studies have mainly focused on inter-regional connectivity, representing the brain as a network of brain regions, requiring an arbitrary definition of such regions. However, using a voxel-wise approach allows for the model-free examination of both inter-regional as well as intra-regional connectivity and might reveal new information on network organization. Especially, a voxel-based study could give information about a possible scale-free organization of functional connectivity in the human brain. Resting-state 3 Tesla fMRI recordings of 28 healthy subjects were acquired and individual connectivity graphs were formed out of all cortical and sub-cortical voxels with connections reflecting inter-voxel functional connectivity. Graph characteristics from these connectivity networks were computed. The clustering-coefficient of these networks turned out to be much higher than the clustering-coefficient of comparable random graphs, together with a short average path length, indicating a small-world organization. Furthermore, the connectivity distribution of the number of inter-voxel connections followed a power-law scaling with an exponent close to 2, suggesting a scale-free network topology. Our findings suggest a combined small-world and scale-free organization of the functionally connected human brain. The results are interpreted as evidence for a highly efficient organization of the functionally connected
Prado-Prado, Francisco; García-Mera, Xerardo; Escobar, Manuel; Sobarzo-Sánchez, Eduardo; Yañez, Matilde; Riera-Fernandez, Pablo; González-Díaz, Humberto
2011-12-01
There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interactions for a single drug. In this sense, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs+336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore
Yu, Xiao-Yang; Cui, Xiao-Bing; Lu, Jing; Luo, Yu-Hui; Zhang, Hong; Gao, Wei-Ping
2014-01-15
Five new inorganic–organic hybrids based on 4,4′-bipyridine and Keggin-type polyoxometalate [SiMo{sub 12}O{sub 40}]{sup 4−}, (SiMo{sub 12}O{sub 40})(H{sub 2}bipy){sub 2}·2H{sub 2}O (1), [Cu(Hbipy){sub 4}(HSiMo{sub 12}O{sub 40})(SiMo{sub 12}O{sub 40})](H{sub 2}bipy){sub 0.5}·7H{sub 2}O (2), [Cu{sub 2}(Hbipy){sub 6}(bipy)(SiMo{sub 12}O{sub 40}){sub 3}](Hbipy){sub 2}·6H{sub 2}O (3), [Cu(bipy){sub 2}(SiMo{sub 12}O{sub 40})](H{sub 2}bipy)·2H{sub 2}O (4) and [Cu{sub 2}(bipy){sub 4}(H{sub 2}O){sub 4}](SiMo{sub 12}O{sub 40})·13H{sub 2}O (5) (bipy=4,4′-bipyridine), have been hydrothermally synthesized. 1 consists of H{sub 2}bipy{sup 2+} and [SiMo{sub 12}O{sub 40}]{sup 4−} units. In 2, two [SiMo{sub 12}O{sub 40}]{sup 4−} are bridged by [Cu(Hbipy){sub 4}]{sup 6+} to form a [Cu(Hbipy){sub 4}(SiMo{sub 12}O{sub 40}){sub 2}]{sup 2−} dimmer. In 3, [SiMo{sub 12}O{sub 40}]{sup 4−} polyanions acting as bidentated bridging ligands and monodentated auxiliary ligands connect [Cu{sub 2}(Hbipy){sub 6}(bipy)]{sup 8+} units into a 1D zigzag chain. In 4, [SiMo{sub 12}O{sub 40}]{sup 4−} polyanions bridge neighboring 1D [Cu(bipy){sub 2}]{sup 2+} double chains into a 2D extended layer. In 5, [SiMo{sub 12}O{sub 40}]{sup 4−} polyanions acting as templates site alternately upon the grids from both sides of the square grid [Cu{sub 2}(bipy){sub 4}(H{sub 2}O){sub 4}]{sup 4+} layer. In addition, the electrochemical behaviors of 1, 3 and 4 and the photocatalysis property of 1 have been investigated. - Graphical abstract: Five new compounds based on [SiMo{sub 12}O{sub 40}]{sup 4−} have been successfully generated. [SiMo{sub 12}O{sub 40}]{sup 4−} anions play different roles in the structures of the five compounds. Display Omitted - Highlights: • Five new compounds based on [SiMo{sub 12}O{sub 40}]{sup 4−} have been generated. • [SiMo{sub 12}O{sub 40}]{sup 4−} anions play different roles in the five structures. • The electrochemical behaviors of 1, 3 and 4 have been
A Space-Filling Visualization Technique for Multivariate Small World Graphs
Wong, Pak C.; Foote, Harlan P.; Mackey, Patrick S.; Chin, George; Huang, Zhenyu; Thomas, James J.
2012-03-15
We introduce an information visualization technique, known as GreenCurve, for large sparse graphs that exhibit small world properties. Our fractal-based design approach uses spatial cues to approximate the node connections and thus eliminates the links between the nodes in the visualization. The paper describes a sophisticated algorithm to order the neighboring nodes of a large sparse graph by solving the Fiedler vector of its graph Laplacian, and then fold the graph nodes into a space-filling fractal curve based on the Fiedler vector. The result is a highly compact visualization that gives a succinct overview of the graph with guaranteed visibility of every graph node. We show in the paper that the GreenCurve technology is (1) theoretically sustainable by introducing an error estimation metric to measure the fidelity of the new graph representation, (2) empirically rigorous by conducting a usability study to investigate its strengths and weaknesses against the traditional graph layout, and (3) pragmatically feasible by applying it to analyze stressed conditions of the large scale electric power grid on the west coast.
Three-state Potts model on non-local directed small-world lattices
NASA Astrophysics Data System (ADS)
Ferraz, Carlos Handrey Araujo; Lima, José Luiz Sousa
2017-10-01
In this paper, we study the non-local directed Small-World (NLDSW) disorder effects in the three-state Potts model as a form to capture the essential features shared by real complex systems where non-locality effects play a important role in the behavior of these systems. Using Monte Carlo techniques and finite-size scaling analysis, we estimate the infinite lattice critical temperatures and the leading critical exponents in this model. In particular, we investigate the first- to second-order phase transition crossover when NLDSW links are inserted. A cluster-flip algorithm was used to reduce the critical slowing down effect in our simulations. We find that for a NLDSW disorder densities p
The Tutte polynomial of an infinite family of outerplanar, small-world and self-similar graphs
NASA Astrophysics Data System (ADS)
Liao, Yunhua; Fang, Aixiang; Hou, Yaoping
2013-10-01
In this paper we recursively describe the Tutte polynomial of an infinite family of outerplanar, small-world and self-similar graphs. In particular, we study the Abelian Sandpile Model on these graphs and obtain the generating function of the recurrent configurations. Further, we give some exact analytical expression for the Tutte polynomial at several special points
Optoelectronics with 2D semiconductors
NASA Astrophysics Data System (ADS)
Mueller, Thomas
2015-03-01
Two-dimensional (2D) atomic crystals, such as graphene and layered transition-metal dichalcogenides, are currently receiving a lot of attention for applications in electronics and optoelectronics. In this talk, I will review our research activities on electrically driven light emission, photovoltaic energy conversion and photodetection in 2D semiconductors. In particular, WSe2 monolayer p-n junctions formed by electrostatic doping using a pair of split gate electrodes, type-II heterojunctions based on MoS2/WSe2 and MoS2/phosphorene van der Waals stacks, 2D multi-junction solar cells, and 3D/2D semiconductor interfaces will be presented. Upon optical illumination, conversion of light into electrical energy occurs in these devices. If an electrical current is driven, efficient electroluminescence is obtained. I will present measurements of the electrical characteristics, the optical properties, and the gate voltage dependence of the device response. In the second part of my talk, I will discuss photoconductivity studies of MoS2 field-effect transistors. We identify photovoltaic and photoconductive effects, which both show strong photoconductive gain. A model will be presented that reproduces our experimental findings, such as the dependence on optical power and gate voltage. We envision that the efficient photon conversion and light emission, combined with the advantages of 2D semiconductors, such as flexibility, high mechanical stability and low costs of production, could lead to new optoelectronic technologies.
Sevrin, A.
1993-06-01
After reviewing some aspects of gravity in two dimensions, I show that non-trivial embeddings of sl(2) in a semi-simple (super) Lie algebra give rise to a very large class of extensions of 2D gravity. The induced action is constructed as a gauged WZW model and an exact expression for the effective action is given.
Highly crystalline 2D superconductors
NASA Astrophysics Data System (ADS)
Saito, Yu; Nojima, Tsutomu; Iwasa, Yoshihiro
2017-02-01
Recent advances in materials fabrication have enabled the manufacturing of ordered 2D electron systems, such as heterogeneous interfaces, atomic layers grown by molecular beam epitaxy, exfoliated thin flakes and field-effect devices. These 2D electron systems are highly crystalline, and some of them, despite their single-layer thickness, exhibit a sheet resistance more than an order of magnitude lower than that of conventional amorphous or granular thin films. In this Review, we explore recent developments in the field of highly crystalline 2D superconductors and highlight the unprecedented physical properties of these systems. In particular, we explore the quantum metallic state (or possible metallic ground state), the quantum Griffiths phase observed in out-of-plane magnetic fields and the superconducting state maintained in anomalously large in-plane magnetic fields. These phenomena are examined in the context of weakened disorder and/or broken spatial inversion symmetry. We conclude with a discussion of how these unconventional properties make highly crystalline 2D systems promising platforms for the exploration of new quantum physics and high-temperature superconductors.
Highly crystalline 2D superconductors
NASA Astrophysics Data System (ADS)
Saito, Yu; Nojima, Tsutomu; Iwasa, Yoshihiro
2016-12-01
Recent advances in materials fabrication have enabled the manufacturing of ordered 2D electron systems, such as heterogeneous interfaces, atomic layers grown by molecular beam epitaxy, exfoliated thin flakes and field-effect devices. These 2D electron systems are highly crystalline, and some of them, despite their single-layer thickness, exhibit a sheet resistance more than an order of magnitude lower than that of conventional amorphous or granular thin films. In this Review, we explore recent developments in the field of highly crystalline 2D superconductors and highlight the unprecedented physical properties of these systems. In particular, we explore the quantum metallic state (or possible metallic ground state), the quantum Griffiths phase observed in out-of-plane magnetic fields and the superconducting state maintained in anomalously large in-plane magnetic fields. These phenomena are examined in the context of weakened disorder and/or broken spatial inversion symmetry. We conclude with a discussion of how these unconventional properties make highly crystalline 2D systems promising platforms for the exploration of new quantum physics and high-temperature superconductors.
E-2D Advanced Hawkeye Aircraft (E-2D AHE)
2015-12-01
and Homeland Defense. As a part of the E-2D AHE radar modernization effort, the Navy also invested in integrating a full glass cockpit and full...Communication Navigation Surveillance/Air Traffic Management capability. The glass cockpit will also provide the capability for the pilot or co-pilot to...hours at a station distance of 200nm Flat Turn Service Ceiling =>25,000 feet above MSL at mission profile =>25,000 feet above MSL at mission
2-D or not 2-D, that is the question: A Northern California test
Mayeda, K; Malagnini, L; Phillips, W S; Walter, W R; Dreger, D
2005-06-06
Reliable estimates of the seismic source spectrum are necessary for accurate magnitude, yield, and energy estimation. In particular, how seismic radiated energy scales with increasing earthquake size has been the focus of recent debate within the community and has direct implications on earthquake source physics studies as well as hazard mitigation. The 1-D coda methodology of Mayeda et al. has provided the lowest variance estimate of the source spectrum when compared against traditional approaches that use direct S-waves, thus making it ideal for networks that have sparse station distribution. The 1-D coda methodology has been mostly confined to regions of approximately uniform complexity. For larger, more geophysically complicated regions, 2-D path corrections may be required. The complicated tectonics of the northern California region coupled with high quality broadband seismic data provides for an ideal ''apples-to-apples'' test of 1-D and 2-D path assumptions on direct waves and their coda. Using the same station and event distribution, we compared 1-D and 2-D path corrections and observed the following results: (1) 1-D coda results reduced the amplitude variance relative to direct S-waves by roughly a factor of 8 (800%); (2) Applying a 2-D correction to the coda resulted in up to 40% variance reduction from the 1-D coda results; (3) 2-D direct S-wave results, though better than 1-D direct waves, were significantly worse than the 1-D coda. We found that coda-based moment-rate source spectra derived from the 2-D approach were essentially identical to those from the 1-D approach for frequencies less than {approx}0.7-Hz, however for the high frequencies (0.7{le} f {le} 8.0-Hz), the 2-D approach resulted in inter-station scatter that was generally 10-30% smaller. For complex regions where data are plentiful, a 2-D approach can significantly improve upon the simple 1-D assumption. In regions where only 1-D coda correction is available it is still preferable over 2
Sánchez-Sánchez, C; Desplanches, C; Clemente-Juan, J M; Clemente-León, M; Coronado, E
2017-02-21
The Fe(ii) complex of the L1 ligand (L1 = 6-(3,5-diamino-2,4,6-triazinyl)-2,2'-bipyridine) has been used as a templating cation for the growth of oxalate-based networks. The magnetic characterization of the [Fe(II)(L1)2](ClO4)2·CH3CN (1) precursor in the solid state has been performed for the first time showing that the low-spin (LS) state is predominating from 2 to 400 K with 10% of Fe(ii), which undergoes a gradual and irreversible spin-crossover above 350 K. 1 presents the LIESST effect with a photo-conversion close to 25% and a T(LIESST) of 49 K. During the preparation of 1, a secondary product of the formula [Fe(II)(L1)(CH3CN)2(H2O)](ClO4)2·CH3CN (2) has been obtained. The magnetic characterization of 2 shows that it contains high-spin (HS) Fe(ii). 1 has afforded two novel oxalate-based compounds, the 2D compound of the formula [Fe(II)(L1)2][Mn(II)Cr(III)(ox)3]2·(CH3NO2)6·(CH3OH)·(H2O)2 (3) and the 3D compound of the formula [Fe(II)(L1)2][Mn(II)Cr(III)(ox)3]2·(CH3CN)3 (4), which have been obtained by changing the synthetic conditions. The magnetic properties show that in 3 the inserted Fe(ii) cation remains in the LS state from 2 to 340 K and presents a partial and irreversible spin-crossover of ∼20% at higher temperatures. In 4, most of the Fe(ii) complexes remain in the LS state from 2 to 230 K and present a partial and irreversible spin-crossover of ∼50% from 230 to 400 K. 3 and 4 do not present the LIESST effect.
Energy Efficiency of D2D Multi-User Cooperation.
Zhang, Zufan; Wang, Lu; Zhang, Jie
2017-03-28
The Device-to-Device (D2D) communication system is an important part of heterogeneous networks. It has great potential to improve spectrum efficiency, throughput and energy efficiency cooperation of multiple D2D users with the advantage of direct communication. When cooperating, D2D users expend extraordinary energy to relay data to other D2D users. Hence, the remaining energy of D2D users determines the life of the system. This paper proposes a cooperation scheme for multiple D2D users who reuse the orthogonal spectrum and are interested in the same data by aiming to solve the energy problem of D2D users. Considering both energy availability and the Signal to Noise Ratio (SNR) of each D2D user, the Kuhn-Munkres algorithm is introduced in the cooperation scheme to solve relay selection problems. Thus, the cooperation issue is transformed into a maximum weighted matching (MWM) problem. In order to enhance energy efficiency without the deterioration of Quality of Service (QoS), the link outage probability is derived according to the Shannon Equation by considering the data rate and delay. The simulation studies the relationships among the number of cooperative users, the length of shared data, the number of data packets and energy efficiency.
Energy Efficiency of D2D Multi-User Cooperation
Zhang, Zufan; Wang, Lu; Zhang, Jie
2017-01-01
The Device-to-Device (D2D) communication system is an important part of heterogeneous networks. It has great potential to improve spectrum efficiency, throughput and energy efficiency cooperation of multiple D2D users with the advantage of direct communication. When cooperating, D2D users expend extraordinary energy to relay data to other D2D users. Hence, the remaining energy of D2D users determines the life of the system. This paper proposes a cooperation scheme for multiple D2D users who reuse the orthogonal spectrum and are interested in the same data by aiming to solve the energy problem of D2D users. Considering both energy availability and the Signal to Noise Ratio (SNR) of each D2D user, the Kuhn-Munkres algorithm is introduced in the cooperation scheme to solve relay selection problems. Thus, the cooperation issue is transformed into a maximum weighted matching (MWM) problem. In order to enhance energy efficiency without the deterioration of Quality of Service (QoS), the link outage probability is derived according to the Shannon Equation by considering the data rate and delay. The simulation studies the relationships among the number of cooperative users, the length of shared data, the number of data packets and energy efficiency. PMID:28350374
Competing coexisting phases in 2D water
NASA Astrophysics Data System (ADS)
Zanotti, Jean-Marc; Judeinstein, Patrick; Dalla-Bernardina, Simona; Creff, Gaëlle; Brubach, Jean-Blaise; Roy, Pascale; Bonetti, Marco; Ollivier, Jacques; Sakellariou, Dimitrios; Bellissent-Funel, Marie-Claire
2016-05-01
The properties of bulk water come from a delicate balance of interactions on length scales encompassing several orders of magnitudes: i) the Hydrogen Bond (HBond) at the molecular scale and ii) the extension of this HBond network up to the macroscopic level. Here, we address the physics of water when the three dimensional extension of the HBond network is frustrated, so that the water molecules are forced to organize in only two dimensions. We account for the large scale fluctuating HBond network by an analytical mean-field percolation model. This approach provides a coherent interpretation of the different events experimentally (calorimetry, neutron, NMR, near and far infra-red spectroscopies) detected in interfacial water at 160, 220 and 250 K. Starting from an amorphous state of water at low temperature, these transitions are respectively interpreted as the onset of creation of transient low density patches of 4-HBonded molecules at 160 K, the percolation of these domains at 220 K and finally the total invasion of the surface by them at 250 K. The source of this surprising behaviour in 2D is the frustration of the natural bulk tetrahedral local geometry and the underlying very significant increase in entropy of the interfacial water molecules.
Competing coexisting phases in 2D water
Zanotti, Jean-Marc; Judeinstein, Patrick; Dalla-Bernardina, Simona; Creff, Gaëlle; Brubach, Jean-Blaise; Roy, Pascale; Bonetti, Marco; Ollivier, Jacques; Sakellariou, Dimitrios; Bellissent-Funel, Marie-Claire
2016-01-01
The properties of bulk water come from a delicate balance of interactions on length scales encompassing several orders of magnitudes: i) the Hydrogen Bond (HBond) at the molecular scale and ii) the extension of this HBond network up to the macroscopic level. Here, we address the physics of water when the three dimensional extension of the HBond network is frustrated, so that the water molecules are forced to organize in only two dimensions. We account for the large scale fluctuating HBond network by an analytical mean-field percolation model. This approach provides a coherent interpretation of the different events experimentally (calorimetry, neutron, NMR, near and far infra-red spectroscopies) detected in interfacial water at 160, 220 and 250 K. Starting from an amorphous state of water at low temperature, these transitions are respectively interpreted as the onset of creation of transient low density patches of 4-HBonded molecules at 160 K, the percolation of these domains at 220 K and finally the total invasion of the surface by them at 250 K. The source of this surprising behaviour in 2D is the frustration of the natural bulk tetrahedral local geometry and the underlying very significant increase in entropy of the interfacial water molecules. PMID:27185018
2D packing using the Myriad framework
NASA Astrophysics Data System (ADS)
Chatburn, Luke T.; Batchelor, Bruce G.
2004-02-01
Myriad is a framework for building networked and distributed vision systems and is described in a companion paper in this conference. Myriad allows the components of a multi-camera, multi-user vision system (web-cameras, image processing engines, intelligent device controllers, databases and the user interface terminals) to be interconnected and operated together, even if they are physically separated by many hundreds, or thousands, of kilometres. This is achieved by operating them as Internet services. The principal objective in this article is to illustrate the simplicity of harmonising visual control with an existing system using Myriad. However, packing of 2-dimensional blob-like objects is of considerable commercial importance in some industries and involves robotic handling and/or cutting. The shapes to be packed may be cut from sheet metal, glass, cloth, leather, wood, card, paper, composite board, or flat food materials. In addition, many 3D packing applications can realistically be tackled only by regarding them as multi-layer 2D applications. Using Myriad to perform 2D packing, a set of blob-like input objects ("shapes") can be digitised using a standard camera (e.g. a "webcam"). The resulting digital images are then analysed, using a separate processing engine, perhaps located on a different continent. The packing is planned by another processing system, perhaps on a third continent. Finally, the assembly is performed using a robot, usually but not necessarily, located close to the camera.
Parademo: e-Democracy Based on a Delegated Expert Selection Process in a Small-World Network
NASA Astrophysics Data System (ADS)
Siebes, Ronny
Many countries have a representative democracy where their governments consist of a relatively small group of politicians that represent the values and beliefs of the majority of the voters. Unfortunately, many citizens are un- satisfied with their rather limited influence on politics especially regarding governments on national level or even higher like the EU or the UN. On the other side, referenda or direct democracies seem to be a too risky way of letting un- knowledgeable or uninterested individuals decide over complex issues. We mainly have these extreme opposites in our democracies due to the limitations of our manually maintained ballot system. Initiatives like Vivarto propose an alternative, called 'Delegated voting' where parts of a vote can be delegated to people with more knowledge on a certain topic. This leads to a convenient position in the middle between both mentioned extremes. We want to use the vast amount of expertise of many online citizens in our societies in selecting the right politicians and solutions. In this paper we propose the design of system called Parademo, that enables a fine-grained e-democracy. Next to this we briefly describe how we can achieve more transparency and third-party functionality by allowing listeners to subscribe to specific information-streams within communities that are formalized in a Semantic-Web language.
Jamming in 2D Prolate Granular Materials
NASA Astrophysics Data System (ADS)
Franklin, Scott
2003-11-01
We have been looking at how 2D piles of prolate (L/D>1) granular materials respond when disturbed. A test object is pushed slowly through a horizontal network of particles; the packing fraction and particle aspect ratio can be varied independently. Particles are cut from square brass rods; the square cross-section reduces the chances of a particle rolling on top of another and keeps the pile two-dimensinal. The initial condition of the pile is quantified with an orientational order parameter which measures the inter-particle alignment. At a critical packing fraction the pile jams and the force needed to push the test object through the pile increases. The jammed state also corresponds to an increase in the number of particles undergoing large-scale motion. This is revealed both in video analysis, which measures particle rearrangments within the pile, and in the number of particles that are pushed off the end of the table.
Label-based routing for a family of small-world Farey graphs
Zhai, Yinhu; Wang, Yinhe
2016-01-01
We introduce an informative labelling method for vertices in a family of Farey graphs, and deduce a routing algorithm on all the shortest paths between any two vertices in Farey graphs. The label of a vertex is composed of the precise locating position in graphs and the exact time linking to graphs. All the shortest paths routing between any pair of vertices, which number is exactly the product of two Fibonacci numbers, are determined only by their labels, and the time complexity of the algorithm is O(n). It is the first algorithm to figure out all the shortest paths between any pair of vertices in a kind of deterministic graphs. For Farey networks, the existence of an efficient routing protocol is of interest to design practical communication algorithms in relation to dynamical processes (including synchronization and structural controllability) and also to understand the underlying mechanisms that have shaped their particular structure. PMID:27167605
Label-based routing for a family of small-world Farey graphs
NASA Astrophysics Data System (ADS)
Zhai, Yinhu; Wang, Yinhe
2016-05-01
We introduce an informative labelling method for vertices in a family of Farey graphs, and deduce a routing algorithm on all the shortest paths between any two vertices in Farey graphs. The label of a vertex is composed of the precise locating position in graphs and the exact time linking to graphs. All the shortest paths routing between any pair of vertices, which number is exactly the product of two Fibonacci numbers, are determined only by their labels, and the time complexity of the algorithm is O(n). It is the first algorithm to figure out all the shortest paths between any pair of vertices in a kind of deterministic graphs. For Farey networks, the existence of an efficient routing protocol is of interest to design practical communication algorithms in relation to dynamical processes (including synchronization and structural controllability) and also to understand the underlying mechanisms that have shaped their particular structure.
Label-based routing for a family of small-world Farey graphs.
Zhai, Yinhu; Wang, Yinhe
2016-05-11
We introduce an informative labelling method for vertices in a family of Farey graphs, and deduce a routing algorithm on all the shortest paths between any two vertices in Farey graphs. The label of a vertex is composed of the precise locating position in graphs and the exact time linking to graphs. All the shortest paths routing between any pair of vertices, which number is exactly the product of two Fibonacci numbers, are determined only by their labels, and the time complexity of the algorithm is O(n). It is the first algorithm to figure out all the shortest paths between any pair of vertices in a kind of deterministic graphs. For Farey networks, the existence of an efficient routing protocol is of interest to design practical communication algorithms in relation to dynamical processes (including synchronization and structural controllability) and also to understand the underlying mechanisms that have shaped their particular structure.
Big brains, small worlds: material culture and the evolution of the mind.
Coward, Fiona; Gamble, Clive
2008-06-12
New developments in neuroimaging have demonstrated that the basic capacities underpinning human social skills are shared by our closest extant primate relatives. The challenge for archaeologists is to explain how complex human societies evolved from this shared pattern of face-to-face social interaction. We argue that a key process was the gradual incorporation of material culture into social networks over the course of hominin evolution. Here we use three long-term processes in hominin evolution-encephalization, the global human diaspora and sedentism/agriculture-to illustrate how the cultural transmission of material culture allowed the 'scaling up' of face-to-face social interactions to the global societies known today. We conclude that future research by neuroimagers and archaeologists will need to investigate the cognitive mechanisms behind human engagement with material culture as well as other persons.
2D quasiperiodic plasmonic crystals
Bauer, Christina; Kobiela, Georg; Giessen, Harald
2012-01-01
Nanophotonic structures with irregular symmetry, such as quasiperiodic plasmonic crystals, have gained an increasing amount of attention, in particular as potential candidates to enhance the absorption of solar cells in an angular insensitive fashion. To examine the photonic bandstructure of such systems that determines their optical properties, it is necessary to measure and model normal and oblique light interaction with plasmonic crystals. We determine the different propagation vectors and consider the interaction of all possible waveguide modes and particle plasmons in a 2D metallic photonic quasicrystal, in conjunction with the dispersion relations of a slab waveguide. Using a Fano model, we calculate the optical properties for normal and inclined light incidence. Comparing measurements of a quasiperiodic lattice to the modelled spectra for angle of incidence variation in both azimuthal and polar direction of the sample gives excellent agreement and confirms the predictive power of our model. PMID:23209871
NASA Astrophysics Data System (ADS)
Schaibley, John R.; Yu, Hongyi; Clark, Genevieve; Rivera, Pasqual; Ross, Jason S.; Seyler, Kyle L.; Yao, Wang; Xu, Xiaodong
2016-11-01
Semiconductor technology is currently based on the manipulation of electronic charge; however, electrons have additional degrees of freedom, such as spin and valley, that can be used to encode and process information. Over the past several decades, there has been significant progress in manipulating electron spin for semiconductor spintronic devices, motivated by potential spin-based information processing and storage applications. However, experimental progress towards manipulating the valley degree of freedom for potential valleytronic devices has been limited until very recently. We review the latest advances in valleytronics, which have largely been enabled by the isolation of 2D materials (such as graphene and semiconducting transition metal dichalcogenides) that host an easily accessible electronic valley degree of freedom, allowing for dynamic control.
Georgi, Howard; Kats, Yevgeny
2008-09-26
We discuss what can be learned about unparticle physics by studying simple quantum field theories in one space and one time dimension. We argue that the exactly soluble 2D theory of a massless fermion coupled to a massive vector boson, the Sommerfield model, is an interesting analog of a Banks-Zaks model, approaching a free theory at high energies and a scale-invariant theory with nontrivial anomalous dimensions at low energies. We construct a toy standard model coupling to the fermions in the Sommerfield model and study how the transition from unparticle behavior at low energies to free particle behavior at high energies manifests itself in interactions with the toy standard model particles.
2D quasiperiodic plasmonic crystals.
Bauer, Christina; Kobiela, Georg; Giessen, Harald
2012-01-01
Nanophotonic structures with irregular symmetry, such as quasiperiodic plasmonic crystals, have gained an increasing amount of attention, in particular as potential candidates to enhance the absorption of solar cells in an angular insensitive fashion. To examine the photonic bandstructure of such systems that determines their optical properties, it is necessary to measure and model normal and oblique light interaction with plasmonic crystals. We determine the different propagation vectors and consider the interaction of all possible waveguide modes and particle plasmons in a 2D metallic photonic quasicrystal, in conjunction with the dispersion relations of a slab waveguide. Using a Fano model, we calculate the optical properties for normal and inclined light incidence. Comparing measurements of a quasiperiodic lattice to the modelled spectra for angle of incidence variation in both azimuthal and polar direction of the sample gives excellent agreement and confirms the predictive power of our model.
Quantum coherence selective 2D Raman-2D electronic spectroscopy
NASA Astrophysics Data System (ADS)
Spencer, Austin P.; Hutson, William O.; Harel, Elad
2017-03-01
Electronic and vibrational correlations report on the dynamics and structure of molecular species, yet revealing these correlations experimentally has proved extremely challenging. Here, we demonstrate a method that probes correlations between states within the vibrational and electronic manifold with quantum coherence selectivity. Specifically, we measure a fully coherent four-dimensional spectrum which simultaneously encodes vibrational-vibrational, electronic-vibrational and electronic-electronic interactions. By combining near-impulsive resonant and non-resonant excitation, the desired fifth-order signal of a complex organic molecule in solution is measured free of unwanted lower-order contamination. A critical feature of this method is electronic and vibrational frequency resolution, enabling isolation and assignment of individual quantum coherence pathways. The vibronic structure of the system is then revealed within an otherwise broad and featureless 2D electronic spectrum. This method is suited for studying elusive quantum effects in which electronic transitions strongly couple to phonons and vibrations, such as energy transfer in photosynthetic pigment-protein complexes.
Quantum coherence selective 2D Raman–2D electronic spectroscopy
Spencer, Austin P.; Hutson, William O.; Harel, Elad
2017-01-01
Electronic and vibrational correlations report on the dynamics and structure of molecular species, yet revealing these correlations experimentally has proved extremely challenging. Here, we demonstrate a method that probes correlations between states within the vibrational and electronic manifold with quantum coherence selectivity. Specifically, we measure a fully coherent four-dimensional spectrum which simultaneously encodes vibrational–vibrational, electronic–vibrational and electronic–electronic interactions. By combining near-impulsive resonant and non-resonant excitation, the desired fifth-order signal of a complex organic molecule in solution is measured free of unwanted lower-order contamination. A critical feature of this method is electronic and vibrational frequency resolution, enabling isolation and assignment of individual quantum coherence pathways. The vibronic structure of the system is then revealed within an otherwise broad and featureless 2D electronic spectrum. This method is suited for studying elusive quantum effects in which electronic transitions strongly couple to phonons and vibrations, such as energy transfer in photosynthetic pigment–protein complexes. PMID:28281541
Quantum coherence selective 2D Raman-2D electronic spectroscopy.
Spencer, Austin P; Hutson, William O; Harel, Elad
2017-03-10
Electronic and vibrational correlations report on the dynamics and structure of molecular species, yet revealing these correlations experimentally has proved extremely challenging. Here, we demonstrate a method that probes correlations between states within the vibrational and electronic manifold with quantum coherence selectivity. Specifically, we measure a fully coherent four-dimensional spectrum which simultaneously encodes vibrational-vibrational, electronic-vibrational and electronic-electronic interactions. By combining near-impulsive resonant and non-resonant excitation, the desired fifth-order signal of a complex organic molecule in solution is measured free of unwanted lower-order contamination. A critical feature of this method is electronic and vibrational frequency resolution, enabling isolation and assignment of individual quantum coherence pathways. The vibronic structure of the system is then revealed within an otherwise broad and featureless 2D electronic spectrum. This method is suited for studying elusive quantum effects in which electronic transitions strongly couple to phonons and vibrations, such as energy transfer in photosynthetic pigment-protein complexes.
Structure of brain functional networks.
Kuchaiev, Oleksii; Wang, Po T; Nenadic, Zoran; Przulj, Natasa
2009-01-01
Brain is a complex network optimized both for segregated and distributed information processing. To perform cognitive tasks, different areas of the brain must "cooperate," thereby forming complex networks of interactions also known as brain functional networks. Previous studies have shown that these networks exhibit "small-world" characteristics. Small-world topology, however, is a general property of all brain functional networks and does not capture structural changes in these networks in response to different stimuli or cognitive tasks. Here we show how novel graph theoretic techniques can be utilized for precise analysis of brain functional networks. These techniques allow us to detect structural changes in brain functional networks in response to different stimuli or cognitive tasks. For certain types of cognitive tasks we have found that these networks exhibit geometric structure in addition to the small-world topology. The method has been applied to the electrocorticographic signals of six epileptic patients.
NASA Astrophysics Data System (ADS)
Cappon, Giacomo; Pedersen, Morten Gram
2016-05-01
Many multicellular systems consist of coupled cells that work as a syncytium. The pancreatic islet of Langerhans is a well-studied example of such a microorgan. The islets are responsible for secretion of glucose-regulating hormones, mainly glucagon and insulin, which are released in distinct pulses. In order to observe pulsatile insulin secretion from the β-cells within the islets, the cellular responses must be synchronized. It is now well established that gap junctions provide the electrical nearest-neighbor coupling that allows excitation waves to spread across islets to synchronize the β-cell population. Surprisingly, functional coupling analysis of calcium responses in β-cells shows small-world properties, i.e., a high degree of local coupling with a few long-range "short-cut" connections that reduce the average path-length greatly. Here, we investigate how such long-range functional coupling can appear as a result of heterogeneity, nearest-neighbor coupling, and wave propagation. Heterogeneity is also able to explain a set of experimentally observed synchronization and wave properties without introducing all-or-none cell coupling and percolation theory. Our theoretical results highlight how local biological coupling can give rise to functional small-world properties via heterogeneity and wave propagation.
2D transition metal dichalcogenides
NASA Astrophysics Data System (ADS)
Manzeli, Sajedeh; Ovchinnikov, Dmitry; Pasquier, Diego; Yazyev, Oleg V.; Kis, Andras
2017-08-01
Graphene is very popular because of its many fascinating properties, but its lack of an electronic bandgap has stimulated the search for 2D materials with semiconducting character. Transition metal dichalcogenides (TMDCs), which are semiconductors of the type MX2, where M is a transition metal atom (such as Mo or W) and X is a chalcogen atom (such as S, Se or Te), provide a promising alternative. Because of its robustness, MoS2 is the most studied material in this family. TMDCs exhibit a unique combination of atomic-scale thickness, direct bandgap, strong spin-orbit coupling and favourable electronic and mechanical properties, which make them interesting for fundamental studies and for applications in high-end electronics, spintronics, optoelectronics, energy harvesting, flexible electronics, DNA sequencing and personalized medicine. In this Review, the methods used to synthesize TMDCs are examined and their properties are discussed, with particular attention to their charge density wave, superconductive and topological phases. The use of TMCDs in nanoelectronic devices is also explored, along with strategies to improve charge carrier mobility, high frequency operation and the use of strain engineering to tailor their properties.
A distance constrained synaptic plasticity model of C. elegans neuronal network
NASA Astrophysics Data System (ADS)
Badhwar, Rahul; Bagler, Ganesh
2017-03-01
Brain research has been driven by enquiry for principles of brain structure organization and its control mechanisms. The neuronal wiring map of C. elegans, the only complete connectome available till date, presents an incredible opportunity to learn basic governing principles that drive structure and function of its neuronal architecture. Despite its apparently simple nervous system, C. elegans is known to possess complex functions. The nervous system forms an important underlying framework which specifies phenotypic features associated to sensation, movement, conditioning and memory. In this study, with the help of graph theoretical models, we investigated the C. elegans neuronal network to identify network features that are critical for its control. The 'driver neurons' are associated with important biological functions such as reproduction, signalling processes and anatomical structural development. We created 1D and 2D network models of C. elegans neuronal system to probe the role of features that confer controllability and small world nature. The simple 1D ring model is critically poised for the number of feed forward motifs, neuronal clustering and characteristic path-length in response to synaptic rewiring, indicating optimal rewiring. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a distance constrained synaptic plasticity model that simultaneously explains small world nature, saturation of feed forward motifs as well as observed number of driver neurons. The distance constrained model suggests optimum long distance synaptic connections as a key feature specifying control of the network.
NKG2D ligands as therapeutic targets
Spear, Paul; Wu, Ming-Ru; Sentman, Marie-Louise; Sentman, Charles L.
2013-01-01
The Natural Killer Group 2D (NKG2D) receptor plays an important role in protecting the host from infections and cancer. By recognizing ligands induced on infected or tumor cells, NKG2D modulates lymphocyte activation and promotes immunity to eliminate ligand-expressing cells. Because these ligands are not widely expressed on healthy adult tissue, NKG2D ligands may present a useful target for immunotherapeutic approaches in cancer. Novel therapies targeting NKG2D ligands for the treatment of cancer have shown preclinical success and are poised to enter into clinical trials. In this review, the NKG2D receptor and its ligands are discussed in the context of cancer, infection, and autoimmunity. In addition, therapies targeting NKG2D ligands in cancer are also reviewed. PMID:23833565
Closed-shell and open-shell 2D nanographenes.
Sun, Zhe; Wu, Jishan
2014-01-01
This chapter describes a series of two-dimensional (2D) expanded arene networks, also known as nanographenes, with either closed-shell or open-shell electronic structure in the ground state. These systems are further categorized into three classes on a basis of different edge structures: those with zigzag edges only, those with armchair edges only, and those possessing both. Distinctive physical properties of these 2D aromatic systems are closely related to their structural characteristics and provide great potential for them as materials for different applications.
Using 2-D arrays for sensing multimodal Lamb waves
NASA Astrophysics Data System (ADS)
Engholm, Marcus; Stepinski, Tadeusz
2010-04-01
Monitoring structural integrity of large planar structures requires normally a relatively dense network of uniformly distributed ultrasonic sensors. A 2-D ultrasonic phased array with all azimuth angle coverage would be extremely useful for the structural health monitoring (SHM) of such structures. Known techniques for estimating direction of arriving (DOA) waves cannot efficiently cope with dispersive and multimodal Lamb waves (LWs). In the paper we propose an adaptive spectral estimation technique capable of handling broadband LWs sensed by 2-D arrays, the modified Capon method. Performance of the technique is evaluated using simulated multiple-mode LWs, and verified using experimental data.
NASA Astrophysics Data System (ADS)
Stone, Thomas E., Jr.
This study of network structure and phase transitions focuses on three systems with different dynamical rules: the Ising model with competing ferromagnetic and antiferromagnetic interactions on a 2D triangular lattice, the susceptible-infected-recovered (SIR) epidemic model on an adaptive small-world network, and the SIR model on the Saramaki-Kaski dynamic small-world network. In the Ising model with competing interactions, we employ a novel network construction using the individual spins as nodes and links occurring between two nodes if their spin-spin correlation function exceeds a set threshold. This construction yields the emergence of multiple networks of correlated fluctuations. In the spin-glass-like phase, we find spatially non-contiguous networks of correlated fluctuations, as had been previously predicted by chaotic renormalization-group trajectory arguments, but not confirmed. In the second part of this thesis we turn to a dynamical process, disease spreading, on an adaptive small-world network. The adaptive nature of the contact network means that the social connections can evolve in time, in response to the current states of the individual nodes, creating a feedback mechanism. Unlike previous work, we introduce a method by which this adaptive rewiring is included while maintaining the underlying community structure. This more realistic method can have significant effects on the final size of an outbreak. We also develop a mean-field theory to verify our simulation results in certain limits based on master equation considerations. The third part of this thesis treats a dynamic small-world network, in order to utilize its computational advantages to study the critical phenomena of the disease-free to epidemic phase transition. We solve the dynamical equations for the predicted critical point, and verify this point via finite size scaling arguments. The associated critical exponents are found in a similar manner, which show this model to be in a new
ERIC Educational Resources Information Center
Crane, Willow Soltow
1987-01-01
Contains a set of learning activities which deal with the accelerated loss of the earth's tropical rain forests. Includes four lessons that take 15 minutes or less to teach, background information for the teacher, and a longer lesson which provides an in-depth look at the issues that affect this topic. (TW)
Quantitative 2D liquid-state NMR.
Giraudeau, Patrick
2014-06-01
Two-dimensional (2D) liquid-state NMR has a very high potential to simultaneously determine the absolute concentration of small molecules in complex mixtures, thanks to its capacity to separate overlapping resonances. However, it suffers from two main drawbacks that probably explain its relatively late development. First, the 2D NMR signal is strongly molecule-dependent and site-dependent; second, the long duration of 2D NMR experiments prevents its general use for high-throughput quantitative applications and affects its quantitative performance. Fortunately, the last 10 years has witnessed an increasing number of contributions where quantitative approaches based on 2D NMR were developed and applied to solve real analytical issues. This review aims at presenting these recent efforts to reach a high trueness and precision in quantitative measurements by 2D NMR. After highlighting the interest of 2D NMR for quantitative analysis, the different strategies to determine the absolute concentrations from 2D NMR spectra are described and illustrated by recent applications. The last part of the manuscript concerns the recent development of fast quantitative 2D NMR approaches, aiming at reducing the experiment duration while preserving - or even increasing - the analytical performance. We hope that this comprehensive review will help readers to apprehend the current landscape of quantitative 2D NMR, as well as the perspectives that may arise from it.
Davis, Elizabeth; Sloan, Tyler; Aurelius, Krista; Barbour, Angela; Bodey, Elijah; Clark, Brigette; Dennis, Celeste; Drown, Rachel; Fleming, Megan; Humbert, Allison; Glasgo, Elizabeth; Kerns, Trent; Lingro, Kelly; McMillin, MacKenzie; Meyer, Aaron; Pope, Breanna; Stalevicz, April; Steffen, Brittney; Steindl, Austin; Williams, Carolyn; Wimberley, Carmen; Zenas, Robert; Butela, Kristen; Wildschutte, Hans
2017-01-22
The emergence of bacterial pathogens resistant to all known antibiotics is a global health crisis. Adding to this problem is that major pharmaceutical companies have shifted away from antibiotic discovery due to low profitability. As a result, the pipeline of new antibiotics is essentially dry and many bacteria now resist the effects of most commonly used drugs. To address this global health concern, citizen science through the Small World Initiative (SWI) was formed in 2012. As part of SWI, students isolate bacteria from their local environments, characterize the strains, and assay for antibiotic production. During the 2015 fall semester at Bowling Green State University, students isolated 77 soil-derived bacteria and genetically characterized strains using the 16S rRNA gene, identified strains exhibiting antagonistic activity, and performed an expanded SWI workflow using transposon mutagenesis to identify a biosynthetic gene cluster involved in toxigenic compound production. We identified one mutant with loss of antagonistic activity and through subsequent whole-genome sequencing and linker-mediated PCR identified a 24.9 kb biosynthetic gene locus likely involved in inhibitory activity in that mutant. Further assessment against human pathogens demonstrated the inhibition of Bacillus cereus, Listeria monocytogenes, and methicillin-resistant Staphylococcus aureus in the presence of this compound, thus supporting our molecular strategy as an effective research pipeline for SWI antibiotic discovery and genetic characterization.
Staring 2-D hadamard transform spectral imager
Gentry, Stephen M.; Wehlburg, Christine M.; Wehlburg, Joseph C.; Smith, Mark W.; Smith, Jody L.
2006-02-07
A staring imaging system inputs a 2D spatial image containing multi-frequency spectral information. This image is encoded in one dimension of the image with a cyclic Hadamarid S-matrix. The resulting image is detecting with a spatial 2D detector; and a computer applies a Hadamard transform to recover the encoded image.
Annotated Bibliography of EDGE2D Use
J.D. Strachan and G. Corrigan
2005-06-24
This annotated bibliography is intended to help EDGE2D users, and particularly new users, find existing published literature that has used EDGE2D. Our idea is that a person can find existing studies which may relate to his intended use, as well as gain ideas about other possible applications by scanning the attached tables.
Fernández de Luis, Roberto; Larrea, Edurne S; Orive, Joseba; Lezama, Luis; Arriortua, María I
2016-11-21
The average and commensurate superstructures of the one-dimensional coordination polymer {Cu(NO3)(H2O)}(HTae)(Bpy) (H2Tae = 1,1,2,2-tetraacetylethane, Bpy = 4,4'-bipyridine) were determined by single-crystal X-ray diffraction, and the possible symmetry relations between the space group of the average structure and the superstructure were checked. The crystal structure consists in parallel and oblique {Cu(HTae)(Bpy)} zigzag metal-organic chains stacked along the [100] crystallographic direction. The origin of the fivefold c axis in the commensurate superstructure is ascribed to a commensurate modulation of the coordination environment of the copper atoms. The commensurately ordered nitrate groups and coordinated water molecules establish a two-dimensional hydrogen-bonding network. Moreover, the crystal structure shows a commensurate to incommensurate transition at room temperature. The release of the coordination water molecules destabilizes the crystal framework, and the compound shows an irreversible structure transformation above 100 °C. Despite the loss of crystallinity, the spectroscopic studies indicate that the main building blocks of the crystal framework are retained after the transformation. The hydrogen-bonding network not only plays a crucial role stabilizing the crystal structure but also is an important pathway for magnetic exchange transmission. In fact, the magnetic susceptibility curves indicate that after the loss of coordinated water molecules, and hence the collapse of the hydrogen-bonding network, the weak anti-ferromagnetic coupling observed in the initial compound is broken. The electron paramagnetic resonance spectra are the consequence of the average signals from Cu(II) with different orientations, indicating that the magnetic coupling is effective between them. In fact, X- and Q-band data are reflecting different situations; the X-band spectra show the characteristics of an exchange g-tensor, while the Q-band signals are coming from both
ERIC Educational Resources Information Center
Burnett, Gary; Jaeger, Paul T.
2008-01-01
Introduction: This paper attempts to build bridges between two sets of theoretical concepts related to information behaviour: the macro-level concepts of Jurgen Habermas related to lifeworlds and the micro-level concepts of Elfreda Chatman related to small worlds. Argument: Habermas and Chatman explored similar issues of information behaviour at…
Lieffrig, Julien; Jeannin, Olivier; Fourmigué, Marc
2013-04-24
Halogen bonding interactions between halide anions and neutral polyiodinated linkers are used for the elaboration of anion organic frameworks, by analogy with well-known MOF derivatives. The extended, 3-fold symmetry, 1,3,5-tris(iodoethynyl)-2,4,6-trifluorobenzene (1) cocrystallizes with a variety of halide salts, namely, Et3S(+)I(-), Et3MeN(+)I(-), Et4N(+)Br(-), Et3BuN(+)Br(-), Me-DABCO(+)I(-), Bu3S(+)I(-), Bu4N(+)Br(-), Ph3S(+)Br(-), Ph4P(+)Br(-), and PPN(+)Br(-). Salts with 1:1 stoichiometry formulated as (1)·(C(+),X(-)) show recurrent formation of corrugated (6,3) networks, with the large cavities thus generated, filled either by the cations and solvent (CHCl3) molecules and/or by interpenetration (up to 4-fold interpenetration). The 2:1 salt formulated as (1)2·(Et3BuN(+)Br(-)) crystallizes in the cubic Ia3 space group (a = 22.573(5) Å, V = 11502(4) Å(3)), with the Br(-) ion located on 3 site and molecule 1 on a 3-fold axis. The 6-fold, unprecedented octahedral coordination of the bromide anion generates an hexagonal three-dimensional network of Pa3 symmetry, as observed in the pyrite model structure, at variance with the usual, but lower-symmetry, rutile-type topology. In this complex system, the I centering gives rise to a 2-fold interpenetration of class Ia, while the cations and solvent molecules are found disordered within interconnected cavities. Another related cubic structure of comparable unit cell volume (space group Pa3̅, a = 22.4310(15) Å, V = 11286.2(13) Å(3)) is found with (1)2·(Et3S(+)I(-)).
Ginsparg, P.
1991-01-01
These are introductory lectures for a general audience that give an overview of the subject of matrix models and their application to random surfaces, 2d gravity, and string theory. They are intentionally 1.5 years out of date.
Ginsparg, P.
1991-12-31
These are introductory lectures for a general audience that give an overview of the subject of matrix models and their application to random surfaces, 2d gravity, and string theory. They are intentionally 1.5 years out of date.
Brittle damage models in DYNA2D
Faux, D.R.
1997-09-01
DYNA2D is an explicit Lagrangian finite element code used to model dynamic events where stress wave interactions influence the overall response of the system. DYNA2D is often used to model penetration problems involving ductile-to-ductile impacts; however, with the advent of the use of ceramics in the armor-anti-armor community and the need to model damage to laser optics components, good brittle damage models are now needed in DYNA2D. This report will detail the implementation of four brittle damage models in DYNA2D, three scalar damage models and one tensor damage model. These new brittle damage models are then used to predict experimental results from three distinctly different glass damage problems.
NASA Astrophysics Data System (ADS)
Dekker, T.; de Zwart, S. T.; Willemsen, O. H.; Hiddink, M. G. H.; IJzerman, W. L.
2006-02-01
A prerequisite for a wide market acceptance of 3D displays is the ability to switch between 3D and full resolution 2D. In this paper we present a robust and cost effective concept for an auto-stereoscopic switchable 2D/3D display. The display is based on an LCD panel, equipped with switchable LC-filled lenticular lenses. We will discuss 3D image quality, with the focus on display uniformity. We show that slanting the lenticulars in combination with a good lens design can minimize non-uniformities in our 20" 2D/3D monitors. Furthermore, we introduce fractional viewing systems as a very robust concept to further improve uniformity in the case slanting the lenticulars and optimizing the lens design are not sufficient. We will discuss measurements and numerical simulations of the key optical characteristics of this display. Finally, we discuss 2D image quality, the switching characteristics and the residual lens effect.
2-d Finite Element Code Postprocessor
Sanford, L. A.; Hallquist, J. O.
1996-07-15
ORION is an interactive program that serves as a postprocessor for the analysis programs NIKE2D, DYNA2D, TOPAZ2D, and CHEMICAL TOPAZ2D. ORION reads binary plot files generated by the two-dimensional finite element codes currently used by the Methods Development Group at LLNL. Contour and color fringe plots of a large number of quantities may be displayed on meshes consisting of triangular and quadrilateral elements. ORION can compute strain measures, interface pressures along slide lines, reaction forces along constrained boundaries, and momentum. ORION has been applied to study the response of two-dimensional solids and structures undergoing finite deformations under a wide variety of large deformation transient dynamic and static problems and heat transfer analyses.
Chemical Approaches to 2D Materials.
Samorì, Paolo; Palermo, Vincenzo; Feng, Xinliang
2016-08-01
Chemistry plays an ever-increasing role in the production, functionalization, processing and applications of graphene and other 2D materials. This special issue highlights a selection of enlightening chemical approaches to 2D materials, which nicely reflect the breadth of the field and convey the excitement of the individuals involved in it, who are trying to translate graphene and related materials from the laboratory into a real, high-impact technology.
Resolving 2D Amorphous Materials with Scanning Probe Microscopy
NASA Astrophysics Data System (ADS)
Burson, Kristen M.; Buechner, Christin; Lewandowski, Adrian; Heyde, Markus; Freund, Hans-Joachim
Novel two-dimensional (2D) materials have garnered significant scientific interest due to their potential technological applications. Alongside the emphasis on crystalline materials, such as graphene and hexagonal BN, a new class of 2D amorphous materials must be pursued. For amorphous materials, a detailed understanding of the complex structure is necessary. Here we present a structural study of 2D bilayer silica on Ru(0001), an insulating material which is weakly coupled to the substrate. Atomic structure has been determined with a dual mode atomic force microscopy (AFM) and scanning tunneling microscopy (STM) sensor in ultra-high vacuum (UHV) at low temperatures, revealing a network of different ring sizes. Liquid AFM measurements with sub-nanometer resolution bridge the gap between clean UHV conditions and the environments that many material applications demand. Samples are grown and characterized in vacuum and subsequently transferred to the liquid AFM. Notably, the key structural features observed, namely nanoscale ring networks and larger holes to the substrate, show strong quantitative agreement between the liquid and UHV microscopy measurements. This provides direct evidence for the structural stability of these silica films for nanoelectronics and other applications. KMB acknowledges support from the Alexander von Humboldt Foundation.
Alcón, Isaac; Reta, Daniel; Moreira, Iberio de P. R.
2017-01-01
Triarylmethyls (TAMs) are prominent highly attractive open shell organic molecular building blocks for materials science, having been used in breakthrough syntheses of organic magnetic polymers and metal organic frameworks. With their radical π-conjugated nature and a proven capacity to possess high stability via suitable chemical design, TAMs display a variety of desirable characteristics which can be exploited for a wide range of applications. Due to their particular molecular and electronic structure, the spin localization in TAMs almost entirely depends on the dihedral angles of their three aryl rings with respect to the central methyl carbon atom plane, which opens up the possibility of controlling their fundamental properties by twisting the three aryl rings. Aryl ring twist angles can be tuned to a single value by specific chemical functionalisation but controlling them by external means in organic materials or devices represents a challenging task which has not yet been experimentally achieved. Herein, through rational chemical design we propose two 2D covalent organic frameworks (2D-COFs) based on specific TAM building blocks. By employing ab initio computational modeling we demonstrate that it is possible to externally manipulate the aryl ring twist angles in these 2D-linked TAM frameworks by external mechanical means. Furthermore, we show this structural manipulation allows for finely tuning the most important characteristics of these materials such as spin localization, optical electronic transitions and magnetic interactions. Due to the enormous technological potential offered by this new class of material and the fact that our work is guided by real advances in organic materials synthesis, we believe that our predictions will inspire the experimental realization of radical-2D-COFs with externally controllable characteristics. PMID:28451241
Universality in complex networks: random matrix analysis.
Bandyopadhyay, Jayendra N; Jalan, Sarika
2007-08-01
We apply random matrix theory to complex networks. We show that nearest neighbor spacing distribution of the eigenvalues of the adjacency matrices of various model networks, namely scale-free, small-world, and random networks follow universal Gaussian orthogonal ensemble statistics of random matrix theory. Second, we show an analogy between the onset of small-world behavior, quantified by the structural properties of networks, and the transition from Poisson to Gaussian orthogonal ensemble statistics, quantified by Brody parameter characterizing a spectral property. We also present our analysis for a protein-protein interaction network in budding yeast.
2D microwave imaging reflectometer electronics
Spear, A. G.; Domier, C. W. Hu, X.; Muscatello, C. M.; Ren, X.; Luhmann, N. C.; Tobias, B. J.
2014-11-15
A 2D microwave imaging reflectometer system has been developed to visualize electron density fluctuations on the DIII-D tokamak. Simultaneously illuminated at four probe frequencies, large aperture optics image reflections from four density-dependent cutoff surfaces in the plasma over an extended region of the DIII-D plasma. Localized density fluctuations in the vicinity of the plasma cutoff surfaces modulate the plasma reflections, yielding a 2D image of electron density fluctuations. Details are presented of the receiver down conversion electronics that generate the in-phase (I) and quadrature (Q) reflectometer signals from which 2D density fluctuation data are obtained. Also presented are details on the control system and backplane used to manage the electronics as well as an introduction to the computer based control program.
Large Area Synthesis of 2D Materials
NASA Astrophysics Data System (ADS)
Vogel, Eric
Transition metal dichalcogenides (TMDs) have generated significant interest for numerous applications including sensors, flexible electronics, heterostructures and optoelectronics due to their interesting, thickness-dependent properties. Despite recent progress, the synthesis of high-quality and highly uniform TMDs on a large scale is still a challenge. In this talk, synthesis routes for WSe2 and MoS2 that achieve monolayer thickness uniformity across large area substrates with electrical properties equivalent to geological crystals will be described. Controlled doping of 2D semiconductors is also critically required. However, methods established for conventional semiconductors, such as ion implantation, are not easily applicable to 2D materials because of their atomically thin structure. Redox-active molecular dopants will be demonstrated which provide large changes in carrier density and workfunction through the choice of dopant, treatment time, and the solution concentration. Finally, several applications of these large-area, uniform 2D materials will be described including heterostructures, biosensors and strain sensors.
Orthotropic Piezoelectricity in 2D Nanocellulose
NASA Astrophysics Data System (ADS)
García, Y.; Ruiz-Blanco, Yasser B.; Marrero-Ponce, Yovani; Sotomayor-Torres, C. M.
2016-10-01
The control of electromechanical responses within bonding regions is essential to face frontier challenges in nanotechnologies, such as molecular electronics and biotechnology. Here, we present Iβ-nanocellulose as a potentially new orthotropic 2D piezoelectric crystal. The predicted in-layer piezoelectricity is originated on a sui-generis hydrogen bonds pattern. Upon this fact and by using a combination of ab-initio and ad-hoc models, we introduce a description of electrical profiles along chemical bonds. Such developments lead to obtain a rationale for modelling the extended piezoelectric effect originated within bond scales. The order of magnitude estimated for the 2D Iβ-nanocellulose piezoelectric response, ~pm V‑1, ranks this material at the level of currently used piezoelectric energy generators and new artificial 2D designs. Such finding would be crucial for developing alternative materials to drive emerging nanotechnologies.
Orthotropic Piezoelectricity in 2D Nanocellulose
García, Y.; Ruiz-Blanco, Yasser B.; Marrero-Ponce, Yovani; Sotomayor-Torres, C. M.
2016-01-01
The control of electromechanical responses within bonding regions is essential to face frontier challenges in nanotechnologies, such as molecular electronics and biotechnology. Here, we present Iβ-nanocellulose as a potentially new orthotropic 2D piezoelectric crystal. The predicted in-layer piezoelectricity is originated on a sui-generis hydrogen bonds pattern. Upon this fact and by using a combination of ab-initio and ad-hoc models, we introduce a description of electrical profiles along chemical bonds. Such developments lead to obtain a rationale for modelling the extended piezoelectric effect originated within bond scales. The order of magnitude estimated for the 2D Iβ-nanocellulose piezoelectric response, ~pm V−1, ranks this material at the level of currently used piezoelectric energy generators and new artificial 2D designs. Such finding would be crucial for developing alternative materials to drive emerging nanotechnologies. PMID:27708364
Orthotropic Piezoelectricity in 2D Nanocellulose.
García, Y; Ruiz-Blanco, Yasser B; Marrero-Ponce, Yovani; Sotomayor-Torres, C M
2016-10-06
The control of electromechanical responses within bonding regions is essential to face frontier challenges in nanotechnologies, such as molecular electronics and biotechnology. Here, we present Iβ-nanocellulose as a potentially new orthotropic 2D piezoelectric crystal. The predicted in-layer piezoelectricity is originated on a sui-generis hydrogen bonds pattern. Upon this fact and by using a combination of ab-initio and ad-hoc models, we introduce a description of electrical profiles along chemical bonds. Such developments lead to obtain a rationale for modelling the extended piezoelectric effect originated within bond scales. The order of magnitude estimated for the 2D Iβ-nanocellulose piezoelectric response, ~pm V(-1), ranks this material at the level of currently used piezoelectric energy generators and new artificial 2D designs. Such finding would be crucial for developing alternative materials to drive emerging nanotechnologies.
2D microwave imaging reflectometer electronics.
Spear, A G; Domier, C W; Hu, X; Muscatello, C M; Ren, X; Tobias, B J; Luhmann, N C
2014-11-01
A 2D microwave imaging reflectometer system has been developed to visualize electron density fluctuations on the DIII-D tokamak. Simultaneously illuminated at four probe frequencies, large aperture optics image reflections from four density-dependent cutoff surfaces in the plasma over an extended region of the DIII-D plasma. Localized density fluctuations in the vicinity of the plasma cutoff surfaces modulate the plasma reflections, yielding a 2D image of electron density fluctuations. Details are presented of the receiver down conversion electronics that generate the in-phase (I) and quadrature (Q) reflectometer signals from which 2D density fluctuation data are obtained. Also presented are details on the control system and backplane used to manage the electronics as well as an introduction to the computer based control program.
Caruso, Joseph P; Israel, Natalie; Rowland, Kimberly; Lovelace, Matthew J; Saunders, Mary Jane
2016-03-01
Course-based undergraduate research is known to improve science, technology, engineering, and mathematics student achievement. We tested "The Small World Initiative, a Citizen-Science Project to Crowdsource Novel Antibiotic Discovery" to see if it also improved student performance and the critical thinking of non-science majors in Introductory Biology at Florida Atlantic University (a large, public, minority-dominant institution) in academic year 2014-15. California Critical Thinking Skills Test pre- and posttests were offered to both Small World Initiative (SWI) and control lab students for formative amounts of extra credit. SWI lab students earned significantly higher lecture grades than control lab students, had significantly fewer lecture grades of D+ or lower, and had significantly higher critical thinking posttest total scores than control students. Lastly, more SWI students were engaged while taking critical thinking tests. These results support the hypothesis that utilizing independent course-based undergraduate science research improves student achievement even in nonscience students.
Assessing 2D electrophoretic mobility spectroscopy (2D MOSY) for analytical applications.
Fang, Yuan; Yushmanov, Pavel V; Furó, István
2016-12-08
Electrophoretic displacement of charged entity phase modulates the spectrum acquired in electrophoretic NMR experiments, and this modulation can be presented via 2D FT as 2D mobility spectroscopy (MOSY) spectra. We compare in various mixed solutions the chemical selectivity provided by 2D MOSY spectra with that provided by 2D diffusion-ordered spectroscopy (DOSY) spectra and demonstrate, under the conditions explored, a superior performance of the former method. 2D MOSY compares also favourably with closely related LC-NMR methods. The shape of 2D MOSY spectra in complex mixtures is strongly modulated by the pH of the sample, a feature that has potential for areas such as in drug discovery and metabolomics. Copyright © 2016 The Authors. Magnetic Resonance in Chemistry published by John Wiley & Sons Ltd. StartCopTextCopyright © 2016 The Authors. Magnetic Resonance in Chemistry published by John Wiley & Sons Ltd.
2D Distributed Sensing Via TDR
2007-11-02
plate VEGF CompositeSensor Experimental Setup Air 279 mm 61 78 VARTM profile: slope RTM profile: rectangle 22 1 Jul 2003© 2003 University of Delaware...2003 University of Delaware All rights reserved Vision: Non-contact 2D sensing ü VARTM setup constructed within TL can be sensed by its EM field: 2D...300.0 mm/ns. 1 2 1 Jul 2003© 2003 University of Delaware All rights reserved Model Validation “ RTM Flow” TDR Response to 139 mm VEGC
Inkjet printing of 2D layered materials.
Li, Jiantong; Lemme, Max C; Östling, Mikael
2014-11-10
Inkjet printing of 2D layered materials, such as graphene and MoS2, has attracted great interests for emerging electronics. However, incompatible rheology, low concentration, severe aggregation and toxicity of solvents constitute critical challenges which hamper the manufacturing efficiency and product quality. Here, we introduce a simple and general technology concept (distillation-assisted solvent exchange) to efficiently overcome these challenges. By implementing the concept, we have demonstrated excellent jetting performance, ideal printing patterns and a variety of promising applications for inkjet printing of 2D layered materials.
Parallel Stitching of 2D Materials.
Ling, Xi; Lin, Yuxuan; Ma, Qiong; Wang, Ziqiang; Song, Yi; Yu, Lili; Huang, Shengxi; Fang, Wenjing; Zhang, Xu; Hsu, Allen L; Bie, Yaqing; Lee, Yi-Hsien; Zhu, Yimei; Wu, Lijun; Li, Ju; Jarillo-Herrero, Pablo; Dresselhaus, Mildred; Palacios, Tomás; Kong, Jing
2016-03-23
Diverse parallel stitched 2D heterostructures, including metal-semiconductor, semiconductor-semiconductor, and insulator-semiconductor, are synthesized directly through selective "sowing" of aromatic molecules as the seeds in the chemical vapor deposition (CVD) method. The methodology enables the large-scale fabrication of lateral heterostructures, which offers tremendous potential for its application in integrated circuits.
Parallel stitching of 2D materials
Ling, Xi; Wu, Lijun; Lin, Yuxuan; Ma, Qiong; Wang, Ziqiang; Song, Yi; Yu, Lili; Huang, Shengxi; Fang, Wenjing; Zhang, Xu; Hsu, Allen L.; Bie, Yaqing; Lee, Yi -Hsien; Zhu, Yimei; Li, Ju; Jarillo-Herrero, Pablo; Dresselhaus, Mildred; Palacios, Tomas; Kong, Jing
2016-01-27
Diverse parallel stitched 2D heterostructures, including metal–semiconductor, semiconductor–semiconductor, and insulator–semiconductor, are synthesized directly through selective “sowing” of aromatic molecules as the seeds in the chemical vapor deposition (CVD) method. Lastly, the methodology enables the large-scale fabrication of lateral heterostructures, which offers tremendous potential for its application in integrated circuits.
Beckett, Phil
2012-01-01
The technique of two-dimensional (2D) gel electrophoresis is a powerful tool for separating complex mixtures of proteins, but since its inception in the mid 1970s, it acquired the stigma of being a very difficult application to master and was generally used to its best effect by experts. The introduction of commercially available immobilized pH gradients in the early 1990s provided enhanced reproducibility and easier protocols, leading to a pronounced increase in popularity of the technique. However gel-to-gel variation was still difficult to control without the use of technical replicates. In the mid 1990s (at the same time as the birth of "proteomics"), the concept of multiplexing fluorescently labeled proteins for 2D gel separation was realized by Jon Minden's group and has led to the ability to design experiments to virtually eliminate gel-to-gel variation, resulting in biological replicates being used for statistical analysis with the ability to detect very small changes in relative protein abundance. This technology is referred to as 2D difference gel electrophoresis (2D DIGE).
Parallel stitching of 2D materials
Ling, Xi; Wu, Lijun; Lin, Yuxuan; ...
2016-01-27
Diverse parallel stitched 2D heterostructures, including metal–semiconductor, semiconductor–semiconductor, and insulator–semiconductor, are synthesized directly through selective “sowing” of aromatic molecules as the seeds in the chemical vapor deposition (CVD) method. Lastly, the methodology enables the large-scale fabrication of lateral heterostructures, which offers tremendous potential for its application in integrated circuits.
VIEWNET: a neural architecture for learning to recognize 3D objects from multiple 2D views
NASA Astrophysics Data System (ADS)
Grossberg, Stephen; Bradski, Gary
1994-10-01
A self-organizing neural network is developed for recognition of 3-D objects from sequences of their 2-D views. Called VIEWNET because it uses view information encoded with networks, the model processes 2-D views of 3-D objects using the CORT-X 2 filter, which discounts the illuminant, regularizes and completes figural boundaries, and removes noise from the images. A log-polar transform is taken with respect to the centroid of the resulting figure and then re-centered to achieve 2-D scale and rotation invariance. The invariant images are coarse coded to further reduce noise, reduce foreshortening effects, and increase generalization. These compressed codes are input into a supervised learning system based on the Fuzzy ARTMAP algorithm which learns 2-D view categories. Evidence from sequences of 2-D view categories is stored in a working memory. Voting based on the unordered set of stored categories determines object recognition. Recognition is studied with noisy and clean images using slow and fast learning. VIEWNET is demonstrated on an MIT Lincoln Laboratory database of 2-D views of aircraft with and without additive noise. A recognition rate of up to 90% is achieved with one 2-D view category and of up to 98.5% correct with three 2-D view categories.
2D optical beam splitter using diffractive optical elements (DOE)
NASA Astrophysics Data System (ADS)
Wen, Fung J.; Chung, Po S.
2006-09-01
A novel approach for optical beam distribution into a 2-dimensional (2-D) packaged fiber arrays using 2-D Dammann gratings is investigated. This paper focuses on the design and fabrication of the diffractive optical element (DOE) and investigates the coupling efficiencies of the beamlets into a packaged V-grooved 2x2 fibre array. We report for the first time experimental results of a 2-D optical signal distribution into a packaged 2x2 fibre array using Dammann grating. This grating may be applicable to the FTTH network as it can support sufficient channels with good output uniformity together with low polarization dependent loss (PDL) and acceptable insertion loss. Using an appropriate optimization algorithm (the steepest descent algorithm in this case), the optimum profile for the gratings can be calculated. The gratings are then fabricated on ITO glass using electron-beam lithography. The overall performance of the design shows an output uniformity of around 0.14 dB and an insertion loss of about 12.63 dB, including the DOE, focusing lens and the packaged fiber array.
Quantitive and Sociological Analysis of Blog Networks
NASA Astrophysics Data System (ADS)
Bachnik, W.; Szymczyk, S.; Leszczynski, S.; Podsiadlo, R.; Rymszewicz, E.; Kurylo, L.; Makowiec, D.; Bykowska, B.
2005-10-01
This paper examines the emerging phenomenon of blogging, using three different Polish blogging services as the base of the research. Authors show that blog networks are sharing their characteristics with complex networks (gamma coefficients, small worlds, cliques, etc.). Elements of sociometric analysis were used to prove existence of some social structures in the blog networks.
Application of 2D Non-Graphene Materials and 2D Oxide Nanostructures for Biosensing Technology
Shavanova, Kateryna; Bakakina, Yulia; Burkova, Inna; Shtepliuk, Ivan; Viter, Roman; Ubelis, Arnolds; Beni, Valerio; Starodub, Nickolaj; Yakimova, Rositsa; Khranovskyy, Volodymyr
2016-01-01
The discovery of graphene and its unique properties has inspired researchers to try to invent other two-dimensional (2D) materials. After considerable research effort, a distinct “beyond graphene” domain has been established, comprising the library of non-graphene 2D materials. It is significant that some 2D non-graphene materials possess solid advantages over their predecessor, such as having a direct band gap, and therefore are highly promising for a number of applications. These applications are not limited to nano- and opto-electronics, but have a strong potential in biosensing technologies, as one example. However, since most of the 2D non-graphene materials have been newly discovered, most of the research efforts are concentrated on material synthesis and the investigation of the properties of the material. Applications of 2D non-graphene materials are still at the embryonic stage, and the integration of 2D non-graphene materials into devices is scarcely reported. However, in recent years, numerous reports have blossomed about 2D material-based biosensors, evidencing the growing potential of 2D non-graphene materials for biosensing applications. This review highlights the recent progress in research on the potential of using 2D non-graphene materials and similar oxide nanostructures for different types of biosensors (optical and electrochemical). A wide range of biological targets, such as glucose, dopamine, cortisol, DNA, IgG, bisphenol, ascorbic acid, cytochrome and estradiol, has been reported to be successfully detected by biosensors with transducers made of 2D non-graphene materials. PMID:26861346
Application of 2D Non-Graphene Materials and 2D Oxide Nanostructures for Biosensing Technology.
Shavanova, Kateryna; Bakakina, Yulia; Burkova, Inna; Shtepliuk, Ivan; Viter, Roman; Ubelis, Arnolds; Beni, Valerio; Starodub, Nickolaj; Yakimova, Rositsa; Khranovskyy, Volodymyr
2016-02-06
The discovery of graphene and its unique properties has inspired researchers to try to invent other two-dimensional (2D) materials. After considerable research effort, a distinct "beyond graphene" domain has been established, comprising the library of non-graphene 2D materials. It is significant that some 2D non-graphene materials possess solid advantages over their predecessor, such as having a direct band gap, and therefore are highly promising for a number of applications. These applications are not limited to nano- and opto-electronics, but have a strong potential in biosensing technologies, as one example. However, since most of the 2D non-graphene materials have been newly discovered, most of the research efforts are concentrated on material synthesis and the investigation of the properties of the material. Applications of 2D non-graphene materials are still at the embryonic stage, and the integration of 2D non-graphene materials into devices is scarcely reported. However, in recent years, numerous reports have blossomed about 2D material-based biosensors, evidencing the growing potential of 2D non-graphene materials for biosensing applications. This review highlights the recent progress in research on the potential of using 2D non-graphene materials and similar oxide nanostructures for different types of biosensors (optical and electrochemical). A wide range of biological targets, such as glucose, dopamine, cortisol, DNA, IgG, bisphenol, ascorbic acid, cytochrome and estradiol, has been reported to be successfully detected by biosensors with transducers made of 2D non-graphene materials.
DNN-state identification of 2D distributed parameter systems
NASA Astrophysics Data System (ADS)
Chairez, I.; Fuentes, R.; Poznyak, A.; Poznyak, T.; Escudero, M.; Viana, L.
2012-02-01
There are many examples in science and engineering which are reduced to a set of partial differential equations (PDEs) through a process of mathematical modelling. Nevertheless there exist many sources of uncertainties around the aforementioned mathematical representation. Moreover, to find exact solutions of those PDEs is not a trivial task especially if the PDE is described in two or more dimensions. It is well known that neural networks can approximate a large set of continuous functions defined on a compact set to an arbitrary accuracy. In this article, a strategy based on the differential neural network (DNN) for the non-parametric identification of a mathematical model described by a class of two-dimensional (2D) PDEs is proposed. The adaptive laws for weights ensure the 'practical stability' of the DNN-trajectories to the parabolic 2D-PDE states. To verify the qualitative behaviour of the suggested methodology, here a non-parametric modelling problem for a distributed parameter plant is analysed.
Extrinsic Cation Selectivity of 2D Membranes
2017-01-01
From a systematic study of the concentration driven diffusion of positive and negative ions across porous 2D membranes of graphene and hexagonal boron nitride (h-BN), we prove their cation selectivity. Using the current–voltage characteristics of graphene and h-BN monolayers separating reservoirs of different salt concentrations, we calculate the reversal potential as a measure of selectivity. We tune the Debye screening length by exchanging the salt concentrations and demonstrate that negative surface charge gives rise to cation selectivity. Surprisingly, h-BN and graphene membranes show similar characteristics, strongly suggesting a common origin of selectivity in aqueous solvents. For the first time, we demonstrate that the cation flux can be increased by using ozone to create additional pores in graphene while maintaining excellent selectivity. We discuss opportunities to exploit our scalable method to use 2D membranes for applications including osmotic power conversion. PMID:28157333
Schottky diodes from 2D germanane
NASA Astrophysics Data System (ADS)
Sahoo, Nanda Gopal; Esteves, Richard J.; Punetha, Vinay Deep; Pestov, Dmitry; Arachchige, Indika U.; McLeskey, James T.
2016-07-01
We report on the fabrication and characterization of a Schottky diode made using 2D germanane (hydrogenated germanene). When compared to germanium, the 2D structure has higher electron mobility, an optimal band-gap, and exceptional stability making germanane an outstanding candidate for a variety of opto-electronic devices. One-atom-thick sheets of hydrogenated puckered germanium atoms have been synthesized from a CaGe2 framework via intercalation and characterized by XRD, Raman, and FTIR techniques. The material was then used to fabricate Schottky diodes by suspending the germanane in benzonitrile and drop-casting it onto interdigitated metal electrodes. The devices demonstrate significant rectifying behavior and the outstanding potential of this material.
Schottky diodes from 2D germanane
Sahoo, Nanda Gopal; Punetha, Vinay Deep; Esteves, Richard J; Arachchige, Indika U.; Pestov, Dmitry; McLeskey, James T.
2016-07-11
We report on the fabrication and characterization of a Schottky diode made using 2D germanane (hydrogenated germanene). When compared to germanium, the 2D structure has higher electron mobility, an optimal band-gap, and exceptional stability making germanane an outstanding candidate for a variety of opto-electronic devices. One-atom-thick sheets of hydrogenated puckered germanium atoms have been synthesized from a CaGe{sub 2} framework via intercalation and characterized by XRD, Raman, and FTIR techniques. The material was then used to fabricate Schottky diodes by suspending the germanane in benzonitrile and drop-casting it onto interdigitated metal electrodes. The devices demonstrate significant rectifying behavior and the outstanding potential of this material.
Compatible embedding for 2D shape animation.
Baxter, William V; Barla, Pascal; Anjyo, Ken-Ichi
2009-01-01
We present new algorithms for the compatible embedding of 2D shapes. Such embeddings offer a convenient way to interpolate shapes having complex, detailed features. Compared to existing techniques, our approach requires less user input, and is faster, more robust, and simpler to implement, making it ideal for interactive use in practical applications. Our new approach consists of three parts. First, our boundary matching algorithm locates salient features using the perceptually motivated principles of scale-space and uses these as automatic correspondences to guide an elastic curve matching algorithm. Second, we simplify boundaries while maintaining their parametric correspondence and the embedding of the original shapes. Finally, we extend the mapping to shapes' interiors via a new compatible triangulation algorithm. The combination of our algorithms allows us to demonstrate 2D shape interpolation with instant feedback. The proposed algorithms exhibit a combination of simplicity, speed, and accuracy that has not been achieved in previous work.
Stochastic Inversion of 2D Magnetotelluric Data
Chen, Jinsong
2010-07-01
The algorithm is developed to invert 2D magnetotelluric (MT) data based on sharp boundary parametrization using a Bayesian framework. Within the algorithm, we consider the locations and the resistivity of regions formed by the interfaces are as unknowns. We use a parallel, adaptive finite-element algorithm to forward simulate frequency-domain MT responses of 2D conductivity structure. Those unknown parameters are spatially correlated and are described by a geostatistical model. The joint posterior probability distribution function is explored by Markov Chain Monte Carlo (MCMC) sampling methods. The developed stochastic model is effective for estimating the interface locations and resistivity. Most importantly, it provides details uncertainty information on each unknown parameter. Hardware requirements: PC, Supercomputer, Multi-platform, Workstation; Software requirements C and Fortan; Operation Systems/version is Linux/Unix or Windows
Static & Dynamic Response of 2D Solids
Lin, Jerry
1996-07-15
NIKE2D is an implicit finite-element code for analyzing the finite deformation, static and dynamic response of two-dimensional, axisymmetric, plane strain, and plane stress solids. The code is fully vectorized and available on several computing platforms. A number of material models are incorporated to simulate a wide range of material behavior including elasto-placicity, anisotropy, creep, thermal effects, and rate dependence. Slideline algorithms model gaps and sliding along material interfaces, including interface friction, penetration and single surface contact. Interactive-graphics and rezoning is included for analyses with large mesh distortions. In addition to quasi-Newton and arc-length procedures, adaptive algorithms can be defined to solve the implicit equations using the solution language ISLAND. Each of these capabilities and more make NIKE2D a robust analysis tool.
Explicit 2-D Hydrodynamic FEM Program
Lin, Jerry
1996-08-07
DYNA2D* is a vectorized, explicit, two-dimensional, axisymmetric and plane strain finite element program for analyzing the large deformation dynamic and hydrodynamic response of inelastic solids. DYNA2D* contains 13 material models and 9 equations of state (EOS) to cover a wide range of material behavior. The material models implemented in all machine versions are: elastic, orthotropic elastic, kinematic/isotropic elastic plasticity, thermoelastoplastic, soil and crushable foam, linear viscoelastic, rubber, high explosive burn, isotropic elastic-plastic, temperature-dependent elastic-plastic. The isotropic and temperature-dependent elastic-plastic models determine only the deviatoric stresses. Pressure is determined by one of 9 equations of state including linear polynomial, JWL high explosive, Sack Tuesday high explosive, Gruneisen, ratio of polynomials, linear polynomial with energy deposition, ignition and growth of reaction in HE, tabulated compaction, and tabulated.
2D Metals by Repeated Size Reduction.
Liu, Hanwen; Tang, Hao; Fang, Minghao; Si, Wenjie; Zhang, Qinghua; Huang, Zhaohui; Gu, Lin; Pan, Wei; Yao, Jie; Nan, Cewen; Wu, Hui
2016-10-01
A general and convenient strategy for manufacturing freestanding metal nanolayers is developed on large scale. By the simple process of repeatedly folding and calendering stacked metal sheets followed by chemical etching, free-standing 2D metal (e.g., Ag, Au, Fe, Cu, and Ni) nanosheets are obtained with thicknesses as small as 1 nm and with sizes of the order of several micrometers.
Realistic and efficient 2D crack simulation
NASA Astrophysics Data System (ADS)
Yadegar, Jacob; Liu, Xiaoqing; Singh, Abhishek
2010-04-01
Although numerical algorithms for 2D crack simulation have been studied in Modeling and Simulation (M&S) and computer graphics for decades, realism and computational efficiency are still major challenges. In this paper, we introduce a high-fidelity, scalable, adaptive and efficient/runtime 2D crack/fracture simulation system by applying the mathematically elegant Peano-Cesaro triangular meshing/remeshing technique to model the generation of shards/fragments. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level-of-detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanism used for mesh element splitting and merging with minimal memory requirements essential for realistic 2D fragment formation. Upon load impact/contact/penetration, a number of factors including impact angle, impact energy, and material properties are all taken into account to produce the criteria of crack initialization, propagation, and termination leading to realistic fractal-like rubble/fragments formation. The aforementioned parameters are used as variables of probabilistic models of cracks/shards formation, making the proposed solution highly adaptive by allowing machine learning mechanisms learn the optimal values for the variables/parameters based on prior benchmark data generated by off-line physics based simulation solutions that produce accurate fractures/shards though at highly non-real time paste. Crack/fracture simulation has been conducted on various load impacts with different initial locations at various impulse scales. The simulation results demonstrate that the proposed system has the capability to realistically and efficiently simulate 2D crack phenomena (such as window shattering and shards generation) with diverse potentials in military and civil M&S applications such as training and mission planning.
Quasiparticle interference in unconventional 2D systems
NASA Astrophysics Data System (ADS)
Chen, Lan; Cheng, Peng; Wu, Kehui
2017-03-01
At present, research of 2D systems mainly focuses on two kinds of materials: graphene-like materials and transition-metal dichalcogenides (TMDs). Both of them host unconventional 2D electronic properties: pseudospin and the associated chirality of electrons in graphene-like materials, and spin-valley-coupled electronic structures in the TMDs. These exotic electronic properties have attracted tremendous interest for possible applications in nanodevices in the future. Investigation on the quasiparticle interference (QPI) in 2D systems is an effective way to uncover these properties. In this review, we will begin with a brief introduction to 2D systems, including their atomic structures and electronic bands. Then, we will discuss the formation of Friedel oscillation due to QPI in constant energy contours of electron bands, and show the basic concept of Fourier-transform scanning tunneling microscopy/spectroscopy (FT-STM/STS), which can resolve Friedel oscillation patterns in real space and consequently obtain the QPI patterns in reciprocal space. In the next two parts, we will summarize some pivotal results in the investigation of QPI in graphene and silicene, in which systems the low-energy quasiparticles are described by the massless Dirac equation. The FT-STM experiments show there are two different interference channels (intervalley and intravalley scattering) and backscattering suppression, which associate with the Dirac cones and the chirality of quasiparticles. The monolayer and bilayer graphene on different substrates (SiC and metal surfaces), and the monolayer and multilayer silicene on a Ag(1 1 1) surface will be addressed. The fifth part will introduce the FT-STM research on QPI in TMDs (monolayer and bilayer of WSe2), which allow us to infer the spin texture of both conduction and valence bands, and present spin-valley coupling by tracking allowed and forbidden scattering channels.
Compact 2-D graphical representation of DNA
NASA Astrophysics Data System (ADS)
Randić, Milan; Vračko, Marjan; Zupan, Jure; Novič, Marjana
2003-05-01
We present a novel 2-D graphical representation for DNA sequences which has an important advantage over the existing graphical representations of DNA in being very compact. It is based on: (1) use of binary labels for the four nucleic acid bases, and (2) use of the 'worm' curve as template on which binary codes are placed. The approach is illustrated on DNA sequences of the first exon of human β-globin and gorilla β-globin.
2D materials: Graphene and others
Bansal, Suneev Anil Singh, Amrinder Pal; Kumar, Suresh
2016-05-06
Present report reviews the recent advancements in new atomically thick 2D materials. Materials covered in this review are Graphene, Silicene, Germanene, Boron Nitride (BN) and Transition metal chalcogenides (TMC). These materials show extraordinary mechanical, electronic and optical properties which make them suitable candidates for future applications. Apart from unique properties, tune-ability of highly desirable properties of these materials is also an important area to be emphasized on.
NASA Astrophysics Data System (ADS)
Smith, Greg; Lankshear, Allan
1998-07-01
2dF is a multi-object instrument mounted at prime focus at the AAT capable of spectroscopic analysis of 400 objects in a single 2 degree field. It also prepares a second 2 degree 400 object field while the first field is being observed. At its heart is a high precision robotic positioner that places individual fiber end magnetic buttons on one of two field plates. The button gripper is carried on orthogonal gantries powered by linear synchronous motors and contains a TV camera which precisely locates backlit buttons to allow placement in user defined locations to 10 (mu) accuracy. Fiducial points on both plates can also be observed by the camera to allow repeated checks on positioning accuracy. Field plates rotate to follow apparent sky rotation. The spectrographs both analyze light from the 200 observing fibers each and back- illuminate the 400 fibers being re-positioned during the observing run. The 2dF fiber position and spectrograph system is a large and complex instrument located at the prime focus of the Anglo Australian Telescope. The mechanical design has departed somewhat from the earlier concepts of Gray et al, but still reflects the audacity of those first ideas. The positioner is capable of positioning 400 fibers on a field plate while another 400 fibers on another plate are observing at the focus of the telescope and feeding the twin spectrographs. When first proposed it must have seemed like ingenuity unfettered by caution. Yet now it works, and works wonderfully well. 2dF is a system which functions as the result of the combined and coordinated efforts of the astronomers, the mechanical designers and tradespeople, the electronic designers, the programmers, the support staff at the telescope, and the manufacturing subcontractors. The mechanical design of the 2dF positioner and spectrographs was carried out by the mechanical engineering staff of the AAO and the majority of the manufacture was carried out in the AAO workshops.
Engineering light outcoupling in 2D materials.
Lien, Der-Hsien; Kang, Jeong Seuk; Amani, Matin; Chen, Kevin; Tosun, Mahmut; Wang, Hsin-Ping; Roy, Tania; Eggleston, Michael S; Wu, Ming C; Dubey, Madan; Lee, Si-Chen; He, Jr-Hau; Javey, Ali
2015-02-11
When light is incident on 2D transition metal dichalcogenides (TMDCs), it engages in multiple reflections within underlying substrates, producing interferences that lead to enhancement or attenuation of the incoming and outgoing strength of light. Here, we report a simple method to engineer the light outcoupling in semiconducting TMDCs by modulating their dielectric surroundings. We show that by modulating the thicknesses of underlying substrates and capping layers, the interference caused by substrate can significantly enhance the light absorption and emission of WSe2, resulting in a ∼11 times increase in Raman signal and a ∼30 times increase in the photoluminescence (PL) intensity of WSe2. On the basis of the interference model, we also propose a strategy to control the photonic and optoelectronic properties of thin-layer WSe2. This work demonstrates the utilization of outcoupling engineering in 2D materials and offers a new route toward the realization of novel optoelectronic devices, such as 2D LEDs and solar cells.
Irreversibility-inversions in 2D turbulence
NASA Astrophysics Data System (ADS)
Bragg, Andrew; de Lillo, Filippo; Boffetta, Guido
2016-11-01
We consider a recent theoretical prediction that for inertial particles in 2D turbulence, the nature of the irreversibility of their pair dispersion inverts when the particle inertia exceeds a certain value. In particular, when the particle Stokes number, St , is below a certain value, the forward-in-time (FIT) dispersion should be faster than the backward-in-time (BIT) dispersion, but for St above this value, this should invert so that BIT becomes faster than FIT dispersion. This non-trivial behavior arises because of the competition between two physically distinct irreversibility mechanisms that operate in different regimes of St . In 3D turbulence, both mechanisms act to produce faster BIT than FIT dispersion, but in 2D, the two mechanisms have opposite effects because of the inverse energy cascade in the turbulent velocity field. We supplement the qualitative argument given by Bragg et al. by deriving quantitative predictions of this effect in the short-time dispersion limit. These predictions are then confirmed by results of inertial particle dispersion in a direct numerical simulation of 2D turbulence.
MAGNUM-2D computer code: user's guide
England, R.L.; Kline, N.W.; Ekblad, K.J.; Baca, R.G.
1985-01-01
Information relevant to the general use of the MAGNUM-2D computer code is presented. This computer code was developed for the purpose of modeling (i.e., simulating) the thermal and hydraulic conditions in the vicinity of a waste package emplaced in a deep geologic repository. The MAGNUM-2D computer computes (1) the temperature field surrounding the waste package as a function of the heat generation rate of the nuclear waste and thermal properties of the basalt and (2) the hydraulic head distribution and associated groundwater flow fields as a function of the temperature gradients and hydraulic properties of the basalt. MAGNUM-2D is a two-dimensional numerical model for transient or steady-state analysis of coupled heat transfer and groundwater flow in a fractured porous medium. The governing equations consist of a set of coupled, quasi-linear partial differential equations that are solved using a Galerkin finite-element technique. A Newton-Raphson algorithm is embedded in the Galerkin functional to formulate the problem in terms of the incremental changes in the dependent variables. Both triangular and quadrilateral finite elements are used to represent the continuum portions of the spatial domain. Line elements may be used to represent discrete conduits. 18 refs., 4 figs., 1 tab.
Stability analysis and breast tumor classification from 2D ARMA models of ultrasound images.
Abdulsadda, A; Bouaynaya, N; Iqbal, K
2009-01-01
Two-dimensional (2D) autoregressive moving average (ARMA) random fields have been proven to be accurate models of ultrasound breast images. However, the stability properties of these models have not been examined. In this paper, we investigate the stability of 2D ARMA models in ultrasound breast images, and use the estimated 2D ARMA coefficients as a basis for statistical inference using artificial neural networks. Specifically, we use the estimated 2D ARMA coefficients as inputs to a Multi layer perceptron (MLP) neural network to classify the ultrasound breast image into three regions: healthy tissue, benign tumor, and cancerous tumor. Our simulation results on various cancerous and benign ultrasound breast images illustrate the power of the proposed algorithm as attested by different learning algorithms and classification accuracy measures.
2D superconductivity by ionic gating
NASA Astrophysics Data System (ADS)
Iwasa, Yoshi
2D superconductivity is attracting a renewed interest due to the discoveries of new highly crystalline 2D superconductors in the past decade. Superconductivity at the oxide interfaces triggered by LaAlO3/SrTiO3 has become one of the promising routes for creation of new 2D superconductors. Also, the MBE grown metallic monolayers including FeSe are also offering a new platform of 2D superconductors. In the last two years, there appear a variety of monolayer/bilayer superconductors fabricated by CVD or mechanical exfoliation. Among these, electric field induced superconductivity by electric double layer transistor (EDLT) is a unique platform of 2D superconductivity, because of its ability of high density charge accumulation, and also because of the versatility in terms of materials, stemming from oxides to organics and layered chalcogenides. In this presentation, the following issues of electric filed induced superconductivity will be addressed; (1) Tunable carrier density, (2) Weak pinning, (3) Absence of inversion symmetry. (1) Since the sheet carrier density is quasi-continuously tunable from 0 to the order of 1014 cm-2, one is able to establish an electronic phase diagram of superconductivity, which will be compared with that of bulk superconductors. (2) The thickness of superconductivity can be estimated as 2 - 10 nm, dependent on materials, and is much smaller than the in-plane coherence length. Such a thin but low resistance at normal state results in extremely weak pinning beyond the dirty Boson model in the amorphous metallic films. (3) Due to the electric filed, the inversion symmetry is inherently broken in EDLT. This feature appears in the enhancement of Pauli limit of the upper critical field for the in-plane magnetic fields. In transition metal dichalcogenide with a substantial spin-orbit interactions, we were able to confirm the stabilization of Cooper pair due to its spin-valley locking. This work has been supported by Grant-in-Aid for Specially
2D non-separable linear canonical transform (2D-NS-LCT) based cryptography
NASA Astrophysics Data System (ADS)
Zhao, Liang; Muniraj, Inbarasan; Healy, John J.; Malallah, Ra'ed; Cui, Xiao-Guang; Ryle, James P.; Sheridan, John T.
2017-05-01
The 2D non-separable linear canonical transform (2D-NS-LCT) can describe a variety of paraxial optical systems. Digital algorithms to numerically evaluate the 2D-NS-LCTs are not only important in modeling the light field propagations but also of interest in various signal processing based applications, for instance optical encryption. Therefore, in this paper, for the first time, a 2D-NS-LCT based optical Double-random- Phase-Encryption (DRPE) system is proposed which offers encrypting information in multiple degrees of freedom. Compared with the traditional systems, i.e. (i) Fourier transform (FT); (ii) Fresnel transform (FST); (iii) Fractional Fourier transform (FRT); and (iv) Linear Canonical transform (LCT), based DRPE systems, the proposed system is more secure and robust as it encrypts the data with more degrees of freedom with an augmented key-space.
Codon Constraints on Closed 2D Shapes,
2014-09-26
19843$ CODON CONSTRAINTS ON CLOSED 2D SHAPES Go Whitman Richards "I Donald D. Hoffman’ D T 18 Abstract: Codons are simple primitives for describing plane...RSONAL AUT"ORtIS) Richards, Whitman & Hoffman, Donald D. 13&. TYPE OF REPORT 13b. TIME COVERED N/A P8 AT F RRrT t~r. Ago..D,) is, PlE COUNT Reprint...outlines, if figure and ground are ignored. Later, we will address the problem of indexing identical codon descriptors that have different figure
ENERGY LANDSCAPE OF 2D FLUID FORMS
Y. JIANG; ET AL
2000-04-01
The equilibrium states of 2D non-coarsening fluid foams, which consist of bubbles with fixed areas, correspond to local minima of the total perimeter. (1) The authors find an approximate value of the global minimum, and determine directly from an image how far a foam is from its ground state. (2) For (small) area disorder, small bubbles tend to sort inwards and large bubbles outwards. (3) Topological charges of the same sign repel while charges of opposite sign attract. (4) They discuss boundary conditions and the uniqueness of the pattern for fixed topology.
Periodically sheared 2D Yukawa systems
Kovács, Anikó Zsuzsa; Hartmann, Peter; Donkó, Zoltán
2015-10-15
We present non-equilibrium molecular dynamics simulation studies on the dynamic (complex) shear viscosity of a 2D Yukawa system. We have identified a non-monotonic frequency dependence of the viscosity at high frequencies and shear rates, an energy absorption maximum (local resonance) at the Einstein frequency of the system at medium shear rates, an enhanced collective wave activity, when the excitation is near the plateau frequency of the longitudinal wave dispersion, and the emergence of significant configurational anisotropy at small frequencies and high shear rates.
NASA Astrophysics Data System (ADS)
Lacava, C.; Carrol, L.; Bozzola, A.; Marchetti, R.; Minzioni, P.; Cristiani, I.; Fournier, M.; Bernabe, S.; Gerace, D.; Andreani, L. C.
2016-03-01
We present the characterization of Silicon-on-insulator (SOI) photonic-crystal based 2D grating-couplers (2D-GCs) fabricated by CEA-Leti in the frame of the FP7 Fabulous project, which is dedicated to the realization of devices and systems for low-cost and high-performance passives-optical-networks. On the analyzed samples different test structures are present, including 2D-GC connected to another 2D-GC by different waveguides (in a Mach-Zehnder like configuration), and 2D-GC connected to two separate 2D-GCs, so as to allow a complete assessment of different parameters. Measurements were carried out using a tunable laser source operating in the extended telecom bandwidth and a fiber-based polarization controlling system at the input of device-under-test. The measured data yielded an overall fiber-to-fiber loss of 7.5 dB for the structure composed by an input 2D-GC connected to two identical 2D-GCs. This value was obtained at the peak wavelength of the grating, and the 3-dB bandwidth of the 2D-GC was assessed to be 43 nm. Assuming that the waveguide losses are negligible, so as to make a worst-case analysis, the coupling efficiency of the single 2D-GC results to be equal to -3.75 dB, constituting, to the best of our knowledge, the lowest value ever reported for a fully CMOS compatible 2D-GC. It is worth noting that both the obtained values are in good agreement with those expected by the numerical simulations performed using full 3D analysis by Lumerical FDTD-solutions.
Social network structures and bank runs
NASA Astrophysics Data System (ADS)
Li, Shouwei; Li, Jiaheng
2016-05-01
This paper investigates the impact of social network structures of depositors on bank runs. The analyzed network structures include random networks, small-world networks and scale-free networks. Simulation results show that the probability of bank run occurrence in random networks is larger than that in small-world networks, but the probability of bank run occurrence in scale-free networks drops from the highest to the lowest among the three types of network structures with the increase of the proportion of impatient depositors. The average degree of depositor networks has a significant impact on bank runs, but this impact is related to the proportion of impatient depositors and the confidence levels of depositors in banks.
Formation and properties of a terpyridine-based 2D MOF on the surface of water
NASA Astrophysics Data System (ADS)
Koitz, Ralph; Hutter, Jürg; Iannuzzi, Marcella
2016-06-01
Two-dimensional networks inspired by graphene are of prime importance in nanoscience. We present a computational study of an infinite molecular sheet confined on a water surface to assess its properties and formation mechanism. Terpyridine-based ligand molecules are interlinked by Zn ions to form an extended 2D metal-organic framework. We show that the network is stable on the water surface, and that the substrate affects the dynamic properties of the sheet, exhibiting a confining effect and flattening the sheet by 30%. We use metadynamics to characterize the process of network formation and breaking and determine an intra-network binding energy of 143 kJ mol-1. Based on this mechanistic insight we propose that the 2D network strength can be tuned by varying the rigidity of the ligand through its chemical structure.
Remarks on thermalization in 2D CFT
NASA Astrophysics Data System (ADS)
de Boer, Jan; Engelhardt, Dalit
2016-12-01
We revisit certain aspects of thermalization in 2D conformal field theory (CFT). In particular, we consider similarities and differences between the time dependence of correlation functions in various states in rational and non-rational CFTs. We also consider the distinction between global and local thermalization and explain how states obtained by acting with a diffeomorphism on the ground state can appear locally thermal, and we review why the time-dependent expectation value of the energy-momentum tensor is generally a poor diagnostic of global thermalization. Since all 2D CFTs have an infinite set of commuting conserved charges, generic initial states might be expected to give rise to a generalized Gibbs ensemble rather than a pure thermal ensemble at late times. We construct the holographic dual of the generalized Gibbs ensemble and show that, to leading order, it is still described by a Banados-Teitelboim-Zanelli black hole. The extra conserved charges, while rendering c <1 theories essentially integrable, therefore seem to have little effect on large-c conformal field theories.
NASA Astrophysics Data System (ADS)
Yang, Shengxue; Jiang, Chengbao; Wei, Su-huai
2017-06-01
Two-dimensional (2D) layered inorganic nanomaterials have attracted huge attention due to their unique electronic structures, as well as extraordinary physical and chemical properties for use in electronics, optoelectronics, spintronics, catalysts, energy generation and storage, and chemical sensors. Graphene and related layered inorganic analogues have shown great potential for gas-sensing applications because of their large specific surface areas and strong surface activities. This review aims to discuss the latest advancements in the 2D layered inorganic materials for gas sensors. We first elaborate the gas-sensing mechanisms and introduce various types of gas-sensing devices. Then, we describe the basic parameters and influence factors of the gas sensors to further enhance their performance. Moreover, we systematically present the current gas-sensing applications based on graphene, graphene oxide (GO), reduced graphene oxide (rGO), functionalized GO or rGO, transition metal dichalcogenides, layered III-VI semiconductors, layered metal oxides, phosphorene, hexagonal boron nitride, etc. Finally, we conclude the future prospects of these layered inorganic materials in gas-sensing applications.
Microwave Assisted 2D Materials Exfoliation
NASA Astrophysics Data System (ADS)
Wang, Yanbin
Two-dimensional materials have emerged as extremely important materials with applications ranging from energy and environmental science to electronics and biology. Here we report our discovery of a universal, ultrafast, green, solvo-thermal technology for producing excellent-quality, few-layered nanosheets in liquid phase from well-known 2D materials such as such hexagonal boron nitride (h-BN), graphite, and MoS2. We start by mixing the uniform bulk-layered material with a common organic solvent that matches its surface energy to reduce the van der Waals attractive interactions between the layers; next, the solutions are heated in a commercial microwave oven to overcome the energy barrier between bulk and few-layers states. We discovered the minutes-long rapid exfoliation process is highly temperature dependent, which requires precise thermal management to obtain high-quality inks. We hypothesize a possible mechanism of this proposed solvo-thermal process; our theory confirms the basis of this novel technique for exfoliation of high-quality, layered 2D materials by using an as yet unknown role of the solvent.
Caruso, Joseph P.; Israel, Natalie; Rowland, Kimberly; Lovelace, Matthew J.; Saunders, Mary Jane
2016-01-01
Course-based undergraduate research is known to improve science, technology, engineering, and mathematics student achievement. We tested “The Small World Initiative, a Citizen-Science Project to Crowdsource Novel Antibiotic Discovery” to see if it also improved student performance and the critical thinking of non-science majors in Introductory Biology at Florida Atlantic University (a large, public, minority-dominant institution) in academic year 2014–15. California Critical Thinking Skills Test pre- and posttests were offered to both Small World Initiative (SWI) and control lab students for formative amounts of extra credit. SWI lab students earned significantly higher lecture grades than control lab students, had significantly fewer lecture grades of D+ or lower, and had significantly higher critical thinking posttest total scores than control students. Lastly, more SWI students were engaged while taking critical thinking tests. These results support the hypothesis that utilizing independent course-based undergraduate science research improves student achievement even in nonscience students. PMID:27047613
WFR-2D: an analytical model for PWAS-generated 2D ultrasonic guided wave propagation
NASA Astrophysics Data System (ADS)
Shen, Yanfeng; Giurgiutiu, Victor
2014-03-01
This paper presents WaveFormRevealer 2-D (WFR-2D), an analytical predictive tool for the simulation of 2-D ultrasonic guided wave propagation and interaction with damage. The design of structural health monitoring (SHM) systems and self-aware smart structures requires the exploration of a wide range of parameters to achieve best detection and quantification of certain types of damage. Such need for parameter exploration on sensor dimension, location, guided wave characteristics (mode type, frequency, wavelength, etc.) can be best satisfied with analytical models which are fast and efficient. The analytical model was constructed based on the exact 2-D Lamb wave solution using Bessel and Hankel functions. Damage effects were inserted in the model by considering the damage as a secondary wave source with complex-valued directivity scattering coefficients containing both amplitude and phase information from wave-damage interaction. The analytical procedure was coded with MATLAB, and a predictive simulation tool called WaveFormRevealer 2-D was developed. The wave-damage interaction coefficients (WDICs) were extracted from harmonic analysis of local finite element model (FEM) with artificial non-reflective boundaries (NRB). The WFR-2D analytical simulation results were compared and verified with full scale multiphysics finite element models and experiments with scanning laser vibrometer. First, Lamb wave propagation in a pristine aluminum plate was simulated with WFR-2D, compared with finite element results, and verified by experiments. Then, an inhomogeneity was machined into the plate to represent damage. Analytical modeling was carried out, and verified by finite element simulation and experiments. This paper finishes with conclusions and suggestions for future work.
Reconstruction of a 2D seismic wavefield by seismic gradiometry
NASA Astrophysics Data System (ADS)
Maeda, Takuto; Nishida, Kiwamu; Takagi, Ryota; Obara, Kazushige
2016-12-01
We reconstructed a 2D seismic wavefield and obtained its propagation properties by using the seismic gradiometry method together with dense observations of the Hi-net seismograph network in Japan. The seismic gradiometry method estimates the wave amplitude and its spatial derivative coefficients at any location from a discrete station record by using a Taylor series approximation. From the spatial derivatives in horizontal directions, the properties of a propagating wave packet, including the arrival direction, slowness, geometrical spreading, and radiation pattern can be obtained. In addition, by using spatial derivatives together with free-surface boundary conditions, the 2D vector elastic wavefield can be decomposed into divergence and rotation components. First, as a feasibility test, we performed an analysis with a synthetic seismogram dataset computed by a numerical simulation for a realistic 3D medium and the actual Hi-net station layout. We confirmed that the wave amplitude and its spatial derivatives were very well-reproduced for period bands longer than 25 s. Applications to a real large earthquake showed that the amplitude and phase of the wavefield were well reconstructed, along with slowness vector. The slowness of the reconstructed wavefield showed a clear contrast between body and surface waves and regional non-great-circle-path wave propagation, possibly owing to scattering. Slowness vectors together with divergence and rotation decomposition are expected to be useful for determining constituents of observed wavefields in inhomogeneous media.
2D and 3D heterogeneous photonic integrated circuits
NASA Astrophysics Data System (ADS)
Yoo, S. J. Ben
2014-03-01
Exponential increases in the amount of data that need to be sensed, communicated, and processed are continuing to drive the complexity of our computing, networking, and sensing systems. High degrees of integration is essential in scalable, practical, and cost-effective microsystems. In electronics, high-density 2D integration has naturally evolved towards 3D integration by stacking of memory and processor chips with through-silicon-vias. In photonics, too, we anticipate highdegrees of 3D integration of photonic components to become a prevailing method in realizing future microsystems for information and communication technologies. However, compared to electronics, photonic 3D integration face a number of challenges. This paper will review two methods of 3D photonic integration --- fs laser inscription and layer stacking, and discuss applications and future prospects.
Characteristics on hub networks of urban rail transit networks
NASA Astrophysics Data System (ADS)
Zhang, Jianhua; Wang, Shuliang; Zhang, Zhaojun; Zou, Kuansheng; Shu, Zhan
2016-04-01
This paper proposes an approach to extract the hub networks from urban rail transit networks, and analyzes the characteristics of the hub networks. Minsk metro and Shanghai metro networks are given to illustrate the feasibility and effectiveness of the presented method in this paper. By simulations, we discover that the hub networks of urban rail transit networks possess small-world property and scale-free property. Meanwhile, this paper shows that the hub networks are completely different from the corresponding metro networks. Moreover, we find that the hub network is a hierarchical network, and the root of hub network corresponds to the transfer station of metro network which is passed by the most lines in metro network, and the root controls the main characteristics of hub network. In other words, the transfer station corresponding to this root plays the most important role in the urban rail transit networks.
Simulation of Yeast Cooperation in 2D.
Wang, M; Huang, Y; Wu, Z
2016-03-01
Evolution of cooperation has been an active research area in evolutionary biology in decades. An important type of cooperation is developed from group selection, when individuals form spatial groups to prevent them from foreign invasions. In this paper, we study the evolution of cooperation in a mixed population of cooperating and cheating yeast strains in 2D with the interactions among the yeast cells restricted to their small neighborhoods. We conduct a computer simulation based on a game theoretic model and show that cooperation is increased when the interactions are spatially restricted, whether the game is of a prisoner's dilemma, snow drifting, or mutual benefit type. We study the evolution of homogeneous groups of cooperators or cheaters and describe the conditions for them to sustain or expand in an opponent population. We show that under certain spatial restrictions, cooperator groups are able to sustain and expand as group sizes become large, while cheater groups fail to expand and keep them from collapse.
Variational regularized 2-D nonnegative matrix factorization.
Gao, Bin; Woo, W L; Dlay, S S
2012-05-01
A novel approach for adaptive regularization of 2-D nonnegative matrix factorization is presented. The proposed matrix factorization is developed under the framework of maximum a posteriori probability and is adaptively fine-tuned using the variational approach. The method enables: (1) a generalized criterion for variable sparseness to be imposed onto the solution; and (2) prior information to be explicitly incorporated into the basis features. The method is computationally efficient and has been demonstrated on two applications, that is, extracting features from image and separating single channel source mixture. In addition, it is shown that the basis features of an information-bearing matrix can be extracted more efficiently using the proposed regularized priors. Experimental tests have been rigorously conducted to verify the efficacy of the proposed method.
Graphene suspensions for 2D printing
NASA Astrophysics Data System (ADS)
Soots, R. A.; Yakimchuk, E. A.; Nebogatikova, N. A.; Kotin, I. A.; Antonova, I. V.
2016-04-01
It is shown that, by processing a graphite suspension in ethanol or water by ultrasound and centrifuging, it is possible to obtain particles with thicknesses within 1-6 nm and, in the most interesting cases, 1-1.5 nm. Analogous treatment of a graphite suspension in organic solvent yields eventually thicker particles (up to 6-10 nm thick) even upon long-term treatment. Using the proposed ink based on graphene and aqueous ethanol with ethylcellulose and terpineol additives for 2D printing, thin (~5 nm thick) films with sheet resistance upon annealing ~30 MΩ/□ were obtained. With the ink based on aqueous graphene suspension, the sheet resistance was ~5-12 kΩ/□ for 6- to 15-nm-thick layers with a carrier mobility of ~30-50 cm2/(V s).
2D quantum gravity from quantum entanglement.
Gliozzi, F
2011-01-21
In quantum systems with many degrees of freedom the replica method is a useful tool to study the entanglement of arbitrary spatial regions. We apply it in a way that allows them to backreact. As a consequence, they become dynamical subsystems whose position, form, and extension are determined by their interaction with the whole system. We analyze, in particular, quantum spin chains described at criticality by a conformal field theory. Its coupling to the Gibbs' ensemble of all possible subsystems is relevant and drives the system into a new fixed point which is argued to be that of the 2D quantum gravity coupled to this system. Numerical experiments on the critical Ising model show that the new critical exponents agree with those predicted by the formula of Knizhnik, Polyakov, and Zamolodchikov.
2D Electrostatic Actuation of Microshutter Arrays
NASA Technical Reports Server (NTRS)
Burns, Devin E.; Oh, Lance H.; Li, Mary J.; Jones, Justin S.; Kelly, Daniel P.; Zheng, Yun; Kutyrev, Alexander S.; Moseley, Samuel H.
2015-01-01
An electrostatically actuated microshutter array consisting of rotational microshutters (shutters that rotate about a torsion bar) were designed and fabricated through the use of models and experiments. Design iterations focused on minimizing the torsional stiffness of the microshutters, while maintaining their structural integrity. Mechanical and electromechanical test systems were constructed to measure the static and dynamic behavior of the microshutters. The torsional stiffness was reduced by a factor of four over initial designs without sacrificing durability. Analysis of the resonant behavior of the microshutter arrays demonstrates that the first resonant mode is a torsional mode occurring around 3000 Hz. At low vacuum pressures, this resonant mode can be used to significantly reduce the drive voltage necessary for actuation requiring as little as 25V. 2D electrostatic latching and addressing was demonstrated using both a resonant and pulsed addressing scheme.
Canard configured aircraft with 2-D nozzle
NASA Technical Reports Server (NTRS)
Child, R. D.; Henderson, W. P.
1978-01-01
A closely-coupled canard fighter with vectorable two-dimensional nozzle was designed for enhanced transonic maneuvering. The HiMAT maneuver goal of a sustained 8g turn at a free-stream Mach number of 0.9 and 30,000 feet was the primary design consideration. The aerodynamic design process was initiated with a linear theory optimization minimizing the zero percent suction drag including jet effects and refined with three-dimensional nonlinear potential flow techniques. Allowances were made for mutual interference and viscous effects. The design process to arrive at the resultant configuration is described, and the design of a powered 2-D nozzle model to be tested in the LRC 16-foot Propulsion Wind Tunnel is shown.
Transition to turbulence: 2D directed percolation
NASA Astrophysics Data System (ADS)
Chantry, Matthew; Tuckerman, Laurette; Barkley, Dwight
2016-11-01
The transition to turbulence in simple shear flows has been studied for well over a century, yet in the last few years has seen major leaps forward. In pipe flow, this transition shows the hallmarks of (1 + 1) D directed percolation, a universality class of continuous phase transitions. In spanwisely confined Taylor-Couette flow the same class is found, suggesting the phenomenon is generic to shear flows. However in plane Couette flow the largest simulations and experiments to-date find evidence for a discrete transition. Here we study a planar shear flow, called Waleffe flow, devoid of walls yet showing the fundamentals of planar transition to turbulence. Working with a quasi-2D yet Navier-Stokes derived model of this flow we are able to attack the (2 + 1) D transition problem. Going beyond the system sizes previously possible we find all of the required scalings of directed percolation and thus establish planar shears flow in this class.
2D Electrostatic Actuation of Microshutter Arrays
NASA Technical Reports Server (NTRS)
Burns, Devin E.; Oh, Lance H.; Li, Mary J.; Kelly, Daniel P.; Kutyrev, Alexander S.; Moseley, Samuel H.
2015-01-01
Electrostatically actuated microshutter arrays consisting of rotational microshutters (shutters that rotate about a torsion bar) were designed and fabricated through the use of models and experiments. Design iterations focused on minimizing the torsional stiffness of the microshutters, while maintaining their structural integrity. Mechanical and electromechanical test systems were constructed to measure the static and dynamic behavior of the microshutters. The torsional stiffness was reduced by a factor of four over initial designs without sacrificing durability. Analysis of the resonant behavior of the microshutters demonstrates that the first resonant mode is a torsional mode occurring around 3000 Hz. At low vacuum pressures, this resonant mode can be used to significantly reduce the drive voltage necessary for actuation requiring as little as 25V. 2D electrostatic latching and addressing was demonstrated using both a resonant and pulsed addressing scheme.
Canard configured aircraft with 2-D nozzle
NASA Technical Reports Server (NTRS)
Child, R. D.; Henderson, W. P.
1978-01-01
A closely-coupled canard fighter with vectorable two-dimensional nozzle was designed for enhanced transonic maneuvering. The HiMAT maneuver goal of a sustained 8g turn at a free-stream Mach number of 0.9 and 30,000 feet was the primary design consideration. The aerodynamic design process was initiated with a linear theory optimization minimizing the zero percent suction drag including jet effects and refined with three-dimensional nonlinear potential flow techniques. Allowances were made for mutual interference and viscous effects. The design process to arrive at the resultant configuration is described, and the design of a powered 2-D nozzle model to be tested in the LRC 16-foot Propulsion Wind Tunnel is shown.
Numerical Evaluation of 2D Ground States
NASA Astrophysics Data System (ADS)
Kolkovska, Natalia
2016-02-01
A ground state is defined as the positive radial solution of the multidimensional nonlinear problem
Metrology for graphene and 2D materials
NASA Astrophysics Data System (ADS)
Pollard, Andrew J.
2016-09-01
The application of graphene, a one atom-thick honeycomb lattice of carbon atoms with superlative properties, such as electrical conductivity, thermal conductivity and strength, has already shown that it can be used to benefit metrology itself as a new quantum standard for resistance. However, there are many application areas where graphene and other 2D materials, such as molybdenum disulphide (MoS2) and hexagonal boron nitride (h-BN), may be disruptive, areas such as flexible electronics, nanocomposites, sensing and energy storage. Applying metrology to the area of graphene is now critical to enable the new, emerging global graphene commercial world and bridge the gap between academia and industry. Measurement capabilities and expertise in a wide range of scientific areas are required to address this challenge. The combined and complementary approach of varied characterisation methods for structural, chemical, electrical and other properties, will allow the real-world issues of commercialising graphene and other 2D materials to be addressed. Here, examples of metrology challenges that have been overcome through a multi-technique or new approach are discussed. Firstly, the structural characterisation of defects in both graphene and MoS2 via Raman spectroscopy is described, and how nanoscale mapping of vacancy defects in graphene is also possible using tip-enhanced Raman spectroscopy (TERS). Furthermore, the chemical characterisation and removal of polymer residue on chemical vapour deposition (CVD) grown graphene via secondary ion mass spectrometry (SIMS) is detailed, as well as the chemical characterisation of iron films used to grow large domain single-layer h-BN through CVD growth, revealing how contamination of the substrate itself plays a role in the resulting h-BN layer. In addition, the role of international standardisation in this area is described, outlining the current work ongoing in both the International Organization of Standardization (ISO) and the
Contreras, Alejandra V; Monge-Cazares, Tulia; Alfaro-Ruiz, Luis; Hernandez-Morales, Salvador; Miranda-Ortiz, Haydee; Carrillo-Sanchez, Karol; Jimenez-Sanchez, Gerardo; Silva-Zolezzi, Irma
2011-05-01
The CYP2D6 enzyme participates in the metabolism of commonly prescribed drugs: antidepressants, antipsychotics and antihypertensives. The CYP2D6 gene shows a high degree of interindividual and interethnic variability that influences its expression and function. Mexican Mestizos are a recently admixed population resulting from the combination of Amerindian, European and, to a lesser extent, African populations. This study aimed to comprehensively characterize the CYP2D6 gene in Mexican Mestizos. We performed linkage disequilibrium and network analyses in resequencing data of 96 individuals from two regions within Mexico with a different history of admixture and particular population dynamics, the Northwestern state of Sonora and the Central-Pacific state of Guerrero. We identified 64 polymorphisms, including 14 novel variants: 13 SNPs and a CYP2D7 exon 2 conversion, that was assigned CYP2D6*82 by the Human Cytochrome P450 (CYP) Allele Nomenclature Committee. Three novel SNPs were predicted to have functional effects. For CYP2D6*82 we hypothesize an Amerindian origin that is supported by its identification in three Mexican Amerindian groups (Mayas, Tepehuanos and Mixtecos). Frequencies of CYP2D6*1, *2, *4, *5, *10, *29, *53, *82 and its duplications were 50.0, 25.5, 14.1, 2.0, 2.6, 1.0, 0.5, 2.1 and 3.6%, respectively. We found significant frequency differences in CYP2D6*1 and *2 between Mexican Mestizos and in CYP2D6*1, *2, *4, *5, *10 and *29 between Mexicans and at least one other population. We observed strong linkage disequilibrium and phylogenetic relationships between haplotypes. To our knowledge, this study is the first comprehensive resequencing analysis of CYP2D6 in Mexicans or any other Latin American population, providing information about genetic diversity relevant in the development of pharmacogenomics in this region.
The dynamic correlation between degree and betweenness of complex network under attack
NASA Astrophysics Data System (ADS)
Nie, Tingyuan; Guo, Zheng; Zhao, Kun; Lu, Zhe-Ming
2016-09-01
Complex networks are often subjected to failure and attack. Recent work has addressed the resilience of complex networks to either random or intentional deletion of nodes or links. Here we simulate the breakdown of the small-world network and the scale-free network under node failure or attacks. We analyze and discuss the dynamic correlation between degree and betweenness in the process of attack. The simulation results show that the correlation for scale-free network obeys a power law distribution until the network collapses, while it represents irregularly for small-world network.
Hirobe, Tomohisa; Ito, Shosuke; Wakamatsu, Kazumasa
2013-09-01
The novel mutation named ru2(d) /Hps5(ru2-d) , characterized by light-colored coats and ruby-eyes, prohibits differentiation of melanocytes by inhibiting tyrosinase (Tyr) activity, expression of Tyr, Tyr-related protein 1 (Tyrp1), Tyrp2, and Kit. However, it is not known whether the ru2(d) allele affects pheomelanin synthesis in recessive yellow (e/Mc1r(e) ) or in pheomelanic stage in agouti (A) mice. In this study, effects of the ru2(d) allele on pheomelanin synthesis were investigated by chemical analysis of melanin present in dorsal hairs of 5-week-old mice from F2 generation between C57BL/10JHir (B10)-co-isogenic ruby-eye 2(d) and B10-congenic recessive yellow or agouti. Eumelanin content was decreased in ruby-eye 2(d) and ruby-eye 2(d) agouti mice, whereas pheomelanin con