Development of pacemaker properties and rhythmogenic mechanisms in the mouse embryonic respiratory network

Science.gov (United States)

Chevalier, Marc; Toporikova, Natalia; Simmers, John; Thoby-Brisson, Muriel

2016-01-01

Breathing is a vital rhythmic behavior generated by hindbrain neuronal circuitry, including the preBötzinger complex network (preBötC) that controls inspiration. The emergence of preBötC network activity during prenatal development has been described, but little is known regarding inspiratory neurons expressing pacemaker properties at embryonic stages. Here, we combined calcium imaging and electrophysiological recordings in mouse embryo brainstem slices together with computational modeling to reveal the existence of heterogeneous pacemaker oscillatory properties relying on distinct combinations of burst-generating INaP and ICAN conductances. The respective proportion of the different inspiratory pacemaker subtypes changes during prenatal development. Concomitantly, network rhythmogenesis switches from a purely INaP/ICAN-dependent mechanism at E16.5 to a combined pacemaker/network-driven process at E18.5. Our results provide the first description of pacemaker bursting properties in embryonic preBötC neurons and indicate that network rhythmogenesis undergoes important changes during prenatal development through alterations in both circuit properties and the biophysical characteristics of pacemaker neurons. DOI: http://dx.doi.org/10.7554/eLife.16125.001 PMID:27434668

Tribological properties and morphology of bimodal elastomeric nitrile butadiene rubber networks

International Nuclear Information System (INIS)

Guo, Yin; Wang, Jiaxu; Li, Kang; Ding, Xingwu

2013-01-01

Highlights: • Bimodal elastomeric NBR as a new material was developed. • The structure of bimodal elastomeric NBR networks was determined. • The relationship between structure and mechanical properties was investigated. • The tribological properties and mechanisms of bimodal NBR were analyzed. • The benefits of bimodal NBR in the field of tribology were discussed. - Abstract: Bimodal nitrile butadiene rubber (NBR) was examined in this study. The molecular structure was determined by dynamic mechanical analysis and transmission electron microscopy. The relationship between the structure and the mechanical properties related to elastomeric tribological properties was investigated. The properties and the mechanisms of friction and wear of bimodal elastomeric NBR networks were also analyzed. The lubricating characteristics of bimodal NBR networks were revealed based on the mechanisms of friction and wear. Results show that bimodal NBR networks are similar to bimodal polydimethylsiloxane networks. The form and density of the network structure can be controlled from elastomeric networks to thermosetting resin networks. The mechanical properties of bimodal NBR networks, such as elasticity, elongation at break, fatigue characteristic, tensile strength, elastic modulus, and thermal stability can be precisely controlled following the variation in network structure. The friction, wear, and lubrication of bimodal NBR networks can be clearly described according to the principles of tribology. Common elastomers cannot simultaneously reduce friction and wear because of the different mechanisms of friction and wear; however, bimodal elastomer networks can efficiently address this problem

Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

Science.gov (United States)

Pesavento, Michael J; Pinto, David J

2012-11-01

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.

Analyzing self-similar and fractal properties of the C. elegans neural network.

Directory of Open Access Journals (Sweden)

Tyler M Reese

Full Text Available The brain is one of the most studied and highly complex systems in the biological world. While much research has concentrated on studying the brain directly, our focus is the structure of the brain itself: at its core an interconnected network of nodes (neurons. A better understanding of the structural connectivity of the brain should elucidate some of its functional properties. In this paper we analyze the connectome of the nematode Caenorhabditis elegans. Consisting of only 302 neurons, it is one of the better-understood neural networks. Using a Laplacian Matrix of the 279-neuron "giant component" of the network, we use an eigenvalue counting function to look for fractal-like self similarity. This matrix representation is also used to plot visualizations of the neural network in eigenfunction coordinates. Small-world properties of the system are examined, including average path length and clustering coefficient. We test for localization of eigenfunctions, using graph energy and spacial variance on these functions. To better understand results, all calculations are also performed on random networks, branching trees, and known fractals, as well as fractals which have been "rewired" to have small-world properties. We propose algorithms for generating Laplacian matrices of each of these graphs.

Photometric Properties of Network and Faculae Derived from HMI Data Compensated for Scattered Light

Energy Technology Data Exchange (ETDEWEB)

Criscuoli, Serena; Whitney, Taylor [National Solar Observatory, 3665 Discovery Drive, Boulder, CO 80303 (United States); Norton, Aimee [Hansen Experimental Physics Laboratory, Stanford University, Stanford, CA, 94305 (United States)

2017-10-01

We report on the photometric properties of faculae and network, as observed in full-disk, scattered-light-corrected images from the Helioseismic Magnetic Imager. We use a Lucy–Richardson deconvolution routine that corrects an image in less than one second. Faculae are distinguished from network through proximity to active regions. This is the first report that full-disk observations, including center-to-limb variations, reproduce the photometric properties of faculae and network observed previously only in sub-arcsecond-resolution; small field-of-view studies, i.e. that network, as defined by distance from active regions, exhibit higher photometric contrasts. Specifically, for magnetic flux values larger than approximately 300 G, the network is brighter than faculae and the contrast differences increase toward the limb, where the network contrast is about twice the facular one. For lower magnetic flux values, network appear darker than faculae. Contrary to reports from previous full-disk observations, we also found that network exhibits a higher center-to-limb variation. Our results are in agreement with reports from simulations that indicate magnetic flux alone is a poor proxy of the photometric properties of magnetic features. We estimate that the contribution of faculae and network to Total Solar Irradiance variability of the current Cycle 24 is overestimated by at least 11%, due to the photometric properties of network and faculae not being recognized as different. This estimate is specific to the method employed in this study to reconstruct irradiance variations, so caution should be paid when extending it to other techniques.

Photometric Properties of Network and Faculae Derived from HMI Data Compensated for Scattered Light

International Nuclear Information System (INIS)

Criscuoli, Serena; Whitney, Taylor; Norton, Aimee

2017-01-01

We report on the photometric properties of faculae and network, as observed in full-disk, scattered-light-corrected images from the Helioseismic Magnetic Imager. We use a Lucy–Richardson deconvolution routine that corrects an image in less than one second. Faculae are distinguished from network through proximity to active regions. This is the first report that full-disk observations, including center-to-limb variations, reproduce the photometric properties of faculae and network observed previously only in sub-arcsecond-resolution; small field-of-view studies, i.e. that network, as defined by distance from active regions, exhibit higher photometric contrasts. Specifically, for magnetic flux values larger than approximately 300 G, the network is brighter than faculae and the contrast differences increase toward the limb, where the network contrast is about twice the facular one. For lower magnetic flux values, network appear darker than faculae. Contrary to reports from previous full-disk observations, we also found that network exhibits a higher center-to-limb variation. Our results are in agreement with reports from simulations that indicate magnetic flux alone is a poor proxy of the photometric properties of magnetic features. We estimate that the contribution of faculae and network to Total Solar Irradiance variability of the current Cycle 24 is overestimated by at least 11%, due to the photometric properties of network and faculae not being recognized as different. This estimate is specific to the method employed in this study to reconstruct irradiance variations, so caution should be paid when extending it to other techniques.

Statistical properties of random clique networks

Science.gov (United States)

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

2017-10-01

In this paper, a random clique network model to mimic the large clustering coefficient and the modular structure that exist in many real complex networks, such as social networks, artificial networks, and protein interaction networks, is introduced by combining the random selection rule of the Erdös and Rényi (ER) model and the concept of cliques. We find that random clique networks having a small average degree differ from the ER network in that they have a large clustering coefficient and a power law clustering spectrum, while networks having a high average degree have similar properties as the ER model. In addition, we find that the relation between the clustering coefficient and the average degree shows a non-monotonic behavior and that the degree distributions can be fit by multiple Poisson curves; we explain the origin of such novel behaviors and degree distributions.

Thermoelectric properties of semiconductor nanowire networks

Science.gov (United States)

Roslyak, Oleksiy; Piryatinski, Andrei

2016-03-01

To examine the thermoelectric (TE) properties of a semiconductor nanowire (NW) network, we propose a theoretical approach mapping the TE network on a two-port network. In contrast to a conventional single-port (i.e., resistor) network model, our model allows for large scale calculations showing convergence of TE figure of merit, ZT, with an increasing number of junctions. Using this model, numerical simulations are performed for the Bi2Te3 branched nanowire (BNW) and Cayley tree NW (CTNW) network. We find that the phonon scattering at the network junctions plays a dominant role in enhancing the network ZT. Specifically, disordered BNW and CTNW demonstrate an order of magnitude higher ZT enhancement compared to their ordered counterparts. Formation of preferential TE pathways in CTNW makes the network effectively behave as its BNW counterpart. We provide formalism for simulating large scale nanowire networks hinged upon experimentally measurable TE parameters of a single T-junction.

Methods for generating complex networks with selected structural properties for simulations: A review and tutorial for neuroscientists

Directory of Open Access Journals (Sweden)

Brenton J Prettejohn

2011-03-01

Full Text Available Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erd"{o}s-R'{e}nyi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the `scale-free' and `small-world' properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks.

Hydraulic properties of fracture networks

International Nuclear Information System (INIS)

Dreuzy, J.R. de

1999-12-01

Fractured medium are studied in the general framework of oil and water supply and more recently for the underground storage of high level nuclear wastes. As fractures are generally far more permeable than the embedding medium, flow is highly channeled in a complex network of fractures. The complexity of the network comes from the broad distributions of fracture length and permeability at the fracture scale and appears through the increase of the equivalent permeability at the network scale. The goal of this thesis is to develop models of fracture networks consistent with both local-scale and global-scale observations. Bidimensional models of fracture networks display a wide variety of flow structures ranging from the sole permeable fracture to the equivalent homogeneous medium. The type of the relevant structure depends not only on the density and the length and aperture distributions but also on the observation scale. In several models, a crossover scale separates complex structures highly channeled from more distributed and homogeneous-like flow patterns at larger scales. These models, built on local characteristics and validated by global properties, have been settled in steady state. They have also been compared to natural well test data obtained in Ploemeur (Morbihan) in transient state. The good agreement between models and data reinforces the relevance of the models. Once validated and calibrated, the models are used to estimate the global tendencies of the main flow properties and the risk associated with the relative lack of data on natural fractures media. (author)

Effect of the social influence on topological properties of user-object bipartite networks

Science.gov (United States)

Liu, Jian-Guo; Hu, Zhaolong; Guo, Qiang

2013-11-01

Social influence plays an important role in analyzing online users' collective behaviors [Salganik et al., Science 311, 854 (2006)]. However, the effect of the social influence from the viewpoint of theoretical model is missing. In this paper, by taking into account the social influence and users' preferences, we develop a theoretical model to analyze the topological properties of user-object bipartite networks, including the degree distribution, average nearest neighbor degree and the bipartite clustering coefficient, as well as topological properties of the original user-object networks and their unipartite projections. According to the users' preferences and the global ranking effect, we analyze the theoretical results for two benchmark data sets, Amazon and Bookcrossing, which are approximately consistent with the empirical results. This work suggests that this model is feasible to analyze topological properties of bipartite networks in terms of the social influence and the users' preferences.

Pore network properties of sandstones in a fault damage zone

Science.gov (United States)

Bossennec, Claire; Géraud, Yves; Moretti, Isabelle; Mattioni, Luca; Stemmelen, Didier

2018-05-01

The understanding of fluid flow in faulted sandstones is based on a wide range of techniques. These depend on the multi-method determination of petrological and structural features, porous network properties and both spatial and temporal variations and interactions of these features. The question of the multi-parameter analysis on fluid flow controlling properties is addressed for an outcrop damage zone in the hanging wall of a normal fault zone on the western border of the Upper Rhine Graben, affecting the Buntsandstein Group (Early Triassic). Diagenetic processes may alter the original pore type and geometry in fractured and faulted sandstones. Therefore, these may control the ultimate porosity and permeability of the damage zone. The classical model of evolution of hydraulic properties with distance from the major fault core is nuanced here. The hydraulic behavior of the rock media is better described by a pluri-scale model including: 1) The grain scale, where the hydraulic properties are controlled by sedimentary features, the distance from the fracture, and the impact of diagenetic processes. These result in the ultimate porous network characteristics observed. 2) A larger scale, where the structural position and characteristics (density, connectivity) of the fracture corridors are strongly correlated with both geo-mechanical and hydraulic properties within the damage zone.

Structural properties of the Chinese air transportation multilayer network

International Nuclear Information System (INIS)

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

2016-01-01

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

Network optimization including gas lift and network parameters under subsurface uncertainty

Energy Technology Data Exchange (ETDEWEB)

Schulze-Riegert, R.; Baffoe, J.; Pajonk, O. [SPT Group GmbH, Hamburg (Germany); Badalov, H.; Huseynov, S. [Technische Univ. Clausthal, Clausthal-Zellerfeld (Germany). ITE; Trick, M. [SPT Group, Calgary, AB (Canada)

2013-08-01

Optimization of oil and gas field production systems poses a great challenge to field development due to complex and multiple interactions between various operational design parameters and subsurface uncertainties. Conventional analytical methods are capable of finding local optima based on single deterministic models. They are less applicable for efficiently generating alternative design scenarios in a multi-objective context. Practical implementations of robust optimization workflows integrate the evaluation of alternative design scenarios and multiple realizations of subsurface uncertainty descriptions. Production or economic performance indicators such as NPV (Net Present Value) are linked to a risk-weighted objective function definition to guide the optimization processes. This work focuses on an integrated workflow using a reservoir-network simulator coupled to an optimization framework. The work will investigate the impact of design parameters while considering the physics of the reservoir, wells, and surface facilities. Subsurface uncertainties are described by well parameters such as inflow performance. Experimental design methods are used to investigate parameter sensitivities and interactions. Optimization methods are used to find optimal design parameter combinations which improve key performance indicators of the production network system. The proposed workflow will be applied to a representative oil reservoir coupled to a network which is modelled by an integrated reservoir-network simulator. Gas-lift will be included as an explicit measure to improve production. An objective function will be formulated for the net present value of the integrated system including production revenue and facility costs. Facility and gas lift design parameters are tuned to maximize NPV. Well inflow performance uncertainties are introduced with an impact on gas lift performance. Resulting variances on NPV are identified as a risk measure for the optimized system design. A

Unraveling spurious properties of interaction networks with tailored random networks.

Directory of Open Access Journals (Sweden)

Stephan Bialonski

Full Text Available We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

Association of structural global brain network properties with intelligence in normal aging.

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Florian U Fischer

Full Text Available Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60-85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience.

Association of Structural Global Brain Network Properties with Intelligence in Normal Aging

Science.gov (United States)

Fischer, Florian U.; Wolf, Dominik; Scheurich, Armin; Fellgiebel, Andreas

2014-01-01

Higher general intelligence attenuates age-associated cognitive decline and the risk of dementia. Thus, intelligence has been associated with cognitive reserve or resilience in normal aging. Neurophysiologically, intelligence is considered as a complex capacity that is dependent on a global cognitive network rather than isolated brain areas. An association of structural as well as functional brain network characteristics with intelligence has already been reported in young adults. We investigated the relationship between global structural brain network properties, general intelligence and age in a group of 43 cognitively healthy elderly, age 60–85 years. Individuals were assessed cross-sectionally using Wechsler Adult Intelligence Scale-Revised (WAIS-R) and diffusion-tensor imaging. Structural brain networks were reconstructed individually using deterministic tractography, global network properties (global efficiency, mean shortest path length, and clustering coefficient) were determined by graph theory and correlated to intelligence scores within both age groups. Network properties were significantly correlated to age, whereas no significant correlation to WAIS-R was observed. However, in a subgroup of 15 individuals aged 75 and above, the network properties were significantly correlated to WAIS-R. Our findings suggest that general intelligence and global properties of structural brain networks may not be generally associated in cognitively healthy elderly. However, we provide first evidence of an association between global structural brain network properties and general intelligence in advanced elderly. Intelligence might be affected by age-associated network deterioration only if a certain threshold of structural degeneration is exceeded. Thus, age-associated brain structural changes seem to be partially compensated by the network and the range of this compensation might be a surrogate of cognitive reserve or brain resilience. PMID:24465994

Size-dependent mechanical properties of 2D random nanofibre networks

International Nuclear Information System (INIS)

Lu, Zixing; Zhu, Man; Liu, Qiang

2014-01-01

The mechanical properties of nanofibre networks (NFNs) are size dependent with respect to different fibre diameters. In this paper, a continuum model is developed to reveal the size-dependent mechanical properties of 2D random NFNs. Since such size-dependent behaviours are attributed to different micromechanical mechanisms, the surface effects and the strain gradient (SG) effects are, respectively, introduced into the mechanical analysis of NFNs. Meanwhile, a modified fibre network model is proposed, in which the axial, bending and shearing deformations are incorporated. The closed-form expressions of effective modulus and Poisson's ratio are obtained for NFNs. Different from the results predicted by conventional fibre network model, the present model predicts the size-dependent mechanical properties of NFNs. It is found that both surface effects and SG effects have significant influences on the effective mechanical properties. Moreover, the present results show that the shearing deformation of fibre segment is also crucial to precisely evaluate the effective mechanical properties of NFNs. This work mainly aims to provide an insight into the micromechanical mechanisms of NFNs. Besides, this work is also expected to provide a more accurate theoretical model for 2D fibre networks. (paper)

26 CFR 1.1013-1 - Property included in inventory.

Science.gov (United States)

2010-04-01

... 26 Internal Revenue 11 2010-04-01 2010-04-01 true Property included in inventory. 1.1013-1 Section... inventory. The basis of property required to be included in inventory is the last inventory value of such property in the hands of the taxpayer. The requirements with respect to the valuation of an inventory are...

Three-dimensional graphene networks: synthesis,properties and applications

Institute of Scientific and Technical Information of China (English)

Yanfeng Ma; Yongsheng Chen

2015-01-01

Recently, three-dimensional graphene/graphene oxide(GO) networks(3DGNs) in the form of foams,sponges and aerogels have atracted much atention. 3D structures provide graphene materials with high speciic surface areas, large pore volumes, strong mechanical strengths and fast mass and electron transport,owing to the combination of the 3D porous structures and the excellent intrinsic properties of graphene.his review focuses on the latest advances in the preparation, properties and potential applications of 3D micro-/nano-architectures made of graphene/GO-based networks, with emphasis on graphene foams and sponges.

Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

Directory of Open Access Journals (Sweden)

Ram Prahlad T

2008-08-01

Full Text Available Abstract Background In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior. Results We introduce PathwayOracle, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates PathwayOracle from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, PathwayOracle includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model. Conclusion PathwayOracle provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models

Evolution properties of the community members for dynamic networks

Science.gov (United States)

Yang, Kai; Guo, Qiang; Li, Sheng-Nan; Han, Jing-Ti; Liu, Jian-Guo

2017-03-01

The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities they joined in for these data sets. Then we introduce a parameter φ to describe community evolution between different slices and investigate the dynamic community properties of the new community members. The empirical analyses for the Facebook, APS, Enron and Wiki data sets indicate that both the number of new members and joint communities increase, the ratio declines rapidly and then becomes stable over time, and most of the new members will join in the small size communities that is s ≤ 10. Furthermore, the proportion of new members in existed communities decreases firstly and then becomes stable and relatively small for these data sets. Our work may be helpful for deeply understanding the evolution properties of community members for social networks.

Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks

International Nuclear Information System (INIS)

Sigala, R; Smerieri, A; Camorani, P; Schüz, A; Erokhin, V

2013-01-01

Memristors are passive two-terminal circuit elements that combine resistance and memory. Although in theory memristors are a very promising approach to fabricate hardware with adaptive properties, there are only very few implementations able to show their basic properties. We recently developed stochastic polymeric matrices with a functionality that evidences the formation of self-assembled three-dimensional (3D) networks of memristors. We demonstrated that those networks show the typical hysteretic behavior observed in the ‘one input-one output’ memristive configuration. Interestingly, using different protocols to electrically stimulate the networks, we also observed that their adaptive properties are similar to those present in the nervous system. Here, we model and simulate the electrical properties of these self-assembled polymeric networks of memristors, the topology of which is defined stochastically. First, we show that the model recreates the hysteretic behavior observed in the real experiments. Second, we demonstrate that the networks modeled indeed have a 3D instead of a planar functionality. Finally, we show that the adaptive properties of the networks depend on their connectivity pattern. Our model was able to replicate fundamental qualitative behavior of the real organic 3D memristor networks; yet, through the simulations, we also explored other interesting properties, such as the relation between connectivity patterns and adaptive properties. Our model and simulations represent an interesting tool to understand the very complex behavior of self-assembled memristor networks, which can finally help to predict and formulate hypotheses for future experiments. (paper)

Properties of four real world collaboration--competition networks

Science.gov (United States)

Fu, Chun-Hua; Xu, Xiu-Lian; He, Da-Ren

2009-03-01

Our research group has empirically investigated 9 real world collaboration networks and 25 real world cooperation-competition networks. Among the 34 real world systems, all the 9 real world collaboration networks and 6 real world cooperation-competition networks show the unimodal act-size distribution and the shifted power law distribution of degree and act-degree. We have proposed a collaboration network evolution model for an explanation of the rules [1]. The other 14 real world cooperation-competition networks show that the act-size distributions are not unimodal; instead, they take qualitatively the same shifted power law forms as the degree and act-degree distributions. The properties of four systems (the main land movie film network, Beijing restaurant network, 2004 Olympic network, and Tao-Bao notebook computer sale network) are reported in detail as examples. Via a numerical simulation, we show that the new rule can still be explained by the above-mentioned model. [1] H. Chang, B. B. Su, et al. Phsica A, 2007, 383: 687-702.

Node-node correlations and transport properties in scale-free networks

Science.gov (United States)

Obregon, Bibiana; Guzman, Lev

2011-03-01

We study some transport properties of complex networks. We focus our attention on transport properties of scale-free and small-world networks and compare two types of transport: Electric and max-flow cases. In particular, we construct scale-free networks, with a given degree sequence, to estimate the distribution of conductances for different values of assortative/dissortative mixing. For the electric case we find that the distributions of conductances are affect ed by the assortative mixing of the network whereas for the max-flow case, the distributions almost do not show changes when node-node correlations are altered. Finally, we compare local and global transport in terms of the average conductance for the small-world (Watts-Strogatz) model

Multi-equilibrium property of metabolic networks: SSI module

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Chen Luonan

2011-06-01

Full Text Available Abstract Background Revealing the multi-equilibrium property of a metabolic network is a fundamental and important topic in systems biology. Due to the complexity of the metabolic network, it is generally a difficult task to study the problem as a whole from both analytical and numerical viewpoint. On the other hand, the structure-oriented modularization idea is a good choice to overcome such a difficulty, i.e. decomposing the network into several basic building blocks and then studying the whole network through investigating the dynamical characteristics of the basic building blocks and their interactions. Single substrate and single product with inhibition (SSI metabolic module is one type of the basic building blocks of metabolic networks, and its multi-equilibrium property has important influence on that of the whole metabolic networks. Results In this paper, we describe what the SSI metabolic module is, characterize the rates of the metabolic reactions by Hill kinetics and give a unified model for SSI modules by using a set of nonlinear ordinary differential equations with multi-variables. Specifically, a sufficient and necessary condition is first given to describe the injectivity of a class of nonlinear systems, and then, the sufficient condition is used to study the multi-equilibrium property of SSI modules. As a main theoretical result, for the SSI modules in which each reaction has no more than one inhibitor, a sufficient condition is derived to rule out multiple equilibria, i.e. the Jacobian matrix of its rate function is nonsingular everywhere. Conclusions In summary, we describe SSI modules and give a general modeling framework based on Hill kinetics, and provide a sufficient condition for ruling out multiple equilibria of a key type of SSI module.

Heuristic algorithm for determination of local properties of scale-free networks

CERN Document Server

Mitrovic, M

2006-01-01

Complex networks are everywhere. Many phenomena in nature can be modeled as networks: - brain structures - protein-protein interaction networks - social interactions - the Internet and WWW. They can be represented in terms of nodes and edges connecting them. Important characteristics: - these networks are not random; they have a structured architecture. Structure of different networks are similar: - all have power law degree distribution (scale-free property) - despite large size there is usually relatively short path between any two nodes (small world property). Global characteristics: - degree distribution, clustering coefficient and the diameter. Local structure: - frequency of subgraphs of given type (subgraph of order k is a part of the network consisting of k nodes and edges between them). There are different types of subgraphs of the same order.

Structural and robustness properties of smart-city transportation networks

International Nuclear Information System (INIS)

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

2015-01-01

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

Scaling properties in time-varying networks with memory

Science.gov (United States)

Kim, Hyewon; Ha, Meesoon; Jeong, Hawoong

2015-12-01

The formation of network structure is mainly influenced by an individual node's activity and its memory, where activity can usually be interpreted as the individual inherent property and memory can be represented by the interaction strength between nodes. In our study, we define the activity through the appearance pattern in the time-aggregated network representation, and quantify the memory through the contact pattern of empirical temporal networks. To address the role of activity and memory in epidemics on time-varying networks, we propose temporal-pattern coarsening of activity-driven growing networks with memory. In particular, we focus on the relation between time-scale coarsening and spreading dynamics in the context of dynamic scaling and finite-size scaling. Finally, we discuss the universality issue of spreading dynamics on time-varying networks for various memory-causality tests.

Structure-Property Relationships and the Mixed Network Former Effect in Boroaluminosilicate Glasses

DEFF Research Database (Denmark)

Zheng, Qiuju; Potuzak, Marcel; Mauro, John C.

compositions by substituting Al2O3 for SiO2. We also investigate the various roles of sodium in the glasses including charge compensation of tetrahedral aluminum and boron atoms and formation of non-bridging oxygen. We find that mechanical properties (density, elastic moduli, and hardness), glass transition......Boroaluminosilicate glasses are important materials for various applications, e.g., liquid crystal display substrates, glass fibers for reinforcement, and thermal shock-resistant glass containers. The complicated structural speciation in these glasses leads to a mixed network former effect yielding...... nonlinear variation in many macroscopic properties. It is therefore crucial to investigate and understand structure-property correlations in boroaluminosilicate glasses. Here we study the structure-property relationships of a range of sodium boroaluminosilicate glasses from peralkaline to peraluminous...

Computational Models and Emergent Properties of Respiratory Neural Networks

Science.gov (United States)

Lindsey, Bruce G.; Rybak, Ilya A.; Smith, Jeffrey C.

2012-01-01

Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. PMID:23687564

Electrical Properties of an m × n Hammock Network

Science.gov (United States)

Tan, Zhen; Tan, Zhi-Zhong; Zhou, Ling

2018-05-01

Electrical property is an important problem in the field of natural science and physics, which usually involves potential, current and resistance in the electric circuit. We investigate the electrical properties of an arbitrary hammock network, which has not been resolved before, and propose the exact potential formula of an arbitrary m × n hammock network by means of the Recursion-Transform method with current parameters (RT-I) pioneered by one of us [Z. Z. Tan, Phys. Rev. E 91 (2015) 052122], and the branch currents and equivalent resistance of the network are derived naturally. Our key technique is to setting up matrix equations and making matrix transformation, the potential formula derived is a meaningful discovery, which deduces many novel applications. The discovery of potential formula of the hammock network provides new theoretical tools and techniques for related scientific research. Supported by the Natural Science Foundation of Jiangsu Province under Grant No. BK20161278

Confinement properties of 2D porous molecular networks on metal surfaces

International Nuclear Information System (INIS)

Müller, Kathrin; Enache, Mihaela; Stöhr, Meike

2016-01-01

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

Mean Green operators of deformable fiber networks embedded in a compliant matrix and property estimates

Science.gov (United States)

Franciosi, Patrick; Spagnuolo, Mario; Salman, Oguz Umut

2018-04-01

Composites comprising included phases in a continuous matrix constitute a huge class of meta-materials, whose effective properties, whether they be mechanical, physical or coupled, can be selectively optimized by using appropriate phase arrangements and architectures. An important subclass is represented by "network-reinforced matrices," say those materials in which one or more of the embedded phases are co-continuous with the matrix in one or more directions. In this article, we present a method to study effective properties of simple such structures from which more complex ones can be accessible. Effective properties are shown, in the framework of linear elasticity, estimable by using the global mean Green operator for the entire embedded fiber network which is by definition through sample spanning. This network operator is obtained from one of infinite planar alignments of infinite fibers, which the network can be seen as an interpenetrated set of, with the fiber interactions being fully accounted for in the alignments. The mean operator of such alignments is given in exact closed form for isotropic elastic-like or dielectric-like matrices. We first exemplify how these operators relevantly provide, from classic homogenization frameworks, effective properties in the case of 1D fiber bundles embedded in an isotropic elastic-like medium. It is also shown that using infinite patterns with fully interacting elements over their whole influence range at any element concentration suppresses the dilute approximation limit of these frameworks. We finally present a construction method for a global operator of fiber networks described as interpenetrated such bundles.

Structural and robustness properties of smart-city transportation networks

Science.gov (United States)

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

2015-09-01

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

Mechanical properties of polymer-infiltrated-ceramic-network materials.

Science.gov (United States)

Coldea, Andrea; Swain, Michael V; Thiel, Norbert

2013-04-01

To determine and identify correlations between flexural strength, strain at failure, elastic modulus and hardness versus ceramic network densities of a range of novel polymer-infiltrated-ceramic-network (PICN) materials. Four ceramic network densities ranging from 59% to 72% of theoretical density, resin infiltrated PICN as well as pure polymer and dense ceramic cross-sections were subjected to Vickers Indentations (HV 5) for hardness evaluation. The flexural strength and elastic modulus were measured using three-point-bending. The fracture response of PICNs was determined for cracks induced by Vickers-indentation. Optical and scanning electron microscopy (SEM) was employed to observe the indented areas. Depending on the density of the porous ceramic the flexural strength of PICNs ranged from 131 to 160MPa, the hardness values ranged between 1.05 and 2.10GPa and the elastic modulus between 16.4 and 28.1GPa. SEM observations of the indentation induced cracks indicate that the polymer network causes greater crack deflection than the dense ceramic material. The results were compared with simple analytical expressions for property variation of two phase composite materials. This study points out the correlation between ceramic network density, elastic modulus and hardness of PICNs. These materials are considered to more closely imitate natural tooth properties compared with existing dental restorative materials. Copyright © 2013 Academy of Dental Materials. All rights reserved.

Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset.

Science.gov (United States)

Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J

2013-06-01

Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset

Generic Properties of Random Gene Regulatory Networks.

Science.gov (United States)

Li, Zhiyuan; Bianco, Simone; Zhang, Zhaoyang; Tang, Chao

2013-12-01

Modeling gene regulatory networks (GRNs) is an important topic in systems biology. Although there has been much work focusing on various specific systems, the generic behavior of GRNs with continuous variables is still elusive. In particular, it is not clear typically how attractors partition among the three types of orbits: steady state, periodic and chaotic, and how the dynamical properties change with network's topological characteristics. In this work, we first investigated these questions in random GRNs with different network sizes, connectivity, fraction of inhibitory links and transcription regulation rules. Then we searched for the core motifs that govern the dynamic behavior of large GRNs. We show that the stability of a random GRN is typically governed by a few embedding motifs of small sizes, and therefore can in general be understood in the context of these short motifs. Our results provide insights for the study and design of genetic networks.

Impact of visual repetition rate on intrinsic properties of low frequency fluctuations in the visual network.

Directory of Open Access Journals (Sweden)

Yi-Chia Li

Full Text Available BACKGROUND: Visual processing network is one of the functional networks which have been reliably identified to consistently exist in human resting brains. In our work, we focused on this network and investigated the intrinsic properties of low frequency (0.01-0.08 Hz fluctuations (LFFs during changes of visual stimuli. There were two main questions to be discussed in this study: intrinsic properties of LFFs regarding (1 interactions between visual stimuli and resting-state; (2 impact of repetition rate of visual stimuli. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed scanning sessions that contained rest and visual stimuli in various repetition rates with a novel method. The method included three numerical approaches involving ICA (Independent Component Analyses, fALFF (fractional Amplitude of Low Frequency Fluctuation, and Coherence, to respectively investigate the modulations of visual network pattern, low frequency fluctuation power, and interregional functional connectivity during changes of visual stimuli. We discovered when resting-state was replaced by visual stimuli, more areas were involved in visual processing, and both stronger low frequency fluctuations and higher interregional functional connectivity occurred in visual network. With changes of visual repetition rate, the number of areas which were involved in visual processing, low frequency fluctuation power, and interregional functional connectivity in this network were also modulated. CONCLUSIONS/SIGNIFICANCE: To combine the results of prior literatures and our discoveries, intrinsic properties of LFFs in visual network are altered not only by modulations of endogenous factors (eye-open or eye-closed condition; alcohol administration and disordered behaviors (early blind, but also exogenous sensory stimuli (visual stimuli with various repetition rates. It demonstrates that the intrinsic properties of LFFs are valuable to represent physiological states of human brains.

Sparse and shrunken estimates of MRI networks in the brain and their influence on network properties

DEFF Research Database (Denmark)

Romero-Garcia, Rafael; Clemmensen, Line Katrine Harder

2014-01-01

approaches showed more stable results with a relative low variance at the expense of a little bias. Interestingly, topological properties as local and global efficiency estimated in networks constructed from traditional non-regularized correlations also showed higher variability when compared to those from...... regularized networks. Our findings suggest that a population-based connectivity study can achieve a more robust description of cortical topology through regularization of the correlation estimates. Four regularization methods were examined: Two with shrinkage (Ridge and Schäfer’s shrinkage), one with sparsity...... (Lasso) and one with both shrinkage and sparsity (Elastic net). Furthermore, the different regularizations resulted in different correlation estimates as well as network properties. The shrunken estimates resulted in lower variance of the estimates than the sparse estimates....

Large-scale structure of a network of co-occurring MeSH terms: statistical analysis of macroscopic properties.

Directory of Open Access Journals (Sweden)

Andrej Kastrin

Full Text Available Concept associations can be represented by a network that consists of a set of nodes representing concepts and a set of edges representing their relationships. Complex networks exhibit some common topological features including small diameter, high degree of clustering, power-law degree distribution, and modularity. We investigated the topological properties of a network constructed from co-occurrences between MeSH descriptors in the MEDLINE database. We conducted the analysis on two networks, one constructed from all MeSH descriptors and another using only major descriptors. Network reduction was performed using the Pearson's chi-square test for independence. To characterize topological properties of the network we adopted some specific measures, including diameter, average path length, clustering coefficient, and degree distribution. For the full MeSH network the average path length was 1.95 with a diameter of three edges and clustering coefficient of 0.26. The Kolmogorov-Smirnov test rejects the power law as a plausible model for degree distribution. For the major MeSH network the average path length was 2.63 edges with a diameter of seven edges and clustering coefficient of 0.15. The Kolmogorov-Smirnov test failed to reject the power law as a plausible model. The power-law exponent was 5.07. In both networks it was evident that nodes with a lower degree exhibit higher clustering than those with a higher degree. After simulated attack, where we removed 10% of nodes with the highest degrees, the giant component of each of the two networks contains about 90% of all nodes. Because of small average path length and high degree of clustering the MeSH network is small-world. A power-law distribution is not a plausible model for the degree distribution. The network is highly modular, highly resistant to targeted and random attack and with minimal dissortativity.

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-04-01

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

Fractal properties and small-scale structure of cosmic string networks

International Nuclear Information System (INIS)

Martins, C.J.A.P.; Shellard, E.P.S.

2006-01-01

We present results from a detailed numerical study of the small-scale and loop production properties of cosmic string networks, based on the largest and highest resolution string simulations to date. We investigate the nontrivial fractal properties of cosmic strings, in particular, the fractal dimension and renormalized string mass per unit length, and we also study velocity correlations. We demonstrate important differences between string networks in flat (Minkowski) spacetime and the two very similar expanding cases. For high resolution matter era network simulations, we provide strong evidence that small-scale structure has converged to 'scaling' on all dynamical length scales, without the need for other radiative damping mechanisms. We also discuss preliminary evidence that the dominant loop production size is also approaching scaling

76 FR 19466 - Masco Builder Cabinet Group Including On-Site Leased Workers From Reserves Network, Reliable...

Science.gov (United States)

2011-04-07

... Builder Cabinet Group Including On-Site Leased Workers From Reserves Network, Reliable Staffing, and Third Dimension Waverly, OH; Masco Builder Cabinet Group Including On-Site Leased Workers From Reserves Network... Group including on-site leased workers from Reserves Network, Jackson, Ohio. The workers produce...

Elastic properties and short-range structural order in mixed network former glasses.

Science.gov (United States)

Wang, Weimin; Christensen, Randilynn; Curtis, Brittany; Hynek, David; Keizer, Sydney; Wang, James; Feller, Steve; Martin, Steve W; Kieffer, John

2017-06-21

Elastic properties of alkali containing glasses are of great interest not only because they provide information about overall structural integrity but also they are related to other properties such as thermal conductivity and ion mobility. In this study, we investigate two mixed-network former glass systems, sodium borosilicate 0.2Na 2 O + 0.8[xBO 1.5 + (1 - x)SiO 2 ] and sodium borogermanate 0.2Na 2 O + 0.8[xBO 1.5 + (1 - x)GeO 2 ] glasses. By mixing network formers, the network topology can be changed while keeping the network modifier concentration constant, which allows for the effect of network structure on elastic properties to be analyzed over a wide parametric range. In addition to non-linear, non-additive mixed-glass former effects, maxima are observed in longitudinal, shear and Young's moduli with increasing atomic number density. By combining results from NMR spectroscopy and Brillouin light scattering with a newly developed statistical thermodynamic reaction equilibrium model, it is possible to determine the relative proportions of all network structural units. This new analysis reveals that the structural characteristic predominantly responsible for effective mechanical load transmission in these glasses is a high density of network cations coordinated by four or more bridging oxygens, as it provides for establishing a network of covalent bonds among these cations with connectivity in three dimensions.

Graph analysis of structural brain networks in Alzheimer's disease: beyond small world properties.

Science.gov (United States)

John, Majnu; Ikuta, Toshikazu; Ferbinteanu, Janina

2017-03-01

Changes in brain connectivity in patients with early Alzheimer's disease (AD) have been investigated using graph analysis. However, these studies were based on small data sets, explored a limited range of network parameters, and did not focus on more restricted sub-networks, where neurodegenerative processes may introduce more prominent alterations. In this study, we constructed structural brain networks out of 87 regions using data from 135 healthy elders and 100 early AD patients selected from the Open Access Series of Imaging Studies (OASIS) database. We evaluated the graph properties of these networks by investigating metrics of network efficiency, small world properties, segregation, product measures of complexity, and entropy. Because degenerative processes take place at different rates in different brain areas, analysis restricted to sub-networks may reveal changes otherwise undetected. Therefore, we first analyzed the graph properties of a network encompassing all brain areas considered together, and then repeated the analysis after dividing the brain areas into two sub-networks constructed by applying a clustering algorithm. At the level of large scale network, the analysis did not reveal differences between AD patients and controls. In contrast, the same analysis performed on the two sub-networks revealed that small worldness diminished with AD only in the sub-network containing the areas of medial temporal lobe known to be heaviest and earliest affected. The second sub-network, which did not present significant AD-induced modifications of 'classical' small world parameters, nonetheless showed a trend towards an increase in small world propensity, a novel metric that unbiasedly quantifies small world structure. Beyond small world properties, complexity and entropy measures indicated that the intricacy of connection patterns and structural diversity decreased in both sub-networks. These results show that neurodegenerative processes impact volumetric

Property transfer assessments should include radon gas testing

International Nuclear Information System (INIS)

Nardi, M.A.

1992-01-01

There are two emerging influences that will require radon gas testing as part of many property transfers and most environmental assessments. These requirements come from lending regulators and state legislatures and affect single family, multifamily, and commercial properties. Fannie Mae and others have developed environmental investigation guidelines for protection from long term legal liabilities in the purchase of environmentally contaminated real estate. These guidelines include radon gas testing for many properties. Several states have enacted laws that require environmental disclosure forms be prepared to ensure that the parties involved in certain real estate transactions are aware of the environmental liabilities that may come with the transfer of property. Indiana has recently enacted legislation that would require the disclosure of the presence of radon gas on many commercial real estate transactions. With more banks and state governments following this trend, radon gas testing should be performed during all property transfers and environmental assessments to protect the parties involved from any long term legal liabilities

Modeling and dynamical topology properties of VANET based on complex networks theory

Energy Technology Data Exchange (ETDEWEB)

Zhang, Hong; Li, Jie, E-mail: prof.li@foxmail.com [School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, 430074 (China)

2015-01-15

Vehicular Ad hoc Network (VANET) is a special subset of multi-hop Mobile Ad hoc Networks in which vehicles can not only communicate with each other but also with the fixed equipments along the roads through wireless interfaces. Recently, it has been discovered that essential systems in real world share similar properties. When they are regarded as networks, among which the dynamic topology structure of VANET system is an important issue. Many real world networks are actually growing with preferential attachment like Internet, transportation system and telephone network. Those phenomena have brought great possibility in finding a strategy to calibrate and control the topology parameters which can help find VANET topology change regulation to relieve traffic jam, prevent traffic accident and improve traffic safety. VANET is a typical complex network which has its basic characteristics. In this paper, we focus on the macroscopic Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) inter-vehicle communication network with complex network theory. In particular, this paper is the first one to propose a method analyzing the topological structure and performance of VANET and present the communications in VANET from a new perspective. Accordingly, we propose degree distribution, clustering coefficient and the short path length of complex network to implement our strategy by numerical example and simulation. All the results demonstrate that VANET shows small world network features and is characterized by a truncated scale-free degree distribution with power-law degree distribution. The average path length of the network is simulated numerically, which indicates that the network shows small-world property and is rarely affected by the randomness. What’s more, we carry out extensive simulations of information propagation and mathematically prove the power law property when γ > 2. The results of this study provide useful information for VANET optimization from a

Prediction of thermophysical properties of mixed refrigerants using artificial neural network

International Nuclear Information System (INIS)

Sencan, Arzu; Koese, Ismail Ilke; Selbas, Resat

2011-01-01

The determination of thermophysical properties of the refrigerants is very important for thermodynamic analysis of vapor compression refrigeration systems. In this paper, an artificial neural network (ANN) is proposed to determine properties as heat conduction coefficient, dynamic viscosity, kinematic viscosity, thermal diffusivity, density, specific heat capacity of refrigerants. Five alternative refrigerants are considered: R413A, R417A, R422A, R422D and R423A. The training and validation were performed with good accuracy. The thermophysical properties of the refrigerants are formulated using artificial neural network (ANN) methodology. Liquid and vapor thermophysical properties of refrigerants with new formulation obtained from ANN can be easily estimated. The method proposed offers more flexibility and therefore thermodynamic analysis of vapor compression refrigeration systems is fairly simplified.

Topological properties of complex networks in protein structures

Science.gov (United States)

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

2014-03-01

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

Evaluating the mechanical properties of E-Glass fiber/carbon fiber reinforced interpenetrating polymer networks

Directory of Open Access Journals (Sweden)

G. Suresh

2015-02-01

Full Text Available A series of vinyl ester and polyurethane interpenetrating polymer networks were prepared by changing the component ratios of VER (Vinyl ester and PU (Polyurethane and the polymerization process was confirmed with Fourier Transform infrared spectroscopy. IPN (Inter Penetrating Polymer Network - VER/PU reinforced Glass and carbon fiber composite laminates were made using the Hand lay up technique. The Mechanical properties of the E-glass and carbon fiber specimens were compared from tests including Tensile, Compressive, Flexural, ILSS (Inter Laminar Shear Strength, Impact & Head Deflection Test (HDT. The IPN Reinforced Carbon fiber specimen showed better results in all the tests than E-Glass fibre reinforced IPN laminate with same thickness of the specimen, according to ASTM standards. It was found that the combination of 60%VER and 40%PU IPN exhibits better impact strength and maximum elongation at break, but at the slight expense of mechanical properties such as tensile, compressive, flexural, ILSS properties. The morphology of the unreinforced and reinforced composites was analyzed with help of scanning electron microscopy.

Design and Optimization of Capacitated Supply Chain Networks Including Quality Measures

Directory of Open Access Journals (Sweden)

Krystel K. Castillo-Villar

2014-01-01

Full Text Available This paper presents (1 a novel capacitated model for supply chain network design which considers manufacturing, distribution, and quality costs (named SCND-COQ model and (2 five combinatorial optimization methods, based on nonlinear optimization, heuristic, and metaheuristic approaches, which are used to solve realistic instances of practical size. The SCND-COQ model is a mixed-integer nonlinear problem which can be used at a strategic planning level to design a supply chain network that maximizes the total profit subject to meeting an overall quality level of the final product at minimum costs. The SCND-COQ model computes the quality-related costs for the whole supply chain network considering the interdependencies among business entities. The effectiveness of the proposed solution approaches is shown using numerical experiments. These methods allow solving more realistic (capacitated supply chain network design problems including quality-related costs (inspections, rework, opportunity costs, and others within a reasonable computational time.

Engineering the Mechanical Properties of Polymer Networks with Precise Doping of Primary Defects.

Science.gov (United States)

Chan, Doreen; Ding, Yichuan; Dauskardt, Reinhold H; Appel, Eric A

2017-12-06

Polymer networks are extensively utilized across numerous applications ranging from commodity superabsorbent polymers and coatings to high-performance microelectronics and biomaterials. For many applications, desirable properties are known; however, achieving them has been challenging. Additionally, the accurate prediction of elastic modulus has been a long-standing difficulty owing to the presence of loops. By tuning the prepolymer formulation through precise doping of monomers, specific primary network defects can be programmed into an elastomeric scaffold, without alteration of their resulting chemistry. The addition of these monomers that respond mechanically as primary defects is used both to understand their impact on the resulting mechanical properties of the materials and as a method to engineer the mechanical properties. Indeed, these materials exhibit identical bulk and surface chemistry, yet vastly different mechanical properties. Further, we have adapted the real elastic network theory (RENT) to the case of primary defects in the absence of loops, thus providing new insights into the mechanism for material strength and failure in polymer networks arising from primary network defects, and to accurately predict the elastic modulus of the polymer system. The versatility of the approach we describe and the fundamental knowledge gained from this study can lead to new advancements in the development of novel materials with precisely defined and predictable chemical, physical, and mechanical properties.

Modeling mechanical properties of cast aluminum alloy using artificial neural network

International Nuclear Information System (INIS)

Jokhio, M.H.; Panhwar, M.I.

2009-01-01

Modeling is widely used to investigate the mechanical properties of engineering materials due to increasing demand of low cost and high strength to weight ratio for many engineering applications. The aluminum casting alloys are cost competitive material and possess the desired properties. The mechanical properties largely depend upon composition of alloys and their processing method. Alloy design involves controlling mechanical properties via optimization of the composition and processing parameters. For optimization the possible root is empirical modeling and its more refined version is the analysis of the wide range of data using ANN (Artificial Neural Networks) modeling. The modeling of mechanical properties of the aluminum alloys are the main objective of present work. For this purpose, some data were collected and experimentally prepared using conventional casting method. A MLP (Multilayer Perceptron) network was developed, which is trained by using the error back propagation algorithm. (author)

Spectral properties of Google matrix of Wikipedia and other networks

Science.gov (United States)

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

2013-05-01

We study the properties of eigenvalues and eigenvectors of the Google matrix of the Wikipedia articles hyperlink network and other real networks. With the help of the Arnoldi method, we analyze the distribution of eigenvalues in the complex plane and show that eigenstates with significant eigenvalue modulus are located on well defined network communities. We also show that the correlator between PageRank and CheiRank vectors distinguishes different organizations of information flow on BBC and Le Monde web sites.

A comparative analysis of the statistical properties of large mobile phone calling networks.

Science.gov (United States)

Li, Ming-Xia; Jiang, Zhi-Qiang; Xie, Wen-Jie; Miccichè, Salvatore; Tumminello, Michele; Zhou, Wei-Xing; Mantegna, Rosario N

2014-05-30

Mobile phone calling is one of the most widely used communication methods in modern society. The records of calls among mobile phone users provide us a valuable proxy for the understanding of human communication patterns embedded in social networks. Mobile phone users call each other forming a directed calling network. If only reciprocal calls are considered, we obtain an undirected mutual calling network. The preferential communication behavior between two connected users can be statistically tested and it results in two Bonferroni networks with statistically validated edges. We perform a comparative analysis of the statistical properties of these four networks, which are constructed from the calling records of more than nine million individuals in Shanghai over a period of 110 days. We find that these networks share many common structural properties and also exhibit idiosyncratic features when compared with previously studied large mobile calling networks. The empirical findings provide us an intriguing picture of a representative large social network that might shed new lights on the modelling of large social networks.

Integration Strategy Is a Key Step in Network-Based Analysis and Dramatically Affects Network Topological Properties and Inferring Outcomes

Science.gov (United States)

Jin, Nana; Wu, Deng; Gong, Yonghui; Bi, Xiaoman; Jiang, Hong; Li, Kongning; Wang, Qianghu

2014-01-01

An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches. PMID:25243127

Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

OpenAIRE

Zhao, Liang; Liao, Siyu; Wang, Yanzhi; Li, Zhe; Tang, Jian; Pan, Victor; Yuan, Bo

2017-01-01

Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neural networks with weight matrices of LDR matrices, referred as LDR neural networks, can achieve significant reduction in space and computational complexity while retaining high accuracy. We formally study LDR matrices in deep learning. First, we prove the universal approximation property of LDR neural networks with a ...

The relations between network-operation and topological-property in a scale-free and small-world network with community structure

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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.

The 'emergent scaling' phenomenon and the dielectric properties of random resistor-capacitor networks

CERN Document Server

Bouamrane, R

2003-01-01

An efficient algorithm, based on the Frank-Lobb reduction scheme, for calculating the equivalent dielectric properties of very large random resistor-capacitor (R-C) networks has been developed. It has been used to investigate the network size and composition dependence of dielectric properties and their statistical variability. The dielectric properties of 256 samples of random networks containing: 512, 2048, 8192 and 32 768 components distributed randomly in the ratios 60% R-40% C, 50% R-50% C and 40% R-60% C have been computed. It has been found that these properties exhibit the anomalous power law dependences on frequency known as the 'universal dielectric response' (UDR). Attention is drawn to the contrast between frequency ranges across which percolation determines dielectric response, where considerable variability is found amongst the samples, and those across which power laws define response where very little variability is found between samples. It is concluded that the power law UDRs are emergent pr...

Dynamic properties of epidemic spreading on finite size complex networks

Science.gov (United States)

Li, Ying; Liu, Yang; Shan, Xiu-Ming; Ren, Yong; Jiao, Jian; Qiu, Ben

2005-11-01

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS (susceptible-infected-susceptible) model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Investigation of global and local network properties of music perception with culturally different styles of music.

Science.gov (United States)

Li, Yan; Rui, Xue; Li, Shuyu; Pu, Fang

2014-11-01

Graph theoretical analysis has recently become a popular research tool in neuroscience, however, there have been very few studies on brain responses to music perception, especially when culturally different styles of music are involved. Electroencephalograms were recorded from ten subjects listening to Chinese traditional music, light music and western classical music. For event-related potentials, phase coherence was calculated in the alpha band and then constructed into correlation matrices. Clustering coefficients and characteristic path lengths were evaluated for global properties, while clustering coefficients and efficiency were assessed for local network properties. Perception of light music and western classical music manifested small-world network properties, especially with a relatively low proportion of weights of correlation matrices. For local analysis, efficiency was more discernible than clustering coefficient. Nevertheless, there was no significant discrimination between Chinese traditional and western classical music perception. Perception of different styles of music introduces different network properties, both globally and locally. Research into both global and local network properties has been carried out in other areas; however, this is a preliminary investigation aimed at suggesting a possible new approach to brain network properties in music perception. Copyright © 2014 Elsevier Ltd. All rights reserved.

Statistical properties and attack tolerance of growing networks with algebraic preferential attachment

International Nuclear Information System (INIS)

Liu Zonghua; Lai Yingcheng; Ye Nong

2002-01-01

We consider growing networks with algebraic preferential attachment and address two questions: (1) what is the effect of temporal fluctuations in the number of new links acquired by the network? and (2) what is the network tolerance against random failures and intentional attacks? We find that the fluctuations generally have little effect on the network properties, although they lead to a plateau behavior for small degrees in the connectivity distribution. Formulas are derived for the evolution and distribution of the network connectivity, which are tested by numerical simulations. Numerical study of the effect of failures and attacks suggests that networks constructed under algebraic preferential attachment are more robust than scale-free networks

Hydrogen bond networks determine emergent mechanical and thermodynamic properties across a protein family

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Dallakyan Sargis

2008-08-01

Full Text Available Abstract Background Gram-negative bacteria use periplasmic-binding proteins (bPBP to transport nutrients through the periplasm. Despite immense diversity within the recognized substrates, all members of the family share a common fold that includes two domains that are separated by a conserved hinge. The hinge allows the protein to cycle between open (apo and closed (ligated conformations. Conformational changes within the proteins depend on a complex interplay of mechanical and thermodynamic response, which is manifested as an increase in thermal stability and decrease of flexibility upon ligand binding. Results We use a distance constraint model (DCM to quantify the give and take between thermodynamic stability and mechanical flexibility across the bPBP family. Quantitative stability/flexibility relationships (QSFR are readily evaluated because the DCM links mechanical and thermodynamic properties. We have previously demonstrated that QSFR is moderately conserved across a mesophilic/thermophilic RNase H pair, whereas the observed variance indicated that different enthalpy-entropy mechanisms allow similar mechanical response at their respective melting temperatures. Our predictions of heat capacity and free energy show marked diversity across the bPBP family. While backbone flexibility metrics are mostly conserved, cooperativity correlation (long-range couplings also demonstrate considerable amount of variation. Upon ligand removal, heat capacity, melting point, and mechanical rigidity are, as expected, lowered. Nevertheless, significant differences are found in molecular cooperativity correlations that can be explained by the detailed nature of the hydrogen bond network. Conclusion Non-trivial mechanical and thermodynamic variation across the family is explained by differences within the underlying H-bond networks. The mechanism is simple; variation within the H-bond networks result in altered mechanical linkage properties that directly affect

Security Property Validation of the Sensor Network Encryption Protocol (SNEP

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Salekul Islam

2015-07-01

Full Text Available Since wireless sensor networks (WSNs have been designed to be deployed in an unsecured, public environment, secured communication is really vital for their wide-spread use. Among all of the communication protocols developed for WSN, the Security Protocols for Sensor Networks (SPINS is exceptional, as it has been designed with security as a goal. SPINS is composed of two building blocks: Secure Network Encryption Protocol (SNEP and the “micro” version of the Timed Efficient Streaming Loss-tolerant Authentication (TESLA, named μTESLA. From the inception of SPINS, a number of efforts have been made to validate its security properties. In this paper, we have validated the security properties of SNEP by using an automated security protocol validation tool, named AVISPA. Using the protocol specification language, HLPSL, we model two combined scenarios—node to node key agreement and counter exchange protocols—followed by data transmission. Next, we validate the security properties of these combined protocols, using different AVISPA back-ends. AVISPA reports the models we have developed free from attacks. However, by analyzing the key distribution sub-protocol, we find one threat of a potential DoS attack that we have demonstrated by modeling in AVISPA. Finally, we propose a modification, and AVISPA reports this modified version free from the potential DoS attack.

Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network

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ZHANG Yongzhi

2016-10-01

Full Text Available A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the training speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for predicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process.

Regime-dependent topological properties of biofuels networks

Czech Academy of Sciences Publication Activity Database

Krištoufek, Ladislav; Janda, K.; Zilberman, D.

2013-01-01

Roč. 86, č. 2 (2013), 40-1-40-12 ISSN 1434-6028 R&D Projects: GA ČR GA402/09/0965 Grant - others:GA UK(CZ) 118310; GA ČR(CZ) GAP402/11/0948 Program:GA Institutional support: RVO:67985556 Keywords : topology * biofuels * correlations Subject RIV: AH - Economics Impact factor: 1.463, year: 2013 http://library.utia.cas.cz/separaty/2013/E/kristoufek-regime-dependent topological properties of biofuels networks.pdf

Investigating changes in brain network properties in HIV-associated neurocognitive disease (HAND) using mutual connectivity analysis (MCA)

Science.gov (United States)

Abidin, Anas Zainul; D'Souza, Adora M.; Nagarajan, Mahesh B.; Wismüller, Axel

2016-03-01

About 50% of subjects infected with HIV present deficits in cognitive domains, which are known collectively as HIV associated neurocognitive disorder (HAND). The underlying synaptodendritic damage can be captured using resting state functional MRI, as has been demonstrated by a few earlier studies. Such damage may induce topological changes of brain connectivity networks. We test this hypothesis by capturing the functional interdependence of 90 brain network nodes using a Mutual Connectivity Analysis (MCA) framework with non-linear time series modeling based on Generalized Radial Basis function (GRBF) neural networks. The network nodes are selected based on the regions defined in the Automated Anatomic Labeling (AAL) atlas. Each node is represented by the average time series of the voxels of that region. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We tested for differences in these properties in network graphs obtained for 10 subjects (6 male and 4 female, 5 HIV+ and 5 HIV-). Global network properties captured some differences between these subject cohorts, though significant differences were seen only with the clustering coefficient measure. Local network properties, such as local efficiency and the degree of connections, captured significant differences in regions of the frontal lobe, precentral and cingulate cortex amongst a few others. These results suggest that our method can be used to effectively capture differences occurring in brain network connectivity properties revealed by resting-state functional MRI in neurological disease states, such as HAND.

Thermo-Mechanical Properties of Semi-Degradable Poly(β-amino ester)-co-Methyl Methacrylate Networks under Simulated Physiological Conditions

Science.gov (United States)

Safranski, David L.; Crabtree, Jacob C.; Huq, Yameen R.; Gall, Ken

2011-01-01

Poly(β-amino ester) networks are being explored for biomedical applications, but they may lack the mechanical properties necessary for long term implantation. The objective of this study is to evaluate the effect of adding methyl methacrylate on networks' mechanical properties under simulated physiological conditions. The networks were synthesized in two parts: (1) a biodegradable crosslinker was formed from a diacrylate and amine, (2) and then varying concentrations of methyl methacrylate were added prior to photopolymerizing the network. Degradation rate, mechanical properties, and glass transition temperature were studied as a function of methyl methacrylate composition. The crosslinking density played a limited role on mechanical properties for these networks, but increasing methyl methacrylate concentration improved the toughness by several orders of magnitude. Under simulated physiological conditions, networks showed increasing toughness or sustained toughness as degradation occurred. This work establishes a method of creating degradable networks with tailorable toughness while undergoing partial degradation. PMID:21966028

PDMS Network Structure-Property Relationships: Influence of Molecular Architecture on Mechanical and Wetting Properties

Science.gov (United States)

Melillo, Matthew Joseph

Poly(dimethylsiloxane) (PDMS) is one of the most common elastomers, with applications ranging from sealants and marine-antifouling coatings to medical devices and absorbents for water treatment. Fundamental understanding of how liquids spread on the surface of and absorb into and leach out of PDMS networks is of critical importance for the design and use in another application - microfluidic devices. The growing use of PDMS in microfluidic devices raises the concern that some researchers may use this material without fully understanding all of its advantages, drawbacks, and intricacies. The primary goal of this Ph.D. dissertation is to elucidate PDMS network molecular structure to macroscopic property relationships and to demonstrate how molecular architecture can alter dynamic mechanical and wetting characteristics. We prepare PDMS materials by using vinyl/ tetrakis(dimethylsiloxy)silane (TDSS) and silanol/ tetraethylorthosilicate (TEOS) combinations of PDMS end-groups and crosslinkers as two model systems. Under constant curing conditions, we systematically study the effects of polymer molecular weight, loading of crosslinker, and end-group chemical functionality on the extent of gelation and the dynamic mechanical and water wetting properties of end-linked PDMS networks. The extent of the gelation reaction is determined using the Soxhlet extraction to quantify the amount of material that did and did not participate in the crosslinking reactions, termed the gel and sol fractions, respectively. We use the Miller-Macosko model in conjunction with the gel fraction and precise chemical composition (i.e., stoichiometric ratio and molecular weight) to determine the fractions of elastic and pendant material, the molecular weight between chemical crosslinks, and the average effective functionality of the crosslinker molecule. Based on dynamic mechanical testing, we find that the maximum storage moduli are achieved at optimal stoichiometric conditions in the vinyl

Tailoring the mechanical properties by molecular integration of flexible and stiff polymer networks.

Science.gov (United States)

Wan, Haixiao; Shen, Jianxiang; Gao, Naishen; Liu, Jun; Gao, Yangyang; Zhang, Liqun

2018-03-28

Designing a multiple-network structure at the molecular level to tailor the mechanical properties of polymeric materials is of great scientific and technological importance. Through the coarse-grained molecular dynamics simulation, we successfully construct an interpenetrating polymer network (IPN) composed of a flexible polymer network and a stiff polymer network. First, we find that there is an optimal chain stiffness for a single network (SN) to achieve the best stress-strain behavior. Then we turn to study the mechanical behaviors of IPNs. The result shows that the stress-strain behaviors of the IPNs appreciably exceed the sum of that of the corresponding single flexible and stiff network, which highlights the advantage of the IPN structure. By systematically varying the stiffness of the stiff polymer network of the IPNs, optimal stiffness also exists to achieve the best performance. We attribute this to a much larger contribution of the non-bonded interaction energy. Last, the effect of the component concentration ratio is probed. With the increase of the concentration of the flexible network, the stress-strain behavior of the IPNs is gradually enhanced, while an optimized concentration (around 60% molar ration) of the stiff network occurs, which could result from the dominant role of the enthalpy rather than the entropy. In general, our work is expected to provide some guidelines to better tailor the mechanical properties of the IPNs made of a flexible network and a stiff network, by manipulating the stiffness of the stiff polymer network and the component concentration ratio.

Neural Networks Relating Alloy Composition, Microstructure, and Tensile Properties of α/ β-Processed TIMETAL 6-4

Science.gov (United States)

Collins, Peter C.; Koduri, Santhosh; Welk, Brian; Tiley, Jaimie; Fraser, Hamish L.

2013-03-01

Bayesian neural networks have been developed, which relate composition, microstructure, and tensile properties of the alloy TIMETAL 6-4 (nominal composition: Ti-6Al-4V (wt pct) after thermomechanical processing (TMP) in the two-phase ( α + β)-phase field. The developed networks are able to make interpolative predictions of properties within the ranges of composition and microstructural features that are in the population of the database used for training and testing of the networks. In addition, the neural networks have been used to conduct virtual experiments which permit the functional dependencies of properties on composition and microstructural features to be determined. In this way, it is shown that in the microstructural condition resulting from TMP in the two-phase ( α + β) phase field, the most significant contribution to strength is from solid solution strengthening, with microstructural features apparently influencing the balance of a number of properties.

Formation of actin networks in microfluidic concentration gradients

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Natalja eStrelnikova

2016-05-01

Full Text Available The physical properties of cytoskeletal networks are contributors in a number of mechanical responses of cells including cellular deformation and locomotion, and are crucial for the proper action of living cells. Local chemical gradients modulate cytoskeletal functionality including the interactions of the cytoskeleton with other cellular components. Actin is a major constituent of the cytoskeleton. Introducing a microfluidic-based platform, we explored the impact of concentration gradients on the formation and structural properties of actin networks. Microfluidics-controlled flow-free steady state experimental conditions allow for the generation of chemical gradients of different profiles, such as linear or step-like. We discovered specific features of actin networks emerging in defined gradients. In particular, we analyzed the effects of spatial conditions on network properties, bending rigidities of network links, and the network elasticity.

Meso-decorated self-healing gels: network structure and properties

Science.gov (United States)

Gong, Jin; Sawamura, Kensuke; Igarashi, Susumu; Furukawa, Hidemitsu

2013-04-01

Gels are a new material having three-dimensional network structures of macromolecules. They possess excellent properties as swellability, high permeability and biocompatibility, and have been applied in various fields of daily life, food, medicine, architecture, and chemistry. In this study, we tried to prepare new multi-functional and high-strength gels by using Meso-Decoration (Meso-Deco), one new method of structure design at intermediate mesoscale. High-performance rigid-rod aromatic polymorphic crystals, and the functional groups of thermoreversible Diels-Alder reaction were introduced into soft gels as crosslinkable pendent chains. The functionalization and strengthening of gels can be realized by meso-decorating the gels' structure using high-performance polymorphic crystals and thermoreversible pendent chains. New gels with good mechanical properties, novel optical properties and thermal properties are expected to be developed.

Statistical properties of the personal social network in the Facebook

Science.gov (United States)

Guo, Q.; Shao, F.; Hu, Z. L.; Liu, J. G.

2013-10-01

The statistical properties of the user interaction behaviors in a city have great significance for developing the network marketing strategy, promoting personalized service and so on. In this paper, we investigate the interaction property of the users from New Orleans network in the Facebook, and find that one's out-degree and in-degree are approximately the same. In addition, when the number of a user friends is less than 65, the number of their posts would linearly grow with the slope 4.2, but when one user's friends are more than 65, their posts would grow with the slope 2.1. Further, the average link weight is relatively flat when the out-degree ranges from 28 to 65, and before or after the section it is on the rise or in decline, respectively, from which we can conclude that one could not maintain stable and meaningful relationships with more than 65 people in a single city. We present a null model to reshuffle the network to guarantee that the empirical results are not obtained by accident. The result obtained after reshuffling suggests that there exists a limit that restricts people's social activities.

Relating Topological Determinants of Complex Networks to Their Spectral Properties: Structural and Dynamical Effects

Science.gov (United States)

Castellano, Claudio; Pastor-Satorras, Romualdo

2017-10-01

The largest eigenvalue of a network's adjacency matrix and its associated principal eigenvector are key elements for determining the topological structure and the properties of dynamical processes mediated by it. We present a physically grounded expression relating the value of the largest eigenvalue of a given network to the largest eigenvalue of two network subgraphs, considered as isolated: the hub with its immediate neighbors and the densely connected set of nodes with maximum K -core index. We validate this formula by showing that it predicts, with good accuracy, the largest eigenvalue of a large set of synthetic and real-world topologies. We also present evidence of the consequences of these findings for broad classes of dynamics taking place on the networks. As a by-product, we reveal that the spectral properties of heterogeneous networks built according to the linear preferential attachment model are qualitatively different from those of their static counterparts.

The effect of the neural activity on topological properties of growing neural networks.

Science.gov (United States)

Gafarov, F M; Gafarova, V R

2016-09-01

The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

Thermochemical Stability and Friction Properties of Soft Organosilica Networks for Solid Lubrication

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Pablo Gonzalez Rodriguez

2018-01-01

Full Text Available In view of their possible application as high temperature solid lubricants, the tribological and thermochemical properties of several organosilica networks were investigated over a range of temperatures between 25 and 580 °C. Organosilica networks, obtained from monomers with terminal and bridging organic groups, were synthesized by a sol-gel process. The influence of carbon content, crosslink density, rotational freedom of incorporated hydrocarbon groups, and network connectivity on the high temperature friction properties of the polymer was studied for condensed materials from silicon alkoxide precursors with terminating organic groups, i.e., methyltrimethoxysilane, propyltrimethoxysilane, diisopropyldimethoxysilane, cyclohexyltrimethoxysilane, phenyltrimethoxysilane and 4-biphenylyltriethoxysilane networks, as well as precursors with organic bridging groups between Si centers, i.e., 1,4-bis(triethoxysilylbenzene and 4,4′-bis(triethoxysilyl-1,1′-biphenyl. Pin-on-disc measurements were performed using all selected solid lubricants. It was found that materials obtained from phenyltrimethoxysilane and cyclohexyltrimethoxysilane precursors showed softening above 120 °C and performed best in terms of friction reduction, reaching friction coefficients as low as 0.01. This value is lower than that of graphite films (0.050 ± 0.005, a common bench mark for solid lubricants.

Species-free species distribution models describe macroecological properties of protected area networks.

Science.gov (United States)

Robinson, Jason L; Fordyce, James A

2017-01-01

narrow geographic distributions, and are thus prone to future shifts away from the climatic conditions in these parks in current climates. In other cases, some parks are broadly similar to large geographic regions surrounding the park or have climatic envelopes that may persist into near-term climate change. Larger parks predict larger climatic envelopes, in current conditions, but on average the predicted area of climate envelopes are smaller in our single future conditions scenario. Individual units in a protected area network may vary in the potential for climate adaptation, and adaptive management strategies for the network should account for the landscape contexts of the geodiversity or climate diversity within individual units. Conservation strategies, including maintaining connectivity, assessing the feasibility of assisted migration and other landscape restoration or enhancements can be optimized using analysis methods to assess the spatial properties of protected area networks in biogeographic and macroecological contexts.

Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

Directory of Open Access Journals (Sweden)

Alexandru D. Iordan

2018-01-01

Full Text Available Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA and 20 older adults (OA were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of

Influence of Bulk PDMS Network Properties on Water Wettability

Science.gov (United States)

Melillo, Matthew; Walker, Edwin; Klein, Zoe; Efimenko, Kirill; Genzer, Jan

Poly(dimethylsiloxane) (PDMS) is one of the most common elastomers, with applications ranging from sealants and marine antifouling coatings to absorbents for water treatment. Fundamental understanding of how liquids spread on the surface of and absorb into PDMS networks is of critical importance for the design and use of another application - medical devices. We have systematically studied the effects of polymer molecular weight, loading of tetra-functional crosslinker, and end-group chemical functionality on the mechanical and surface properties of end-linked PDMS networks. Wettability was investigated through the sessile drop technique, wherein a DI water droplet was placed on the bulk network surface and droplet volume, shape, surface area, and contact angle were monitored as a function of time. Various silicone substrates ranging from incredibly soft and flexible materials (E' 50 kPa) to highly rigid networks (E' 5 MPa) were tested. The dynamic behavior of the droplet on the surfaces demonstrated equilibration times between the droplet and surface on the order of 5 minutes. Similar trends were observed for the commercial PDMS material, Sylgard-184. Our results have provided new evidence for the strong influence that substrate modulus and molecular network structure have on the wettability of PDMS elastomers. These findings will aid in the design and implementation of efficient, accurate, and safe PDMS-based medical devices and microfluidic materials that involve aqueous media.

Criticality is an emergent property of genetic networks that exhibit evolvability.

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Christian Torres-Sosa

Full Text Available Accumulating experimental evidence suggests that the gene regulatory networks of living organisms operate in the critical phase, namely, at the transition between ordered and chaotic dynamics. Such critical dynamics of the network permits the coexistence of robustness and flexibility which are necessary to ensure homeostatic stability (of a given phenotype while allowing for switching between multiple phenotypes (network states as occurs in development and in response to environmental change. However, the mechanisms through which genetic networks evolve such critical behavior have remained elusive. Here we present an evolutionary model in which criticality naturally emerges from the need to balance between the two essential components of evolvability: phenotype conservation and phenotype innovation under mutations. We simulated the Darwinian evolution of random Boolean networks that mutate gene regulatory interactions and grow by gene duplication. The mutating networks were subjected to selection for networks that both (i preserve all the already acquired phenotypes (dynamical attractor states and (ii generate new ones. Our results show that this interplay between extending the phenotypic landscape (innovation while conserving the existing phenotypes (conservation suffices to cause the evolution of all the networks in a population towards criticality. Furthermore, the networks produced by this evolutionary process exhibit structures with hubs (global regulators similar to the observed topology of real gene regulatory networks. Thus, dynamical criticality and certain elementary topological properties of gene regulatory networks can emerge as a byproduct of the evolvability of the phenotypic landscape.

Interpenetrated polymer networks based on commercial silicone elastomers and ionic networks with high dielectric permittivity and self-healing properties

DEFF Research Database (Denmark)

Ogliani, Elisa; Yu, Liyun; Skov, Anne Ladegaard

the applicability. One method used to avoid this limitation is to increase the dielectric permittivity of the material in order to improve the actuation response at a given field. Recently, interpenetrating polymer networks (IPNs) based on covalently cross-linked commercial silicone elastomers and ionic networks...... from amino- and carboxylic acid- functional silicones have been designed[2] (Figure 1). This novel system provides both the mechanical stability and the high breakdown strength given by the silicone part of the IPNs and the high permittivity and the softening effect of the ionic network. Thus......,1 Hz), and the commercial elastomers RT625 and LR3043/30 provide thebest viscoelastic properties to the systems, since they maintain low viscous losses upon addition of ionic network. The values ofthe breakdown strength in all cases remain higher than that of the reference pure PDMS network (ranging...

The Vulnerability of Some Networks including Cycles via Domination Parameters

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Tufan Turaci

2016-01-01

Full Text Available Let G=(V(G,E(G be an undirected simple connected graph. A network is usually represented by an undirected simple graph where vertices represent processors and edges represent links between processors. Finding the vulnerability values of communication networks modeled by graphs is important for network designers. The vulnerability value of a communication network shows the resistance of the network after the disruption of some centers or connection lines until a communication breakdown. The domination number and its variations are the most important vulnerability parameters for network vulnerability. Some variations of domination numbers are the 2-domination number, the bondage number, the reinforcement number, the average lower domination number, the average lower 2-domination number, and so forth. In this paper, we study the vulnerability of cycles and related graphs, namely, fans, k-pyramids, and n-gon books, via domination parameters. Then, exact solutions of the domination parameters are obtained for the above-mentioned graphs.

Social network properties and self-rated health in later life: comparisons from the Korean social life, health, and aging project and the national social life, health and aging project.

Science.gov (United States)

Youm, Yoosik; Laumann, Edward O; Ferraro, Kenneth F; Waite, Linda J; Kim, Hyeon Chang; Park, Yeong-Ran; Chu, Sang Hui; Joo, Won-Tak; Lee, Jin A

2014-09-14

This paper has two objectives. Firstly, it provides an overview of the social network module, data collection procedures, and measurement of ego-centric and complete-network properties in the Korean Social Life, Health, and Aging Project (KSHAP). Secondly, it directly compares the KSHAP structure and results to the ego-centric network structure and results of the National Social Life, Health, and Aging Project (NSHAP), which conducted in-home interviews with 3,005 persons 57 to 85 years of age in the United States. The structure of the complete social network of 814 KSHAP respondents living in Township K was measured and examined at two levels of networks. Ego-centric network properties include network size, composition, volume of contact with network members, density, and bridging potential. Complete-network properties are degree centrality, closeness centrality, betweenness centrality, and brokerage role. We found that KSHAP respondents with a smaller number of social network members were more likely to be older and tended to have poorer self-rated health. Compared to the NSHAP, the KSHAP respondents maintained a smaller network size with a greater network density among their members and lower bridging potential. Further analysis of the complete network properties of KSHAP respondents revealed that more brokerage roles inside the same neighborhood (Ri) were significantly associated with better self-rated health. Socially isolated respondents identified by network components had the worst self-rated health. The findings demonstrate the importance of social network analysis for the study of older adults' health status in Korea. The study also highlights the importance of complete-network data and its ability to reveal mechanisms beyond ego-centric network data.

ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

Energy Technology Data Exchange (ETDEWEB)

Goh, Garrett B.; Siegel, Charles M.; Vishnu, Abhinav; Hodas, Nathan O.

2017-12-08

With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed from the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.

Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network

Science.gov (United States)

Merrison-Hort, Robert; Soffe, Stephen R; Borisyuk, Roman

2018-01-01

Although, in most animals, brain connectivity varies between individuals, behaviour is often similar across a species. What fundamental structural properties are shared across individual networks that define this behaviour? We describe a probabilistic model of connectivity in the hatchling Xenopus tadpole spinal cord which, when combined with a spiking model, reliably produces rhythmic activity corresponding to swimming. The probabilistic model allows calculation of structural characteristics that reflect common network properties, independent of individual network realisations. We use the structural characteristics to study examples of neuronal dynamics, in the complete network and various sub-networks, and this allows us to explain the basis for key experimental findings, and make predictions for experiments. We also study how structural and functional features differ between detailed anatomical connectomes and those generated by our new, simpler, model (meta-model). PMID:29589828

Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network

Directory of Open Access Journals (Sweden)

Zhibin Yu

2017-01-01

Full Text Available Underwater inherent optical properties (IOPs are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network.

Science.gov (United States)

Yu, Zhibin; Wang, Yubo; Zheng, Bing; Zheng, Haiyong; Wang, Nan; Gu, Zhaorui

2017-01-01

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.

Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

Science.gov (United States)

Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

2012-06-01

This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

Impact of Cross-Tie Properties on the Modal Behavior of Cable Networks on Cable-Stayed Bridges.

Science.gov (United States)

Ahmad, Javaid; Cheng, Shaohong; Ghrib, Faouzi

2015-01-01

Dynamic behaviour of cable networks is highly dependent on the installation location, stiffness, and damping of cross-ties. Thus, these are the important design parameters for a cable network. While the effects of the former two on the network response have been investigated to some extent in the past, the impact of cross-tie damping has rarely been addressed. To comprehend our knowledge of mechanics associated with cable networks, in the current study, an analytical model of a cable network will be proposed by taking into account both cross-tie stiffness and damping. In addition, the damping property of main cables in the network will also be considered in the formulation. This would allow exploring not only the effectiveness of a cross-tie design on enhancing the in-plane stiffness of a constituted cable network, but also its energy dissipation capacity. The proposed analytical model will be applied to networks with different configurations. The influence of cross-tie stiffness and damping on the modal response of various types of networks will be investigated by using the corresponding undamped rigid cross-tie network as a reference base. Results will provide valuable information on the selection of cross-tie properties to achieve more effective cable vibration control.

Impact of Cross-Tie Properties on the Modal Behavior of Cable Networks on Cable-Stayed Bridges

Directory of Open Access Journals (Sweden)

Javaid Ahmad

2015-01-01

Full Text Available Dynamic behaviour of cable networks is highly dependent on the installation location, stiffness, and damping of cross-ties. Thus, these are the important design parameters for a cable network. While the effects of the former two on the network response have been investigated to some extent in the past, the impact of cross-tie damping has rarely been addressed. To comprehend our knowledge of mechanics associated with cable networks, in the current study, an analytical model of a cable network will be proposed by taking into account both cross-tie stiffness and damping. In addition, the damping property of main cables in the network will also be considered in the formulation. This would allow exploring not only the effectiveness of a cross-tie design on enhancing the in-plane stiffness of a constituted cable network, but also its energy dissipation capacity. The proposed analytical model will be applied to networks with different configurations. The influence of cross-tie stiffness and damping on the modal response of various types of networks will be investigated by using the corresponding undamped rigid cross-tie network as a reference base. Results will provide valuable information on the selection of cross-tie properties to achieve more effective cable vibration control.

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

Directory of Open Access Journals (Sweden)

Ryan C Williamson

2016-12-01

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

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

Directory of Open Access Journals (Sweden)

T. A. Mityushkina

2012-12-01

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

[Network structures in biological systems].

Science.gov (United States)

Oleskin, A V

2013-01-01

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

Wood Modification at High Temperature and Pressurized Steam: a Relational Model of Mechanical Properties Based on a Neural Network

Directory of Open Access Journals (Sweden)

Hong Yang

2015-07-01

Full Text Available Thermally modified wood has high dimensional stability and biological durability.But if the process parameters of thermal modification are not appropriate, then there will be a decline in the physical properties of wood.A neural network algorithm was employed in this study to establish the relationship between the process parameters of high-temperature and high-pressure thermal modification and the mechanical properties of the wood. Three important parameters: temperature, relative humidity, and treatment time, were considered as the inputs to the neural network. Back propagation (BP neural network and radial basis function (RBF neural network models for prediction were built and compared. The comparison showed that the RBF neural network model had advantages in network structure, convergence speed, and generalization capacity. On this basis, the inverse model, reflecting the relationship between the process parameters and the mechanical properties of wood, was established. Given the desired mechanical properties of the wood, the thermal modification process parameters could be inversely optimized and predicted. The results indicated that the model has good learning ability and generalization capacity. This is of great importance for the theoretical and applicational studies of the thermal modification of wood.

The microwave properties of composites including lightweight core–shell ellipsoids

International Nuclear Information System (INIS)

Yuan, Liming; Xu, Yonggang; Dai, Fei; Liao, Yi; Zhang, Deyuan

2016-01-01

In order to study the microwave properties of suspensions including lightweight core–shell ellipsoids, the calculation formula was obtained by substituting an equivalent ellipsoid for the original core–shell ellipsoid. Simulations for Fe-coated diatomite/paraffin suspensions were performed. Results reveal that the calculated results fitted the measured results very well when the inclusion concentration was no more than 15 vol%, but there was an obvious deviation when the inclusion concentration reached 24 vol%. By comparisons, the formula for less diluted suspensions was more suitable for calculating the electromagnetic parameter of suspensions especially when the ratio was smaller between the electromagnetic parameter of the inclusion and that of the host medium. - Highlights: • The microwave properties of suspensions with core-shell inclusions were studied. • Less diluted suspensions were considered. • Flaky Fe-coated diatomite/paraffin suspensions were studied. • The microwave properties could be simulated successfully.

The microwave properties of composites including lightweight core–shell ellipsoids

Energy Technology Data Exchange (ETDEWEB)

Yuan, Liming, E-mail: lming_y@163.com [Science and Technology on Electromagnetic Scattering Laboratory, Shanghai 200438 (China); Xu, Yonggang; Dai, Fei; Liao, Yi [Science and Technology on Electromagnetic Scattering Laboratory, Shanghai 200438 (China); Zhang, Deyuan [School of Mechanical Engineering and Automation, Beihang University, Beijing 100191 (China)

2016-12-01

In order to study the microwave properties of suspensions including lightweight core–shell ellipsoids, the calculation formula was obtained by substituting an equivalent ellipsoid for the original core–shell ellipsoid. Simulations for Fe-coated diatomite/paraffin suspensions were performed. Results reveal that the calculated results fitted the measured results very well when the inclusion concentration was no more than 15 vol%, but there was an obvious deviation when the inclusion concentration reached 24 vol%. By comparisons, the formula for less diluted suspensions was more suitable for calculating the electromagnetic parameter of suspensions especially when the ratio was smaller between the electromagnetic parameter of the inclusion and that of the host medium. - Highlights: • The microwave properties of suspensions with core-shell inclusions were studied. • Less diluted suspensions were considered. • Flaky Fe-coated diatomite/paraffin suspensions were studied. • The microwave properties could be simulated successfully.

THE MORPHOLOGIC PROPERTIES OF MAGNETIC NETWORKS OVER THE SOLAR CYCLE 23

Energy Technology Data Exchange (ETDEWEB)

Huang Chong; Yan Yihua; Zhang Yin; Tan Baolin; Li Gang, E-mail: chuang@nao.cas.cn, E-mail: yyh@nao.cas.cn [Key Laboratory of Solar Activity, National Astronomical Observatories of Chinese Academy of Sciences, Beijing 100012 (China)

2012-11-10

The morphologic properties of the magnetic networks during Carrington Rotations (CRs) 1955-2091 (from 1999 to 2010) have been analyzed by applying the watershed algorithm to magnetograms observed by the Michelson Doppler Interferometer on board the Solar and Heliospheric Observatory spacecraft. We find that the average area of magnetic cells on the solar surface at lower latitudes (within {+-}50 Degree-Sign ) is smaller than that at higher latitudes (beyond {+-}50 Degree-Sign ). Statistical analysis of these data indicates that the magnetic networks are fractal in nature and the average fractal dimension is D{sub f} = 1.253 {+-} 0.011. We also find that both the fractal dimension and the size of the magnetic networks are anti-correlated with the sunspot area. This is perhaps because a strong magnetic field can suppress spatially modulated oscillation and compress the boundaries of network cells, leading to smoother cell boundaries. The fractal dimension of the cell deviates from that predicted from an isobar of Kolmogorov k {sup -5/3} homogeneous turbulence.

Interconnected networks

CERN Document Server

2016-01-01

This volume provides an introduction to and overview of the emerging field of interconnected networks which include multi layer or multiplex networks, as well as networks of networks. Such networks present structural and dynamical features quite different from those observed in isolated networks. The presence of links between different networks or layers of a network typically alters the way such interconnected networks behave – understanding the role of interconnecting links is therefore a crucial step towards a more accurate description of real-world systems. While examples of such dissimilar properties are becoming more abundant – for example regarding diffusion, robustness and competition – the root of such differences remains to be elucidated. Each chapter in this topical collection is self-contained and can be read on its own, thus making it also suitable as reference for experienced researchers wishing to focus on a particular topic.

Disrupted topological properties of brain white matter networks in left temporal lobe epilepsy: a diffusion tensor imaging study.

Science.gov (United States)

Xu, Y; Qiu, S; Wang, J; Liu, Z; Zhang, R; Li, S; Cheng, L; Liu, Z; Wang, W; Huang, R

2014-10-24

Mesial temporal lobe epilepsy (mTLE) is the most common drug-refractory focal epilepsy in adults. Although previous functional and morphological studies have revealed abnormalities in the brain networks of mTLE, the topological organization of the brain white matter (WM) networks in mTLE patients is still ambiguous. In this study, we constructed brain WM networks for 14 left mTLE patients and 22 age- and gender-matched normal controls using diffusion tensor tractography and estimated the alterations of network properties in the mTLE brain networks using graph theoretical analysis. We found that networks for both the mTLE patients and the controls exhibited prominent small-world properties, suggesting a balanced topology of integration and segregation. However, the brain WM networks of mTLE patients showed a significant increased characteristic path length but significant decreased global efficiency, which indicate a disruption in the organization of the brain WM networks in mTLE patients. Moreover, we found significant between-group differences in the nodal properties in several brain regions, such as the left superior temporal gyrus, left hippocampus, the right occipital and right temporal cortices. The robustness analysis showed that the results were likely to be consistent for the networks constructed with different definitions of node and edge weight. Taken together, our findings may suggest an adverse effect of epileptic seizures on the organization of large-scale brain WM networks in mTLE patients. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Reliability–based economic model predictive control for generalised flow–based networks including actuators’ health–aware capabilities

Directory of Open Access Journals (Sweden)

Grosso Juan M.

2016-09-01

Full Text Available This paper proposes a reliability-based economic model predictive control (MPC strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.

76 FR 79169 - Power Network New Mexico, LLC; Supplemental Notice That Initial Market-Based Rate Filing Includes...

Science.gov (United States)

2011-12-21

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. ER12-605-000] Power Network New Mexico, LLC; Supplemental Notice That Initial Market-Based Rate Filing Includes Request for... Power Network New Mexico, LLC's application for market-based rate authority, with an accompanying rate...

Neural networks (NN applied to the commercial properties valuation

Directory of Open Access Journals (Sweden)

J. M. Núñez Tabales

2017-03-01

Full Text Available Several agents, such as buyers and sellers, or local or tax authorities need to estimate the value of properties. There are different approaches to obtain the market price of a dwelling. Many papers have been produced in the academic literature for such purposes, but, these are, almost always, oriented to estimate hedonic prices of residential properties, such as houses or apartments. Here these methodologies are used in the field of estimate market price of commercial premises, using AI techniques. A case study is developed in Cordova —city in the South of Spain—. Neural Networks are an attractive alternative to the traditional hedonic modelling approaches, as they are better adapted to non-linearities of causal relationships and they also produce smaller valuation errors. It is also possible, from the NN model, to obtain implicit prices associated to the main attributes that can explain the variability of the market price of commercial properties.

Evaluation of EOR Processes Using Network Models

DEFF Research Database (Denmark)

Winter, Anatol; Larsen, Jens Kjell; Krogsbøll, Anette

1998-01-01

The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels......) including estimation of their "petrophysical" properties (e.g. absolute permeability). 3) Mathematical modelling and computer studies of multiphase transport through pore space using mathematical network models. 4) Investigation of link between pore-scale and macroscopic recovery mechanisms....

Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks.

Science.gov (United States)

Arbabi, Vahid; Pouran, Behdad; Campoli, Gianni; Weinans, Harrie; Zadpoor, Amir A

2016-03-21

One of the most widely used techniques to determine the mechanical properties of cartilage is based on indentation tests and interpretation of the obtained force-time or displacement-time data. In the current computational approaches, one needs to simulate the indentation test with finite element models and use an optimization algorithm to estimate the mechanical properties of cartilage. The modeling procedure is cumbersome, and the simulations need to be repeated for every new experiment. For the first time, we propose a method for fast and accurate estimation of the mechanical and physical properties of cartilage as a poroelastic material with the aid of artificial neural networks. In our study, we used finite element models to simulate the indentation for poroelastic materials with wide combinations of mechanical and physical properties. The obtained force-time curves are then divided into three parts: the first two parts of the data is used for training and validation of an artificial neural network, while the third part is used for testing the trained network. The trained neural network receives the force-time curves as the input and provides the properties of cartilage as the output. We observed that the trained network could accurately predict the properties of cartilage within the range of properties for which it was trained. The mechanical and physical properties of cartilage could therefore be estimated very fast, since no additional finite element modeling is required once the neural network is trained. The robustness of the trained artificial neural network in determining the properties of cartilage based on noisy force-time data was assessed by introducing noise to the simulated force-time data. We found that the training procedure could be optimized so as to maximize the robustness of the neural network against noisy force-time data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multilevel method for modeling large-scale networks.

Energy Technology Data Exchange (ETDEWEB)

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

2012-02-24

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

Evaluation of the vibration attenuation properties of an air-inflated cushion with two different heavy machinery seats in multi-axis vibration environments including jolts.

Science.gov (United States)

Ji, Xiaoxu; Eger, Tammy R; Dickey, James P

2017-03-01

Seats and cushions can attenuate whole-body vibration (WBV) exposures and minimize health risks for heavy machine operators. We successfully developed neural network (NN) algorithms to identify the vibration attenuation properties for four different seating conditions (seat/cushion combinations), and implemented each of the NN models to predict the equivalent daily exposure A(8) values for various vehicles in the forestry and mining environments. We also evaluated the performance of the new prototype No-Jolt™ air-inflated cushion and the original cushion of each seat with jolt exposures. We observed that the air cushion significantly improved the vibration attenuation properties of the seat that initially had good performance, but not for the seat that had relatively poor vibration attenuation properties. In addition, operator's anthropometrics and sex influenced the performance of the air-inflated cushion when the vibration environment included jolt exposures. Copyright © 2016 Elsevier Ltd. All rights reserved.

Nestedness of ectoparasite-vertebrate host networks.

Directory of Open Access Journals (Sweden)

Sean P Graham

2009-11-01

Full Text Available Determining the structure of ectoparasite-host networks will enable disease ecologists to better understand and predict the spread of vector-borne diseases. If these networks have consistent properties, then studying the structure of well-understood networks could lead to extrapolation of these properties to others, including those that support emerging pathogens. Borrowing a quantitative measure of network structure from studies of mutualistic relationships between plants and their pollinators, we analyzed 29 ectoparasite-vertebrate host networks--including three derived from molecular bloodmeal analysis of mosquito feeding patterns--using measures of nestedness to identify non-random interactions among species. We found significant nestedness in ectoparasite-vertebrate host lists for habitats ranging from tropical rainforests to polar environments. These networks showed non-random patterns of nesting, and did not differ significantly from published estimates of nestedness from mutualistic networks. Mutualistic and antagonistic networks appear to be organized similarly, with generalized ectoparasites interacting with hosts that attract many ectoparasites and more specialized ectoparasites usually interacting with these same "generalized" hosts. This finding has implications for understanding the network dynamics of vector-born pathogens. We suggest that nestedness (rather than random ectoparasite-host associations can allow rapid transfer of pathogens throughout a network, and expand upon such concepts as the dilution effect, bridge vectors, and host switching in the context of nested ectoparasite-vertebrate host networks.

Properties and toughening mechanisms of PVA/PAM double-network hydrogels prepared by freeze-thawing and anneal-swelling.

Science.gov (United States)

Ou, Kangkang; Dong, Xia; Qin, Chengling; Ji, Xinan; He, Jinxin

2017-08-01

It is well known that preparation method of hydrogels has a significant effect on their properties. In this paper, freeze-thawing and anneal-swelling were applied to prepare poly(vinyl alcohol)/polyacrylamide (PVA/PAM) double-network hydrogels with covalently and physically cross-linked networks. The properties of these hydrogels were investigated and compared to control hydrogels. Results indicated that hydrogels fabricated by freeze-thawing show larger pores size and higher swelling capacity than those made by anneal-swelling and control hydrogels. Hydrogels prepared by anneal-swelling exhibit higher mechanical strength, energy dissipation, fracture energy, gel fraction and crystallinity than those made by freeze-thawing and control hydrogels. Physical cross-linking plays a key role in formation of physical-chemical double-network. The toughening mechanism of double-network hydrogel is related to their chain-fracture behavior and elasticity. The results also indicated that appropriate methods can endow hydrogels with specific microstructures and properties which would broaden current hydrogels research and applications in biomedical fields. Copyright © 2017 Elsevier B.V. All rights reserved.

Network reconfiguration and neuronal plasticity in rhythm-generating networks.

Science.gov (United States)

Koch, Henner; Garcia, Alfredo J; Ramirez, Jan-Marino

2011-12-01

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.

A practical algorithm for optimal operation management of distribution network including fuel cell power plants

Energy Technology Data Exchange (ETDEWEB)

Niknam, Taher; Meymand, Hamed Zeinoddini; Nayeripour, Majid [Electrical and Electronic Engineering Department, Shiraz University of Technology, Shiraz (Iran)

2010-08-15

Fuel cell power plants (FCPPs) have been taken into a great deal of consideration in recent years. The continuing growth of the power demand together with environmental constraints is increasing interest to use FCPPs in power system. Since FCPPs are usually connected to distribution network, the effect of FCPPs on distribution network is more than other sections of power system. One of the most important issues in distribution networks is optimal operation management (OOM) which can be affected by FCPPs. This paper proposes a new approach for optimal operation management of distribution networks including FCCPs. In the article, we consider the total electrical energy losses, the total electrical energy cost and the total emission as the objective functions which should be minimized. Whereas the optimal operation in distribution networks has a nonlinear mixed integer optimization problem, the optimal solution could be obtained through an evolutionary method. We use a new evolutionary algorithm based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) to solve the optimal operation problem and compare this method with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO) and Tabu Search (TS) over two distribution test feeders. (author)

Organisational adaptation in an activist network: social networks, leadership, and change in al-Muhajiroun.

Science.gov (United States)

Kenney, Michael; Horgan, John; Horne, Cale; Vining, Peter; Carley, Kathleen M; Bigrigg, Michael W; Bloom, Mia; Braddock, Kurt

2013-09-01

Social networks are said to facilitate learning and adaptation by providing the connections through which network nodes (or agents) share information and experience. Yet, our understanding of how this process unfolds in real-world networks remains underdeveloped. This paper explores this gap through a case study of al-Muhajiroun, an activist network that continues to call for the establishment of an Islamic state in Britain despite being formally outlawed by British authorities. Drawing on organisation theory and social network analysis, we formulate three hypotheses regarding the learning capacity and social network properties of al-Muhajiroun (AM) and its successor groups. We then test these hypotheses using mixed methods. Our methods combine quantitative analysis of three agent-based networks in AM measured for structural properties that facilitate learning, including connectedness, betweenness centrality and eigenvector centrality, with qualitative analysis of interviews with AM activists focusing organisational adaptation and learning. The results of these analyses confirm that al-Muhajiroun activists respond to government pressure by changing their operations, including creating new platforms under different names and adjusting leadership roles among movement veterans to accommodate their spiritual leader's unwelcome exodus to Lebanon. Simple as they are effective, these adaptations have allowed al-Muhajiroun and its successor groups to continue their activism in an increasingly hostile environment. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

43 CFR 30.127 - What happens if property was improperly included in the inventory?

Science.gov (United States)

2010-10-01

... Interior INDIAN PROBATE HEARINGS PROCEDURES Judicial Authority and Duties § 30.127 What happens if property... proceeding, it is found that property has been improperly included in the inventory of an estate, the...

Application of backpropagation neural networks to evaluate residual properties of thermally damaged concrete

International Nuclear Information System (INIS)

Vasconcelos, W.L.; Shigaki, Y.; Tolentino, E.

2009-01-01

In this work it was analyzed the residual performance of Portland cement concretes, when cold after heat-treated up to 600 deg C. Granite-gneiss was used in the three concrete mix proportions as the coarse aggregate, and river sand with finesses modulus of 2.7 as the fine aggregate. Ultrasonic pulse tests were performed on all the specimens and ultrasonic dynamic modulus were obtained. An artificial neural network of the backpropagation type was trained to evaluate and apply models in predicting residual properties of Portland cement concretes. The input layer for both models consists of an external layer input vector of the temperature. The hidden layer has two processing units with hyperbolic tangent sigmoid transfer functions (tansig for short), and the output layer contains one processing unit that represents the network's output (ultrasonic pulse velocity or modulus of elasticity) for each input vector. The training phase of the network converged for reasonable results after 5.000 epochs approximately, resulting in mean squared errors less than 0.02 for the normalized data. The neural network developed for modeling residual properties of Portland cement concretes was shown to be efficient in both the training phase and the test. From the results reasonable predictions could be made for the ultrasonic pulse velocity or dynamic modulus of elasticity by using temperature. (author)

A protein interaction atlas for the nuclear receptors: properties and quality of a hub-based dimerisation network

Directory of Open Access Journals (Sweden)

De Graaf David

2007-07-01

Full Text Available Abstract Background The nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved. Results Phylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors. Conclusion We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources.

Probing the topological properties of complex networks modeling short written texts.

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Diego R Amancio

Full Text Available In recent years, graph theory has been widely employed to probe several language properties. More specifically, the so-called word adjacency model has been proven useful for tackling several practical problems, especially those relying on textual stylistic analysis. The most common approach to treat texts as networks has simply considered either large pieces of texts or entire books. This approach has certainly worked well-many informative discoveries have been made this way-but it raises an uncomfortable question: could there be important topological patterns in small pieces of texts? To address this problem, the topological properties of subtexts sampled from entire books was probed. Statistical analyses performed on a dataset comprising 50 novels revealed that most of the traditional topological measurements are stable for short subtexts. When the performance of the authorship recognition task was analyzed, it was found that a proper sampling yields a discriminability similar to the one found with full texts. Surprisingly, the support vector machine classification based on the characterization of short texts outperformed the one performed with entire books. These findings suggest that a local topological analysis of large documents might improve its global characterization. Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks. As a consequence, the techniques described here can be extended in a straightforward fashion to analyze texts as time-varying complex networks.

Short-term memory capacity in networks via the restricted isometry property.

Science.gov (United States)

Charles, Adam S; Yap, Han Lun; Rozell, Christopher J

2014-06-01

Cortical networks are hypothesized to rely on transient network activity to support short-term memory (STM). In this letter, we study the capacity of randomly connected recurrent linear networks for performing STM when the input signals are approximately sparse in some basis. We leverage results from compressed sensing to provide rigorous nonasymptotic recovery guarantees, quantifying the impact of the input sparsity level, the input sparsity basis, and the network characteristics on the system capacity. Our analysis demonstrates that network memory capacities can scale superlinearly with the number of nodes and in some situations can achieve STM capacities that are much larger than the network size. We provide perfect recovery guarantees for finite sequences and recovery bounds for infinite sequences. The latter analysis predicts that network STM systems may have an optimal recovery length that balances errors due to omission and recall mistakes. Furthermore, we show that the conditions yielding optimal STM capacity can be embodied in several network topologies, including networks with sparse or dense connectivities.

Softening in Random Networks of Non-Identical Beams.

Science.gov (United States)

Ban, Ehsan; Barocas, Victor H; Shephard, Mark S; Picu, Catalin R

2016-02-01

Random fiber networks are assemblies of elastic elements connected in random configurations. They are used as models for a broad range of fibrous materials including biopolymer gels and synthetic nonwovens. Although the mechanics of networks made from the same type of fibers has been studied extensively, the behavior of composite systems of fibers with different properties has received less attention. In this work we numerically and theoretically study random networks of beams and springs of different mechanical properties. We observe that the overall network stiffness decreases on average as the variability of fiber stiffness increases, at constant mean fiber stiffness. Numerical results and analytical arguments show that for small variabilities in fiber stiffness the amount of network softening scales linearly with the variance of the fiber stiffness distribution. This result holds for any beam structure and is expected to apply to a broad range of materials including cellular solids.

Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method.

Science.gov (United States)

Barkaoui, Abdelwahed; Chamekh, Abdessalem; Merzouki, Tarek; Hambli, Ridha; Mkaddem, Ali

2014-03-01

The complexity and heterogeneity of bone tissue require a multiscale modeling to understand its mechanical behavior and its remodeling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network (NN) computation and homogenization equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained NN simulation. Finite element calculation is performed at nanoscopic levels to provide a database to train an in-house NN program; and (iii) in steps 2-10 from fibril to continuum cortical bone tissue, homogenization equations are used to perform the computation at the higher scales. The NN outputs (elastic properties of the microfibril) are used as inputs for the homogenization computation to determine the properties of mineralized collagen fibril. The mechanical and geometrical properties of bone constituents (mineral, collagen, and cross-links) as well as the porosity were taken in consideration. This paper aims to predict analytically the effective elastic constants of cortical bone by modeling its elastic response at these different scales, ranging from the nanostructural to mesostructural levels. Our findings of the lowest scale's output were well integrated with the other higher levels and serve as inputs for the next higher scale modeling. Good agreement was obtained between our predicted results and literature data. Copyright © 2013 John Wiley & Sons, Ltd.

Application of CAPEC Lipid Property Databases in the Synthesis and Design of Biorefinery Networks

DEFF Research Database (Denmark)

Bertran, Maria-Ona; Cunico, Larissa; Gani, Rafiqul

Petroleum is currently the primary raw material for the production of fuels and chemicals. Consequently, our society is highly dependent on fossil non-renewable resources. However, renewable raw materials are recently receiving increasing interest for the production of chemicals and fuels, so a n...... of biorefinery networks. The objective of this work is to show the application of databases of physical and thermodynamic properties of lipid components to the synthesis and design of biorefinery networks.......]. The wide variety and complex nature of components in biorefineries poses a challenge with respect to the synthesis and design of these types of processes. Whereas physical and thermodynamic property data or models for petroleum-based processes are widely available, most data and models for biobased...

Complex quantum network geometries: Evolution and phase transitions

Science.gov (United States)

Bianconi, Ginestra; Rahmede, Christoph; Wu, Zhihao

2015-08-01

Networks are topological and geometric structures used to describe systems as different as the Internet, the brain, or the quantum structure of space-time. Here we define complex quantum network geometries, describing the underlying structure of growing simplicial 2-complexes, i.e., simplicial complexes formed by triangles. These networks are geometric networks with energies of the links that grow according to a nonequilibrium dynamics. The evolution in time of the geometric networks is a classical evolution describing a given path of a path integral defining the evolution of quantum network states. The quantum network states are characterized by quantum occupation numbers that can be mapped, respectively, to the nodes, links, and triangles incident to each link of the network. We call the geometric networks describing the evolution of quantum network states the quantum geometric networks. The quantum geometric networks have many properties common to complex networks, including small-world property, high clustering coefficient, high modularity, and scale-free degree distribution. Moreover, they can be distinguished between the Fermi-Dirac network and the Bose-Einstein network obeying, respectively, the Fermi-Dirac and Bose-Einstein statistics. We show that these networks can undergo structural phase transitions where the geometrical properties of the networks change drastically. Finally, we comment on the relation between quantum complex network geometries, spin networks, and triangulations.

Empirical investigation of topological and weighted properties of a bus transport network from China

Science.gov (United States)

Shu-Min, Feng; Bao-Yu, Hu; Cen, Nie; Xiang-Hao, Shen; Yu-Sheng, Ci

2016-03-01

Many bus transport networks (BTNs) have evolved into directed networks. A new representation model for BTNs is proposed, called directed-space P. The bus transport network of Harbin (BTN-H) is described as a directed and weighted complex network by the proposed representation model and by giving each node weights. The topological and weighted properties are revealed in detail. In-degree and out-degree distributions, in-weight and out-weight distributions are presented as an exponential law, respectively. There is a strong relation between in-weight and in-degree (also between out-weight and out-degree), which can be fitted by a power function. Degree-degree and weight-weight correlations are investigated to reveal that BTN-H has a disassortative behavior as the nodes have relatively high degree (or weight). The disparity distributions of out-degree and in-degree follow an approximate power-law. Besides, the node degree shows a near linear increase with the number of routes that connect to the corresponding station. These properties revealed in this paper can help public transport planners to analyze the status quo of the BTN in nature. Project supported by the National High Technology Research and Development Program of China (Grant No. 2014AA110304).

Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics.

Science.gov (United States)

Sadeh, Sadra; Rotter, Stefan

2015-01-01

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not

Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics.

Directory of Open Access Journals (Sweden)

Sadra Sadeh

2015-01-01

Full Text Available The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are

Exploring network operations for data and information networks

Science.gov (United States)

Yao, Bing; Su, Jing; Ma, Fei; Wang, Xiaomin; Zhao, Xiyang; Yao, Ming

2017-01-01

Barabási and Albert, in 1999, formulated scale-free models based on some real networks: World-Wide Web, Internet, metabolic and protein networks, language or sexual networks. Scale-free networks not only appear around us, but also have high qualities in the world. As known, high quality information networks can transfer feasibly and efficiently data, clearly, their topological structures are very important for data safety. We build up network operations for constructing large scale of dynamic networks from smaller scale of network models having good property and high quality. We focus on the simplest operators to formulate complex operations, and are interesting on the closeness of operations to desired network properties.

Study Under AC Stimulation on Excitement Properties of Weighted Small-World Biological Neural Networks with Side-Restrain Mechanism

International Nuclear Information System (INIS)

Yuan Wujie; Luo Xiaoshu; Jiang Pinqun

2007-01-01

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.

Networks of networks – An introduction

International Nuclear Information System (INIS)

Kenett, Dror Y.; Perc, Matjaž; Boccaletti, Stefano

2015-01-01

Graphical abstract: Interdependent network reciprocity. Only those blue cooperative domains that are initially present on both networks survive. Abstract: This is an introduction to the special issue titled “Networks of networks” that is in the making at Chaos, Solitons & Fractals. Recent research and reviews attest to the fact that networks of networks are the next frontier in network science [1–7]. Not only are interactions limited and thus inadequately described by well-mixed models, it is also a fact that the networks that should be an integral part of such models are often interconnected, thus making the processes that are unfolding on them interdependent. From the World economy and transportation systems to social media, it is clear that processes taking place in one network might significantly affect what is happening in many other networks. Within an interdependent system, each type of interaction has a certain relevance and meaning, so that treating all the links identically inevitably leads to information loss. Networks of networks, interdependent networks, or multilayer networks are therefore a much better and realistic description of such systems, and this Special Issue is devoted to their structure, dynamics and evolution, as well as to the study of emergent properties in multi-layered systems in general. Topics of interest include but are not limited to the spread of epidemics and information, percolation, diffusion, synchronization, collective behavior, and evolutionary games on networks of networks. Interdisciplinary work on all aspects of networks of networks, regardless of background and motivation, is very welcome.

ODMBP: Behavior Forwarding for Multiple Property Destinations in Mobile Social Networks

Directory of Open Access Journals (Sweden)

Jia Xu

2016-01-01

Full Text Available The smartphones are widely available in recent years. Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. The behavior based forwarding has become a new transmission paradigm for supporting many novel applications. However, the commodities, services, and individuals usually have multiple properties of their interests and behaviors. In this paper, we profile these multiple properties and propose an Opportunistic Dissemination Protocol based on Multiple Behavior Profile, ODMBP, in mobile social networks. We first map the interest space to the behavior space and extract the multiple behavior profiles from the behavior space. Then, we propose the correlation computing model based on the principle of BM25 to calculate the correlation metric of multiple behavior profiles. The correlation metric is used to forward the message to the users who are more similar to the target in our protocol. ODMBP consists of three stages: user initialization, gradient ascent, and group spread. Through extensive simulations, we demonstrate that the proposed multiple behavior profile and correlation computing model are correct and efficient. Compared to other classical routing protocols, ODMBP can significantly improve the performance in the aspect of delivery ratio, delay, and overhead ratio.

Structural Quality of Service in Large-Scale Networks

DEFF Research Database (Denmark)

Pedersen, Jens Myrup

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

Best Signal Quality in Cellular Networks: Asymptotic Properties and Applications to Mobility Management in Small Cell Networks

Directory of Open Access Journals (Sweden)

Baccelli François

2010-01-01

Full Text Available The quickly increasing data traffic and the user demand for a full coverage of mobile services anywhere and anytime are leading mobile networking into a future of small cell networks. However, due to the high-density and randomness of small cell networks, there are several technical challenges. In this paper, we investigate two critical issues: best signal quality and mobility management. Under the assumptions that base stations are uniformly distributed in a ring-shaped region and that shadowings are lognormal, independent, and identically distributed, we prove that when the number of sites in the ring tends to infinity, then (i the maximum signal strength received at the center of the ring tends in distribution to a Gumbel distribution when properly renormalized, and (ii it is asymptotically independent of the interference. Using these properties, we derive the distribution of the best signal quality. Furthermore, an optimized random cell scanning scheme is proposed, based on the evaluation of the optimal number of sites to be scanned for maximizing the user data throughput.

Percolation of interdependent network of networks

International Nuclear Information System (INIS)

Havlin, Shlomo; Stanley, H. Eugene; Bashan, Amir; Gao, Jianxi; Kenett, Dror Y.

2015-01-01

Complex networks appear in almost every aspect of science and technology. Previous work in network theory has focused primarily on analyzing single networks that do not interact with other networks, despite the fact that many real-world networks interact with and depend on each other. Very recently an analytical framework for studying the percolation properties of interacting networks has been introduced. Here we review the analytical framework and the results for percolation laws for a Network Of Networks (NONs) formed by n interdependent random networks. The percolation properties of a network of networks differ greatly from those of single isolated networks. In particular, because the constituent networks of a NON are connected by node dependencies, a NON is subject to cascading failure. When there is strong interdependent coupling between networks, the percolation transition is discontinuous (first-order) phase transition, unlike the well-known continuous second-order transition in single isolated networks. Moreover, although networks with broader degree distributions, e.g., scale-free networks, are more robust when analyzed as single networks, they become more vulnerable in a NON. We also review the effect of space embedding on network vulnerability. It is shown that for spatially embedded networks any finite fraction of dependency nodes will lead to abrupt transition

Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy.

Science.gov (United States)

Wang, Junjing; Qiu, Shijun; Xu, Yong; Liu, Zhenyin; Wen, Xue; Hu, Xiangshu; Zhang, Ruibin; Li, Meng; Wang, Wensheng; Huang, Ruiwang

2014-09-01

Temporal lobe epilepsy (TLE) is one of the most common forms of drug-resistant epilepsy. Previous studies have indicated that the TLE-related impairments existed in extensive local functional networks. However, little is known about the alterations in the topological properties of whole brain functional networks. In this study, we acquired resting-state BOLD-fMRI (rsfMRI) data from 26 TLE patients and 25 healthy controls, constructed their whole brain functional networks, compared the differences in topological parameters between the TLE patients and the controls, and analyzed the correlation between the altered topological properties and the epilepsy duration. The TLE patients showed significant increases in clustering coefficient and characteristic path length, but significant decrease in global efficiency compared to the controls. We also found altered nodal parameters in several regions in the TLE patients, such as the bilateral angular gyri, left middle temporal gyrus, right hippocampus, triangular part of left inferior frontal gyrus, left inferior parietal but supramarginal and angular gyri, and left parahippocampus gyrus. Further correlation analysis showed that the local efficiency of the TLE patients correlated positively with the epilepsy duration. Our results indicated the disrupted topological properties of whole brain functional networks in TLE patients. Our findings indicated the TLE-related impairments in the whole brain functional networks, which may help us to understand the clinical symptoms of TLE patients and offer a clue for the diagnosis and treatment of the TLE patients. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Terrorism cover in France for property damage including nuclear risks

International Nuclear Information System (INIS)

Stanislas, A.

2004-01-01

The obligation to include terrorism cover in all Property Damage policies issued on the French Market is ruled by an Act of 1986 and introduced under Section R 126-2 of the French Code of Insurance. This section stipulates that Property Damage policies must provide cover for damage resulting from acts of terrorism, with the same deductible and the same limit than that of the other damage covered in the policy. Soon after the dramatic events of September 11, 2001 in the United States and although reinsurers worldwide restricted their offer of capacities, French insurers recognized that they had to maintain this global cover for the benefit of their insurers. After difficult discussions between insurers, reinsurers, brokers, risk managers and representatives of the State, the creation of a new Pool, backed with a State guarantee, was decided in less than three months. Effective January 1, 2002 and called Gestion d'Assurance et de Reassurance des Risques Attentats et Actes de Terrorisme (GAREAT), the Pool offers a multiple layers stop-loss cover for Property Damage only, i.e. excluding TPL policies. Considering that nuclear risks should be treated in the same way as other industrial risks, it was decided that they would be covered by GAREAT as well. In the meantime, by a Decree of December 28, 2001 modifying Section R 126-2, a special provision, aiming at reducing the limit and thus the price of this cover, was introduced in the Code. The purpose of this paper is to expose the present situation applying through GAREAT and, after two years of operation to discuss future developments, including other sources of capacity for the coverage of acts of terrorism in nuclear risks insurance.(author)

Mutational robustness of gene regulatory networks.

Directory of Open Access Journals (Sweden)

Aalt D J van Dijk

Full Text Available Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor-target gene interactions but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive. In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

PROPERTIES AND MICROSTRUCTURE OF CEMENT PASTE INCLUDING RECYCLED CONCRETE POWDER

Directory of Open Access Journals (Sweden)

Jaroslav Topič

2017-02-01

Full Text Available The disposal and further recycling of concrete is being investigated worldwide, because the issue of complete recycling has not yet been fully resolved. A fundamental difficulty faced by researchers is the reuse of the recycled concrete fines which are very small (< 1 mm. Currently, full recycling of such waste fine fractions is highly energy intensive and resulting in production of CO2. Because of this, the only recycling methods that can be considered as sustainable and environmentally friendly are those which involve recycled concrete powder (RCP in its raw form. This article investigates the performance of RCP with the grain size < 0.25 mm as a potential binder replacement, and also as a microfiller in cement-based composites. Here, the RCP properties are assessed, including how mechanical properties and the microstructure are influenced by increasing the amount of the RCP in a cement paste (≤ 25 wt%.

Relation between the development of structure and the properties of network polymers

International Nuclear Information System (INIS)

Williams, D.R.G.; Allen, P.E.M.; Simon, G.P.

1989-01-01

It was shown by various techniques that the development of structure in the early stages of cure determines the final structure and properties of glassy three-dimensional networks. Solid state [ 13 C]-nuclear magnetic resonance (NMR) techniques identified the structure groupings and the extent of heterogeneity soon after the commencement of polymerization through to the glassy state. Observations of the changes in mobility of groups during cure were correlated with the development of bulk properties such as the glass transition and compressive strength. Computer simulation of the kinetics of the polymerization based on a three-dimensional lattice closely followed the trends observed by experiment and provided insight into the formation of clusters of reacted polymer surrounded by pools of unreacted monomer. The difficulty in obtaining full cure was clearly associated with the presence of trapped reactive centres within the cluster. Observation of the behaviour of small probe or tracer molecules by NMR was shown to be a potent technique for investigating the organization of networks. 28 refs., 12 figs

Chemical structure, network topology, and porosity effects on the mechanical properties of Zeolitic Imidazolate Frameworks

OpenAIRE

Tan, J. C.; Bennett, T. D.; Cheetham, A. K.

2010-01-01

The mechanical properties of seven zeolitic imidazolate frameworks (ZIFs) based on five unique network topologies have been systematically characterized by single-crystal nanoindentation studies. We demonstrate that the elastic properties of ZIF crystal structures are strongly correlated to the framework density and the underlying porosity. For the systems considered here, the elastic modulus was found to range from 3 to 10 GPa, whereas the hardness property lies between 300 MPa and 1.1 GPa. ...

Stabilizing weighted complex networks

International Nuclear Information System (INIS)

Xiang Linying; Chen Zengqiang; Liu Zhongxin; Chen Fei; Yuan Zhuzhi

2007-01-01

Real networks often consist of local units which interact with each other via asymmetric and heterogeneous connections. In this paper, the V-stability problem is investigated for a class of asymmetric weighted coupled networks with nonidentical node dynamics, which includes the unweighted network as a special case. Pinning control is suggested to stabilize such a coupled network. The complicated stabilization problem is reduced to measuring the semi-negative property of the characteristic matrix which embodies not only the network topology, but also the node self-dynamics and the control gains. It is found that network stabilizability depends critically on the second largest eigenvalue of the characteristic matrix. The smaller the second largest eigenvalue is, the more the network is pinning controllable. Numerical simulations of two representative networks composed of non-chaotic systems and chaotic systems, respectively, are shown for illustration and verification

Network structure and functional properties of transparent hydrogel sanxan produced by Sphingomonas sanxanigenens NX02.

Science.gov (United States)

Wu, Mengmeng; Shi, Zhong; Huang, Haidong; Qu, Jianmei; Dai, Xiaohui; Tian, Xuefeng; Wei, Weiying; Li, Guoqiang; Ma, Ting

2017-11-15

The micro-network structure and functional properties of sanxan, a novel polysaccharide produced by Sphingomonas sanxanigenens NX02, were investigated. Transparent hydrogel sanxan was a high acyl polymer containing 8.96% acetyl and 4.75% glyceroyl. The micro-network structure of sanxan was mainly cyclic configurations composed of side-by-side intermolecular associations, with many rounded nodes found. Sanxan exhibited predominant gelation behavior at concentrations above 0.1%, which was enhanced by adding cations, especially Ca 2+ . The gel strength of sanxan was much higher than that of low acyl gellan, but slightly lower than that of high acyl gellan. Furthermore, the conformation transition temperature was increased in the presence of added cations. Moreover, sanxan showed excellent emulsifying and emulsion stabilizing properties. Consequently, such excellent functional properties make sanxan a good candidate as a gelling, stabilizing, emulsifying, or suspending agent in food and cosmetics industries, and in medical and pharmaceutical usage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Complex networks as an emerging property of hierarchical preferential attachment

Science.gov (United States)

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

2015-12-01

Real complex systems are not rigidly structured; no clear rules or blueprints exist for their construction. Yet, amidst their apparent randomness, complex structural properties universally emerge. We propose that an important class of complex systems can be modeled as an organization of many embedded levels (potentially infinite in number), all of them following the same universal growth principle known as preferential attachment. We give examples of such hierarchy in real systems, for instance, in the pyramid of production entities of the film industry. More importantly, we show how real complex networks can be interpreted as a projection of our model, from which their scale independence, their clustering, their hierarchy, their fractality, and their navigability naturally emerge. Our results suggest that complex networks, viewed as growing systems, can be quite simple, and that the apparent complexity of their structure is largely a reflection of their unobserved hierarchical nature.

Applying network theory to animal movements to identify properties of landscape space use.

Science.gov (United States)

Bastille-Rousseau, Guillaume; Douglas-Hamilton, Iain; Blake, Stephen; Northrup, Joseph M; Wittemyer, George

2018-04-01

Network (graph) theory is a popular analytical framework to characterize the structure and dynamics among discrete objects and is particularly effective at identifying critical hubs and patterns of connectivity. The identification of such attributes is a fundamental objective of animal movement research, yet network theory has rarely been applied directly to animal relocation data. We develop an approach that allows the analysis of movement data using network theory by defining occupied pixels as nodes and connection among these pixels as edges. We first quantify node-level (local) metrics and graph-level (system) metrics on simulated movement trajectories to assess the ability of these metrics to pull out known properties in movement paths. We then apply our framework to empirical data from African elephants (Loxodonta africana), giant Galapagos tortoises (Chelonoidis spp.), and mule deer (Odocoileous hemionus). Our results indicate that certain node-level metrics, namely degree, weight, and betweenness, perform well in capturing local patterns of space use, such as the definition of core areas and paths used for inter-patch movement. These metrics were generally applicable across data sets, indicating their robustness to assumptions structuring analysis or strategies of movement. Other metrics capture local patterns effectively, but were sensitive to specified graph properties, indicating case specific applications. Our analysis indicates that graph-level metrics are unlikely to outperform other approaches for the categorization of general movement strategies (central place foraging, migration, nomadism). By identifying critical nodes, our approach provides a robust quantitative framework to identify local properties of space use that can be used to evaluate the effect of the loss of specific nodes on range wide connectivity. Our network approach is intuitive, and can be implemented across imperfectly sampled or large-scale data sets efficiently, providing a

Universal properties of mythological networks

Science.gov (United States)

Mac Carron, Pádraig; Kenna, Ralph

2012-07-01

As in statistical physics, the concept of universality plays an important, albeit qualitative, role in the field of comparative mythology. Here we apply statistical mechanical tools to analyse the networks underlying three iconic mythological narratives with a view to identifying common and distinguishing quantitative features. Of the three narratives, an Anglo-Saxon and a Greek text are mostly believed by antiquarians to be partly historically based while the third, an Irish epic, is often considered to be fictional. Here we use network analysis in an attempt to discriminate real from imaginary social networks and place mythological narratives on the spectrum between them. This suggests that the perceived artificiality of the Irish narrative can be traced back to anomalous features associated with six characters. Speculating that these are amalgams of several entities or proxies, renders the plausibility of the Irish text comparable to the others from a network-theoretic point of view.

Semi-Degradable Poly(β-amino ester) Networks with Temporally-Controlled Enhancement of Mechanical Properties

Science.gov (United States)

Safranski, David L.; Weiss, Daiana; Clark, J. Brian; Taylor, W.R.; Gall, Ken

2014-01-01

Biodegradable polymers are clinically used in numerous biomedical applications, and classically show a loss in mechanical properties within weeks of implantation. This work demonstrates a new class of semi-degradable polymers that show an increase in mechanical properties through degradation via a controlled shift in a thermal transition. Semi-degradable polymer networks, poly(β-amino ester)-co-methyl methacrylate, were formed from a low glass transition temperature crosslinker, poly(β-amino ester), and high glass transition temperature monomer, methyl methacrylate, which degraded in a manner dependent upon the crosslinker chemical structure. In vitro and in vivo degradation revealed changes in mechanical behavior due to the degradation of the crosslinker from the polymer network. This novel polymer system demonstrates a strategy to temporally control the mechanical behavior of polymers and to enhance the initial performance of smart biomedical devices. PMID:24769113

Novel amphiphilic poly(dimethylsiloxane) based polyurethane networks tethered with carboxybetaine and their combined antibacterial and anti-adhesive property

Energy Technology Data Exchange (ETDEWEB)

Jiang, Jingxian; Fu, Yuchen; Zhang, Qinghua, E-mail: qhzhang@zju.edu.cn; Zhan, Xiaoli; Chen, Fengqiu

2017-08-01

Highlights: • An amphiphilic poly(dimethylsiloxane) (PDMS) based polyurethane (PU) network tethered with carboxybetaine is prepared. • The surface distribution of PDMS and zwitterionic segments produces an obvious amphiphilic heterogeneous surface. • This designed PDMS-based amphiphilic PU network exhibits combined antibacterial and anti-adhesive properties. - Abstract: The traditional nonfouling materials are powerless against bacterial cells attachment, while the hydrophobic bactericidal surfaces always suffer from nonspecific protein adsorption and dead bacterial cells accumulation. Here, amphiphilic polyurethane (PU) networks modified with poly(dimethylsiloxane) (PDMS) and cationic carboxybetaine diol through simple crosslinking reaction were developed, which had an antibacterial efficiency of 97.7%. Thereafter, the hydrolysis of carboxybetaine ester into zwitterionic groups brought about anti-adhesive properties against bacteria and proteins. The surface chemical composition and wettability performance of the PU network surfaces were investigated by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), X-ray photoelectron spectroscopy (XPS) and contact angle analysis. The surface distribution of PDMS and zwitterionic segments produced an obvious amphiphilic heterogeneous surface, which was demonstrated by atomic force microscopy (AFM). Enzyme-linked immunosorbent assays (ELISA) were used to test the nonspecific protein adsorption behaviors. With the advantages of the transition from excellent bactericidal performance to anti-adhesion and the combination of fouling resistance and fouling release property, the designed PDMS-based amphiphilic PU network shows great application potential in biomedical devices and marine facilities.

Discovering disease-associated genes in weighted protein-protein interaction networks

Science.gov (United States)

Cui, Ying; Cai, Meng; Stanley, H. Eugene

2018-04-01

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

Maximum entropy networks are more controllable than preferential attachment networks

International Nuclear Information System (INIS)

Hou, Lvlin; Small, Michael; Lao, Songyang

2014-01-01

A maximum entropy (ME) method to generate typical scale-free networks has been recently introduced. We investigate the controllability of ME networks and Barabási–Albert preferential attachment networks. Our experimental results show that ME networks are significantly more easily controlled than BA networks of the same size and the same degree distribution. Moreover, the control profiles are used to provide insight into control properties of both classes of network. We identify and classify the driver nodes and analyze the connectivity of their neighbors. We find that driver nodes in ME networks have fewer mutual neighbors and that their neighbors have lower average degree. We conclude that the properties of the neighbors of driver node sensitively affect the network controllability. Hence, subtle and important structural differences exist between BA networks and typical scale-free networks of the same degree distribution. - Highlights: • The controllability of maximum entropy (ME) and Barabási–Albert (BA) networks is investigated. • ME networks are significantly more easily controlled than BA networks of the same degree distribution. • The properties of the neighbors of driver node sensitively affect the network controllability. • Subtle and important structural differences exist between BA networks and typical scale-free networks

Scattering Analysis of a Compact Dipole Array with Series and Parallel Feed Network including Mutual Coupling Effect

Directory of Open Access Journals (Sweden)

H. L. Sneha

2013-01-01

Full Text Available The current focus in defense arena is towards the stealth technology with an emphasis to control the radar cross-section (RCS. The scattering from the antennas mounted over the platform is of prime importance especially for a low-observable aerospace vehicle. This paper presents the analysis of the scattering cross section of a uniformly spaced linear dipole array. Two types of feed networks, that is, series and parallel feed networks, are considered. The total RCS of phased array with either kind of feed network is obtained by following the signal as it enters through the aperture and travels through the feed network. The RCS estimation of array is done including the mutual coupling effect between the dipole elements in three configurations, that is, side-by-side, collinear, and parallel-in-echelon. The results presented can be useful while designing a phased array with optimum performance towards low observability.

Comprehensive risk assessment method of catastrophic accident based on complex network properties

Science.gov (United States)

Cui, Zhen; Pang, Jun; Shen, Xiaohong

2017-09-01

On the macro level, the structural properties of the network and the electrical characteristics of the micro components determine the risk of cascading failures. And the cascading failures, as a process with dynamic development, not only the direct risk but also potential risk should be considered. In this paper, comprehensively considered the direct risk and potential risk of failures based on uncertain risk analysis theory and connection number theory, quantified uncertain correlation by the node degree and node clustering coefficient, then established a comprehensive risk indicator of failure. The proposed method has been proved by simulation on the actual power grid. Modeling a network according to the actual power grid, and verified the rationality of the proposed method.

Thermal conductivity model for nanofiber networks

Science.gov (United States)

Zhao, Xinpeng; Huang, Congliang; Liu, Qingkun; Smalyukh, Ivan I.; Yang, Ronggui

2018-02-01

Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

Thermal conductivity model for nanofiber networks

Energy Technology Data Exchange (ETDEWEB)

Zhao, Xinpeng [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Huang, Congliang [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China; Liu, Qingkun [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Smalyukh, Ivan I. [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Yang, Ronggui [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Buildings and Thermal Systems Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA

2018-02-28

Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

International Nuclear Information System (INIS)

Wang, Y.

2013-01-01

Within the framework of plate tectonics, the deformation that arises from the relative movement of two plates occurs across discontinuities in the earth's crust, known as fault zones. Active fault zones are the causal locations of most earthquakes, which suddenly release tectonic stresses within a very short time. In return, fault zones slowly grow by accumulating slip due to such earthquakes by cumulated damage at their tips, and by branching or linking between pre-existing faults of various sizes. Over the last decades, a large amount of knowledge has been acquired concerning the overall phenomenology and mechanics of individual faults and earthquakes: A deep physical and mechanical understanding of the links and interactions between and among them is still missing, however. One of the main issues lies in our failure to always succeed in assigning an earthquake to its causative fault. Using approaches based in pattern-recognition theory, more insight into the relationship between earthquakes and fault structure can be gained by developing an automatic fault network reconstruction approach using high resolution earthquake data sets at largely different scales and by considering individual event uncertainties. This thesis introduces the Anisotropic Clustering of Location Uncertainty Distributions (ACLUD) method to reconstruct active fault networks on the basis of both earthquake locations and their estimated individual uncertainties. This method consists in fitting a given set of hypocenters with an increasing amount of finite planes until the residuals of the fit compare with location uncertainties. After a massive search through the large solution space of possible reconstructed fault networks, six different validation procedures are applied in order to select the corresponding best fault network. Two of the validation steps (cross-validation and Bayesian Information Criterion (BIC)) process the fit residuals, while the four others look for solutions that

Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

Energy Technology Data Exchange (ETDEWEB)

Wang, Y.

2013-07-01

Within the framework of plate tectonics, the deformation that arises from the relative movement of two plates occurs across discontinuities in the earth's crust, known as fault zones. Active fault zones are the causal locations of most earthquakes, which suddenly release tectonic stresses within a very short time. In return, fault zones slowly grow by accumulating slip due to such earthquakes by cumulated damage at their tips, and by branching or linking between pre-existing faults of various sizes. Over the last decades, a large amount of knowledge has been acquired concerning the overall phenomenology and mechanics of individual faults and earthquakes: A deep physical and mechanical understanding of the links and interactions between and among them is still missing, however. One of the main issues lies in our failure to always succeed in assigning an earthquake to its causative fault. Using approaches based in pattern-recognition theory, more insight into the relationship between earthquakes and fault structure can be gained by developing an automatic fault network reconstruction approach using high resolution earthquake data sets at largely different scales and by considering individual event uncertainties. This thesis introduces the Anisotropic Clustering of Location Uncertainty Distributions (ACLUD) method to reconstruct active fault networks on the basis of both earthquake locations and their estimated individual uncertainties. This method consists in fitting a given set of hypocenters with an increasing amount of finite planes until the residuals of the fit compare with location uncertainties. After a massive search through the large solution space of possible reconstructed fault networks, six different validation procedures are applied in order to select the corresponding best fault network. Two of the validation steps (cross-validation and Bayesian Information Criterion (BIC)) process the fit residuals, while the four others look for solutions that

A social network's changing statistical properties and the quality of human innovation

Science.gov (United States)

Uzzi, Brian

2008-06-01

We examined the entire network of creative artists that made Broadway musicals, in the post-War period, a collaboration network of international acclaim and influence, with an eye to investigating how the network's structural features condition the relationship between individual artistic talent and the success of their musicals. Our findings show that some of the evolving topographical qualities of degree distributions, path lengths and assortativity are relatively stable with time even as collaboration patterns shift, which suggests their changes are only minimally associated with the ebb and flux of the success of new productions. In contrast, the clustering coefficient changed substantially over time and we found that it had a nonlinear association with the production of financially and artistically successful shows. When the clustering coefficient ratio is low or high, the financial and artistic success of the industry is low, while an intermediate level of clustering is associated with successful shows. We supported these findings with sociological theory on the relationship between social structure and collaboration and with tests of statistical inference. Our discussion focuses on connecting the statistical properties of social networks to their performance and the performance of the actors embedded within them.

A social network's changing statistical properties and the quality of human innovation

International Nuclear Information System (INIS)

Uzzi, Brian

2008-01-01

We examined the entire network of creative artists that made Broadway musicals, in the post-War period, a collaboration network of international acclaim and influence, with an eye to investigating how the network's structural features condition the relationship between individual artistic talent and the success of their musicals. Our findings show that some of the evolving topographical qualities of degree distributions, path lengths and assortativity are relatively stable with time even as collaboration patterns shift, which suggests their changes are only minimally associated with the ebb and flux of the success of new productions. In contrast, the clustering coefficient changed substantially over time and we found that it had a nonlinear association with the production of financially and artistically successful shows. When the clustering coefficient ratio is low or high, the financial and artistic success of the industry is low, while an intermediate level of clustering is associated with successful shows. We supported these findings with sociological theory on the relationship between social structure and collaboration and with tests of statistical inference. Our discussion focuses on connecting the statistical properties of social networks to their performance and the performance of the actors embedded within them

Semi-degradable poly(β-amino ester) networks with temporally controlled enhancement of mechanical properties.

Science.gov (United States)

Safranski, David L; Weiss, Daiana; Clark, J Brian; Taylor, W Robert; Gall, Ken

2014-08-01

Biodegradable polymers are clinically used in numerous biomedical applications, and classically show a loss of mechanical properties within weeks of implantation. This work demonstrates a new class of semi-degradable polymers that show an increase in mechanical properties through degradation via a controlled shift in a thermal transition. Semi-degradable polymer networks, poly(β-amino ester)-co-methyl methacrylate, were formed from a low glass transition temperature crosslinker, poly(β-amino ester), and high glass transition temperature monomer, methyl methacrylate, which degraded in a manner dependent upon the crosslinker chemical structure. In vitro and in vivo degradation revealed changes in mechanical behavior due to the degradation of the crosslinker from the polymer network. This novel polymer system demonstrates a strategy to temporally control the mechanical behavior of polymers and to enhance the initial performance of smart biomedical devices. Copyright © 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Coarse graining for synchronization in directed networks

Science.gov (United States)

Zeng, An; Lü, Linyuan

2011-05-01

Coarse-graining model is a promising way to analyze and visualize large-scale networks. The coarse-grained networks are required to preserve statistical properties as well as the dynamic behaviors of the initial networks. Some methods have been proposed and found effective in undirected networks, while the study on coarse-graining directed networks lacks of consideration. In this paper we proposed a path-based coarse-graining (PCG) method to coarse grain the directed networks. Performing the linear stability analysis of synchronization and numerical simulation of the Kuramoto model on four kinds of directed networks, including tree networks and variants of Barabási-Albert networks, Watts-Strogatz networks, and Erdös-Rényi networks, we find our method can effectively preserve the network synchronizability.

The art of wireless sensor networks

CERN Document Server

2014-01-01

During the last one and a half decades, wireless sensor networks have witnessed significant growth and tremendous development in both academia and industry.   “The Art of Wireless Sensor Networks: Volume 1: Fundamentals” focuses on the fundamentals concepts in the design, analysis, and implementation of wireless sensor networks. It covers the various layers of the lifecycle of this type of network from the physical layer up to the application layer. Its rationale is that the first volume covers contemporary design issues, tools, and protocols for radio-based two-dimensional terrestrial sensor networks. All the book chapters in this volume include up-to-date research work spanning various classic facets of the physical properties and functional behavior of wireless sensor networks, including physical layer, medium access control, data routing, topology management, mobility management, localization, task management, data management, data gathering, security, middleware, sensor technology, standards, and ...

Interdisciplinary and physics challenges of network theory

Science.gov (United States)

Bianconi, Ginestra

2015-09-01

Network theory has unveiled the underlying structure of complex systems such as the Internet or the biological networks in the cell. It has identified universal properties of complex networks, and the interplay between their structure and dynamics. After almost twenty years of the field, new challenges lie ahead. These challenges concern the multilayer structure of most of the networks, the formulation of a network geometry and topology, and the development of a quantum theory of networks. Making progress on these aspects of network theory can open new venues to address interdisciplinary and physics challenges including progress on brain dynamics, new insights into quantum technologies, and quantum gravity.

The correlation of metrics in complex networks with applications in functional brain networks

International Nuclear Information System (INIS)

Li, C; Wang, H; Van Mieghem, P; De Haan, W; Stam, C J

2011-01-01

An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (Bárabasi–Albert graphs, Erdös–Rényi random graphs and Watts–Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös–Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected

Exploring biological network structure with clustered random networks

Directory of Open Access Journals (Sweden)

Bansal Shweta

2009-12-01

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

Computer systems and networks status and perspectives

CERN Document Server

Zacharov, V

1981-01-01

The properties of computers are discussed, both as separate units and in inter-coupled systems. The main elements of modern processor technology are reviewed and the associated peripheral components are discussed in the light of the prevailing rapid pace of developments. Particular emphasis is given to the impact of very large scale integrated circuitry in these developments. Computer networks are considered in some detail, including common-carrier and local-area networks, and the problem of inter-working is included in the discussion. Components of network systems and the associated technology are also among the topics treated.

Computer systems and networks: Status and perspectives

International Nuclear Information System (INIS)

Zacharov, Z.

1981-01-01

The properties of computers are discussed, both as separate units and in inter-coupled systems. The main elements of modern processor thechnology are reviewed and the associated peripheral components are disscussed in the light of the prevailling rapid pace of developments. Particular emphais is given to the impact of very large scale integrated circuitry in these developments. Computer networks, and considered in some detail, including comon-carrier and local-area networks and the problem of inter-working is included in the discussion. Components of network systems and the associated technology are also among the topics treated. (orig.)

More randomized and resilient in the topological properties of functional brain networks in patients with major depressive disorder.

Science.gov (United States)

Li, Huaizhou; Zhou, Haiyan; Yang, Yang; Wang, Haiyuan; Zhong, Ning

2017-10-01

Previous studies have reported the enhanced randomization of functional brain networks in patients with major depressive disorder (MDD). However, little is known about the changes of key nodal attributes for randomization, the resilience of network, and the clinical significance of the alterations. In this study, we collected the resting-state functional MRI data from 19 MDD patients and 19 healthy control (HC) individuals. Graph theory analysis showed that decreases were found in the small-worldness, clustering coefficient, local efficiency, and characteristic path length (i.e., increase of global efficiency) in the network of MDD group compared with HC group, which was consistent with previous findings and suggested the development toward randomization in the brain network in MDD. In addition, the greater resilience under the targeted attacks was also found in the network of patients with MDD. Furthermore, the abnormal nodal properties were found, including clustering coefficients and nodal efficiencies in the left orbital superior frontal gyrus, bilateral insula, left amygdala, right supramarginal gyrus, left putamen, left posterior cingulate cortex, left angular gyrus. Meanwhile, the correlation analysis showed that most of these abnormal areas were associated with the clinical status. The observed increased randomization and resilience in MDD might be related to the abnormal hub nodes in the brain networks, which were attacked by the disease pathology. Our findings provide new evidence to indicate that the weakening of specialized regions and the enhancement of whole brain integrity could be the potential endophenotype of the depressive pathology. Copyright © 2017 Elsevier Ltd. All rights reserved.

Syntactic computations in the language network: Characterising dynamic network properties using representational similarity analysis

Directory of Open Access Journals (Sweden)

Lorraine Komisarjevsky Tyler

2013-05-01

Full Text Available The core human capacity of syntactic analysis involves a left hemisphere network involving left inferior frontal gyrus (LIFG and posterior middle temporal gyrus (LMTG and the anatomical connections between them. Here we use MEG to determine the spatio-temporal properties of syntactic computations in this network. Listeners heard spoken sentences containing a local syntactic ambiguity (e.g. …landing planes…, at the offset of which they heard a disambiguating verb and decided whether it was an acceptable/unacceptable continuation of the sentence. We charted the time-course of processing and resolving syntactic ambiguity by measuring MEG responses from the onset of each word in the ambiguous phrase and the disambiguating word. We used representational similarity analysis (RSA to characterize syntactic information represented in the LIFG and LpMTG over time and to investigate their relationship to each other. Testing a variety of lexico-syntactic and ambiguity models against the MEG data, our results suggest early lexico-syntactic responses in the LpMTG and later effects of ambiguity in the LIFG, pointing to a clear differentiation in the functional roles of these two regions. Our results suggest the LpMTG represents and transmits lexical information to the LIFG, which responds to and resolves the ambiguity.

Local dependency in networks

Directory of Open Access Journals (Sweden)

Kudĕlka Miloš

2015-06-01

Full Text Available Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network’s nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank.

Epidemic spreading in scale-free networks including the effect of individual vigilance

International Nuclear Information System (INIS)

Gong Yong-Wang; Song Yu-Rong; Jiang Guo-Ping

2012-01-01

In this paper, we study the epidemic spreading in scale-free networks and propose a new susceptible-infected-recovered (SIR) model that includes the effect of individual vigilance. In our model, the effective spreading rate is dynamically adjusted with the time evolution at the vigilance period. Using the mean-field theory, an analytical result is derived. It shows that individual vigilance has no effect on the epidemic threshold. The numerical simulations agree well with the analytical result. Furthermore, we investigate the effect of individual vigilance on the epidemic spreading speed. It is shown that individual vigilance can slow the epidemic spreading speed effectively and delay the arrival of peak epidemic infection. (general)

Endogenous network of firms and systemic risk

Science.gov (United States)

Ma, Qianting; He, Jianmin; Li, Shouwei

2018-02-01

We construct an endogenous network characterized by commercial credit relationships connecting the upstream and downstream firms. Simulation results indicate that the endogenous network model displays a scale-free property which exists in real-world firm systems. In terms of the network structure, with the expansion of the scale of network nodes, the systemic risk increases significantly, while the heterogeneities of network nodes have no effect on systemic risk. As for firm micro-behaviors, including the selection range of trading partners, actual output, labor requirement, price of intermediate products and employee salaries, increase of all these parameters will lead to higher systemic risk.

Sync in Complex Dynamical Networks: Stability, Evolution, Control, and Application

OpenAIRE

Li, Xiang

2005-01-01

In the past few years, the discoveries of small-world and scale-free properties of many natural and artificial complex networks have stimulated significant advances in better understanding the relationship between the topology and the collective dynamics of complex networks. This paper reports recent progresses in the literature of synchronization of complex dynamical networks including stability criteria, network synchronizability and uniform synchronous criticality in different topologies, ...

Including Internet insurance as part of a hospital computer network security plan.

Science.gov (United States)

Riccardi, Ken

2002-01-01

Cyber attacks on a hospital's computer network is a new crime to be reckoned with. Should your hospital consider internet insurance? The author explains this new phenomenon and presents a risk assessment for determining network vulnerabilities.

Bose-Einstein Condensation in Complex Networks

International Nuclear Information System (INIS)

Bianconi, Ginestra; Barabasi, Albert-Laszlo

2001-01-01

The evolution of many complex systems, including the World Wide Web, business, and citation networks, is encoded in the dynamic web describing the interactions between the system's constituents. Despite their irreversible and nonequilibrium nature these networks follow Bose statistics and can undergo Bose-Einstein condensation. Addressing the dynamical properties of these nonequilibrium systems within the framework of equilibrium quantum gases predicts that the 'first-mover-advantage,' 'fit-get-rich,' and 'winner-takes-all' phenomena observed in competitive systems are thermodynamically distinct phases of the underlying evolving networks

Percolation on shopping and cashback electronic commerce networks

Science.gov (United States)

Fu, Tao; Chen, Yini; Qin, Zhen; Guo, Liping

2013-06-01

Many realistic networks live in the form of multiple networks, including interacting networks and interdependent networks. Here we study percolation properties of a special kind of interacting networks, namely Shopping and Cashback Electronic Commerce Networks (SCECNs). We investigate two actual SCECNs to extract their structural properties, and develop a mathematical framework based on generating functions for analyzing directed interacting networks. Then we derive the necessary and sufficient condition for the absence of the system-wide giant in- and out- component, and propose arithmetic to calculate the corresponding structural measures in the sub-critical and supercritical regimes. We apply our mathematical framework and arithmetic to those two actual SCECNs to observe its accuracy, and give some explanations on the discrepancies. We show those structural measures based on our mathematical framework and arithmetic are useful to appraise the status of SCECNs. We also find that the supercritical regime of the whole network is maintained mainly by hyperlinks between different kinds of websites, while those hyperlinks between the same kinds of websites can only enlarge the sizes of in-components and out-components.

LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.

Energy Technology Data Exchange (ETDEWEB)

MASLOV,S.SNEPPEN,K.

2003-11-17

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

Social inheritance can explain the structure of animal social networks

Science.gov (United States)

Ilany, Amiyaal; Akçay, Erol

2016-01-01

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

Prediction of Thermal Properties of Sweet Sorghum Bagasse as a Function of Moisture Content Using Artificial Neural Networks and Regression Models

Directory of Open Access Journals (Sweden)

Gosukonda Ramana

2017-06-01

Full Text Available Artificial neural networks (ANN and traditional regression models were developed for prediction of thermal properties of sweet sorghum bagasse as a function of moisture content and room temperature. Predictions were made for three thermal properties: 1 thermal conductivity, 2 volumetric specific heat, and 3 thermal diffusivity. Each thermal property had five levels of moisture content (8.52%, 12.93%, 18.94%, 24.63%, and 28.62%, w. b. and room temperature as inputs. Data were sub-partitioned for training, testing, and validation of models. Backpropagation (BP and Kalman Filter (KF learning algorithms were employed to develop nonparametric models between input and output data sets. Statistical indices including correlation coefficient (R between actual and predicted outputs were produced for selecting the suitable models. Prediction plots for thermal properties indicated that the ANN models had better accuracy from unseen patterns as compared to regression models. In general, ANN models were able to strongly generalize and interpolate unseen patterns within the domain of training.

Histone chaperone networks shaping chromatin function

DEFF Research Database (Denmark)

Hammond, Colin; Strømme, Caroline Bianchi; Huang, Hongda

2017-01-01

and fate, which affects all chromosomal processes, including gene expression, chromosome segregation and genome replication and repair. Here, we review the distinct structural and functional properties of the expanding network of histone chaperones. We emphasize how chaperones cooperate in the histone...... chaperone network and via co-chaperone complexes to match histone supply with demand, thereby promoting proper nucleosome assembly and maintaining epigenetic information by recycling modified histones evicted from chromatin....

Complex Network Analysis of Guangzhou Metro

OpenAIRE

Yasir Tariq Mohmand; Fahad Mehmood; Fahd Amjad; Nedim Makarevic

2015-01-01

The structure and properties of public transportation networks can provide suggestions for urban planning and public policies. This study contributes a complex network analysis of the Guangzhou metro. The metro network has 236 kilometers of track and is the 6th busiest metro system of the world. In this paper topological properties of the network are explored. We observed that the network displays small world properties and is assortative in nature. The network possesses a high average degree...

Looking for robust properties in the growth of an academic network: the case of the Uruguayan biological research community.

Science.gov (United States)

Cabana, Alvaro; Mizraji, Eduardo; Pomi, Andrés; Valle-Lisboa, Juan Carlos

2008-04-01

Graph-theoretical methods have recently been used to analyze certain properties of natural and social networks. In this work, we have investigated the early stages in the growth of a Uruguayan academic network, the Biology Area of the Programme for the Development of Basic Science (PEDECIBA). This transparent social network is a territory for the exploration of the reliability of clustering methods that can potentially be used when we are confronted with opaque natural systems that provide us with a limited spectrum of observables (happens in research on the relations between brain, thought and language). From our social net, we constructed two different graph representations based on the relationships among researchers revealed by their co-participation in Master's thesis committees. We studied these networks at different times and found that they achieve connectedness early in their evolution and exhibit the small-world property (i.e. high clustering with short path lengths). The data seem compatible with power law distributions of connectivity, clustering coefficients and betweenness centrality. Evidence of preferential attachment of new nodes and of new links between old nodes was also found in both representations. These results suggest that there are topological properties observed throughout the growth of the network that do not depend on the representations we have chosen but reflect intrinsic properties of the academic collective under study. Researchers in PEDECIBA are classified according to their specialties. We analysed the community structure detected by a standard algorithm in both representations. We found that much of the pre-specified structure is recovered and part of the mismatches can be attributed to convergent interests between scientists from different sub-disciplines. This result shows the potentiality of some clustering methods for the analysis of partially known natural systems.

Investigating the effects of streamline-based fiber tractography on matrix scaling in brain connective network.

Science.gov (United States)

Jan, Hengtai; Chao, Yi-Ping; Cho, Kuan-Hung; Kuo, Li-Wei

2013-01-01

Investigating the brain connective network using the modern graph theory has been widely applied in cognitive and clinical neuroscience research. In this study, we aimed to investigate the effects of streamline-based fiber tractography on the change of network properties and established a systematic framework to understand how an adequate network matrix scaling can be determined. The network properties, including degree, efficiency and betweenness centrality, show similar tendency in both left and right hemispheres. By employing the curve-fitting process with exponential law and measuring the residuals, the association between changes of network properties and threshold of track numbers is found and an adequate range of investigating the lateralization of brain network is suggested. The proposed approach can be further applied in clinical applications to improve the diagnostic sensitivity using network analysis with graph theory.

Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells

Energy Technology Data Exchange (ETDEWEB)

Lobato, Justo; Canizares, Pablo; Rodrigo, Manuel A.; Linares, Jose J. [Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004 Ciudad Real (Spain); Piuleac, Ciprian-George; Curteanu, Silvia [Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, ' ' Gh. Asachi' ' Technical University Iasi Bd. D. Mangeron, No. 71A, 700050 IASI (Romania)

2010-08-15

This article shows the application of a very useful mathematical tool, artificial neural networks, to predict the fuel cells results (the value of the tortuosity and the cell voltage, at a given current density, and therefore, the power) on the basis of several properties that define a Gas Diffusion Layer: Teflon content, air permeability, porosity, mean pore size, hydrophobia level. Four neural networks types (multilayer perceptron, generalized feedforward network, modular neural network, and Jordan-Elman neural network) have been applied, with a good fitting between the predicted and the experimental values in the polarization curves. A simple feedforward neural network with one hidden layer proved to be an accurate model with good generalization capability (error about 1% in the validation phase). A procedure based on inverse neural network modelling was able to determine, with small errors, the initial conditions leading to imposed values for characteristics of the fuel cell. In addition, the use of this tool has been proved to be very attractive in order to predict the cell performance, and more interestingly, the influence of the properties of the gas diffusion layer on the cell performance, allowing possible enhancements of this material by changing some of its properties. (author)

Intrinsic properties of channel network structure and the hierarchical classification approach for stream-limits delineation

Energy Technology Data Exchange (ETDEWEB)

Afana, A.; Barrio, G. del

2009-07-01

Delineation of drainage networks is an essential task in hydrological and geomorphologic analysis. Manual channel definition depends on topographic contrast and is highly subjective, leading to important errors at high resolutions. different automatic methods have proposed the use of a constant threshold of up sole contributing are to define channel initiation. Actually, these are the most commonly used for the automatic-channel network extraction from Digital Models (DEMs). However, these methods fall to detect and appropriate threshold when the basin is made up to heterogeneous sub-zones, as they only work either lumped or locally. In this study, the critical threshold area for channel delineation has been defined through the analysis of dominant geometric and topologic properties of stream network formation. (Author) 5 refs.

Multiscale unfolding of real networks by geometric renormalization

Science.gov (United States)

García-Pérez, Guillermo; Boguñá, Marián; Serrano, M. Ángeles

2018-06-01

Symmetries in physical theories denote invariance under some transformation, such as self-similarity under a change of scale. The renormalization group provides a powerful framework to study these symmetries, leading to a better understanding of the universal properties of phase transitions. However, the small-world property of complex networks complicates application of the renormalization group by introducing correlations between coexisting scales. Here, we provide a framework for the investigation of complex networks at different resolutions. The approach is based on geometric representations, which have been shown to sustain network navigability and to reveal the mechanisms that govern network structure and evolution. We define a geometric renormalization group for networks by embedding them into an underlying hidden metric space. We find that real scale-free networks show geometric scaling under this renormalization group transformation. We unfold the networks in a self-similar multilayer shell that distinguishes the coexisting scales and their interactions. This in turn offers a basis for exploring critical phenomena and universality in complex networks. It also affords us immediate practical applications, including high-fidelity smaller-scale replicas of large networks and a multiscale navigation protocol in hyperbolic space, which betters those on single layers.

Microstructure and Mechanical Properties of Heterogeneous Ceramic-Polymer Composite Using Interpenetrating Network

International Nuclear Information System (INIS)

Eun-Hee, K.; Yeon-Gil, J.; Chang-Yong, J.

2012-01-01

Prepolymer, which can be polymerized by a photo, has been infiltrated into a porous ceramic to improve the addition effect of polymer into the ceramic, as a function of the functionality of prepolymer. It induces the increase in the mechanical properties of the ceramic. The porous alumina (Al 2 O 3 ) and the polyurethane acrylate (PUA) with a network structure by photo-polymerization were used as the matrix and infiltration materials, respectively. The porous Al 2 O 3 matrix without the polymer shows lower values in fracture strength than the composites, since the stress is transmitted more quickly via propagation of cracks from intrinsic defects in the porous matrix. However, in the case of composites, the distribution of stress between hetero phases results in the improved mechanical properties. In addition, the mechanical properties of composites, such as elastic modulus and fracture strength, are enhanced with increasing the functionality of prepolymer attributed to the crosslinking density of polymer.

The dependence of the optoelectrical properties of silver nanowire networks on nanowire length and diameter

International Nuclear Information System (INIS)

Sorel, Sophie; Lyons, Philip E; De, Sukanta; Coleman, Jonathan N; Dickerson, Janet C

2012-01-01

We have characterized the optoelectrical properties of networks of silver nanowires as a function of nanowire dimension by measuring transmittance (T) and sheet resistance (R s ) for a large number of networks of different thicknesses fabricated from wires of different diameters (D) and lengths (L). We have analysed these data using both bulk-like and percolative models. We find the network DC conductivity to scale linearly with wire length while the optical conductivity is approximately invariant with nanowire length. The ratio of DC to optical conductivity, often taken as a figure of merit for transparent conductors, scales approximately as L/D. Interestingly, the percolative exponent, n, scales empirically as D 2 , while the percolative figure of merit, Π, displays large values at low D. As high T and low R s are associated with low n and high Π, these data are consistent with improved optoelectrical performance for networks of low-D wires. We predict that networks of wires with D = 25 nm could give sheet resistance as low as 25 Ω/□ for T = 90%. (paper)

Using large-scale Granger causality to study changes in brain network properties in the Clinically Isolated Syndrome (CIS) stage of multiple sclerosis

Science.gov (United States)

Abidin, Anas Z.; Chockanathan, Udaysankar; DSouza, Adora M.; Inglese, Matilde; Wismüller, Axel

2017-03-01

Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques. We test this hypothesis by capturing the effective relationships of 90 brain regions, defined in the Automated Anatomic Labeling (AAL) atlas, using a large-scale Granger Causality (lsGC) framework. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We study for differences in these properties in network graphs obtained for 18 subjects (10 male and 8 female, 9 with CIS and 9 healthy controls). Global network properties captured trending differences with modularity and clustering coefficient (pdifferences in some regions of the inferior frontal and parietal lobe. We conclude that multivariate analysis of fMRI time-series can reveal interesting information about changes occurring in the brain in early stages of MS.

The Effects of Long-term Abacus Training on Topological Properties of Brain Functional Networks.

Science.gov (United States)

Weng, Jian; Xie, Ye; Wang, Chunjie; Chen, Feiyan

2017-08-18

Previous studies in the field of abacus-based mental calculation (AMC) training have shown that this training has the potential to enhance a wide variety of cognitive abilities. It can also generate specific changes in brain structure and function. However, there is lack of studies investigating the impact of AMC training on the characteristics of brain networks. In this study, utilizing graph-based network analysis, we compared topological properties of brain functional networks between an AMC group and a matched control group. Relative to the control group, the AMC group exhibited higher nodal degrees in bilateral calcarine sulcus and increased local efficiency in bilateral superior occipital gyrus and right cuneus. The AMC group also showed higher nodal local efficiency in right fusiform gyrus, which was associated with better math ability. However, no relationship was significant in the control group. These findings provide evidence that long-term AMC training may improve information processing efficiency in visual-spatial related regions, which extend our understanding of training plasticity at the brain network level.

Equilibrium switching and mathematical properties of nonlinear interaction networks with concurrent antagonism and self-stimulation

International Nuclear Information System (INIS)

Rabajante, Jomar Fajardo; Talaue, Cherryl Ortega

2015-01-01

Graphical abstract: Display Omitted -- Highlights: •Properties of n-dimensional decision model of competitive interaction networks. •Graphical technique for component-wise and steady state stability analysis. •Search for parameter conditions that control equilibrium switching. •Illustrations of multi-stable systems and repressilators. -- Abstract: Concurrent decision-making model (CDM) of interaction networks with more than two antagonistic components represents various biological systems, such as gene interaction, species competition and mental cognition. The CDM model assumes sigmoid kinetics where every component stimulates itself but concurrently represses the others. Here we prove generic mathematical properties (e.g., location and stability of steady states) of n-dimensional CDM with either symmetric or asymmetric reciprocal antagonism between components. Significant modifications in parameter values serve as biological regulators for inducing steady state switching by driving a temporal state to escape an undesirable equilibrium. Increasing the maximal growth rate and decreasing the decay rate can expand the basin of attraction of a steady state that contains the desired dominant component. Perpetually adding an external stimulus could shut down multi-stability of the system which increases the robustness of the system against stochastic noise. We further show that asymmetric interaction forming a repressilator-type network generates oscillatory behavior

Animal transportation networks

Science.gov (United States)

Perna, Andrea; Latty, Tanya

2014-01-01

Many group-living animals construct transportation networks of trails, galleries and burrows by modifying the environment to facilitate faster, safer or more efficient movement. Animal transportation networks can have direct influences on the fitness of individuals, whereas the shape and structure of transportation networks can influence community dynamics by facilitating contacts between different individuals and species. In this review, we discuss three key areas in the study of animal transportation networks: the topological properties of networks, network morphogenesis and growth, and the behaviour of network users. We present a brief primer on elements of network theory, and then discuss the different ways in which animal groups deal with the fundamental trade-off between the competing network properties of travel efficiency, robustness and infrastructure cost. We consider how the behaviour of network users can impact network efficiency, and call for studies that integrate both network topology and user behaviour. We finish with a prospectus for future research. PMID:25165598

Structural Properties of the Brazilian Air Transportation Network.

Science.gov (United States)

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

2015-09-01

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

Structural Properties of the Brazilian Air Transportation Network

Directory of Open Access Journals (Sweden)

GUILHERME S. COUTO

2015-09-01

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

Fractal and multifractal analyses of bipartite networks

Science.gov (United States)

Liu, Jin-Long; Wang, Jian; Yu, Zu-Guo; Xie, Xian-Hua

2017-03-01

Bipartite networks have attracted considerable interest in various fields. Fractality and multifractality of unipartite (classical) networks have been studied in recent years, but there is no work to study these properties of bipartite networks. In this paper, we try to unfold the self-similarity structure of bipartite networks by performing the fractal and multifractal analyses for a variety of real-world bipartite network data sets and models. First, we find the fractality in some bipartite networks, including the CiteULike, Netflix, MovieLens (ml-20m), Delicious data sets and (u, v)-flower model. Meanwhile, we observe the shifted power-law or exponential behavior in other several networks. We then focus on the multifractal properties of bipartite networks. Our results indicate that the multifractality exists in those bipartite networks possessing fractality. To capture the inherent attribute of bipartite network with two types different nodes, we give the different weights for the nodes of different classes, and show the existence of multifractality in these node-weighted bipartite networks. In addition, for the data sets with ratings, we modify the two existing algorithms for fractal and multifractal analyses of edge-weighted unipartite networks to study the self-similarity of the corresponding edge-weighted bipartite networks. The results show that our modified algorithms are feasible and can effectively uncover the self-similarity structure of these edge-weighted bipartite networks and their corresponding node-weighted versions.

How and to what end may consciousness contribute to action? Attributing properties of consciousness to an embodied, minimally cognitive artificial neural network.

Science.gov (United States)

Cruse, Holk; Schilling, Malte

2013-01-01

An artificial neural network called reaCog is described which is based on a decentralized, reactive and embodied architecture developed to control non-trivial hexapod walking in an unpredictable environment (Walknet) while using insect-like navigation (Navinet). In reaCog, these basic networks are extended in such a way that the complete system, reaCog, adopts the capability of inventing new behaviors and - via internal simulation - of planning ahead. This cognitive expansion enables the reactive system to be enriched with additional procedures. Here, we focus on the question to what extent properties of phenomena to be characterized on a different level of description as for example consciousness can be found in this minimally cognitive system. Adopting a monist view, we argue that the phenomenal aspect of mental phenomena can be neglected when discussing the function of such a system. Under this condition, reaCog is discussed to be equipped with properties as are bottom-up and top-down attention, intentions, volition, and some aspects of Access Consciousness. These properties have not been explicitly implemented but emerge from the cooperation between the elements of the network. The aspects of Access Consciousness found in reaCog concern the above mentioned ability to plan ahead and to invent and guide (new) actions. Furthermore, global accessibility of memory elements, another aspect characterizing Access Consciousness is realized by this network. reaCog allows for both reactive/automatic control and (access-) conscious control of behavior. We discuss examples for interactions between both the reactive domain and the conscious domain. Metacognition or Reflexive Consciousness is not a property of reaCog. Possible expansions are discussed to allow for further properties of Access Consciousness, verbal report on internal states, and for Metacognition. In summary, we argue that already simple networks allow for properties of consciousness if leaving the phenomenal

How and to what end may consciousness contribute to action? Attributing properties of consciousness to an embodied, minimally cognitive artificial neural network

Directory of Open Access Journals (Sweden)

Holk eCruse

2013-06-01

Full Text Available An artificial neural network called reaCog is described which is based on a decentralized, reactive and embodied architecture to control non-trivial hexapod walking in unpredictable environment (Walknet as well as insect-like navigation (Navinet. In reaCog, these basic networks are extended in such a way that the complete system, reaCog, adopts the capability of inventing new behaviors and - via internal simulation - of planning ahead. This cognitive expansion enables the reactive system to be enriched with additional procedures. Here, we focus on the question to what extent properties of phenomena to be characterized on a different level of description as for example consciousness can be found in this minimally cognitive system. Adopting a monist view, we argue that the phenomenal aspect of mental phenomena can be neglected when discussing the function of such a system. Under this condition, reaCog is discussed to be equipped with properties as are bottom-up and top-down attention, intentions, volition and some aspects of Access Consciousness. These properties have not been explicitly implemented but emerge from the cooperation between the elements of the network. The aspects of access consciousness found in reaCog concern the above mentioned ability to plan ahead and to invent and guide (new actions. Furthermore, global accessibility of memory elements, another aspect characterizing Access Consciousness is realized by this network. reaCog allows for both reactive/automatic control and (access- conscious control of behavior. We discuss examples for interactions between both the reactive domain and the conscious domain. Metacognition or Reflexive Consciousness is not a property of reaCog. Possible expansions are discussed to allow for further properties of Access Consciousness, verbal report on internal states, and for Metacognition. In summary, we argue that already simple networks allow for properties of consciousness if leaving the phenomenal

Network cohesion

OpenAIRE

Cavalcanti, Tiago Vanderlei; Giannitsarou, Chrysi; Johnson, CR

2017-01-01

We define a measure of network cohesion and show how it arises naturally in a broad class of dynamic models of endogenous perpetual growth with network externalities. Via a standard growth model, we show why network cohesion is crucial for conditional convergence and explain that as cohesion increases, convergence is faster. We prove properties of network cohesion and define a network aggregator that preserves network cohesion.

Numerical Feedback Stabilization with Applications to Networks

Directory of Open Access Journals (Sweden)

Simone Göttlich

2017-01-01

Full Text Available The focus is on the numerical consideration of feedback boundary control problems for linear systems of conservation laws including source terms. We explain under which conditions the numerical discretization can be used to design feedback boundary values for network applications such as electric transmission lines or traffic flow systems. Several numerical examples illustrate the properties of the results for different types of networks.

On the structural properties of small-world networks with range-limited shortcut links

Science.gov (United States)

Jia, Tao; Kulkarni, Rahul V.

2013-12-01

We explore a new variant of Small-World Networks (SWNs), in which an additional parameter (r) sets the length scale over which shortcuts are uniformly distributed. When r=0 we have an ordered network, whereas r=1 corresponds to the original Watts-Strogatz SWN model. These limited range SWNs have a similar degree distribution and scaling properties as the original SWN model. We observe the small-world phenomenon for r≪1, indicating that global shortcuts are not necessary for the small-world effect. For limited range SWNs, the average path length changes nonmonotonically with system size, whereas for the original SWN model it increases monotonically. We propose an expression for the average path length for limited range SWNs based on numerical simulations and analytical approximations.

Nanoporous ionic organic networks: from synthesis to materials applications

OpenAIRE

Sun, Jian-Ke; Antonietti, Markus; Yuan, Jiayin

2016-01-01

The past decade has witnessed rapid progress in the synthesis of nanoporous organic networks or polymer frameworks for various potential applications. Generally speaking, functionalization of porous networks to add extra properties and enhance materials performance could be achieved either during the pore formation (thus a concurrent approach) or by post-synthetic modification (a sequential approach). Nanoporous organic networks which include ion pairs bound in a covalent manner are of specia...

Accelerating networks

International Nuclear Information System (INIS)

Smith, David M D; Onnela, Jukka-Pekka; Johnson, Neil F

2007-01-01

Evolving out-of-equilibrium networks have been under intense scrutiny recently. In many real-world settings the number of links added per new node is not constant but depends on the time at which the node is introduced in the system. This simple idea gives rise to the concept of accelerating networks, for which we review an existing definition and-after finding it somewhat constrictive-offer a new definition. The new definition provided here views network acceleration as a time dependent property of a given system as opposed to being a property of the specific algorithm applied to grow the network. The definition also covers both unweighted and weighted networks. As time-stamped network data becomes increasingly available, the proposed measures may be easily applied to such empirical datasets. As a simple case study we apply the concepts to study the evolution of three different instances of Wikipedia, namely, those in English, German, and Japanese, and find that the networks undergo different acceleration regimes in their evolution

Diamond network: template-free fabrication and properties.

Science.gov (United States)

Zhuang, Hao; Yang, Nianjun; Fu, Haiyuan; Zhang, Lei; Wang, Chun; Huang, Nan; Jiang, Xin

2015-03-11

A porous diamond network with three-dimensionally interconnected pores is of technical importance but difficult to be produced. In this contribution, we demonstrate a simple, controllable, and "template-free" approach to fabricate diamond networks. It combines the deposition of diamond/β-SiC nanocomposite film with a wet-chemical selective etching of the β-SiC phase. The porosity of these networks was tuned from 15 to 68%, determined by the ratio of the β-SiC phase in the composite films. The electrochemical working potential and the reactivity of redox probes on the diamond networks are similar to those of a flat nanocrystalline diamond film, while their surface areas are hundreds of times larger than that of a flat diamond film (e.g., 490-fold enhancement for a 3 μm thick diamond network). The marriage of the unprecedented physical/chemical features of diamond with inherent advantages of the porous structure makes the diamond network a potential candidate for various applications such as water treatment, energy conversion (batteries or fuel cells), and storage (capacitors), as well as electrochemical and biochemical sensing.

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Science.gov (United States)

Xie, Tian; Grossman, Jeffrey C.

2018-04-01

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

Research on Some Bus Transport Networks with Random Overlapping Clique Structure

International Nuclear Information System (INIS)

Yang Xuhua; Sun Youxian; Wang Bo; Wang Wanliang

2008-01-01

On the basis of investigating the statistical data of bus transport networks of three big cities in China, we propose that each bus route is a clique (maximal complete subgraph) and a bus transport network (BTN) consists of a lot of cliques, which intensively connect and overlap with each other. We study the network properties, which include the degree distribution, multiple edges' overlapping time distribution, distribution of the overlap size between any two overlapping cliques, distribution of the number of cliques that a node belongs to. Naturally, the cliques also constitute a network, with the overlapping nodes being their multiple links. We also research its network properties such as degree distribution, clustering, average path length, and so on. We propose that a BTN has the properties of random clique increment and random overlapping clique, at the same time, a BTN is a small-world network with highly clique-clustered and highly clique-overlapped. Finally, we introduce a BTN evolution model, whose simulation results agree well with the statistical laws that emerge in real BTNs

76 FR 2145 - Masco Builder Cabinet Group Including On-Site Leased Workers From Reserves Network, Jackson, OH...

Science.gov (United States)

2011-01-12

...,287B; TA-W-71,287C] Masco Builder Cabinet Group Including On-Site Leased Workers From Reserves Network, Jackson, OH; Masco Builder Cabinet Group, Waverly, OH; Masco Builder Cabinet Group, Seal Township, OH; Masco Builder Cabinet Group, Seaman, OH; Amended Certification Regarding Eligibility To Apply for Worker...

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

Science.gov (United States)

Zhang, Liang; Lu, Na

2017-12-01

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

Microstructure and Mechanical Properties of Heterogeneous Ceramic-Polymer Composite Using Interpenetrating Network

OpenAIRE

Kim, Eun-Hee; Jung, Yeon-Gil; Jo, Chang-Yong

2012-01-01

Prepolymer, which can be polymerized by a photo, has been infiltrated into a porous ceramic to improve the addition effect of polymer into the ceramic, as a function of the functionality of prepolymer. It induces the increase in the mechanical properties of the ceramic. The porous alumina (Al2O3) and the polyurethane acrylate (PUA) with a network structure by photo-polymerization were used as the matrix and infiltration materials, respectively. The porous Al2O3 matrix without t...

Google matrix analysis of directed networks

Science.gov (United States)

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

2015-10-01

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

Printing continuously graded interpenetrating polymer networks of acrylate/epoxy by manipulating cationic network formation during stereolithography

Directory of Open Access Journals (Sweden)

W. Li

2016-12-01

Full Text Available Ultra-violet (UV laser assisted stereolithography is used to print graded interpenetrating polymer networks (IPNs by controlling network formation. Unlike the traditional process where structural change in IPNs is achieved by varying the feeding ratio of monomers or polymer precursors, in this demonstration property is changed by controlled termination of network formation. A photo-initiated process is used to construct IPNs by a combination of radical and cationic network formation in an acrylate/epoxy system. The extent of the cationic network formation is used to control the final properties of the system. Rapid-Scan Fourier Transformation Infrared Spectroscopy (RS-FTIR is used to track the curing kinetics of the two networks and identify key parameters to control the final properties. Atomic force microscopy (AFM and differential scanning calorimetry (DSC confirm the formation of homogenous IPNs, whereas nano-indentation indicates that properties vary with the extent of cationic network formation. The curing characteristics are used to design and demonstrate printing of graded IPNs that show two orders of magnitude variation in mechanical properties in the millimeter scale.

Analysis of Semantic Networks using Complex Networks Concepts

DEFF Research Database (Denmark)

Ortiz-Arroyo, Daniel

2013-01-01

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

Composing Music with Complex Networks

Science.gov (United States)

Liu, Xiaofan; Tse, Chi K.; Small, Michael

In this paper we study the network structure in music and attempt to compose music artificially. Networks are constructed with nodes and edges corresponding to musical notes and their co-occurrences. We analyze sample compositions from Bach, Mozart, Chopin, as well as other types of music including Chinese pop music. We observe remarkably similar properties in all networks constructed from the selected compositions. Power-law exponents of degree distributions, mean degrees, clustering coefficients, mean geodesic distances, etc. are reported. With the network constructed, music can be created by using a biased random walk algorithm, which begins with a randomly chosen note and selects the subsequent notes according to a simple set of rules that compares the weights of the edges, weights of the nodes, and/or the degrees of nodes. The newly created music from complex networks will be played in the presentation.

3D printing of an interpenetrating network hydrogel material with tunable viscoelastic properties.

Science.gov (United States)

Bootsma, Katherine; Fitzgerald, Martha M; Free, Brandon; Dimbath, Elizabeth; Conjerti, Joe; Reese, Greg; Konkolewicz, Dominik; Berberich, Jason A; Sparks, Jessica L

2017-06-01

Interpenetrating network (IPN) hydrogel materials are recognized for their unique mechanical properties. While IPN elasticity and toughness properties have been explored in previous studies, the factors that impact the time-dependent stress relaxation behavior of IPN materials are not well understood. Time-dependent (i.e. viscoelastic) mechanical behavior is a critical design parameter in the development of materials for a variety of applications, such as medical simulation devices, flexible substrate materials, cellular mechanobiology substrates, or regenerative medicine applications. This study reports a novel technique for 3D printing alginate-polyacrylamide IPN gels with tunable elastic and viscoelastic properties. The viscoelastic stress relaxation behavior of the 3D printed alginate-polyacrylamide IPN hydrogels was influenced most strongly by varying the concentration of the acrylamide cross-linker (MBAA), while the elastic modulus was affected most by varying the concentration of total monomer material. The material properties of our 3D printed IPN constructs were consistent with those reported in the biomechanics literature for soft tissues such as skeletal muscle, cardiac muscle, skin and subcutaneous tissue. Copyright © 2017 Elsevier Ltd. All rights reserved.

Efficient weighting strategy for enhancing synchronizability of complex networks

Science.gov (United States)

Wang, Youquan; Yu, Feng; Huang, Shucheng; Tu, Juanjuan; Chen, Yan

2018-04-01

Networks with high propensity to synchronization are desired in many applications ranging from biology to engineering. In general, there are two ways to enhance the synchronizability of a network: link rewiring and/or link weighting. In this paper, we propose a new link weighting strategy based on the concept of the neighborhood subgroup. The neighborhood subgroup of a node i through node j in a network, i.e. Gi→j, means that node u belongs to Gi→j if node u belongs to the first-order neighbors of j (not include i). Our proposed weighting schema used the local and global structural properties of the networks such as the node degree, betweenness centrality and closeness centrality measures. We applied the method on scale-free and Watts-Strogatz networks of different structural properties and show the good performance of the proposed weighting scheme. Furthermore, as model networks cannot capture all essential features of real-world complex networks, we considered a number of undirected and unweighted real-world networks. To the best of our knowledge, the proposed weighting strategy outperformed the previously published weighting methods by enhancing the synchronizability of these real-world networks.

Optical protocols for advanced spacecraft networks

Science.gov (United States)

Bergman, Larry A.

1991-01-01

Most present day fiber optic networks are in fact extensions of copper wire networks. As a result, their speed is still limited by electronics even though optics is capable of running three orders of magnitude faster. Also, the fact that photons do not interact with one another (as electrons do) provides optical communication systems with some unique properties or new functionality that is not readily taken advantage of with conventional approaches. Some of the motivation for implementing network protocols in the optical domain, a few possible approaches including optical code-division multiple-access (CDMA), and how this class of networks can extend the technology life cycle of the Space Station Freedom (SSF) with increased performance and functionality are described.

Optical protocols for advanced spacecraft networks

Science.gov (United States)

Bergman, Larry A.

1991-09-01

Most present day fiber optic networks are in fact extensions of copper wire networks. As a result, their speed is still limited by electronics even though optics is capable of running three orders of magnitude faster. Also, the fact that photons do not interact with one another (as electrons do) provides optical communication systems with some unique properties or new functionality that is not readily taken advantage of with conventional approaches. Some of the motivation for implementing network protocols in the optical domain, a few possible approaches including optical code-division multiple-access (CDMA), and how this class of networks can extend the technology life cycle of the Space Station Freedom (SSF) with increased performance and functionality are described.

Exploring network structure, dynamics, and function using networkx

Energy Technology Data Exchange (ETDEWEB)

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

2008-01-01

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

Topological properties of random wireless networks

Indian Academy of Sciences (India)

Wireless networks in which the node locations are random are best modelled as random geometric graphs (RGGs). In addition to their extensive application in the modelling of wireless networks, RGGs find many new applications and are being studied in their own right. In this paper we first provide a brief introduction to the ...

Effect of the metal work function on the electrical properties of carbon nanotube network transistors

International Nuclear Information System (INIS)

Kim, Un Jeong; Ko, Dae Young; Kil, Joon Pyo; Lee, Jung Wha; Park, Wan Jun

2012-01-01

A nearly perfect semiconducting single-walled carbon nanotube random network thin film transistor array was fabricated, and its reproducible transport properties were investigated. The effects of the metal work function for both the source and the drain on the electrical properties of the transistors were systematically investigated. Three different metal electrodes, Al, Ti, and Pd, were employed. As the metal work function increased, p-type behavior became dominant, and the field effect hole mobility dramatically increased. Also, the Schottky barrier of the Ti-nanotube contact was invariant to the molecular adsorption of species in air.

[Aberrant topological properties of whole-brain functional network in chronic right-sided sensorineural hearing loss: a resting-state functional MRI study].

Science.gov (United States)

Zhang, Lingling; Liu, Bin; Xu, Yangwen; Yang, Ming; Feng, Yuan; Huang, Yaqing; Huan, Zhichun; Hou, Zhaorui

2015-02-03

To investigate the topological properties of the functional brain network in unilateral sensorineural hearing loss patients. In this study, we acquired resting-state BOLD- fMRI data from 19 right-sided SNHL patients and 31 healthy controls with normal hearing and constructed their whole brain functional networks. Two-sample two-tailed t-tests were performed to investigate group differences in topological parameters between the USNHL patients and the controls. Partial correlation analysis was conducted to determine the relationships between the network metrics and USNHL-related variables. Both USNHL patients and controls exhibited small-word architecture in their brain functional networks within the range 0. 1 - 0. 2 of sparsity. Compared to the controls, USNHL patients showed significant increase in characteristic path length and normalized characteristic path length, but significant decrease in global efficiency. Clustering coefficient, local efficiency and normalized clustering coefficient demonstrated no significant difference. Furthermore, USNHL patients exhibited no significant association between the altered network metrics and the duration of USNHL or the severity of hearing loss. Our results indicated the altered topological properties of whole brain functional networks in USNHL patients, which may help us to understand pathophysiologic mechanism of USNHL patients.

Asymptotic stability and disturbance attenuation properties for a class of networked control systems

Institute of Scientific and Technical Information of China (English)

无

2006-01-01

In this paper, stability and disturbance attenuation issues for a class of Networked Control Systems (NCSs)under uncertain access delay and packet dropout effects are considered. Our aim is to find conditions on the delay and packet dropout rate, under which the system stability and H∞ disturbance attenuation properties are preserved to a desired level. The basic idea in this paper is to formulate such Networked Control System as a discrete-time switched system. Then the NCSs' stability and performance problems can be reduced to the corresponding problems for switched systems, which have been studied for decades and for which a number of results are available in the literature. The techniques in this paper are based on recent progress in the discrete-time switched systems and piecewise Lyapunov functions.

Power Laws, Scale-Free Networks and Genome Biology

CERN Document Server

Koonin, Eugene V; Karev, Georgy P

2006-01-01

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

Solution processed zinc oxide nanopyramid/silver nanowire transparent network films with highly tunable light scattering properties

KAUST Repository

Mehra, Saahil

2013-01-01

Metal nanowire transparent networks are promising replacements to indium tin oxide (ITO) transparent electrodes for optoelectronic devices. While the transparency and sheet resistance are key metrics for transparent electrode performance, independent control of the film light scattering properties is important to developing multifunctional electrodes for improved photovoltaic absorption. Here we show that controlled incorporation of ZnO nanopyramids into a metal nanowire network film affords independent, highly tunable control of the scattering properties (haze) with minimal effects on the transparency and sheet resistance. Varying the zinc oxide/silver nanostructure ratios prior to spray deposition results in sheet resistances, transmission (600 nm), and haze (600 nm) of 6-30 Ω □^-1, 68-86%, and 34-66%, respectively. Incorporation of zinc oxide nanopyramid scattering agents into the conducting nanowire mesh has a negligible effect on mesh connectivity, providing a straightforward method of controlling electrode scattering properties. The decoupling of the film scattering power and electrical characteristics makes these films promising candidates for highly scattering transparent electrodes in optoelectronic devices and can be generalized to other metal nanowire films as well as carbon nanotube transparent electrodes. © 2013 The Royal Society of Chemistry.

Resolving structural variability in network models and the brain.

Directory of Open Access Journals (Sweden)

Florian Klimm

2014-03-01

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

Network analysis of mesoscale optical recordings to assess regional, functional connectivity.

Science.gov (United States)

Lim, Diana H; LeDue, Jeffrey M; Murphy, Timothy H

2015-10-01

With modern optical imaging methods, it is possible to map structural and functional connectivity. Optical imaging studies that aim to describe large-scale neural connectivity often need to handle large and complex datasets. In order to interpret these datasets, new methods for analyzing structural and functional connectivity are being developed. Recently, network analysis, based on graph theory, has been used to describe and quantify brain connectivity in both experimental and clinical studies. We outline how to apply regional, functional network analysis to mesoscale optical imaging using voltage-sensitive-dye imaging and channelrhodopsin-2 stimulation in a mouse model. We include links to sample datasets and an analysis script. The analyses we employ can be applied to other types of fluorescence wide-field imaging, including genetically encoded calcium indicators, to assess network properties. We discuss the benefits and limitations of using network analysis for interpreting optical imaging data and define network properties that may be used to compare across preparations or other manipulations such as animal models of disease.

A social network's changing statistical properties and the quality of human innovation

Energy Technology Data Exchange (ETDEWEB)

Uzzi, Brian [Kellogg School of Management, Northwestern University, Evanston, IL (United States)], E-mail: uzzi@northwestern.edu

2008-06-06

We examined the entire network of creative artists that made Broadway musicals, in the post-War period, a collaboration network of international acclaim and influence, with an eye to investigating how the network's structural features condition the relationship between individual artistic talent and the success of their musicals. Our findings show that some of the evolving topographical qualities of degree distributions, path lengths and assortativity are relatively stable with time even as collaboration patterns shift, which suggests their changes are only minimally associated with the ebb and flux of the success of new productions. In contrast, the clustering coefficient changed substantially over time and we found that it had a nonlinear association with the production of financially and artistically successful shows. When the clustering coefficient ratio is low or high, the financial and artistic success of the industry is low, while an intermediate level of clustering is associated with successful shows. We supported these findings with sociological theory on the relationship between social structure and collaboration and with tests of statistical inference. Our discussion focuses on connecting the statistical properties of social networks to their performance and the performance of the actors embedded within them.

Gene Prioritization by Integrated Analysis of Protein Structural and Network Topological Properties for the Protein-Protein Interaction Network of Neurological Disorders

Directory of Open Access Journals (Sweden)

Yashna Paul

2016-01-01

Full Text Available Neurological disorders are known to show similar phenotypic manifestations like anxiety, depression, and cognitive impairment. There is a need to identify shared genetic markers and molecular pathways in these diseases, which lead to such comorbid conditions. Our study aims to prioritize novel genetic markers that might increase the susceptibility of patients affected with one neurological disorder to other diseases with similar manifestations. Identification of pathways involving common candidate markers will help in the development of improved diagnosis and treatments strategies for patients affected with neurological disorders. This systems biology study for the first time integratively uses 3D-structural protein interface descriptors and network topological properties that characterize proteins in a neurological protein interaction network, to aid the identification of genes that are previously not known to be shared between these diseases. Results of protein prioritization by machine learning have identified known as well as new genetic markers which might have direct or indirect involvement in several neurological disorders. Important gene hubs have also been identified that provide an evidence for shared molecular pathways in the neurological disease network.

Detection of material property errors in handbooks and databases using artificial neural networks with hidden correlations

Science.gov (United States)

Zhang, Y. M.; Evans, J. R. G.; Yang, S. F.

2010-11-01

The authors have discovered a systematic, intelligent and potentially automatic method to detect errors in handbooks and stop their transmission using unrecognised relationships between materials properties. The scientific community relies on the veracity of scientific data in handbooks and databases, some of which have a long pedigree covering several decades. Although various outlier-detection procedures are employed to detect and, where appropriate, remove contaminated data, errors, which had not been discovered by established methods, were easily detected by our artificial neural network in tables of properties of the elements. We started using neural networks to discover unrecognised relationships between materials properties and quickly found that they were very good at finding inconsistencies in groups of data. They reveal variations from 10 to 900% in tables of property data for the elements and point out those that are most probably correct. Compared with the statistical method adopted by Ashby and co-workers [Proc. R. Soc. Lond. Ser. A 454 (1998) p. 1301, 1323], this method locates more inconsistencies and could be embedded in database software for automatic self-checking. We anticipate that our suggestion will be a starting point to deal with this basic problem that affects researchers in every field. The authors believe it may eventually moderate the current expectation that data field error rates will persist at between 1 and 5%.

Dynamical properties of fractal networks: Scaling, numerical simulations, and physical realizations

International Nuclear Information System (INIS)

Nakayama, T.; Yakubo, K.; Orbach, R.L.

1994-01-01

This article describes the advances that have been made over the past ten years on the problem of fracton excitations in fractal structures. The relevant systems to this subject are so numerous that focus is limited to a specific structure, the percolating network. Recent progress has followed three directions: scaling, numerical simulations, and experiment. In a happy coincidence, large-scale computations, especially those involving array processors, have become possible in recent years. Experimental techniques such as light- and neutron-scattering experiments have also been developed. Together, they form the basis for a review article useful as a guide to understanding these developments and for charting future research directions. In addition, new numerical simulation results for the dynamical properties of diluted antiferromagnets are presented and interpreted in terms of scaling arguments. The authors hope this article will bring the major advances and future issues facing this field into clearer focus, and will stimulate further research on the dynamical properties of random systems

Characterisation of anisotropic etching in KOH using network etch rate function model: influence of an applied potential in terms of microscopic properties

International Nuclear Information System (INIS)

Nguyen, Q D; Elwenspoek, M

2006-01-01

Using the network etch rate function model, the anisotropic etch rate of p-type single crystal silicon was characterised in terms of microscopic properties including step velocity, step and terrace roughening. The anisotropic etch rate data needed have been obtained using a combination of 2 wagon wheel patterns on different substrate and 1 offset trench pattern. Using this procedure the influence of an applied potential has been investigated in terms of microscopic properties. Model parameter trends show a good correlation with chemical/electrochemical reaction mechanism and mono- and dihydride terminated steps reactivity difference. Results also indicate a minimum in (111) terrace roughening which results in a peak in anisotropic ratio at the non-OCP applied potential of -1250 mV vs OCP

Antagonistic Phenomena in Network Dynamics

Science.gov (United States)

Motter, Adilson E.; Timme, Marc

2018-03-01

Recent research on the network modeling of complex systems has led to a convenient representation of numerous natural, social, and engineered systems that are now recognized as networks of interacting parts. Such systems can exhibit a wealth of phenomena that not only cannot be anticipated from merely examining their parts, as per the textbook definition of complexity, but also challenge intuition even when considered in the context of what is now known in network science. Here, we review the recent literature on two major classes of such phenomena that have far-reaching implications: (a) antagonistic responses to changes of states or parameters and (b) coexistence of seemingly incongruous behaviors or properties - both deriving from the collective and inherently decentralized nature of the dynamics. They include effects as diverse as negative compressibility in engineered materials, rescue interactions in biological networks, negative resistance in fluid networks, and the Braess paradox occurring across transport and supply networks. They also include remote synchronization, chimera states, and the converse of symmetry breaking in brain, power-grid, and oscillator networks as well as remote control in biological and bioinspired systems. By offering a unified view of these various scenarios, we suggest that they are representative of a yet broader class of unprecedented network phenomena that ought to be revealed and explained by future research.

Hybrid Organic/Inorganic Thiol-ene-Based Photopolymerized Networks

OpenAIRE

Schreck, Kathleen M.; Leung, Diana; Bowman, Christopher N.

2011-01-01

The thiol-ene reaction serves as a more oxygen tolerant alternative to traditional (meth)acrylate chemistry for forming photopolymerized networks with numerous desirable attributes including energy absorption, optical clarity, and reduced shrinkage stress. However, when utilizing commercially available monomers, many thiol-ene networks also exhibit decreases in properties such as glass transition temperature (Tg) and crosslink density. In this study, hybrid organic/inorganic thiol-ene resins ...

Self-Healing Natural Rubber with Tailorable Mechanical Properties Based on Ionic Supramolecular Hybrid Network.

Science.gov (United States)

Xu, Chuanhui; Cao, Liming; Huang, Xunhui; Chen, Yukun; Lin, Baofeng; Fu, Lihua

2017-08-30

In most cases, the strength of self-healing supramolecular rubber based on noncovalent bonds is in the order of KPa, which is a challenge for their further applications. Incorporation of conventional fillers can effectively enhance the strength of rubbers, but usually accompanied by a sacrifice of self-healing capability due to that the filler system is independent of the reversible supramolecular network. In the present work, in situ reaction of methacrylic acid (MAA) and excess zinc oxide (ZnO) was realized in natural rubber (NR). Ionic cross-links in NR matrix were obtained by limiting the covalent cross-linking of NR molecules and allowing the in situ polymerization of MAA/ZnO. Because of the natural affinity between Zn 2+ ion-rich domains and ZnO, the residual nano ZnO participated in formation of a reversible ionic supramolecular hybrid network, thus having little obstructions on the reconstruction of ionic cross-links. Meanwhile, the well dispersed residual ZnO could tailor the mechanical properties of NR by changing the MAA/ZnO molar ratios. The present study thus provides a simple method to fabricate a new self-healing NR with tailorable mechanical properties that may have more potential applications.

Evolving phenotypic networks in silico.

Science.gov (United States)

François, Paul

2014-11-01

Evolved gene networks are constrained by natural selection. Their structures and functions are consequently far from being random, as exemplified by the multiple instances of parallel/convergent evolution. One can thus ask if features of actual gene networks can be recovered from evolutionary first principles. I review a method for in silico evolution of small models of gene networks aiming at performing predefined biological functions. I summarize the current implementation of the algorithm, insisting on the construction of a proper "fitness" function. I illustrate the approach on three examples: biochemical adaptation, ligand discrimination and vertebrate segmentation (somitogenesis). While the structure of the evolved networks is variable, dynamics of our evolved networks are usually constrained and present many similar features to actual gene networks, including properties that were not explicitly selected for. In silico evolution can thus be used to predict biological behaviours without a detailed knowledge of the mapping between genotype and phenotype. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

Analysis of transmission properties in an indoor wireless sensor network based on a full-factorial design

International Nuclear Information System (INIS)

Christmann, Dennis; Martinovic, Ivan; Schmitt, Jens B

2010-01-01

In this paper, we systematically investigate different factors and their effects on the wireless transmission properties using a full-factorial experimental design of a wireless sensor network in a real-world indoor environment. We quantify the impact of primary factors such as the wireless channel, physical position, transmission power and line of sight, as well as their interactions on the received signal strength (RSS). While some of our results support conventional assumptions, this study also shows that there are several properties which are in contrast to existing findings. For example, there is no significant correlation in the measured RSS between differently located but equally distant transmitters, yet the correlation coefficient for the two directions of a single link between two transmitters is above 94%, leading to very symmetric links that differ only in a few dBm for the two directions. Further analysis reveals the strong interaction of transmission frequency and physical position, while the transmission power has only an isolated, non-interacting effect on the RSS. Since the analyzed network consists of commodity motes utilizing TI's well-known IEEE 802.15.4 compliant CC2420 transceiver, the results of this experimental analysis can serve as valuable insights in planning and deploying wireless sensor networks in different application scenarios

Network access charges, vertical integration, and property rights structure - experiences from the German electricity markets

International Nuclear Information System (INIS)

Growitsch, C.; Wein, T.

2005-01-01

After the deregulation of the German electricity markets in 1998, the German government opted for a regulatory regime called negotiated third party access, which would be subject to ex-post control by the federal cartel office. Network access charges for new competitors are based on contractual arrangements between energy producers and industrial consumers. As the electricity networks are incontestable natural monopolies, the local and regional network operators are able to set (monopolistic) charges at their own discretion, restricted only by the possible interference of the federal cartel office (Bundeskartellamt). In this paper we analyze if there is evidence for varying charging behaviour depending on the supplier's economic independence (structure of property rights) or its level of vertical integration. For this purpose, we hypothesise that incorporated and vertically integrated suppliers set different charges than independent utility companies. Multivariate estimations show a relation between network access charges and the network operator's economic independence as well as level of vertical integration: on the low voltage level for an estimated annual consumption of 1700 kW/h, vertically integrated firms set-in accordance with our hypothesis-significantly lower access charges than vertically separated suppliers, whereas incorporated network operators charge significantly higher charges compared to independent suppliers. These results could not have been confirmed for other consumptions or voltage levels. (author)

Signatures of arithmetic simplicity in metabolic network architecture.

Directory of Open Access Journals (Sweden)

William J Riehl

2010-04-01

Full Text Available Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity.

Influence of the Training Set Value on the Quality of the Neural Network to Identify Selected Moulding Sand Properties

Directory of Open Access Journals (Sweden)

Jakubski J.

2013-06-01

Full Text Available Artificial neural networks are one of the modern methods of the production optimisation. An attempt to apply neural networks for controlling the quality of bentonite moulding sands is presented in this paper. This is the assessment method of sands suitability by means of detecting correlations between their individual parameters. This paper presents the next part of the study on usefulness of artificial neural networks to support rebonding of green moulding sand, using chosen properties of moulding sands, which can be determined fast. The effect of changes in the training set quantity on the quality of the network is presented in this article. It has been shown that a small change in the data set would change the quality of the network, and may also make it necessary to change the type of network in order to obtain good results.

Conductivity and properties of polysiloxane-polyether cluster-LiTFSI networks as hybrid polymer electrolytes

Science.gov (United States)

Boaretto, Nicola; Joost, Christine; Seyfried, Mona; Vezzù, Keti; Di Noto, Vito

2016-09-01

This report describes the synthesis and the properties of a series of polymer electrolytes, composed of a hybrid inorganic-organic matrix doped with LiTFSI. The matrix is based on ring-like oligo-siloxane clusters, bearing pendant, partially cross-linked, polyether chains. The dependency of the thermo-mechanic and of the transport properties on several structural parameters, such as polyether chains' length, cross-linkers' concentration, and salt concentration is studied. Altogether, the materials show good thermo-mechanical and electrochemical stabilities, with conductivities reaching, at best, 8·10-5 S cm-1 at 30 °C. In conclusion, the cell performances of one representative sample are shown. The scope of this report is to analyze the correlations between structure and properties in networked and hybrid polymer electrolytes. This could help the design of optimized polymer electrolytes for application in lithium metal batteries.

Novel amphiphilic poly(dimethylsiloxane) based polyurethane networks tethered with carboxybetaine and their combined antibacterial and anti-adhesive property

Science.gov (United States)

Jiang, Jingxian; Fu, Yuchen; Zhang, Qinghua; Zhan, Xiaoli; Chen, Fengqiu

2017-08-01

The traditional nonfouling materials are powerless against bacterial cells attachment, while the hydrophobic bactericidal surfaces always suffer from nonspecific protein adsorption and dead bacterial cells accumulation. Here, amphiphilic polyurethane (PU) networks modified with poly(dimethylsiloxane) (PDMS) and cationic carboxybetaine diol through simple crosslinking reaction were developed, which had an antibacterial efficiency of 97.7%. Thereafter, the hydrolysis of carboxybetaine ester into zwitterionic groups brought about anti-adhesive properties against bacteria and proteins. The surface chemical composition and wettability performance of the PU network surfaces were investigated by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), X-ray photoelectron spectroscopy (XPS) and contact angle analysis. The surface distribution of PDMS and zwitterionic segments produced an obvious amphiphilic heterogeneous surface, which was demonstrated by atomic force microscopy (AFM). Enzyme-linked immunosorbent assays (ELISA) were used to test the nonspecific protein adsorption behaviors. With the advantages of the transition from excellent bactericidal performance to anti-adhesion and the combination of fouling resistance and fouling release property, the designed PDMS-based amphiphilic PU network shows great application potential in biomedical devices and marine facilities.

Organization of signal flow in directed networks

International Nuclear Information System (INIS)

Bányai, M; Bazsó, F; Négyessy, L

2011-01-01

Confining an answer to the question of whether and how the coherent operation of network elements is determined by the network structure is the topic of our work. We map the structure of signal flow in directed networks by analysing the degree of edge convergence and the overlap between the in- and output sets of an edge. Definitions of convergence degree and overlap are based on the shortest paths, thus they encapsulate global network properties. Using the defining notions of convergence degree and overlapping set we clarify the meaning of network causality and demonstrate the crucial role of chordless circles. In real-world networks the flow representation distinguishes nodes according to their signal transmitting, processing and control properties. The analysis of real-world networks in terms of flow representation was in accordance with the known functional properties of the network nodes. It is shown that nodes with different signal processing, transmitting and control properties are randomly connected at the global scale, while local connectivity patterns depart from randomness. The grouping of network nodes according to their signal flow properties was unrelated to the network's community structure. We present evidence that the signal flow properties of small-world-like, real-world networks cannot be reconstructed by algorithms used to generate small-world networks. Convergence degree values were calculated for regular oriented trees, and the probability density function for networks grown with the preferential attachment mechanism. For Erdos–Rényi graphs we calculated the probability density function of both convergence degrees and overlaps

Energy star compliant voice over internet protocol (VoIP) telecommunications network including energy star compliant VoIP devices

Science.gov (United States)

Kouchri, Farrokh Mohammadzadeh

2012-11-06

A Voice over Internet Protocol (VoIP) communications system, a method of managing a communications network in such a system and a program product therefore. The system/network includes an ENERGY STAR (E-star) aware softswitch and E-star compliant communications devices at system endpoints. The E-star aware softswitch allows E-star compliant communications devices to enter and remain in power saving mode. The E-star aware softswitch spools messages and forwards only selected messages (e.g., calls) to the devices in power saving mode. When the E-star compliant communications devices exit power saving mode, the E-star aware softswitch forwards spooled messages.

Hybrid Organic/Inorganic Thiol-ene-Based Photopolymerized Networks.

Science.gov (United States)

Schreck, Kathleen M; Leung, Diana; Bowman, Christopher N

2011-09-15

The thiol-ene reaction serves as a more oxygen tolerant alternative to traditional (meth)acrylate chemistry for forming photopolymerized networks with numerous desirable attributes including energy absorption, optical clarity, and reduced shrinkage stress. However, when utilizing commercially available monomers, many thiol-ene networks also exhibit decreases in properties such as glass transition temperature (T(g)) and crosslink density. In this study, hybrid organic/inorganic thiol-ene resins incorporating silsesquioxane (SSQ) species into the photopolymerized networks were investigated as a route to improve these properties. Thiol- and ene-functionalized SSQs (SH-SSQ and allyl-SSQ, respectively) were synthesized via alkoxysilane hydrolysis/condensation chemistry, using a photopolymerizable monomer [either pentaerythriol tetrakis(3-mercaptopropionate) (PETMP) or 1,3,5-triallyl-1,3,5-triazine-2,4,6(1H,3H,5H)-trione (TATATO)] as the reaction solvent. The resulting SSQ-containing solutions (SSQ-PETMP and SSQ-TATATO) were characterized, and their incorporation into photopolymerized networks was evaluated.

GABAergic synapse properties may explain genetic variation in hippocampal network oscillations in mice

Directory of Open Access Journals (Sweden)

Tim S Heistek

2010-06-01

Full Text Available Cognitive ability and the properties of brain oscillation are highly heritable in humans. Genetic variation underlying oscillatory activity might give rise to differences in cognition and behavior. How genetic diversity translates into altered properties of oscillations and synchronization of neuronal activity is unknown. To address this issue, we investigated cellular and synaptic mechanisms of hippocampal fast network oscillations in eight genetically distinct inbred mouse strains. The frequency of carbachol-induced oscillations differed substantially between mouse strains. Since GABAergic inhibition sets oscillation frequency, we studied the properties of inhibitory synaptic inputs (IPSCs received by CA3 and CA1 pyramidal cells of three mouse strains that showed the highest, lowest and intermediate frequencies of oscillations. In CA3 pyramidal cells, the frequency of rhythmic IPSC input showed the same strain differences as the frequency of field oscillations. Furthermore, IPSC decay times in both CA1 and CA3 pyramidal cells were faster in mouse strains with higher oscillation frequencies than in mouse strains with lower oscillation frequency, suggesting that differences in GABAA-receptor subunit composition exist between these strains. Indeed, gene expression of GABAA-receptor β2 (Gabrb2 and β3 (Gabrb2 subunits was higher in mouse strains with faster decay kinetics compared with mouse strains with slower decay kinetics. Hippocampal pyramidal neurons in mouse strains with higher oscillation frequencies and faster decay kinetics fired action potential at higher frequencies. These data indicate that differences in genetic background may result in different GABAA-receptor subunit expression, which affects the rhythm of pyramidal neuron firing and fast network activity through GABA synapse kinetics.

Prediction of properties of polymer concrete composite with tire rubber using neural networks

International Nuclear Information System (INIS)

Diaconescu, Rodica-Mariana; Barbuta, Marinela; Harja, Maria

2013-01-01

Highlights: ► Using waste a new composite material was obtained with specific characteristics. ► The objective was to maximize tire powder content with the minimum resin content. ► By direct modeling, the maximum compressive strength was obtained for 30% tire powder. ► Inverse neural modeling was used for obtaining maximum values of strengths. -- Abstract: The neural network method was used to investigate the influence of filler and resin content on the mechanical properties of polymer concrete with powdered tire waste. The mechanical strengths of 10 experimentally determined combinations using mixed epoxy resin, aggregates and tire powder as filler were optimized using direct neural modeling and inverse neural modeling, by imposing a minimum cost (content in resin). Direct neural modeling gave the optimum composition for obtaining maximum values for compressive strength, flexural strength and split tensile strength. Inverse neural modeling analyzed the possibility of obtaining maximum values of mechanical properties by variations in the dosages of the epoxy resin and tire powder. Neural network modeling generated the mixes with the lowest cost and maximum strength. The modeling method has shown that two mechanical properties can be simultaneously optimized in the investigation domain. From direct modeling, the maximum compressive strength was obtained for a composition with 0.215 (fraction weight) epoxy resin and 0.3 (fraction weight) tire powder. Maximum flexural strength was obtained for experimental values of 0.23 epoxy resin and 0.17 tire powder with a severe reduction noted for smaller resin dosages. The maximum split tensile strength was obtained for a resin dosage of 0.24 and tire powder dosage of 0.17

Networks in Cell Biology

Science.gov (United States)

Buchanan, Mark; Caldarelli, Guido; De Los Rios, Paolo; Rao, Francesco; Vendruscolo, Michele

2010-05-01

Introduction; 1. Network views of the cell Paolo De Los Rios and Michele Vendruscolo; 2. Transcriptional regulatory networks Sarath Chandra Janga and M. Madan Babu; 3. Transcription factors and gene regulatory networks Matteo Brilli, Elissa Calistri and Pietro Lió; 4. Experimental methods for protein interaction identification Peter Uetz, Björn Titz, Seesandra V. Rajagopala and Gerard Cagney; 5. Modeling protein interaction networks Francesco Rao; 6. Dynamics and evolution of metabolic networks Daniel Segré; 7. Hierarchical modularity in biological networks: the case of metabolic networks Erzsébet Ravasz Regan; 8. Signalling networks Gian Paolo Rossini; Appendix 1. Complex networks: from local to global properties D. Garlaschelli and G. Caldarelli; Appendix 2. Modelling the local structure of networks D. Garlaschelli and G. Caldarelli; Appendix 3. Higher-order topological properties S. Ahnert, T. Fink and G. Caldarelli; Appendix 4. Elementary mathematical concepts A. Gabrielli and G. Caldarelli; References.

Scaling properties of domain wall networks

International Nuclear Information System (INIS)

Leite, A. M. M.; Martins, C. J. A. P.

2011-01-01

We revisit the cosmological evolution of domain wall networks, taking advantage of recent improvements in computing power. We carry out high-resolution field theory simulations in two, three and four spatial dimensions to study the effects of dimensionality and damping on the evolution of the network. Our results are consistent with the expected scale-invariant evolution of the network, which suggests that previous hints of deviations from this behavior may have been due to the limited dynamical range of those simulations. We also use the results of very large (1024 3 ) simulations in three cosmological epochs to provide a calibration for the velocity-dependent one-scale model for domain walls: we numerically determine the two free model parameters to have the values c w =0.5±0.2 and k w =1.1±0.3.

Mean-field approach to evolving spatial networks, with an application to osteocyte network formation

Science.gov (United States)

Taylor-King, Jake P.; Basanta, David; Chapman, S. Jonathan; Porter, Mason A.

2017-07-01

We consider evolving networks in which each node can have various associated properties (a state) in addition to those that arise from network structure. For example, each node can have a spatial location and a velocity, or it can have some more abstract internal property that describes something like a social trait. Edges between nodes are created and destroyed, and new nodes enter the system. We introduce a "local state degree distribution" (LSDD) as the degree distribution at a particular point in state space. We then make a mean-field assumption and thereby derive an integro-partial differential equation that is satisfied by the LSDD. We perform numerical experiments and find good agreement between solutions of the integro-differential equation and the LSDD from stochastic simulations of the full model. To illustrate our theory, we apply it to a simple model for osteocyte network formation within bones, with a view to understanding changes that may take place during cancer. Our results suggest that increased rates of differentiation lead to higher densities of osteocytes, but with a smaller number of dendrites. To help provide biological context, we also include an introduction to osteocytes, the formation of osteocyte networks, and the role of osteocytes in bone metastasis.

Synchronization in an array of coupled Boolean networks

International Nuclear Information System (INIS)

Li, Rui; Chu, Tianguang

2012-01-01

This Letter presents an analytical study of synchronization in an array of coupled deterministic Boolean networks. A necessary and sufficient criterion for synchronization is established based on algebraic representations of logical dynamics in terms of the semi-tensor product of matrices. Some basic properties of a synchronized array of Boolean networks are then derived for the existence of transient states and the upper bound of the number of fixed points. Particularly, an interesting consequence indicates that a “large” mismatch between two coupled Boolean networks in the array may result in loss of synchrony in the entire system. Examples, including the Boolean model of coupled oscillations in the cell cycle, are given to illustrate the present results. -- Highlights: ► We analytically study synchronization in an array of coupled Boolean networks. ► The study is based on the algebraic representations of logical dynamics. ► A necessary and sufficient algebraic criterion for synchronization is established. ► It reveals some basic properties of a synchronized array of Boolean networks. ► A large mismatch between two coupled networks may result in the loss of synchrony.

Maximum-entropy networks pattern detection, network reconstruction and graph combinatorics

CERN Document Server

Squartini, Tiziano

2017-01-01

This book is an introduction to maximum-entropy models of random graphs with given topological properties and their applications. Its original contribution is the reformulation of many seemingly different problems in the study of both real networks and graph theory within the unified framework of maximum entropy. Particular emphasis is put on the detection of structural patterns in real networks, on the reconstruction of the properties of networks from partial information, and on the enumeration and sampling of graphs with given properties.  After a first introductory chapter explaining the motivation, focus, aim and message of the book, chapter 2 introduces the formal construction of maximum-entropy ensembles of graphs with local topological constraints. Chapter 3 focuses on the problem of pattern detection in real networks and provides a powerful way to disentangle nontrivial higher-order structural features from those that can be traced back to simpler local constraints. Chapter 4 focuses on the problem o...

Spectral Analysis of Rich Network Topology in Social Networks

Science.gov (United States)

Wu, Leting

2013-01-01

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

Mixed random walks with a trap in scale-free networks including nearest-neighbor and next-nearest-neighbor jumps

Science.gov (United States)

Zhang, Zhongzhi; Dong, Yuze; Sheng, Yibin

2015-10-01

Random walks including non-nearest-neighbor jumps appear in many real situations such as the diffusion of adatoms and have found numerous applications including PageRank search algorithm; however, related theoretical results are much less for this dynamical process. In this paper, we present a study of mixed random walks in a family of fractal scale-free networks, where both nearest-neighbor and next-nearest-neighbor jumps are included. We focus on trapping problem in the network family, which is a particular case of random walks with a perfect trap fixed at the central high-degree node. We derive analytical expressions for the average trapping time (ATT), a quantitative indicator measuring the efficiency of the trapping process, by using two different methods, the results of which are consistent with each other. Furthermore, we analytically determine all the eigenvalues and their multiplicities for the fundamental matrix characterizing the dynamical process. Our results show that although next-nearest-neighbor jumps have no effect on the leading scaling of the trapping efficiency, they can strongly affect the prefactor of ATT, providing insight into better understanding of random-walk process in complex systems.

Dislocation concepts applied to fatigue properties of austenitic stainless steels including time-dependent modes

Energy Technology Data Exchange (ETDEWEB)

Tavassoli, A.A.

1986-10-01

Dislocation substructures formed in austenitic stainless steel 304L and 316L, fatigued at 673 K, 823 K and 873 K under total imposed strain ranges of 0.7 to 2.25%, and their correlation with mechanical properties have been investigated. In addition substructures formed at lower strain ranges have been examined using foils prepared from parts of the specimens with larger cross-sections. Investigation has also been extended to include the effect of intermittent hold-times up to 1.8 x 10/sup 4/s and sequential creep-fatigue and fatigue-creep. The experimental results obtained are analysed and their implications for current dislocation concepts and mechanical properties are discussed.

A Complex Network Approach to Distributional Semantic Models.

Directory of Open Access Journals (Sweden)

Akira Utsumi

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

Border trees of complex networks

International Nuclear Information System (INIS)

Villas Boas, Paulino R; Rodrigues, Francisco A; Travieso, Gonzalo; Fontoura Costa, Luciano da

2008-01-01

The comprehensive characterization of the structure of complex networks is essential to understand the dynamical processes which guide their evolution. The discovery of the scale-free distribution and the small-world properties of real networks were fundamental to stimulate more realistic models and to understand important dynamical processes related to network growth. However, the properties of the network borders (nodes with degree equal to 1), one of its most fragile parts, remained little investigated and understood. The border nodes may be involved in the evolution of structures such as geographical networks. Here we analyze the border trees of complex networks, which are defined as the subgraphs without cycles connected to the remainder of the network (containing cycles) and terminating into border nodes. In addition to describing an algorithm for identification of such tree subgraphs, we also consider how their topological properties can be quantified in terms of their depth and number of leaves. We investigate the properties of border trees for several theoretical models as well as real-world networks. Among the obtained results, we found that more than half of the nodes of some real-world networks belong to the border trees. A power-law with cut-off was observed for the distribution of the depth and number of leaves of the border trees. An analysis of the local role of the nodes in the border trees was also performed

Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples

International Nuclear Information System (INIS)

Soezen, Adnan; Arcaklioglu, Erol; Oezalp, Mehmet

2005-01-01

This paper presents a new approach based on artificial neural networks (ANNs) to determine the properties of liquid and two phase boiling and condensing of two alternative refrigerant/absorbent couples (methanol/LiBr and methanol/LiCl). These couples do not cause ozone depletion and use in the absorption thermal systems (ATSs). ANNs are able to learn the key information patterns within multidimensional information domain. ANNs operate such as a 'black box' model, requiring no detailed information about the system. On the other hand, they learn the relationship between the input and the output. In order to train the neural network, limited experimental measurements were used as training data and test data. In this study, in input layer, there are temperatures in the range of 298-498 K, pressures (0.1-40 MPa) and concentrations of 2%, 7%, 12% of the couples; specific volume is in output layer. The back-propagation learning algorithm with three different variants, namely scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM), and logistic sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as SCG with 8 neurons. For this number level, after the training, it is found that maximum error is less than 3%, average error is about 1% and R 2 value are 99.999%. As seen from the results obtained the thermodynamic equations for each pair by using the weights of network have been obviously predicted within acceptable errors. This paper shows that values predicted with ANN can be used to define the thermodynamic properties instead of approximate and complex analytic equations

The correlation of metrics in complex networks with applications in functional brain networks

NARCIS (Netherlands)

Li, C.; Wang, H.; De Haan, W.; Stam, C.J.; Van Mieghem, P.F.A.

2011-01-01

An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we

Conditions for extinction events in chemical reaction networks with discrete state spaces.

Science.gov (United States)

Johnston, Matthew D; Anderson, David F; Craciun, Gheorghe; Brijder, Robert

2018-05-01

We study chemical reaction networks with discrete state spaces and present sufficient conditions on the structure of the network that guarantee the system exhibits an extinction event. The conditions we derive involve creating a modified chemical reaction network called a domination-expanded reaction network and then checking properties of this network. Unlike previous results, our analysis allows algorithmic implementation via systems of equalities and inequalities and suggests sequences of reactions which may lead to extinction events. We apply the results to several networks including an EnvZ-OmpR signaling pathway in Escherichia coli.

Research on the Topological Properties of Air Quality Index Based on a Complex Network

Directory of Open Access Journals (Sweden)

Yongli Zhang

2018-04-01

Full Text Available To analyze the dynamic characteristics of air quality for enforcing effective measures to prevent and evade air pollution harm, air quality index (AQI time series data was selected and transformed into a symbol sequence consisting of characters (H, M, L through the coarse graining process; then each 6-symbols series was treated as one vertex by time sequence to construct the AQI directed-weighted network; finally the centrality, clusterability, and ranking of the AQI network were analyzed. The results indicated that vertex strength and cumulative strength distribution, vertex strength and strength rank presented power law distributions, and the AQI network is a scale-free network. Only 17 vertices possessed a higher weighted clustering coefficient; meanwhile weighted clustering coefficient and vertex strength didn’t show a strong correlation. The AQI network did not have an obvious central tendency towards intermediaries in general, but 20.55% of vertices accounted for nearly 1/2 of the intermediaries, and the varieties still existed. The mean distance of 68.4932% of vertices was 6.120–9.973, the AQI network did not have obvious small-world phenomena, the conversion of AQI patterns presented the characteristics of periodicity and regularity, and 20.2055% of vertices had high proximity prestige. The vertices fell into six islands, the AQI pattern indicating heavy or serious air pollution lasting six days always lingered for a long time. The number of triads 2-012 was the largest, and the AQI network followed the transitivity model. The study has instructional significance in understanding time change regulation of air quality in Beijing, opening a new way for time series prediction research. Additionally, the factors causing the change of topological properties should be analyzed in the future research.

Effects of Ceramic Density and Sintering Temperature on the Mechanical Properties of a Novel Polymer-Infiltrated Ceramic-Network Zirconia Dental Restorative (Filling) Material.

Science.gov (United States)

Li, Weiyan; Sun, Jian

2018-05-10

BACKGROUND Polymer-infiltrated ceramic-network (PICN) dental material is a new and practical development in orthodontics. Sintering is the process of forming a stable solid mass from a powder by heating without melting. The aim of this study was to evaluate the effects of sintering temperature on the mechanical properties of a PICN zirconia dental material. MATERIAL AND METHODS A dense zirconia ceramic and four PICN zirconia dental materials, with varying porosities, were sintered at three different temperatures; 12 PICN zirconia dental materials based on these porous ceramics were prepared, as well as a pure polymer. After the specimen preparation, flexural strength and elastic modulus values were measured using the three-point bending test, and fracture toughness were determined by the single-edge notched beam (SENB) method. The Vickers hardness test method was used with an indentation strength (IS) test. Scanning electron microscopy (SEM) was used to examine the microstructure of the ceramic surface and the fracture surface. RESULTS Mechanical properties of the PICN dental materials, including flexural strength, elastic modulus, fracture toughness, and hardness, were more similar to the properties of natural teeth when compared with traditional dental ceramic materials, and were affected by the density and sintering temperature. SEM showed that the porous ceramic network became cohesive and that the length of cracks in the PICN dental material was reduced. CONCLUSIONS PICN zirconia dental materials were characterized by similar mechanical properties to natural dental tissues, but further studies are required continue to improve the similarities with natural human enamel and dentin.

Tunable photonic bandgap fiber based devices for optical networks

DEFF Research Database (Denmark)

Alkeskjold, Thomas Tanggaard; Scolari, Lara; Rottwitt, Karsten

2005-01-01

In future all optical networks one of the enabling technologies is tunable elements including reconfigurable routers, switches etc. Thus, the development of a technology platform that allows construction of tuning components is critical. Lately, microstructured optical fibers, filled with liquid......, for example a liquid crystal that changes optical properties when subjected to, for example, an optical or an electrical field. The utilization of these two basic properties allows design of tunable optical devices for optical networks. In this work, we focus on applications of such devices and discuss recent...... crystals, have proven to be a candidate for such a platform. Microstructured optical fibers offer unique wave-guiding properties that are strongly related to the design of the air holes in the cladding of the fiber. These wave-guiding properties may be altered by filling the air holes with a material...

Time series analysis of temporal networks

Science.gov (United States)

Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

2016-01-01

A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

Smart city networks through the internet of things

CERN Document Server

Pardalos, Panos

2017-01-01

This book both analyzes and synthesizes new cutting-edge theories and methods for future design implementations in smart cities through interdisciplinary synergizing of architecture, technology, and the Internet of Things (IoT). Implementation of IoT enables the collection and data exchange of objects embedded with electronics, software, sensors, and network connectivity. Recently IoT practices have moved into uniquely identifiable objects that are able to transfer data directly into networks. This book features new technologically advanced ideas, highlighting properties of smart future city networks. Chapter contributors include theorists, computer scientists, mathematicians, and interdisciplinary planners, who currently work on identifying theories, essential elements, and practices where the IoT can impact the formation of smart cities and sustainability via optimization, network analyses, data mining, mathematical modeling and engineering. Moreover, this book includes research-based theories and real wo...

Evaluating the hydraulic and transport properties of peat soil using pore network modeling and X-ray micro computed tomography

Science.gov (United States)

Gharedaghloo, Behrad; Price, Jonathan S.; Rezanezhad, Fereidoun; Quinton, William L.

2018-06-01

Micro-scale properties of peat pore space and their influence on hydraulic and transport properties of peat soils have been given little attention so far. Characterizing the variation of these properties in a peat profile can increase our knowledge on the processes controlling contaminant transport through peatlands. As opposed to the common macro-scale (or bulk) representation of groundwater flow and transport processes, a pore network model (PNM) simulates flow and transport processes within individual pores. Here, a pore network modeling code capable of simulating advective and diffusive transport processes through a 3D unstructured pore network was developed; its predictive performance was evaluated by comparing its results to empirical values and to the results of computational fluid dynamics (CFD) simulations. This is the first time that peat pore networks have been extracted from X-ray micro-computed tomography (μCT) images of peat deposits and peat pore characteristics evaluated in a 3D approach. Water flow and solute transport were modeled in the unstructured pore networks mapped directly from μCT images. The modeling results were processed to determine the bulk properties of peat deposits. Results portray the commonly observed decrease in hydraulic conductivity with depth, which was attributed to the reduction of pore radius and increase in pore tortuosity. The increase in pore tortuosity with depth was associated with more decomposed peat soil and decreasing pore coordination number with depth, which extended the flow path of fluid particles. Results also revealed that hydraulic conductivity is isotropic locally, but becomes anisotropic after upscaling to core-scale; this suggests the anisotropy of peat hydraulic conductivity observed in core-scale and field-scale is due to the strong heterogeneity in the vertical dimension that is imposed by the layered structure of peat soils. Transport simulations revealed that for a given solute, the effective

Poly(ethylene glycol)-grafted cyclic acetals based polymer networks with non-water-swellable, biodegradable and surface hydrophilic properties

Energy Technology Data Exchange (ETDEWEB)

Yin, Ruixue, E-mail: qdruinyan@hotmail.com [Complex and Intelligent Research Center, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai (China); Zhang, Nan; Wu, Wentao [School of Materials Science and Engineering, Changzhou University, Changzhou 213164 (China); Wang, Kemin, E-mail: kemin-wang@hotmail.com [School of Materials Science and Engineering, Changzhou University, Changzhou 213164 (China)

2016-05-01

Cyclic acetals based biomaterial without acidic products during hydrolytic degradation is a promising candidate for tissue engineering applications; however, low hydrophilicity is still one limitation for its biomedical application. In this work, we aim to achieve non-water-swellable cyclic acetal networks with improved hydrophilicity and surface wettability by copolymerization of cyclic acetal units based monomer, 5-ethyl-5-(hydroxymethyl)-β,β-dimethyl-1, 3-dioxane-2-ethanol diacrylate (EHD) and methoxy poly(ethylene glycol) monoacrylate (mPEGA) under UV irradiation, to avoid swelling of conventional hydrogels which could limit their applicability in particular of the mechanical properties and geometry integrity. Various EHD/mPEGA networks were fabricated with different concentrations of mPEGA from 0 to 30%, and the results showed photopolymerization behavior, mechanical property and thermal stability could not be significantly affected by addition of mPEGA, while the surface hydrophilicity was dramatically improved with the increase of mPEGA and could achieve a water contact angle of 37° with 30% mPEGA concentration. The obtained EHD/mPEGA network had comparative degradation rate to the PECA hydrogels reported previously, and MTT assay indicated it was biocompatible to L929 cells. - Highlights: • Cyclic acetals contained EHD/mPEGA networks were fabricated by photopolymerization. • It can be degraded under simulated physiological condition without acidic products. • Surface hydrophilicity was increased without swelling in water.

Surrogate-assisted identification of influences of network construction on evolving weighted functional networks

Science.gov (United States)

Stahn, Kirsten; Lehnertz, Klaus

2017-12-01

We aim at identifying factors that may affect the characteristics of evolving weighted networks derived from empirical observations. To this end, we employ various chains of analysis that are often used in field studies for a data-driven derivation and characterization of such networks. As an example, we consider fully connected, weighted functional brain networks before, during, and after epileptic seizures that we derive from multichannel electroencephalographic data recorded from epilepsy patients. For these evolving networks, we estimate clustering coefficient and average shortest path length in a time-resolved manner. Lastly, we make use of surrogate concepts that we apply at various levels of the chain of analysis to assess to what extent network characteristics are dominated by properties of the electroencephalographic recordings and/or the evolving weighted networks, which may be accessible more easily. We observe that characteristics are differently affected by the unavoidable referencing of the electroencephalographic recording, by the time-series-analysis technique used to derive the properties of network links, and whether or not networks were normalized. Importantly, for the majority of analysis settings, we observe temporal evolutions of network characteristics to merely reflect the temporal evolutions of mean interaction strengths. Such a property of the data may be accessible more easily, which would render the weighted network approach—as used here—as an overly complicated description of simple aspects of the data.

The effects of topology on the structural, dynamic and mechanical properties of network-forming materials

International Nuclear Information System (INIS)

Wilson, Mark

2012-01-01

The effects of network topology on the static structural, mechanical and dynamic properties of MX 2 network-forming liquids (with tetrahedral short-range order) are discussed. The network topology is controlled via a single model parameter (the anion polarizability) which effectively constrains the inter-tetrahedral linkages in a physically transparent manner. Critically, it is found to control the balance between the stability of corner- and edge-sharing tetrahedra. A potential rigidity transformation is investigated. The vibrational density of states is investigated, using an instantaneous normal model analysis, as a function of both anion polarizability and temperature. A low frequency peak is seen to appear and is shown to be correlated with the fraction of cations which are linked through solely edge-sharing structural motifs. A modified effective mean atom coordination number is proposed which allows the appearance of the low frequency feature to be understood in terms of a mean field rigidity percolation threshold. (paper)

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

Science.gov (United States)

Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

Electrogenerated networks from poly[4-(diphenylaminobenzyl methacrylate] and their electrochromic properties

Directory of Open Access Journals (Sweden)

O. I. Negru

2014-09-01

Full Text Available Poly[4-(diphenylaminobenzyl methacrylate] with well-defined molecular weight and low polydispersity was prepared by atom transfer radical polymerization (ATRP using 4-(diphenylamino benzyl 2-bromo-2-methyl-propanoate as initiator and CuBr/2,2'-bipyridine as catalytic complex. Electrochemical behavior and optical properties of the polymers were investigated by cyclic voltammetry and UV-Vis and fluorescence spectroscopy. Cyclic votammetric studies revealed that the redox processes were accompanied by dimerization of triphenylamine pendant groups. The initial polymer was postmodified, in solution or bulk, by electrochemical oxidation leading to a crosslinked and insoluble network with electro -chromic properties, accompanied by strong color changes with high coloration efficiency. The crosslinking reaction took place between triphenylamine groups through para free positions leading to tetraphenylbenzidine bridges. The structure of polymers was confirmed by Fourier transform infrared (FT-IR spectroscopy and 1H and 13C-nuclear magnetic resonance spectroscopy. Morphology studies of the cross-linked film have evidenced a smooth and continuous aspect without any pinholes or defects.

Gradient networks on uncorrelated random scale-free networks

International Nuclear Information System (INIS)

Pan Guijun; Yan Xiaoqing; Huang Zhongbing; Ma Weichuan

2011-01-01

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

Inferring general relations between network characteristics from specific network ensembles.

Science.gov (United States)

Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

2012-01-01

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

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

Directory of Open Access Journals (Sweden)

Cedric E Ginestet

2014-05-01

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

Resting state brain networks in the prairie vole.

Science.gov (United States)

Ortiz, Juan J; Portillo, Wendy; Paredes, Raul G; Young, Larry J; Alcauter, Sarael

2018-01-19

Resting state functional magnetic resonance imaging (rsfMRI) has shown the hierarchical organization of the human brain into large-scale complex networks, referred as resting state networks. This technique has turned into a promising translational research tool after the finding of similar resting state networks in non-human primates, rodents and other animal models of great value for neuroscience. Here, we demonstrate and characterize the presence of resting states networks in Microtus ochrogaster, the prairie vole, an extraordinary animal model to study complex human-like social behavior, with potential implications for the research of normal social development, addiction and neuropsychiatric disorders. Independent component analysis of rsfMRI data from isoflurane-anestethized prairie voles resulted in cortical and subcortical networks, including primary motor and sensory networks, but also included putative salience and default mode networks. We further discuss how future research could help to close the gap between the properties of the large scale functional organization and the underlying neurobiology of several aspects of social cognition. These results contribute to the evidence of preserved resting state brain networks across species and provide the foundations to explore the use of rsfMRI in the prairie vole for basic and translational research.

Network structure of subway passenger flows

Science.gov (United States)

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

2016-03-01

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

Topological and functional properties of the small GTPases protein interaction network.

Directory of Open Access Journals (Sweden)

Anna Delprato

Full Text Available Small GTP binding proteins of the Ras superfamily (Ras, Rho, Rab, Arf, and Ran regulate key cellular processes such as signal transduction, cell proliferation, cell motility, and vesicle transport. A great deal of experimental evidence supports the existence of signaling cascades and feedback loops within and among the small GTPase subfamilies suggesting that these proteins function in a coordinated and cooperative manner. The interplay occurs largely through association with bi-partite regulatory and effector proteins but can also occur through the active form of the small GTPases themselves. In order to understand the connectivity of the small GTPases signaling routes, a systems-level approach that analyzes data describing direct and indirect interactions was used to construct the small GTPases protein interaction network. The data were curated from the Search Tool for the Retrieval of Interacting Genes (STRING database and include only experimentally validated interactions. The network method enables the conceptualization of the overall structure as well as the underlying organization of the protein-protein interactions. The interaction network described here is comprised of 778 nodes and 1943 edges and has a scale-free topology. Rac1, Cdc42, RhoA, and HRas are identified as the hubs. Ten sub-network motifs are also identified in this study with themes in apoptosis, cell growth/proliferation, vesicle traffic, cell adhesion/junction dynamics, the nicotinamide adenine dinucleotide phosphate (NADPH oxidase response, transcription regulation, receptor-mediated endocytosis, gene silencing, and growth factor signaling. Bottleneck proteins that bridge signaling paths and proteins that overlap in multiple small GTPase networks are described along with the functional annotation of all proteins in the network.

DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

Energy Technology Data Exchange (ETDEWEB)

MASLOV,S.SNEPPEN,K.

2003-11-17

Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulations in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An

Resilient Amorphous Networks Prepared by Photo-Crosslinking High-Molecular-Weight D,L-Lactide and Trimethylene Carbonate Macromers: Mechanical Properties and Shape-Memory Behavior

NARCIS (Netherlands)

Sharifi, Shahriar; Grijpma, Dirk W.

2012-01-01

Tough networks are prepared by photo-crosslinking high-molecular-weight DLLA and TMC macromers. These amorphous networks exhibit tunable thermal and mechanical properties and have excellent shape-memory features. Variation of the monomer ratio allows adjustment of Tg between approximately −13 and

How optimal synchronization of oscillators depends on the network structure and the individual dynamical properties of the oscillators

International Nuclear Information System (INIS)

Markovic, R; Gosak, M; Marhl, M

2013-01-01

The problem of making a network of dynamical systems synchronize onto a common evolution is the subject of much ongoing research in several scientific disciplines. It is nowadays a well-known fact that the synchronization processes are gradually influenced by the interaction topology between the dynamically interacting units. A complex coupling configuration can significantly affect the synchronization abilities of a networked system. However, the question arises what is the optimal network topology that provides enhancement of the synchronization features under given circumstances. In order to address this issue we make use of a network model in which we can smoothly tune the topology from a highly heterogeneous and efficient scale-free network to a homogeneous and less efficient network. The network is then populated with Poincaré oscillators, a paradigmatic model for limit-cycle oscillations. This oscillator model exhibits a parameter that enables changes of the limit cycle attraction and is thus immediately related to flexibility/rigidity properties of the oscillator. Our results reveal that for weak attractions of the limit cycle, intermediate homogeneous topology ensures maximal synchronization, whereas highly heterogeneous scale-free topology ensures maximal synchronization for strong attractions of the limit cycle. We argue that the flexibility/rigidity of individual nodes of the networks defines the topology, where maximal global coherence is achieved.

Complex Network Analysis of Guangzhou Metro

Directory of Open Access Journals (Sweden)

Yasir Tariq Mohmand

2015-11-01

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

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

Directory of Open Access Journals (Sweden)

Zimmer Ralf

2007-08-01

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

Thermoelastic steam turbine rotor control based on neural network

Science.gov (United States)

Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

2015-12-01

Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

Linear control theory for gene network modeling.

Science.gov (United States)

Shin, Yong-Jun; Bleris, Leonidas

2010-09-16

Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.

Science.gov (United States)

Jie, Biao; Liu, Mingxia; Shen, Dinggang

2018-07-01

Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of brain disease, such as Alzheimer's disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Different from conventional studies focusing on static descriptions on functional connectivity (FC) between brain regions in rs-fMRI, recent studies have resorted to dynamic connectivity networks (DCNs) to characterize the dynamic changes of FC, since dynamic changes of FC may indicate changes in macroscopic neural activity patterns in cognitive and behavioral aspects. However, most of the existing studies only investigate the temporal properties of DCNs (e.g., temporal variability of FC between specific brain regions), ignoring the important spatial properties of the network (e.g., spatial variability of FC associated with a specific brain region). Also, emerging evidence on FCNs has suggested that, besides temporal variability, there is significant spatial variability of activity foci over time. Hence, integrating both temporal and spatial properties of DCNs can intuitively promote the performance of connectivity-network-based learning methods. In this paper, we first define a new measure to characterize the spatial variability of DCNs, and then propose a novel learning framework to integrate both temporal and spatial variabilities of DCNs for automatic brain disease diagnosis. Specifically, we first construct DCNs from the rs-fMRI time series at successive non-overlapping time windows. Then, we characterize the spatial variability of a specific brain region by computing the correlation of functional sequences (i.e., the changing profile of FC between a pair of brain regions within all time windows) associated with this region. Furthermore, we extract both temporal variabilities and spatial variabilities from DCNs as features, and integrate them for classification by using manifold regularized multi

Dually cross-linked single network poly(acrylic acid) hydrogels with superior mechanical properties and water absorbency.

Science.gov (United States)

Zhong, Ming; Liu, Yi-Tao; Liu, Xiao-Ying; Shi, Fu-Kuan; Zhang, Li-Qin; Zhu, Mei-Fang; Xie, Xu-Ming

2016-06-28

Poly(acrylic acid) (PAA) hydrogels with superior mechanical properties, based on a single network structure with dual cross-linking, are prepared by one-pot free radical polymerization. The network structure of the PAA hydrogels is composed of dual cross-linking: a dynamic and reversible ionic cross-linking among the PAA chains enabled by Fe(3+) ions, and a sparse covalent cross-linking enabled by a covalent cross-linker (Bis). Under deformation, the covalently cross-linked PAA chains remain intact to maintain their original configuration, while the Fe(3+)-enabled ionic cross-linking among the PAA chains is broken to dissipate energy and then recombined. It is found that the mechanical properties of the PAA hydrogels are significantly influenced by the contents of covalent cross-linkers, Fe(3+) ions and water, which can be adjusted within a substantial range and thus broaden the applications of the hydrogels. Meanwhile, the PAA hydrogels have excellent recoverability based on the dynamic and reversible ionic cross-linking enabled by Fe(3+) ions. Moreover, the swelling capacity of the PAA hydrogels is as high as 1800 times in deionized water due to the synergistic effects of ionic and covalent cross-linkings. The combination of balanced mechanical properties, efficient recoverability, high swelling capacity and facile preparation provides a new method to obtain high-performance hydrogels.

Enhancing Electrophoretic Display Lifetime: Thiol-Polybutadiene Evaporation Barrier Property Response to Network Microstructure

Energy Technology Data Exchange (ETDEWEB)

Cook, Caitlyn Christian [California State Polytechnic State Univ., San Luis Obispo, CA (United States)

2017-02-27

An evaporation barrier is required to enhance the lifetime of electrophoretic deposition (EPD) displays. As EPD functions on the basis of reversible deposition and resuspension of colloids suspended in a solvent, evaporation of the solvent ultimately leads to device failure. Incorporation of a thiol-polybutadiene elastomer into EPD displays enabled display lifetime surpassing six months in counting and catalyzed rigid display transition into a flexible package. Final flexible display transition to mass production compels an electronic-ink approach to encapsulate display suspension within an elastomer shell. Final thiol-polybutadiene photosensitive resin network microstructure was idealized to be dense, homogeneous, and expose an elastic response to deformation. Research at hand details an approach to understanding microstructural change within display elastomers. Polybutadiene-based resin properties are modified via polymer chain structure, with and without added aromatic urethane methacrylate difunctionality, and in measuring network response to variation in thiol and initiator concentration. Dynamic mechanical analysis results signify that cross-linked segments within a difunctionalized polybutadiene network were on average eight times more elastically active than that of linked segments within a non-functionalized polybutadiene network. Difunctionalized polybutadiene samples also showed a 2.5 times greater maximum elastic modulus than non-functionalized samples. Hybrid polymer composed of both polybutadiene chains encompassed TE-2000 stiffness and B-1000 elasticity for use in encapsulating display suspension. Later experiments measured kinetic and rheological response due to alteration in dithiol cross-linker chain length via real time Fourier transform infrared spectroscopy and real-time dynamic rheology. Distinct differences were discovered between dithiol resin systems, as maximum thiol conversion achieved in short and long chain length dithiols was 86% and

Investigation of the properties of fully reacted unstoichiometric polydimethylsiloxane networks and their extracted network fractions

DEFF Research Database (Denmark)

Frankær, Sarah Maria Grundahl; Jensen, Mette Krog; Bejenariu, Anca Gabriela

2012-01-01

We investigated the linear dynamic response of a series of fully reacted unstoichiometric polydimethylsiloxane (PDMS) networks and of the two corresponding network fractions namely the sol and the washed network. The sol and the washed network were separated by a simple extraction process. This way...

Milk Technological Properties as Affected by Including Artichoke By-Products Silages in the Diet of Dairy Goats

Directory of Open Access Journals (Sweden)

Raquel Muelas

2017-12-01

Full Text Available Traditional farming practices include the use of local agricultural by-products in the diet of ruminants. Artichoke harvesting and transformation yield high amounts of by-products that, if properly used, may reduce farming costs and the environmental impact of farming. The present study tests the inclusion of silages from artichoke by-products (plant and outer bracts in the diet of dairy goats (0%, 12.5% and 25% inclusion on the technological and sensory properties of milk during a five-month study. Milk composition, color, stability, coagulation and fermentation properties remained unaffected by diet changes. Panelists were not able to differentiate among yogurts obtained from those milks by discriminant triangular sensory tests. Silages of artichoke by-products can be included in isoproteic and isoenergetic diets for dairy goats, up to a 25% (feed dry matter, without negatively affecting milk technological and sensory properties whereas reducing feeding costs.

A new information dimension of complex networks

Energy Technology Data Exchange (ETDEWEB)

Wei, Daijun [School of Computer and Information Science, Southwest University, Chongqing 400715 (China); School of Science, Hubei University for Nationalities, Enshi 445000 (China); Wei, Bo [School of Computer and Information Science, Southwest University, Chongqing 400715 (China); Hu, Yong [Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Guangzhou 510006 (China); Zhang, Haixin [School of Computer and Information Science, Southwest University, Chongqing 400715 (China); Deng, Yong, E-mail: ydeng@swu.edu.cn [School of Computer and Information Science, Southwest University, Chongqing 400715 (China); School of Engineering, Vanderbilt University, TN 37235 (United States)

2014-03-01

Highlights: •The proposed measure is more practical than the classical information dimension. •The difference of information for box in the box-covering algorithm is considered. •Results indicate the measure can capture the fractal property of complex networks. -- Abstract: The fractal and self-similarity properties are revealed in many complex networks. The classical information dimension is an important method to study fractal and self-similarity properties of planar networks. However, it is not practical for real complex networks. In this Letter, a new information dimension of complex networks is proposed. The nodes number in each box is considered by using the box-covering algorithm of complex networks. The proposed method is applied to calculate the fractal dimensions of some real networks. Our results show that the proposed method is efficient when dealing with the fractal dimension problem of complex networks.

A new information dimension of complex networks

International Nuclear Information System (INIS)

Wei, Daijun; Wei, Bo; Hu, Yong; Zhang, Haixin; Deng, Yong

2014-01-01

Highlights: •The proposed measure is more practical than the classical information dimension. •The difference of information for box in the box-covering algorithm is considered. •Results indicate the measure can capture the fractal property of complex networks. -- Abstract: The fractal and self-similarity properties are revealed in many complex networks. The classical information dimension is an important method to study fractal and self-similarity properties of planar networks. However, it is not practical for real complex networks. In this Letter, a new information dimension of complex networks is proposed. The nodes number in each box is considered by using the box-covering algorithm of complex networks. The proposed method is applied to calculate the fractal dimensions of some real networks. Our results show that the proposed method is efficient when dealing with the fractal dimension problem of complex networks.

Synaptic integration of transplanted interneuron progenitor cells into native cortical networks.

Science.gov (United States)

Howard, MacKenzie A; Baraban, Scott C

2016-08-01

Interneuron-based cell transplantation is a powerful method to modify network function in a variety of neurological disorders, including epilepsy. Whether new interneurons integrate into native neural networks in a subtype-specific manner is not well understood, and the therapeutic mechanisms underlying interneuron-based cell therapy, including the role of synaptic inhibition, are debated. In this study, we tested subtype-specific integration of transplanted interneurons using acute cortical brain slices and visualized patch-clamp recordings to measure excitatory synaptic inputs, intrinsic properties, and inhibitory synaptic outputs. Fluorescently labeled progenitor cells from the embryonic medial ganglionic eminence (MGE) were used for transplantation. At 5 wk after transplantation, MGE-derived parvalbumin-positive (PV+) interneurons received excitatory synaptic inputs, exhibited mature interneuron firing properties, and made functional synaptic inhibitory connections to native pyramidal cells that were comparable to those of native PV+ interneurons. These findings demonstrate that MGE-derived PV+ interneurons functionally integrate into subtype-appropriate physiological niches within host networks following transplantation. Copyright © 2016 the American Physiological Society.

Quasirandom geometric networks from low-discrepancy sequences

Science.gov (United States)

Estrada, Ernesto

2017-08-01

We define quasirandom geometric networks using low-discrepancy sequences, such as Halton, Sobol, and Niederreiter. The networks are built in d dimensions by considering the d -tuples of digits generated by these sequences as the coordinates of the vertices of the networks in a d -dimensional Id unit hypercube. Then, two vertices are connected by an edge if they are at a distance smaller than a connection radius. We investigate computationally 11 network-theoretic properties of two-dimensional quasirandom networks and compare them with analogous random geometric networks. We also study their degree distribution and their spectral density distributions. We conclude from this intensive computational study that in terms of the uniformity of the distribution of the vertices in the unit square, the quasirandom networks look more random than the random geometric networks. We include an analysis of potential strategies for generating higher-dimensional quasirandom networks, where it is know that some of the low-discrepancy sequences are highly correlated. In this respect, we conclude that up to dimension 20, the use of scrambling, skipping and leaping strategies generate quasirandom networks with the desired properties of uniformity. Finally, we consider a diffusive process taking place on the nodes and edges of the quasirandom and random geometric graphs. We show that the diffusion time is shorter in the quasirandom graphs as a consequence of their larger structural homogeneity. In the random geometric graphs the diffusion produces clusters of concentration that make the process more slow. Such clusters are a direct consequence of the heterogeneous and irregular distribution of the nodes in the unit square in which the generation of random geometric graphs is based on.

Efficient Synthesis of Network Updates

Science.gov (United States)

2015-06-17

mal Languages ]: Mathematical Logic—Temporal logic; C.2.3 [Computer-communication Networks]: Network Operations— Network Management Keywords synthesis...problem, and prove this algorithm to be correct (§4). • We present an incremental LTL model checker for loop-free models (§5). • We describe an OCaml ...canned” properties, we use a specification language that is expressive enough to encode these properties and others, as well as conjunctions

Reverse engineering large-scale genetic networks: synthetic versus

Indian Academy of Sciences (India)

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

Photocrosslinked PLA-PEO-PLA Hydrogels from Self-Assembled Physical Networks: Mechanical Properties and Influence of Assumed Constitutive Relationships

Science.gov (United States)

Sanabria-DeLong, Naomi; Crosby, Alfred J.; Tew, Gregory N.

2014-01-01

Poly(lactide) – block – poly(ethylene oxide) – block – poly(lactide) [PLA-PEO-PLA] triblock copolymers are known to form physical hydrogels in water, due to the polymer's amphiphilicity. Their mechanical properties, biocompatibility, and biodegradability have made them attractive for use as soft tissue scaffolds. However, the network junction points are not covalently crosslinked and in a highly aqueous environment these hydrogels adsorb more water, transform from gel to sol, and lose the designed mechanical properties. In this report, a hydrogel was formed by using a novel two step approach. In the first step end-functionalized PLA-PEOPLA triblock was self-assembled into a physical hydrogel through hydrophobic micelle network junctions, and then, in the second step, this self-assembled physical network structure was locked into place by photocrosslinking the terminal acrylate groups. In contrast to physical hydrogels, the photocrosslinked gels remained intact in phosphate buffered solution at body temperature. The swelling, degradation, and mechanical properties were characterized and demonstrated extended degradation time (~ 65 days), exponential decrease in modulus with degradation time, and tunable shear modulus (1.6 – 133 kPa) by varying concentration. We also discuss the various constitutive relationships (Hookean, Neo-Hookean, and Mooney-Rivlin) that can be used to describe the stress-strain behavior of these hydrogels. The chosen model and assumptions used for data fitting influences the obtained modulus values by as much as a factor of 3.5, demonstrating the importance of clearly stating one's data fitting parameters so that accurate comparisons can be made within the literature. PMID:18817440

SYNAPTIC DEPRESSION IN DEEP NEURAL NETWORKS FOR SPEECH PROCESSING.

Science.gov (United States)

Zhang, Wenhao; Li, Hanyu; Yang, Minda; Mesgarani, Nima

2016-03-01

A characteristic property of biological neurons is their ability to dynamically change the synaptic efficacy in response to variable input conditions. This mechanism, known as synaptic depression, significantly contributes to the formation of normalized representation of speech features. Synaptic depression also contributes to the robust performance of biological systems. In this paper, we describe how synaptic depression can be modeled and incorporated into deep neural network architectures to improve their generalization ability. We observed that when synaptic depression is added to the hidden layers of a neural network, it reduces the effect of changing background activity in the node activations. In addition, we show that when synaptic depression is included in a deep neural network trained for phoneme classification, the performance of the network improves under noisy conditions not included in the training phase. Our results suggest that more complete neuron models may further reduce the gap between the biological performance and artificial computing, resulting in networks that better generalize to novel signal conditions.

The properties of genome conformation and spatial gene interaction and regulation networks of normal and malignant human cell types.

Directory of Open Access Journals (Sweden)

Zheng Wang

Full Text Available The spatial conformation of a genome plays an important role in the long-range regulation of genome-wide gene expression and methylation, but has not been extensively studied due to lack of genome conformation data. The recently developed chromosome conformation capturing techniques such as the Hi-C method empowered by next generation sequencing can generate unbiased, large-scale, high-resolution chromosomal interaction (contact data, providing an unprecedented opportunity to investigate the spatial structure of a genome and its applications in gene regulation, genomics, epigenetics, and cell biology. In this work, we conducted a comprehensive, large-scale computational analysis of this new stream of genome conformation data generated for three different human leukemia cells or cell lines by the Hi-C technique. We developed and applied a set of bioinformatics methods to reliably generate spatial chromosomal contacts from high-throughput sequencing data and to effectively use them to study the properties of the genome structures in one-dimension (1D and two-dimension (2D. Our analysis demonstrates that Hi-C data can be effectively applied to study tissue-specific genome conformation, chromosome-chromosome interaction, chromosomal translocations, and spatial gene-gene interaction and regulation in a three-dimensional genome of primary tumor cells. Particularly, for the first time, we constructed genome-scale spatial gene-gene interaction network, transcription factor binding site (TFBS - TFBS interaction network, and TFBS-gene interaction network from chromosomal contact information. Remarkably, all these networks possess the properties of scale-free modular networks.

Brain Interaction during Cooperation: Evaluating Local Properties of Multiple-Brain Network.

Science.gov (United States)

Sciaraffa, Nicolina; Borghini, Gianluca; Aricò, Pietro; Di Flumeri, Gianluca; Colosimo, Alfredo; Bezerianos, Anastasios; Thakor, Nitish V; Babiloni, Fabio

2017-07-21

Subjects' interaction is the core of most human activities. This is the reason why a lack of coordination is often the cause of missing goals, more than individual failure. While there are different subjective and objective measures to assess the level of mental effort required by subjects while facing a situation that is getting harder, that is, mental workload, to define an objective measure based on how and if team members are interacting is not so straightforward. In this study, behavioral, subjective and synchronized electroencephalographic data were collected from couples involved in a cooperative task to describe the relationship between task difficulty and team coordination, in the sense of interaction aimed at cooperatively performing the assignment. Multiple-brain connectivity analysis provided information about the whole interacting system. The results showed that averaged local properties of a brain network were affected by task difficulty. In particular, strength changed significantly with task difficulty and clustering coefficients strongly correlated with the workload itself. In particular, a higher workload corresponded to lower clustering values over the central and parietal brain areas. Such results has been interpreted as less efficient organization of the network when the subjects' activities, due to high workload tendencies, were less coordinated.

Neural Networks Modelling of Municipal Real Estate Market Rent Rates

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Muczyński Andrzej

2016-12-01

Full Text Available This paper presents the results of research on the application of neural networks modelling of municipal real estate market rent rates. The test procedure was based on selected networks trained on the local real estate market data and transformation of the detected dependencies – through established models – to estimate the potential market rent rates of municipal premises. On this basis, the assessment of the adequacy of the actual market rent rates of municipal properties was made. Empirical research was conducted on the local real estate market of the city of Olsztyn in Poland. In order to describe the phenomenon of market rent rates formation an unidirectional three-layer network and a network of radial base was selected. Analyses showed a relatively low degree of convergence of the actual municipal rent rents with potential market rent rates. This degree was strongly varied depending on the type of business ran on the property and its’ social and economic impact. The applied research methodology and the obtained results can be used in order to rationalize municipal property management, including the activation of rental policy.

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

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Rich, Scott; Zochowski, Michal; Booth, Victoria

2018-01-01

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

Evolving spiking neural networks: a novel growth algorithm exhibits unintelligent design

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Schaffer, J. David

2015-06-01

Spiking neural networks (SNNs) have drawn considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains. Yet progress building these systems has been limited because we lack a design methodology. We present a gene-driven network growth algorithm that enables a genetic algorithm (evolutionary computation) to generate and test SNNs. The genome for this algorithm grows O(n) where n is the number of neurons; n is also evolved. The genome not only specifies the network topology, but all its parameters as well. Experiments show the algorithm producing SNNs that effectively produce a robust spike bursting behavior given tonic inputs, an application suitable for central pattern generators. Even though evolution did not include perturbations of the input spike trains, the evolved networks showed remarkable robustness to such perturbations. In addition, the output spike patterns retain evidence of the specific perturbation of the inputs, a feature that could be exploited by network additions that could use this information for refined decision making if required. On a second task, a sequence detector, a discriminating design was found that might be considered an example of "unintelligent design"; extra non-functional neurons were included that, while inefficient, did not hamper its proper functioning.

Study of self-organization in network glasses and physical properties in some non-crystalline systems

International Nuclear Information System (INIS)

Mohamed, M.A.M.

2007-01-01

to understand the effects of structural features of the Ag-As-Te glassy system, various properties are separately studied as functions of the average coordination number (r). the relation between the chemical ordered covalent network model and the constraint theory, of the structural features, is examined. the self-organization model is widely used as a realistic description for the structure of both covalent glasses and amorphous solids. the overall mean energy, (E) , of a covalent network for the Ag-As-Te ternary glasses is determined. the average coordination number (r) for the Ag-As-Te system based on recently suggested models for network glasses has been examined. it was found that, two topological effects namely; the rigid to floppy transition and the structural transition, occurred resulting in some changes in the chemical ordering. the values of the glass transition temperature, T g were found to depend on the compositions. the thermal stability and the glass-forming tendency were calculated and they were found to have the same trend

Stochastic resonance in feedforward acupuncture networks

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Qin, Ying-Mei; Wang, Jiang; Men, Cong; Deng, Bin; Wei, Xi-Le; Yu, Hai-Tao; Chan, Wai-Lok

2014-10-01

Effects of noises and some other network properties on the weak signal propagation are studied systematically in feedforward acupuncture networks (FFN) based on FitzHugh-Nagumo neuron model. It is found that noises with medium intensity can enhance signal propagation and this effect can be further increased by the feedforward network structure. Resonant properties in the noisy network can also be altered by several network parameters, such as heterogeneity, synapse features, and feedback connections. These results may also provide a novel potential explanation for the propagation of acupuncture signal.

Using network properties to evaluate targeted immunization algorithms

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Bita Shams

2014-09-01

Full Text Available Immunization of complex network with minimal or limited budget is a challenging issue for research community. In spite of much literature in network immunization, no comprehensive research has been conducted for evaluation and comparison of immunization algorithms. In this paper, we propose an evaluation framework for immunization algorithms regarding available amount of vaccination resources, goal of immunization program, and time complexity. The evaluation framework is designed based on network topological metrics which is extensible to all epidemic spreading model. Exploiting evaluation framework on well-known targeted immunization algorithms shows that in general, immunization based on PageRank centrality outperforms other targeting strategies in various types of networks, whereas, closeness and eigenvector centrality exhibit the worst case performance.

Parzen neural networks: Fundamentals, properties, and an application to forensic anthropology.

Science.gov (United States)

Trentin, Edmondo; Lusnig, Luca; Cavalli, Fabio

2018-01-01

A novel, unsupervised nonparametric model of multivariate probability density functions (pdf) is introduced, namely the Parzen neural network (PNN). The PNN is intended to overcome the major limitations of traditional (either statistical or neural) pdf estimation techniques. Besides being profitably simple, the PNN turns out to have nice properties in terms of unbiased modeling capability, asymptotic convergence, and efficiency at test time. Several matters pertaining the practical application of the PNN are faced in the paper, too. Experiments are reported, involving (i) synthetic datasets, and (ii) a challenging sex determination task from 1400 scout-view CT-scan images of human crania. Incidentally, the empirical evidence entails also some conclusions of high anthropological relevance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Biomimetic oral mucin from polymer micelle networks

Science.gov (United States)

Authimoolam, Sundar Prasanth

Mucin networks are formed by the complexation of bottlebrush-like mucin glycoprotein with other small molecule glycoproteins. These glycoproteins create nanoscale strands that then arrange into a nanoporous mesh. These networks play an important role in ensuring surface hydration, lubricity and barrier protection. In order to understand the functional behavior in mucin networks, it is important to decouple their chemical and physical effects responsible for generating the fundamental property-function relationship. To achieve this goal, we propose to develop a synthetic biomimetic mucin using a layer-by-layer (LBL) deposition approach. In this work, a hierarchical 3-dimensional structures resembling natural mucin networks was generated using affinity-based interactions on synthetic and biological surfaces. Unlike conventional polyelectrolyte-based LBL methods, pre-assembled biotin-functionalized filamentous (worm-like) micelles was utilized as the network building block, which from complementary additions of streptavidin generated synthetic networks of desired thickness. The biomimetic nature in those synthetic networks are studied by evaluating its structural and bio-functional properties. Structurally, synthetic networks formed a nanoporous mesh. The networks demonstrated excellent surface hydration property and were able capable of microbial capture. Those functional properties are akin to that of natural mucin networks. Further, the role of synthetic mucin as a drug delivery vehicle, capable of providing localized and tunable release was demonstrated. By incorporating antibacterial curcumin drug loading within synthetic networks, bacterial growth inhibition was also demonstrated. Thus, such bioactive interfaces can serve as a model for independently characterizing mucin network properties and through its role as a drug carrier vehicle it presents exciting future opportunities for localized drug delivery, in regenerative applications and as bio

Scalable Approaches to Control Network Dynamics: Prospects for City Networks

Science.gov (United States)

Motter, Adilson E.; Gray, Kimberly A.

2014-07-01

A city is a complex, emergent system and as such can be conveniently represented as a network of interacting components. A fundamental aspect of networks is that the systemic properties can depend as much on the interactions as they depend on the properties of the individual components themselves. Another fundamental aspect is that changes to one component can affect other components, in a process that may cause the entire or a substantial part of the system to change behavior. Over the past 2 decades, much research has been done on the modeling of large and complex networks involved in communication and transportation, disease propagation, and supply chains, as well as emergent phenomena, robustness and optimization in such systems...

Network cohesion

OpenAIRE

Cavalcanti, Tiago V. V.; Giannitsarou, Chryssi; Johnson, Charles R.

2016-01-01

This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s00199-016-0992-1 We define a measure of network cohesion and show how it arises naturally in a broad class of dynamic models of endogenous perpetual growth with network externalities. Via a standard growth model, we show why network cohesion is crucial for conditional convergence and explain that as cohesion increases, convergence is faster. We prove properties of network cohesion and d...

Random walks on generalized Koch networks

International Nuclear Information System (INIS)

Sun, Weigang

2013-01-01

For deterministically growing networks, it is a theoretical challenge to determine the topological properties and dynamical processes. In this paper, we study random walks on generalized Koch networks with features that include an initial state that is a globally connected network to r nodes. In each step, every existing node produces m complete graphs. We then obtain the analytical expressions for first passage time (FPT), average return time (ART), i.e. the average of FPTs for random walks from node i to return to the starting point i for the first time, and average sending time (AST), defined as the average of FPTs from a hub node to all other nodes, excluding the hub itself with regard to network parameters m and r. For this family of Koch networks, the ART of the new emerging nodes is identical and increases with the parameters m or r. In addition, the AST of our networks grows with network size N as N ln N and also increases with parameter m. The results obtained in this paper are the generalizations of random walks for the original Koch network. (paper)

Social Networks in context of cyberspace. Consumers, electronic commerce and intellectual property in the light of the Cuban case

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Nelvys Mendoza Gurdián

2018-06-01

Full Text Available Face the state of vulnerability in the context of cyberspace, it is necessary to reflect on the social networks and law, from a holistic approach aimed at the vulnerability of rights associated with the information in this environment. This work general objective is to analyse the phenomenon of online social networks and the information society, emphasizing on the study of the legal aspects related to consumers, electronic commerce and intellectual property. The methodology used aims to conceptualize the category of social networks, examinate the aspects associated with law in the use of social networks and establish the conceptual, legal and conflicting points of relevance. This will allow describing the problems under study and propose alternatives for a sphere of integrative protection that harmonizes the edges of the preventive, the corrective and the prophylactic.

Linear control theory for gene network modeling.

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Yong-Jun Shin

Full Text Available Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain and linear state-space (time domain can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

Proposta de um quadro de referência para integrar o consumidor nos conceitos de redes [Proposed Reference Table to Include the Consumer in Network Concepts

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Ernesto Michelangelo Giglio

2011-06-01

Full Text Available O artigo apresenta uma proposta e defesa da inclusão do ator consumidor nos raciocínios e pesquisas sobre redes, a partir da teoria das redes sociais. A proposta decorre da análise e reflexão sobre 82 artigos de redes selecionados, cujos objetivos incluíam o consumidor. Esta análise mostrou que o consumidor está ausente como ator, tanto teoricamente, quanto nas sugestões gerenciais. Seu papel na rede é secundário e são raros os estudos sobre a gestão de sua participação. Entre as causas dessa ausência, destacam-se a dominância de modelos sócio técnicos de redes na bibliografia e o uso de teorias da psicologia do indivíduo, quando se aborda o consumidor, o que se entende como inadequado num raciocínio de redes a partir das redes sociais. Nas conclusões, propõe-se um conjunto de princípios que inclui o consumidor como ator da rede, ampliando o campo de reflexões e de pesquisas da área. --- Proposed Reference Table to Include the Consumer in Network Concepts --- Abstract --- The article presents a model that includes the consumer in the principles and research on networks, using the concepts of social networks. The model arises from the analysis and reflections of 82 articles about networks, whose objectives included the consumer. It showed that he/she is absent as an actor in both theoretically and management proposals. His/her role in the network is secondary and there are few studies into the management of his/her participation. Among the causes of this absence we identify the dominance of socio-technical models in the bibliography and the use of theories of individual psychology, which are inadequate in a reasoning of social networks. Finally we propose a set of principles that includes the consumer as an actor in a network, widening the reflections and research in this area.

Graph-Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory

Science.gov (United States)

Gruenenfelder, Thomas M.; Recchia, Gabriel; Rubin, Tim; Jones, Michael N.

2016-01-01

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network…

A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links

Science.gov (United States)

Türker, Ilker; Sulak, Eyüb Ekmel

2018-02-01

Complex network studies, as an interdisciplinary framework, span a large variety of subjects including social media. In social networks, several mechanisms generate miscellaneous structures like friendship networks, mention networks, tag networks, etc. Focusing on tag networks (namely, hashtags in twitter), we made a two-layer analysis of tag networks from a massive dataset of Twitter entries. The first layer is constructed by converting the co-occurrences of these tags in a single entry (tweet) into links, while the second layer is constructed converting the semantic relations of the tags into links. We observed that the universal properties of the real networks like small-world property, clustering and power-law distributions in various network parameters are also evident in the multilayer network of hashtags. Moreover, we outlined that co-occurrences of hashtags in tweets are mostly coupled with semantic relations, whereas a small number of semantically unrelated, therefore random links reduce node separation and network diameter in the co-occurrence network layer. Together with the degree distributions, the power-law consistencies of degree difference, edge weight and cosine similarity distributions in both layers are also appealing forms of Zipf’s law evident in nature.

RMOD: a tool for regulatory motif detection in signaling network.

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Jinki Kim

Full Text Available Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

The evolving network structure of US airline system during 1990-2010

Science.gov (United States)

Lin, Jingyi; Ban, Yifang

2014-09-01

This paper analyzes the growth and evolution of topological features of the US airline network over a 20-year period. It captures the change in the network system from different dimensions of complex networks such as centrality distribution and various structural properties of the network over time. We first illustrate the results of a set of measures, including degree, strength, betweenness centrality, and clustering structure. The geographic features of airport systems, spatial distance and network efficiency are also discussed in this section. In order to further capture the dynamics of the system, this paper also explores the correlation between different measures, and investigates various interactions inside the network. Overall this study offers a novel approach to understanding the growth and evolution of real physical networks.

A unifying view of synchronization for data assimilation in complex nonlinear networks

Science.gov (United States)

Abarbanel, Henry D. I.; Shirman, Sasha; Breen, Daniel; Kadakia, Nirag; Rey, Daniel; Armstrong, Eve; Margoliash, Daniel

2017-12-01

Networks of nonlinear systems contain unknown parameters and dynamical degrees of freedom that may not be observable with existing instruments. From observable state variables, we want to estimate the connectivity of a model of such a network and determine the full state of the model at the termination of a temporal observation window during which measurements transfer information to a model of the network. The model state at the termination of a measurement window acts as an initial condition for predicting the future behavior of the network. This allows the validation (or invalidation) of the model as a representation of the dynamical processes producing the observations. Once the model has been tested against new data, it may be utilized as a predictor of responses to innovative stimuli or forcing. We describe a general framework for the tasks involved in the "inverse" problem of determining properties of a model built to represent measured output from physical, biological, or other processes when the measurements are noisy, the model has errors, and the state of the model is unknown when measurements begin. This framework is called statistical data assimilation and is the best one can do in estimating model properties through the use of the conditional probability distributions of the model state variables, conditioned on observations. There is a very broad arena of applications of the methods described. These include numerical weather prediction, properties of nonlinear electrical circuitry, and determining the biophysical properties of functional networks of neurons. Illustrative examples will be given of (1) estimating the connectivity among neurons with known dynamics in a network of unknown connectivity, and (2) estimating the biophysical properties of individual neurons in vitro taken from a functional network underlying vocalization in songbirds.

Conversion of KEGG metabolic pathways to SBGN maps including automatic layout.

Science.gov (United States)

Czauderna, Tobias; Wybrow, Michael; Marriott, Kim; Schreiber, Falk

2013-08-16

Biologists make frequent use of databases containing large and complex biological networks. One popular database is the Kyoto Encyclopedia of Genes and Genomes (KEGG) which uses its own graphical representation and manual layout for pathways. While some general drawing conventions exist for biological networks, arbitrary graphical representations are very common. Recently, a new standard has been established for displaying biological processes, the Systems Biology Graphical Notation (SBGN), which aims to unify the look of such maps. Ideally, online repositories such as KEGG would automatically provide networks in a variety of notations including SBGN. Unfortunately, this is non-trivial, since converting between notations may add, remove or otherwise alter map elements so that the existing layout cannot be simply reused. Here we describe a methodology for automatic translation of KEGG metabolic pathways into the SBGN format. We infer important properties of the KEGG layout and treat these as layout constraints that are maintained during the conversion to SBGN maps. This allows for the drawing and layout conventions of SBGN to be followed while creating maps that are still recognizably the original KEGG pathways. This article details the steps in this process and provides examples of the final result.

Knowledge-based artificial neural network model to predict the properties of alpha+ beta titanium alloys

Energy Technology Data Exchange (ETDEWEB)

Banu, P. S. Noori; Rani, S. Devaki [Dept. of Metallurgical Engineering, Jawaharlal Nehru Technological University, HyderabadI (India)

2016-08-15

In view of emerging applications of alpha+beta titanium alloys in aerospace and defense, we have aimed to develop a Back propagation neural network (BPNN) model capable of predicting the properties of these alloys as functions of alloy composition and/or thermomechanical processing parameters. The optimized BPNN model architecture was based on the sigmoid transfer function and has one hidden layer with ten nodes. The BPNN model showed excellent predictability of five properties: Tensile strength (r: 0.96), yield strength (r: 0.93), beta transus (r: 0.96), specific heat capacity (r: 1.00) and density (r: 0.99). The developed BPNN model was in agreement with the experimental data in demonstrating the individual effects of alloying elements in modulating the above properties. This model can serve as the platform for the design and development of new alpha+beta titanium alloys in order to attain desired strength, density and specific heat capacity.

Specialists meeting on properties of primary circuit structural materials including environmental effects

Energy Technology Data Exchange (ETDEWEB)

NONE

1977-07-01

The Specialists Meeting on Properties of Primary Circuit Structural Materials of LMFBRs covered the following topics: overview of materials program in different countries; mechanical properties of materials in air; fracture mechanics studies - component related activities; impact of environmental influences on mechanical properties; relationship of material properties and design methods. The purpose of the meeting was to provide a forum for exchange of information on structural materials behaviour in primary circuit of fast breeder reactors. Special emphasis was placed on environmental effects such as influence of sodium and irradiation on mechanical properties of reactor materials.

Specialists meeting on properties of primary circuit structural materials including environmental effects

International Nuclear Information System (INIS)

1977-01-01

The Specialists Meeting on Properties of Primary Circuit Structural Materials of LMFBRs covered the following topics: overview of materials program in different countries; mechanical properties of materials in air; fracture mechanics studies - component related activities; impact of environmental influences on mechanical properties; relationship of material properties and design methods. The purpose of the meeting was to provide a forum for exchange of information on structural materials behaviour in primary circuit of fast breeder reactors. Special emphasis was placed on environmental effects such as influence of sodium and irradiation on mechanical properties of reactor materials

Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome.

Science.gov (United States)

Rzhetsky, A; Gomez, S M

2001-10-01

Current growth in the field of genomics has provided a number of exciting approaches to the modeling of evolutionary mechanisms within the genome. Separately, dynamical and statistical analyses of networks such as the World Wide Web and the social interactions existing between humans have shown that these networks can exhibit common fractal properties-including the property of being scale-free. This work attempts to bridge these two fields and demonstrate that the fractal properties of molecular networks are linked to the fractal properties of their underlying genomes. We suggest a stochastic model capable of describing the evolutionary growth of metabolic or signal-transduction networks. This model generates networks that share important statistical properties (so-called scale-free behavior) with real molecular networks. In particular, the frequency of vertices connected to exactly k other vertices follows a power-law distribution. The shape of this distribution remains invariant to changes in network scale: a small subgraph has the same distribution as the complete graph from which it is derived. Furthermore, the model correctly predicts that the frequencies of distinct DNA and protein domains also follow a power-law distribution. Finally, the model leads to a simple equation linking the total number of different DNA and protein domains in a genome with both the total number of genes and the overall network topology. MatLab (MathWorks, Inc.) programs described in this manuscript are available on request from the authors. ar345@columbia.edu.

Resistance Genes in Global Crop Breeding Networks.

Science.gov (United States)

Garrett, K A; Andersen, K F; Asche, F; Bowden, R L; Forbes, G A; Kulakow, P A; Zhou, B

2017-10-01

Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

The role of syntax in complex networks: Local and global importance of verbs in a syntactic dependency network

Science.gov (United States)

Čech, Radek; Mačutek, Ján; Žabokrtský, Zdeněk

2011-10-01

Syntax of natural language has been the focus of linguistics for decades. The complex network theory, being one of new research tools, opens new perspectives on syntax properties of the language. Despite numerous partial achievements, some fundamental problems remain unsolved. Specifically, although statistical properties typical for complex networks can be observed in all syntactic networks, the impact of syntax itself on these properties is still unclear. The aim of the present study is to shed more light on the role of syntax in the syntactic network structure. In particular, we concentrate on the impact of the syntactic function of a verb in the sentence on the complex network structure. Verbs play the decisive role in the sentence structure (“local” importance). From this fact we hypothesize the importance of verbs in the complex network (“global” importance). The importance of verb in the complex network is assessed by the number of links which are directed from the node representing verb to other nodes in the network. Six languages (Catalan, Czech, Dutch, Hungarian, Italian, Portuguese) were used for testing the hypothesis.

Hyperconnectivity in juvenile myoclonic epilepsy: a network analysis.

Science.gov (United States)

Caeyenberghs, K; Powell, H W R; Thomas, R H; Brindley, L; Church, C; Evans, J; Muthukumaraswamy, S D; Jones, D K; Hamandi, K

2015-01-01

Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas. Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients. Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance. Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients.

Structural and electronic properties of InN nanowire network grown by vapor-liquid-solid method

Science.gov (United States)

Barick, B. K.; Rodríguez-Fernández, Carlos; Cantarero, Andres; Dhar, S.

2015-05-01

Growth of InN nanowires have been carried out on quartz substrates at different temperatures by vapor-liquid-solid (VLS) technique using different thicknesses of Au catalyst layer. It has been found that a narrow window of Au layer thickness and growth temperature leads to multi-nucleation, in which each site acts as the origin of several nanowires. In this multi-nucleation regime, several tens of micrometer long wires with diameter as small as 20 nm are found to grow along [ 11 2 ¯ 0 ] direction (a-plane) to form a dense network. Structural and electronic properties of these wires are studied. As grown nanowires show degenerate n-type behavior. Furthermore, x-ray photoemission study reveals an accumulation of electrons on the surface of these nanowires. Interestingly, the wire network shows persistence of photoconductivity for several hours after switching off the photoexcitation.

Structural and electronic properties of InN nanowire network grown by vapor-liquid-solid method

Energy Technology Data Exchange (ETDEWEB)

Barick, B. K., E-mail: bkbarick@gmail.com, E-mail: subho-dh@yahoo.co.in; Dhar, S., E-mail: bkbarick@gmail.com, E-mail: subho-dh@yahoo.co.in [Department of Physics, Indian Institute of Technology, Bombay, Mumbai-400076 (India); Rodríguez-Fernández, Carlos; Cantarero, Andres [Materials Science Institute, University of Valencia, PO Box 22085, 46071 Valencia (Spain)

2015-05-15

Growth of InN nanowires have been carried out on quartz substrates at different temperatures by vapor-liquid-solid (VLS) technique using different thicknesses of Au catalyst layer. It has been found that a narrow window of Au layer thickness and growth temperature leads to multi-nucleation, in which each site acts as the origin of several nanowires. In this multi-nucleation regime, several tens of micrometer long wires with diameter as small as 20 nm are found to grow along [112{sup -}0] direction (a-plane) to form a dense network. Structural and electronic properties of these wires are studied. As grown nanowires show degenerate n-type behavior. Furthermore, x-ray photoemission study reveals an accumulation of electrons on the surface of these nanowires. Interestingly, the wire network shows persistence of photoconductivity for several hours after switching off the photoexcitation.

Place-based attributes predict community membership in a mobile phone communication network.

Directory of Open Access Journals (Sweden)

T Trevor Caughlin

Full Text Available Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97 between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

Place-based attributes predict community membership in a mobile phone communication network.

Science.gov (United States)

Caughlin, T Trevor; Ruktanonchai, Nick; Acevedo, Miguel A; Lopiano, Kenneth K; Prosper, Olivia; Eagle, Nathan; Tatem, Andrew J

2013-01-01

Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

Robustness and structure of complex networks

Science.gov (United States)

Shao, Shuai

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

Phase synchronization on small-world networks with community structure

International Nuclear Information System (INIS)

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

2010-01-01

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

Homophyly/Kinship Model: Naturally Evolving Networks

Science.gov (United States)

Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

2015-10-01

It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

Science.gov (United States)

Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

2016-09-14

The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

A three-dimensional computational model of collagen network mechanics.

Directory of Open Access Journals (Sweden)

Byoungkoo Lee

Full Text Available Extracellular matrix (ECM strongly influences cellular behaviors, including cell proliferation, adhesion, and particularly migration. In cancer, the rigidity of the stromal collagen environment is thought to control tumor aggressiveness, and collagen alignment has been linked to tumor cell invasion. While the mechanical properties of collagen at both the single fiber scale and the bulk gel scale are quite well studied, how the fiber network responds to local stress or deformation, both structurally and mechanically, is poorly understood. This intermediate scale knowledge is important to understanding cell-ECM interactions and is the focus of this study. We have developed a three-dimensional elastic collagen fiber network model (bead-and-spring model and studied fiber network behaviors for various biophysical conditions: collagen density, crosslinker strength, crosslinker density, and fiber orientation (random vs. prealigned. We found the best-fit crosslinker parameter values using shear simulation tests in a small strain region. Using this calibrated collagen model, we simulated both shear and tensile tests in a large linear strain region for different network geometry conditions. The results suggest that network geometry is a key determinant of the mechanical properties of the fiber network. We further demonstrated how the fiber network structure and mechanics evolves with a local formation, mimicking the effect of pulling by a pseudopod during cell migration. Our computational fiber network model is a step toward a full biomechanical model of cellular behaviors in various ECM conditions.

Voxel Scale Complex Networks of Functional Connectivity in the Rat Brain: Neurochemical State Dependence of Global and Local Topological Properties

Directory of Open Access Journals (Sweden)

Adam J. Schwarz

2012-01-01

Full Text Available Network analysis of functional imaging data reveals emergent features of the brain as a function of its topological properties. However, the brain is not a homogeneous network, and the dependence of functional connectivity parameters on neuroanatomical substrate and parcellation scale is a key issue. Moreover, the extent to which these topological properties depend on underlying neurochemical changes remains unclear. In the present study, we investigated both global statistical properties and the local, voxel-scale distribution of connectivity parameters of the rat brain. Different neurotransmitter systems were stimulated by pharmacological challenge (d-amphetamine, fluoxetine, and nicotine to discriminate between stimulus-specific functional connectivity and more general features of the rat brain architecture. Although global connectivity parameters were similar, mapping of local connectivity parameters at high spatial resolution revealed strong neuroanatomical dependence of functional connectivity in the rat brain, with clear differentiation between the neocortex and older brain regions. Localized foci of high functional connectivity independent of drug challenge were found in the sensorimotor cortices, consistent with the high neuronal connectivity in these regions. Conversely, the topological properties and node roles in subcortical regions varied with neurochemical state and were dependent on the specific dynamics of the different functional processes elicited.

Horton Ratios Link Self-Similarity with Maximum Entropy of Eco-Geomorphological Properties in Stream Networks

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Bruce T. Milne

2017-05-01

Full Text Available Stream networks are branched structures wherein water and energy move between land and atmosphere, modulated by evapotranspiration and its interaction with the gravitational dissipation of potential energy as runoff. These actions vary among climates characterized by Budyko theory, yet have not been integrated with Horton scaling, the ubiquitous pattern of eco-hydrological variation among Strahler streams that populate river basins. From Budyko theory, we reveal optimum entropy coincident with high biodiversity. Basins on either side of optimum respond in opposite ways to precipitation, which we evaluated for the classic Hubbard Brook experiment in New Hampshire and for the Whitewater River basin in Kansas. We demonstrate that Horton ratios are equivalent to Lagrange multipliers used in the extremum function leading to Shannon information entropy being maximal, subject to constraints. Properties of stream networks vary with constraints and inter-annual variation in water balance that challenge vegetation to match expected resource supply throughout the network. The entropy-Horton framework informs questions of biodiversity, resilience to perturbations in water supply, changes in potential evapotranspiration, and land use changes that move ecosystems away from optimal entropy with concomitant loss of productivity and biodiversity.

Magnetic properties of nickel halide hydrates including deuteration effects

Energy Technology Data Exchange (ETDEWEB)

DeFotis, G.C., E-mail: gxdefo@wm.edu [Chemistry Department, College of William & Mary, Williamsburg, VA, 23187 United States (United States); Van Dongen, M.J.; Hampton, A.S.; Komatsu, C.H.; Trowell, K.T.; Havas, K.C.; Davis, C.M.; DeSanto, C.L. [Chemistry Department, College of William & Mary, Williamsburg, VA, 23187 United States (United States); Hays, K.; Wagner, M.J. [Chemistry Department, George Washington University, Washington, DC, 20052 United States (United States)

2017-01-01

Magnetic measurements on variously hydrated nickel chlorides and bromides, including deuterated forms, are reported. Results include locations and sizes of susceptibility maxima, T{sub max} and χ{sub max}, ordering temperatures T{sub c}, Curie constants and Weiss theta in the paramagnetic regime, and primary and secondary exchange interactions from analysis of low temperature data. For the latter a 2D Heisenberg model augmented by interlayer exchange in a mean-field approximation is applied. Magnetization data to 16 kG as a function of temperature show curvature and hysteresis characteristics quite system dependent. For four materials high field magnetization data to 70 kG at 2.00 K are also obtained. Comparison is made with theoretical relations for spin-1 models. Trends are apparent, primarily that T{sub max} of each bromide hydrate is less than for the corresponding chloride, and that for a given halide nD{sub 2}O (n=1 or 2) deuterates exhibit lesser T{sub max} than do nH{sub 2}O hydrates. A monoclinic unit cell determined from powder X-ray diffraction data on NiBr{sub 2}·2D{sub 2}O is different from and slightly larger than that of NiBr{sub 2}·2H{sub 2}O. This provides some rationale for the difference in magnetic properties between these. - Highlights: • The magnetism of Ni(II) chloride and bromide dihydrates and monohydrates is studied. • Effects of replacing H{sub 2}O by D{sub 2}O are examined for both hydration states and both halides. • Exchange interactions in bromides are weaker than in corresponding chlorides. • Exchange interactions are weaker in D{sub 2}O than in corresponding H{sub 2}O containing systems. • The unit cell of NiBr{sub 2}·2D{sub 2}O is different from and slightly larger than that of NiBr{sub 2}·2H{sub 2}O.

A new approach using artificial neural networks for determination of the thermodynamic properties of fluid couples

International Nuclear Information System (INIS)

Sencan, Arzu; Kalogirou, Soteris A.

2005-01-01

This paper presents a new approach using artificial neural networks (ANN) to determine the thermodynamic properties of two alternative refrigerant/absorbent couples (LiCl-H 2 O and LiBr + LiNO 3 + LiI + LiCl-H 2 O). These pairs can be used in absorption heat pump systems, and their main advantage is that they do not cause ozone depletion. In order to train the network, limited experimental measurements were used as training and test data. Two feedforward ANNs were trained, one for each pair, using the Levenberg-Marquardt algorithm. The training and validation were performed with good accuracy. The correlation coefficient obtained when unknown data were applied to the networks was 0.9997 and 0.9987 for the two pairs, respectively, which is very satisfactory. The present methodology proved to be much better than linear multiple regression analysis. Using the weights obtained from the trained network, a new formulation is presented for determination of the vapor pressures of the two refrigerant/absorbent couples. The use of this new formulation, which can be employed with any programming language or spreadsheet program for estimation of the vapor pressures of fluid couples, as described in this paper, may make the use of dedicated ANN software unnecessary

Network Properties of the Ensemble of RNA Structures

Science.gov (United States)

Clote, Peter; Bayegan, Amir

2015-01-01

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

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

Science.gov (United States)

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

2018-04-01

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

Tree-Based Unrooted Phylogenetic Networks.

Science.gov (United States)

Francis, A; Huber, K T; Moulton, V

2018-02-01

Phylogenetic networks are a generalization of phylogenetic trees that are used to represent non-tree-like evolutionary histories that arise in organisms such as plants and bacteria, or uncertainty in evolutionary histories. An unrooted phylogenetic network on a non-empty, finite set X of taxa, or network, is a connected, simple graph in which every vertex has degree 1 or 3 and whose leaf set is X. It is called a phylogenetic tree if the underlying graph is a tree. In this paper we consider properties of tree-based networks, that is, networks that can be constructed by adding edges into a phylogenetic tree. We show that although they have some properties in common with their rooted analogues which have recently drawn much attention in the literature, they have some striking differences in terms of both their structural and computational properties. We expect that our results could eventually have applications to, for example, detecting horizontal gene transfer or hybridization which are important factors in the evolution of many organisms.

Challenges in network science: Applications to infrastructures, climate, social systems and economics

Science.gov (United States)

Havlin, S.; Kenett, D. Y.; Ben-Jacob, E.; Bunde, A.; Cohen, R.; Hermann, H.; Kantelhardt, J. W.; Kertész, J.; Kirkpatrick, S.; Kurths, J.; Portugali, J.; Solomon, S.

2012-11-01

Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and in natural and anthropogenic structures. New applications are developed at an ever-increasing rate and the promise for future growth is high, since increasingly we interact with one another within these vital and complex environments. Despite all the great successes of this field, crucial aspects of multi-level complex systems have been largely ignored. Important challenges of network science are to take into account many of these missing realistic features such as strong coupling between networks (networks are not isolated), the dynamics of networks (networks are not static), interrelationships between structure, dynamics and function of networks, interdependencies in given networks (and other classes of links, including different signs of interactions), and spatial properties (including geographical aspects) of networks. This aim of this paper is to introduce and discuss the challenges that future network science needs to address, and how different disciplines will be accordingly affected.

Scaling Laws in Chennai Bus Network

OpenAIRE

Chatterjee, Atanu; Ramadurai, Gitakrishnan

2015-01-01

In this paper, we study the structural properties of the complex bus network of Chennai. We formulate this extensive network structure by identifying each bus stop as a node, and a bus which stops at any two adjacent bus stops as an edge connecting the nodes. Rigorous statistical analysis of this data shows that the Chennai bus network displays small-world properties and a scale-free degree distribution with the power-law exponent, $\\gamma > 3$.

Social networks and cooperation in hunter-gatherers.

Science.gov (United States)

Apicella, Coren L; Marlowe, Frank W; Fowler, James H; Christakis, Nicholas A

2012-01-25

Social networks show striking structural regularities, and both theory and evidence suggest that networks may have facilitated the development of large-scale cooperation in humans. Here, we characterize the social networks of the Hadza, a population of hunter-gatherers in Tanzania. We show that Hadza networks have important properties also seen in modernized social networks, including a skewed degree distribution, degree assortativity, transitivity, reciprocity, geographic decay and homophily. We demonstrate that Hadza camps exhibit high between-group and low within-group variation in public goods game donations. Network ties are also more likely between people who give the same amount, and the similarity in cooperative behaviour extends up to two degrees of separation. Social distance appears to be as important as genetic relatedness and physical proximity in explaining assortativity in cooperation. Our results suggest that certain elements of social network structure may have been present at an early point in human history. Also, early humans may have formed ties with both kin and non-kin, based in part on their tendency to cooperate. Social networks may thus have contributed to the emergence of cooperation.

Survey of Network-Based Approaches to Research of Cardiovascular Diseases

Directory of Open Access Journals (Sweden)

Anida Sarajlić

2014-01-01

Full Text Available Cardiovascular diseases (CVDs are the leading health problem worldwide. Investigating causes and mechanisms of CVDs calls for an integrative approach that would take into account its complex etiology. Biological networks generated from available data on biomolecular interactions are an excellent platform for understanding interconnectedness of all processes within a living cell, including processes that underlie diseases. Consequently, topology of biological networks has successfully been used for identifying genes, pathways, and modules that govern molecular actions underlying various complex diseases. Here, we review approaches that explore and use relationships between topological properties of biological networks and mechanisms underlying CVDs.

Memory functions reveal structural properties of gene regulatory networks

Science.gov (United States)

Perez-Carrasco, Ruben

2018-01-01

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

Molecular dynamics study on the evolution of interfacial dislocation network and mechanical properties of Ni-based single crystal superalloys

Science.gov (United States)

Li, Nan-Lin; Wu, Wen-Ping; Nie, Kai

2018-05-01

The evolution of misfit dislocation network at γ /γ‧ phase interface and tensile mechanical properties of Ni-based single crystal superalloys at various temperatures and strain rates are studied by using molecular dynamics (MD) simulations. From the simulations, it is found that with the increase of loading, the dislocation network effectively inhibits dislocations emitted in the γ matrix cutting into the γ‧ phase and absorbs the matrix dislocations to strengthen itself which increases the stability of structure. Under the influence of the temperature, the initial mosaic structure of dislocation network gradually becomes irregular, and the initial misfit stress and the elastic modulus slowly decline as temperature increasing. On the other hand, with the increase of the strain rate, it almost has no effect on the elastic modulus and the way of evolution of dislocation network, but contributes to the increases of the yield stress and tensile strength. Moreover, tension-compression asymmetry of Ni-based single crystal superalloys is also presented based on MD simulations.

Modeling and Vulnerability Analysis of Cyber-Physical Power Systems Considering Network Topology and Power Flow Properties

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Jia Guo

2017-01-01

Full Text Available Conventional power systems are developing into cyber-physical power systems (CPPS with wide applications of communication, computer and control technologies. However, multiple practical cases show that the failure of cyber layers is a major factor leading to blackouts. Therefore, it is necessary to discuss the cascading failure process considering cyber layer failures and analyze the vulnerability of CPPS. In this paper, a CPPS model, which consists of cyber layer, physical layer and cyber-physical interface, is presented using complex network theory. Considering power flow properties, the impacts of cyber node failures on the cascading failure propagation process are studied. Moreover, two vulnerability indices are established from the perspective of both network structure and power flow properties. A vulnerability analysis method is proposed, and the CPPS performance before and after cascading failures is analyzed by the proposed method to calculate vulnerability indices. In the case study, three typical scenarios are analyzed to illustrate the method, and vulnerabilities under different interface strategies and attack strategies are compared. Two thresholds are proposed to value the CPPS vulnerability roughly. The results show that CPPS is more vulnerable under malicious attacks and cyber nodes with high indices are vulnerable points which should be reinforced.

The use of artificial neural networks for mathematical modeling of the effect of composition and production conditions on the properties of PVC floor coverings

Directory of Open Access Journals (Sweden)

Radovanović Rajko M.

2017-01-01

Full Text Available The application of PVC floor coverings is strongly connected with their end-use properties, which depend on the composition and processing conditions. It is very difficult to estimate the proper influence of the production parameters on the characteristics of PVC floor coverings due to their complex composition and various preparation procedures. The effect of different processing variables (such as time of bowling, temperature of bowling and composition of PVC plastisol on the mechanical properties of PVC floor coverings was investigated. The influence of different input parameters on the mechanical properties was successfully determined using an artificial neural network with an optimized number of hidden neurons. The Garson and Yoon models were applied to calculate and describe the variable contributions in the artificial neural networks. [Projekat Ministarstva nauke Republike Srbije, br. III 45022

The Ripple Pond: Enabling Spiking Networks to See

Directory of Open Access Journals (Sweden)

Saeed eAfshar

2013-11-01

Full Text Available We present the biologically inspired Ripple Pond Network (RPN, a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilising the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable temporal patterns and the use of asynchronous frames for information binding.

The ripple pond: enabling spiking networks to see.

Science.gov (United States)

Afshar, Saeed; Cohen, Gregory K; Wang, Runchun M; Van Schaik, André; Tapson, Jonathan; Lehmann, Torsten; Hamilton, Tara J

2013-01-01

We present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns (TP) suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilizing the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable TP and the use of asynchronous frames for information binding.

Spatial networks

Science.gov (United States)

Barthélemy, Marc

2011-02-01

Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, and neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields, ranging from urbanism to epidemiology. An important consequence of space on networks is that there is a cost associated with the length of edges which in turn has dramatic effects on the topological structure of these networks. We will thoroughly explain the current state of our understanding of how the spatial constraints affect the structure and properties of these networks. We will review the most recent empirical observations and the most important models of spatial networks. We will also discuss various processes which take place on these spatial networks, such as phase transitions, random walks, synchronization, navigation, resilience, and disease spread.

Structural and electronic properties of InN nanowire network grown by vapor-liquid-solid method

Directory of Open Access Journals (Sweden)

B. K. Barick

2015-05-01

Full Text Available Growth of InN nanowires have been carried out on quartz substrates at different temperatures by vapor-liquid-solid (VLS technique using different thicknesses of Au catalyst layer. It has been found that a narrow window of Au layer thickness and growth temperature leads to multi-nucleation, in which each site acts as the origin of several nanowires. In this multi-nucleation regime, several tens of micrometer long wires with diameter as small as 20 nm are found to grow along [ 11 2 ̄ 0 ] direction (a-plane to form a dense network. Structural and electronic properties of these wires are studied. As grown nanowires show degenerate n-type behavior. Furthermore, x-ray photoemission study reveals an accumulation of electrons on the surface of these nanowires. Interestingly, the wire network shows persistence of photoconductivity for several hours after switching off the photoexcitation.

Latent geometry of bipartite networks

Science.gov (United States)

Kitsak, Maksim; Papadopoulos, Fragkiskos; Krioukov, Dmitri

2017-03-01

Despite the abundance of bipartite networked systems, their organizing principles are less studied compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model and propose an efficient method to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.

Prediction of mechanical properties of a warm compacted molybdenum prealloy using artificial neural network and adaptive neuro-fuzzy models

International Nuclear Information System (INIS)

Zare, Mansour; Vahdati Khaki, Jalil

2012-01-01

Highlights: ► ANNs and ANFIS fairly predicted UTS and YS of warm compacted molybdenum prealloy. ► Effects of composition, temperature, compaction pressure on output were studied. ► ANFIS model was in better agreement with experimental data from published article. ► Sintering temperature had the most significant effect on UTS and YS. -- Abstract: Predictive models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were successfully developed to predict yield strength and ultimate tensile strength of warm compacted 0.85 wt.% molybdenum prealloy samples. To construct these models, 48 different experimental data were gathered from the literature. A portion of the data set was randomly chosen to train both ANN with back propagation (BP) learning algorithm and ANFIS model with Gaussian membership function and the rest was implemented to verify the performance of the trained network against the unseen data. The generalization capability of the networks was also evaluated by applying new input data within the domain covered by the training pattern. To compare the obtained results, coefficient of determination (R 2 ), root mean squared error (RMSE) and average absolute error (AAE) indexes were chosen and calculated for both of the models. The results showed that artificial neural network and adaptive neuro-fuzzy system were both potentially strong for prediction of the mechanical properties of warm compacted 0.85 wt.% molybdenum prealloy; however, the proposed ANFIS showed better performance than the ANN model. Also, the ANFIS model was subjected to a sensitivity analysis to find the significant inputs affecting mechanical properties of the samples.

Software-defined Quantum Networking Ecosystem

Energy Technology Data Exchange (ETDEWEB)

2017-01-01

The software enables a user to perform modeling and simulation of software-defined quantum networks. The software addresses the problem of how to synchronize transmission of quantum and classical signals through multi-node networks and to demonstrate quantum information protocols such as quantum teleportation. The software approaches this problem by generating a graphical model of the underlying network and attributing properties to each node and link in the graph. The graphical model is then simulated using a combination of discrete-event simulators to calculate the expected state of each node and link in the graph at a future time. A user interacts with the software by providing an initial network model and instantiating methods for the nodes to transmit information with each other. This includes writing application scripts in python that make use of the software library interfaces. A user then initiates the application scripts, which invokes the software simulation. The user then uses the built-in diagnostic tools to query the state of the simulation and to collect statistics on synchronization.

Scaling in public transport networks

Directory of Open Access Journals (Sweden)

C. von Ferber

2005-01-01

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

Complex networks generated by the Penna bit-string model: Emergence of small-world and assortative mixing

Science.gov (United States)

Li, Chunguang; Maini, Philip K.

2005-10-01

The Penna bit-string model successfully encompasses many phenomena of population evolution, including inheritance, mutation, evolution, and aging. If we consider social interactions among individuals in the Penna model, the population will form a complex network. In this paper, we first modify the Verhulst factor to control only the birth rate, and introduce activity-based preferential reproduction of offspring in the Penna model. The social interactions among individuals are generated by both inheritance and activity-based preferential increase. Then we study the properties of the complex network generated by the modified Penna model. We find that the resulting complex network has a small-world effect and the assortative mixing property.

Altering the swelling pressures within in vitro engineered cartilage is predicted to modulate the configuration of the collagen network and hence improve tissue mechanical properties.

Science.gov (United States)

Nagel, Thomas; Kelly, Daniel J

2013-06-01

Prestress in the collagen network has a significant impact on the material properties of cartilaginous tissues. It is closely related to the recruitment configuration of the collagen network which defines the transition from lax collagen fibres to uncrimped, load-bearing collagen fibres. This recruitment configuration can change in response to alterations in the external environmental conditions. In this study, the influence of changes in external salt concentration or sequential proteoglycan digestion on the configuration of the collagen network of tissue engineered cartilage is investigated using a previously developed computational model. Collagen synthesis and network assembly are assumed to occur in the tissue configuration present during in vitro culture. The model assumes that if this configuration is more compact due to changes in tissue swelling, the collagen network will adapt by lowering its recruitment stretch. When returned to normal physiological conditions, these tissues will then have a higher prestress in the collagen network. Based on these assumptions, the model demonstrates that proteoglycan digestion at discrete time points during culture as well as culture in a hypertonic medium can improve the functionality of tissue engineered cartilage, while culture in hypotonic solution is detrimental to the apparent mechanical properties of the graft. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evolution characteristics of the network core in the Facebook.

Directory of Open Access Journals (Sweden)

Jian-Guo Liu

Full Text Available Statistical properties of the static networks have been extensively studied. However, online social networks are evolving dynamically, understanding the evolving characteristics of the core is one of major concerns in online social networks. In this paper, we empirically investigate the evolving characteristics of the Facebook core. Firstly, we separate the Facebook-link(FL and Facebook-wall(FW datasets into 28 snapshots in terms of timestamps. By employing the k-core decomposition method to identify the core of each snapshot, we find that the core sizes of the FL and FW networks approximately contain about 672 and 373 nodes regardless of the exponential growth of the network sizes. Secondly, we analyze evolving topological properties of the core, including the k-core value, assortative coefficient, clustering coefficient and the average shortest path length. Empirical results show that nodes in the core are getting more interconnected in the evolving process. Thirdly, we investigate the life span of nodes belonging to the core. More than 50% nodes stay in the core for more than one year, and 19% nodes always stay in the core from the first snapshot. Finally, we analyze the connections between the core and the whole network, and find that nodes belonging to the core prefer to connect nodes with high k-core values, rather than the high degrees ones. This work could provide new insights into the online social network analysis.

Ecological network analysis: network construction

NARCIS (Netherlands)

Fath, B.D.; Scharler, U.M.; Ulanowicz, R.E.; Hannon, B.

2007-01-01

Ecological network analysis (ENA) is a systems-oriented methodology to analyze within system interactions used to identify holistic properties that are otherwise not evident from the direct observations. Like any analysis technique, the accuracy of the results is as good as the data available, but

Shape-memory properties of magnetically active triple-shape nanocomposites based on a grafted polymer network with two crystallizable switching segments

Directory of Open Access Journals (Sweden)

A. Lendlein

2012-01-01

Full Text Available Thermo-sensitive shape-memory polymers (SMP, which are capable of memorizing two or more different shapes, have generated significant research and technological interest. A triple-shape effect (TSE of SMP can be activated e.g. by increasing the environmental temperature (Tenv, whereby two switching temperatures (Tsw have to be exceeded to enable the subsequent shape changes from shape (A to shape (B and finally the original shape (C. In this work, we explored the thermally and magnetically initiated shape-memory properties of triple-shape nanocomposites with various compositions and particle contents using different shape-memory creation procedures (SMCP. The nanocomposites were prepared by the incorporation of magnetite nanoparticles into a multiphase polymer network matrix with grafted polymer network architecture containing crystallizable poly(ethylene glycol (PEG side chains and poly(ε-caprolactone (PCL crosslinks named CLEGC. Excellent triple-shape properties were achieved for nanocomposites with high PEG weight fraction when two-step programming procedures were applied. In contrast, single-step programming resulted in dual-shape properties for all investigated materials as here the temporary shape (A was predominantly fixed by PCL crystallites.

Determination of Elastic and Dissipative Properties of Material Using Combination of FEM and Complex Artificial Neural Networks

Science.gov (United States)

Soloviev, A. N.; Giang, N. D. T.; Chang, S.-H.

This paper describes the application of complex artificial neural networks (CANN) in the inverse identification problem of the elastic and dissipative properties of solids. Additional information for the inverse problem serves the components of the displacement vector measured on the body boundary, which performs harmonic oscillations at the first resonant frequency. The process of displacement measurement in this paper is simulated using calculation of finite element (FE) software ANSYS. In the shown numerical example, we focus on the accurate identification of elastic modulus and quality of material depending on the number of measurement points and their locations as well as on the architecture of neural network and time of the training process, which is conducted by using algorithms RProp, QuickProp.

Design of fuzzy systems using neurofuzzy networks.

Science.gov (United States)

Figueiredo, M; Gomide, F

1999-01-01

This paper introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, nonnoisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.

Competing dynamic phases of active polymer networks

Science.gov (United States)

Freedman, Simon; Banerjee, Shiladitya; Dinner, Aaron R.

Recent experiments on in-vitro reconstituted assemblies of F-actin, myosin-II motors, and cross-linking proteins show that tuning local network properties can changes the fundamental biomechanical behavior of the system. For example, by varying cross-linker density and actin bundle rigidity, one can switch between contractile networks useful for reshaping cells, polarity sorted networks ideal for directed molecular transport, and frustrated networks with robust structural properties. To efficiently investigate the dynamic phases of actomyosin networks, we developed a coarse grained non-equilibrium molecular dynamics simulation of model semiflexible filaments, molecular motors, and cross-linkers with phenomenologically defined interactions. The simulation's accuracy was verified by benchmarking the mechanical properties of its individual components and collective behavior against experimental results at the molecular and network scales. By adjusting the model's parameters, we can reproduce the qualitative phases observed in experiment and predict the protein characteristics where phase crossovers could occur in collective network dynamics. Our model provides a framework for understanding cells' multiple uses of actomyosin networks and their applicability in materials research. Supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program.